Quantifying the robustness of process manufacturing concept – A medical product case study

DEFF Research Database (Denmark)

Boorla, Srinivasa Murthy; Troldtoft, M.E.; Eifler, Tobias

2017-01-01

Product robustness refers to the consistency of performance of all of the units produced. It is often the case that process manufactured products are not designed concurrently, so by the end of the product design phase the Process Manufacturing Concept (PMC) has yet to be decided. Allocating...... the unit-to-unit robustness of an early-stage for a PMC is proposed. The method uses variability and adjustability information from the manufacturing concept in combination with sensitivity information from products' design to predict its functional performance variation. A Technology maturation factor...... process capable tolerances to the product during the design phase is therefore not possible. The robustness of the concept (how capable it is to achieve the product specification), only becomes clear at this late stage and thus after testing and iteration. In this article, a method for calculating...

Space station automation study. Automation requirements derived from space manufacturing concepts. Volume 1: Executive summary

Science.gov (United States)

1984-01-01

The two manufacturing concepts developed represent innovative, technologically advanced manufacturing schemes. The concepts were selected to facilitate an in depth analysis of manufacturing automation requirements in the form of process mechanization, teleoperation and robotics, and artificial intelligence. While the cost effectiveness of these facilities has not been analyzed as part of this study, both appear entirely feasible for the year 2000 timeframe. The growing demand for high quality gallium arsenide microelectronics may warrant the ventures.

Additive manufacturing for freeform mechatronics design: from concepts to applications

NARCIS (Netherlands)

Baars, G. van; Smeltink, J.; Werff, J. van der; Limpens, M.; Barink, M.; Berg, D. van den; Vreugd, J. de; Witvoet, G.; Galaktionov, O.S.

2015-01-01

This article presents developments of freeform mechatronics concepts, enabled by industrial Additive Manufacturing (AM), aiming at breakthroughs for precision engineering challenges such as lightweight, advanced thermal control, and integrated design. To assess potential impact in future

Learning sequential control in a Neural Blackboard Architecture for in situ concept reasoning

NARCIS (Netherlands)

van der Velde, Frank; van der Velde, Frank; Besold, Tarek R.; Lamb, Luis; Serafini, Luciano; Tabor, Whitney

2016-01-01

Simulations are presented and discussed of learning sequential control in a Neural Blackboard Architecture (NBA) for in situ concept-based reasoning. Sequential control is learned in a reservoir network, consisting of columns with neural circuits. This allows the reservoir to control the dynamics of

Concepts for dynamic modelling of energy-related flows in manufacturing

International Nuclear Information System (INIS)

Wright, A.J.; Oates, M.R.; Greenough, R.

2013-01-01

Highlights: ► Modelling of the thermal flows in factories and processes is usually separate. ► We propose a set of key features for an integrated thermal model. ► Such models can be used to improve the efficiency of manufacturing processes. - Abstract: Industry uses around one third of the world’s energy, and accounts for about 40% of global carbon dioxide emissions. There is increasing economic and social pressure to improve efficiency and create closed-loop industrial systems, in which energy efficiency plays a key role. This paper describes some of the key concepts involved in modelling the energy flows in manufacturing, both for the building services and the industrial processes. Detailed dynamic energy simulation of buildings is well established and routinely used, working on a time series basis – but current tools are inadequate to model the energy flows of many industrial processes. There are also well-established models of manufacturing flows, used to optimise production efficiency, but typically not modelling energy, and usually representing production and material flows as event-driven processes. The THERM project has developed new software tools to model energy-related and other utility flows in manufacturing, incorporating these into existing thermal models of factory buildings. This makes it possible to map out the whole energy system, and hence to test efficiency measures, to understand the effect of processes on building energy use, to investigate recycling of heat or cooling into other processes or building conditioning, and so on. The paper describes some of the key concepts and modelling approaches involved in developing these models, and gives examples of some real processes modelled in factories. It concludes that such models are entirely feasible and potentially very useful, although to develop a tool which comprehensively models both energy and manufacturing flows would be a major undertaking

New concept for in-line OLED manufacturing

Science.gov (United States)

Hoffmann, U.; Landgraf, H.; Campo, M.; Keller, S.; Koening, M.

2011-03-01

A new concept of a vertical In-Line deposition machine for large area white OLED production has been developed. The concept targets manufacturing on large substrates (>= Gen 4, 750 x 920 mm2) using linear deposition source achieving a total material utilization of >= 50 % and tact time down to 80 seconds. The continuously improved linear evaporation sources for the organic material achieve thickness uniformity on Gen 4 substrate of better than +/- 3 % and stable deposition rates down to less than 0.1 nm m/min and up to more than 100 nm m/min. For Lithium-Fluoride but also for other high evaporation temperature materials like Magnesium or Silver a linear source with uniformity better than +/- 3 % has been developed. For Aluminum we integrated a vertical oriented point source using wire feed to achieve high (> 150 nm m/min) and stable deposition rates. The machine concept includes a new vertical vacuum handling and alignment system for Gen 4 shadow masks. A complete alignment cycle for the mask can be done in less than one minute achieving alignment accuracy in the range of several 10 μm.

Medical Concept Normalization in Social Media Posts with Recurrent Neural Networks.

Science.gov (United States)

Tutubalina, Elena; Miftahutdinov, Zulfat; Nikolenko, Sergey; Malykh, Valentin

2018-06-12

Text mining of scientific libraries and social media has already proven itself as a reliable tool for drug repurposing and hypothesis generation. The task of mapping a disease mention to a concept in a controlled vocabulary, typically to the standard thesaurus in the Unified Medical Language System (UMLS), is known as medical concept normalization. This task is challenging due to the differences in the use of medical terminology between health care professionals and social media texts coming from the lay public. To bridge this gap, we use sequence learning with recurrent neural networks and semantic representation of one- or multi-word expressions: we develop end-to-end architectures directly tailored to the task, including bidirectional Long Short-Term Memory, Gated Recurrent Units with an attention mechanism, and additional semantic similarity features based on UMLS. Our evaluation against a standard benchmark shows that recurrent neural networks improve results over an effective baseline for classification based on convolutional neural networks. A qualitative examination of mentions discovered in a dataset of user reviews collected from popular online health information platforms as well as a quantitative evaluation both show improvements in the semantic representation of health-related expressions in social media. Copyright © 2018. Published by Elsevier Inc.

Autonomous dynamics in neural networks: the dHAN concept and associative thought processes

Science.gov (United States)

Gros, Claudius

2007-02-01

The neural activity of the human brain is dominated by self-sustained activities. External sensory stimuli influence this autonomous activity but they do not drive the brain directly. Most standard artificial neural network models are however input driven and do not show spontaneous activities. It constitutes a challenge to develop organizational principles for controlled, self-sustained activity in artificial neural networks. Here we propose and examine the dHAN concept for autonomous associative thought processes in dense and homogeneous associative networks. An associative thought-process is characterized, within this approach, by a time-series of transient attractors. Each transient state corresponds to a stored information, a memory. The subsequent transient states are characterized by large associative overlaps, which are identical to acquired patterns. Memory states, the acquired patterns, have such a dual functionality. In this approach the self-sustained neural activity has a central functional role. The network acquires a discrimination capability, as external stimuli need to compete with the autonomous activity. Noise in the input is readily filtered-out. Hebbian learning of external patterns occurs coinstantaneous with the ongoing associative thought process. The autonomous dynamics needs a long-term working-point optimization which acquires within the dHAN concept a dual functionality: It stabilizes the time development of the associative thought process and limits runaway synaptic growth, which generically occurs otherwise in neural networks with self-induced activities and Hebbian-type learning rules.

Warranty optimisation based on the prediction of costs to the manufacturer using neural network model and Monte Carlo simulation

Science.gov (United States)

Stamenkovic, Dragan D.; Popovic, Vladimir M.

2015-02-01

Warranty is a powerful marketing tool, but it always involves additional costs to the manufacturer. In order to reduce these costs and make use of warranty's marketing potential, the manufacturer needs to master the techniques for warranty cost prediction according to the reliability characteristics of the product. In this paper a combination free replacement and pro rata warranty policy is analysed as warranty model for one type of light bulbs. Since operating conditions have a great impact on product reliability, they need to be considered in such analysis. A neural network model is used to predict light bulb reliability characteristics based on the data from the tests of light bulbs in various operating conditions. Compared with a linear regression model used in the literature for similar tasks, the neural network model proved to be a more accurate method for such prediction. Reliability parameters obtained in this way are later used in Monte Carlo simulation for the prediction of times to failure needed for warranty cost calculation. The results of the analysis make possible for the manufacturer to choose the optimal warranty policy based on expected product operating conditions. In such a way, the manufacturer can lower the costs and increase the profit.

Design and manufacturing challenges of optogenetic neural interfaces: a review

Science.gov (United States)

Goncalves, S. B.; Ribeiro, J. F.; Silva, A. F.; Costa, R. M.; Correia, J. H.

2017-08-01

Optogenetics is a relatively new technology to achieve cell-type specific neuromodulation with millisecond-scale temporal precision. Optogenetic tools are being developed to address neuroscience challenges, and to improve the knowledge about brain networks, with the ultimate aim of catalyzing new treatments for brain disorders and diseases. To reach this ambitious goal the implementation of mature and reliable engineered tools is required. The success of optogenetics relies on optical tools that can deliver light into the neural tissue. Objective/Approach: Here, the design and manufacturing approaches available to the scientific community are reviewed, and current challenges to accomplish appropriate scalable, multimodal and wireless optical devices are discussed. Significance: Overall, this review aims at presenting a helpful guidance to the engineering and design of optical microsystems for optogenetic applications.

Microchannel neural interface manufacture by stacking silicone and metal foil laminae

Science.gov (United States)

Lancashire, Henry T.; Vanhoestenberghe, Anne; Pendegrass, Catherine J.; Ajam, Yazan Al; Magee, Elliot; Donaldson, Nick; Blunn, Gordon W.

2016-06-01

Objective. Microchannel neural interfaces (MNIs) overcome problems with recording from peripheral nerves by amplifying signals independent of node of Ranvier position. Selective recording and stimulation using an MNI requires good insulation between microchannels and a high electrode density. We propose that stacking microchannel laminae will improve selectivity over single layer MNI designs due to the increase in electrode number and an improvement in microchannel sealing. Approach. This paper describes a manufacturing method for creating MNIs which overcomes limitations on electrode connectivity and microchannel sealing. Laser cut silicone—metal foil laminae were stacked using plasma bonding to create an array of microchannels containing tripolar electrodes. Electrodes were DC etched and electrode impedance and cyclic voltammetry were tested. Main results. MNIs with 100 μm and 200 μm diameter microchannels were manufactured. High electrode density MNIs are achievable with electrodes present in every microchannel. Electrode impedances of 27.2 ± 19.8 kΩ at 1 kHz were achieved. Following two months of implantation in Lewis rat sciatic nerve, micro-fascicles were observed regenerating through the MNI microchannels. Significance. Selective MNIs with the peripheral nervous system may allow upper limb amputees to control prostheses intuitively.

Neural reuse leads to associative connections between concrete (physical) and abstract (social) concepts and motives.

Science.gov (United States)

Wang, Yimeng; Bargh, John A

2016-01-01

Consistent with neural reuse theory, empirical tests of the related "scaffolding" principle of abstract concept development show that higher-level concepts "reuse" and are built upon fundamental motives such as survival, safety, and consumption. This produces mutual influence between the two levels, with far-ranging impacts from consumer behavior to political attitudes.

Industrial waste management within manufacturing: a comparative study of tools, policies, visions and concepts

OpenAIRE

Shahbazi, Sasha; Kurdve, Martin; Bjelkemyr, Marcus; Jönsson, Christina; Wiktorsson, Magnus

2013-01-01

Industrial waste is a key factor when assessing the sustainability of a manufacturing process or company. A multitude of visions, concepts, tools, and policies are used both academically and industrially to improve the environmental effect of manufacturing; a majority of these approaches have a direct bearing on industrial waste. The identified approaches have in this paper been categorised according to application area, goals, organisational entity, life cycle phase, and waste hierarchy stag...

Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Krzysztof Gajowniczek

2018-04-01

Full Text Available Artificial neural networks are currently one of the most commonly used classifiers and over the recent years they have been successfully used in many practical applications, including banking and finance, health and medicine, engineering and manufacturing. A large number of error functions have been proposed in the literature to achieve a better predictive power. However, only a few works employ Tsallis statistics, although the method itself has been successfully applied in other machine learning techniques. This paper undertakes the effort to examine the q -generalized function based on Tsallis statistics as an alternative error measure in neural networks. In order to validate different performance aspects of the proposed function and to enable identification of its strengths and weaknesses the extensive simulation was prepared based on the artificial benchmarking dataset. The results indicate that Tsallis entropy error function can be successfully introduced in the neural networks yielding satisfactory results and handling with class imbalance, noise in data or use of non-informative predictors.

Measuring Manufacturing Innovativeness

DEFF Research Database (Denmark)

Blichfeldt, Henrik; Knudsen, Mette Præst

2017-01-01

Globalization and customization increases the pressure on manufacturing companies, and the ability to provide innovativeness is a potential source of competitive advantage. This paper positions the manufacturing entity in the innovation process, and investigates the relation between innovation vers...... technology and organizational concepts. Based on Danish survey data from the European Manufacturing Survey (EMS-2015) this paper finds that there is a relation between innovative companies, and their level of technology and use of organizational concepts. Technology and organizational concepts act...... as manufacturing levers to support the manufacturing and production system to provide innovativeness. The managerial implication lies in building manufacturing capabilities to support the innovative process, by standardization, optimization and creating stability in combination with automation and advanced...

The strategic relevance of manufacturing technology: An overall quality concept to promote innovation preventing drug shortage.

Science.gov (United States)

Panzitta, Michele; Ponti, Mauro; Bruno, Giorgio; Cois, Giancarlo; D'Arpino, Alessandro; Minghetti, Paola; Mendicino, Francesca Romana; Perioli, Luana; Ricci, Maurizio

2017-01-10

Manufacturing is the bridge between research and patient: without product, there is no clinical outcome. Shortage has a variety of causes, in this paper we analyse only causes related to manufacturing technology and we use shortage as a paradigm highliting the relevance of Pharmaceutical Technology. Product and process complexity and capacity issues are the main challenge for the Pharmaceutical Industry Supply chain. Manufacturing Technology should be acknowledged as a R&D step and as a very important matter during University degree in Pharmacy and related disciplines, promoting collaboration between Academia and Industry, measured during HTA step and rewarded in terms of price and reimbursement. The above elements are not yet properly recognised, and manufacturing technology is taken in to consideration only when a shortage is in place. In a previous work, Panzitta et al. proposed to perform a full technology assessment at the Health Technological Assessment stage, evaluating three main technical aspects of a medicine: manufacturing process, physicochemical properties, and formulation characteristics. In this paper, we develop the concept of manufacturing appraisal, providing a technical overview of upcoming challenges, a risk based approach and an economic picture of shortage costs. We develop also an overall quality concept, not limited to GMP factors but broaden to all elements leading to a robust supply and promoting technical innovation. Copyright © 2016 Elsevier B.V. All rights reserved.

The impact of fit manufacturing on green manufacturing: A review

Science.gov (United States)

Qi, Ang Nian; Sin, Tan Chan; Fathullah, M.; Lee, C. C.

2017-09-01

Fit manufacturing and Green manufacturing are a new trend principle and concept. They are getting popular in industrial. This paper is identifying the impact between Fit manufacturing and Green manufacturing. Besides Fit manufacturing, Lean manufacturing, Agile manufacturing and Sustainable manufacturing gives big impacts to Green Manufacturing. On top of that, this paper also discuss the benefits of applying Fit manufacturing and Green manufacturing in industrial as well as environment. Hence, applications of Fit manufacturing and Green Manufacturing are increasing year by year.

Concept of neural genoarchitecture and its genomic fundament.

Directory of Open Access Journals (Sweden)

Luis ePuelles

2012-11-01

Full Text Available The recent concept of neural genoarchitecture (or genoarchitectonics is examined from several angles, aiming to clarify the rationale for this new approach in causal and descriptive neuroanatomy. Gene expression patterns can be used as topographic stains revealing architectonic borders that may clarify, dispute or complicate existing brain anatomical subdivisions based on other methods, while increasing our understanding of how they arise in ontogenesis and evolution. A section of the text deals with differential regulation of gene expression in an ontogenetic causal network, attending to the structure of the genome and the functional peculiarities of enhancer and repressor regulatory regions that modulate gene transcription. The emergence of regionally characteristic sets of active transcription factors represents a critical concept, molecular identity, which can be applied to discrete brain territories and neuronal populations. Gene regulation is tied to positional effects, that is, topologically invariant domains of gene expression and natural boundaries, which can be correlated with anatomic ones. The large-scale stability of these patterns among vertebrates underpins molecularly the structural brain Bauplan, and is the fundament of field homology. The study of genoarchitectonic boundaries is presented as a crucial objective of modern neuroanatomic research. At most brain regions, new neuronal populations are being detected thanks to their differential genoarchitectonic features.

Neural Networks and Micromechanics

Science.gov (United States)

Kussul, Ernst; Baidyk, Tatiana; Wunsch, Donald C.

The title of the book, "Neural Networks and Micromechanics," seems artificial. However, the scientific and technological developments in recent decades demonstrate a very close connection between the two different areas of neural networks and micromechanics. The purpose of this book is to demonstrate this connection. Some artificial intelligence (AI) methods, including neural networks, could be used to improve automation system performance in manufacturing processes. However, the implementation of these AI methods within industry is rather slow because of the high cost of conducting experiments using conventional manufacturing and AI systems. To lower the cost, we have developed special micromechanical equipment that is similar to conventional mechanical equipment but of much smaller size and therefore of lower cost. This equipment could be used to evaluate different AI methods in an easy and inexpensive way. The proved methods could be transferred to industry through appropriate scaling. In this book, we describe the prototypes of low cost microequipment for manufacturing processes and the implementation of some AI methods to increase precision, such as computer vision systems based on neural networks for microdevice assembly and genetic algorithms for microequipment characterization and the increase of microequipment precision.

SLAM: a fast high volume additive manufacturing concept by impact welding; application to Ti6Al4V alloy

NARCIS (Netherlands)

Wentzel, C.M.; Carton, E.P.; Kloosterman, A.

2006-01-01

Against the manufacturing requirement for both lower lead time and reduced machining time for titanium components, a new concept was conceived assembling sheet material and other stock into semi finished parts by (explosive) impact welding. It is believed that this concept (which we named SLAM)

Performing particle image velocimetry using artificial neural networks: a proof-of-concept

Science.gov (United States)

Rabault, Jean; Kolaas, Jostein; Jensen, Atle

2017-12-01

Traditional programs based on feature engineering are underperforming on a steadily increasing number of tasks compared with artificial neural networks (ANNs), in particular for image analysis. Image analysis is widely used in fluid mechanics when performing particle image velocimetry (PIV) and particle tracking velocimetry (PTV), and therefore it is natural to test the ability of ANNs to perform such tasks. We report for the first time the use of convolutional neural networks (CNNs) and fully connected neural networks (FCNNs) for performing end-to-end PIV. Realistic synthetic images are used for training the networks and several synthetic test cases are used to assess the quality of each network’s predictions and compare them with state-of-the-art PIV software. In addition, we present tests on real-world data that prove ANNs can be used not only with synthetic images but also with more noisy, imperfect images obtained in a real experimental setup. While the ANNs we present have slightly higher root mean square error than state-of-the-art cross-correlation methods, they perform better near edges and allow for higher spatial resolution than such methods. In addition, it is likely that one could with further work develop ANNs which perform better that the proof-of-concept we offer.

Neural Networks

Directory of Open Access Journals (Sweden)

Schwindling Jerome

2010-04-01

Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

UAV Trajectory Modeling Using Neural Networks

Science.gov (United States)

Xue, Min

2017-01-01

Massive small unmanned aerial vehicles are envisioned to operate in the near future. While there are lots of research problems need to be addressed before dense operations can happen, trajectory modeling remains as one of the keys to understand and develop policies, regulations, and requirements for safe and efficient unmanned aerial vehicle operations. The fidelity requirement of a small unmanned vehicle trajectory model is high because these vehicles are sensitive to winds due to their small size and low operational altitude. Both vehicle control systems and dynamic models are needed for trajectory modeling, which makes the modeling a great challenge, especially considering the fact that manufactures are not willing to share their control systems. This work proposed to use a neural network approach for modelling small unmanned vehicle's trajectory without knowing its control system and bypassing exhaustive efforts for aerodynamic parameter identification. As a proof of concept, instead of collecting data from flight tests, this work used the trajectory data generated by a mathematical vehicle model for training and testing the neural network. The results showed great promise because the trained neural network can predict 4D trajectories accurately, and prediction errors were less than 2:0 meters in both temporal and spatial dimensions.

Cloud manufacturing: a service-oriented manufacturing paradigm. A review paper

Directory of Open Access Journals (Sweden)

Siderska Julia

2018-03-01

Full Text Available This paper introduces cloud manufacturing (CMfg as a new manufacturing paradigm that joins the emerging technologies – such as the Internet of Things, cloud computing, and service-oriented technologies – for solving complex problems in manufacturing applications and performing large-scale collaborative manufacturing. Using scientific publications indexed in Scopus database during the period 2012–2017, the concept and fundamentals of CMfg are presented and discussed given the results of the most recent research. While focusing on the current state of the art, the recent research trends within CMfg concept were also identified. The review involved the methods of bibliometric analysis and network analysis. A prototype of CMfg and the existing related work conducted by various researchers are presented, and the map of co-occurrence is introduced to indicate the most commonly occurring issues related to the “cloud manufacturing” term. The VOSviewer software was used for this purpose. Finally, cloud-based manufacturing areas for further research are identified.

Alternative Manufacturing Concepts for Solid Oral Dosage Forms From Drug Nanosuspensions Using Fluid Dispensing and Forced Drying Technology.

Science.gov (United States)

Bonhoeffer, Bastian; Kwade, Arno; Juhnke, Michael

2018-03-01

Flexible manufacturing technologies for solid oral dosage forms with a continuous adjustability of the manufactured dose strength are of interest for applications in personalized medicine. This study explored the feasibility of using microvalve technology for the manufacturing of different solid oral dosage form concepts. Hard gelatin capsules filled with excipients, placebo tablets, and polymer films, placed in hard gelatin capsules after drying, were considered as substrates. For each concept, a basic understanding of relevant formulation parameters and their impact on dissolution behavior has been established. Suitable matrix formers, present either on the substrate or directly in the drug nanosuspension, proved to be essential to prevent nanoparticle agglomeration of the drug nanoparticles and to ensure a fast dissolution behavior. Furthermore, convection and radiation drying methods were investigated for the fast drying of drug nanosuspensions dispensed onto polymer films, which were then placed in hard gelatin capsules. Changes in morphology and in drug and matrix former distribution were observed for increasing drying intensity. However, even fast drying times below 1 min could be realized, while maintaining the nanoparticulate drug structure and a good dissolution behavior. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Manufacturing and testing of relevant scale mockup based on monoblock concept

International Nuclear Information System (INIS)

Di Pietro, E.; Orsini, A.; Sacchetti, M.; Libera, S.; Cardella, A.; Vieider, G.

1993-01-01

The results obtained from small-scale mockups manufactured on the monoblock design concept have proven that the solution appears promising for a conventional divertor operating with heat fluxes in the range 10 to 15 MW/m 2 with a thermal fatigue cycle exceeding 1000 cycles at full power. The divertor mock-up consists of six half meter-long armored tubes obtained by brazing CFC to TZM molybdenum alloy. Two types of CFC were used to investigate the advantages of 3-d CFCs with respect to more conventional and cheaper 2-d CFC. The brazing process utilizes three variants of a process developed in laboratory trials and based on selected combinations of active braze filler/CFC surface conditioning procedures. The supporting structure is based on the sliding support concept intended to assure a compromise between the requested thermal stability of the component and the buildup of secondary stresses deriving from mechanical constraints. The FE thermal and thermal mechanical analysis of the divertor mockup structure is reported and the critical areas of sliding support are highlighted for comparison with experimental results. The main results of NDE and experimental high heat flux tests are reported and discussed

Reconfigurable Manufacturing System Design and Implementation

DEFF Research Database (Denmark)

Hadar, Ronen; Bilberg, Arne

2013-01-01

is meant primarily to show the physical feasibility of the system and provide a first-look into a real production application of the RMS ideas. The purpose of this paper is to present the design of the manufacturer’s RMS proof of concept, its implementation, and evaluation results.......Reconfigurable Manufacturing Systems (RMS) have been an increasing area of interest in the research arena. However, it seems that current literature is lacking application and implementation cases where RMS are simulated, tested, and evaluated as a feasible manufacturing concept. A Manufacturer...... of Consumer Goods identified the potential of the RMS concept and decided to investigate the concept in a real production installation. The result of this development is a proof of concept of a changeable and reconfigurable assembly and decoration system based on the principles of RMS. This proof of concept...

Study for Manufacturing of ITER TF Coil Radial Plates

International Nuclear Information System (INIS)

Fietz, W.H.; Muetzel, W.

2006-01-01

During the previous design phase of ITER the ITER Toroidal Field Model Coil (TFMC) has been built to verify the TF coil concept of ITER and to proof the feasibility of an industrial fabrication of such a coil. In April 2004, Forschungszentrum and BNG, started a Manufacturing Study for the full scale Radial Plates (RP) of the TF Coils in the frame of an EFDA task. The main part of the Study was to develop feasible concepts of the technology for the manufacturing of the Full Scale Radial Plates starting with the raw material until final testing. The Feasibility Study has covered all manufacturing steps that are necessary for production of the RP. It has included as well a basic layout for the manufacturing process. During the work several proposals for the single manufacturing work steps have been developed. After that an evaluation of the found proposals has taken place. The most feasible proposals have been combined to manufacturing concepts. Finally two main Concepts were elaborated and evaluated: Concept 1 includes the premachining of segments with grooves, the welding of the segments and the final machining of the RP. Concept 2 includes the welding of not machined small segments to the D-shape of the RP and the following machining of the surface and grooves. Both Concepts will be described in detail with a comparison of tooling and manufacturing details, achievement of technological requirements as well as with the requirements coming from the overall time schedule. Based on the results of the assessment of the different concepts and manufacturing techniques Concept 1 shows some advantages compared to Concept 2. These will be described in the paper. In addition a proposal about additional R(and)D in front of the later manufacturing will be made. (author)

Collaborative Cloud Manufacturing: Design of Business Model Innovations Enabled by Cyberphysical Systems in Distributed Manufacturing Systems

Directory of Open Access Journals (Sweden)

Erwin Rauch

2016-01-01

Full Text Available Collaborative cloud manufacturing, as a concept of distributed manufacturing, allows different opportunities for changing the logic of generating and capturing value. Cyberphysical systems and the technologies behind them are the enablers for new business models which have the potential to be disruptive. This paper introduces the topics of distributed manufacturing as well as cyberphysical systems. Furthermore, the main business model clusters of distributed manufacturing systems are described, including collaborative cloud manufacturing. The paper aims to provide support for developing business model innovations based on collaborative cloud manufacturing. Therefore, three business model architecture types of a differentiated business logic are discussed, taking into consideration the parameters which have an influence and the design of the business model and its architecture. As a result, new business models can be developed systematically and new ideas can be generated to boost the concept of collaborative cloud manufacturing within all sustainable business models.

Application of the threshold of toxicological concern concept to pharmaceutical manufacturing operations.

Science.gov (United States)

Dolan, David G; Naumann, Bruce D; Sargent, Edward V; Maier, Andrew; Dourson, Michael

2005-10-01

A scientific rationale is provided for estimating acceptable daily intake values (ADIs) for compounds with limited or no toxicity information to support pharmaceutical manufacturing operations. These ADIs are based on application of the "thresholds of toxicological concern" (TTC) principle, in which levels of human exposure are estimated that pose no appreciable risk to human health. The same concept has been used by the US Food and Drug Administration (FDA) to establish "thresholds of regulation" for indirect food additives and adopted by the Joint FAO/WHO Expert Committee on Food Additives for flavoring substances. In practice, these values are used as a statement of safety and indicate when no actions need to be taken in a given exposure situation. Pharmaceutical manufacturing relies on ADIs for cleaning validation of process equipment and atypical extraneous matter investigations. To provide practical guidance for handling situations where relatively unstudied compounds with limited or no toxicity data are encountered, recommendations are provided on ADI values that correspond to three categories of compounds: (1) compounds that are likely to be carcinogenic, (2) compounds that are likely to be potent or highly toxic, and (3) compounds that are not likely to be potent, highly toxic or carcinogenic. Corresponding ADIs for these categories of materials are 1, 10, and 100 microg/day, respectively.

Modern manufacturing engineering

CERN Document Server

2015-01-01

This book covers recent research and trends in Manufacturing Engineering. The chapters emphasize different aspects of the transformation from materials to products. It provides the reader with fundamental materials treatments and the integration of processes. Concepts such as green and lean manufacturing are also covered in this book.

Concept-Development of a Structure Supported Membrane for Deployable Space Applications - From Nature to Manufacture and Testing

Science.gov (United States)

Zander, Martin; Belvin, W. K.

2012-01-01

Current space applications of membrane structures include large area solar power arrays, solar sails, antennas, and numerous other large aperture devices like the solar shades of the new James Webb Space Telescope. These expandable structural systems, deployed in-orbit to achieve the desired geometry, are used to collect, reflect and/or transmit electromagnetic radiation. This work, a feasibility study supporting a diploma thesis, describes the systematic process for developing a biologically inspired concept for a structure supported (integrated) membrane, that features a rip stop principle, makes self-deployment possible and is part of an ultra-light weight space application. Novel manufacturing of membrane prototypes and test results are presented for the rip-stop concepts. Test data showed that the new membrane concept has a higher tear resistance than neat film of equivalent mass.

Toward New-Generation Intelligent Manufacturing

Directory of Open Access Journals (Sweden)

Ji Zhou

2018-02-01

Full Text Available Intelligent manufacturing is a general concept that is under continuous development. It can be categorized into three basic paradigms: digital manufacturing, digital-networked manufacturing, and new-generation intelligent manufacturing. New-generation intelligent manufacturing represents an in-depth integration of new-generation artificial intelligence (AI technology and advanced manufacturing technology. It runs through every link in the full life-cycle of design, production, product, and service. The concept also relates to the optimization and integration of corresponding systems; the continuous improvement of enterprises’ product quality, performance, and service levels; and reduction in resources consumption. New-generation intelligent manufacturing acts as the core driving force of the new industrial revolution and will continue to be the main pathway for the transformation and upgrading of the manufacturing industry in the decades to come. Human-cyber-physical systems (HCPSs reveal the technological mechanisms of new-generation intelligent manufacturing and can effectively guide related theoretical research and engineering practice. Given the sequential development, cross interaction, and iterative upgrading characteristics of the three basic paradigms of intelligent manufacturing, a technology roadmap for “parallel promotion and integrated development” should be developed in order to drive forward the intelligent transformation of the manufacturing industry in China. Keywords: Advanced manufacturing, New-generation intelligent manufacturing, Human-cyber-physical system, New-generation AI, Basic paradigms, Parallel promotion, Integrated development

Application of lean manufacturing concepts to drug discovery: rapid analogue library synthesis.

Science.gov (United States)

Weller, Harold N; Nirschl, David S; Petrillo, Edward W; Poss, Michael A; Andres, Charles J; Cavallaro, Cullen L; Echols, Martin M; Grant-Young, Katherine A; Houston, John G; Miller, Arthur V; Swann, R Thomas

2006-01-01

The application of parallel synthesis to lead optimization programs in drug discovery has been an ongoing challenge since the first reports of library synthesis. A number of approaches to the application of parallel array synthesis to lead optimization have been attempted over the years, ranging from widespread deployment by (and support of) individual medicinal chemists to centralization as a service by an expert core team. This manuscript describes our experience with the latter approach, which was undertaken as part of a larger initiative to optimize drug discovery. In particular, we highlight how concepts taken from the manufacturing sector can be applied to drug discovery and parallel synthesis to improve the timeliness and thus the impact of arrays on drug discovery.

Layout design optimization of dynamic environment flexible manufacturing systems

Directory of Open Access Journals (Sweden)

Jaber Abu Qudeiri

2015-06-01

Full Text Available The proper positioning of machine tools in flexible manufacturing system is one of the factors that lead to increase in production efficiency. Choosing the optimum position of machine tools curtails the total part handling cost between machine tools within the flexible manufacturing system. In this article, a two-stage approach is presented to investigate the best locations of the machine tools in flexible manufacturing system. The location of each machine tool is selected from the available specific and fixed locations in such a way that it will result in best throughput of the flexible manufacturing system. In the first stage of the two-stage approach, the throughput of randomly selected locations of the machine tool in flexible manufacturing system is computed by proposing a production simulation system. The production simulation system utilizes genetic algorithms to find the locations of the machine tools in flexible manufacturing system that achieve the maximum throughput of the flexible manufacturing system. In the second stage, the generated locations are fed into artificial neural network to find a relation between a machine tool’s location and the throughput that can be used to predict the throughput for any other set of locations. Artificial neural network will result in mitigating the computational time.

Computer-assisted generation of individual training concepts for advanced education in manufacturing metrology

International Nuclear Information System (INIS)

Werner, Teresa; Weckenmann, Albert

2010-01-01

Due to increasing requirements on the accuracy and reproducibility of measurement results together with a rapid development of novel technologies for the execution of measurements, there is a high demand for adequately qualified metrologists. Accordingly, a variety of training offers are provided by machine manufacturers, universities and other institutions. Yet, for an interested learner it is very difficult to define an optimal training schedule for his/her individual demands. Therefore, a computer-based assistance tool is developed to support a demand-responsive scheduling of training. Based on the difference between the actual and intended competence profile and under consideration of amending requirements, an optimally customized qualification concept is derived. For this, available training offers are categorized according to different dimensions: regarding contents of the course, but also intended target groups, focus of the imparted competences, implemented methods of learning and teaching, expected constraints for learning and necessary preknowledge. After completing a course, the achieved competences and the transferability of gathered knowledge are evaluated. Based on the results, recommendations for amending measures of learning are provided. Thus, a customized qualification for manufacturing metrology is facilitated, adapted to the specific needs and constraints of each individual learner

Rolling Force Prediction in Heavy Plate Rolling Based on Uniform Differential Neural Network

Directory of Open Access Journals (Sweden)

Fei Zhang

2016-01-01

Full Text Available Accurate prediction of the rolling force is critical to assuring the quality of the final product in steel manufacturing. Exit thickness of plate for each pass is calculated from roll gap, mill spring, and predicted roll force. Ideal pass scheduling is dependent on a precise prediction of the roll force in each pass. This paper will introduce a concept that allows obtaining the material model parameters directly from the rolling process on an industrial scale by the uniform differential neural network. On the basis of the characteristics that the uniform distribution can fully characterize the solution space and enhance the diversity of the population, uniformity research on differential evolution operator is made to get improved crossover with uniform distribution. When its original function is transferred with a transfer function, the uniform differential evolution algorithms can quickly solve complex optimization problems. Neural network structure and weights threshold are optimized by uniform differential evolution algorithm, and a uniform differential neural network is formed to improve rolling force prediction accuracy in process control system.

Virtual manufacturing in reality

Science.gov (United States)

Papstel, Jyri; Saks, Alo

2000-10-01

SMEs play an important role in manufacturing industry. But from time to time there is a shortage in resources to complete the particular order in time. Number of systems is introduced to produce digital information in order to support product and process development activities. Main problem is lack of opportunity for direct data transition within design system modules when needed temporary extension of design capacity (virtuality) or to implement integrated concurrent product development principles. The planning experience in the field is weakly used as well. The concept of virtual manufacturing is a supporting idea to solve this problem. At the same time a number of practical problems should be solved like information conformity, data transfer, unified technological concepts acceptation etc. In the present paper the proposed ways to solve the practical problems of virtual manufacturing are described. General objective is to introduce the knowledge-based CAPP system as missing module for Virtual Manufacturing in the selected product domain. Surface-centered planning concept based on STEP- based modeling principles, and knowledge-based process planning methodology will be used to gain the objectives. As a result the planning module supplied by design data with direct access, and supporting advising environment is expected. Mould producing SME would be as test basis.

Developing effective campaign messages to prevent neural tube defects: a qualitative assessment of women's reactions to advertising concepts.

Science.gov (United States)

Massi Lindsey, Lisa L; Silk, Kami J; Von Friederichs-Fitzwater, Marlene M; Hamner, Heather C; Prue, Christine E; Boster, Franklin J

2009-03-01

The incidence of neural tube defects (NTDs), serious birth defects of the brain and spine that affect approximately 3,000 pregnancies in the United States each year, can be reduced by 50-70% with daily periconceptional consumption of the B vitamin folic acid. Two studies were designed to assess college women's reactions to and perceptions of potential campaign advertising concepts derived from preproduction formative research to increase folic acid consumption through the use of a daily multivitamin. Study one assessed draft advertising concepts in eight focus groups (N = 71) composed of college-enrolled women in four cities geographically dispersed across the United States. Based on study one results, the concepts were revised and reassessed in study two with a different sample (eight focus groups; N = 73) of college women in the same four cities. Results indicated that participants generally responded favorably to concepts in each of the two studies, and provided insight into individual concepts to increase their overall appeal and effectiveness. The specific findings and implications of these results are discussed.

Case study of lean manufacturing application in a die casting manufacturing company

Science.gov (United States)

Ching, Ng Tan; Hoe, Clarence Chan Kok; Hong, Tang Sai; Ghobakhloo, Morteza; Pin, Chen Kah

2015-05-01

The case study of lean manufacturing aims to study the application of lean manufacturing in a die casting manufacturing company located in Pulau Penang, Malaysia. This case study describes mainly about the important concepts and applications of lean manufacturing which could gradually help the company in increasing the profit by studying and analyzing their current manufacturing process and company culture. Many approaches of lean manufacturing are studied in this project which includes: 5S housekeeping, Kaizen, and Takt Time. Besides, the lean tools mentioned, quality tool such as the House of Quality is being used as an analysis tool to continuously improve the product quality. In short, the existing lean culture in the company is studied and analyzed, with recommendations written at the end of this paper.

Lean Application to Manufacturing Ramp-Up

DEFF Research Database (Denmark)

Christensen, Irene; Rymaszewska, Anna

2016-01-01

. Abstracting from the extant literature, the authors considered the competitiveness of manufacturing companies from two principal perspectives: the leanness of the ramp-up process and the new-value creation of quality managers. While much of the literature fails to acknowledge that the roots of lean actually......This article provides a theoretical overview of the concepts of lean and manufacturing ramp-up in an attempt to conceptualize the strategic areas in which lean philosophy and principles can be applied for continuous improvements. The application of lean principles during the final stage of a new...... product development process, that is, the ramp-up process, is a critical, early enabler of lean manufacturing. The manufacturing strategy literature conceptualizes a state of “leanness in operations,” which can consolidate both the concepts of lean and manufacturing ramp-up, providing a dual perspective...

New Concepts and Theories For Intelligent Control of Cellular Manufacturing Systems

DEFF Research Database (Denmark)

Langer, Gilad

1996-01-01

This paper will present some new theories such as biological manufacturing system, the fractal factory theory, holonic manufacturing systems, agile manufacturing, object orientation, multi-agent theory, artificial intelligence, and artificial life in the context of manufacturing systems....... The paper tries to encapsulate the main area of my Ph.D. thesis research which will evolve around the idea of integrating intelligent elements into the control systems of the manufacturing systems. Furthermore it intends to show how the curriculum and discussions of the IPS Ph.D. course will and have...... contributed to my research. The research will concentrate on integration of manufacturing units by use of intelligent control mechanisms, information technology and the material handling as the key integrators....

Analysis Of Lean Accounting JIT And Balance Scorecard In The Companys Lean Manufacturing

OpenAIRE

Irwan Sutirman Wahdiat

2015-01-01

This research purpose to analyze the concept of Lean Manufacturing which is influenced by the role of JIT. This research uses a theoretical approach. This study portrait thinking companies that have yet to implement lean manufacturing and after doing the concept of lean manufacturing. This study shows that the concept of lean manufacturing can make the company more efficient and effective. This paper shows some lean manufacturing dimensions of the researchers previous researchers. This study ...

The neural signature of self-concept development in adolescence: The role of domain and valence distinctions

Directory of Open Access Journals (Sweden)

R. van der Cruijsen

2018-04-01

Full Text Available Neuroimaging studies in adults showed that cortical midline regions including medial prefrontal cortex (mPFC and posterior parietal cortex (PPC are important in self-evaluations. The goals of this study were to investigate the contribution of these regions to self-evaluations in late childhood, adolescence, and early adulthood, and to examine whether these differed per domain (academic, physical and prosocial and valence (positive versus negative. Also, we tested whether this activation changes across adolescence. For this purpose, participants between ages 11–21-years (N = 150 evaluated themselves on trait sentences in an fMRI session. Behaviorally, adolescents rated their academic traits less positively than children and young adults. The neural analyses showed that evaluating self-traits versus a control condition was associated with increased activity in mPFC (domain-general effect, and positive traits were associated with increased activity in ventral mPFC (valence effect. Self-related mPFC activation increased linearly with age, but only for evaluating physical traits. Furthermore, an adolescent-specific decrease in striatum activation for positive self traits was found. Finally, we found domain-specific neural activity for evaluating traits in physical (dorsolateral PFC, dorsal mPFC and academic (PPC domains. Together, these results highlight the importance of domain distinctions when studying self-concept development in late childhood, adolescence, and early adulthood. Keywords: Self, fMRI, Adolescence, Development, Medial prefrontal cortex, Self-concept

An Overview of Cloud Implementation in the Manufacturing Process Life Cycle

Science.gov (United States)

Kassim, Noordiana; Yusof, Yusri; Hakim Mohamad, Mahmod Abd; Omar, Abdul Halim; Roslan, Rosfuzah; Aryanie Bahrudin, Ida; Ali, Mohd Hatta Mohamed

2017-08-01

The advancement of information and communication technology (ICT) has changed the structure and functions of various sectors and it has also started to play a significant role in modern manufacturing in terms of computerized machining and cloud manufacturing. It is important for industries to keep up with the current trend of ICT for them to be able survive and be competitive. Cloud manufacturing is an approach that wanted to realize a real-world manufacturing processes that will apply the basic concept from the field of Cloud computing to the manufacturing domain called Cloud-based manufacturing (CBM) or cloud manufacturing (CM). Cloud manufacturing has been recognized as a new paradigm for manufacturing businesses. In cloud manufacturing, manufacturing companies need to support flexible and scalable business processes in the shop floor as well as the software itself. This paper provides an insight or overview on the implementation of cloud manufacturing in the modern manufacturing processes and at the same times analyses the requirements needed regarding process enactment for Cloud manufacturing and at the same time proposing a STEP-NC concept that can function as a tool to support the cloud manufacturing concept.

Manufacturing engineering and technology

CERN Document Server

Kalpakjian, Serope; Vijai Sekar, K S

2014-01-01

For courses in manufacturing processes at two- or four-year schools. An up-to-date text that provides a solid background in manufacturing processes. Manufacturing Engineering and Technology, SI Edition, 7e, presents a mostly qualitative description of the science, technology, and practice of manufacturing. This includes detailed descriptions of manufacturing processes and the manufacturing enterprise that will help introduce students to important concepts. With a total of 120 examples and case studies, up-to-date and comprehensive coverage of all topics, and superior two-color graphics, this text provides a solid background for manufacturing students and serves as a valuable reference text for professionals. Teaching and Learning Experience To provide a better teaching and learning experience, for both instructors and students, this program will: * Apply Theory and/or Research: An excellent overview of manufacturing conceptswith a balance of relevant fundamentals and real-world practices. * Engage Students: E...

Composite fuselage crown panel manufacturing technology

Science.gov (United States)

Willden, Kurtis; Metschan, S.; Grant, C.; Brown, T.

1992-01-01

Commercial fuselage structures contain significant challenges in attempting to save manufacturing costs with advanced composite technology. Assembly issues, material costs, and fabrication of elements with complex geometry are each expected to drive the cost of composite fuselage structures. Boeing's efforts under the NASA ACT program have pursued key technologies for low-cost, large crown panel fabrication. An intricate bond panel design and manufacturing concepts were selected based on the efforts of the Design Build Team (DBT). The manufacturing processes selected for the intricate bond design include multiple large panel fabrication with the Advanced Tow Placement (ATP) process, innovative cure tooling concepts, resin transfer molding of long fuselage frames, and utilization of low-cost material forms. The process optimization for final design/manufacturing configuration included factory simulations and hardware demonstrations. These efforts and other optimization tasks were instrumental in reducing cost by 18 percent and weight by 45 percent relative to an aluminum baseline. The qualitative and quantitative results of the manufacturing demonstrations were used to assess manufacturing risks and technology readiness.

Modernization of the Radioisotopes Production Laboratory of the La Reina Nuclear Center in Chile: Incorporating advanced concepts of safety and good manufacturing practices

International Nuclear Information System (INIS)

Lagos Espinoza, Silvia

2014-01-01

A radioisotopes and radiopharmaceuticals production laboratory was established in Chile in the 1960s for research activities. From 1967 until January 2012, it was dedicated to the manufacturing of radioisotopes and radiopharmaceuticals for medical diagnosis and treatment purposes. In 2012, modernization of the facility’s design and technology began as part of the IAEA technical cooperation project, Modernizing the Radioisotopes Production Laboratory of La Reina Nuclear Centre by Incorporating Advanced Concepts of Safety and Good Manufacturing Practices, (CHI4022)

Implementation Issues and Challenges in Applying Lean Manufacturing Tools & Techniques in Different Manufacturing Environments

OpenAIRE

Low, Kwee Ang

2005-01-01

Lean Manufacturing has made a significant impact on both the academic and manufacturing circles in the last decade. Fostered by a rapid spread into many other industrial sectors beyond the automotive industry, there has been significant development and "localisation" of the Lean Manufacturing concept in both developed and developing countries worldwide. Despite its successful application in a wide range of industries, little research has been carried out on its successful application outside ...

On the neural mechanisms subserving consciousness and attention

Directory of Open Access Journals (Sweden)

Catherine eTallon-Baudry

2012-01-01

Full Text Available Consciousness, as described in the experimental literature, is a multi-faceted phenomenon, that impinges on other well-studied concepts such as attention and control. Do consciousness and attention refer to different aspects of the same core phenomenon, or do they correspond to distinct functions? One possibility to address this question is to examine the neural mechanisms underlying consciousness and attention. If consciousness and attention pertain to the same concept, they should rely on shared neural mechanisms. Conversely, if their underlying mechanisms are distinct, then consciousness and attention should be considered as distinct entities. This paper therefore reviews neurophysiological facts arguing in favor or against a tight relationship between consciousness and attention. Three neural mechanisms that have been associated with both attention and consciousness are examined (neural amplification, involvement of the fronto-parietal network, and oscillatory synchrony, to conclude that the commonalities between attention and consciousness at the neural level may have been overestimated. Last but not least, experiments in which both attention and consciousness were probed at the neural level point toward a dissociation between the two concepts. It therefore appears from this review that consciousness and attention rely on distinct neural properties, although they can interact at the behavioral level. It is proposed that a "cumulative influence model", in which attention and consciousness correspond to distinct neural mechanisms feeding a single decisional process leading to behavior, fits best with available neural and behavioral data. In this view, consciousness should not be considered as a top-level executive function but should rather be defined by its experiential properties.

An Activity for Demonstrating the Concept of a Neural Circuit

Science.gov (United States)

Kreiner, David S.

2012-01-01

College students in two sections of a general psychology course participated in a demonstration of a simple neural circuit. The activity was based on a neural circuit that Jeffress proposed for localizing sounds. Students in one section responded to a questionnaire prior to participating in the activity, while students in the other section…

CAD And Distributed Manufacturing Solutions for Pellet Boiler Producers

Directory of Open Access Journals (Sweden)

Timur Mamut

2016-12-01

Full Text Available The paper is summarizing the research activities that had been carried out for defining an appropriate manufacturing concept and the system architecture for a manufacturing plant of pellet boilers. The concept has been validated through the implementation of a solution of computer integrated manufacturing that includes a CAD platform and a CAM facility including laser cutting machines, rolling and welding machines and advanced technologies for assembly, quality control and testing.

Neural synchrony in cortical networks: history, concept and current status

Directory of Open Access Journals (Sweden)

Peter Uhlhaas

2009-07-01

Full Text Available Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies.

Adaptive training of neural networks for control of autonomous mobile robots

NARCIS (Netherlands)

Steur, E.; Vromen, T.; Nijmeijer, H.; Fossen, T.I.; Nijmeijer, H.; Pettersen, K.Y.

2017-01-01

We present an adaptive training procedure for a spiking neural network, which is used for control of a mobile robot. Because of manufacturing tolerances, any hardware implementation of a spiking neural network has non-identical nodes, which limit the performance of the controller. The adaptive

Green manufacturing processes and systems

Energy Technology Data Exchange (ETDEWEB)

Davim, J. Paulo (ed.) [Aveiro Univ. (Portugal). Dept. of Mechanical Engineering, Campus Universitario de Santiago

2013-02-01

This book provides the recent advances on green manufacturing processes and systems for modern industry. Chapter 1 provides information on sustainable manufacturing through environmentally-friendly machining. Chapter 2 is dedicated to environmentally-friendly machining: vegetable based cutting fluids. Chapter 3 describes environmental-friendly joining of tubes. Chapter 4 contains information on concepts, methods and strategies for zero-waste in manufacturing. Finally, chapter 5 is dedicated to the application of hybrid MCDM approach for selecting the best tyre recycling process.

Quality assurance in tube manufacture

International Nuclear Information System (INIS)

Depken, H.

1976-01-01

Reliability in service essential for many high-technology products fabricated today. This is particularly the case within the nuclear industry. Here defective materials or components may have diastrous consequences to the safety of human beings and the environment. A new concept - Quality Assurance - originates from this industry. The concept implies that all contractors, fabricators and material manufactures involved must prove that the quality control system used, fulfits particular requirements at all manufacturing, inspection and testing stages. These requirement are laid down in two standards issued by the U.S. Atomic Energy Commission and the American Society of Mechanical Engineers. These standards are discussed in the paper. As a manufacturer of steel products for nuclear applications Sandvik has been forced to establish a quality assurance system according to these principles. The Sandvik approach is briefly described with regard to organisation and other major quality assurance activities. Further the education and training of operators and technicians is touched upon. Finally some viewpoints regarding audits performed by customers of steel manufacturers are expressed. (author)

Additive Feed Forward Control with Neural Networks

DEFF Research Database (Denmark)

Sørensen, O.

1999-01-01

This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied....... A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...

[Chinese medicine industry 4.0：advancing digital pharmaceutical manufacture toward intelligent pharmaceutical manufacture].

Science.gov (United States)

Cheng, Yi-Yu; Qu, Hai-Bin; Zhang, Bo-Li

2016-01-01

A perspective analysis on the technological innovation in pharmaceutical engineering of Chinese medicine unveils a vision on "Future Factory" of Chinese medicine industry in mind. The strategy as well as the technical roadmap of "Chinese medicine industry 4.0" is proposed, with the projection of related core technology system. It is clarified that the technical development path of Chinese medicine industry from digital manufacture to intelligent manufacture. On the basis of precisely defining technical terms such as process control, on-line detection and process quality monitoring for Chinese medicine manufacture, the technical concepts and characteristics of intelligent pharmaceutical manufacture as well as digital pharmaceutical manufacture are elaborated. Promoting wide applications of digital manufacturing technology of Chinese medicine is strongly recommended. Through completely informationized manufacturing processes and multi-discipline cluster innovation, intelligent manufacturing technology of Chinese medicine should be developed, which would provide a new driving force for Chinese medicine industry in technology upgrade, product quality enhancement and efficiency improvement. Copyright© by the Chinese Pharmaceutical Association.

QUALITY IN WORLD CLASS MANUFACTURING

Directory of Open Access Journals (Sweden)

Slavko Arsovski

2011-12-01

Full Text Available The World Class Manufacturing (WCM is a contemporary concept that is applied by the world leaders in the business. In this concept, one of the nine pillars is directly related to the quality and the other eight are related to it indirectly. That is why is very important to investigate relations between this concept and concept of model of quality. In the end of this paper are appointed the examples of best practice.

Fuzzy neural network theory and application

CERN Document Server

Liu, Puyin

2004-01-01

This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to he

Features of the Manufacturing Vision Development Process

DEFF Research Database (Denmark)

Dukovska-Popovska, Iskra; Riis, Jens Ove; Boer, Harry

2005-01-01

of action research. The methodology recommends wide participation of people from different hierarchical and functional positions, who engage in a relatively short, playful and creative process and come up with a vision (concept) for the future manufacturing system in the company. Based on three case studies......This paper discusses the key features of the process of Manufacturing Vision Development, a process that enables companies to develop their future manufacturing concept. The basis for the process is a generic five-phase methodology (Riis and Johansen, 2003) developed as a result of ten years...... of companies going through the initial phases of the methodology, this research identified the key features of the Manufacturing Vision Development process. The paper elaborates the key features by defining them, discussing how and when they can appear, and how they influence the process....

Predictive Manufacturing: A Classification Strategy to Predict Product Failures

DEFF Research Database (Denmark)

Khan, Abdul Rauf; Schiøler, Henrik; Kulahci, Murat

2018-01-01

manufacturing analytics model that employs a big data approach to predicting product failures; third, we illustrate the issue of high dimensionality, along with statistically redundant information; and, finally, our proposed method will be compared against the well-known classification methods (SVM, K......-nearest neighbor, artificial neural networks). The results from real data show that our predictive manufacturing analytics approach, using genetic algorithms and Voronoi tessellations, is capable of predicting product failure with reasonable accuracy. The potential application of this method contributes...... to accurately predicting product failures, which would enable manufacturers to reduce production costs without compromising product quality....

Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system

International Nuclear Information System (INIS)

Fazlollahtabar, Hamed; Saidi-Mehrabad, Mohammad; Balakrishnan, Jaydeep

2015-01-01

This paper proposes an integrated Markovian and back propagation neural network approaches to compute reliability of a system. While states of failure occurrences are significant elements for accurate reliability computation, Markovian based reliability assessment method is designed. Due to drawbacks shown by Markovian model for steady state reliability computations and neural network for initial training pattern, integration being called Markov-neural is developed and evaluated. To show efficiency of the proposed approach comparative analyses are performed. Also, for managerial implication purpose an application case for multiple automated guided vehicles (AGVs) in manufacturing networks is conducted. - Highlights: • Integrated Markovian and back propagation neural network approach to compute reliability. • Markovian based reliability assessment method. • Managerial implication is shown in an application case for multiple automated guided vehicles (AGVs) in manufacturing networks

Boolean Factor Analysis by Attractor Neural Network

Czech Academy of Sciences Publication Activity Database

Frolov, A. A.; Húsek, Dušan; Muraviev, I. P.; Polyakov, P.Y.

2007-01-01

Roč. 18, č. 3 (2007), s. 698-707 ISSN 1045-9227 R&D Projects: GA AV ČR 1ET100300419; GA ČR GA201/05/0079 Institutional research plan: CEZ:AV0Z10300504 Keywords : recurrent neural network * Hopfield-like neural network * associative memory * unsupervised learning * neural network architecture * neural network application * statistics * Boolean factor analysis * dimensionality reduction * features clustering * concepts search * information retrieval Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.769, year: 2007

More Exact Approaches to Modernization and Renewal of the Manufacturing Base

Directory of Open Access Journals (Sweden)

Naqib Daneshjo

2017-08-01

Full Text Available Globalized development strategies in industry are currently focused on developing intelligent manufacturing concepts called Industry 4.0. Companies around the world will be forced to adopt this concept, especially in terms of maintaining competitiveness. One of the most serious obstacles of developing the concept of intelligent production is physical and moral obsolescence of the manufacturing base in general. Despite the fact that companies have historically renewed their manufacturing base, automated and robotized manufacturing processes and systems, nowadays highly current question of determining the form and timing of further modernization and renewal of the manufacturing base for intelligent production purposes. The authors present a model to determine optimal time to start upgrading and renewing the production base based on formulating and comparing costs of means of production throughout their lifecycle, including consideration of their moral obsolescence.

Semiconductors integrated circuit design for manufacturability

CERN Document Server

Balasinki, Artur

2011-01-01

Because of the continuous evolution of integrated circuit manufacturing (ICM) and design for manufacturability (DfM), most books on the subject are obsolete before they even go to press. That's why the field requires a reference that takes the focus off of numbers and concentrates more on larger economic concepts than on technical details. Semiconductors: Integrated Circuit Design for Manufacturability covers the gradual evolution of integrated circuit design (ICD) as a basis to propose strategies for improving return-on-investment (ROI) for ICD in manufacturing. Where most books put the spotl

Research overview: Advanced Manufacturing in Switzerland

OpenAIRE

Schärer, Claudia

2016-01-01

SATW is convinced that industrial production methods will see fundamental changes over the coming years. Mastering new production technologies (advanced manufacturing) such as additive manufacturing and industry 4.0 will be vital to keep Swiss production at a competitive level. New additive manufacturing processes such as 3D printing offer revolutionary opportunities and have the potential to replace traditional production methods. Industry 4.0 has seen the definition of a new concept for...

UAV Trajectory Modeling Using Neural Networks

Science.gov (United States)

Xue, Min

2017-01-01

Large amount of small Unmanned Aerial Vehicles (sUAVs) are projected to operate in the near future. Potential sUAV applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. sUAVs are expected to operate in the uncontrolled Class G airspace, which is at or below 500 feet above ground level (AGL), where many static and dynamic constraints exist, such as ground properties and terrains, restricted areas, various winds, manned helicopters, and conflict avoidance among sUAVs. How to enable safe, efficient, and massive sUAV operations at the low altitude airspace remains a great challenge. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative works on establishing infrastructure and developing policies, requirement, and rules to enable safe and efficient sUAVs' operations. To achieve this goal, it is important to gain insights of future UTM traffic operations through simulations, where the accurate trajectory model plays an extremely important role. On the other hand, like what happens in current aviation development, trajectory modeling should also serve as the foundation for any advanced concepts and tools in UTM. Accurate models of sUAV dynamics and control systems are very important considering the requirement of the meter level precision in UTM operations. The vehicle dynamics are relatively easy to derive and model, however, vehicle control systems remain unknown as they are usually kept by manufactures as a part of intellectual properties. That brings challenges to trajectory modeling for sUAVs. How to model the vehicle's trajectories with unknown control system? This work proposes to use a neural network to model a vehicle's trajectory. The neural network is first trained to learn the vehicle's responses at numerous conditions. Once being fully trained, given current vehicle states, winds, and desired future trajectory, the neural

A relativity concept in mesenchymal stromal cell manufacturing.

Science.gov (United States)

Martin, Ivan; De Boer, Jan; Sensebe, Luc

2016-05-01

Mesenchymal stromal cells (MSCs) are being experimentally tested in several biological systems and clinical settings with the aim of verifying possible therapeutic effects for a variety of indications. MSCs are also known to be heterogeneous populations, with phenotypic and functional features that depend heavily on the individual donor, the harvest site, and the culture conditions. In the context of this multidimensional complexity, a recurrent question is whether it is feasible to produce MSC batches as "standard" therapeutics, possibly within scalable manufacturing systems. Here, we provide a short overview of the literature on different culture methods for MSCs, including those employing innovative technologies, and of some typically assessed functional features (e.g., growth, senescence, genomic stability, clonogenicity, etc.). We then offer our perspective of a roadmap on how to identify and refine manufacturing systems for MSCs intended for specific clinical indications. We submit that the vision of producing MSCs according to a unique standard, although commercially attractive, cannot yet be scientifically substantiated. Instead, efforts should be concentrated on standardizing methods for characterization of MSCs generated by different groups, possibly covering a vast gamut of functionalities. Such assessments, combined with hypotheses on the therapeutic mode of action and associated clinical data, should ultimately allow definition of in-process controls and measurable release criteria for MSC manufacturing. These will have to be validated as predictive of potency in suitable pre-clinical models and of therapeutic efficacy in patients. Copyright © 2016 International Society for Cellular Therapy. Published by Elsevier Inc. All rights reserved.

An intuitive concept for manufacturing and inspecting of aspherical components

Science.gov (United States)

Chou, Hsiao-Yu; Chang, Keng-Shou

2011-09-01

In this paper we propose an intuitive concept for manufacturing and inspecting of aspherical components. Two types, parabolic and cylinder, of plano-convex and plano-concave aspherical lenses were made by LOH 120S form generation machine. Three form error measurement methods were used known as coordinate measuring machine (CMM), interferometer with CGH null lens and inspection with combined pair lenses. Ultra high accuracy CMM from Panasonic Co., CGH cylinder null and CGH aspheric null from Diffraction International and OWI 150 ASPH CGH interferometer from OptoTech GmbH play the roll for measurement. CMM was used as a surface profiler to inspect the surface shape, and the software GRAPHER was also used as analysis tool to exam asphere numerical datum. The difference between theoretical and practical is as a surface polishing revised reference. The finished plano-convex and plano-concave aspherical lenses can be combined to be a plane lens. The individual and combined lenses were inspected on OPTOTECH OWI 150 ASPH CGH interferometer. The compared interference patterns have shown with the Diffration International CGH Aspheric Null "ASPHERIC 1" and CGH Cylinder Null "H80F2C". Through the procedure, the combined plano-convex and plano-concave aspherical lenses should be a perfect match plane lens and the individual lens might be an aspherical test standard element for quick inspection.

A universal concept based on cellular neural networks for ultrafast and flexible solving of differential equations.

Science.gov (United States)

Chedjou, Jean Chamberlain; Kyamakya, Kyandoghere

2015-04-01

This paper develops and validates a comprehensive and universally applicable computational concept for solving nonlinear differential equations (NDEs) through a neurocomputing concept based on cellular neural networks (CNNs). High-precision, stability, convergence, and lowest-possible memory requirements are ensured by the CNN processor architecture. A significant challenge solved in this paper is that all these cited computing features are ensured in all system-states (regular or chaotic ones) and in all bifurcation conditions that may be experienced by NDEs.One particular quintessence of this paper is to develop and demonstrate a solver concept that shows and ensures that CNN processors (realized either in hardware or in software) are universal solvers of NDE models. The solving logic or algorithm of given NDEs (possible examples are: Duffing, Mathieu, Van der Pol, Jerk, Chua, Rössler, Lorenz, Burgers, and the transport equations) through a CNN processor system is provided by a set of templates that are computed by our comprehensive templates calculation technique that we call nonlinear adaptive optimization. This paper is therefore a significant contribution and represents a cutting-edge real-time computational engineering approach, especially while considering the various scientific and engineering applications of this ultrafast, energy-and-memory-efficient, and high-precise NDE solver concept. For illustration purposes, three NDE models are demonstratively solved, and related CNN templates are derived and used: the periodically excited Duffing equation, the Mathieu equation, and the transport equation.

Lean Manufacturing, Mass Customization and their relationships - empirical findings

DEFF Research Database (Denmark)

Christiansen, Thomas Bøhm

2004-01-01

manufacturing companies in 2001-02. This study fills a void in existing research by exploring relationships between bundles of lean manufacturing practices and bundles of mass customization practices. This study is based on a questionnaire that is developed from two existing questionnaires each investigating...... bundles of lean manufacturing practices and bundles of mass customization practices separately. Here, these bundles of practices are related. The results indicate that there are no direct relationships between the lean manufacturing and the mass customization practices, but that the combination of some...... sets of practices can explain differences in performance on important dimensions. The general conclusion, however, is that there are only weak relationships between the two concepts, hence this study suggests that the concepts of lean manufacturing and mass customization at present are more mutually...

A brain-based account of "basic-level" concepts.

Science.gov (United States)

Bauer, Andrew James; Just, Marcel Adam

2017-11-01

This study provides a brain-based account of how object concepts at an intermediate (basic) level of specificity are represented, offering an enriched view of what it means for a concept to be a basic-level concept, a research topic pioneered by Rosch and others (Rosch et al., 1976). Applying machine learning techniques to fMRI data, it was possible to determine the semantic content encoded in the neural representations of object concepts at basic and subordinate levels of abstraction. The representation of basic-level concepts (e.g. bird) was spatially broad, encompassing sensorimotor brain areas that encode concrete object properties, and also language and heteromodal integrative areas that encode abstract semantic content. The representation of subordinate-level concepts (robin) was less widely distributed, concentrated in perceptual areas that underlie concrete content. Furthermore, basic-level concepts were representative of their subordinates in that they were neurally similar to their typical but not atypical subordinates (bird was neurally similar to robin but not woodpecker). The findings provide a brain-based account of the advantages that basic-level concepts enjoy in everyday life over subordinate-level concepts: the basic level is a broad topographical representation that encompasses both concrete and abstract semantic content, reflecting the multifaceted yet intuitive meaning of basic-level concepts. Copyright © 2017 Elsevier Inc. All rights reserved.

Cross-brain neurofeedback: scientific concept and experimental platform.

Directory of Open Access Journals (Sweden)

Lian Duan

Full Text Available The present study described a new type of multi-person neurofeedback with the neural synchronization between two participants as the direct regulating target, termed as "cross-brain neurofeedback." As a first step to implement this concept, an experimental platform was built on the basis of functional near-infrared spectroscopy, and was validated with a two-person neurofeedback experiment. This novel concept as well as the experimental platform established a framework for investigation of the relationship between multiple participants' cross-brain neural synchronization and their social behaviors, which could provide new insight into the neural substrate of human social interactions.

A New Controller to Enhance PV System Performance Based on Neural Network

Directory of Open Access Journals (Sweden)

Roshdy A AbdelRassoul

2017-06-01

Full Text Available In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.

An empirical review of lean manufacturing and their strategies

Directory of Open Access Journals (Sweden)

Virender Chahal

2017-07-01

Full Text Available The theory of lean manufacturing provides the quality of the products in minimum cost and pro-vides customer satisfaction. Today, the competition level is very high and every industry tries to supply high quality products in nominal cost, so lean is the latest tool to achieve. The objective of this paper is to study different lean concepts under various lean strategies. This study helps to find out the status of lean manufacturing and its ways of implementation. Also in this paper, there is a discussion about lean manufacturing concept, lean waste, lean strategies, lean barriers and cycle of lean implementation. This paper presents a literature review to clear the status of lean manufacturing and their strategies with help of collection of relevant papers.

Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems.

Science.gov (United States)

Chou, Zane; Lim, Jeffrey; Brown, Sophie; Keller, Melissa; Bugbee, Joseph; Broccard, Frédéric D; Khraiche, Massoud L; Silva, Gabriel A; Cauwenberghs, Gert

2015-01-01

Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing.

Applying Fuzzy Artificial Neural Network OSPF to develop Smart ...

African Journals Online (AJOL)

pc

2018-03-05

Mar 5, 2018 ... Fuzzy Artificial Neural Network to create Smart Routing. Protocol Algorithm. ... manufactured mental aptitude strategy. The capacity to study .... Based Energy Efficiency in Wireless Sensor Networks: A Survey",. International ...

Key Features of the Manufacturing Vision Development Process

DEFF Research Database (Denmark)

Dukovska-Popovska, Iskra; Riis, Jens Ove; Boer, Harry

2005-01-01

of action research. The methodology recommends wide participation of people from different hierarchical and functional positions, who engage in a relatively short, playful and creative process and come up with a vision (concept) for the future manufacturing system in the company. Based on three case studies......This paper discusses the key features of the process of Manufacturing Vision Development, a process that enables companies to develop their future manufacturing concept. The basis for the process is a generic five-phase methodology (Riis and Johansen 2003) developed as a result of ten years...... of companies going through the initial phases of the methodology, this research identified the key features of the Manufacturing Vision Development process. The paper elaborates the key features by defining them, discussing how and when they can appear, and how they influence the process....

Space station automation study: Automation requirements derived from space manufacturing concepts. Volume 1: Executive summary

Science.gov (United States)

1984-01-01

The electroepitaxial process and the Very Large Scale Integration (VLSI) circuits (chips) facilities were chosen because each requires a very high degree of automation, and therefore involved extensive use of teleoperators, robotics, process mechanization, and artificial intelligence. Both cover a raw materials process and a sophisticated multi-step process and are therfore highly representative of the kinds of difficult operation, maintenance, and repair challenges which can be expected for any type of space manufacturing facility. Generic areas were identified which will require significant further study. The initial design will be based on terrestrial state-of-the-art hard automation. One hundred candidate missions were evaluated on the basis of automation portential and availability of meaning ful knowldege. The design requirements and unconstrained design concepts developed for the two missions are presented.

Manufacturing technology for practical Josephson voltage normals

International Nuclear Information System (INIS)

Kohlmann, Johannes; Kieler, Oliver

2016-01-01

In this contribution we present the manufacturing technology for the fabrication of integrated superconducting Josephson serial circuits for voltage normals. First we summarize some foundations for Josephson voltage normals and sketch the concept and the setup of the circuits, before we describe the manufacturing technology form modern practical Josephson voltage normals.

Neural networks. A new analytical tool, applicable also in nuclear technology

International Nuclear Information System (INIS)

Stritar, A.

1992-01-01

The basic concept of neural networks and back propagation learning algorithm are described. The behaviour of typical neural network is demonstrated on a simple graphical case. A short literature survey about the application of neural networks in nuclear science and engineering is made. The application of the neural network to the probability density calculation is shown. (author) [sl

Design and Evaluation of a Reconfigurable Manufacturing System

DEFF Research Database (Denmark)

Zhang, Shuai; Li, Yang; Bilberg, Arne

2014-01-01

In modern manufacturing industry, reconfigurable manufacturing system (RMS) is a promising concept in the research arena. A new RMS system structure has been recently designed by a large consumer goods manufacturer in Europe, aiming to improve its production efficiency. This article shows...... implemented in this consumer goods manufacturer can be effectively (re)configured as part of the daily operations, and the configuration is analyzed by computer simulation before release. Flexibility can be increased considerably meanwhile the system can maintain an acceptable productivity....

Additive manufacturing technologies 3D printing, rapid prototyping, and direct digital manufacturing

CERN Document Server

Gibson, Ian; Stucker, Brent

2015-01-01

This book covers in detail the various aspects of joining materials to form parts. A conceptual overview of rapid prototyping and layered manufacturing is given,  beginning with the fundamentals so that readers can get up to speed quickly. Unusual and emerging applications such as micro-scale manufacturing, medical applications, aerospace, and rapid manufacturing are also discussed. This book provides a comprehensive overview of rapid prototyping technologies as well as support technologies such as software systems, vacuum casting, investment casting, plating, infiltration and other systems. This book also: Reflects recent developments and trends and adheres to the ASTM, SI, and other standards Includes chapters on automotive technology, aerospace technology and low-cost AM technologies Provides a broad range of technical questions to ensure comprehensive understanding of the concepts covered  

CONCEPTION OF USE VIBROACOUSTIC SIGNALS AND NEURAL NETWORKS FOR DIAGNOSING OF CHOSEN ELEMENTS OF INTERNAL COMBUSTION ENGINES IN CAR VEHICLES

Directory of Open Access Journals (Sweden)

Piotr CZECH

2014-03-01

Full Text Available Currently used diagnostics systems are not always efficient and do not give straightforward results which allow for the assessment of the technological condition of the engine or for the identification of the possible damages in their early stages of development. Growing requirements concerning durability, reliability, reduction of costs to minimum and decrease of negative influence on the natural environment are the reasons why there is a need to acquire information about the technological condition of each of the elements of a vehicle during its exploitation. One of the possibilities to achieve information about technological condition of a vehicle are vibroacoustic phenomena. Symptoms of defects, achieved as a result of advanced methods of vibroacoustic signals processing can serve as models which can be used during construction of intelligent diagnostic system based on artificial neural networks. The work presents conception of use artificial neural networks in the task of combustion engines diagnosis.

Model-based Engineering for the Integration of Manufacturing Systems with Advanced Analytics

OpenAIRE

Lechevalier , David; Narayanan , Anantha; Rachuri , Sudarsan; Foufou , Sebti; Lee , Y Tina

2016-01-01

Part 3: Interoperability and Systems Integration; International audience; To employ data analytics effectively and efficiently on manufacturing systems, engineers and data scientists need to collaborate closely to bring their domain knowledge together. In this paper, we introduce a domain-specific modeling approach to integrate a manufacturing system model with advanced analytics, in particular neural networks, to model predictions. Our approach combines a set of meta-models and transformatio...

Collaborative networked organizations - Concepts and practice in manufacturing enterprises

NARCIS (Netherlands)

Camarinha-Matos, L.M.; Afsarmanesh, H.; Galeano, N.; Molina, A.

2009-01-01

Participation in networks has nowadays become very important for any organization that strives to achieve a differentiated competitive advantage, especially if the company is small or medium sized. Collaboration is a key issue to rapidly answer market demands in a manufacturing company, through

A factory concept for processing and manufacturing with lunar material

Science.gov (United States)

Driggers, G. W.

1977-01-01

A conceptual design for an orbital factory sized to process 1.5 million metric tons per year of raw lunar fines into 0.3 million metric tons of manufacturing materials is presented. A conservative approach involving application of present earth-based technology leads to a design devoid of new inventions. Earth based counterparts to the factory machinery were used to generate subsystem masses and lumped parameters for volume and mass estimates. The results are considered to be conservative since technologies more advanced than those assumed are presently available in many areas. Some attributes of potential space processing technologies applied to material refinement and component manufacture are discussed.

Complex-valued neural networks advances and applications

CERN Document Server

Hirose, Akira

2013-01-01

Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and

The concept of biologically motivated time-pulse information processing for design and construction of multifunctional devices of neural logic

Science.gov (United States)

Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Lazarev, Alexander A.; Sholohov, V. I.

2004-04-01

On the basis of the analysis of advanced approaches and optoelectronic systems for realization of various logics: two-valued, multi-valued, neural, continuous and others the biologically motivated time-pulse conception for building of multifunctional reconfigurable universal elements with programmable tuning for neurobiologic is grounded. The concept consists in usage of preliminary conversion of multi-level or continuous optic 2D signals into durations of time intervals (the conversion to a temporal area) and further use of time-pulse two-level digital signals that allows to ensure fast tuning to a required function of two-valued, multi-valued and other logics. It is shown that optoelectronic pulse-phase and pulse-width modulators (PPM and PWM) are the base elements for that. Time-pulse coding universal elements for matrix two-valued and multi-valued logics and structural-functional design of universal time-pulse coding elements for neural (continuous) logic are considered in the article. PPMs realized on 1.5μm technology CMOS transistors are considered. The PPMs have parameters: the input photocurrent range is 10nA...10μA the conversion period is 10μs...1ms the conversion relative error is 0.1...1%; the conversion law is ramp; the supply voltage is 3V and the power consumption is 83μW. The small power consumption of such PPMs enables successfully their integration in 2Darray with size of 128x128 elements and more and productivity equals 1...10 Giga continuous logic operations per sec.

Good manufacturing practice - quality assurance programs

International Nuclear Information System (INIS)

Masefield, John; Thompson, Steven

1986-01-01

The concept of good manufacturing practice (GMP) in the medical device industry requires the use of controlled methods and equipment in performing each step in the device manufacturing process. Quality assurance programs are used to maintain compliance with GMP requirements by prescribing the operating and control procedures to be used. The specific elements of a quality assurance program for the radiation sterilization of medical devices are described. (author)

Future of business models in manufacturing

OpenAIRE

Seidel, Johannes; Barquet, Ana-Paula; Seliger, Günther; Kohl, Holger

2017-01-01

In order to achieve systematic change in pursuit of sustainable manufacturing, both a strategic long-term perspective employing methods from future studies and a concrete implementation of the knowledge gained in sustainable business models are necessary. In this chapter, the concepts and exemplary methods for sustainable business model innovation are introduced with a special focus on sustainable manufacturing. Circular Economy-based business models and Product Service Systems are explained ...

Introduction to semiconductor manufacturing technology

CERN Document Server

2012-01-01

IC chip manufacturing processes, such as photolithography, etch, CVD, PVD, CMP, ion implantation, RTP, inspection, and metrology, are complex methods that draw upon many disciplines. [i]Introduction to Semiconductor Manufacturing Technologies, Second Edition[/i] thoroughly describes the complicated processes with minimal mathematics, chemistry, and physics; it covers advanced concepts while keeping the contents accessible to readers without advanced degrees. Designed as a textbook for college students, this book provides a realistic picture of the semiconductor industry and an in-depth discuss

Preventing neural tube defects in Europe : A missed opportunity

NARCIS (Netherlands)

Busby, A; Armstrong, B; Dolk, H; Armstrong, N; Haeusler, M; Berghold, A; Gillerot, Y; Baguette, A; Gjerga, R; Barisic, [No Value; Christiansen, M; Goujard, J; Steinbicker, [No Value; Rosch, C; McDonnell, R; Scarano, G; Calzolari, E; Neville, A; Cocchi, G; Bianca, S; Gatt, M; De Walle, H; Braz, P; Latos-Bielenska, A; Gener, B; Portillor, [No Value; Addor, MC; Abramsky, L; Ritvanen, A; Robert-Gnansia, E; Daltveit, AK; Aneren, G; Olars, B; Edwards, G

2005-01-01

Each year, more than 4500 pregnancies in the European Union are affected by neural tube defects (NTD). Unambiguous evidence of the effectiveness of peri conceptional folic acid in preventing the majority of neural tube defects has been available since 1991. We report on trends in the total

Strategi Bersaing dengan Agile Manufacturing

Directory of Open Access Journals (Sweden)

Hamidah Tussifah

2017-06-01

Full Text Available Competitive advantage now increasingly rests upon a dynamic capability to compete successfully in an environment of frequent, challenging and unpredictable change. The agile manufacturing a recently popularized concept has been advocated as the 21st century manufacturing paradigm. In adopting and developing the key elements of agile manufactruring, there is requirement for enterprises to overcome the philosophical challenges of a shift from mass/lean production to the customization of agility. Beside that, enterprises should explore the key success factors to support succesfull agile implementation.

Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations

OpenAIRE

Harradon, Michael; Druce, Jeff; Ruttenberg, Brian

2018-01-01

Deep neural networks are complex and opaque. As they enter application in a variety of important and safety critical domains, users seek methods to explain their output predictions. We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained in a CNN. We develop methods to extract salient concepts throughout a target network by using autoencoders trained to extract human-understandable representations of network activations. We then bu...

Promethus Hot Leg Piping Concept

International Nuclear Information System (INIS)

AM Girbik; PA Dilorenzo

2006-01-01

The Naval Reactors Prime Contractor Team (NRPCT) recommended the development of a gas cooled reactor directly coupled to a Brayton energy conversion system as the Space Nuclear Power Plant (SNPP) for NASA's Project Prometheus. The section of piping between the reactor outlet and turbine inlet, designated as the hot leg piping, required unique design features to allow the use of a nickel superalloy rather than a refractory metal as the pressure boundary. The NRPCT evaluated a variety of hot leg piping concepts for performance relative to SNPP system parameters, manufacturability, material considerations, and comparison to past high temperature gas reactor (HTGR) practice. Manufacturability challenges and the impact of pressure drop and turbine entrance temperature reduction on cycle efficiency were discriminators between the piping concepts. This paper summarizes the NRPCT hot leg piping evaluation, presents the concept recommended, and summarizes developmental issues for the recommended concept

Simulation approach towards energy flexible manufacturing systems

CERN Document Server

Beier, Jan

2017-01-01

This authored monograph provides in-depth analysis and methods for aligning electricity demand of manufacturing systems to VRE supply. The book broaches both long-term system changes and real-time manufacturing execution and control, and the author presents a concept with different options for improved energy flexibility including battery, compressed air and embodied energy storage. The reader will also find a detailed application procedure as well as an implementation into a simulation prototype software. The book concludes with two case studies. The target audience primarily comprises research experts in the field of green manufacturing systems. .

EDITORIAL: Commercial opportunities for neural engineers

Science.gov (United States)

Cavuoto, James

2008-03-01

Research and academic professionals in neural engineering know well the promise the field offers for advancing our understanding of basic neuroscience and devising new therapies for treating neurological diseases and disorders. But there is also considerable commercial opportunity for new start-up companies in several areas of neural engineering. The neurotechnology industry, which includes firms that manufacture neuromodulation devices, neural prostheses, neurorehabilitation systems, and neurosensing devices, is forecast to grow to grow from 3.6 billion this year to 8.8 billion in 2012, according to a recently published market research study from Neurotech Reports. In recent years, there have been several successful spinoffs of neurotechnology startup firms that originated with research at universities and clinical institutions. In many cases, the academic researchers who invented the new technology or product innovation have stayed on with their startup firms after receiving funding from venture capital firms, or after going public. Among the most successful neurotechnology industry spinoffs in recent years were: Cyberkinetics Inc., Foxborough, MA, a manufacturer of brain-computer interface devices based on research at Brown University. John Donoghue, a professor and chairman of the department of neuroscience at Brown University and executive director of the brain science program at Brown, founded the company in 2001 and remains on board as the chief scientific officer. Synapse Biomedical, Inc., Oberlin, OH, a manufacturer of diaphragm pacing systems, based on research at Case Western Reserve University. Raymond Onders, director of minimal invasive surgery and associate professor at University Hospital Case Medical Center in Cleveland, was the primary researcher. He was helped by J. Thomas Mortimer, professor emeritus of biomedical engineering at Case, and it was Mortimer who came up with the name NeuRx for the device. Onders performed his first surgical implant

Compensatory recruitment of neural resources in chronic alcoholism.

Science.gov (United States)

Chanraud, Sandra; Sullivan, Edith V

2014-01-01

Functional recovery occurs with sustained sobriety, but the neural mechanisms enabling recovery are only now emerging. Theories about promising mechanisms involve concepts of neuroadaptation, where excessive alcohol consumption results in untoward structural and functional brain changes which are subsequently candidates for reversal with sobriety. Views on functional adaptation in chronic alcoholism have expanded with results from neuroimaging studies. Here, we first describe and define the concept of neuroadaptation according to emerging theories based on the growing literature in aging-related cognitive functioning. Then we describe findings as they apply to chronic alcoholism and factors that could influence compensation, such as functional brain reserve and the integrity of brain structure. Finally, we review brain plasticity based on physiologic mechanisms that could underlie mechanisms of neural compensation. Where possible, we provide operational criteria to define functional and neural compensation. © 2014 Elsevier B.V. All rights reserved.

Estimates of emergency operating capacity in US manufacturing and nonmanufacturing industries - Volume 1: Concepts and Methodology

Energy Technology Data Exchange (ETDEWEB)

Belzer, D.B. (Pacific Northwest Lab., Richland, WA (USA)); Serot, D.E. (D/E/S Research, Richland, WA (USA)); Kellogg, M.A. (ERCE, Inc., Portland, OR (USA))

1991-03-01

Development of integrated mobilization preparedness policies requires planning estimates of available productive capacity during national emergency conditions. Such estimates must be developed in a manner to allow evaluation of current trends in capacity and the consideration of uncertainties in various data inputs and in engineering assumptions. This study developed estimates of emergency operating capacity (EOC) for 446 manufacturing industries at the 4-digit Standard Industrial Classification (SIC) level of aggregation and for 24 key nonmanufacturing sectors. This volume lays out the general concepts and methods used to develop the emergency operating estimates. The historical analysis of capacity extends from 1974 through 1986. Some nonmanufacturing industries are included. In addition to mining and utilities, key industries in transportation, communication, and services were analyzed. Physical capacity and efficiency of production were measured. 3 refs., 2 figs., 12 tabs. (JF)

Improved Large Aperture Collector Manufacturing

Energy Technology Data Exchange (ETDEWEB)

O' Rourke, Deven [Abengoa Solar LLC, Lakewood, CO (United States); Farr, Adrian [Abengoa Solar LLC, Lakewood, CO (United States)

2015-12-01

The parabolic trough is the most established CSP technology and carries a long history of design experimentation dating back to the 1970’s. This has led to relatively standardized collector architectures, a maturing global supply chain, and a fairly uniform cost reduction strategy. Abengoa has deployed more than 1,500MWe of CSP troughs across several countries and has built and tested full-scale prototypes of many R&D concepts. The latest trough R&D efforts involved efforts to internalize non-CSP industry experience including a preliminary DFMA principles review done with Boothroyd Dewhurst, a construction literature review by the Arizona State University School of Construction Management, and two more focused manufacturing engineering subcontracts done by Ricardo Inc. and the nonprofit Edison Welding Institute. The first two studies highlighted strong opportunities in lowering part count, standardizing components and fasteners, developing modular designs to support prefabrication and automation, and devising simple, error-proof manual assembly methods. These principles have delivered major new cost savings in otherwise “mature” products in analogous industries like automotive, truck trailer manufacture, metal building fabrication, and shipbuilding. For this reason, they were core in the design development of the SpaceTube® collector, and arguably key to its early successes. The latter two studies were applied specifically to the first-generation SpaceTube® design and were important in setting the direction of the present SolarMat project. These studies developed a methodology to analyze the costs of manufacture and assembly, and identify new tooling concepts for more efficient manufacture. Among the main opportunities identified in these studies were the automated mirror arm manufacturing concept and the need for a less infrastructure-intensive assembly line, both of which now form central pillars of the SolarMat project strategy. These new designs will be

Social aspects in additive manufacturing of pharmaceutical products

DEFF Research Database (Denmark)

Lind, Johanna Lena Maria; Kälvemark Sporrong, Sofia; Kaae, Susanne

2016-01-01

INTRODUCTION: Additive manufacturing (AM) techniques, such as drug printing, represent a new engineering approach that can implement the concept of personalized medicine via on-demand manufacturing of dosage forms with individually adjusted doses. Implementation of AM principles...... will be used for production of on-demand medicine. The impact of such changes in the distribution chain on regulation, healthcare professionals and patients are highlighted. Expert opinion: Drug manufacturing by traditional methods is well-established, but it lacks the possibility for on-demand personalized...

Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing

Directory of Open Access Journals (Sweden)

James Moyne

2017-07-01

Full Text Available Smart manufacturing (SM is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries, thus there is an opportunity to look to other industries to determine solution and roadmap paths for industries such as biochemistry or biology. The big data evolution affords an opportunity for managing significantly larger amounts of information and acting on it with analytics for improved diagnostics and prognostics. The analytics approaches can be defined in terms of dimensions to understand their requirements and capabilities, and to determine technology gaps. The semiconductor manufacturing industry has been taking advantage of the big data and analytics evolution by improving existing capabilities such as fault detection, and supporting new capabilities such as predictive maintenance. For most of these capabilities: (1 data quality is the most important big data factor in delivering high quality solutions; and (2 incorporating subject matter expertise in analytics is often required for realizing effective on-line manufacturing solutions. In the future, an improved big data environment incorporating smart manufacturing concepts such as digital twin will further enable analytics; however, it is anticipated that the need for incorporating subject matter expertise in solution design will remain.

Manufacturing data analytics using a virtual factory representation.

Science.gov (United States)

Jain, Sanjay; Shao, Guodong; Shin, Seung-Jun

2017-01-01

Large manufacturers have been using simulation to support decision-making for design and production. However, with the advancement of technologies and the emergence of big data, simulation can be utilised to perform and support data analytics for associated performance gains. This requires not only significant model development expertise, but also huge data collection and analysis efforts. This paper presents an approach within the frameworks of Design Science Research Methodology and prototyping to address the challenge of increasing the use of modelling, simulation and data analytics in manufacturing via reduction of the development effort. The use of manufacturing simulation models is presented as data analytics applications themselves and for supporting other data analytics applications by serving as data generators and as a tool for validation. The virtual factory concept is presented as the vehicle for manufacturing modelling and simulation. Virtual factory goes beyond traditional simulation models of factories to include multi-resolution modelling capabilities and thus allowing analysis at varying levels of detail. A path is proposed for implementation of the virtual factory concept that builds on developments in technologies and standards. A virtual machine prototype is provided as a demonstration of the use of a virtual representation for manufacturing data analytics.

Artificial neural networks for plasma spectroscopy analysis

International Nuclear Information System (INIS)

Morgan, W.L.; Larsen, J.T.; Goldstein, W.H.

1992-01-01

Artificial neural networks have been applied to a variety of signal processing and image recognition problems. Of the several common neural models the feed-forward, back-propagation network is well suited for the analysis of scientific laboratory data, which can be viewed as a pattern recognition problem. The authors present a discussion of the basic neural network concepts and illustrate its potential for analysis of experiments by applying it to the spectra of laser produced plasmas in order to obtain estimates of electron temperatures and densities. Although these are high temperature and density plasmas, the neural network technique may be of interest in the analysis of the low temperature and density plasmas characteristic of experiments and devices in gaseous electronics

Are temporal concepts embodied? A challenge for cognitive neuroscience

Directory of Open Access Journals (Sweden)

Alexander eKranjec

2010-12-01

Full Text Available Is time an embodied concept? People often talk and think about temporal concepts in terms of space. This observation, along with linguistic and experimental behavioral data documenting a close conceptual relation between space and time, is often interpreted as evidence that temporal concepts are embodied. However, there is little neural data supporting the idea that our temporal concepts are grounded in sensorimotor representations. This lack of evidence may be because it is still unclear how an embodied concept of time should be expressed in the brain. The present paper sets out to characterize the kinds of evidence that would support or challenge embodied accounts of time. Of main interest are theoretical issues concerning (1 whether space, as a mediating concept for time, is itself best understood as embodied and (2 whether embodied theories should attempt to bypass space by investigating temporal conceptual grounding in neural systems that instantiate time perception.

Configurable intelligent optimization algorithm design and practice in manufacturing

CERN Document Server

Tao, Fei; Laili, Yuanjun

2014-01-01

Presenting the concept and design and implementation of configurable intelligent optimization algorithms in manufacturing systems, this book provides a new configuration method to optimize manufacturing processes. It provides a comprehensive elaboration of basic intelligent optimization algorithms, and demonstrates how their improvement, hybridization and parallelization can be applied to manufacturing. Furthermore, various applications of these intelligent optimization algorithms are exemplified in detail, chapter by chapter. The intelligent optimization algorithm is not just a single algorit

Neural redundancy applied to the parity space for signal validation

International Nuclear Information System (INIS)

Mol, Antonio Carlos de Abreu; Pereira, Claudio Marcio Nascimento Abreu; Martinez, Aquilino Senra

2005-01-01

The objective of signal validation is to provide more reliable information from the plant sensor data The method presented in this work introduces the concept of neural redundancy and applies it to the space parity method [1] to overcome an inherent deficiency of this method - the determination of the best estimative of the redundant measures when they are inconsistent. The concept of neural redundancy consists on the calculation of a redundancy through neural networks based on the time series of the own state variable. Therefore, neural networks, dynamically trained with the time series, will estimate the current value of the own measure, which will be used as referee of the redundant measures in the parity space. For this purpose the neural network should have the capacity to supply the neural redundancy in real time and with maximum error corresponding to the group deviation. The historical series should be enough to allow the estimate of the next value, during transients and at the same time, it should be optimized to facilitate the retraining of the neural network to each acquisition. In order to have the capacity to reproduce the tendency of the time series even under accident condition, the dynamic training of the neural network privileges the recent points of the time series. The tests accomplished with simulated data of a nuclear plant, demonstrated that this method applied on the parity space method improves the signal validation process. (author)

Neural redundancy applied to the parity space for signal validation

Energy Technology Data Exchange (ETDEWEB)

Mol, Antonio Carlos de Abreu; Pereira, Claudio Marcio Nascimento Abreu [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil)]. E-mail: cmnap@ien.gov.br; Martinez, Aquilino Senra [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia]. E-mail: aquilino@lmp.br

2005-07-01

The objective of signal validation is to provide more reliable information from the plant sensor data The method presented in this work introduces the concept of neural redundancy and applies it to the space parity method [1] to overcome an inherent deficiency of this method - the determination of the best estimative of the redundant measures when they are inconsistent. The concept of neural redundancy consists on the calculation of a redundancy through neural networks based on the time series of the own state variable. Therefore, neural networks, dynamically trained with the time series, will estimate the current value of the own measure, which will be used as referee of the redundant measures in the parity space. For this purpose the neural network should have the capacity to supply the neural redundancy in real time and with maximum error corresponding to the group deviation. The historical series should be enough to allow the estimate of the next value, during transients and at the same time, it should be optimized to facilitate the retraining of the neural network to each acquisition. In order to have the capacity to reproduce the tendency of the time series even under accident condition, the dynamic training of the neural network privileges the recent points of the time series. The tests accomplished with simulated data of a nuclear plant, demonstrated that this method applied on the parity space method improves the signal validation process. (author)

The application of artificial neural networks to TLD dose algorithm

International Nuclear Information System (INIS)

Moscovitch, M.

1997-01-01

We review the application of feed forward neural networks to multi element thermoluminescence dosimetry (TLD) dose algorithm development. A Neural Network is an information processing method inspired by the biological nervous system. A dose algorithm based on a neural network is a fundamentally different approach from conventional algorithms, as it has the capability to learn from its own experience. The neural network algorithm is shown the expected dose values (output) associated with a given response of a multi-element dosimeter (input) many times.The algorithm, being trained that way, eventually is able to produce its own unique solution to similar (but not exactly the same) dose calculation problems. For personnel dosimetry, the output consists of the desired dose components: deep dose, shallow dose, and eye dose. The input consists of the TL data obtained from the readout of a multi-element dosimeter. For this application, a neural network architecture was developed based on the concept of functional links network (FLN). The FLN concept allowed an increase in the dimensionality of the input space and construction of a neural network without any hidden layers. This simplifies the problem and results in a relatively simple and reliable dose calculation algorithm. Overall, the neural network dose algorithm approach has been shown to significantly improve the precision and accuracy of dose calculations. (authors)

The roles of communication process for an effective lean manufacturing implementation

OpenAIRE

Puvanasvaran, Perumal; Megat, Hamdan; Hong, Tang Sai; Razali, Muhamad Mohd.

2009-01-01

Many companies are implementing lean manufacturing concept in order to remain competitive and sustainable, however, not many of them are successful in the process due to various reasons. Communication is an important aspect of lean process in order to successfully implement lean manufacturing. This paper determines the roles of communication process in ensuring a successful implementation of leanness in manufacturing companies. All the information of lean manufacturing practice...

Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks.

Science.gov (United States)

Duda, Piotr; Jaworski, Maciej; Rutkowski, Leszek

2018-03-01

One of the greatest challenges in data mining is related to processing and analysis of massive data streams. Contrary to traditional static data mining problems, data streams require that each element is processed only once, the amount of allocated memory is constant and the models incorporate changes of investigated streams. A vast majority of available methods have been developed for data stream classification and only a few of them attempted to solve regression problems, using various heuristic approaches. In this paper, we develop mathematically justified regression models working in a time-varying environment. More specifically, we study incremental versions of generalized regression neural networks, called IGRNNs, and we prove their tracking properties - weak (in probability) and strong (with probability one) convergence assuming various concept drift scenarios. First, we present the IGRNNs, based on the Parzen kernels, for modeling stationary systems under nonstationary noise. Next, we extend our approach to modeling time-varying systems under nonstationary noise. We present several types of concept drifts to be handled by our approach in such a way that weak and strong convergence holds under certain conditions. Finally, in the series of simulations, we compare our method with commonly used heuristic approaches, based on forgetting mechanism or sliding windows, to deal with concept drift. Finally, we apply our concept in a real life scenario solving the problem of currency exchange rates prediction.

Nano Manufacturing - Products and Technologies

DEFF Research Database (Denmark)

Hansen, Hans Nørgaard; Alting, Leo

2004-01-01

The use of micro and nano technologies in components and products not only sets new demands to the manufacturing technologies. Product concepts have to be rethought and redefined in order to implement the micro and nano technologies into functional systems. Both a technology driven and a product ...

Integrated evolutionary computation neural network quality controller for automated systems

Energy Technology Data Exchange (ETDEWEB)

Patro, S.; Kolarik, W.J. [Texas Tech Univ., Lubbock, TX (United States). Dept. of Industrial Engineering

1999-06-01

With increasing competition in the global market, more and more stringent quality standards and specifications are being demands at lower costs. Manufacturing applications of computing power are becoming more common. The application of neural networks to identification and control of dynamic processes has been discussed. The limitations of using neural networks for control purposes has been pointed out and a different technique, evolutionary computation, has been discussed. The results of identifying and controlling an unstable, dynamic process using evolutionary computation methods has been presented. A framework for an integrated system, using both neural networks and evolutionary computation, has been proposed to identify the process and then control the product quality, in a dynamic, multivariable system, in real-time.

Fundamentals of semiconductor manufacturing and process control

CERN Document Server

May, Gary S

2006-01-01

A practical guide to semiconductor manufacturing from process control to yield modeling and experimental design Fundamentals of Semiconductor Manufacturing and Process Control covers all issues involved in manufacturing microelectronic devices and circuits, including fabrication sequences, process control, experimental design, process modeling, yield modeling, and CIM/CAM systems. Readers are introduced to both the theory and practice of all basic manufacturing concepts. Following an overview of manufacturing and technology, the text explores process monitoring methods, including those that focus on product wafers and those that focus on the equipment used to produce wafers. Next, the text sets forth some fundamentals of statistics and yield modeling, which set the foundation for a detailed discussion of how statistical process control is used to analyze quality and improve yields. The discussion of statistical experimental design offers readers a powerful approach for systematically varying controllable p...

Identifikasi Waste Pada Proses Produksi Key Set Clarinet Dengan Pendekatan Lean Manufacturing

Directory of Open Access Journals (Sweden)

Dana Marsetiya Utama

2016-07-01

Full Text Available This study discusses lean manufacturing concept to identify waste in Key Set Clarinet production process at Yamaha Musical Products Indonesia, Ltd. The lean manufacturing concept is done intially by big picture mapping, waste assessment model (WAM, value stream mapping (VSM and cause and effect diagram respectively. The results show that the dominant waste is defect (26.04%, motion (19.34%, inventory (19.22%, and waiting (13.91%.

On the origin of reproducible sequential activity in neural circuits

Science.gov (United States)

Afraimovich, V. S.; Zhigulin, V. P.; Rabinovich, M. I.

2004-12-01

Robustness and reproducibility of sequential spatio-temporal responses is an essential feature of many neural circuits in sensory and motor systems of animals. The most common mathematical images of dynamical regimes in neural systems are fixed points, limit cycles, chaotic attractors, and continuous attractors (attractive manifolds of neutrally stable fixed points). These are not suitable for the description of reproducible transient sequential neural dynamics. In this paper we present the concept of a stable heteroclinic sequence (SHS), which is not an attractor. SHS opens the way for understanding and modeling of transient sequential activity in neural circuits. We show that this new mathematical object can be used to describe robust and reproducible sequential neural dynamics. Using the framework of a generalized high-dimensional Lotka-Volterra model, that describes the dynamics of firing rates in an inhibitory network, we present analytical results on the existence of the SHS in the phase space of the network. With the help of numerical simulations we confirm its robustness in presence of noise in spite of the transient nature of the corresponding trajectories. Finally, by referring to several recent neurobiological experiments, we discuss possible applications of this new concept to several problems in neuroscience.

Meaning and value of cloud manufacturing platform for aerospace enterprises

Science.gov (United States)

Tang, Wei; Xu, Wei; Xin, Xin

2017-08-01

Aerospace manufacturing engineering technology status it is important symbol to measure the comprehensive strength of nation. This paper analyzes the meaning and value of aerospace enterprises, based on the concept of cloud manufacturing to the practical production and application, combined with the characteristics of aerospace enterprises.

Crowd wisdom drives intelligent manufacturing

Directory of Open Access Journals (Sweden)

Jiaqi Lu

2017-03-01

Full Text Available Purpose – A fundamental problem for intelligent manufacturing is to equip the agents with the ability to automatically make judgments and decisions. This paper aims to introduce the basic principle for intelligent crowds in an attempt to show that crowd wisdom could help in making accurate judgments and proper decisions. This further shows the positive effects that crowd wisdom could bring to the entire manufacturing process. Design/methodology/approach – Efforts to support the critical role of crowd wisdom in intelligent manufacturing involve theoretical explanation, including a discussion of several prevailing concepts, such as consumer-to-business (C2B, crowdfunding and an interpretation of the contemporary Big Data mania. In addition, an empirical study with three business cases was conducted to prove the conclusion that our ideas could well explain the current business phenomena and guide the future of manufacturing. Findings – This paper shows that crowd wisdom could help make accurate judgments and proper decisions. It further shows the positive effects that crowd wisdom could bring to the entire manufacturing process. Originality/value – The paper highlights the importance of crowd wisdom in manufacturing with sufficient theoretical and empirical analysis, potentially providing a guideline for future industry.

Prediction of compression strength of high performance concrete using artificial neural networks

International Nuclear Information System (INIS)

Torre, A; Moromi, I; Garcia, F; Espinoza, P; Acuña, L

2015-01-01

High-strength concrete is undoubtedly one of the most innovative materials in construction. Its manufacture is simple and is carried out starting from essential components (water, cement, fine and aggregates) and a number of additives. Their proportions have a high influence on the final strength of the product. This relations do not seem to follow a mathematical formula and yet their knowledge is crucial to optimize the quantities of raw materials used in the manufacture of concrete. Of all mechanical properties, concrete compressive strength at 28 days is most often used for quality control. Therefore, it would be important to have a tool to numerically model such relationships, even before processing. In this aspect, artificial neural networks have proven to be a powerful modeling tool especially when obtaining a result with higher reliability than knowledge of the relationships between the variables involved in the process. This research has designed an artificial neural network to model the compressive strength of concrete based on their manufacturing parameters, obtaining correlations of the order of 0.94

Data systems and computer science: Neural networks base R/T program overview

Science.gov (United States)

Gulati, Sandeep

1991-01-01

The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.

Recurrent Neural Network Based Boolean Factor Analysis and its Application to Word Clustering

Czech Academy of Sciences Publication Activity Database

Frolov, A. A.; Húsek, Dušan; Polyakov, P.Y.

2009-01-01

Roč. 20, č. 7 (2009), s. 1073-1086 ISSN 1045-9227 R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : recurrent neural network * Hopfield-like neural network * associative memory * unsupervised learning * neural network architecture * neural network application * statistics * Boolean factor analysis * concepts search * information retrieval Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.889, year: 2009

Global dissipativity of continuous-time recurrent neural networks with time delay

International Nuclear Information System (INIS)

Liao Xiaoxin; Wang Jun

2003-01-01

This paper addresses the global dissipativity of a general class of continuous-time recurrent neural networks. First, the concepts of global dissipation and global exponential dissipation are defined and elaborated. Next, the sets of global dissipativity and global exponentially dissipativity are characterized using the parameters of recurrent neural network models. In particular, it is shown that the Hopfield network and cellular neural networks with or without time delays are dissipative systems

Artificial Astrocytes Improve Neural Network Performance

Science.gov (United States)

Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-01-01

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

Artificial astrocytes improve neural network performance.

Directory of Open Access Journals (Sweden)

Ana B Porto-Pazos

Full Text Available Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN and artificial neuron-glia networks (NGN to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

Artificial astrocytes improve neural network performance.

Science.gov (United States)

Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-04-19

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

The technological conception

International Nuclear Information System (INIS)

Parrochia, D.

1998-01-01

The 'technological conception' examines how a project can be concretized or how it is possible to 'conceive', i.e. to produce operative ideas that can be directly use. The first part of this book, called 'concepts and methods', analyzes the logics of conceiving and its philosophy in the construction of its objects and in the management of its programs or projects. The second part is devoted to some exemplary technologies: roads, tunnels, bridges, dams, nuclear power plants, aerospace constructions, and analyzes different concrete logics of technological conception. Finally, the author shows how todays conception faces the risks and complexity increase of systems and considers the possibility of an entirely automated manufacturing shop in the future. (J.S.)

Introduction to neural networks

International Nuclear Information System (INIS)

Pavlopoulos, P.

1996-01-01

This lecture is a presentation of today's research in neural computation. Neural computation is inspired by knowledge from neuro-science. It draws its methods in large degree from statistical physics and its potential applications lie mainly in computer science and engineering. Neural networks models are algorithms for cognitive tasks, such as learning and optimization, which are based on concepts derived from research into the nature of the brain. The lecture first gives an historical presentation of neural networks development and interest in performing complex tasks. Then, an exhaustive overview of data management and networks computation methods is given: the supervised learning and the associative memory problem, the capacity of networks, the Perceptron networks, the functional link networks, the Madaline (Multiple Adalines) networks, the back-propagation networks, the reduced coulomb energy (RCE) networks, the unsupervised learning and the competitive learning and vector quantization. An example of application in high energy physics is given with the trigger systems and track recognition system (track parametrization, event selection and particle identification) developed for the CPLEAR experiment detectors from the LEAR at CERN. (J.S.). 56 refs., 20 figs., 1 tab., 1 appendix

Introduction to spiking neural networks: Information processing, learning and applications.

Science.gov (United States)

Ponulak, Filip; Kasinski, Andrzej

2011-01-01

The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.

Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects.

Science.gov (United States)

Tan, Shing Chiang; Watada, Junzo; Ibrahim, Zuwairie; Khalid, Marzuki

2015-05-01

Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.

Specification process reengineering: concepts and experiences from Danish industry

DEFF Research Database (Denmark)

Hansen, Benjamin Loer; Riis, Jesper; Hvam, Lars

2003-01-01

This paper presents terminologies and concepts related to the IT automation of specification processes in companies manufacturing custom made products. Based on 11 cases from the Danish industry the most significant development trends are discussed.......This paper presents terminologies and concepts related to the IT automation of specification processes in companies manufacturing custom made products. Based on 11 cases from the Danish industry the most significant development trends are discussed....

Production process stability - core assumption of INDUSTRY 4.0 concept

Science.gov (United States)

Chromjakova, F.; Bobak, R.; Hrusecka, D.

2017-06-01

Today’s industrial enterprises are confronted by implementation of INDUSTRY 4.0 concept with basic problem - stabilised manufacturing and supporting processes. Through this phenomenon of stabilisation, they will achieve positive digital management of both processes and continuously throughput. There is required structural stability of horizontal (business) and vertical (digitized) manufacturing processes, supported through digitalised technologies of INDUSTRY 4.0 concept. Results presented in this paper based on the research results and survey realised in more industrial companies. Following will described basic model for structural process stabilisation in manufacturing environment.

Concepts and Relations in Neurally Inspired In Situ Concept-Based Computing

NARCIS (Netherlands)

van der Velde, Frank; van der Velde, Frank

2016-01-01

In situ concept-based computing is based on the notion that conceptual representations in the human brain are “in situ.” In this way, they are grounded in perception and action. Examples are neuronal assemblies, whose connection structures develop over time and are distributed over different brain

Hardware Acceleration of Adaptive Neural Algorithms.

Energy Technology Data Exchange (ETDEWEB)

James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-11-01

As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

Higher-order neural network software for distortion invariant object recognition

Science.gov (United States)

Reid, Max B.; Spirkovska, Lilly

1991-01-01

The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

Qualitative analysis and control of complex neural networks with delays

CERN Document Server

Wang, Zhanshan; Zheng, Chengde

2016-01-01

This book focuses on the stability of the dynamical neural system, synchronization of the coupling neural system and their applications in automation control and electrical engineering. The redefined concept of stability, synchronization and consensus are adopted to provide a better explanation of the complex neural network. Researchers in the fields of dynamical systems, computer science, electrical engineering and mathematics will benefit from the discussions on complex systems. The book will also help readers to better understand the theory behind the control technique and its design.

Additive Manufacturing: An Enabling Technology for the MoonBEAM 6U CubeSat Missions

Science.gov (United States)

Hopkins, R. C.; Hickman, R. R.; Cavender, D. P.; Dominquez, A.; Schnell, A. R.; Baysinger, M.; Capizzo, P.; Garcia, J.; Fabisinski, L. L.

2017-01-01

The Advanced Concepts Office at the NASA Marshall Space Flight Center completed a mission concept study for the Moon Burst Energetics All-sky Monitor (MoonBEAM). The goal of the concept study was to show the enabling aspects that additive manufacturing can provide to CubeSats. In addition to using the additively manufactured tanks as part of the spacecraft structure, the main propulsion system uses a green propellant, which is denser than hydrazine. Momentum unloading is achieved with electric microthrusters, eliminating much of the propellant plumbing. The science mission, requirements, and spacecraft design are described.

An Indexing Scheme for Case-Based Manufacturing Vision Development

DEFF Research Database (Denmark)

Wang, Chengbo; Johansen, John; Luxhøj, James T.

2004-01-01

with the competence improvement of an enterprises manufacturing system. There are two types of cases within the CBRM – an event case (EC) and a general supportive case (GSC). We designed one set of indexing vocabulary for the two types of cases, but a different indexing representation structure for each of them......This paper focuses on one critical element, indexing – retaining and representing knowledge in an applied case-based reasoning (CBR) model for supporting strategic manufacturing vision development (CBRM). Manufacturing vision (MV) is a kind of knowledge management concept and process concerned...

Automatic inspection for remotely manufactured fuel elements

International Nuclear Information System (INIS)

Reifman, J.; Vitela, J.E.; Gibbs, K.S.; Benedict, R.W.

1995-01-01

Two classification techniques, standard control charts and artificial neural networks, are studied as a means for automating the visual inspection of the welding of end plugs onto the top of remotely manufactured reprocessed nuclear fuel element jackets. Classificatory data are obtained through measurements performed on pre- and post-weld images captured with a remote camera and processed by an off-the-shelf vision system. The two classification methods are applied in the classification of 167 dummy stainless steel (HT9) fuel jackets yielding comparable results

Energy Efficiency in Manufacturing Systems

CERN Document Server

Thiede, Sebastian

2012-01-01

Energy consumption is of great interest to manufacturing companies. Beyond considering individual processes and machines, the perspective on process chains and factories as a whole holds major potentials for energy efficiency improvements. To exploit these potentials, dynamic interactions of different processes as well as auxiliary equipment (e.g. compressed air generation) need to be taken into account. In addition, planning and controlling manufacturing systems require  balancing technical, economic and environmental objectives. Therefore, an innovative and comprehensive methodology – with a generic energy flow-oriented manufacturing simulation environment as a core element – is developed and embedded into a step-by-step application cycle. The concept is applied in its entirety to a wide range of case studies such as aluminium die casting, weaving mills, and printed circuit board assembly in order to demonstrate the broad applicability and the benefits that can be achieved.

Spin glasses and neural networks

International Nuclear Information System (INIS)

Parga, N.; Universidad Nacional de Cuyo, San Carlos de Bariloche

1989-01-01

The mean-field theory of spin glass models has been used as a prototype of systems with frustration and disorder. One of the most interesting related systems are models of associative memories. In these lectures we review the main concepts developed to solve the Sherrington-Kirkpatrick model and its application to neural networks. (orig.)

IMPLEMENTATION OF LEAN MANUFACTURING IN FISH CANNING COMPANY: A CASE STUDY OF A CANNED SARDINES PRODUCTION COMPANY IN MOROCCO

Directory of Open Access Journals (Sweden)

I. Idrıssi

2015-12-01

Full Text Available Lean is a powerful tool, which can bring significant benefit to manufacturing industries by creating value through reduction of waste. Although the lean concept has become very popular in mass production industries such as the automotive industry, more recently the concept has been adopted in different batch processing industries and service sectors. The application of lean tools into the food processing industry has not received the same level of attention compared to the traditional manufacturing industries. The paper discusses how the lean concept could be applied to a fish manufacturing company. The paper first presents the lean concept tools. The empirical section discusses how a case company, operating as a contract manufacturer in the food industry, has applied the lean production concept and tools. In the case study, three analysis tools are examined and the structures of demand chains of different customers are presented. The delivery times will decrease and more flexibility will be needed from the contract manufacturer. The case study shows that much movement is possible toward the lean supply chain and partnership-based cooperation. By implementing the lean concept, food companies can increase customer value through cost reduction or through provision of additional value-enhanced services.

Neural networks. A new analytical tool, applicable also in nuclear technology

Energy Technology Data Exchange (ETDEWEB)

Stritar, A [Inst. Jozef Stefan, Ljubljana (Slovenia)

1992-07-01

The basic concept of neural networks and back propagation learning algorithm are described. The behaviour of typical neural network is demonstrated on a simple graphical case. A short literature survey about the application of neural networks in nuclear science and engineering is made. The application of the neural network to the probability density calculation is shown. (author) [Slovenian] Opisana je osnova nevronskih mrez in back propagation nacina njihovega ucenja. Obnasanje enostavne nevronske mreze je prikazano na graficnem primeru. Podan je kratek pregled literaure o uporabi nevronskih mrez v jedrski znanosti in tehnologiji. Prikazana je tudi uporaba nevronske mreze pri izracunu verjetnostne porazdelitve. (author)

Supervised Learning with Complex-valued Neural Networks

CERN Document Server

Suresh, Sundaram; Savitha, Ramasamy

2013-01-01

Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

Controlled neural network application in track-match problem

International Nuclear Information System (INIS)

Baginyan, S.A.; Ososkov, G.A.

1993-01-01

Track-match problem of high energy physics (HEP) data handling is formulated in terms of incidence matrices. The corresponding Hopfield neural network is developed to solve this type of constraint satisfaction problems (CSP). A special concept of the controlled neural network is proposed as a basis of an algorithm for the effective CSP solution. Results of comparable calculations show the very high performance of this algorithm against conventional search procedures. 8 refs.; 1 fig.; 1 tab

Empirical study on entropy models of cellular manufacturing systems

Institute of Scientific and Technical Information of China (English)

Zhifeng Zhang; Renbin Xiao

2009-01-01

From the theoretical point of view,the states of manufacturing resources can be monitored and assessed through the amount of information needed to describe their technological structure and operational state.The amount of information needed to describe cellular manufacturing systems is investigated by two measures:the structural entropy and the operational entropy.Based on the Shannon entropy,the models of the structural entropy and the operational entropy of cellular manufacturing systems are developed,and the cognizance of the states of manufacturing resources is also illustrated.Scheduling is introduced to measure the entropy models of cellular manufacturing systems,and the feasible concepts of maximum schedule horizon and schedule adherence are advanced to quantitatively evaluate the effectiveness of schedules.Finally,an example is used to demonstrate the validity of the proposed methodology.

PENERAPAN LEAN MANUFACTURING UNTUK MEREDUKSI WASTE DI INDUSTRI SKALA UKM

Directory of Open Access Journals (Sweden)

Darminto Pujotomo

2012-08-01

Full Text Available Lean Manufacturing merupakan konsep manufaktur untuk menghasilkan produk yang efisien dengan mengurangi biaya produksi melalui efisiensi. Dalam konsep Lean, dikenal 7 macam pemborosan yang meliputi produksi berlebih, transportasi material yang berlebihan, menunggu, proses yang tidak perlu, persediaan, pergerakan dan cacat produk. Penelitian ini menggunakan value stream mapping dalam mengidentifikasi pemborosan dan menelusuri potensi terjadinya pemborosan. Potensi pemborosan yang terjadi akan direduksi dengan menggunakan instrumen yang sesuai berdasarkan indikator terpilih. Penelitian diharapkan dapat menghasilkan proses produksi yang lebih efisiensi sehingga mampu mereduksi biaya produksi. Pada akhirnya akan menghasilkan profit yang lebih besar. Kata kunci : lean manufacturing, value stream mapping, waste   Lean Manufacturing is a manufacturing concepts to produce products that are efficient by reducing production costs through efficiencies. In the Lean concept, known seven kinds of waste, including overproduction, excessive material transportation, waiting, unnecessary processes, inventory, motion and product defects. This study uses value stream mapping to identify waste and exploring the potential for waste. Potential waste that occurs will be reduced by using appropriate instruments based on selected indicators. The study is expected to produce a more efficient production processes so as to reduce production costs.Will ultimately result in greater profits. Keywords: lean manufacturing, value stream mapping, waste

Advanced neural network-based computational schemes for robust fault diagnosis

CERN Document Server

Mrugalski, Marcin

2014-01-01

The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

Neural and Fuzzy Adaptive Control of Induction Motor Drives

International Nuclear Information System (INIS)

Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.

2008-01-01

This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller

Quantum neural networks: Current status and prospects for development

Science.gov (United States)

Altaisky, M. V.; Kaputkina, N. E.; Krylov, V. A.

2014-11-01

The idea of quantum artificial neural networks, first formulated in [34], unites the artificial neural network concept with the quantum computation paradigm. Quantum artificial neural networks were first systematically considered in the PhD thesis by T. Menneer (1998). Based on the works of Menneer and Narayanan [42, 43], Kouda, Matsui, and Nishimura [35, 36], Altaisky [2, 68], Zhou [67], and others, quantum-inspired learning algorithms for neural networks were developed, and are now used in various training programs and computer games [29, 30]. The first practically realizable scaled hardware-implemented model of the quantum artificial neural network is obtained by D-Wave Systems, Inc. [33]. It is a quantum Hopfield network implemented on the basis of superconducting quantum interference devices (SQUIDs). In this work we analyze possibilities and underlying principles of an alternative way to implement quantum neural networks on the basis of quantum dots. A possibility of using quantum neural network algorithms in automated control systems, associative memory devices, and in modeling biological and social networks is examined.

Mechatronic sensory system for computer integrated manufacturing

CSIR Research Space (South Africa)

Kumile, CM

2007-05-01

Full Text Available . The framework has been created that defines a formalisation of shop-floor control using sensors previously missing in manufacturing research. The contribution is in the ease and the elegance that the concept provides finite state/ automata activities as well...

World class manufacturing in metallurgical enterprise

OpenAIRE

B. Gajdzik

2013-01-01

World Class Manufacturing (WCM) assumes increase of efficiency of the company by elimination of all the losses, wastage and dangers of safety. In ArcelorMittal the concept of WCM is implemented in particular enterprises within the capital group. In this publication the activities conducted in some of those enterprises are described.

Neural control of magnetic suspension systems

Science.gov (United States)

Gray, W. Steven

1993-01-01

The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.

Efficient and Rapid Derivation of Primitive Neural Stem Cells and Generation of Brain Subtype Neurons From Human Pluripotent Stem Cells

OpenAIRE

Yan, Yiping; Shin, Soojung; Jha, Balendu Shekhar; Liu, Qiuyue; Sheng, Jianting; Li, Fuhai; Zhan, Ming; Davis, Janine; Bharti, Kapil; Zeng, Xianmin; Rao, Mahendra; Malik, Nasir; Vemuri, Mohan C.

2013-01-01

This study developed a highly efficient serum-free pluripotent stem cell (PSC) neural induction medium that can induce human PSCs into primitive neural stem cells (NSCs) in 7 days, obviating the need for time-consuming, laborious embryoid body generation or rosette picking. This method of primitive NSC derivation sets the stage for the scalable production of clinically relevant neural cells for cell therapy applications in good manufacturing practice conditions.

Outsourcing. The Concept

Directory of Open Access Journals (Sweden)

Victor-Adrian TROACĂ

2012-06-01

Full Text Available In the last two decades, an economic phenomenon took place, phenomenon characterized by the transfer of manufacturing activity from the developed countries to those in developing process. This practice can be considered as the response found by the large companies to the problem of production costs that were in a continuous rising, concomitant with the rising of living standards and remuneration.This paper aims to analyze the concept of outsourcing in terms of its evolution, but also in terms of incentives, ups and downs associated with the concept. On the other hand, this paper seeks to capture the fundamentals of this concept veracity and whether it could be implemented in the public service.

A case study of lean, sustainable manufacturing

Directory of Open Access Journals (Sweden)

Geoff Miller

2010-06-01

Full Text Available A small furniture production company has integrated lean tools and sustainability concepts with discrete event simulation modeling and analysis as well as mathematical optimization to make a positive impact on the environment, society and its own financial success. The principles of lean manufacturing that aid in the elimination of waste have helped the company meet ever increasing customer demands while preserving valuable resources for future generations. The implementation of lean and sustainable manufacturing was aided by the use of discrete event simulation and optimization to overcome deficits in lean’s traditional implementation strategies. Lean and green manufacturing can have a more significant, positive impact on multiple measures of operational performance when implemented concurrently rather than separately. These ideas are demonstrated by three applications.

ECO INVESTMENT PROJECT MANAGEMENT THROUGH TIME APPLYING ARTIFICIAL NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

Tamara Gvozdenović

2007-06-01

Full Text Available he concept of project management expresses an indispensable approach to investment projects. Time is often the most important factor in these projects. The artificial neural network is the paradigm of data processing, which is inspired by the one used by the biological brain, and it is used in numerous, different fields, among which is the project management. This research is oriented to application of artificial neural networks in managing time of investment project. The artificial neural networks are used to define the optimistic, the most probable and the pessimistic time in PERT method. The program package Matlab: Neural Network Toolbox is used in data simulation. The feed-forward back propagation network is chosen.

Grounded understanding of abstract concepts: The case of STEM learning.

Science.gov (United States)

Hayes, Justin C; Kraemer, David J M

2017-01-01

Characterizing the neural implementation of abstract conceptual representations has long been a contentious topic in cognitive science. At the heart of the debate is whether the "sensorimotor" machinery of the brain plays a central role in representing concepts, or whether the involvement of these perceptual and motor regions is merely peripheral or epiphenomenal. The domain of science, technology, engineering, and mathematics (STEM) learning provides an important proving ground for sensorimotor (or grounded) theories of cognition, as concepts in science and engineering courses are often taught through laboratory-based and other hands-on methodologies. In this review of the literature, we examine evidence suggesting that sensorimotor processes strengthen learning associated with the abstract concepts central to STEM pedagogy. After considering how contemporary theories have defined abstraction in the context of semantic knowledge, we propose our own explanation for how body-centered information, as computed in sensorimotor brain regions and visuomotor association cortex, can form a useful foundation upon which to build an understanding of abstract scientific concepts, such as mechanical force. Drawing from theories in cognitive neuroscience, we then explore models elucidating the neural mechanisms involved in grounding intangible concepts, including Hebbian learning, predictive coding, and neuronal recycling. Empirical data on STEM learning through hands-on instruction are considered in light of these neural models. We conclude the review by proposing three distinct ways in which the field of cognitive neuroscience can contribute to STEM learning by bolstering our understanding of how the brain instantiates abstract concepts in an embodied fashion.

Prototype development of educational program for production manager leading new perspectives on manufacturing technology

OpenAIRE

Ishii, Kazuyoshi; Ikeda, Hiroshi; Tsuchiya, Akinori; Shikida, Asami; Abe, Takehiko

2006-01-01

In this paper proposes the basic concept of an educational system and shows the result of educational program developed for manufacturing manager in leadership roles who wish to create new values in manufacturing technology. The basic concept combines an intelligent knowledge-based approach with the kaizen activity program in a framework of new value creation and comparative advantage models based on the ABC-G network (Academia, Business, Consultants, and Governmental officers). The education...

Manufacturing routes for stainless steel first wall panels

International Nuclear Information System (INIS)

Bucci, Ph.; Federzoni, L.; Le Marois, G.; Lorenzetto, P.

2001-01-01

Hot isostatic pressing (HIP) techniques are being considered in the European Home Team as one of the fabrication routes to produce ITER-FEAT primary first wall panels (PFWP). To demonstrate the potential and the availability of such techniques, material development, innovative mock-up fabrications and numerical modeling for the production of near-net shape components are currently been studied by CEA/CEREM in collaboration with the EFDA-CSU Garching. The aim of this work is to investigate the manufacturing feasibility of advanced PFWP concepts, with reduced pitch between FW cooling channels and reduced material thickness between the FW cooling channels and the front surface, in order to improve the thermal fatigue performance of these concepts. In order to select the best fabrication route, two different manufacturing methods based on the HIP process are being considered. The first one consists in manufacturing of the first wall panel by a HIP forming technique. Mock-ups are made of a serpentine tube expanded into a proper matrix. 2-D computer modeling has been performed to estimate the serpentine deformation. The second manufacturing route is based on the powder HIP technique. Mock-ups have been made of a serpentine embedded into SS powder. In both cases, the objective was to obtain the minimum pitch between the stainless steel (SS) tubes and between the SS tubes and the front face

Service Orientation in Holonic and Multi-Agent Manufacturing Control

CERN Document Server

Thomas, André; Trentesaux, Damien

2012-01-01

Service orientation is emerging nowadays at multiple organizational levels in enterprise business, and it leverages technology in response to the growing need for greater business integration, flexibility and agility of manufacturing enterprises. This book gathers contributions from scientists, researchers and industrialists on concepts, methods, frameworks and implementing issues addressing trends in the service orientation of control technology and management applied to manufacturing enterprise. It analyzes a Service Oriented Architecture (SOA) representing a technical architecture, a business modelling concept, a type of infrastructure, an integration source and a new way of viewing units of automation within the enterprise. The presents how SOA aligns the business world with the world of information technology in a way that makes both more effective.  

Lean Manufacturing Auto Cluster at Chennai

Science.gov (United States)

Bhaskaran, E.

2012-10-01

Due the presence of lot of automotive Industry, Chennai is known as Detroit of India, that producing over 40 % of the Indian vehicle and components. Lean manufacturing concepts have been widely recognized as an important tool in improving the competitiveness of industries. This is a continuous process involving everyone, starting from management to the shop floor. Automotive Component Industries (ACIs) in Ambattur Industrial Estate, Chennai has formed special purpose vehicle (SPV) society namely Ambattur Industrial Estate Manufacturers Association (AIEMA) Technology Centre (ATC) lean manufacturing cluster (ATC-LMC) during July 2010 under lean manufacturing competitiveness scheme, that comes under National Manufacturing Competitiveness Programme of Government of India. The Tripartite Agreement is taken place between National Productivity Council, consultants and cluster (ATC-LMC). The objective is to conduct diagnostic study, study on training and application of various lean manufacturing techniques and auditing in ten ACIs. The methodology adopted is collection of primary data/details from ten ACIs. In the first phase, diagnostic study is done and the areas for improvement in each of the cluster member companies are identified. In the second phase, training programs and implementation is done on 5S and other areas. In the third phase auditing is done and found that the lean manufacturing techniques implementation in ATC-LMC is sustainable and successful in every cluster companies, which will not only enhance competitiveness but also decrease cost, time and increase productivity. The technical efficiency of LMC companies also increases significantly.

Implementing Signature Neural Networks with Spiking Neurons.

Science.gov (United States)

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence

Wireless synapses in bio-inspired neural networks

Science.gov (United States)

Jannson, Tomasz; Forrester, Thomas; Degrood, Kevin

2009-05-01

Wireless (virtual) synapses represent a novel approach to bio-inspired neural networks that follow the infrastructure of the biological brain, except that biological (physical) synapses are replaced by virtual ones based on cellular telephony modeling. Such synapses are of two types: intracluster synapses are based on IR wireless ones, while intercluster synapses are based on RF wireless ones. Such synapses have three unique features, atypical of conventional artificial ones: very high parallelism (close to that of the human brain), very high reconfigurability (easy to kill and to create), and very high plasticity (easy to modify or upgrade). In this paper we analyze the general concept of wireless synapses with special emphasis on RF wireless synapses. Also, biological mammalian (vertebrate) neural models are discussed for comparison, and a novel neural lensing effect is discussed in detail.

World class manufacturing in metallurgical enterprise

Directory of Open Access Journals (Sweden)

B. Gajdzik

2013-01-01

Full Text Available World Class Manufacturing (WCM assumes increase of efficiency of the company by elimination of all the losses, wastage and dangers of safety. In ArcelorMittal the concept of WCM is implemented in particular enterprises within the capital group. In this publication the activities conducted in some of those enterprises are described.

Social aspects in additive manufacturing of pharmaceutical products.

Science.gov (United States)

Lind, Johanna; Kälvemark Sporrong, Sofia; Kaae, Susanne; Rantanen, Jukka; Genina, Natalja

2017-08-01

Additive manufacturing (AM) techniques, such as drug printing, represent a new engineering approach that can implement the concept of personalized medicine via on-demand manufacturing of dosage forms with individually adjusted doses. Implementation of AM principles, such as pharmacoprinting, will challenge the entire drug distribution chain and affect the society at different levels. Areas covered: This work summarizes the concept of personalized medicine and gives an overview of possibilities for monitoring patients' health. The most recent activities in the field of printing technologies for fabrication of dosage forms and 'polypills' with flexible doses and tailored release profiles are reviewed. Different scenarios for the drug distribution chain with the required adjustments in drug logistics, quality systems and environmental safety are discussed, as well as whether AM will be used for production of on-demand medicine. The impact of such changes in the distribution chain on regulation, healthcare professionals and patients are highlighted. Expert opinion: Drug manufacturing by traditional methods is well-established, but it lacks the possibility for on-demand personalized drug production. With the recent approval of the first printed medicine, society should be prepared for the changes that will follow the introduction of printed pharmaceuticals.

Regulatory Perspectives on Continuous Pharmaceutical Manufacturing: Moving From Theory to Practice: September 26-27, 2016, International Symposium on the Continuous Manufacturing of Pharmaceuticals.

Science.gov (United States)

Nasr, Moheb M; Krumme, Markus; Matsuda, Yoshihiro; Trout, Bernhardt L; Badman, Clive; Mascia, Salvatore; Cooney, Charles L; Jensen, Keith D; Florence, Alastair; Johnston, Craig; Konstantinov, Konstantin; Lee, Sau L

2017-11-01

Continuous manufacturing plays a key role in enabling the modernization of pharmaceutical manufacturing. The fate of this emerging technology will rely, in large part, on the regulatory implementation of this novel technology. This paper, which is based on the 2nd International Symposium on the Continuous Manufacturing of Pharmaceuticals, describes not only the advances that have taken place since the first International Symposium on Continuous Manufacturing of Pharmaceuticals in 2014, but the regulatory landscape that exists today. Key regulatory concepts including quality risk management, batch definition, control strategy, process monitoring and control, real-time release testing, data processing and management, and process validation/verification are outlined. Support from regulatory agencies, particularly in the form of the harmonization of regulatory expectations, will be crucial to the successful implementation of continuous manufacturing. Collaborative efforts, among academia, industry, and regulatory agencies, are the optimal solution for ensuring a solid future for this promising manufacturing technology. Copyright © 2017 American Pharmacists Association®. All rights reserved.

Implementing the South African additive manufacturing technology roadmap - the role of an additive manufacturing centre of competence

Directory of Open Access Journals (Sweden)

Du Preez, Willie Bouwer

2015-08-01

Full Text Available The Rapid Product Development Association of South Africa (RAPDASA expressed the need for a national Additive Manufacturing Roadmap. Consequentially, the South African Department of Science and Technology commissioned the development of a South African Additive Manufacturing Technology Roadmap. This was intended to guide role-players in identifying business opportunities, addressing technology gaps, focusing development programmes, and informing investment decisions that would enable local companies and industry sectors to become global leaders in selected areas of additive manufacturing. The challenge remains now for South Africa to decide on an implementation approach that will maximize the impact in the shortest possible time. This article introduces the concept of a national Additive Manufacturing Centre of Competence (AMCoC as a primary implementation vehicle for the roadmap. The support of the current leading players in additive manufacturing in South Africa for such a centre of competence is shared and their key roles are indicated. A summary of the investments that the leading players have already made in the focus areas of the AMCoC over the past two decades is given as confirmation of their commitment towards the advancement of the additive manufacturing technology. An exposition is given of how the AMCoC could indeed become the primary initiative for achieving the agreed national goals on additive manufacturing. The conclusion is that investment by public and private institutions in an AMCoC would be the next step towards ensuring South Africa’s continued progress in the field.

Sensor fusion control system for computer integrated manufacturing

CSIR Research Space (South Africa)

Kumile, CM

2007-08-01

Full Text Available -floor control using sensors previously missing in manufacturing research. The contribution is in the ease and the elegance that the concept provides finite state/ automata activities as well as the production engineering elements such as planning...

Simulation Modeling by Classification of Problems: A Case of Cellular Manufacturing

International Nuclear Information System (INIS)

Afiqah, K N; Mahayuddin, Z R

2016-01-01

Cellular manufacturing provides good solution approach to manufacturing area by applying Group Technology concept. The evolution of cellular manufacturing can enhance performance of the cell and to increase the quality of the product manufactured but it triggers other problem. Generally, this paper highlights factors and problems which emerge commonly in cellular manufacturing. The aim of the research is to develop a thorough understanding of common problems in cellular manufacturing. A part from that, in order to find a solution to the problems exist using simulation technique, this classification framework is very useful to be adapted during model building. Biology evolution tool was used in the research in order to classify the problems emerge. The result reveals 22 problems and 25 factors using cladistic technique. In this research, the expected result is the cladogram established based on the problems in cellular manufacturing gathered. (paper)

A TLD dose algorithm using artificial neural networks

International Nuclear Information System (INIS)

Moscovitch, M.; Rotunda, J.E.; Tawil, R.A.; Rathbone, B.A.

1995-01-01

An artificial neural network was designed and used to develop a dose algorithm for a multi-element thermoluminescence dosimeter (TLD). The neural network architecture is based on the concept of functional links network (FLN). Neural network is an information processing method inspired by the biological nervous system. A dose algorithm based on neural networks is fundamentally different as compared to conventional algorithms, as it has the capability to learn from its own experience. The neural network algorithm is shown the expected dose values (output) associated with given responses of a multi-element dosimeter (input) many times. The algorithm, being trained that way, eventually is capable to produce its own unique solution to similar (but not exactly the same) dose calculation problems. For personal dosimetry, the output consists of the desired dose components: deep dose, shallow dose and eye dose. The input consists of the TL data obtained from the readout of a multi-element dosimeter. The neural network approach was applied to the Harshaw Type 8825 TLD, and was shown to significantly improve the performance of this dosimeter, well within the U.S. accreditation requirements for personnel dosimeters

Manufacturing technology for practical Josephson voltage normals; Fertigungstechnologie fuer praxistaugliche Josephson-Spannungsnormale

Energy Technology Data Exchange (ETDEWEB)

Kohlmann, Johannes; Kieler, Oliver [Physikalisch-Technische Bundesanstalt (PTB), Braunschweig (Germany). Arbeitsgruppe 2.43 ' ' Josephson-Schaltungen' '

2016-09-15

In this contribution we present the manufacturing technology for the fabrication of integrated superconducting Josephson serial circuits for voltage normals. First we summarize some foundations for Josephson voltage normals and sketch the concept and the setup of the circuits, before we describe the manufacturing technology form modern practical Josephson voltage normals.

Robot skills for manufacturing

DEFF Research Database (Denmark)

Pedersen, Mikkel Rath; Nalpantidis, Lazaros; Andersen, Rasmus Skovgaard

2016-01-01

-asserting robot skills for manufacturing. We show how a relatively small set of skills are derived from current factory worker instructions, and how these can be transferred to industrial mobile manipulators. General robot skills can not only be implemented on these robots, but also be intuitively concatenated...... products are introduced by manufacturers. In order to compete on global markets, the factories of tomorrow need complete production lines, including automation technologies that can effortlessly be reconfigured or repurposed, when the need arises. In this paper we present the concept of general, self...... in running production facilities at an industrial partner. It follows from these experiments that the use of robot skills, and associated task-level programming framework, is a viable solution to introducing robots that can intuitively and on the fly be programmed to perform new tasks by factory workers....

Human-automation collaboration in manufacturing: identifying key implementation factors

OpenAIRE

Charalambous, George; Fletcher, Sarah; Webb, Philip

2013-01-01

Human-automation collaboration refers to the concept of human operators and intelligent automation working together interactively within the same workspace without conventional physical separation. This concept has commanded significant attention in manufacturing because of the potential applications, such as the installation of large sub-assemblies. However, the key human factors relevant to human-automation collaboration have not yet been fully investigated. To maximise effective implement...

Introduction to neural networks in high energy physics

International Nuclear Information System (INIS)

Therhaag, J.

2013-01-01

Artificial neural networks are a well established tool in high energy physics, playing an important role in both online and offline data analysis. Nevertheless they are often perceived as black boxes which perform obscure operations beyond the control of the user, resulting in a skepticism against any results that may be obtained using them. The situation is not helped by common explanations which try to draw analogies between artificial neural networks and the human brain, for the brain is an even more complex black box itself. In this introductory text, I will take a problem-oriented approach to neural network techniques, showing how the fundamental concepts arise naturally from the demand to solve classification tasks which are frequently encountered in high energy physics. Particular attention is devoted to the question how probability theory can be used to control the complexity of neural networks. (authors)

Multiple simultaneous fault diagnosis via hierarchical and single artificial neural networks

International Nuclear Information System (INIS)

Eslamloueyan, R.; Shahrokhi, M.; Bozorgmehri, R.

2003-01-01

Process fault diagnosis involves interpreting the current status of the plant given sensor reading and process knowledge. There has been considerable work done in this area with a variety of approaches being proposed for process fault diagnosis. Neural networks have been used to solve process fault diagnosis problems in chemical process, as they are well suited for recognizing multi-dimensional nonlinear patterns. In this work, the use of Hierarchical Artificial Neural Networks in diagnosing the multi-faults of a chemical process are discussed and compared with that of Single Artificial Neural Networks. The lower efficiency of Hierarchical Artificial Neural Networks , in comparison to Single Artificial Neural Networks, in process fault diagnosis is elaborated and analyzed. Also, the concept of a multi-level selection switch is presented and developed to improve the performance of hierarchical artificial neural networks. Simulation results indicate that application of multi-level selection switch increase the performance of the hierarchical artificial neural networks considerably

Achievement report on developing inverse manufacturing system in fiscal 1998; 1998 nendo inverse manufacturing system no kaihatsu seika hokokusho

Energy Technology Data Exchange (ETDEWEB)

NONE

2000-03-01

Research and development has been performed on a circulating type production system, or an inverse manufacturing system, to minimize the environmental load in the entire life cycle of industrial products from design to manufacturing, maintenance, recovery, and re-utilization. In developing the recycling information system, discussion and extraction were executed on the data to be exchanged between manufacturers and users in the inverse society. A new individual parts history control system was developed. In developing the recycling processing system, a prototype system was evaluated by using hypothetical data, wherein the possibility of improving the recycling processing efficiency and reducing the cost was verified. In addition, trial design was made on a recycling processing plant in consideration of the cost effect to get the future plant image. In fabricating the inverse manufacturing products, a prototype concept model was made for information terminal devices. Development was performed on such elementary technologies as the easy-to-disintegrate structure technology, self-integration connecting structure and environmental hysteresis detection system. (NEDO)

Neural networks prove effective at NOx reduction

Energy Technology Data Exchange (ETDEWEB)

Radl, B.J. [Pegasus Technologies, Mentor, OH (USA)

2000-05-01

The availability of low cost computer hardware and software is opening up possibilities for the use of artificial intelligence concepts, notably neural networks, in power plant control applications, delivering lower costs, greater efficiencies and reduced emissions. One example of a neural network system is the NeuSIGHT combustion optimisation system, developed by Pegasus Technologies, a subsidiary of KFx Inc. It can help reduce NOx emissions, improve heat rate and enable either deferral or elimination of capital expenditures. on other NOx control technologies, such as low NOx burners, SNCR and SCR. This paper illustrates these benefits using three recent case studies. 4 figs.

Relationship Between Lean Production and Operational Performance in the Manufacturing Industry

Science.gov (United States)

Rasi, Raja Zuraidah R. M.; Syamsyul Rakiman, Umol; Ahmad, Md Fauzi Bin

2015-05-01

Nowadays, more and more manufacturing firms have started to implement lean production system in their operations. Lean production viewed as one of the mechanism to maintain the organisation's position and to compete globally. However, many fail to apply the lean concepts successfully in their operations. Based on previous studies, implementation of lean production in the manufacturing industry is more focused on the relationship between Lean and Operational Performance of one dimension only. Therefore, this study attempted to examine the relationship between Lean Production (LP) and Operational Performance in 4 dimensions which are quality, delivery, cost and flexibility. This study employed quantitative study using questionnaires. Data was collected from 50 manufacturing industries. The data was analysed using Statistical Package for Social Science (SPSS) 22.0. This study is hoped to shed new understanding on the concept of Lean Production (LP) in regards of Operational Performance covering the 4 dimensions.

Low Cost Method of Manufacturing Cooled Axisymmetric Scramjets, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — Scramjet engine developers are working on advanced axisymmetric engine concepts that may not be feasible due to limitations of currently available manufacturing...

Hierarchical modular granular neural networks with fuzzy aggregation

CERN Document Server

Sanchez, Daniela

2016-01-01

In this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus resulting in a new concept of MNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL) and hierarchical genetic algorithms (HGAs) are techniques used in this research work to improve results. These techniques are chosen because in other works have demonstrated to be a good option, and in the case of MNNs and HGAs, these techniques allow to improve the results obtained than with their conventional versions; respectively artificial neural networks and genetic algorithms.

Multimedia Image Technology and Computer Aided Manufacturing Engineering Analysis

Science.gov (United States)

Nan, Song

2018-03-01

Since the reform and opening up, with the continuous development of science and technology in China, more and more advanced science and technology have emerged under the trend of diversification. Multimedia imaging technology, for example, has a significant and positive impact on computer aided manufacturing engineering in China. From the perspective of scientific and technological advancement and development, the multimedia image technology has a very positive influence on the application and development of computer-aided manufacturing engineering, whether in function or function play. Therefore, this paper mainly starts from the concept of multimedia image technology to analyze the application of multimedia image technology in computer aided manufacturing engineering.

The Role of Design Concepts in the Development of Industrial Services

DEFF Research Database (Denmark)

Pekkala, Janne; Ylirisku, Salu

2017-01-01

B-to-B industrial manufacturing organisations are moving focus from designing products to services. This transition challenges the management of innovating, which is increasingly collaborative and networked. Organisations need to be able to tackle the related uncertainty in order to prepare, secure......-to-B industrial manufacturing. Eight roles for design concepts are identified in the 11-month study, and these are presented as stories concretising how design concepts functioned. Design concepts were utilised in 1) anticipating future, 2) implementing design, 3) training, 4) engaging in dialogue, 5) setting...... goals, 6) establishing vocabulary in organisation, 7) planning and securing resources, and 8) linking projects....

NN-Es Fault Diagnosis Method in Nuclear Power Equipment Based on Concept Lattice

International Nuclear Information System (INIS)

Liu Yongkuo; Xie Chunli; Xia Hong

2010-01-01

In order to improve the fault diagnosis accuracy of nuclear power plant,neural network and expert systems were combined to give full play to their advantages. In this paper, the concept lattice was applied to get the object properties, extracting the core attributes, dispensable attributes and relative necessary attributes from a large number raw data of fault symptoms.Based on these attributes, neural networks with different levels of importance were designed to improve the learning speed and diagnosis accuracy, and the diagnosis results of the neural networks were verified by using rule-based reasoning expert system. To verify the accuracy of this method, some simulation experiments about the typical faults of nuclear power plant were conducted. And the simulation results show that it is feasible to diagnose nuclear power plant faults with the confederation diagnosis methods combined the neural networks based on the concept lattice theory and expert system, with the distinctive features such as the efficiency of neural network learning, less calculation and reliability of diagnosis results and so on. (authors)

Identification of the actual state and entity availability forecasting in power engineering using neural-network technologies

Science.gov (United States)

Protalinsky, O. M.; Shcherbatov, I. A.; Stepanov, P. V.

2017-11-01

A growing number of severe accidents in RF call for the need to develop a system that could prevent emergency situations. In a number of cases accident rate is stipulated by careless inspections and neglects in developing repair programs. Across the country rates of accidents are growing because of a so-called “human factor”. In this regard, there has become urgent the problem of identification of the actual state of technological facilities in power engineering using data on engineering processes running and applying artificial intelligence methods. The present work comprises four model states of manufacturing equipment of engineering companies: defect, failure, preliminary situation, accident. Defect evaluation is carried out using both data from SCADA and ASEPCR and qualitative information (verbal assessments of experts in subject matter, photo- and video materials of surveys processed using pattern recognition methods in order to satisfy the requirements). Early identification of defects makes possible to predict the failure of manufacturing equipment using mathematical techniques of artificial neural network. In its turn, this helps to calculate predicted characteristics of reliability of engineering facilities using methods of reliability theory. Calculation of the given parameters provides the real-time estimation of remaining service life of manufacturing equipment for the whole operation period. The neural networks model allows evaluating possibility of failure of a piece of equipment consistent with types of actual defects and their previous reasons. The article presents the grounds for a choice of training and testing samples for the developed neural network, evaluates the adequacy of the neural networks model, and shows how the model can be used to forecast equipment failure. There have been carried out simulating experiments using a computer and retrospective samples of actual values for power engineering companies. The efficiency of the developed

The neural subjective frame: from bodily signals to perceptual consciousness.

Science.gov (United States)

Park, Hyeong-Dong; Tallon-Baudry, Catherine

2014-05-05

The report 'I saw the stimulus' operationally defines visual consciousness, but where does the 'I' come from? To account for the subjective dimension of perceptual experience, we introduce the concept of the neural subjective frame. The neural subjective frame would be based on the constantly updated neural maps of the internal state of the body and constitute a neural referential from which first person experience can be created. We propose to root the neural subjective frame in the neural representation of visceral information which is transmitted through multiple anatomical pathways to a number of target sites, including posterior insula, ventral anterior cingulate cortex, amygdala and somatosensory cortex. We review existing experimental evidence showing that the processing of external stimuli can interact with visceral function. The neural subjective frame is a low-level building block of subjective experience which is not explicitly experienced by itself which is necessary but not sufficient for perceptual experience. It could also underlie other types of subjective experiences such as self-consciousness and emotional feelings. Because the neural subjective frame is tightly linked to homeostatic regulations involved in vigilance, it could also make a link between state and content consciousness.

Ocean wave prediction using numerical and neural network models

Digital Repository Service at National Institute of Oceanography (India)

Mandal, S.; Prabaharan, N.

This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...

Utilizing Mass Customization Methods for Modular Manufacturing System Design

DEFF Research Database (Denmark)

Jørgensen, Steffen; Jacobsen, Alexia; Nielsen, Kjeld

2011-01-01

Markets today have become dynamic and demand rapid product changes, product variety, and customized products. In order to operate under and taking advantages of such conditions requires, amongst other aspects, manufacturing processes robust to product changes - a contradiction to traditional...... manufacturing systems developed as dedicated engineer-to-order solutions, tailored to production of a specific product or a limited product assortment. In response, modular manufacturing concepts are evolving, which are aimed at possessing the needed responsiveness and aimed at being the manufacturing paradigm...... of Mass Customization (MC). Research focus has been on the basic principles and enabling technologies, while modular architectures and system design have received less attention. A potential to fill these gaps by applying selected design theories and methods of MC have been seen. Based on a communality...

The concept of managerial accounting for business clothing industry

OpenAIRE

Luchko, М.

2010-01-01

The article discusses the problem of constructing the management accounting concept for business clothing industry taking into account factors of production decline in the current context. Concept of separate components is studies, which depend on the characteristics of clothing manufacture.

Neural network-based run-to-run controller using exposure and resist thickness adjustment

Science.gov (United States)

Geary, Shane; Barry, Ronan

2003-06-01

This paper describes the development of a run-to-run control algorithm using a feedforward neural network, trained using the backpropagation training method. The algorithm is used to predict the critical dimension of the next lot using previous lot information. It is compared to a common prediction algorithm - the exponentially weighted moving average (EWMA) and is shown to give superior prediction performance in simulations. The manufacturing implementation of the final neural network showed significantly improved process capability when compared to the case where no run-to-run control was utilised.

Laser polishing of additive manufactured Ti alloys

Science.gov (United States)

Ma, C. P.; Guan, Y. C.; Zhou, W.

2017-06-01

Laser-based additive manufacturing has attracted much attention as a promising 3D printing method for metallic components in recent years. However, surface roughness of additive manufactured components has been considered as a challenge to achieve high performance. In this work, we demonstrate the capability of fiber laser in polishing rough surface of additive manufactured Ti-based alloys as Ti-6Al-4V and TC11. Both as-received surface and laser-polished surfaces as well as cross-section subsurfaces were analyzed carefully by White-Light Interference, Confocal Microscope, Focus Ion Beam, Scanning Electron Microscopy, Energy Dispersive Spectrometer, and X-ray Diffraction. Results revealed that as-received Ti-based alloys with surface roughness more than 5 μm could be reduce to less than 1 μm through laser polishing process. Moreover, microstructure, microhardness and wear resistance of laser-polished zone was investigated in order to examine the thermal effect of laser polishing processing on the substrate of additive manufactured Ti alloys. This proof-of-concept process has the potential to effectively improve the surface roughness of additive manufactured metallic alloy by local polishing method without damage to the substrate.

Future Role of Application of New Technologies in Small-and Medium Scale Manufacturing Systems - Regarding Intelligent and Advanced Manufacturing Systems in Northern Peripheral Area

OpenAIRE

Somlò, Kinga; Sziebig, Gabor

2017-01-01

Accepted manuscript version. Link to publishers version: http://doi.org/10.1109/ISIE.2017.8001510 Nowadays the concept of Industry 4.0. and the relating intelligent manufacturing system are getting more and more current and well-known. In the past years the outstanding development of different areas such as information technology computer science, machining, robotics and so on, made possible a comprehensive transformation of the manufacturing systems. Present paper aims to give a gener...

Application of Contact Mode AFM to Manufacturing Processes

Science.gov (United States)

Giordano, Michael A.; Schmid, Steven R.

A review of the application of contact mode atomic force microscopy (AFM) to manufacturing processes is presented. A brief introduction to common experimental techniques including hardness, scratch, and wear testing is presented, with a discussion of challenges in the extension of manufacturing scale investigations to the AFM. Differences between the macro- and nanoscales tests are discussed, including indentation size effects and their importance in the simulation of processes such as grinding. The basics of lubrication theory are presented and friction force microscopy is introduced as a method of investigating metal forming lubrication on the nano- and microscales that directly simulates tooling/workpiece asperity interactions. These concepts are followed by a discussion of their application to macroscale industrial manufacturing processes and direct correlations are made.

Manufacturing and joining technologies for helium cooled divertors

International Nuclear Information System (INIS)

Aktaa, J.; Basuki, W.W.; Weber, T.; Norajitra, P.; Krauss, W.; Konys, J.

2014-01-01

Highlights: • The manufacturing and joining technologies developed at KIT for helium cooled divertors are reviewed and critically discussed. • Various technologies have been pursued and further developed aiming divertor components with very high quality and sufficient reliability. • Very promising routes have been found for which however still R and D works are necessary. • Technologies developed are also useful for other divertor and even blanket concepts, particularly those with tungsten armor. - Abstract: In the helium cooled (HC) divertor, developed at KIT for a fusion power plant, tungsten has been selected as armor as well as structural material due to its crucial properties: high melting point, very low sputtering yield, good thermal conductivity, high temperature strength, low thermal expansion and low activation. Thereby the armor tungsten is attached to the structural tungsten by thermally conductive joint. Due to the brittleness of tungsten at low temperatures its use as structural material is limited to the high temperature part of the component and a structural joint to the reduced activation ferritic martensitic steel EUROFER97 is foreseen. Hence, to realize the selected hybrid material concept reliable tungsten–steel and tungsten–tungsten joints have been developed and will be reported in this paper. In addition, the modular design of the HC divertor requires tungsten armor tiles and tungsten structural thimbles to be manufactured in high numbers with very high quality. Due to the high strength and low temperature brittleness of tungsten special manufacturing techniques need to be developed for the production of parts with no cavities inside and/or surface flaws. The main achievement in developing the respective manufacturing technologies will be presented and discussed. To achieve the objectives mentioned above various manufacturing and joining technologies are pursued. Their later applicability depends on the level of development

Image Encryption and Chaotic Cellular Neural Network

Science.gov (United States)

Peng, Jun; Zhang, Du

Machine learning has been playing an increasingly important role in information security and assurance. One of the areas of new applications is to design cryptographic systems by using chaotic neural network due to the fact that chaotic systems have several appealing features for information security applications. In this chapter, we describe a novel image encryption algorithm that is based on a chaotic cellular neural network. We start by giving an introduction to the concept of image encryption and its main technologies, and an overview of the chaotic cellular neural network. We then discuss the proposed image encryption algorithm in details, which is followed by a number of security analyses (key space analysis, sensitivity analysis, information entropy analysis and statistical analysis). The comparison with the most recently reported chaos-based image encryption algorithms indicates that the algorithm proposed in this chapter has a better security performance. Finally, we conclude the chapter with possible future work and application prospects of the chaotic cellular neural network in other information assurance and security areas.

Automated implementation of rule-based expert systems with neural networks for time-critical applications

Science.gov (United States)

Ramamoorthy, P. A.; Huang, Song; Govind, Girish

1991-01-01

In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

Medical image segmentation by means of constraint satisfaction neural network

International Nuclear Information System (INIS)

Chen, C.T.; Tsao, C.K.; Lin, W.C.

1990-01-01

This paper applies the concept of constraint satisfaction neural network (CSNN) to the problem of medical image segmentation. Constraint satisfaction (or constraint propagation), the procedure to achieve global consistency through local computation, is an important paradigm in artificial intelligence. CSNN can be viewed as a three-dimensional neural network, with the two-dimensional image matrix as its base, augmented by various constraint labels for each pixel. These constraint labels can be interpreted as the connections and the topology of the neural network. Through parallel and iterative processes, the CSNN will approach a solution that satisfies the given constraints thus providing segmented regions with global consistency

Knowledge management in secondary pharmaceutical manufacturing by mining of data historians-A proof-of-concept study.

Science.gov (United States)

Meneghetti, Natascia; Facco, Pierantonio; Bezzo, Fabrizio; Himawan, Chrismono; Zomer, Simeone; Barolo, Massimiliano

2016-05-30

In this proof-of-concept study, a methodology is proposed to systematically analyze large data historians of secondary pharmaceutical manufacturing systems using data mining techniques. The objective is to develop an approach enabling to automatically retrieve operation-relevant information that can assist the management in the periodic review of a manufactory system. The proposed methodology allows one to automatically perform three tasks: the identification of single batches within the entire data-sequence of the historical dataset, the identification of distinct operating phases within each batch, and the characterization of a batch with respect to an assigned multivariate set of operating characteristics. The approach is tested on a six-month dataset of a commercial-scale granulation/drying system, where several millions of data entries are recorded. The quality of results and the generality of the approach indicate that there is a strong potential for extending the method to even larger historical datasets and to different operations, thus making it an advanced PAT tool that can assist the implementation of continual improvement paradigms within a quality-by-design framework. Copyright © 2016 Elsevier B.V. All rights reserved.

Image object recognition based on the Zernike moment and neural networks

Science.gov (United States)

Wan, Jianwei; Wang, Ling; Huang, Fukan; Zhou, Liangzhu

1998-03-01

This paper first give a comprehensive discussion about the concept of artificial neural network its research methods and the relations with information processing. On the basis of such a discussion, we expound the mathematical similarity of artificial neural network and information processing. Then, the paper presents a new method of image recognition based on invariant features and neural network by using image Zernike transform. The method not only has the invariant properties for rotation, shift and scale of image object, but also has good fault tolerance and robustness. Meanwhile, it is also compared with statistical classifier and invariant moments recognition method.

Study on manufacturing technology of fuel guide tube using HANA alloys

International Nuclear Information System (INIS)

Kim, Hyungil; Jung, Yangil; Park, Dongjun; Park, Jeongyong; Kim, Ilhyun; Choi, Byungkwon; Jeong, Yonghwan; Park, Sangyoon

2013-04-01

This research was focused on the study for the manufacturing technology of HANA alloys to crease the corrosion resistance of 30% as well as the to improve the strength of 10% when compared to the commercial zirconium alloys. The new manufacturing concept having higher corrosion resistance and strength than commercial alloy performance can be obtained in this research. This result was transferred to the KNF and, that will be commercialized. This research result can be summarized like this; Ο Parameter study to increase formability of HANA alloy tube - Study on alloy element and heat-treatment effect - Study on texture development mechanism - Study on final annealing effect Ο Out-of-pile performance evaluation of HANA alloy tube - Corrosion performance evaluation of HANA alloy manufactured at KNF - Mechanical performance evaluation of HANA alloy manufactured at KNF - Recrystallization behavior evaluation of HANA alloy manufactured at KNF - Texture characterization of HANA alloy manufactured at KNF - Microstructure characterization of HANA alloy manufactured at KNF Ο Manufacturing guideline setup to increase formability of HANA alloy tube - Manufacturing guideline setup to decrease surface defect - Manufacturing guideline setup to increase strength and corrosion resistance - Manufacturing guideline setup to control texture

Mechanical Prototyping and Manufacturing Internship

Science.gov (United States)

Grenfell, Peter

2016-01-01

The internship was located at the Johnson Space Center (JSC) Innovation Design Center (IDC), which is a facility where the JSC workforce can meet and conduct hands-on innovative design, fabrication, evaluation, and testing of ideas and concepts relevant to NASA's mission. The tasks of the internship included mechanical prototyping design and manufacturing projects in service of research and development as well as assisting the users of the IDC in completing their manufacturing projects. The first project was to manufacture hatch mechanisms for a team in the Systems Engineering and Project Advancement Program (SETMAP) hexacopter competition. These mechanisms were intended to improve the performance of the servomotors and offer an access point that would also seal to prevent cross-contamination. I also assisted other teams as they were constructing and modifying their hexacopters. The success of this competition demonstrated a proof of concept for aerial reconnaissance and sample return to be potentially used in future NASA missions. I also worked with Dr. Kumar Krishen to prototype an improved thermos and a novel, portable solar array. Computer-aided design (CAD) software was used to model the parts for both of these projects. Then, 3D printing as well as conventional techniques were used to produce the parts. These prototypes were then subjected to trials to determine the success of the designs. The solar array is intended to work in a cluster that is easy to set up and take down and doesn't require powered servomechanisms. It could be used terrestrially in areas not serviced by power grids. Both projects improve planetary exploration capabilities to future astronauts. Other projects included manufacturing custom rail brackets for EG-2, assisting engineers working on underwater instrument and tool cases for the NEEMO project, and helping to create mock-up parts for Space Center Houston. The use of the IDC enabled efficient completion of these projects at

A fuzzy Hopfield neural network for medical image segmentation

International Nuclear Information System (INIS)

Lin, J.S.; Cheng, K.S.; Mao, C.W.

1996-01-01

In this paper, an unsupervised parallel segmentation approach using a fuzzy Hopfield neural network (FHNN) is proposed. The main purpose is to embed fuzzy clustering into neural networks so that on-line learning and parallel implementation for medical image segmentation are feasible. The idea is to cast a clustering problem as a minimization problem where the criteria for the optimum segmentation is chosen as the minimization of the Euclidean distance between samples to class centers. In order to generate feasible results, a fuzzy c-means clustering strategy is included in the Hopfield neural network to eliminate the need of finding weighting factors in the energy function, which is formulated and based on a basic concept commonly used in pattern classification, called the within-class scatter matrix principle. The suggested fuzzy c-means clustering strategy has also been proven to be convergent and to allow the network to learn more effectively than the conventional Hopfield neural network. The fuzzy Hopfield neural network based on the within-class scatter matrix shows the promising results in comparison with the hard c-means method

Structural Design and Sizing of a Metallic Cryotank Concept

Science.gov (United States)

Sleight, David W.; Martin, Robert A.; Johnson, Theodore F.

2013-01-01

This paper presents the structural design and sizing details of a 33-foot (10 m) metallic cryotank concept used as the reference design to compare with the composite cryotank concepts developed by industry as part of NASA s Composite Cryotank Technology Development (CCTD) Project. The structural design methodology and analysis results for the metallic cryotank concept are reported in the paper. The paper describes the details of the metallic cryotank sizing assumptions for the baseline and reference tank designs. In particular, the paper discusses the details of the cryotank weld land design and analyses performed to obtain a reduced weight metallic cryotank design using current materials and manufacturing techniques. The paper also discusses advanced manufacturing techniques to spin-form the cryotank domes and compares the potential mass savings to current friction stir-welded technology.

Neural network algorithm for image reconstruction using the grid friendly projections

International Nuclear Information System (INIS)

Cierniak, R.

2011-01-01

Full text: The presented paper describes a development of original approach to the reconstruction problem using a recurrent neural network. Particularly, the 'grid-friendly' angles of performed projections are selected according to the discrete Radon transform (DRT) concept to decrease the number of projections required. The methodology of our approach is consistent with analytical reconstruction algorithms. Reconstruction problem is reformulated in our approach to optimization problem. This problem is solved in present concept using method based on the maximum likelihood methodology. The reconstruction algorithm proposed in this work is consequently adapted for more practical discrete fan beam projections. Computer simulation results show that the neural network reconstruction algorithm designed to work in this way improves obtained results and outperforms conventional methods in reconstructed image quality. (author)

Amorphous silicon carbide ultramicroelectrode arrays for neural stimulation and recording

Science.gov (United States)

Deku, Felix; Cohen, Yarden; Joshi-Imre, Alexandra; Kanneganti, Aswini; Gardner, Timothy J.; Cogan, Stuart F.

2018-02-01

Objective. Foreign body response to indwelling cortical microelectrodes limits the reliability of neural stimulation and recording, particularly for extended chronic applications in behaving animals. The extent to which this response compromises the chronic stability of neural devices depends on many factors including the materials used in the electrode construction, the size, and geometry of the indwelling structure. Here, we report on the development of microelectrode arrays (MEAs) based on amorphous silicon carbide (a-SiC). Approach. This technology utilizes a-SiC for its chronic stability and employs semiconductor manufacturing processes to create MEAs with small shank dimensions. The a-SiC films were deposited by plasma enhanced chemical vapor deposition and patterned by thin-film photolithographic techniques. To improve stimulation and recording capabilities with small contact areas, we investigated low impedance coatings on the electrode sites. The assembled devices were characterized in phosphate buffered saline for their electrochemical properties. Main results. MEAs utilizing a-SiC as both the primary structural element and encapsulation were fabricated successfully. These a-SiC MEAs had 16 penetrating shanks. Each shank has a cross-sectional area less than 60 µm2 and electrode sites with a geometric surface area varying from 20 to 200 µm2. Electrode coatings of TiN and SIROF reduced 1 kHz electrode impedance to less than 100 kΩ from ~2.8 MΩ for 100 µm2 Au electrode sites and increased the charge injection capacities to values greater than 3 mC cm‑2. Finally, we demonstrated functionality by recording neural activity from basal ganglia nucleus of Zebra Finches and motor cortex of rat. Significance. The a-SiC MEAs provide a significant advancement in the development of microelectrodes that over the years has relied on silicon platforms for device manufacture. These flexible a-SiC MEAs have the potential for decreased tissue damage and reduced

Industry 4.0 and representative participation in innovation in manufacturing industries

OpenAIRE

Ramioul, Monique

2017-01-01

Industry 4.0 and representative participation in innovation in manufacturing industries Prof. dr. Monique Ramioul HIVA-KU Leuven, Belgium Abstract submitted for the ETUI-workshop “Digitalization of manufacturing and restructuring of value chains: technology adoption, upgrading, and the changing geography of production in Europe.” (23-24 February 2017, Naples Italy). Under the umbrella concept Industry4.0, the advanced digitalisation and robotisation of industry is presented as a pro...

Adaptive Filtering Using Recurrent Neural Networks

Science.gov (United States)

Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.

2005-01-01

A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

PROSPECTS OF APPLICATION OF THE CONCEPTOF QUICK RESPONSE MANUFACTURING AT THE RUSSIAN INDUSTRIAL ENTERPRISES

Directory of Open Access Journals (Sweden)

Ch. V. Shipilova

2016-01-01

Full Text Available This article is devoted to research of such concept of the organization of production, as Quick Response Manufacturing or Quick-response production, at the center which, time of implementation of the order is put. Today domestic producers are compelled to function in new conditions, competing with a great number of players of the global market, as a result the emphasis on timely implementation of orders and a tendency of reduction of time of production will allow to reach competitive advantages. Fundamental aspects of Quick Response Manufacturing within functioning of the enterprise of branch are considered, and also the essential lack of this concept is revealed.The purpose / objectives. The purpose of article is research of the economic contents of the concept of the organization of production Quick Response Manufacturing and area of its use at the modern industrial enterprises. Article tasks: to investigate economic contents of the concept of Quick Response Manufacturing, to reveal its features and scopes in the modern industry.Methods. A methodical basis of this article are comparative methods of the analysis.Results. The economic contents of the concept of Quick Response Manufacturing are investigated. Four main key aspects which are the cornerstone of this concept are investigated: time force, organizational structure of the enterprise, system dynamics and possibilities of its application in scales of all enterprise. Components of positive work of system dynamics of the industrial enterprise are revealed, the trend of critical process of production is built. Considering that modern conditions demand from the enterprises to react, adapt quickly for the changing conditions, the factor of time plays an important role. Therefore the concept of QRM has rather perspective character, focusing attention on time of implementation of the order, in that communication that this parameter is one of key both for the enterprise, and for increase of its

Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

Energy Technology Data Exchange (ETDEWEB)

Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)

2006-10-15

The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

Science.gov (United States)

Wang, L.; Zhang, Y. Y.; Ding, L.

2006-10-01

The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

International Nuclear Information System (INIS)

Wang, L; Zhang, Y Y; Ding, L

2006-01-01

The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module

Use of neural network based auto-associative memory as a data compressor for pre-processing optical emission spectra in gas thermometry with the help of neural network

International Nuclear Information System (INIS)

Dolenko, S.A.; Filippov, A.V.; Pal, A.F.; Persiantsev, I.G.; Serov, A.O.

2003-01-01

Determination of temperature from optical emission spectra is an inverse problem that is often very difficult to solve, especially when substantial noise is present. One of the means that can be used to solve such a problem is a neural network trained on the results of modeling of spectra at different temperatures (Dolenko, et al., in: I.C. Parmee (Ed.), Adaptive Computing in Design and Manufacture, Springer, London, 1998, p. 345). Reducing the dimensionality of the input data prior to application of neural network can increase the accuracy and stability of temperature determination. In this study, such pre-processing is performed with another neural network working as an auto-associative memory with a narrow bottleneck in the hidden layer. The improvement in the accuracy and stability of temperature determination in presence of noise is demonstrated on model spectra similar to those recorded in a DC-discharge CVD reactor

Neural dichotomy of word concreteness: a view from functional neuroimaging.

Science.gov (United States)

Kumar, Uttam

2016-02-01

Our perception about the representation and processing of concrete and abstract concepts is based on the fact that concrete words are highly imagined and remembered faster than abstract words. In order to explain the processing differences between abstract and concrete concepts, various theories have been proposed, yet there is no unanimous consensus about its neural implication. The present study investigated the processing of concrete and abstract words during an orthography judgment task (implicit semantic processing) using functional magnetic resonance imaging to validate the involvement of the neural regions. Relative to non-words, both abstract and concrete words show activation in the regions of bilateral hemisphere previously associated with semantic processing. The common areas (conjunction analyses) observed for abstract and concrete words are bilateral inferior frontal gyrus (BA 44/45), left superior parietal (BA 7), left fusiform gyrus and bilateral middle occipital. The additional areas for abstract words were noticed in bilateral superior temporal and bilateral middle temporal region, whereas no distinct region was noticed for concrete words. This suggests that words with abstract concepts recruit additional language regions in the brain.

Integration of Sensor and Actuator Networks and the SCADA System to Promote the Migration of the Legacy Flexible Manufacturing System towards the Industry 4.0 Concept

Directory of Open Access Journals (Sweden)

Antonio José Calderón Godoy

2018-05-01

Full Text Available Networks of sensors and actuators in automated manufacturing processes are implemented using industrial fieldbuses, where automation units and supervisory systems are also connected to exchange operational information. In the context of the incoming fourth industrial revolution, called Industry 4.0, the management of legacy facilities is a paramount issue to deal with. This paper presents a solution to enhance the connectivity of a legacy Flexible Manufacturing System, which constitutes the first step in the adoption of the Industry 4.0 concept. Such a system includes the fieldbus PROcess FIeld BUS (PROFIBUS around which sensors, actuators, and controllers are interconnected. In order to establish effective communication between the sensors and actuators network and a supervisory system, a hardware and software approach including Ethernet connectivity is implemented. This work is envisioned to contribute to the migration of legacy systems towards the challenging Industry 4.0 framework. The experimental results prove the proper operation of the FMS and the feasibility of the proposal.

Towards a Lifecycle Information Framework and Technology in Manufacturing.

Science.gov (United States)

Hedberg, Thomas; Feeney, Allison Barnard; Helu, Moneer; Camelio, Jaime A

2017-06-01

Industry has been chasing the dream of integrating and linking data across the product lifecycle and enterprises for decades. However, industry has been challenged by the fact that the context in which data is used varies based on the function / role in the product lifecycle that is interacting with the data. Holistically, the data across the product lifecycle must be considered an unstructured data-set because multiple data repositories and domain-specific schema exist in each phase of the lifecycle. This paper explores a concept called the Lifecycle Information Framework and Technology (LIFT). LIFT is a conceptual framework for lifecycle information management and the integration of emerging and existing technologies, which together form the basis of a research agenda for dynamic information modeling in support of digital-data curation and reuse in manufacturing. This paper provides a discussion of the existing technologies and activities that the LIFT concept leverages. Also, the paper describes the motivation for applying such work to the domain of manufacturing. Then, the LIFT concept is discussed in detail, while underlying technologies are further examined and a use case is detailed. Lastly, potential impacts are explored.

The roles of communication process for an effective lean manufacturing implementation

Directory of Open Access Journals (Sweden)

Perumal Puvanasvaran

2009-07-01

Full Text Available Many companies are implementing lean manufacturing concept in order to remain competitive and sustainable, however, not many of them are successful in the process due to various reasons. Communication is an important aspect of lean process in order to successfully implement lean manufacturing.  This paper determines the roles of communication process in ensuring a successful implementation of leanness in manufacturing companies. All the information of lean manufacturing practices and roles of communication in the implementation were compiled from related journals, books and websites. A study was conducted in an aerospace manufacturing in Malaysia. A five-point scale questionnaire is used as the study instrument. These questionnaires were distributed to 45 employees working in a kitting department and to 8 top management people. The results indicate that the degree of leanness were moderate.

Manufacturing of small scale W monoblock mockups by hot radial pressing

International Nuclear Information System (INIS)

Visca, Eliseo; Testani, C.; Libera, S.; Sacchetti, M.

2003-01-01

In the frame of the European Technology R and D programme for International thermonuclear experimental reactor (ITER) and in the area of high heat flux plasma facing components (HHFC), representative small-scale mock-ups were manufactured and tested to compare different concepts and joining technologies (i.e. active brazing, hot isostatic pressing (HIPping), diffusion bonding, etc.). On the basis of the results obtained by thermal fatigue tests, the monoblock concept resulted to be the most robust one, particularly when the HIPping manufacturing technology is used. Within this programme, ENEA developed an alternative technique for manufacturing plasma-facing components with a monoblock geometry of the ITER machine. The basic idea of this technique, named hot radial pressing (HRP), is to perform a radial diffusion bonding between the cooling tube and the armour tile by pressurising the internal tube only and by keeping the process parameters within the range in which the thermo-mechanical properties of the copper alloys are not yet degraded. The HRP is performed by a standard furnace, in which only a section of the canister is heated. The manufacturing procedure and the results of the screening and fatigue thermal tests performed on the ENEA mock-ups are reported in this paper

Research on networked manufacturing system for reciprocating pump industry

Science.gov (United States)

Wu, Yangdong; Qi, Guoning; Xie, Qingsheng; Lu, Yujun

2005-12-01

Networked manufacturing is a trend of reciprocating pump industry. According to the enterprises' requirement, the architecture of networked manufacturing system for reciprocating pump industry was proposed, which composed of infrastructure layer, system management layer, application service layer and user layer. Its main functions included product data management, ASP service, business management, and customer relationship management, its physics framework was a multi-tier internet-based model; the concept of ASP service integration was put forward and its process model was also established. As a result, a networked manufacturing system aimed at the characteristics of reciprocating pump industry was built. By implementing this system, reciprocating pump industry can obtain a new way to fully utilize their own resources and enhance the capabilities to respond to the global market quickly.

Efficacy of Lean Manufacturing to Improve Production Performance

Directory of Open Access Journals (Sweden)

Israel Balogun

2016-12-01

Full Text Available The lean manufacturing system is a technique of manufacturing products in time. The concept of lean manufacturing principles employs simpler ways of communicating required materials as well as manual technique in ensuring the provision of signals for replenishment of materials the production companies require. Performance on the other hand, can be considered the attainment of value effectively and efficiently. Lean manufacturing and performance production goes hand in hand. Through utilizing lean manufacturing method, most companies would be able to tailor their processes in achieving effective performance and meeting unique requests from their consumers. The exploratory observations conducted in the study was purposely for examining the nature of complex interactions involved between major constructs and environmental sustainability at the parent company and the tire part vendors. The method of the research was a qualitative case study. The research data were obtained from with the case company and through structured interviews the case company's consumers. The case-specific tools were first developed in close co-operation with the case company. Future research agenda addresses gaps in the current literature and suggests relevant framework from which to explore this phenomenon.

Artificial-neural-network-based failure detection and isolation

Science.gov (United States)

Sadok, Mokhtar; Gharsalli, Imed; Alouani, Ali T.

1998-03-01

This paper presents the design of a systematic failure detection and isolation system that uses the concept of failure sensitive variables (FSV) and artificial neural networks (ANN). The proposed approach was applied to tube leak detection in a utility boiler system. Results of the experimental testing are presented in the paper.

Validated Feasibility Study of Integrally Stiffened Metallic Fuselage Panels for Reducing Manufacturing Costs

Science.gov (United States)

Pettit, R. G.; Wang, J. J.; Toh, C.

2000-01-01

The continual need to reduce airframe cost and the emergence of high speed machining and other manufacturing technologies has brought about a renewed interest in large-scale integral structures for aircraft applications. Applications have been inhibited, however, because of the need to demonstrate damage tolerance, and by cost and manufacturing risks associated with the size and complexity of the parts. The Integral Airframe Structures (IAS) Program identified a feasible integrally stiffened fuselage concept and evaluated performance and manufacturing cost compared to conventional designs. An integral skin/stiffener concept was produced both by plate hog-out and near-net extrusion. Alloys evaluated included 7050-T7451 plate, 7050-T74511 extrusion, 6013-T6511 extrusion, and 7475-T7351 plate. Mechanical properties, structural details, and joint performance were evaluated as well as repair, static compression, and two-bay crack residual strength panels. Crack turning behavior was characterized through panel tests and improved methods for predicting crack turning were developed. Manufacturing cost was evaluated using COSTRAN. A hybrid design, made from high-speed machined extruded frames that are mechanically fastened to high-speed machined plate skin/stringer panels, was identified as the most cost-effective manufacturing solution. Recurring labor and material costs of the hybrid design are up to 61 percent less than the current technology baseline.

Waste reduction possibilities for manufacturing systems in the industry 4.0

Science.gov (United States)

Tamás, P.; Illés, B.; Dobos, P.

2016-11-01

The industry 4.0 creates some new possibilities for the manufacturing companies’ waste reduction for example by appearance of the cyber physical systems and the big data concept and spreading the „Internet of things (IoT)”. This paper presents in details the fourth industrial revolutions’ more important achievements and tools. In addition there will be also numerous new research directions in connection with the waste reduction possibilities of the manufacturing systems outlined.

Doctor, Teacher, and Stethoscope: Neural Representation of Different Types of Semantic Relations.

Science.gov (United States)

Xu, Yangwen; Wang, Xiaosha; Wang, Xiaoying; Men, Weiwei; Gao, Jia-Hong; Bi, Yanchao

2018-03-28

Concepts can be related in many ways. They can belong to the same taxonomic category (e.g., "doctor" and "teacher," both in the category of people) or be associated with the same event context (e.g., "doctor" and "stethoscope," both associated with medical scenarios). How are these two major types of semantic relations coded in the brain? We constructed stimuli from three taxonomic categories (people, manmade objects, and locations) and three thematic categories (school, medicine, and sports) and investigated the neural representations of these two dimensions using representational similarity analyses in human participants (10 men and nine women). In specific regions of interest, the left anterior temporal lobe (ATL) and the left temporoparietal junction (TPJ), we found that, whereas both areas had significant effects of taxonomic information, the taxonomic relations had stronger effects in the ATL than in the TPJ ("doctor" and "teacher" closer in ATL neural activity), with the reverse being true for thematic relations ("doctor" and "stethoscope" closer in TPJ neural activity). A whole-brain searchlight analysis revealed that widely distributed regions, mainly in the left hemisphere, represented the taxonomic dimension. Interestingly, the significant effects of the thematic relations were only observed after the taxonomic differences were controlled for in the left TPJ, the right superior lateral occipital cortex, and other frontal, temporal, and parietal regions. In summary, taxonomic grouping is a primary organizational dimension across distributed brain regions, with thematic grouping further embedded within such taxonomic structures. SIGNIFICANCE STATEMENT How are concepts organized in the brain? It is well established that concepts belonging to the same taxonomic categories (e.g., "doctor" and "teacher") share neural representations in specific brain regions. How concepts are associated in other manners (e.g., "doctor" and "stethoscope," which are thematically

Generic Challenges and Automation Solutions in Manufacturing SMEs

DEFF Research Database (Denmark)

Grube Hansen, David; Malik, Ali Ahmad; Bilberg, Arne

2017-01-01

Evermore research is conducted on smart manufacturing, digital manufacturing and other aspects of what is expected from the fourth industrial revolution known as Industry 4.0. Most of the research of Industry 4.0 is currently a better fit for large corporations than for SMEs, which in Europe...... of the project, identifies a correlation between the challenges, age and size of the companies. The identified correlation lay ground for an Industry 4.0 light concept, targeting the identified generic challenges of companies employing 10-50 people. The solutions presented are based on cloud computing, Internet...... however represent 98% of the manufacturing industry. In general, SMEs produce high-mix low-volume products, which require a high degree of flexibility. Historically flexibility and automation have been contradictory, but as automation becomes smarter, digitalized and less expensive, this may change which...

"Industrie 4.0" and Smart Manufacturing – A Review of Research Issues and Application Examples

OpenAIRE

Klaus-Dieter Thoben; Stefan Wiesner; Thorsten Wuest

2017-01-01

A fourth industrial revolution is occurring in global manufacturing. It is based on the introduction of Internet of things and servitization concepts into manufacturing companies, leading to vertically and horizontally integrated production systems. The resulting smart factories are able to fulfill dynamic customer demands with high variability in small lot sizes while integrating human ingenuity and automation. To support the manufacturing industry in this conversion process and enhance glob...

A feasibility study for a manufacturing technology deployment center

Energy Technology Data Exchange (ETDEWEB)

1994-10-31

The Automation & Robotics Research Institute (ARRI) and the Texas Engineering Extension Service (TEEX) were funded by the U.S. Department of Energy to determine the feasibility of a regional industrial technology institute to be located at the Superconducting Super Collider (SSC) Central Facility in Waxahachie, Texas. In response to this opportunity, ARRI and TEEX teamed with the DOE Kansas City Plant (managed by Allied Signal, Inc.), Los Alamos National Laboratory (managed by the University of California), Vought Aircraft Company, National Center for Manufacturing Sciences (NCMS), SSC Laboratory, KPMG Peat Marwick, Dallas County Community College, Navarro Community College, Texas Department of Commerce (TDOC), Texas Manufacturing Assistance Center (TMAC), Oklahoma Center for the Advancement of Science and Technology, Arkansas Science and Technology Authority, Louisiana Productivity Center, and the NASA Mid-Continent Technology Transfer Center (MCTTC) to develop a series of options, perform the feasibility analysis and secure industrial reviews of the selected concepts. The final report for this study is presented in three sections: Executive Summary, Business Plan, and Technical Plan. The results from the analysis of the proposed concept support the recommendation of creating a regional technology alliance formed by the states of Texas, New Mexico, Oklahoma, Arkansas and Louisiana through the conversion of the SSC Central facility into a Manufacturing Technology Deployment Center (MTDC).

ROBOTICALLY ENHANCED ADVANCED MANUFACTURING CONCEPTS TO OPTIMIZE ENERGY, PRODUCTIVITY, AND ENVIRONMENTAL PERFORMANCE

Energy Technology Data Exchange (ETDEWEB)

Larry L. Keller; Joseph M. Pack; Robert V. Kolarik II

2007-11-05

In the first phase of the REML project, major assets were acquired for a manufacturing line for follow-on installation, capability studies and optimization. That activity has been documented in the DE-FC36-99ID13819 final report. In this the second phase of the REML project, most of the major assets have been installed in a manufacturing line arrangement featuring a green cell, a thermal treatment cell and a finishing cell. Most of the secondary and support assets have been acquired and installed. Assets have been integrated with a commercial, machine-tending gantry robot in the thermal treatment cell and with a low-mass, high-speed gantry robot in the finish cell. Capabilities for masterless gauging of product’s dimensional and form characteristics were advanced. Trial production runs across the entire REML line have been undertaken. Discrete event simulation modeling has aided in line balancing and reduction of flow time. Energy, productivity and cost, and environmental comparisons to baselines have been made. Energy The REML line in its current state of development has been measured to be about 22% (338,000 kVA-hrs) less energy intensive than the baseline conventional low volume line assuming equivalent annual production volume of approximately 51,000 races. The reduction in energy consumption is largely attributable to the energy reduction in the REML thermal treatment cell where the heating devices are energized on demand and are appropriately sized to the heating load of a near single piece flow line. If additional steps such as power factor correction and use of high-efficiency motors were implemented to further reduce energy consumption, it is estimated, but not yet demonstrated, that the REML line would be about 30% less energy intensive than the baseline conventional low volume line assuming equivalent annual production volume. Productivity The capital cost of an REML line would be roughly equivalent to the capital cost of a new conventional line. The

Manufacturing concepts and development trends in the industrial production of microelectromechanical systems

Science.gov (United States)

Schuenemann, Matthias; Grimme, Ralf; Kaufmann, Thomas; Schwaab, Gerhard; Baeder, Uwe; Schaefer, Wolfgang; Dorner, Johann

1998-01-01

During the past few years, remarkable affords have been made for the realization of microscale sensors, actuators and microelectromechanical system. Due to advances in solid state and micromachining technologies, significant advances in designing, fabricating and testing of microminiaturized devices have been achieved at laboratory level. However, the technical and economical realization of microelectromechanical systems is considerably impeded by the lack of satisfying device technology for their industrial production. A production concept for the industrial production of hybrid microelectromechanical systems was developed and investigated. The concept is based on the resources and requirements of medium-sized enterprises and is characterized by its flexibility. Microsystem fabrication is separated into microfabrication steps performed in-house and technological steps performed by external technology providers. The modularity of the concept allows for a gradual increase in the degree of automation and the in-house production depth, depending on market capacity and financial resources. To demonstrate the feasibility of this approach, the design and realization of a microfabrication process center, which includes tasks like transport and handling, processing, cleaning, testing and storing are discussed. Special attention is given to the supply and feeding of microparts, to the necessary magazines, trays and transport systems, to the implementation of homogeneous mechanical, environmental and information interfaces, to the employment of advanced control, scheduling, and lot tracking concepts, and to the application of highly modular and cost-efficient clean production concepts.

Analytic network process model for sustainable lean and green manufacturing performance indicator

Science.gov (United States)

Aminuddin, Adam Shariff Adli; Nawawi, Mohd Kamal Mohd; Mohamed, Nik Mohd Zuki Nik

2014-09-01

Sustainable manufacturing is regarded as the most complex manufacturing paradigm to date as it holds the widest scope of requirements. In addition, its three major pillars of economic, environment and society though distinct, have some overlapping among each of its elements. Even though the concept of sustainability is not new, the development of the performance indicator still needs a lot of improvement due to its multifaceted nature, which requires integrated approach to solve the problem. This paper proposed the best combination of criteria en route a robust sustainable manufacturing performance indicator formation via Analytic Network Process (ANP). The integrated lean, green and sustainable ANP model can be used to comprehend the complex decision system of the sustainability assessment. The finding shows that green manufacturing is more sustainable than lean manufacturing. It also illustrates that procurement practice is the most important criteria in the sustainable manufacturing performance indicator.

HOURLY STABILITY ANALYSIS AS THE KEY PARAMETER OF LEAN MANUFACTURING AND LOGISTICS

Directory of Open Access Journals (Sweden)

Petr Besta

2015-12-01

Full Text Available Lean manufacturing belongs to the basic philosophies originating in automotive industry. It was originally based on a number of elementary principles and methods. Companies from other industrial areas have also been gradually trying to apply these principles. This leads to the incorporation of other tools from various areas into this concept. The fundamental techniques of lean manufacturing include the hourly stability (output analysis. This method can be applied in a wide variety of manufacturing fields. The aim is a stable working worker, not a worker working rapidly and with large fluctuations. Speed and sudden changes mean inaccuracy, poor quality and problems to the manufacturing companies. The research has also carried out the hourly stability analysis in a company manufacturing components for a variety of global car manufacturers. The objective of this article is to evaluate the research of hourly stability for the selected workplaces.

Application of Artificial Neural Network to Predict the use of Runway at Juanda International Airport

Science.gov (United States)

Putra, J. C. P.; Safrilah

2017-06-01

Artificial neural network approaches are useful to solve many complicated problems. It solves a number of problems in various areas such as engineering, medicine, business, manufacturing, etc. This paper presents an application of artificial neural network to predict a runway capacity at Juanda International Airport. An artificial neural network model of backpropagation and multi-layer perceptron is adopted to this research to learning process of runway capacity at Juanda International Airport. The results indicate that the training data is successfully recognizing the certain pattern of runway use at Juanda International Airport. Whereas, testing data indicate vice versa. Finally, it can be concluded that the approach of uniformity data and network architecture is the critical part to determine the accuracy of prediction results.

Unitized Stiffened Composite Textile Panels: Manufacturing, Characterization, Experiments, and Analysis

Science.gov (United States)

Kosztowny, Cyrus Joseph Robert

Use of carbon fiber textiles in complex manufacturing methods creates new implementations of structural components by increasing performance, lowering manufacturing costs, and making composites overall more attractive across industry. Advantages of textile composites include high area output, ease of handling during the manufacturing process, lower production costs per material used resulting from automation, and provide post-manufacturing assembly mainstreaming because significantly more complex geometries such as stiffened shell structures can be manufactured with fewer pieces. One significant challenge with using stiffened composite structures is stiffener separation under compression. Axial compression loading conditions have frequently observed catastrophic structural failure due to stiffeners separating from the shell skin. Characterizing stiffener separation behavior is often costly computationally and experimentally. The objectives of this research are to demonstrate unitized stiffened textile composite panels can be manufactured to produce quality test specimens, that existing characterization techniques applied to state-of-the-art high-performance composites provide valuable information in modeling such structures, that the unitized structure concept successfully removes stiffener separation as a primary structural failure mode, and that modeling textile material failure modes are sufficient to accurately capture postbuckling and final failure responses of the stiffened structures. The stiffened panels in this study have taken the integrally stiffened concept to an extent such that the stiffeners and skin are manufactured at the same time, as one single piece, and from the same composite textile layers. Stiffener separation is shown to be removed as a primary structural failure mode for unitized stiffened composite textile panels loaded under axial compression well into the postbuckling regime. Instead of stiffener separation, a material damaging and

Additive Manufacturing for Highly Efficient Window Inserts CRADA Report

Energy Technology Data Exchange (ETDEWEB)

Roschli, Alex C. [ORNL; Chesser, Phillip C. [ORNL; Love, Lonnie J. [ORNL

2018-04-01

ORNL partnered with the Mackinac Technology Company to demonstrate how additive manufacturing can be used to create highly energy efficient window inserts for retrofit in pre-existing buildings. Many early iterations of the window inserts were fabricated using carbon fiber reinforced thermoplastics and polycarbonate films as a stand in for the low-e coated films produced by the Mackinac Technology Company. After demonstration of the proof of concept, i.e. custom window inserts with tensioned film, the materials used for the manufacture of the frames was more closely examined. Hollow particle-filled syntactic foam and low-density polymer composites formed by expandable microspheres were explored as the materials used to additively manufacture the frames of the inserts. It was concluded that low-cost retrofit window inserts in custom sizes could be easily fabricated using large scale additive manufacturing. Furthermore, the syntactic and expanded foams developed and tested satisfy the mechanical performance requirements for the application.

Nano-Magnets and Additive Manufacturing for Electric Motors

Science.gov (United States)

Misra, Ajay K.

2014-01-01

High power density is required for application of electric motors in hybrid electric propulsion. Potential path to achieve high power density in electric motors include advanced materials, lightweight thermal management, lightweight structural concepts, high power density power electronics, and advanced manufacturing. This presentation will focus on two key technologies for achieving high power density, advanced magnets and additive manufacturing. The maximum energy product in current magnets is reaching their theoretical limits as a result of material and process improvements. Future improvements in the maximum energy product for magnets can be achieved through development of nanocomposite magnets combining the hard magnetic phase and soft magnetic phase at the nanoscale level. The presentation will provide an overview of the current state of development for nanocomposite magnets and the future path for doubling the maximum energy product. The other part of the presentation will focus on the role of additive manufacturing in fabrication of high power density electric motors. The presentation will highlight the potential opportunities for applying additive manufacturing to fabricate electric motors.

Neural crest stem cell population in craniomaxillofacial development and tissue repair

Directory of Open Access Journals (Sweden)

M La Noce

2014-10-01

Full Text Available Neural crest cells, delaminating from the neural tube during migration, undergo an epithelial-mesenchymal transition and differentiate into several cell types strongly reinforcing the mesoderm of the craniofacial body area – giving rise to bone, cartilage and other tissues and cells of this human body area. Recent studies on craniomaxillofacial neural crest-derived cells have provided evidence for the tremendous plasticity of these cells. Actually, neural crest cells can respond and adapt to the environment in which they migrate and the cranial mesoderm plays an important role toward patterning the identity of the migrating neural crest cells. In our experience, neural crest-derived stem cells, such as dental pulp stem cells, can actively proliferate, repair bone and give rise to other tissues and cytotypes, including blood vessels, smooth muscle, adipocytes and melanocytes, highlighting that their use in tissue engineering is successful. In this review, we provide an overview of the main pathways involved in neural crest formation, delamination, migration and differentiation; and, in particular, we concentrate our attention on the translatability of the latest scientific progress. Here we try to suggest new ideas and strategies that are needed to fully develop the clinical use of these cells. This effort should involve both researchers/clinicians and improvements in good manufacturing practice procedures. It is important to address studies towards clinical application or take into consideration that studies must have an effective therapeutic prospect for humans. New approaches and ideas must be concentrated also toward stem cell recruitment and activation within the human body, overcoming the classical grafting.

Neural mechanisms of the mind, Aristotle, Zadeh, and fMRI.

Science.gov (United States)

Perlovsky, Leonid I

2010-05-01

Processes in the mind: perception, cognition, concepts, instincts, emotions, and higher cognitive abilities for abstract thinking, beautiful music are considered here within a neural modeling fields (NMFs) paradigm. Its fundamental mathematical mechanism is a process "from vague-fuzzy to crisp," called dynamic logic (DL). This paper discusses why this paradigm is necessary mathematically, and relates it to a psychological description of the mind. Surprisingly, the process from "vague to crisp" corresponds to Aristotelian understanding of mental functioning. Recent functional magnetic resonance imaging (fMRI) measurements confirmed this process in neural mechanisms of perception.

The application of Quick Response Manufacturing practices in Brazil, Europe, and the USA : An exploratory study

NARCIS (Netherlands)

Godinho Filho, Moacir; Marchesini, Antonio Gilberto; Riezebos, Jan; Vandaele, Nico; Devos Ganga, Gilberto Miller

2017-01-01

This research investigates the application of Quick Response Manufacturing (QRM) practices by enterprises in Brazil, Europe, and the USA. QRM is a management concept that focuses on time as the key factor in competitive manufacturing, particularly in customer-oriented Engineer and Make to Order

Technological and economical assessment of alternative process chains for blisk manufacture

OpenAIRE

Klocke, Fritz; Schmitt, Robert; Zeis, Markus; Heidemanns, Lukas; Kerkhoff, Johannes; Heinen, Daniel; Klink, Andreas

2015-01-01

Due to the increase of blisk (blade integrated disk) demands instead of the conventional fir-tree design in current aero-engine concepts there is a high resource-driven need for a comprehensive evaluation of different process chain alternatives for blisk manufacture. Therefore, in this paper different manufacturing chains consisting of roughing, pre-finishing and finishing/polishing are compared to each other by the example of a HPC-blisk out of Inconel 718. Beside conventional milling and el...

Resin Infusion Rigidized Inflatable Concept Development and Demonstration

Data.gov (United States)

National Aeronautics and Space Administration — A novel concept utilizing resin infusion to rigidize inflatable structures was developed at JSC ES. This ICA project intends to complete manufacturing of a prototype...

Object‐Oriented RFID with IoT: A Design Concept of  Information Systems in Manufacturing

Directory of Open Access Journals (Sweden)

Tamotsu Kamigaki

2017-02-01

Full Text Available The Internet of Things (IoT has increasingly become important in industry. Connectivity  over the internet of not only people but also devices (such as sensors, appliances, machines, robots,  and vehicles is leading to a paradigm shift in manufacturing. The Japanese government recognizes  this and has stated that IoT connectivity and the methodologies to exploit it are paramount for  Japanese  industry.  Currently,  the  necessary  changes  have  been  realized  in  large  Japanese  companies;  however,  implementation  in  smaller  companies  has  been  lagging,  despite  the  advantages of introducing IoT technologies, due to the high cost. The objective of this research is to  suggest a design concept which combines IoT and object‐oriented radio frequency identification  (RFID.  IoT technology is  used for  collecting, analyzing, and managing data, and an object‐oriented  RFID  system  is used  as a control  process in manufacturing  systems.  In previous research,  the objectoriented  RFID system  was shown to provide  flexible management through the use of a variety of  OORFID tags. The current research extends this by introducing object‐oriented RFID into IoT  systems to  improve  the  flexibility  in the  manufacturing systems. For the verification of this concept,  an experimental IoT system using object‐oriented  RFID was  designed  and  implemented

Starting manufacturing phase of ITER upper ports

Energy Technology Data Exchange (ETDEWEB)

Utin, Yuri, E-mail: yuri.utin@iter.org [ITER Organization, Route de Vinon-sur-Verdon, CS 90 046, 13067 St. Paul Lez Durance Cedex (France); Alekseev, Alexander; Sborchia, Carlo; Choi, Changho; Albin, Vincent; Barabash, Vladimir; Davis, James [ITER Organization, Route de Vinon-sur-Verdon, CS 90 046, 13067 St. Paul Lez Durance Cedex (France); Fabritsiev, Sergey [NTC Sintez, Efremov Inst., 189631 Metallostroy, St. Petersburg (Russian Federation); Giraud, Benoit; Guirao, Julio [ITER Organization, Route de Vinon-sur-Verdon, CS 90 046, 13067 St. Paul Lez Durance Cedex (France); Koenig, Werner [MAN Diesel & Turbo SE, Werftstrasse 17, Deggendorf (Germany); Kedrov, Igor; Kuzmin, Evgeny [NTC Sintez, Efremov Inst., 189631 Metallostroy, St. Petersburg (Russian Federation); Levesy, Bruno; Martinez, Jean-Marc [ITER Organization, Route de Vinon-sur-Verdon, CS 90 046, 13067 St. Paul Lez Durance Cedex (France); Prebeck, Markus [MAN Diesel & Turbo SE, Werftstrasse 17, Deggendorf (Germany); Privalova, Elena [NTC Sintez, Efremov Inst., 189631 Metallostroy, St. Petersburg (Russian Federation); Ranzinger, Franz [MAN Diesel & Turbo SE, Werftstrasse 17, Deggendorf (Germany); Savrukhin, Petr [Russian Federation ITER Domestic Agency, Kurchatov sq.1, 123182 Moscow (Russian Federation); Schiller, Thomas [MAN Diesel & Turbo SE, Werftstrasse 17, Deggendorf (Germany); and others

2015-10-15

Highlights: • The port plugs are attached to the ports with high-strength fasteners. • Tightening of the fasteners via inductive heating was tested. • A concept for the port/plug sealing with metal-type gaskets has progressed. • Manufacturing design of the Upper Ports is in progress. • A full-scale mock-up of double-wall part of the port stub extension is in manufacturing process – acceptable final tolerances are expected. - Abstract: The ITER Vacuum Vessel (VV) features upper, equatorial and lower ports. The upper and regular equatorial ports are occupied by the port plugs. Although the port design has been overall completed in the past, the design of some remaining interfaces was still in progress: in particular, the Sealing Flange package, which includes the high-vacuum seals and the plug fasteners. As the ITER construction phase has started, the procurement of the VV ports has been launched. The VV upper ports will be procured by the Russian Federation Domestic Agency. The main suppliers were selected and the manufacturing design of the first parts is in full progress now. Since the VV is classified at nuclear level N2, the design and manufacture of its components are to be compliant with the French RCC-MR code and regulations for nuclear pressure equipment in France. These regulations make a strong impact to the port design and manufacturing process.

Starting manufacturing phase of ITER upper ports

International Nuclear Information System (INIS)

Utin, Yuri; Alekseev, Alexander; Sborchia, Carlo; Choi, Changho; Albin, Vincent; Barabash, Vladimir; Davis, James; Fabritsiev, Sergey; Giraud, Benoit; Guirao, Julio; Koenig, Werner; Kedrov, Igor; Kuzmin, Evgeny; Levesy, Bruno; Martinez, Jean-Marc; Prebeck, Markus; Privalova, Elena; Ranzinger, Franz; Savrukhin, Petr; Schiller, Thomas

2015-01-01

Highlights: • The port plugs are attached to the ports with high-strength fasteners. • Tightening of the fasteners via inductive heating was tested. • A concept for the port/plug sealing with metal-type gaskets has progressed. • Manufacturing design of the Upper Ports is in progress. • A full-scale mock-up of double-wall part of the port stub extension is in manufacturing process – acceptable final tolerances are expected. - Abstract: The ITER Vacuum Vessel (VV) features upper, equatorial and lower ports. The upper and regular equatorial ports are occupied by the port plugs. Although the port design has been overall completed in the past, the design of some remaining interfaces was still in progress: in particular, the Sealing Flange package, which includes the high-vacuum seals and the plug fasteners. As the ITER construction phase has started, the procurement of the VV ports has been launched. The VV upper ports will be procured by the Russian Federation Domestic Agency. The main suppliers were selected and the manufacturing design of the first parts is in full progress now. Since the VV is classified at nuclear level N2, the design and manufacture of its components are to be compliant with the French RCC-MR code and regulations for nuclear pressure equipment in France. These regulations make a strong impact to the port design and manufacturing process.

Cloud-Based Automated Design and Additive Manufacturing: A Usage Data-Enabled Paradigm Shift.

Science.gov (United States)

Lehmhus, Dirk; Wuest, Thorsten; Wellsandt, Stefan; Bosse, Stefan; Kaihara, Toshiya; Thoben, Klaus-Dieter; Busse, Matthias

2015-12-19

Integration of sensors into various kinds of products and machines provides access to in-depth usage information as basis for product optimization. Presently, this large potential for more user-friendly and efficient products is not being realized because (a) sensor integration and thus usage information is not available on a large scale and (b) product optimization requires considerable efforts in terms of manpower and adaptation of production equipment. However, with the advent of cloud-based services and highly flexible additive manufacturing techniques, these obstacles are currently crumbling away at rapid pace. The present study explores the state of the art in gathering and evaluating product usage and life cycle data, additive manufacturing and sensor integration, automated design and cloud-based services in manufacturing. By joining and extrapolating development trends in these areas, it delimits the foundations of a manufacturing concept that will allow continuous and economically viable product optimization on a general, user group or individual user level. This projection is checked against three different application scenarios, each of which stresses different aspects of the underlying holistic concept. The following discussion identifies critical issues and research needs by adopting the relevant stakeholder perspectives.

Cloud-Based Automated Design and Additive Manufacturing: A Usage Data-Enabled Paradigm Shift

Science.gov (United States)

Lehmhus, Dirk; Wuest, Thorsten; Wellsandt, Stefan; Bosse, Stefan; Kaihara, Toshiya; Thoben, Klaus-Dieter; Busse, Matthias

2015-01-01

Integration of sensors into various kinds of products and machines provides access to in-depth usage information as basis for product optimization. Presently, this large potential for more user-friendly and efficient products is not being realized because (a) sensor integration and thus usage information is not available on a large scale and (b) product optimization requires considerable efforts in terms of manpower and adaptation of production equipment. However, with the advent of cloud-based services and highly flexible additive manufacturing techniques, these obstacles are currently crumbling away at rapid pace. The present study explores the state of the art in gathering and evaluating product usage and life cycle data, additive manufacturing and sensor integration, automated design and cloud-based services in manufacturing. By joining and extrapolating development trends in these areas, it delimits the foundations of a manufacturing concept that will allow continuous and economically viable product optimization on a general, user group or individual user level. This projection is checked against three different application scenarios, each of which stresses different aspects of the underlying holistic concept. The following discussion identifies critical issues and research needs by adopting the relevant stakeholder perspectives. PMID:26703606

Probability concepts in quality risk management.

Science.gov (United States)

Claycamp, H Gregg

2012-01-01

Essentially any concept of risk is built on fundamental concepts of chance, likelihood, or probability. Although risk is generally a probability of loss of something of value, given that a risk-generating event will occur or has occurred, it is ironic that the quality risk management literature and guidelines on quality risk management tools are relatively silent on the meaning and uses of "probability." The probability concept is typically applied by risk managers as a combination of frequency-based calculation and a "degree of belief" meaning of probability. Probability as a concept that is crucial for understanding and managing risk is discussed through examples from the most general, scenario-defining and ranking tools that use probability implicitly to more specific probabilistic tools in risk management. A rich history of probability in risk management applied to other fields suggests that high-quality risk management decisions benefit from the implementation of more thoughtful probability concepts in both risk modeling and risk management. Essentially any concept of risk is built on fundamental concepts of chance, likelihood, or probability. Although "risk" generally describes a probability of loss of something of value, given that a risk-generating event will occur or has occurred, it is ironic that the quality risk management literature and guidelines on quality risk management methodologies and respective tools focus on managing severity but are relatively silent on the in-depth meaning and uses of "probability." Pharmaceutical manufacturers are expanding their use of quality risk management to identify and manage risks to the patient that might occur in phases of the pharmaceutical life cycle from drug development to manufacture, marketing to product discontinuation. A probability concept is typically applied by risk managers as a combination of data-based measures of probability and a subjective "degree of belief" meaning of probability. Probability as

IS ATTENTION AN APPROPRIATE CONCEPT FOR EXPLAINING BRAIN PROCESSES

NARCIS (Netherlands)

DALENOORT, GJ

In interpreting measurements of brain processes it is necessary to make the model used explicit. A concept such as attention cannot be used in the description of brain activities without a model of the relation of mental and neural processes.

An Investigation into the Use of Manufactured Sand as a 100% Replacement for Fine Aggregate in Concrete

Directory of Open Access Journals (Sweden)

Martins Pilegis

2016-06-01

Full Text Available Manufactured sand differs from natural sea and river dredged sand in its physical and mineralogical properties. These can be both beneficial and detrimental to the fresh and hardened properties of concrete. This paper presents the results of a laboratory study in which manufactured sand produced in an industry sized crushing plant was characterised with respect to its physical and mineralogical properties. The influence of these characteristics on concrete workability and strength, when manufactured sand completely replaced natural sand in concrete, was investigated and modelled using artificial neural networks (ANN. The results show that the manufactured sand concrete made in this study generally requires a higher water/cement (w/c ratio for workability equal to that of natural sand concrete due to the higher angularity of the manufactured sand particles. Water reducing admixtures can be used to compensate for this if the manufactured sand does not contain clay particles. At the same w/c ratio, the compressive and flexural strength of manufactured sand concrete exceeds that of natural sand concrete. ANN proved a valuable and reliable method of predicting concrete strength and workability based on the properties of the fine aggregate (FA and the concrete mix composition.

An Investigation into the Use of Manufactured Sand as a 100% Replacement for Fine Aggregate in Concrete.

Science.gov (United States)

Pilegis, Martins; Gardner, Diane; Lark, Robert

2016-06-02

Manufactured sand differs from natural sea and river dredged sand in its physical and mineralogical properties. These can be both beneficial and detrimental to the fresh and hardened properties of concrete. This paper presents the results of a laboratory study in which manufactured sand produced in an industry sized crushing plant was characterised with respect to its physical and mineralogical properties. The influence of these characteristics on concrete workability and strength, when manufactured sand completely replaced natural sand in concrete, was investigated and modelled using artificial neural networks (ANN). The results show that the manufactured sand concrete made in this study generally requires a higher water/cement (w/c) ratio for workability equal to that of natural sand concrete due to the higher angularity of the manufactured sand particles. Water reducing admixtures can be used to compensate for this if the manufactured sand does not contain clay particles. At the same w/c ratio, the compressive and flexural strength of manufactured sand concrete exceeds that of natural sand concrete. ANN proved a valuable and reliable method of predicting concrete strength and workability based on the properties of the fine aggregate (FA) and the concrete mix composition.

Economic justification for LAN installation in an oilfield equipment manufacturing operation

International Nuclear Information System (INIS)

Frishmuth, R.E.; Gariepy, J.A.

1992-01-01

The oil field equipment manufacturing business, like any other business working in a world wide environment, is becoming interested in reducing the amount of time required to take a product from initial concept to the market place. The most recent concept being employed to achieve this goal is concurrent engineering. This paper discusses the use of local area networks connecting personal computers to facilitate both traditional engineering and manufacturing organizations and concurrent engineering concepts. The key to making either type of organizational structure work well is communication. As companies have moved away from large mainframe computers toward individual, stand alone, personal computers, communications as well as various databases have been difficult to maintain. The authors attempt to show how a LAN would help to solve problems with data integrity, communication and speed of product development. These ideas are combined with discussion of anticipated cost savings as well as LAN installation cost. The authors show that the initial cost of LAN installation can easily be justified by the costs saved in product development

Recent advances in fuel product and manufacturing process development

International Nuclear Information System (INIS)

Slember, R.J.; Doshi, P.K.

1987-01-01

This paper discusses advancements in commercial nuclear fuel products and manufacturing made by the Westinghouse Electric Corporation in response to the commercial nuclear fuel industry's demand for high reliability, increased plant availability and improved operating flexibility. The features and benefits of Westinghouse's most advanced fuel products--VANTAGE 5 for PWR plants and QUAD+ for BWR plants--are described, as well as 'high performance' fuel concepts now under development for delivery in the late 1980s. The paper also disusses the importance of in-process quality control throughout manufacturing towards reducing product variability and improving fuel reliability. (author)

Heliostat Manufacturing for near-term markets. Phase II final report

International Nuclear Information System (INIS)

1998-01-01

This report describes a project by Science Applications International Corporation and its subcontractors Boeing/Rocketdyne and Bechtel Corp. to develop manufacturing technology for production of SAIC stretched membrane heliostats. The project consists of three phases, of which two are complete. This first phase had as its goals to identify and complete a detailed evaluation of manufacturing technology, process changes, and design enhancements to be pursued for near-term heliostat markets. In the second phase, the design of the SAIC stretched membrane heliostat was refined, manufacturing tooling for mirror facet and structural component fabrication was implemented, and four proof-of-concept/test heliostats were produced and installed in three locations. The proposed plan for Phase III calls for improvements in production tooling to enhance product quality and prepare increased production capacity. This project is part of the U.S. Department of Energy's Solar Manufacturing Technology Program (SolMaT)

Neural overlap of L1 and L2 semantic representations in speech: A decoding approach.

Science.gov (United States)

Van de Putte, Eowyn; De Baene, Wouter; Brass, Marcel; Duyck, Wouter

2017-11-15

Although research has now converged towards a consensus that both languages of a bilingual are represented in at least partly shared systems for language comprehension, it remains unclear whether both languages are represented in the same neural populations for production. We investigated the neural overlap between L1 and L2 semantic representations of translation equivalents using a production task in which the participants had to name pictures in L1 and L2. Using a decoding approach, we tested whether brain activity during the production of individual nouns in one language allowed predicting the production of the same concepts in the other language. Because both languages only share the underlying semantic representation (sensory and lexical overlap was maximally avoided), this would offer very strong evidence for neural overlap in semantic representations of bilinguals. Based on the brain activation for the individual concepts in one language in the bilateral occipito-temporal cortex and the inferior and the middle temporal gyrus, we could accurately predict the equivalent individual concepts in the other language. This indicates that these regions share semantic representations across L1 and L2 word production. Copyright © 2017 Elsevier Inc. All rights reserved.

Using Additive Manufacturing to Print a CubeSat Propulsion System

Science.gov (United States)

Marshall, William M.

2015-01-01

CubeSats are increasingly being utilized for missions traditionally ascribed to larger satellites CubeSat unit (1U) defined as 10 cm x 10 cm x 11 cm. Have been built up to 6U sizes. CubeSats are typically built up from commercially available off-the-shelf components, but have limited capabilities. By using additive manufacturing, mission specific capabilities (such as propulsion), can be built into a system. This effort is part of STMD Small Satellite program Printing the Complete CubeSat. Interest in propulsion concepts for CubeSats is rapidly gaining interest-Numerous concepts exist for CubeSat scale propulsion concepts. The focus of this effort is how to incorporate into structure using additive manufacturing. End-use of propulsion system dictates which type of system to develop-Pulse-mode RCS would require different system than a delta-V orbital maneuvering system. Team chose an RCS system based on available propulsion systems and feasibility of printing using a materials extrusion process. Initially investigated a cold-gas propulsion system for RCS applications-Materials extrusion process did not permit adequate sealing of part to make this a functional approach.

Anti-synchronization control of BAM memristive neural networks with multiple proportional delays and stochastic perturbations

Science.gov (United States)

Wang, Weiping; Yuan, Manman; Luo, Xiong; Liu, Linlin; Zhang, Yao

2018-01-01

Proportional delay is a class of unbounded time-varying delay. A class of bidirectional associative memory (BAM) memristive neural networks with multiple proportional delays is concerned in this paper. First, we propose the model of BAM memristive neural networks with multiple proportional delays and stochastic perturbations. Furthermore, by choosing suitable nonlinear variable transformations, the BAM memristive neural networks with multiple proportional delays can be transformed into the BAM memristive neural networks with constant delays. Based on the drive-response system concept, differential inclusions theory and Lyapunov stability theory, some anti-synchronization criteria are obtained. Finally, the effectiveness of proposed criteria are demonstrated through numerical examples.

An analytic framework for developing inherently-manufacturable pop-up laminate devices

International Nuclear Information System (INIS)

Aukes, Daniel M; Goldberg, Benjamin; Wood, Robert J; Cutkosky, Mark R

2014-01-01

Spurred by advances in manufacturing technologies developed around layered manufacturing technologies such as PC-MEMS, SCM, and printable robotics, we propose a new analytic framework for capturing the geometry of folded composite laminate devices and the mechanical processes used to manufacture them. These processes can be represented by combining a small set of geometric operations which are general enough to encompass many different manufacturing paradigms. Furthermore, such a formulation permits one to construct a variety of geometric tools which can be used to analyze common manufacturability concepts, such as tool access, part removability, and device support. In order to increase the speed of development, reduce the occurrence of manufacturing problems inherent with current design methods, and reduce the level of expertise required to develop new devices, the framework has been implemented in a new design tool called popupCAD, which is suited for the design and development of complex folded laminate devices. We conclude with a demonstration of utility of the tools by creating a folded leg mechanism. (paper)

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

Science.gov (United States)

Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

2017-01-01

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

Neural representations of emotion are organized around abstract event features.

Science.gov (United States)

Skerry, Amy E; Saxe, Rebecca

2015-08-03

Research on emotion attribution has tended to focus on the perception of overt expressions of at most five or six basic emotions. However, our ability to identify others' emotional states is not limited to perception of these canonical expressions. Instead, we make fine-grained inferences about what others feel based on the situations they encounter, relying on knowledge of the eliciting conditions for different emotions. In the present research, we provide convergent behavioral and neural evidence concerning the representations underlying these concepts. First, we find that patterns of activity in mentalizing regions contain information about subtle emotional distinctions conveyed through verbal descriptions of eliciting situations. Second, we identify a space of abstract situation features that well captures the emotion discriminations subjects make behaviorally and show that this feature space outperforms competing models in capturing the similarity space of neural patterns in these regions. Together, the data suggest that our knowledge of others' emotions is abstract and high dimensional, that brain regions selective for mental state reasoning support relatively subtle distinctions between emotion concepts, and that the neural representations in these regions are not reducible to more primitive affective dimensions such as valence and arousal. Copyright © 2015 Elsevier Ltd. All rights reserved.

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

Science.gov (United States)

Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

2017-01-01

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures. PMID:29066942

Neural Networks in Control Applications

DEFF Research Database (Denmark)

Sørensen, O.

are examined. The models are separated into three groups representing input/output descriptions as well as state space descriptions: - Models, where all in- and outputs are measurable (static networks). - Models, where some inputs are non-measurable (recurrent networks). - Models, where some in- and some...... outputs are non-measurable (recurrent networks with incomplete state information). The three groups are ordered in increasing complexity, and for each group it is shown how to solve the problems concerning training and application of the specific model type. Of particular interest are the model types...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...

Human Inspired Self-developmental Model of Neural Network (HIM): Introducing Content/Form Computing

Science.gov (United States)

Krajíček, Jiří

This paper presents cross-disciplinary research between medical/psychological evidence on human abilities and informatics needs to update current models in computer science to support alternative methods for computation and communication. In [10] we have already proposed hypothesis introducing concept of human information model (HIM) as cooperative system. Here we continue on HIM design in detail. In our design, first we introduce Content/Form computing system which is new principle of present methods in evolutionary computing (genetic algorithms, genetic programming). Then we apply this system on HIM (type of artificial neural network) model as basic network self-developmental paradigm. Main inspiration of our natural/human design comes from well known concept of artificial neural networks, medical/psychological evidence and Sheldrake theory of "Nature as Alive" [22].

[Folic acid: Primary prevention of neural tube defects. Literature Review].

Science.gov (United States)

Llamas Centeno, M J; Miguélez Lago, C

2016-03-01

Neural tube defects (NTD) are the most common congenital malformations of the nervous system, they have a multifactorial etiology, are caused by exposure to chemical, physical or biological toxic agents, factors deficiency, diabetes, obesity, hyperthermia, genetic alterations and unknown causes. Some of these factors are associated with malnutrition by interfering with the folic acid metabolic pathway, the vitamin responsible for neural tube closure. Its deficit produce anomalies that can cause abortions, stillbirths or newborn serious injuries that cause disability, impaired quality of life and require expensive treatments to try to alleviate in some way the alterations produced in the embryo. Folic acid deficiency is considered the ultimate cause of the production of neural tube defects, it is clear the reduction in the incidence of Espina Bifida after administration of folic acid before conception, this leads us to want to further study the action of folic acid and its application in the primary prevention of neural tube defects. More than 40 countries have made the fortification of flour with folate, achieving encouraging data of decrease in the prevalence of neural tube defects. This paper attempts to make a literature review, which clarify the current situation and future of the prevention of neural tube defects.

Control systems engineering in continuous pharmaceutical manufacturing. May 20-21, 2014 Continuous Manufacturing Symposium.

Science.gov (United States)

Myerson, Allan S; Krumme, Markus; Nasr, Moheb; Thomas, Hayden; Braatz, Richard D

2015-03-01

drug manufacturing that are easily transportable to industry. Industry can facilitate the move to continuous manufacturing by working with universities on the conception of new continuous pharmaceutical manufacturing process unit operations that have the potential to make major improvements in product quality, controllability, or reduced capital and/or operating costs. Regulatory bodies should ensure that: (1) regulations and regulatory practices promote, and do not derail, the development and implementation of continuous manufacturing and control systems engineering approaches; (2) the individuals who approve specific regulatory filings are sufficiently trained to make good decisions regarding control systems approaches; (3) provide regulatory clarity and eliminate/reduce regulatory risks; (4) financially support the development of high-quality training materials for use of undergraduate students, graduate students, industrial employees, and regulatory staff; (5) enhance the training of their own technical staff by financially supporting joint research projects with universities in the development of continuous pharmaceutical manufacturing processes and the associated control systems engineering theory, numerical algorithms, and software; and (6) strongly encourage the federal agencies that support research to fund these research areas. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

Management Concepts and the Navigation of Interessement Devices

DEFF Research Database (Denmark)

Hansen, Per Richard; Clausen, Christian

2017-01-01

principle) and how the concepts configured change in mutual learning processes characterized by conflict, trial and error. This paper contributes to a new understanding of the role of management concepts in change informed by actor-network theory and the concept of interessement devices, and how management......Management concepts are increasingly defining the way we perceive the needs, possibilities and potential outcomes of organizational change. While management concepts such as business process reengineering (BPR), lean manufacturing and stage gate models have been subject to study and debate......, keyconcerns centre on outcomes (or the lack thereof) and the translation of concepts as they are spread and adopted. However, their role in shaping actual organizational change is poorly understood. Very little has been said about how management concepts are used in organizations, and what managers actually...

Knowledge network model of the energy consumption in discrete manufacturing system

Science.gov (United States)

Xu, Binzi; Wang, Yan; Ji, Zhicheng

2017-07-01

Discrete manufacturing system generates a large amount of data and information because of the development of information technology. Hence, a management mechanism is urgently required. In order to incorporate knowledge generated from manufacturing data and production experience, a knowledge network model of the energy consumption in the discrete manufacturing system was put forward based on knowledge network theory and multi-granularity modular ontology technology. This model could provide a standard representation for concepts, terms and their relationships, which could be understood by both human and computer. Besides, the formal description of energy consumption knowledge elements (ECKEs) in the knowledge network was also given. Finally, an application example was used to verify the feasibility of the proposed method.

Educational program for industrial engineers : nurturing new perspectives on manufacturing technology

OpenAIRE

Ishii, Kazuyoshi; Ikeda, Hiroshi; Tsuchiya, Akinori; Shikida, Asami; Abe, Takehiko

2005-01-01

In this paper, we propose the basic concept and result of an educational program developed for industrial engineers and managers in leadership roles who wish to create new values in manufacturing technology. The basic concept combines an intelligent knowledge-based approach with the kaizen activity program in a framework of new value creation and comparative advantage models based on the ABC-G network (Academia, Business, Consultants, and Governmental officers). The educational program is bas...

ATLAS Barrel Hadron Calorimeter: general manufacturing concepts for 300000 absorber plates mass production

International Nuclear Information System (INIS)

Alikov, B.A.; Budagov, Yu.A.; Bylinkin, P.M

1998-01-01

We summarize a 4-year (1994-1997) experience of design and research efforts which led us to the solution of 2 important tasks of a principal significance for precision assembly of one of major elements of ATLAS, - its Hadron Barrel Tile Calorimeter. These tasks were: - to develop the high tolerances (50-100 microns) technology for about 300000 units of calorimeter nuclear absorber plates mass production, - to choose the best manufacturer(s) able to satisfy shop drawings demands in a reasonable balance with some other significant criteria: production period, price acceptable geography location (transport expenses), available storage area and access ways, reliable quality control etc. For the best absorbers producers our final choice was the TATRA PLANT (Czech Republic) for 1.6 m long plates stamping (40800 units) with Argonne punching die and the MINSK TRACTOR PLANT (Belarus Republic) for smaller size plates stamping (about 240000 units). We exclude noticeable (more than 1% of the day production) tolerances violations by the specially developed QUALITY CONTROL Program

In-situ acoustic signature monitoring in additive manufacturing processes

Science.gov (United States)

Koester, Lucas W.; Taheri, Hossein; Bigelow, Timothy A.; Bond, Leonard J.; Faierson, Eric J.

2018-04-01

Additive manufacturing is a rapidly maturing process for the production of complex metallic, ceramic, polymeric, and composite components. The processes used are numerous, and with the complex geometries involved this can make quality control and standardization of the process and inspection difficult. Acoustic emission measurements have been used previously to monitor a number of processes including machining and welding. The authors have identified acoustic signature measurement as a potential means of monitoring metal additive manufacturing processes using process noise characteristics and those discrete acoustic emission events characteristic of defect growth, including cracks and delamination. Results of acoustic monitoring for a metal additive manufacturing process (directed energy deposition) are reported. The work investigated correlations between acoustic emissions and process noise with variations in machine state and deposition parameters, and provided proof of concept data that such correlations do exist.

Extraterrestrial processing and manufacturing of large space systems, volume 1, chapters 1-6

Science.gov (United States)

Miller, R. H.; Smith, D. B. S.

1979-01-01

Space program scenarios for production of large space structures from lunar materials are defined. The concept of the space manufacturing facility (SMF) is presented. The manufacturing processes and equipment for the SMF are defined and the conceptual layouts are described for the production of solar cells and arrays, structures and joints, conduits, waveguides, RF equipment radiators, wire cables, and converters. A 'reference' SMF was designed and its operation requirements are described.

Implementing lean manufacturing system: ISM approach

Directory of Open Access Journals (Sweden)

Naveen Kumar

2013-09-01

Full Text Available Purpose: Lean Manufacturing System has emerged as an important area of research in Indian context. The requirement of Lean Manufacturing has increased due to defects in products (semi finished and finished and subsequent increase in cost. In this context, this study is an attempt to develop a structural model of the variables, important to implement Lean Manufacturing System in Indian automobile industry. Design/Methodology/Approach: Various variables of lean manufacturing system implementation have been identified from literature review and experts’ opinions. Contextual relationship among these identified variables has been set after carrying out brainstorming session. Further, classification of the variables has been carried out based upon the driving power and dependence. In addition to this, a structural model of variables to implement lean concept in Indian automobile industry has also been developed using Interpretive Structural Modeling (ISM technique. Questionnaire based survey has also been conducted to rank these variables. Findings: Eighteen variables have been identified from the literature and subsequent discussions with experts. Out of which, nine variables have been identified as dependent and nine variables have been identified as driver. No variable has been identified as linkage variable and autonomous variable. From the model developed, ‘Relative cost benefits’ has been identified as top level dependent variable and top management commitment as bottom level most independent variable. Research limitations/Implications: The model so developed is a hypothetical model based upon experts’ opinions. The conclusions so drawn may be further modified to apply in real situation. Practical implication: Clear understanding of interactions among these variables will help organizations to prioritize and manage these variables more effectively and efficiently to draw advantage from lean manufacturing system implementation

A novel Rapid Additive Manufacturing concept for architectural composite shell construction inspired by the shell formation in land snails.

Science.gov (United States)

Felbrich, Benjamin; Wulle, Frederik; Allgaier, Christoph; Menges, Achim; Verl, Alexander; Wurst, Karl-Heinz; Nebelsick, James

2018-01-04

State of the art rapid additive manufacturing (RAM), specifically Fused Filament Fabrication (FFF) has gained popularity among architects, engineers and designers for quick prototyping of technical devices, rapid production of small series and even construction scale fabrication of architectural elements. The spectrum of producible shapes and the resolution of detail, however, are determined and constrained by the layer-based nature of the fabrication process. These aspects significantly limit FFF-based approaches for the prefabrication and in-situ fabrication of freeform shells at the architectural scale. Snails exhibit a shell building process that suggests ways to overcome these limits. They produce a soft, pliable proteinaceous film - the periostracum - which later hardens and serves, among other functions, as a form-giving surface for an inner calcium carbonate layer. Snail shell formation behavior is interpreted from a technical point of view to extract potentially useful aspects for a biomimetic transfer. A RAM concept for continuous extrusion of thin free form composite shells inspired by the snail shell formation is presented. © 2018 IOP Publishing Ltd.

Nanotechnology Concepts at MSFC: Engineering Directorate

Science.gov (United States)

Bhat, Biliyar; Kaul, Raj; Shah, Sandeep; Smithers, Gweneth; Watson, Michael D.

2000-01-01

Nanotechnology is the art and science of building materials and devices at the ultimate level of finesse: atom by atom. Our nation's space program has needs for miniaturization of components, minimization of weight and maximization of performance, and nanotechnology will help us get there. MSFC - Engineering Directorate (ED) is committed to developing nanotechnology that will enable MSFC missions in space transportation, space science and space optics manufacturing. MSFC-ED has a dedicated group of technologists who are currently developing high pay-off nanotechnology concepts. This poster presentation will outline some of the concepts being developed at this time including, nanophase structural materials, carbon nanotube reinforced metal and polymer matrix composites, nanotube temperature sensors and aerogels. The poster will outline these concepts and discuss associated technical challenges in turning these concepts into real components and systems.

Design and manufacture of neural tissue engineering scaffolds using hyaluronic acid and polycaprolactone nanofibers with controlled porosity.

Science.gov (United States)

Entekhabi, Elahe; Haghbin Nazarpak, Masoumeh; Moztarzadeh, Fathollah; Sadeghi, Ali

2016-12-01

Given the large differences in nervous tissue and other tissues of the human body and its unique features, such as poor and/or lack of repair, there are many challenges in the repair process of this tissue. Tissue engineering is one of the most effective approaches to repair neural damages. Scaffolds made from electrospun fibers have special potential in cell adhesion, function and cell proliferation. This research attempted to design a high porous nanofibrous scaffold using hyaluronic acid and polycaprolactone to provide ideal conditions for nerve regeneration by applying proper physicochemical and mechanical signals. Chemical and mechanical properties of pure PCL and PCL/HA nanofibrous scaffolds were measured by FTIR and tensile test. Morphology, swelling behavior, and biodegradability of the scaffolds were evaluated too. Porosity of various layers of scaffolds was measured by image analysis method. To assess the cell-scaffold interaction, SH-SY5Y human neuroblastoma cell line were cultured on the electrospun scaffolds. Taken together, these results suggest that the blended nanofibrous scaffolds PCL/HA 95:5 exhibit the most balanced properties to meet all of the required specifications for neural cells and have potential application in neural tissue engineering. Copyright © 2016 Elsevier B.V. All rights reserved.

A Single-use Strategy to Enable Manufacturing of Affordable Biologics

Directory of Open Access Journals (Sweden)

Renaud Jacquemart

2016-01-01

Full Text Available The current processing paradigm of large manufacturing facilities dedicated to single product production is no longer an effective approach for best manufacturing practices. Increasing competition for new indications and the launch of biosimilars for the monoclonal antibody market have put pressure on manufacturers to produce at lower cost. Single-use technologies and continuous upstream processes have proven to be cost-efficient options to increase biomass production but as of today the adoption has been only minimal for the purification operations, partly due to concerns related to cost and scale-up. This review summarizes how a single-use holistic process and facility strategy can overcome scale limitations and enable cost-efficient manufacturing to support the growing demand for affordable biologics. Technologies enabling high productivity, right-sized, small footprint, continuous, and automated upstream and downstream operations are evaluated in order to propose a concept for the flexible facility of the future.

Synchronization of Reaction-Diffusion Neural Networks With Dirichlet Boundary Conditions and Infinite Delays.

Science.gov (United States)

Sheng, Yin; Zhang, Hao; Zeng, Zhigang

2017-10-01

This paper is concerned with synchronization for a class of reaction-diffusion neural networks with Dirichlet boundary conditions and infinite discrete time-varying delays. By utilizing theories of partial differential equations, Green's formula, inequality techniques, and the concept of comparison, algebraic criteria are presented to guarantee master-slave synchronization of the underlying reaction-diffusion neural networks via a designed controller. Additionally, sufficient conditions on exponential synchronization of reaction-diffusion neural networks with finite time-varying delays are established. The proposed criteria herein enhance and generalize some published ones. Three numerical examples are presented to substantiate the validity and merits of the obtained theoretical results.

A study of reactor monitoring method with neural network

Energy Technology Data Exchange (ETDEWEB)

Nabeshima, Kunihiko [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

2001-03-01

The purpose of this study is to investigate the methodology of Nuclear Power Plant (NPP) monitoring with neural networks, which create the plant models by the learning of the past normal operation patterns. The concept of this method is to detect the symptom of small anomalies by monitoring the deviations between the process signals measured from an actual plant and corresponding output signals from the neural network model, which might not be equal if the abnormal operational patterns are presented to the input of the neural network. Auto-associative network, which has same output as inputs, can detect an kind of anomaly condition by using normal operation data only. The monitoring tests of the feedforward neural network with adaptive learning were performed using the PWR plant simulator by which many kinds of anomaly conditions can be easily simulated. The adaptively trained feedforward network could follow the actual plant dynamics and the changes of plant condition, and then find most of the anomalies much earlier than the conventional alarm system during steady state and transient operations. Then the off-line and on-line test results during one year operation at the actual NPP (PWR) showed that the neural network could detect several small anomalies which the operators or the conventional alarm system didn't noticed. Furthermore, the sensitivity analysis suggests that the plant models by neural networks are appropriate. Finally, the simulation results show that the recurrent neural network with feedback connections could successfully model the slow behavior of the reactor dynamics without adaptive learning. Therefore, the recurrent neural network with adaptive learning will be the best choice for the actual reactor monitoring system. (author)

Effects of Aging Stereotype Threat on Working Self-Concepts: An Event-Related Potentials Approach

Science.gov (United States)

Zhang, Baoshan; Lin, Yao; Gao, Qianyun; Zawisza, Magdalena; Kang, Qian; Chen, Xuhai

2017-01-01

Although the influence of stereotype threat (ST) on working self-concepts has been highlighted in recent years, its neural underpinnings are unclear. Notably, the aging ST, which largely influences older adults’ cognitive ability, mental and physical health, did not receive much attention. In order to investigate these issues, electroencephalogram (EEG) data were obtained from older adults during a modified Stroop task using neutral words, positive and negative self-concept words in aging ST vs. neutral control conditions. Results showed longer reaction times (RTs) for identifying colors of words under the aging ST compared to the neutral condition. More importantly, the negative self-concept elicited more positive late P300 amplitudes and enhanced theta band activities compared to the positive self-concept or neutral words under the aging ST condition, whereas no difference was found between these self-concepts and neutral words in the control condition. Furthermore, the aging ST induced smaller theta band synchronization and enhanced alpha band synchronization compared to the control condition. Moreover, we also observed valence differences in self-concepts where the negative self-concept words reduced early P150/N170 complex relative to neutral words. These findings suggest that priming ST could activate negative self-concepts as current working self-concept, and that this influence occurred during a late neural time course. PMID:28747885

Effects of Aging Stereotype Threat on Working Self-Concepts: An Event-Related Potentials Approach

Directory of Open Access Journals (Sweden)

Baoshan Zhang

2017-07-01

Full Text Available Although the influence of stereotype threat (ST on working self-concepts has been highlighted in recent years, its neural underpinnings are unclear. Notably, the aging ST, which largely influences older adults’ cognitive ability, mental and physical health, did not receive much attention. In order to investigate these issues, electroencephalogram (EEG data were obtained from older adults during a modified Stroop task using neutral words, positive and negative self-concept words in aging ST vs. neutral control conditions. Results showed longer reaction times (RTs for identifying colors of words under the aging ST compared to the neutral condition. More importantly, the negative self-concept elicited more positive late P300 amplitudes and enhanced theta band activities compared to the positive self-concept or neutral words under the aging ST condition, whereas no difference was found between these self-concepts and neutral words in the control condition. Furthermore, the aging ST induced smaller theta band synchronization and enhanced alpha band synchronization compared to the control condition. Moreover, we also observed valence differences in self-concepts where the negative self-concept words reduced early P150/N170 complex relative to neutral words. These findings suggest that priming ST could activate negative self-concepts as current working self-concept, and that this influence occurred during a late neural time course.

Manufacturing development for the SAFE 100 kW core

International Nuclear Information System (INIS)

Carter, Robert; Roman, Jose; Salvail, Pat

2002-01-01

In stark contrast to what is sometimes considered the norm in traditional manufacturing processes, engineers at the Marshall Space Flight Center (MSFC) arc in the practice of altering the standard in an effort to realize other potential methods in core manufacturing. While remaining within the bounds of the materials database, we are researching into core manufacturing techniques that may have been overlooked in the past due to funding and/or time constraints. To augment proven core fabrication capabilities we are pursuing plating processes as another possible method for core build-up and assembly. Although brazing and a proprietary HIP cycle are used for module assembly (proven track record for stability and endurance), it is prudent to pursue secondary or backup methods of module and core assembly. For this reason heat tube manufacture and module assembly by means of plating is being investigated. Potentially, the plating processes will give engineers the ability to manufacture replacement modules for any module that might fail to perform nominally, and to assemble/disassemble a complete core in much less time than would be required for the conventional Braze-HIP process. Another area of improvement in core manufacturing capabilities is the installation of a sodium and lithium liquid metal heat pipe fill machine. This, along with the ability to Electron Beam Weld heat pipe seals and wet-in the pipes in the necessary vacuum atmosphere, will eliminate the need to ship potentially hazardous components outside for processing. In addition to developing core manufacturing techniques, the SAFE manufacturing team has been evaluating the thermal heat transfer characteristics, and manufacturability of several heat exchanger design concepts

Moving Segmentation Up the Supply-Chain: Supply Chain Segmentation and Artificial Neural Networks

OpenAIRE

Erevelles, Sunil; Fukawa, Nobuyuki

2008-01-01

This paper explained the concept of supply-side segmentation and transvectional alignment, and applies these concepts in the artificial neural network (ANN). To the best of our knowledge, no research has applied ANN in explaining the heterogeneity of both the supply-side and demand-side of a market in forming relational entity that consists of firms at all levels of the supply chain and the demand chain. The ANN offers a way of operationalizing the concept of supply-side segmentation. In toda...

Application of product life cycle concept to private label management

Directory of Open Access Journals (Sweden)

Sandra Horvat

2013-06-01

Full Text Available Private labels have recorded significant growth rates worldwide, becoming a serious threat to manufacturer brands. Development of private labels in many different product categories increased the complexity of their management. Therefore, this paper examines the possibility of using the product life cycle concept in private label management. Given that private labels are a specific brand type, it is necessary to adjust certain elements of the product life cycle concept, as it was developed on the basis of manufacturer brands. For instance, in the growth stage of the product life cycle, retailers expand private labels to a number of product categories and use the push strategy while manufacturers tend to expand their distribution network in the expansion of their brands and predominantly use the pull strategy in doing so. Furthermore, there is a focus shift from low-price strategy, predominantly used in the introduction phase, to increasing the quality and private label value in the later stages of the product life cycle.

The Molecular Basis of Neural Memory. Part 7: Neural Intelligence (NI versus Artificial Intelligence (AI

Directory of Open Access Journals (Sweden)

Gerard Marx

2017-07-01

Full Text Available The link of memory to intelligence is incontestable, though the development of electronic artifacts with memory has confounded cognitive and computer scientists’ conception of memory and its relevance to “intelligence”. We propose two categories of “Intelligence”: (1 Logical (objective — mathematics, numbers, pattern recognition, games, programmable in binary format. (2 Emotive (subjective — sensations, feelings, perceptions, goals desires, sociability, sex, food, love. The 1st has been reduced to computational algorithms of which we are well versed, witness global technology and the internet. The 2nd relates to the mysterious process whereby (psychic emotive states are achieved by neural beings sensing, comprehending, remembering and dealing with their surroundings. Many theories and philosophies have been forwarded to rationalize this process, but as neuroscientists, we remain dissatisfied. Our own musings on universal neural memory, suggest a tripartite mechanism involving neurons interacting with their surroundings, notably the neural extracellular matrix (nECM with dopants [trace metals and neurotransmitters (NTs]. In particular, the NTs are the molecular encoders of emotive states. We have developed a chemographic representation of such a molecular code.To quote Longuet-Higgins, “Perhaps it is time for the term ‘artificial intelligence’ to be replaced by something more modest and less provisional”. We suggest “artifact intelligence” (ARTI or “machine intelligence” (MI, neither of which imply emulation of emotive neural processes, but simply refer to the ‘demotive’ (lacking emotive quality capability of electronic artifacts that employ a recall function, to calculate algorithms.

New concepts and materials for the manufacturing of MR-compatible guide wires.

Science.gov (United States)

Brecher, Christian; Emonts, Michael; Brack, Alexander; Wasiak, Christian; Schütte, Adrian; Krämer, Nils; Bruhn, Robin

2014-04-01

This paper shows the development of a new magnetic resonance imaging (MRI)-compatible guide wire made from fiber-reinforced plastics. The basic material of the developed guide wire is manufactured using a specially developed micro-pullwinding technology, which allows the adjustment of tensile, bending, and torsional stiffness independent from each other. Additionally, the micro-pullwinding technology provides the possibility to vary the stiffness along the length of the guide wire in a continuous process. With the possibilities of this technology, the mechanical properties of the guide wire were precisely adjusted for the intended usage in MRI-guided interventions. The performance of the guide wire regarding the mechanical properties was investigated. It could be shown, that the mechanical properties could be changed independently from each other by varying the process parameters. Especially, the torsional stiffness could be significantly improved with only a minor influence on bending and tensile properties. The precise influence of the variation of the winding angle on the mechanical and geometrical properties has to be further investigated. The usability of the guide wire as well as its visibility in MRI was investigated by radiologists. With the micro-pullwinding technology, a continuous manufacturing technique for highly stressable, MRI-safe profiles is available and can be the trigger for a new class of medical devices.

In-use product stocks link manufactured capital to natural capital.

Science.gov (United States)

Chen, Wei-Qiang; Graedel, T E

2015-05-19

In-use stock of a product is the amount of the product in active use. In-use product stocks provide various functions or services on which we rely in our daily work and lives, and the concept of in-use product stock for industrial ecologists is similar to the concept of net manufactured capital stock for economists. This study estimates historical physical in-use stocks of 91 products and 9 product groups and uses monetary data on net capital stocks of 56 products to either approximate or compare with in-use stocks of the corresponding products in the United States. Findings include the following: (i) The development of new products and the buildup of their in-use stocks result in the increase in variety of in-use product stocks and of manufactured capital; (ii) substitution among products providing similar or identical functions reflects the improvement in quality of in-use product stocks and of manufactured capital; and (iii) the historical evolution of stocks of the 156 products or product groups in absolute, per capita, or per-household terms shows that stocks of most products have reached or are approaching an upper limit. Because the buildup, renewal, renovation, maintenance, and operation of in-use product stocks drive the anthropogenic cycles of materials that are used to produce products and that originate from natural capital, the determination of in-use product stocks together with modeling of anthropogenic material cycles provides an analytic perspective on the material linkage between manufactured capital and natural capital.

Automation in Siemens fuel manufacturing - the basis for quality improvement by statistical process control (SPC)

International Nuclear Information System (INIS)

Drecker, St.; Hoff, A.; Dietrich, M.; Guldner, R.

1999-01-01

Statistical Process Control (SPC) is one of the systematic tools to perform a valuable contribution to the control and planning activities for manufacturing processes and product quality. Advanced Nuclear Fuels GmbH (ANF) started a program to introduce SPC in all sections of the manufacturing process of fuel assemblies. The concept phase is based on a realization of SPC in 3 pilot projects. The existing manufacturing devices are reviewed for the utilization of SPC. Subsequent modifications were made to provide the necessary interfaces. The processes 'powder/pellet manufacturing'. 'cladding tube manufacturing' and 'laser-welding of spacers' are located at the different locations of ANF. Due to the completion of the first steps and the experience obtained by the pilot projects, the introduction program for SPC has already been extended to other manufacturing processes. (authors)

A system architecture for holonic manufacturing planning and control (EtoPlan)

NARCIS (Netherlands)

Wullink, Gerhard; Giebels, M.M.T.; Kals, H.J.J.

2002-01-01

In this paper, we present the system architecture of a flexible manufacturing planning and control system, named EtoPlan. The concept is based on the holonic control approach of building multiple and temporary hierarchies (holarchies). This paper describes the system architecture for flexible

Measuring the Leanness of Manufacturing system Using Fuzzy TOPSIS : A Case Study of Parizan Sanat Company

Directory of Open Access Journals (Sweden)

Akram, Rasoul

2013-11-01

Full Text Available The implementation of lean manufacturing concepts has had a significant impact on various industries. Many companies around the world have attempted to implement lean manufacturing, but the lack of an obvious understanding of lean measurement and its performance has caused its implementation to fail. This paper presents an innovative approach by using fuzzy TOPSIS to measure the production leanness of manufacturing systems, as a paradigm. This approach is applied to the Parizan Sanat company.

Make-to-order manufacturing - new approach to management of manufacturing processes

Science.gov (United States)

Saniuk, A.; Waszkowski, R.

2016-08-01

Strategic management must now be closely linked to the management at the operational level, because only in such a situation the company can be flexible and can quickly respond to emerging opportunities and pursue ever-changing strategic objectives. In these conditions industrial enterprises seek constantly new methods, tools and solutions which help to achieve competitive advantage. They are beginning to pay more attention to cost management, economic effectiveness and performance of business processes. In the article characteristics of make-to-order systems (MTO) and needs associated with managing such systems is identified based on the literature analysis. The main aim of this article is to present the results of research related to the development of a new solution dedicated to small and medium enterprises manufacture products solely on the basis of production orders (make-to- order systems). A set of indicators to enable continuous monitoring and control of key strategic areas this type of company is proposed. A presented solution includes the main assumptions of the following concepts: the Performance Management (PM), the Balanced Scorecard (BSC) and a combination of strategic management with the implementation of operational management. The main benefits of proposed solution are to increase effectiveness of MTO manufacturing company management.

Lean manufacturing implementation in reducing waste for electronic assembly line

Directory of Open Access Journals (Sweden)

Zakaria Nurul Husna

2017-01-01

Full Text Available Lean manufacturing is the most convenient way to eliminate unnecessary waste and can provide what customers demand. This paper presents possibilities and sustainability of application of lean manufacturing method by using a virtual simulation of the workers performance in a line production of small and medium industry. Actual case study and Witness simulation were used in this study to find the waste that exists in the production and identified the performance of workers in the production line. Lean manufacturing concept has identified and rectified problems related to low productivity in the assembly line. The case study is involved a line production for electronic part assembly. The result of this preliminary study should illustrate the relationship of worker’s performance by lean manufacturing method as well as the productivity improvements which help to reduce cost for manufacturer. Lean manufacturing method has been used during the study to reduce the cost when waste is eliminated by reducing the workstation without reducing the performance of the production. The performance of the production is increased when allocating the labor in a needed working area. Lastly, the study also proves that the new layout has improved the process to be used for future production process.

Impact of information technology on vendor objectives, capabilities, and competences in contract electronic manufacturing

DEFF Research Database (Denmark)

Perunovic, Zoran; Mefford, Robert; Christoffersen, Mads

2012-01-01

IT impacts vendor capabilities. The research framework integrates four concepts/theories: the resource-based view (RBV), the concept of manufacturing strategy, the concept of business performance, and the concept of IT impact on business performance. Two case companies are studied, one with a high level...... proposed. The method gives valuable insights into how IT enables competences, enhances capabilities, and contributes to the fulfillment of vendor objectives. A model of how IT affects a vendor's competitiveness is proposed. In addition, two initiatives for optimizing the utilization of IT are suggested....

Performance Parameters Analysis of an XD3P Peugeot Engine Using Artificial Neural Networks (ANN) Concept in MATLAB

Science.gov (United States)

Rangaswamy, T.; Vidhyashankar, S.; Madhusudan, M.; Bharath Shekar, H. R.

2015-04-01

The current trends of engineering follow the basic rule of innovation in mechanical engineering aspects. For the engineers to be efficient, problem solving aspects need to be viewed in a multidimensional perspective. One such methodology implemented is the fusion of technologies from other disciplines in order to solve the problems. This paper mainly deals with the application of Neural Networks in order to analyze the performance parameters of an XD3P Peugeot engine (used in Ministry of Defence). The basic propaganda of the work is divided into two main working stages. In the former stage, experimentation of an IC engine is carried out in order to obtain the primary data. In the latter stage the primary database formed is used to design and implement a predictive neural network in order to analyze the output parameters variation with respect to each other. A mathematical governing equation for the neural network is obtained. The obtained polynomial equation describes the characteristic behavior of the built neural network system. Finally, a comparative study of the results is carried out.

Epidemiology of neural tube defects in Saudi Arabia.

Science.gov (United States)

AlShail, Essam; De Vol, Edward; Yassen, Ahsan; Elgamal, Essam A

2014-12-01

To evaluate the distribution and pattern of neural tube defects in Saudi Arabia by creating a hospital based registry. All cases registered in the King Faisal Specialist Hospital and Research Center (KFSH&RC) neural tube defect (NTD) registry since it was established in October 2000 until December 2012 were studied through active surveillance comprising a registrar who collects NTD information by reviewing the patient's medical records, and interviewing patient's families. The total number of patients registered from October 2000 to December 2012 was 718 patients. There were more females (417, 58%) than males (301, 42%). Of 620 mothers who underwent antenatal ultrasonography; 392 (63%) were diagnosed at birth, and 204 (33%) were diagnosed with antenatal hydrocephalus. In our registry sample, most mothers (95%) did not take folic acid 3 months prior to pregnancy, and 76% did not take folic acid during the 3 months after conception with the affected child. Only 5% received folic acid prior to conception. The KFSH&RC-NTD registry has met its objectives as a source of data that may significantly contribute to the prevention of NTDs, and improving quality of care for NTD patients through active publication of registry findings and management approaches.

Good manufacturing practices for medicinal products for human use.

Science.gov (United States)

Gouveia, Bruno G; Rijo, Patrícia; Gonçalo, Tânia S; Reis, Catarina P

2015-01-01

At international and national levels, there are public and private organizations, institutions and regulatory authorities, who work and cooperate between them and with Pharmaceutical Industry, in order to achieve a consensus of the guidelines and laws of the manufacturing of medicinal products for human use. This article includes an explanation of how operate and cooperate these participants, between them and expose the current regulations, following the line of European Community/European Economic Area, referencing, wherever appropriate, the practiced guidelines, outside of regulatory action of space mentioned. In this way, it is intended to achieve quality, security and effectiveness exceptional levels in the manufacturing of health products. Good Manufacturing Practice aim the promotion of the human health and consequently, to the improvement of quality of life. For achieve the proposed objectives, it is necessary to ensure the applicability of the presented concepts and show the benefits arising from this applicability.

Good manufacturing practices for medicinal products for human use

Science.gov (United States)

Gouveia, Bruno G.; Rijo, Patrícia; Gonçalo, Tânia S.; Reis, Catarina P.

2015-01-01

At international and national levels, there are public and private organizations, institutions and regulatory authorities, who work and cooperate between them and with Pharmaceutical Industry, in order to achieve a consensus of the guidelines and laws of the manufacturing of medicinal products for human use. This article includes an explanation of how operate and cooperate these participants, between them and expose the current regulations, following the line of European Community/European Economic Area, referencing, wherever appropriate, the practiced guidelines, outside of regulatory action of space mentioned. In this way, it is intended to achieve quality, security and effectiveness exceptional levels in the manufacturing of health products. Good Manufacturing Practice aim the promotion of the human health and consequently, to the improvement of quality of life. For achieve the proposed objectives, it is necessary to ensure the applicability of the presented concepts and show the benefits arising from this applicability. PMID:25883511

The benefit of manufacturing postponement in consumer electronics distribution and retailing

DEFF Research Database (Denmark)

Appelqvist, P.; Gubi, Ebbe

2004-01-01

is required to balance the savings in shops with additional efforts in product development and the complexity of maintaining additional supply chain concepts. The case company is a manufacturer of consumer electronics with over 1200 dedicated retail outlets worldwide. We first interviewed case company....... Results indicate that shop inventory is necessary for high-volume, low-variety products. Manufacturing postponement seems most beneficial when: 1) Customers require a delivery time that is too short to enable ship to order from a central location 2) Product value is high enough to justify additional...

Synchronization of chaotic recurrent neural networks with time-varying delays using nonlinear feedback control

International Nuclear Information System (INIS)

Cui Baotong; Lou Xuyang

2009-01-01

In this paper, a new method to synchronize two identical chaotic recurrent neural networks is proposed. Using the drive-response concept, a nonlinear feedback control law is derived to achieve the state synchronization of the two identical chaotic neural networks. Furthermore, based on the Lyapunov method, a delay independent sufficient synchronization condition in terms of linear matrix inequality (LMI) is obtained. A numerical example with graphical illustrations is given to illuminate the presented synchronization scheme

Estimation of cellular manufacturing cost components using simulation and activity-based costing

OpenAIRE

Paul Savory; Robert Williams

2010-01-01

It can be difficult estimating all of the cost components that are attributed to a machined part.  This problem is more pronounced when a factory uses group technology manufacturing cells as opposed to a functional or process layout of a job shop.  This paper describes how activity-based costing (ABC) concepts can be integrated into a discrete-event simulation model of a U-shaped manufacturing cell producing a part family with four members.  The simulation model generates detai...

Estimation of cellular manufacturing cost components using simulation and activity-based costing

OpenAIRE

Savory, Paul

2010-01-01

It can be difficult estimating all of the cost components that are attributed to a machined part. This problem is more pronounced when a factory uses group technology manufacturing cells as opposed to a functional or process layout of a job shop. This paper describes how activity-based costing (ABC) concepts can be integrated into a discrete-event simulation model of a U-shaped manufacturing cell producing a part family with four members. The simulation model generates detailed Bills of Ac...

Extraterrestrial processing and manufacturing of large space systems. Volume 3: Executive summary

Science.gov (United States)

Miller, R. H.; Smith, D. B. S.

1979-01-01

Facilities and equipment are defined for refining processes to commercial grade of lunar material that is delivered to a 'space manufacturing facility' in beneficiated, primary processed quality. The manufacturing facilities and the equipment for producing elements of large space systems from these materials and providing programmatic assessments of the concepts are also defined. In-space production processes of solar cells (by vapor deposition) and arrays, structures and joints, conduits, waveguides, RF equipment radiators, wire cables, converters, and others are described.

Biofabrication of customized bone grafts by combination of additive manufacturing and bioreactor knowhow.

Science.gov (United States)

Costa, Pedro F; Vaquette, Cédryck; Baldwin, Jeremy; Chhaya, Mohit; Gomes, Manuela E; Reis, Rui L; Theodoropoulos, Christina; Hutmacher, Dietmar W

2014-09-01

This study reports on an original concept of additive manufacturing for the fabrication of tissue engineered constructs (TEC), offering the possibility of concomitantly manufacturing a customized scaffold and a bioreactor chamber to any size and shape. As a proof of concept towards the development of anatomically relevant TECs, this concept was utilized for the design and fabrication of a highly porous sheep tibia scaffold around which a bioreactor chamber of similar shape was simultaneously built. The morphology of the bioreactor/scaffold device was investigated by micro-computed tomography and scanning electron microscopy confirming the porous architecture of the sheep tibiae as opposed to the non-porous nature of the bioreactor chamber. Additionally, this study demonstrates that both the shape, as well as the inner architecture of the device can significantly impact the perfusion of fluid within the scaffold architecture. Indeed, fluid flow modelling revealed that this was of significant importance for controlling the nutrition flow pattern within the scaffold and the bioreactor chamber, avoiding the formation of stagnant flow regions detrimental for in vitro tissue development. The bioreactor/scaffold device was dynamically seeded with human primary osteoblasts and cultured under bi-directional perfusion for two and six weeks. Primary human osteoblasts were observed homogenously distributed throughout the scaffold, and were viable for the six week culture period. This work demonstrates a novel application for additive manufacturing in the development of scaffolds and bioreactors. Given the intrinsic flexibility of the additive manufacturing technology platform developed, more complex culture systems can be fabricated which would contribute to the advances in customized and patient-specific tissue engineering strategies for a wide range of applications.

Biofabrication of customized bone grafts by combination of additive manufacturing and bioreactor knowhow

International Nuclear Information System (INIS)

Costa, Pedro F; Gomes, Manuela E; Reis, Rui L; Vaquette, Cédryck; Baldwin, Jeremy; Chhaya, Mohit; Theodoropoulos, Christina; Hutmacher, Dietmar W

2014-01-01

This study reports on an original concept of additive manufacturing for the fabrication of tissue engineered constructs (TEC), offering the possibility of concomitantly manufacturing a customized scaffold and a bioreactor chamber to any size and shape. As a proof of concept towards the development of anatomically relevant TECs, this concept was utilized for the design and fabrication of a highly porous sheep tibia scaffold around which a bioreactor chamber of similar shape was simultaneously built. The morphology of the bioreactor/scaffold device was investigated by micro-computed tomography and scanning electron microscopy confirming the porous architecture of the sheep tibiae as opposed to the non-porous nature of the bioreactor chamber. Additionally, this study demonstrates that both the shape, as well as the inner architecture of the device can significantly impact the perfusion of fluid within the scaffold architecture. Indeed, fluid flow modelling revealed that this was of significant importance for controlling the nutrition flow pattern within the scaffold and the bioreactor chamber, avoiding the formation of stagnant flow regions detrimental for in vitro tissue development. The bioreactor/scaffold device was dynamically seeded with human primary osteoblasts and cultured under bi-directional perfusion for two and six weeks. Primary human osteoblasts were observed homogenously distributed throughout the scaffold, and were viable for the six week culture period. This work demonstrates a novel application for additive manufacturing in the development of scaffolds and bioreactors. Given the intrinsic flexibility of the additive manufacturing technology platform developed, more complex culture systems can be fabricated which would contribute to the advances in customized and patient-specific tissue engineering strategies for a wide range of applications. (paper)

Neural overlap of L1 and L2 semantic representations across visual and auditory modalities : A decoding approach

NARCIS (Netherlands)

Van De Putte, Eowyn; De Baene, W.; Price, Cathy J; Duyck, Wouter

2018-01-01

This study investigated whether brain activity in Dutch-French bilinguals during semantic access to concepts from one language could be used to predict neural activation during access to the same concepts from another language, in different language modalities/tasks. This was tested using

Intelligent Manufacturing in the Context of Industry 4.0: A Review

Directory of Open Access Journals (Sweden)

Ray Y. Zhong

2017-10-01

Full Text Available Our next generation of industry—Industry 4.0—holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things (IoT-enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the IoT, cyber-physical systems (CPSs, cloud computing, big data analytics (BDA, and information and communications technology (ICT that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

Directory of Open Access Journals (Sweden)

Tayfun Gokmen

2017-10-01

Full Text Available In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU devices to convolutional neural networks (CNNs. We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

Robot-based additive manufacturing for flexible die-modelling in incremental sheet forming

Science.gov (United States)

Rieger, Michael; Störkle, Denis Daniel; Thyssen, Lars; Kuhlenkötter, Bernd

2017-10-01

The paper describes the application concept of additive manufactured dies to support the robot-based incremental sheet metal forming process (`Roboforming') for the production of sheet metal components in small batch sizes. Compared to the dieless kinematic-based generation of a shape by means of two cooperating industrial robots, the supporting robot models a die on the back of the metal sheet by using the robot-based fused layer manufacturing process (FLM). This tool chain is software-defined and preserves the high geometrical form flexibility of Roboforming while flexibly generating support structures adapted to the final part's geometry. Test series serve to confirm the feasibility of the concept by investigating the process challenges of the adhesion to the sheet surface and the general stability as well as the influence on the geometric accuracy compared to the well-known forming strategies.

Single-hidden-layer feed-forward quantum neural network based on Grover learning.

Science.gov (United States)

Liu, Cheng-Yi; Chen, Chein; Chang, Ching-Ter; Shih, Lun-Min

2013-09-01

In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Abnormal neural responses to social exclusion in schizophrenia.

Directory of Open Access Journals (Sweden)

Victoria B Gradin

Full Text Available Social exclusion is an influential concept in politics, mental health and social psychology. Studies on healthy subjects have implicated the medial prefrontal cortex (mPFC, a region involved in emotional and social information processing, in neural responses to social exclusion. Impairments in social interactions are common in schizophrenia and are associated with reduced quality of life. Core symptoms such as delusions usually have a social content. However little is known about the neural underpinnings of social abnormalities. The aim of this study was to investigate the neural substrates of social exclusion in schizophrenia. Patients with schizophrenia and healthy controls underwent fMRI while participating in a popular social exclusion paradigm. This task involves passing a 'ball' between the participant and two cartoon representations of other subjects. The extent of social exclusion (ball not being passed to the participant was parametrically varied throughout the task. Replicating previous findings, increasing social exclusion activated the mPFC in controls. In contrast, patients with schizophrenia failed to modulate mPFC responses with increasing exclusion. Furthermore, the blunted response to exclusion correlated with increased severity of positive symptoms. These data support the hypothesis that the neural response to social exclusion differs in schizophrenia, highlighting the mPFC as a potential substrate of impaired social interactions.

Cognon Neural Model Software Verification and Hardware Implementation Design

Science.gov (United States)

Haro Negre, Pau

Little is known yet about how the brain can recognize arbitrary sensory patterns within milliseconds using neural spikes to communicate information between neurons. In a typical brain there are several layers of neurons, with each neuron axon connecting to ˜104 synapses of neurons in an adjacent layer. The information necessary for cognition is contained in theses synapses, which strengthen during the learning phase in response to newly presented spike patterns. Continuing on the model proposed in "Models for Neural Spike Computation and Cognition" by David H. Staelin and Carl H. Staelin, this study seeks to understand cognition from an information theoretic perspective and develop potential models for artificial implementation of cognition based on neuronal models. To do so we focus on the mathematical properties and limitations of spike-based cognition consistent with existing neurological observations. We validate the cognon model through software simulation and develop concepts for an optical hardware implementation of a network of artificial neural cognons.

DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars

OpenAIRE

Tian, Yuchi; Pei, Kexin; Jana, Suman; Ray, Baishakhi

2017-01-01

Recent advances in Deep Neural Networks (DNNs) have led to the development of DNN-driven autonomous cars that, using sensors like camera, LiDAR, etc., can drive without any human intervention. Most major manufacturers including Tesla, GM, Ford, BMW, and Waymo/Google are working on building and testing different types of autonomous vehicles. The lawmakers of several US states including California, Texas, and New York have passed new legislation to fast-track the process of testing and deployme...

The nuclear fuel rod character recognition system based on neural network technique

International Nuclear Information System (INIS)

Kim, Woong-Ki; Park, Soon-Yong; Lee, Yong-Bum; Kim, Seung-Ho; Lee, Jong-Min; Chien, Sung-Il.

1994-01-01

The nuclear fuel rods should be discriminated and managed systematically by numeric characters which are printed at the end part of each rod in the process of producing fuel assembly. The characters are used to examine manufacturing process of the fuel rods in the inspection process of irradiated fuel rod. Therefore automatic character recognition is one of the most important technologies to establish automatic manufacturing process of fuel assembly. In the developed character recognition system, mesh feature set extracted from each character written in the fuel rod is employed to train a neural network based on back-propagation algorithm as a classifier for character recognition system. Performance evaluation has been achieved on a test set which is not included in a training character set. (author)

Adiabatic superconducting cells for ultra-low-power artificial neural networks

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Andrey E. Schegolev

2016-10-01

Full Text Available We propose the concept of using superconducting quantum interferometers for the implementation of neural network algorithms with extremely low power dissipation. These adiabatic elements are Josephson cells with sigmoid- and Gaussian-like activation functions. We optimize their parameters for application in three-layer perceptron and radial basis function networks.

The economic value of LNG in the Korean manufacturing industry

International Nuclear Information System (INIS)

Park, Sun-Young; Yoo, Seung-Hoon

2013-01-01

Although LNG is an important input to industrial production for manufacturing firms, its economic value has been rarely investigated in the literature. This paper attempts to estimate the economic value of LNG in Korea's manufacturing sector by employing the concept of the value of marginal product (VMP). For this, we used data on 328 firms using LNG as an input. Two types of production functions (the Cobb–Douglas and trans-log functions) are applied. The result of the specification test indicates that the trans-log function is more appropriate for estimating the data. The output elasticity and VMP of industrial LNG are estimated to be 0.1346 and KRW 6844 (USD 6.22) per m 3 , respectively. The results have important implications for various areas of industrial LNG management. For example, any cost–benefit analysis of new projects providing industrial LNG requires information on the economic value of industrial LNG. In addition, such information is useful for the Korean government's future policies on LNG pricing. - Highlights: • We estimate the economic value of LNG in the Korean manufacturing industry. • We employ the concept of the value of marginal product (VMP). • The VMP of industrial LNG is estimated to be KRW 6844 (USD 6.22) per m 3 . • It significantly outweighs the price of industrial LNG (KRW 629.4 per m 3 )

Vascular pattern of the dentate gyrus is regulated by neural progenitors.

Science.gov (United States)

Pombero, Ana; Garcia-Lopez, Raquel; Estirado, Alicia; Martinez, Salvador

2018-05-01

Neurogenesis is a vital process that begins during early embryonic development and continues until adulthood, though in the latter case, it is restricted to the subventricular zone and the subgranular zone of the dentate gyrus (DG). In particular, the DG's neurogenic properties are structurally and functionally unique, which may be related to its singular vascular pattern. Neurogenesis and angiogenesis share molecular signals and act synergistically, supporting the concept of a neurogenic niche as a functional unit between neural precursors cells and their environment, in which the blood vessels play an important role. Whereas it is well known that vascular development controls neural proliferation in the embryonary and in the adult brain, by releasing neurotrophic factors; the potential influence of neural cells on vascular components during angiogenesis is largely unknown. We have demonstrated that the reduction of neural progenitors leads to a significant impairment of vascular development. Since VEGF is a potential regulator in the neurogenesis-angiogenesis crosstalk, we were interested in assessing the possible role of this molecule in the hippocampal neurovascular development. Our results showed that VEGF is the molecule involved in the regulation of vascular development by neural progenitor cells in the DG.

A Simulation of Lean Manufacturing: The Lean Lemonade Tycoon 2

Science.gov (United States)

Ncube, Lisa B.

2010-01-01

This article discusses the functions and effectiveness of games and simulations in the learning processes, in particular as an experiential learning methodology. The application of the game Lemonade Tycoon in the development of lean manufacturing concepts is described. This article addresses the use of the game to teach the principles of lean…

Phylogenetic convolutional neural networks in metagenomics.

Science.gov (United States)

Fioravanti, Diego; Giarratano, Ylenia; Maggio, Valerio; Agostinelli, Claudio; Chierici, Marco; Jurman, Giuseppe; Furlanello, Cesare

2018-03-08

Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.

The neural bases for valuing social equality.

Science.gov (United States)

Aoki, Ryuta; Yomogida, Yukihito; Matsumoto, Kenji

2015-01-01

The neural basis of how humans value and pursue social equality has become a major topic in social neuroscience research. Although recent studies have identified a set of brain regions and possible mechanisms that are involved in the neural processing of equality of outcome between individuals, how the human brain processes equality of opportunity remains unknown. In this review article, first we describe the importance of the distinction between equality of outcome and equality of opportunity, which has been emphasized in philosophy and economics. Next, we discuss possible approaches for empirical characterization of human valuation of equality of opportunity vs. equality of outcome. Understanding how these two concepts are distinct and interact with each other may provide a better explanation of complex human behaviors concerning fairness and social equality. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Manufacturing Experience for Oxide Dispersion Strengthened Alloys

Energy Technology Data Exchange (ETDEWEB)

Bennett, Wendy D. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Doherty, Ann L. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Henager, Charles H. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lavender, Curt A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Montgomery, Robert O. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Omberg, Ronald P. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Smith, Mark T. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Webster, Ryan A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2016-09-22

This report documents the results of the development and the manufacturing experience gained at the Pacific Northwest National Laboratories (PNNL) while working with the oxide dispersion strengthened (ODS) materials MA 956, 14YWT, and 9YWT. The Fuel Cycle Research and Development program of the Office of Nuclear Energy has implemented a program to develop a Uranium-Molybdenum metal fuel for light water reactors. ODS materials have the potential to provide improved performance for the U-Mo concept.

A review on manufacturing technology for long-lived radionuclide fuel compounds

International Nuclear Information System (INIS)

Hwang, Doo Seong; Park, Jin Ho; Kim, Eung Ho; Chung, Won Myung; Lee, Kui Ill; Woo, Moon Sik; Kim, Yeon Ku; Yoo, Jae Hyung

1998-03-01

Thermal neutron reactor (LWR), fast neutron reactor (FBR), accelerator-driven subcritical system have been studied as the potential transmutation devices. The fuel types can be classified according to the concept of each reactor. Oxide fuel is considered in LWR and metal, oxide, and nitride fuels are studied in FBR. In accelerator-driven subcritical system molten salt, metal, and oxide fuels are considered. This review focused on characteristics according to transmutation system, and manufacturing technologies of each fuels. Accelerator-driven system is being proposed as the most reasonable concept in recent, since it has merits in terms of stability and free control of nuclides composition rate in charge of long-lived nuclides. Fluorides molten salt fuel is better chemically stable and corrosion resistant, and lower vapor pressure than chloride molten salt and metal in the fuel type of accelerator-driven system. And then the detail manufacturing technology of fluorides molten salt were reviewed. (author). 62 refs., 23 tabs., 37 figs

Energy-efficiency based classification of the manufacturing workstation

Science.gov (United States)

Frumuşanu, G.; Afteni, C.; Badea, N.; Epureanu, A.

2017-08-01

EU Directive 92/75/EC established for the first time an energy consumption labelling scheme, further implemented by several other directives. As consequence, nowadays many products (e.g. home appliances, tyres, light bulbs, houses) have an EU Energy Label when offered for sale or rent. Several energy consumption models of manufacturing equipments have been also developed. This paper proposes an energy efficiency - based classification of the manufacturing workstation, aiming to characterize its energetic behaviour. The concept of energy efficiency of the manufacturing workstation is defined. On this base, a classification methodology has been developed. It refers to specific criteria and their evaluation modalities, together to the definition & delimitation of energy efficiency classes. The energy class position is defined after the amount of energy needed by the workstation in the middle point of its operating domain, while its extension is determined by the value of the first coefficient from the Taylor series that approximates the dependence between the energy consume and the chosen parameter of the working regime. The main domain of interest for this classification looks to be the optimization of the manufacturing activities planning and programming. A case-study regarding an actual lathe classification from energy efficiency point of view, based on two different approaches (analytical and numerical) is also included.

Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

Science.gov (United States)

Takiyama, Ken

2017-12-01

How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

Moral concepts set decision strategies to abstract values.

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Svenja Caspers

Full Text Available Persons have different value preferences. Neuroimaging studies where value-based decisions in actual conflict situations were investigated suggest an important role of prefrontal and cingulate brain regions. General preferences, however, reflect a superordinate moral concept independent of actual situations as proposed in psychological and socioeconomic research. Here, the specific brain response would be influenced by abstract value systems and moral concepts. The neurobiological mechanisms underlying such responses are largely unknown. Using functional magnetic resonance imaging (fMRI with a forced-choice paradigm on word pairs representing abstract values, we show that the brain handles such decisions depending on the person's superordinate moral concept. Persons with a predominant collectivistic (altruistic value system applied a "balancing and weighing" strategy, recruiting brain regions of rostral inferior and intraparietal, and midcingulate and frontal cortex. Conversely, subjects with mainly individualistic (egocentric value preferences applied a "fight-and-flight" strategy by recruiting the left amygdala. Finally, if subjects experience a value conflict when rejecting an alternative congruent to their own predominant value preference, comparable brain regions are activated as found in actual moral dilemma situations, i.e., midcingulate and dorsolateral prefrontal cortex. Our results demonstrate that superordinate moral concepts influence the strategy and the neural mechanisms in decision processes, independent of actual situations, showing that decisions are based on general neural principles. These findings provide a novel perspective to future sociological and economic research as well as to the analysis of social relations by focusing on abstract value systems as triggers of specific brain responses.

Moral Concepts Set Decision Strategies to Abstract Values

Science.gov (United States)

Caspers, Svenja; Heim, Stefan; Lucas, Marc G.; Stephan, Egon; Fischer, Lorenz; Amunts, Katrin; Zilles, Karl

2011-01-01

Persons have different value preferences. Neuroimaging studies where value-based decisions in actual conflict situations were investigated suggest an important role of prefrontal and cingulate brain regions. General preferences, however, reflect a superordinate moral concept independent of actual situations as proposed in psychological and socioeconomic research. Here, the specific brain response would be influenced by abstract value systems and moral concepts. The neurobiological mechanisms underlying such responses are largely unknown. Using functional magnetic resonance imaging (fMRI) with a forced-choice paradigm on word pairs representing abstract values, we show that the brain handles such decisions depending on the person's superordinate moral concept. Persons with a predominant collectivistic (altruistic) value system applied a “balancing and weighing” strategy, recruiting brain regions of rostral inferior and intraparietal, and midcingulate and frontal cortex. Conversely, subjects with mainly individualistic (egocentric) value preferences applied a “fight-and-flight” strategy by recruiting the left amygdala. Finally, if subjects experience a value conflict when rejecting an alternative congruent to their own predominant value preference, comparable brain regions are activated as found in actual moral dilemma situations, i.e., midcingulate and dorsolateral prefrontal cortex. Our results demonstrate that superordinate moral concepts influence the strategy and the neural mechanisms in decision processes, independent of actual situations, showing that decisions are based on general neural principles. These findings provide a novel perspective to future sociological and economic research as well as to the analysis of social relations by focusing on abstract value systems as triggers of specific brain responses. PMID:21483767

Social manufacturing

OpenAIRE

Hamalainen, Markko; Karjalainen, Jesse

2017-01-01

New business models harnessing the power of individuals have already revolutionized service industries and digital content production. In this study, we investigate whether a similar phenomenon is taking place in manufacturing industries. We start by conceptually defining two distinct forms of firm-individual collaboration in manufacturing industries: (1) social cloud manufacturing, in which firms outsource manufacturing to individuals, and (2) social platform manufacturing, in which firms pr...

Design and manufacture of neural tissue engineering scaffolds using hyaluronic acid and polycaprolactone nanofibers with controlled porosity

International Nuclear Information System (INIS)

Entekhabi, Elahe; Haghbin Nazarpak, Masoumeh; Moztarzadeh, Fathollah; Sadeghi, Ali

2016-01-01

Given the large differences in nervous tissue and other tissues of the human body and its unique features, such as poor and/or lack of repair, there are many challenges in the repair process of this tissue. Tissue engineering is one of the most effective approaches to repair neural damages. Scaffolds made from electrospun fibers have special potential in cell adhesion, function and cell proliferation. This research attempted to design a high porous nanofibrous scaffold using hyaluronic acid and polycaprolactone to provide ideal conditions for nerve regeneration by applying proper physicochemical and mechanical signals. Chemical and mechanical properties of pure PCL and PCL/HA nanofibrous scaffolds were measured by FTIR and tensile test. Morphology, swelling behavior, and biodegradability of the scaffolds were evaluated too. Porosity of various layers of scaffolds was measured by image analysis method. To assess the cell–scaffold interaction, SH-SY5Y human neuroblastoma cell line were cultured on the electrospun scaffolds. Taken together, these results suggest that the blended nanofibrous scaffolds PCL/HA 95:5 exhibit the most balanced properties to meet all of the required specifications for neural cells and have potential application in neural tissue engineering. - Highlights: • This paper focuses on design a high porous nanofibrous scaffold. • Hyaluronic acid and polycaprolactone were used as materials to provide ideal conditions for nerve regeneration. • Proper physicochemical and mechanical signals applied for improving cell attachment

Design and manufacture of neural tissue engineering scaffolds using hyaluronic acid and polycaprolactone nanofibers with controlled porosity

Energy Technology Data Exchange (ETDEWEB)

Entekhabi, Elahe [Department of Biomedical Engineering, Amirkabir University of Technology, P.O. Box: 15875/4413, Tehran 159163/4311 (Iran, Islamic Republic of); Haghbin Nazarpak, Masoumeh, E-mail: mhaghbinn@gmail.com [New Technologies Research Center (NTRC), Amirkabir University of Technology, Tehran 15875-4413 (Iran, Islamic Republic of); Moztarzadeh, Fathollah; Sadeghi, Ali [Department of Biomedical Engineering, Amirkabir University of Technology, P.O. Box: 15875/4413, Tehran 159163/4311 (Iran, Islamic Republic of)

2016-12-01

Given the large differences in nervous tissue and other tissues of the human body and its unique features, such as poor and/or lack of repair, there are many challenges in the repair process of this tissue. Tissue engineering is one of the most effective approaches to repair neural damages. Scaffolds made from electrospun fibers have special potential in cell adhesion, function and cell proliferation. This research attempted to design a high porous nanofibrous scaffold using hyaluronic acid and polycaprolactone to provide ideal conditions for nerve regeneration by applying proper physicochemical and mechanical signals. Chemical and mechanical properties of pure PCL and PCL/HA nanofibrous scaffolds were measured by FTIR and tensile test. Morphology, swelling behavior, and biodegradability of the scaffolds were evaluated too. Porosity of various layers of scaffolds was measured by image analysis method. To assess the cell–scaffold interaction, SH-SY5Y human neuroblastoma cell line were cultured on the electrospun scaffolds. Taken together, these results suggest that the blended nanofibrous scaffolds PCL/HA 95:5 exhibit the most balanced properties to meet all of the required specifications for neural cells and have potential application in neural tissue engineering. - Highlights: • This paper focuses on design a high porous nanofibrous scaffold. • Hyaluronic acid and polycaprolactone were used as materials to provide ideal conditions for nerve regeneration. • Proper physicochemical and mechanical signals applied for improving cell attachment.

Reconfigurability of behavioural specifications for manufacturing systems

Science.gov (United States)

Schmidt, Klaus Werner

2017-12-01

Reconfigurable manufacturing systems (RMS) support flexibility in the product variety and the configuration of the manufacturing system itself in order to enable quick adjustments to new products and production requirements. As a consequence, an essential feature of RMS is their ability to rapidly modify the control strategy during run-time. In this paper, the particular problem of changing the specified operation of a RMS, whose logical behaviour is modelled as a finite state automaton, is addressed. The notion of reconfigurability of specifications (RoS) is introduced and it is shown that the stated reconfiguration problem can be formulated as a controlled language convergence problem. In addition, algorithms for the verification of RoS and the construction of a reconfiguration supervisor are proposed. The supervisor is realised in a modular way which facilitates the extension by new configurations. Finally, it is shown that a supremal nonblocking and controllable strict subautomaton of the plant automaton that fulfils RoS exists in case RoS is violated for the plant automaton itself and an algorithm for the computation of this strict subautomaton is presented. The developed concepts and results are illustrated by a manufacturing cell example.

Raw materials in the manufacture of biotechnology products: regulatory considerations.

Science.gov (United States)

Cordoba-Rodriguez, Ruth

2010-01-01

The Food and Drug Administration's Pharmaceutical cGMPs for the 21st Century initiative emphasizes science and risk-based approaches in the manufacture of drugs. These approaches are reflected in the International Conference on Harmonization (ICH) guidances ICH Q8, Q9, and Q10 and encourage a comprehensive assessment of the manufacture of a biologic, including all aspects of manufacture that have the potential to affect the finished drug product. Appropriate assessment and management of raw materials are an important part of this initiative. Ideally, a raw materials program should strive to assess and minimize the risk to product quality. With this in mind, risk-assessment concepts and control strategies will be discussed and illustrated by examples, with an emphasis on the impact of raw materials on cell substrates. Finally, the life cycle of the raw material will be considered, including its potential to affect the drug product life cycle. In this framework, the supply chain and the vendor-manufacturer relationship will be explored as important parts of an adequate raw materials control strategy.

Dynamical networks: Finding, measuring, and tracking neural population activity using network science

Directory of Open Access Journals (Sweden)

Mark D. Humphries

2017-12-01

Full Text Available Systems neuroscience is in a headlong rush to record from as many neurons at the same time as possible. As the brain computes and codes using neuron populations, it is hoped these data will uncover the fundamentals of neural computation. But with hundreds, thousands, or more simultaneously recorded neurons come the inescapable problems of visualizing, describing, and quantifying their interactions. Here I argue that network science provides a set of scalable, analytical tools that already solve these problems. By treating neurons as nodes and their interactions as links, a single network can visualize and describe an arbitrarily large recording. I show that with this description we can quantify the effects of manipulating a neural circuit, track changes in population dynamics over time, and quantitatively define theoretical concepts of neural populations such as cell assemblies. Using network science as a core part of analyzing population recordings will thus provide both qualitative and quantitative advances to our understanding of neural computation.

JackEx: The new digital manufacturing resource for optimization of Exoskeleton-based factory environments

OpenAIRE

Constantinescu, Carmen; Mureșan, Paul Cristian; Simon, Gabriel-Marian

2016-01-01

The employment of Exoskeletons for manual handling work in manufacturing industries aims at increased employment, productivity, safety and security at workplace. This paper highlights several challenges, current results and future steps of our work in optimization of Exoskeleton based factory environments. “JackEx” is the enhancement of the standard digital humanoid “Jack” with concepts and elements of passive Exoskeletons. For the development of JackEx, a new digital manufacturing resource, ...

Natural language acquisition in large scale neural semantic networks

Science.gov (United States)

Ealey, Douglas

This thesis puts forward the view that a purely signal- based approach to natural language processing is both plausible and desirable. By questioning the veracity of symbolic representations of meaning, it argues for a unified, non-symbolic model of knowledge representation that is both biologically plausible and, potentially, highly efficient. Processes to generate a grounded, neural form of this model-dubbed the semantic filter-are discussed. The combined effects of local neural organisation, coincident with perceptual maturation, are used to hypothesise its nature. This theoretical model is then validated in light of a number of fundamental neurological constraints and milestones. The mechanisms of semantic and episodic development that the model predicts are then used to explain linguistic properties, such as propositions and verbs, syntax and scripting. To mimic the growth of locally densely connected structures upon an unbounded neural substrate, a system is developed that can grow arbitrarily large, data- dependant structures composed of individual self- organising neural networks. The maturational nature of the data used results in a structure in which the perception of concepts is refined by the networks, but demarcated by subsequent structure. As a consequence, the overall structure shows significant memory and computational benefits, as predicted by the cognitive and neural models. Furthermore, the localised nature of the neural architecture also avoids the increasing error sensitivity and redundancy of traditional systems as the training domain grows. The semantic and episodic filters have been demonstrated to perform as well, or better, than more specialist networks, whilst using significantly larger vocabularies, more complex sentence forms and more natural corpora.

Neural Conversion and Patterning of Human Pluripotent Stem Cells: A Developmental Perspective.

Science.gov (United States)

Zirra, Alexandra; Wiethoff, Sarah; Patani, Rickie

2016-01-01

Since the reprogramming of adult human terminally differentiated somatic cells into induced pluripotent stem cells (hiPSCs) became a reality in 2007, only eight years have passed. Yet over this relatively short period, myriad experiments have revolutionized previous stem cell dogmata. The tremendous promise of hiPSC technology for regenerative medicine has fuelled rising expectations from both the public and scientific communities alike. In order to effectively harness hiPSCs to uncover fundamental mechanisms of disease, it is imperative to first understand the developmental neurobiology underpinning their lineage restriction choices in order to predictably manipulate cell fate to desired derivatives. Significant progress in developmental biology provides an invaluable resource for rationalising directed differentiation of hiPSCs to cellular derivatives of the nervous system. In this paper we begin by reviewing core developmental concepts underlying neural induction in order to provide context for how such insights have guided reductionist in vitro models of neural conversion from hiPSCs. We then discuss early factors relevant in neural patterning, again drawing upon crucial knowledge gained from developmental neurobiological studies. We conclude by discussing open questions relating to these concepts and how their resolution might serve to strengthen the promise of pluripotent stem cells in regenerative medicine.

Neural Conversion and Patterning of Human Pluripotent Stem Cells: A Developmental Perspective

Directory of Open Access Journals (Sweden)

Alexandra Zirra

2016-01-01

Full Text Available Since the reprogramming of adult human terminally differentiated somatic cells into induced pluripotent stem cells (hiPSCs became a reality in 2007, only eight years have passed. Yet over this relatively short period, myriad experiments have revolutionized previous stem cell dogmata. The tremendous promise of hiPSC technology for regenerative medicine has fuelled rising expectations from both the public and scientific communities alike. In order to effectively harness hiPSCs to uncover fundamental mechanisms of disease, it is imperative to first understand the developmental neurobiology underpinning their lineage restriction choices in order to predictably manipulate cell fate to desired derivatives. Significant progress in developmental biology provides an invaluable resource for rationalising directed differentiation of hiPSCs to cellular derivatives of the nervous system. In this paper we begin by reviewing core developmental concepts underlying neural induction in order to provide context for how such insights have guided reductionist in vitro models of neural conversion from hiPSCs. We then discuss early factors relevant in neural patterning, again drawing upon crucial knowledge gained from developmental neurobiological studies. We conclude by discussing open questions relating to these concepts and how their resolution might serve to strengthen the promise of pluripotent stem cells in regenerative medicine.

Integrating probabilistic models of perception and interactive neural networks: a historical and tutorial review.

Science.gov (United States)

McClelland, James L

2013-01-01

This article seeks to establish a rapprochement between explicitly Bayesian models of contextual effects in perception and neural network models of such effects, particularly the connectionist interactive activation (IA) model of perception. The article is in part an historical review and in part a tutorial, reviewing the probabilistic Bayesian approach to understanding perception and how it may be shaped by context, and also reviewing ideas about how such probabilistic computations may be carried out in neural networks, focusing on the role of context in interactive neural networks, in which both bottom-up and top-down signals affect the interpretation of sensory inputs. It is pointed out that connectionist units that use the logistic or softmax activation functions can exactly compute Bayesian posterior probabilities when the bias terms and connection weights affecting such units are set to the logarithms of appropriate probabilistic quantities. Bayesian concepts such the prior, likelihood, (joint and marginal) posterior, probability matching and maximizing, and calculating vs. sampling from the posterior are all reviewed and linked to neural network computations. Probabilistic and neural network models are explicitly linked to the concept of a probabilistic generative model that describes the relationship between the underlying target of perception (e.g., the word intended by a speaker or other source of sensory stimuli) and the sensory input that reaches the perceiver for use in inferring the underlying target. It is shown how a new version of the IA model called the multinomial interactive activation (MIA) model can sample correctly from the joint posterior of a proposed generative model for perception of letters in words, indicating that interactive processing is fully consistent with principled probabilistic computation. Ways in which these computations might be realized in real neural systems are also considered.

Local Structure Fixation in the Composite Manufacturing Chain

Science.gov (United States)

Girdauskaite, Lina; Krzywinski, Sybille; Rödel, Hartmut; Wildasin-Werner, Andrea; Böhme, Ralf; Jansen, Irene

2010-12-01

Compared to metal materials, textile reinforced composites show interesting features, but also higher production costs because of low automation rate in the manufacturing chain at this time. Their applicability is also limited due to quality problems, which restrict the production of complex shaped dry textile preforms. New technologies, design concepts, and cost-effective manufacturing methods are needed in order to establish further fields of application. This paper deals with possible ways to improve the textile deformation process by locally applying a fixative to the structure parallel to the cut. This hinders unwanted deformation in the textile stock during the subsequent stacking and formation steps. It is found that suitable thermoplastic binders, applied in the appropriate manner do not restrict formation of the textile and have no negative influence on the mechanical properties of the composite.

The influence of lean manufacturing practices in cellular manufacturing qualifying attributes

Directory of Open Access Journals (Sweden)

Giuliano Almeida Marodin

2013-11-01

Full Text Available This article aims to investigate how of lean production (LP influences to a manufacturing cell (MC performance, based on the theoretical study about the attributes that characterize a MC. It was necessary to develop the concept of MC in a socio-technical system perspective, incorporating a technical attribute, a social attribute and the elements of time, space and information to define a "real cell". The results show that most of LM practices aim to increase time and information. The rapid problem solving process and the use of minimal amount of inventory between the activities of the cell seeks to simultaneously reduces the processing time and increase the degree of information. Although influenced to a lesser degree, the organizational attribute and space element are also positively influenced by the practical application of PE.

Just-in-time Design and Additive Manufacture of Patient-specific Medical Implants

Science.gov (United States)

Shidid, Darpan; Leary, Martin; Choong, Peter; Brandt, Milan

Recent advances in medical imaging and manufacturing science have enabled the design and production of complex, patient-specific orthopaedic implants. Additive Manufacture (AM) generates three-dimensional structures layer by layer, and is not subject to the constraints associated with traditional manufacturing methods. AM provides significant opportunities for the design of novel geometries and complex lattice structures with enhanced functional performance. However, the design and manufacture of patient-specific AM implant structures requires unique expertise in handling various optimization platforms. Furthermore, the design process for complex structures is computationally intensive. The primary aim of this research is to enable the just-in-time customisation of AM prosthesis; whereby AM implant design and manufacture be completed within the time constraints of a single surgical procedure, while minimising prosthesis mass and optimising the lattice structure to match the stiffness of the surrounding bone tissue. In this research, a design approach using raw CT scan data is applied to the AM manufacture of femoral prosthesis. Using the proposed just-in-time concept, the mass of the prosthesis was rapidly designed and manufactured while satisfying the associated structural requirements. Compressive testing of lattice structures manufactured using proposed method shows that the load carrying capacity of the resected composite bone can be recovered by up to 85% and the compressive stiffness of the AM prosthesis is statistically indistinguishable from the stiffness of the initial bone.

INDICATORS FOR SUSTAINABILITY IN INDUSTRIAL SYSTEMS CASE STUDY: PAPER MANUFACTURING

Directory of Open Access Journals (Sweden)

Maria Emiliana Fortună

2011-12-01

Full Text Available The paper describes a framework for promoting sustainability by using indicators for sustainable production. The concept of sustainable production is described as it is viewed by various organisms actions involved in the analysis of the sustainable industrial systems.The measure of sustainability is approached considering indicators of sustainable production, addressing both their dimensions and qualitative and quantitative features.The proposed framework refines the sustainability dimension for a case study which envisages sustainability in paper manufacturing. The analysis takes into account the life cycle analysis for the considered process since the environmental impact is seen as an essential sustainability indicator. Paper recycling and reuse is associated environmental and social costs, as a preferred alternative in waste minimization hierarchy in the manufacturing of non-trees eco-friendly paper.Proactive initiatives to improve the environmental performances of production process are considered as powerful tools for improving the paper manufacturing environmental footprint.

INNOVATION LEAN PRINCIPLES IN AUTOMOTIVE GREEN MANUFACTURING

Directory of Open Access Journals (Sweden)

Dušan Sabadka

2014-12-01

Full Text Available Today, industries such as automotive and manufacturing industries deal with a lot of environmental regulations. Lean is a production strategy whose fundamental principles drive the industry towards a more effective production of goods and services. The eco-efficiency concept is primary to sustainable development and intends to provide more value with less environmental impact. The aim of this study is to identify and explore the contributions of Lean to reduce environmental impacts that naturally result from industrial activity.

Concepts of nuclear quality assurance

International Nuclear Information System (INIS)

Randers, G.; Morris, P.A.; Pomeroy, D.

1976-01-01

While the safety record of the nuclear industry continues to be excellent, the forced outage rates for recent years continue to be 15% or more. Quality assurance, therefore, needs to be applied not only to nuclear safety matters, but to the goals of increased productivity and reduced construction and operating costs. Broadening the application of the general concept of quality assurance in this way leads to the introduction of reliability technology. The total activity might better be called reliability assurance. That effective quality assurance systems do pay off is described by examples from the utility industry, from a manufacturer of instruments and systems and from the experience of Westinghouse Electric Company's manufacturing divisions. The special situation of applying quality assurance to nuclear fuel is discussed. Problems include the lack of a fully developed regulatory policy in this area, incomplete understanding of the mechanism for pellet-clad interaction failures, incomplete access to manufacturers design and process information, inability to make desirable changes on a timely basis and inadequate feedback of irradiation experience. (author)

Evaluate the system reliability for a manufacturing network with reworking actions

International Nuclear Information System (INIS)

Lin, Yi-Kuei; Chang, Ping-Chen

2012-01-01

To measure the system reliability of a manufacturing system with reworking actions is a crucial issue in industry, in which the system reliability could be one of the essential performance indicators to evaluate whether the manufacturing system is capable or not. In a manufacturing system, the input flow (raw materials/WIP) processed by each machine might be defective and thus the output flow (WIP/products) would be less than the input amount. Moreover, defective WIP/products are usually incentive to be reworked for reducing wasting and increasing output. Therefore, reworking actions are necessary to be considered in the manufacturing system. Based on the path concept, we revise such a manufacturing system as a stochastic-flow network in which the capacity of each machine is stochastic (i.e., multistate) due to the failure, partial failure, and maintenance. We decompose the network into one general processing path and several reworking paths. Subsequently, three algorithms for different network models are proposed to generate the lower boundary vector which affords to produce enough products satisfying the demand d. In terms of such a vector, the system reliability can be derived afterwards.

Precision manufacturing

CERN Document Server

Dornfeld, David

2008-01-01

Today there is a high demand for high-precision products. The manufacturing processes are now highly sophisticated and derive from a specialized genre called precision engineering. Precision Manufacturing provides an introduction to precision engineering and manufacturing with an emphasis on the design and performance of precision machines and machine tools, metrology, tooling elements, machine structures, sources of error, precision machining processes and precision process planning. As well as discussing the critical role precision machine design for manufacturing has had in technological developments over the last few hundred years. In addition, the influence of sustainable manufacturing requirements in precision processes is introduced. Drawing upon years of practical experience and using numerous examples and illustrative applications, David Dornfeld and Dae-Eun Lee cover precision manufacturing as it applies to: The importance of measurement and metrology in the context of Precision Manufacturing. Th...

Fiber-reinforced composites materials, manufacturing, and design

CERN Document Server

Mallick, P K

2007-01-01

The newly expanded and revised edition of Fiber-Reinforced Composites: Materials, Manufacturing, and Design presents the most up-to-date resource available on state-of-the-art composite materials. This book is unique in that it not only offers a current analysis of mechanics and properties, but also examines the latest advances in test methods, applications, manufacturing processes, and design aspects involving composites. This third edition presents thorough coverage of newly developed materials including nanocomposites. It also adds more emphasis on underlying theories, practical methods, and problem-solving skills employed in real-world applications of composite materials. Each chapter contains new examples drawn from diverse applications and additional problems to reinforce the practical relevance of key concepts. New in The Third Edition: Contains new sections on material substitution, cost analysis, nano- and natural fibers, fiber architecture, and carbon-carbon composites Provides a new chapter on poly...

The image recognition based on neural network and Bayesian decision

Science.gov (United States)

Wang, Chugege

2018-04-01

The artificial neural network began in 1940, which is an important part of artificial intelligence. At present, it has become a hot topic in the fields of neuroscience, computer science, brain science, mathematics, and psychology. Thomas Bayes firstly reported the Bayesian theory in 1763. After the development in the twentieth century, it has been widespread in all areas of statistics. In recent years, due to the solution of the problem of high-dimensional integral calculation, Bayesian Statistics has been improved theoretically, which solved many problems that cannot be solved by classical statistics and is also applied to the interdisciplinary fields. In this paper, the related concepts and principles of the artificial neural network are introduced. It also summarizes the basic content and principle of Bayesian Statistics, and combines the artificial neural network technology and Bayesian decision theory and implement them in all aspects of image recognition, such as enhanced face detection method based on neural network and Bayesian decision, as well as the image classification based on the Bayesian decision. It can be seen that the combination of artificial intelligence and statistical algorithms has always been the hot research topic.

Strategic research on the sustainable development cost of manufacturing industry under the background of carbon allowance and trade policy

Science.gov (United States)

Ma, Zhongmin; Cheng, Mengting; Wang, Mei

2017-08-01

The important subjects of energy consumption and carbon emission are manufacturing enterprises, with the deepening of international cooperation, and the implementation of carbon limit and trade policy, costs of manufacturing industry will rise sharply. How can the manufacturing industry survive in this reform, and it has to be a problem that the managers of the manufacturing industry need to solve. This paper analyses sustainable development cost connotation and value basis on the basis of sustainable development concept, discusses the influence of carbon allowance and trade policy for cost strategy of manufacturing industry, thinks that manufacturing industry should highlight social responsibility and realize maximization of social value, implement cost strategy the sustainable development, and pointed out the implementation way.

Polymer SU-8 Based Microprobes for Neural Recording and Drug Delivery

Science.gov (United States)

Altuna, Ane; Fernandez, Luis; Berganzo, Javier

2015-06-01

This manuscript makes a reflection about SU-8 based microprobes for neural activity recording and drug delivery. By taking advantage of improvements in microfabrication technologies and using polymer SU-8 as the only structural material, we developed several microprobe prototypes aimed to: a) minimize injury in neural tissue, b) obtain high-quality electrical signals and c) deliver drugs at a micrometer precision scale. Dedicated packaging tools have been developed in parallel to fulfill requirements concerning electric and fluidic connections, size and handling. After these advances have been experimentally proven in brain using in vivo preparation, the technological concepts developed during consecutive prototypes are discussed in depth now.

POLYMER SU-8 BASED MICROPROBES FOR NEURAL RECORDING AND DRUG DELIVERY

Directory of Open Access Journals (Sweden)

Ane eAltuna

2015-06-01

Full Text Available This manuscript makes a reflection about SU-8 based microprobes for neural activity recording and drug delivery. By taking advantage of improvements in microfabrication technologies and using polymer SU-8 as the only structural material, we developed several microprobe prototypes aimed to: a minimize injury in neural tissue, b obtain high-quality electrical signals and c deliver drugs at a micrometer precision scale. Dedicated packaging tools have been developed in parallel to fulfill requirements concerning electric and fluidic connections, size and handling. After these advances have been experimentally proven in brain using in vivo preparation, the technological concepts developed during consecutive prototypes are discussed in depth now.

Manufacturing network evolution

DEFF Research Database (Denmark)

Yang, Cheng; Farooq, Sami; Johansen, John

2011-01-01

Purpose – This paper examines the effect of changes at the manufacturing plant level on other plants in the manufacturing network and also investigates the role of manufacturing plants on the evolution of a manufacturing network. Design/methodology/approach –The research questions are developed...... different manufacturing plants in the network and their impact on network transformation. Findings – The paper highlights the dominant role of manufacturing plants in the continuously changing shape of a manufacturing network. The paper demonstrates that a product or process change at one manufacturing...... by identifying the gaps in the reviewed literature. The paper is based on three case studies undertaken in Danish manufacturing companies to explore in detail their manufacturing plants and networks. The cases provide a sound basis for developing the research questions and explaining the interaction between...

Reliability analysis of C-130 turboprop engine components using artificial neural network

Science.gov (United States)

Qattan, Nizar A.

In this study, we predict the failure rate of Lockheed C-130 Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including (feed-forward back-propagation, radial basis neural network, and multilayer perceptron neural network model); will be utilized to perform this study. For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network (ANN) model utilizing the feed-forward back-propagation algorithm as a learning rule. The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box. In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model. By the end of the study, we forecast the general failure rate of the Lockheed C-130 Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network (MLP) model on DTREG commercial software. The results also give an insight into the reliability of the engine

Simulation Design for Off-Line Training of Practical Lean Manufacturing Concepts for Visual Inspection

Science.gov (United States)

Tetteh, Edem; McWilliams, Douglas

2010-01-01

Customer needs for high-quality goods and the risk of product-liability litigation against businesses have made companies look for a way to sustain quality assurance in their products and services. Lean manufacturing is the latest and most successful system being used by companies to turn their business around. Visual inspection plays an important…

A cost-effective compressed air generation for manufacturing using modified microturbines

International Nuclear Information System (INIS)

Eret, Petr

2016-01-01

Highlights: • A new cost-effective way of compressed air generation for manufacturing in SME is proposed. • The approach is based on a modified microturbine configuration. • Thermodynamic and life cycle analyses are presented and economic benefit is demonstrated. - Abstract: Compressed air is an irreplaceable energy source for some manufacturing processes, and is also common in applications even when there are alternatives. As a result, compressed air is a key utility in manufacturing industry, but unfortunately the cost of compressed air production is one of the most expensive processes in a manufacturing facility. In order to reduce the compressed air generation cost an unconventional way using a microturbine configuration is proposed. The concept is based on an extraction of a certain amount of compressed air from/after the compressor with the residual air flowing to the turbine to produce sufficient back power to drive the compressor. A thermodynamic and life cycle analysis are presented for several system variations, including a simple cycle without a recuperator and a complex configuration with an intercooler, recuperator and reheating. The study is based on the typical requirements (i.e. quantity, pressure) for a small to medium sized industrial compressed air system. The analysis is focused on the North American market due to the low price of natural gas. The lowest life cycle cost alternative is represented by a microturbine concept with a recuperator, air extraction after partial compression, intercooler and aftercooler. A comparison of an electric motor and conventional microturbine prime movers demonstrates the economic benefit of the proposed compressed air generation method, for the design parameters and utility prices considered.

Transforming creativity: Personalized manufacturing meets embodied computing

Directory of Open Access Journals (Sweden)

Alan B. Craig

2012-06-01

Full Text Available This paper discusses aspects of a collaborative investigation of embodied computing and personal manufacturing. We describe the NeuroMaker 1.0, an artwork that playfully implements the concept of “translating of the designer’s ideas into a product”. Visitors to the installation were invited to use their own EEG to fabricate personalized physical objects. While primarily intended to provoke thought about the process of creativity, we also demonstrated that, with the right team, radical new interfaces are well within the reach.

From basic concepts to emerging technologies in regional anesthesia.

Science.gov (United States)

Dillane, Derek; Tsui, Ban C H

2010-10-01

The present article details how our understanding of the basic concepts of regional anesthesia has recently evolved. We will appraise current technological advances and question the commensurate nature of the relationship between tradition and innovation. Ultrasound localization has enhanced our understanding of the needle-nerve relationship. Intraneural injection of local anesthetic may occur with greater frequency than previously thought without inevitably leading to neurological complications. The ratio of neural to non-neural tissue varies both between and within nerves and may be an important determinant of neural injury. Ultrasonographic evidence of intraneural injection is subject to observer expertise and the resolution of the ultrasound image. Current ultrasound resolution capability does not reliably permit differentiation between intrafascicular and extrafascicular drug injection. Perineural electrical impedance may be a determinant of current threshold and conceivably distinguish between intraneural and extraneural tissue. Technology that enhances the sonographic image of both procedure needle and target nerve is the focus of current endeavors in ultrasound innovation.There is inconclusive evidence that the use of ultrasound technology has reduced the incidence of local anesthetic toxicity. Lipid emulsion therapy is now an accepted treatment for systemic local anesthetic toxicity. There are new reports on the development of an ultra long-acting local anesthetic agent that would permit lower doses and superannuate catheter-based continuous regional anesthesia techniques. Over the past decade, our understanding of the fundamental concepts of regional anesthesia continues to be challenged by emerging experimental and clinical evidence.

Hybrid intelligence systems and artificial neural network (ANN approach for modeling of surface roughness in drilling

Directory of Open Access Journals (Sweden)

Ch. Sanjay

2014-12-01

Full Text Available In machining processes, drilling operation is material removal process that has been widely used in manufacturing since industrial revolution. The useful life of cutting tool and its operating conditions largely controls the economics of machining operations. Drilling is most frequently performed material removing process and is used as a preliminary step for many operations, such as reaming, tapping, and boring. Drill wear has a bad effect on the surface finish and dimensional accuracy of the work piece. The surface finish of a machined part is one of the most important quality characteristics in manufacturing industries. The primary objective of this research is the prediction of suitable parameters for surface roughness in drilling. Cutting speed, cutting force, and machining time were given as inputs to the adaptive fuzzy neural network and neuro-fuzzy analysis for estimating the values of surface roughness by using 2, 3, 4, and 5 membership functions. The best structures were selected based on minimum of summation of square with the actual values with the estimated values by artificial neural fuzzy inference system (ANFIS and neuro-fuzzy systems. For artificial neural network (ANN analysis, the number of neurons was selected from 1, 2, 3, … , 20. The learning rate was selected as .5 and .5 smoothing factor was used. The inputs were selected as cutting speed, feed, machining time, and thrust force. The best structures of neural networks were selected based on the criteria as the minimum of summation of square with the actual value of surface roughness. Drilling experiments with 10 mm size were performed at two cutting speeds and feeds. Comparative analysis has been done between the actual values and the estimated values obtained by ANFIS, neuro-fuzzy, and ANN analysis.

Development of manufacturing technology and fabrication of prototype for main coolant pump

Energy Technology Data Exchange (ETDEWEB)

Chung, Koon Seok; Han, C.K.; Chei, J.M.; Chung, K.S.; Youn, M.H.; Shin, S.A.; Choi, D.J.; Kim, H.C. [HALLA Industrial Co., Ltd., Pusan (Korea)

1999-03-01

This study presents the development of the manufacturing technology for the Main Coolant Pump of the SMART. This report contains the followings; (1) Select axial type pump for the MCP (2) MCP is drived by squirrel-cage induction motor that consisted canned motor type. (3) MCP shaft has three horizontal and one vertical support bearings. (4) Design of several part of the MCP (5) Manufacturing of the performance test motor (6) Design and manufacturing of the speed sensor (7) Procedures for three-axial and five-axial M.C.T., Tig welding and Electron Beam Welding were developed. (8) Conceptional design of the MCP test facility for the performance test under operating conditions. (9) Results of standard weld test specimens according to the ASME section IX. (author). 21 refs., 35 figs., 10 tabs.

Real time PV manufacturing diagnostic system

Energy Technology Data Exchange (ETDEWEB)

Kochergin, Vladimir [MicroXact Inc., Blacksburg, VA (United States); Crawford, Michael A. [MicroXact Inc., Blacksburg, VA (United States)

2015-09-01

The main obstacle Photovoltaic (PV) industry is facing at present is the higher cost of PV energy compared to that of fossil energy. While solar cell efficiencies continue to make incremental gains these improvements are so far insufficient to drive PV costs down to match that of fossil energy. Improved in-line diagnostics however, has the potential to significantly increase the productivity and reduce cost by improving the yield of the process. On this Phase I/Phase II SBIR project MicroXact developed and demonstrated at CIGS pilot manufacturing line a high-throughput in-line PV manufacturing diagnostic system, which was verified to provide fast and accurate data on the spatial uniformity of thickness, an composition of the thin films comprising the solar cell as the solar cell is processed reel-to-reel. In Phase II project MicroXact developed a stand-alone system prototype and demonstrated the following technical characteristics: 1) ability of real time defect/composition inconsistency detection over 60cm wide web at web speeds up to 3m/minute; 2) Better than 1mm spatial resolution on 60cm wide web; 3) an average better than 20nm spectral resolution resulting in more than sufficient sensitivity to composition imperfections (copper-rich and copper-poor regions were detected). The system was verified to be high vacuum compatible. Phase II results completely validated both technical and economic feasibility of the proposed concept. MicroXact’s solution is an enabling technique for in-line PV manufacturing diagnostics to increase the productivity of PV manufacturing lines and reduce the cost of solar energy, thus reducing the US dependency on foreign oil while simultaneously reducing emission of greenhouse gasses.

An Assessment of RFID Applications in Manufacturing Companies

Directory of Open Access Journals (Sweden)

Gładysz Bartłomiej

2015-12-01

Full Text Available The meaning of Cyber Physical Systems and an Internet of Things with indication of RFID position in those concepts was outlined. Research program related to assessment of RFID technology was presented. Author deducted on problems related to RFID implementations and RFID essentially for logistics of manufacturing companies. Research goals and problems were formulated. Tools, techniques, models and methods that could be utilized were proposed and discussed. Research was focused on design of a new method to support early decision making phases for RFID application in logistics of manufacturing companies. Author stated that literature and practice lacks of complex method to answer if RFID is strategically important for the company, which processes should be RFID-supported, how RFID-supported processes should be designed and if RFID-support is rational. Framework for assessment of RFID technology with illustrative example was discussed.

Additive Manufacturing (3D Printing) Aircraft Parts and Tooling at the Maintenance Group Level

Science.gov (United States)

The purpose of this research was to evaluate the effectiveness of additive manufacturing (AM) or 3D printing for the Air Force aircraft maintenance...case study of the 552d MXGs 3D printing operation explores their use of a Fused Deposition Modeling (FDM) thermoplastic material to manufacture parts...by applying the case study’s analysis toward a proof of concept, producing a C-130J Aft Cargo Door Rub Strip for 3D printing . The study concluded by

Evolvable synthetic neural system

Science.gov (United States)

Curtis, Steven A. (Inventor)

2009-01-01

An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

Neural control systems for alternatively fuelled vehicles and natural gas fuel injection for DACIA NOVA

Energy Technology Data Exchange (ETDEWEB)

Sulatisky, M. [Saskatchewan Research Council, Saskatoon, SK (Canada); Ghelesel, A. [BC Gas International, Vancouver, BC (Canada)

1999-07-01

The elements of natural gas vehicle conversion technology are described as background to a discussion of the development of bi-fuel injection system for the Rumanian-manufactured DACIA-NOVA automobile. The bi-fuel injection system mirrors the fueling system installed by the original equipment manufacturer; it can also be easily installed on Ford, General Motors and DaimlerChrysler vehicles as well as on most imports.To meet emission standards after 2000, it is envisaged to install on the DACIA NOVA a neural control system (NCS) and a completely adaptive linear control system (ACLS). Details of natural gas vehicles development and the development of NCS and ACLS are discussed, including short-term and long-term objectives.

Neural electrical activity and neural network growth.

Science.gov (United States)

Gafarov, F M

2018-05-01

The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

JT-60SA vacuum vessel manufacturing and assembly

Energy Technology Data Exchange (ETDEWEB)

Masaki, Kei, E-mail: masaki.kei@jaea.go.jp [Japan Atomic Energy Agency, Naka, Ibaraki-ken 311-0193 (Japan); Shibama, Yusuke K.; Sakurai, Shinji; Shibanuma, Kiyoshi; Sakasai, Akira [Japan Atomic Energy Agency, Naka, Ibaraki-ken 311-0193 (Japan)

2012-08-15

Highlights: Black-Right-Pointing-Pointer The design of the JT-60SA vacuum vessel body was completed with the demonstration of manufacturing procedure by the mock-up fabrication of the 20 Degree-Sign upper half of VV. Black-Right-Pointing-Pointer The actual VV manufacturing has started since November 2009. Black-Right-Pointing-Pointer The first product of the VV 40 Degree-Sign sector was completed in May 2011. Black-Right-Pointing-Pointer A basic VV assembly scenario and procedure were studied to complete the 360 Degree-Sign VV including positioning method and joint welding. - Abstract: The JT-60SA vacuum vessel (VV) has a D-shaped poloidal cross section and a toroidal configuration with 10 Degree-Sign segmented facets. A double wall structure is adopted to ensure high rigidity at operational load and high toroidal one-turn resistance. The material is 316L stainless steel with low cobalt content (<0.05%). The design temperatures of the VV at plasma operation and baking are 50 Degree-Sign C and 200 Degree-Sign C, respectively. In the double wall, boric-acid water is circulated at plasma operation to reduce the nuclear heating of the superconducting magnets. For baking, nitrogen gas is circulated in the double wall after draining of the boric-acid water. The manufacturing of the VV started in November 2009 after a fundamental welding R and D and a trial manufacturing of 20 Degree-Sign upper half mock-up. The manufacturing of the first VV 40 Degree-Sign sector was completed in May 2011. A basic concept and required jigs of the VV assembly were studied. This paper describes the design and manufacturing of the vacuum vessel. A plan of VV assembly in torus hall is also presented.

The use of a CAN-bus in a flexible manufacturing cell

NARCIS (Netherlands)

Ing P.N. Klijn; Ir. Dick van Schenk Brill

2000-01-01

One of the goals of this research is to arrive at an implementation of a CAN-bus that can be used for lab exercises in regular student courses. In this paper, an overview is given of our basic ideas concerning the CAN concept and its application to the control of a manufacturing system. This system

Implementation of hierarchical design for manufacture rules in manufacturing processes

OpenAIRE

Parvez, Masud

2008-01-01

In order to shorten the product development cycle time, minimise overall cost and smooth transition into production, early consideration of manufacturing processes is important. Design for Manufacture (DFM) is the practice of designing products with manufacturing issues using an intelligent system, which translates 3D solid models into manufacturable features. Many existing and potential applications, particularly in the field of manufacturing, require various aspects of features technology. ...

Neural-net based unstable machine identification using individual energy functions. [Transient disturbances in power systems

Energy Technology Data Exchange (ETDEWEB)

Djukanovic, M [Institut Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D J; Pao, Yohhan [Case Western Reserve Univ., Cleveland, OH (United States)

1991-10-01

The identification of the mode of instability plays an essential role in generating principal energy boundary hypersurfaces. We present a new method for unstable machine identification based on the use of supervised learning neural-net technology, and the adaptive pattern recognition concept. It is shown that using individual energy functions as pattern features, appropriately trained neural-nets can retrieve the reliable characterization of the transient process including critical clearing time parameter, mode of instability and energy margins. Generalization capabilities of the neural-net processing allow for these assessments to be made independently of load levels. The results obtained from computer simulations are presented using the New England power system, as an example. (author).

Validation of a sterilization dose for products manufactured using a 3D printer

Science.gov (United States)

Wangsgard, Wendy; Winters, Martell

2018-02-01

As more healthcare products are personalized, the use of unique, patient-specific products will increase. Some of these are manufactured using a 3D printing process (also known as additive manufacturing) for either polymers or metals. For these products, processes such as sterilization validations must be handled in a different manner. The concepts typically used are still relevant but are approached from an alternative perspective to account for a potential production batch size of one, and for the great variability that can occur in size and shape of a product.

[Difficulties of the methods for studying environmental exposure and neural tube defects].

Science.gov (United States)

Borja-Aburto, V H; Bermúdez-Castro, O; Lacasaña-Navarro, M; Kuri, P; Bustamante-Montes, P; Torres-Meza, V

1999-01-01

To discuss the attitudes in the assessment of environmental exposures as risk factors associated with neural tube defects, and to present the main risk factors studied to date. Environmental exposures have been suggested to have a roll in the genesis of birth defects. However, studies conducted in human populations have found difficulties in the design and conduction to show such an association for neural tube defects (anencephaly, espina bifida and encephalocele) because of problems raised from: a) the frequency measures used to compare time trends and communities, b) the classification of heterogeneous malformations, c) the inclusion of maternal, paternal and fetal factors as an integrated process and, d) the assessment of environmental exposures. Hypothetically both maternal and paternal environmental exposures can produce damage before and after conception by direct action on the embryo and the fetus-placenta complex. Therefore, in the assessment of environmental exposures we need to take into account: a) both paternal and maternal exposures; b) the critical exposure period, three months before conception for paternal exposures and one month around the conceptional period for maternal exposures; c) quantitatively evaluate environmental exposures when possible, avoiding a dichotomous classification; d) the use of biological markers of exposure is highly recommended as well as markers of genetic susceptibility.

Composites Manufacturing Education and Technology Facility Expedites Manufacturing Innovation

Energy Technology Data Exchange (ETDEWEB)

2017-01-01

The Composites Manufacturing Education and Technology facility (CoMET) at the National Wind Technology Center at the National Renewable Energy Laboratory (NREL) paves the way for innovative wind turbine components and accelerated manufacturing. Available for use by industry partners and university researchers, the 10,000-square-foot facility expands NREL's composite manufacturing research capabilities by enabling researchers to design, prototype, and test composite wind turbine blades and other components -- and then manufacture them onsite. Designed to work in conjunction with NREL's design, analysis, and structural testing capabilities, the CoMET facility expedites manufacturing innovation.

Manufacturing strategy issues in selected Indian manufacturing industry

Directory of Open Access Journals (Sweden)

Mahender Singh

2013-03-01

Full Text Available This paper presents some findings of Indian manufacturing sectors viz. automobile (especially two-wheeler, tractor and general manufacturing industry. Various manufacturing strategy issues such as competitive priorities, improvement activities, and performance measures, have been identified and assessed in Indian context. Sector wise comparison of competitive priorities, improvement activities i.e. advanced manufacturing technology (AMT, integrated information systems (IIS, and advanced management systems (AMS, and performance measure, is provided. Our results showed that most of the Indian companies are still emphasizing on quality. However, automobile sector has set to compete globally with high innovation rate, faster new product development, and continuous improvement. It is also observed that Indian companies are investing more in AMS as compared to IIS and AMT. Manufacturing competence index is also computed for each sector.

Virtual Manufacturing Techniques Designed and Applied to Manufacturing Activities in the Manufacturing Integration and Technology Branch

Science.gov (United States)

Shearrow, Charles A.

1999-01-01

One of the identified goals of EM3 is to implement virtual manufacturing by the time the year 2000 has ended. To realize this goal of a true virtual manufacturing enterprise the initial development of a machinability database and the infrastructure must be completed. This will consist of the containment of the existing EM-NET problems and developing machine, tooling, and common materials databases. To integrate the virtual manufacturing enterprise with normal day to day operations the development of a parallel virtual manufacturing machinability database, virtual manufacturing database, virtual manufacturing paradigm, implementation/integration procedure, and testable verification models must be constructed. Common and virtual machinability databases will include the four distinct areas of machine tools, available tooling, common machine tool loads, and a materials database. The machine tools database will include the machine envelope, special machine attachments, tooling capacity, location within NASA-JSC or with a contractor, and availability/scheduling. The tooling database will include available standard tooling, custom in-house tooling, tool properties, and availability. The common materials database will include materials thickness ranges, strengths, types, and their availability. The virtual manufacturing databases will consist of virtual machines and virtual tooling directly related to the common and machinability databases. The items to be completed are the design and construction of the machinability databases, virtual manufacturing paradigm for NASA-JSC, implementation timeline, VNC model of one bridge mill and troubleshoot existing software and hardware problems with EN4NET. The final step of this virtual manufacturing project will be to integrate other production sites into the databases bringing JSC's EM3 into a position of becoming a clearing house for NASA's digital manufacturing needs creating a true virtual manufacturing enterprise.

Neural correlates of continuous causal word generation.

Science.gov (United States)

Wende, Kim C; Straube, Benjamin; Stratmann, Mirjam; Sommer, Jens; Kircher, Tilo; Nagels, Arne

2012-09-01

Causality provides a natural structure for organizing our experience and language. Causal reasoning during speech production is a distinct aspect of verbal communication, whose related brain processes are yet unknown. The aim of the current study was to investigate the neural mechanisms underlying the continuous generation of cause-and-effect coherences during overt word production. During fMRI data acquisition participants performed three verbal fluency tasks on identical cue words: A novel causal verbal fluency task (CVF), requiring the production of multiple reasons to a given cue word (e.g. reasons for heat are fire, sun etc.), a semantic (free association, FA, e.g. associations with heat are sweat, shower etc.) and a phonological control task (phonological verbal fluency, PVF, e.g. rhymes with heat are meat, wheat etc.). We found that, in contrast to PVF, both CVF and FA activated a left lateralized network encompassing inferior frontal, inferior parietal and angular regions, with further bilateral activation in middle and inferior as well as superior temporal gyri and the cerebellum. For CVF contrasted against FA, we found greater bold responses only in the left middle frontal cortex. Large overlaps in the neural activations during free association and causal verbal fluency indicate that the access to causal relationships between verbal concepts is at least partly based on the semantic neural network. The selective activation in the left middle frontal cortex for causal verbal fluency suggests that distinct neural processes related to cause-and-effect-relations are associated with the recruitment of middle frontal brain areas. Copyright © 2012 Elsevier Inc. All rights reserved.

Exponential H(infinity) synchronization of general discrete-time chaotic neural networks with or without time delays.

Science.gov (United States)

Qi, Donglian; Liu, Meiqin; Qiu, Meikang; Zhang, Senlin

2010-08-01

This brief studies exponential H(infinity) synchronization of a class of general discrete-time chaotic neural networks with external disturbance. On the basis of the drive-response concept and H(infinity) control theory, and using Lyapunov-Krasovskii (or Lyapunov) functional, state feedback controllers are established to not only guarantee exponential stable synchronization between two general chaotic neural networks with or without time delays, but also reduce the effect of external disturbance on the synchronization error to a minimal H(infinity) norm constraint. The proposed controllers can be obtained by solving the convex optimization problems represented by linear matrix inequalities. Most discrete-time chaotic systems with or without time delays, such as Hopfield neural networks, cellular neural networks, bidirectional associative memory networks, recurrent multilayer perceptrons, Cohen-Grossberg neural networks, Chua's circuits, etc., can be transformed into this general chaotic neural network to be H(infinity) synchronization controller designed in a unified way. Finally, some illustrated examples with their simulations have been utilized to demonstrate the effectiveness of the proposed methods.

Identification of determinants for globalization of SMEs using multi-layer perceptron neural networks

International Nuclear Information System (INIS)

Draz, U.; Jahanzaib, M.; Asghar, G.

2016-01-01

SMEs (Small and Medium Sized Enterprises) sector is facing problems relating to implementation of international quality standards. These SMEs need to identify factors affecting business success abroad for intelligent allocation of resources to the process of internationalization. In this paper, MLP NN (Multi-Layer Perceptron Neural Network) has been used for identifying relative importance of key variables related to firm basics, manufacturing, quality inspection labs and level of education in determining the exporting status of Pakistani SMEs. A survey has been conducted for scoring out the pertinent variables in SMEs and coded in MLP NNs. It is found that firm registered with OEM (Original Equipment Manufacturer) and size of firm are the most important in determining exporting status of SMEs followed by other variables. For internationalization, the results aid policy makers in formulating strategies. (author)

Semiconductor Manufacturing equipment introduction

International Nuclear Information System (INIS)

Im, Jong Sun

2001-02-01

This book deals with semiconductor manufacturing equipment. It is comprised of nine chapters, which are manufacturing process of semiconductor device, history of semiconductor manufacturing equipment, kinds and role of semiconductor manufacturing equipment, construction and method of semiconductor manufacturing equipment, introduction of various semiconductor manufacturing equipment, spots of semiconductor manufacturing, technical elements of semiconductor manufacturing equipment, road map of technology of semiconductor manufacturing equipment and semiconductor manufacturing equipment in the 21st century.

Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning

Directory of Open Access Journals (Sweden)

Md. Abdullah-al-mamun

2015-08-01

Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.

On the synchronization of neural networks containing time-varying delays and sector nonlinearity

International Nuclear Information System (INIS)

Yan, J.-J.; Lin, J.-S.; Hung, M.-L.; Liao, T.-L.

2007-01-01

We present a systematic design procedure for synchronization of neural networks subject to time-varying delays and sector nonlinearity in the control input. Based on the drive-response concept and the Lyapunov stability theorem, a memoryless decentralized control law is proposed which guarantees exponential synchronization even when input nonlinearity is present. The supplementary requirement that the time-derivative of time-varying delays must be smaller than one is released for the proposed control scheme. A four-dimensional Hopfield neural network with time-varying delays is presented as the illustrative example to demonstrate the effectiveness of the proposed synchronization scheme

Psychological and neural mechanisms of experimental extinction: a selective review.

Science.gov (United States)

Delamater, Andrew R; Westbrook, R Frederick

2014-02-01

The present review examines key psychological concepts in the study of experimental extinction and implications these have for an understanding of the underlying neurobiology of extinction learning. We suggest that many of the signature characteristics of extinction learning (spontaneous recovery, renewal, reinstatement, rapid reacquisition) can be accommodated by the standard associative learning theory assumption that extinction results in partial erasure of the original learning together with new inhibitory learning. Moreover, we consider recent behavioral and neural evidence that supports the partial erasure view of extinction, but also note shortcomings in our understanding of extinction circuits as these relate to the negative prediction error concept. Recent work suggests that common prediction error and stimulus-specific prediction error terms both may be required to explain neural plasticity both in acquisition and extinction learning. In addition, we suggest that many issues in the content of extinction learning have not been fully addressed in current research, but that neurobiological approaches should be especially helpful in addressing such issues. These include questions about the nature of extinction learning (excitatory CS-No US, inhibitory CS-US learning, occasion setting processes), especially as this relates to studies of the micro-circuitry of extinction, as well as its representational content (sensory, motivational, response). An additional understudied problem in extinction research is the role played by attention processes and their underlying neural networks, although some research and theory converge on the idea that extinction is accompanied by attention decrements (i.e., habituation-like processes). Copyright © 2013 Elsevier Inc. All rights reserved.

Application, manufacturing and trends in development of nucleonic gauges in Poland

International Nuclear Information System (INIS)

Urbanski, P.

1998-01-01

The current status of manufacturing and application of radioisotope gauges in Poland is presented. Metrological performance of the gauges is briefly described and their expected feature prospects on the market of the industrial measuring instruments are discussed. Progress in electronic engineering and common use of the microprocessor systems in the radioisotope gauges made it possible the application of sophisticated methods of signal processing and data treatment, as for example statistical multivariate analysis. Some examples of the multivariate calibration of nucleonic gauges are presented. Application of the partial least square regression (PLS) and artificial neural network (ANN) for the calibration of gauges has been shown. (author)

Reduced Multivariate Polynomial Model for Manufacturing Costs Estimation of Piping Elements

Directory of Open Access Journals (Sweden)

Nibaldo Rodriguez

2013-01-01

Full Text Available This paper discusses the development and evaluation of an estimation model of manufacturing costs of piping elements through the application of a Reduced Multivariate Polynomial (RMP. The model allows obtaining accurate estimations, even when enough and adequate information is not available. This situation typically occurs in the early stages of the design process of industrial products. The experimental evaluations show that the approach is capable, with a low complexity, of reducing uncertainties and to predict costs with significant precision. Comparisons with a neural network showed also that the RMP performs better considering a set of classical performance measures with the corresponding lower complexity and higher accuracy.

Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

CERN Document Server

Melin, Patricia

2012-01-01

This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

Regulatory requirements in the good manufacturing practice production of an epithelial cell graft for ocular surface reconstruction.

Science.gov (United States)

Sheth-Shah, Radhika; Vernon, Amanda J; Seetharaman, Shankar; Neale, Michael H; Daniels, Julie T

2016-04-01

In the past decade, stem cell therapy has been increasingly employed for the treatment of various diseases. Subsequently, there has been a great interest in the manufacture of stem cells under good manufacturing practice, which is required by law for their use in humans. The cells for sight Stem Cell Therapy Research Unit, based at UCL Institute of Ophthalmology, delivers somatic cell-based and tissue-engineered therapies to patients suffering from blinding eye diseases at Moorfields Eye Hospital (London, UK). The following article is based on our experience in the conception, design, construction, validation and manufacturing within a good manufacturing practice manufacturing facility based in the UK. As such the regulations can be extrapolated to the 28 members stated within the EU. However, the principles may have a broad relevance outside the EU.

Polycrystalline-Diamond MEMS Biosensors Including Neural Microelectrode-Arrays

Directory of Open Access Journals (Sweden)

Donna H. Wang

2011-08-01

Full Text Available Diamond is a material of interest due to its unique combination of properties, including its chemical inertness and biocompatibility. Polycrystalline diamond (poly-C has been used in experimental biosensors that utilize electrochemical methods and antigen-antibody binding for the detection of biological molecules. Boron-doped poly-C electrodes have been found to be very advantageous for electrochemical applications due to their large potential window, low background current and noise, and low detection limits (as low as 500 fM. The biocompatibility of poly-C is found to be comparable, or superior to, other materials commonly used for implants, such as titanium and 316 stainless steel. We have developed a diamond-based, neural microelectrode-array (MEA, due to the desirability of poly-C as a biosensor. These diamond probes have been used for in vivo electrical recording and in vitro electrochemical detection. Poly-C electrodes have been used for electrical recording of neural activity. In vitro studies indicate that the diamond probe can detect norepinephrine at a 5 nM level. We propose a combination of diamond micro-machining and surface functionalization for manufacturing diamond pathogen-microsensors.

Neural Network Compensation for Frequency Cross-Talk in Laser Interferometry

Science.gov (United States)

Lee, Wooram; Heo, Gunhaeng; You, Kwanho

The heterodyne laser interferometer acts as an ultra-precise measurement apparatus in semiconductor manufacture. However the periodical nonlinearity property caused from frequency cross-talk is an obstacle to improve the high measurement accuracy in nanometer scale. In order to minimize the nonlinearity error of the heterodyne interferometer, we propose a frequency cross-talk compensation algorithm using an artificial intelligence method. The feedforward neural network trained by back-propagation compensates the nonlinearity error and regulates to minimize the difference with the reference signal. With some experimental results, the improved accuracy is proved through comparison with the position value from a capacitive displacement sensor.

Advanced Manufacturing Technologies

Science.gov (United States)

Fikes, John

2016-01-01

Advanced Manufacturing Technologies (AMT) is developing and maturing innovative and advanced manufacturing technologies that will enable more capable and lower-cost spacecraft, launch vehicles and infrastructure to enable exploration missions. The technologies will utilize cutting edge materials and emerging capabilities including metallic processes, additive manufacturing, composites, and digital manufacturing. The AMT project supports the National Manufacturing Initiative involving collaboration with other government agencies.

Design for Manufacturing – One-Piece, Fibre-Placed Composite Helicopter Tailboom

International Nuclear Information System (INIS)

Marsden, Catharine; Fews, Robert; Oldroyd, Paul; Yousefpour, AH

2011-01-01

Recurring cost has become a critical driver in the design of helicopter airframes, and although composite materials have become widely used in aircraft structures, the hand lay-up manufacturing process in many cases prevents these applications from being cost-effective. Automated manufacturing technologies promise not only reduced production costs but also higher quality, repeatable parts. The introduction of existing automated manufacturing techniques and technologies from industries such as the automotive sector into aerospace can be challenging due to the unique product characteristics as well as the stringent certification and quality control requirements of the industry. The aerospace industry is a low-volume, high value production environment where 'hand-made' products are produced by highly experienced and qualified trades-people. Both metallic and composite components are subjected to precise manufacturing control and documentation requirements. The introduction of automated manufacturing technologies must be done in such a way as to respect these often demanding constraints. The introduction of automation to industrialized processes impacts not only the way parts are produced, but also the way they are designed. Successful composite design and manufacturing automation in the aerospace industry requires the engineering designer and analyst to become increasingly involved in the manufacturing of the product, as machine limitations and producibility become increasingly important drivers for design. This paper presents an overview of a development project intended to evaluate the effectiveness and benefits of the automated fibre placement technology through the design, prototype build and testing of a composite tailboom. The discussion centres on the 'design for manufacturing' concept and provides a perspective on the project objectives, material and process selection and trade-offs, geometric and structural considerations, and component assembly and fastening.

What works in auditory working memory? A neural oscillations perspective.

Science.gov (United States)

Wilsch, Anna; Obleser, Jonas

2016-06-01

Working memory is a limited resource: brains can only maintain small amounts of sensory input (memory load) over a brief period of time (memory decay). The dynamics of slow neural oscillations as recorded using magneto- and electroencephalography (M/EEG) provide a window into the neural mechanics of these limitations. Especially oscillations in the alpha range (8-13Hz) are a sensitive marker for memory load. Moreover, according to current models, the resultant working memory load is determined by the relative noise in the neural representation of maintained information. The auditory domain allows memory researchers to apply and test the concept of noise quite literally: Employing degraded stimulus acoustics increases memory load and, at the same time, allows assessing the cognitive resources required to process speech in noise in an ecologically valid and clinically relevant way. The present review first summarizes recent findings on neural oscillations, especially alpha power, and how they reflect memory load and memory decay in auditory working memory. The focus is specifically on memory load resulting from acoustic degradation. These findings are then contrasted with contextual factors that benefit neural as well as behavioral markers of memory performance, by reducing representational noise. We end on discussing the functional role of alpha power in auditory working memory and suggest extensions of the current methodological toolkit. This article is part of a Special Issue entitled SI: Auditory working memory. Published by Elsevier B.V.

On the future of safety in the manufacturing industry

OpenAIRE

Reniers, G.

2017-01-01

Abstract: This paper argues that a new paradigm is needed in the manufacturing industry to further substantially advance safety as part of the industry 4.0 concept. The different domains that need to be focused upon are Cluster-thinking and cooperation, High transparency and efficient inspections, Education and training, Security integration, and Safety innovation. Since society has fundamentally changed over the last two decades, revolutionizing safety via these domains is truly needed in th...

Contact-Free Support Structures for Part Overhangs in Powder-Bed Metal Additive Manufacturing

Directory of Open Access Journals (Sweden)

Kenneth Cooper

2017-12-01

Full Text Available This study investigates the feasibility of a novel concept, contact-free support structures, for part overhangs in powder-bed metal additive manufacturing. The intent is to develop alternative support designs that require no or little post-processing, and yet, maintain effectiveness in minimizing overhang distortions. The idea is to build, simultaneously during part fabrications, a heat sink (called “heat support”, underneath an overhang to alter adverse thermal behaviors. Thermomechanical modeling and simulations using finite element analysis were applied to numerically research the heat support effect on overhang distortions. Experimentally, a powder-bed electron beam additive manufacturing system was utilized to fabricate heat support designs and examine their functions. The results prove the concept and demonstrate the effectiveness of contact-free heat supports. Moreover, the method was tested with different heat support parameters and applied to various overhang geometries. It is concluded that the heat support proposed has the potential to be implemented in industrial applications.

Morphological neural networks

Energy Technology Data Exchange (ETDEWEB)

Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

1996-12-31

The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

Design and manufacturing of skins based on composite corrugated laminates for morphing aerodynamic surfaces

Science.gov (United States)

Airoldi, Alessandro; Fournier, Stephane; Borlandelli, Elena; Bettini, Paolo; Sala, Giuseppe

2017-04-01

The paper discusses the approaches for the design and manufacturing of morphing skins based on rectangular-shaped composite corrugated laminates and proposes a novel solution to prevent detrimental effects of corrugation on aerodynamic performances. Additionally, more complex corrugated shapes are presented and analysed. The manufacturing issues related to the production of corrugated laminates are discussed and tests are performed to compare different solutions and to assess the validity of analytical and numerical predictions. The solution presented to develop an aerodynamically efficient skin consists in the integration of an elastomeric cover in the corrugated laminate. The related manufacturing process is presented and assessed, and a fully nonlinear numerical model is developed and characterized to study the behaviour of this skin concept in different load conditions. Finally, configurations based on combinations of individual rectangular-shaped corrugated panels are considered. Their structural properties are numerically investigated by varying geometrical parameters. Performance indices are defined to compare structural stiffness contributions in non-morphing directions with the ones of conventional panels of the same weight. Numerical studies also show that the extension of the concept to complex corrugated shapes may improve both the design flexibility and some specific performances with respect to rectangular shaped corrugations. The overall results validate the design approaches and manufacturing processes to produce corrugated laminates and indicate that the solution for the integration of an elastomeric cover is a feasible and promising method to enhance the aerodynamic efficiency of corrugated skins.

Struggling with solutions : a case study of using organisation concepts

NARCIS (Netherlands)

Benders, Jos; Slomp, Jannes

2009-01-01

Engineers contribute to the constant flow of new tools and organisation concepts. These tend to be presented as solutions to existing organisational problems. These solutions may become problems themselves, however. We present a longitudinal case of how a truck manufacturer struggled with various

Neural Stem Cells: Implications for the Conventional Radiotherapy of Central Nervous System Malignancies

International Nuclear Information System (INIS)

Barani, Igor J.; Benedict, Stanley H.; Lin, Peck-Sun

2007-01-01

Advances in basic neuroscience related to neural stem cells and their malignant counterparts are challenging traditional models of central nervous system tumorigenesis and intrinsic brain repair. Neurogenesis persists into adulthood predominantly in two neurogenic centers: subventricular zone and subgranular zone. Subventricular zone is situated adjacent to lateral ventricles and subgranular zone is confined to the dentate gyrus of the hippocampus. Neural stem cells not only self-renew and differentiate along multiple lineages in these regions, but also contribute to intrinsic brain plasticity and repair. Ionizing radiation can depopulate these exquisitely sensitive regions directly or impair in situ neurogenesis by indirect, dose-dependent and inflammation-mediated mechanisms, even at doses <2 Gy. This review discusses the fundamental neural stem cell concepts within the framework of cumulative clinical experience with the treatment of central nervous system malignancies using conventional radiotherapy

Launching the dialogue: Safety and innovation as partners for success in advanced manufacturing.

Science.gov (United States)

Geraci, C L; Tinkle, S S; Brenner, S A; Hodson, L L; Pomeroy-Carter, C A; Neu-Baker, N

2018-06-01

Emerging and novel technologies, materials, and information integrated into increasingly automated and networked manufacturing processes or into traditional manufacturing settings are enhancing the efficiency and productivity of manufacturing. Globally, there is a move toward a new era in manufacturing that is characterized by: (1) the ability to create and deliver more complex designs of products; (2) the creation and use of materials with new properties that meet a design need; (3) the employment of new technologies, such as additive and digital techniques that improve on conventional manufacturing processes; and (4) a compression of the time from initial design concept to the creation of a final product. Globally, this movement has many names, but "advanced manufacturing" has become the shorthand for this complex integration of material and technology elements that enable new ways to manufacture existing products, as well as new products emerging from new technologies and new design methods. As the breadth of activities associated with advanced manufacturing suggests, there is no single advanced manufacturing industry. Instead, aspects of advanced manufacturing can be identified across a diverse set of business sectors that use manufacturing technologies, ranging from the semiconductors and electronics to the automotive and pharmaceutical industries. The breadth and diversity of advanced manufacturing may change the occupational and environmental risk profile, challenge the basic elements of comprehensive health and safety (material, process, worker, environment, product, and general public health and safety), and provide an opportunity for development and dissemination of occupational and environmental health and safety (OEHS) guidance and best practices. It is unknown how much the risk profile of different elements of OEHS will change, thus requiring an evolution of health and safety practices. These changes may be accomplished most effectively through multi

Neural Networks

International Nuclear Information System (INIS)

Smith, Patrick I.

2003-01-01

Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

Development of High Temperature Capacitor Technology and Manufacturing Capability

Energy Technology Data Exchange (ETDEWEB)

None, None

2011-05-15

The goal of the Development of High Temperature Capacitor Technology and Manufacturing Capability program was to mature a production-ready supply chain for reliable 250°C FPE (fluorinated polyester) film capacitors by 2011. These high-temperature film capacitors enable both the down hole drilling and aerospace industries by enabling a variety of benefits including: - Deeper oil exploration in higher temperature and pressure environments - Enabling power electronic and control equipment to operate in higher temperature environments - Enabling reduced cooling requirements of electronics - Increasing reliability and life of capacitors operating below rated temperature - Enabling capacitors to handle higher electrical losses without overheating. The key challenges to bringing the FPE film capacitors to market have been manufacturing challenges including: - FPE Film is difficult to handle and wind, resulting in poor yields - Voltage breakdown strength decreases when the film is wound into capacitors (~70% decrease) - Encapsulation technologies must be improved to enable higher perature operation - Manufacturing and test cycle time is very long As a direct result of this program most of the manufacturing challenges have been met. The FPE film production metalization and winding yield has increased to over 82% from 70%, and the voltage breakdown strength of the wound capacitors has increased 270% to 189 V/μm. The high temperature packaging concepts are showing significant progress including promising results for lead attachments and hermetic packages at 200°C and non-hermetic packages at 250°C. Manufacturing and test cycle time will decrease as the market for FPE capacitors develops.

Building Lean Supply Chain and Manufacturing Skills through an Interactive Case Study

Science.gov (United States)

Ozelkan, Ertunga C.; Teng, S. Gary; Johnson, Thomas; Benson, Tom; Nestvogel, Dean

2007-01-01

With the ongoing global pressure to cut costs and focus on quality, many companies have been implementing "lean manufacturing" concepts to survive in the competitive marketplace. Thus it is imperative that engineering and business graduates are equipped with the lean principles, and are ready to take ownership of lean initiatives as they enter the…

Nanotechnology Concepts at Marshall Space Flight Center: Engineering Directorate

Science.gov (United States)

Bhat, B.; Kaul, R.; Shah, S.; Smithers, G.; Watson, M. D.

2001-01-01

Nanotechnology is the art and science of building materials and devices at the ultimate level of finesse: atom by atom. Our nation's space program has need for miniaturization of components, minimization of weight, and maximization of performance, and nanotechnology will help us get there. Marshall Space Flight Center's (MSFC's) Engineering Directorate is committed to developing nanotechnology that will enable MSFC missions in space transportation, space science, and space optics manufacturing. MSFC has a dedicated group of technologists who are currently developing high-payoff nanotechnology concepts. This poster presentation will outline some of the concepts being developed including, nanophase structural materials, carbon nanotube reinforced metal and polymer matrix composites, nanotube temperature sensors, and aerogels. The poster will outline these concepts and discuss associated technical challenges in turning these concepts into real components and systems.

Micro-manufacturing: design and manufacturing of micro-products

National Research Council Canada - National Science Library

Koç, Muammer; Özel, Tuğrul

2011-01-01

.... After addressing the fundamentals and non-metallic-based micro-manufacturing processes in the semiconductor industry, it goes on to address specific metallic-based micro-manufacturing processes...

Design and application of reconfigurable manufacturing systems in agile mass customization manufacturing environment.

CSIR Research Space (South Africa)

Xing, B

2007-05-01

Full Text Available processes. Many manufacturing techniques are based on the principles of Flexible Manufacturing and Dedicated Manufacturing for mass production. Reconfigurable Manufacturing System, (RMS), is a manufacturing system that can provide for Agile Manufacturing...

Adaptive enhanced sampling by force-biasing using neural networks

Science.gov (United States)

Guo, Ashley Z.; Sevgen, Emre; Sidky, Hythem; Whitmer, Jonathan K.; Hubbell, Jeffrey A.; de Pablo, Juan J.

2018-04-01

A machine learning assisted method is presented for molecular simulation of systems with rugged free energy landscapes. The method is general and can be combined with other advanced sampling techniques. In the particular implementation proposed here, it is illustrated in the context of an adaptive biasing force approach where, rather than relying on discrete force estimates, one can resort to a self-regularizing artificial neural network to generate continuous, estimated generalized forces. By doing so, the proposed approach addresses several shortcomings common to adaptive biasing force and other algorithms. Specifically, the neural network enables (1) smooth estimates of generalized forces in sparsely sampled regions, (2) force estimates in previously unexplored regions, and (3) continuous force estimates with which to bias the simulation, as opposed to biases generated at specific points of a discrete grid. The usefulness of the method is illustrated with three different examples, chosen to highlight the wide range of applicability of the underlying concepts. In all three cases, the new method is found to enhance considerably the underlying traditional adaptive biasing force approach. The method is also found to provide improvements over previous implementations of neural network assisted algorithms.

Implementation of 5S in Manufacturing Industry: A Case of Foreign Workers in Melaka

Directory of Open Access Journals (Sweden)

Chee Houa San

2018-01-01

Full Text Available Lean manufacturing system has been infiltrated in manufacturing sectors across the world. In fact, Lean manufacturing system is a practice which regards the use of the resources, creation of value for the end customers, and as the ways to eliminate the waste. There are several tools that can be used to eliminate the waste within the industry. This research is a study of the implementation of 5S in manufacturing industry. Despite this, the research study focused on manufacturing industry, which has been implemented 5S system in Melaka State. Although there are number of tools and technique available to help in improving the manufacturing process, however, there is only few industries could implement the tools successfully. In this research, foreign workers play a main role in implement the 5S systems as the manufacturing industry in Malaysia adopt large amount of foreign workers to work as employees. Therefore, it is important to ensure the foreign workers truly understand the concept of 5S system and adopt the best ways to implement it in order to have better performance. This research study has been proposed by the research model of the barriers to implementation of 5S in manufacturing industry among foreign workers. A several research method has been adopted to do the research, such as descriptive research design with quantitative methods, survey questionnaire and cross-sectional studies.

Experienced Barriers to Lean in Swedish Manufacturing and Health Care

Directory of Open Access Journals (Sweden)

Bengt Halling

2013-12-01

Full Text Available The purpose is to compare similarities and divergences in how the concepts of Lean and barriers to Lean are described by key informants at a production unit in a large manufacturing company and two emergency health care units in Sweden. Data was collected via semi-structured interviews and analyzed with the constant comparative method (CCM and Porras and Robertson’s (1992 change model. : In both organizations, the view of Lean changed from a toolbox to a human behavior view. Eight barriers were experienced in both organizations. Three barriers were unique to manufacturing or to health care, respectively. Nine barriers were elements of social factors; five were elements of organizing arrangements. Only people practically involved and responsible for the implementation at the two organizations participated in the study. Persons responsible for implementing Lean should consider organizational arrangements and social factors in order to limit barriers to successful implementation. Most research on Lean has been about successful Lean implementations. This study focuses on how Lean is viewed and what barriers personnel in manufacturing and health care have experienced. In comparing the barriers to Lean experienced in the two groups, common, archetypical, and unique barriers for manufacturing and health care can be identified, thus contributing to knowledge about barriers to Lean implementation.

Neural Based Orthogonal Data Fitting The EXIN Neural Networks

CERN Document Server

Cirrincione, Giansalvo

2008-01-01

Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh

Handbook on neural information processing

CERN Document Server

Maggini, Marco; Jain, Lakhmi

2013-01-01

This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include:                         Deep architectures                         Recurrent, recursive, and graph neural networks                         Cellular neural networks                         Bayesian networks                         Approximation capabilities of neural networks                         Semi-supervised learning                         Statistical relational learning                         Kernel methods for structured data                         Multiple classifier systems                         Self organisation and modal learning                         Applications to ...

Cloud Manufacturing Service Paradigm for Group Manufacturing Companies

Directory of Open Access Journals (Sweden)

Jingtao Zhou

2014-07-01

Full Text Available The continuous refinement of specialization requires that the group manufacturing company must be constantly focused on how to concentrate its core resources in special sphere to form its core competitive advantage. However, the resources in enterprise group are usually distributed in different subsidiary companies, which means they cannot be fully used, constraining the competition and development of the enterprise. Conducted as a response to a need for cloud manufacturing studies, systematic and detailed studies on cloud manufacturing schema for group companies are carried out in this paper. A new hybrid private clouds paradigm is proposed to meet the requirements of aggregation and centralized use of heterogeneous resources and business units distributed in different subsidiary companies. After the introduction of the cloud manufacturing paradigm for enterprise group and its architecture, this paper presents a derivation from the abstraction of paradigm and framework to the application of a practical evaluative working mechanism. In short, the paradigm establishes an effective working mechanism to translate collaborative business process composed by the activities into cloud manufacturing process composed by services so as to create a foundation resulting in mature traditional project monitoring and scheduling technologies being able to be used in cloud manufacturing project management.

Efficient universal computing architectures for decoding neural activity.

Directory of Open Access Journals (Sweden)

Benjamin I Rapoport

Full Text Available The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain- machine interfaces (BMIs. Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and accurate performance; in serving that primary function they should also minimize power dissipation in order to avoid damaging neural tissue; and they should transmit data wirelessly in order to minimize the risk of infection associated with chronic, transcutaneous implants. Electronic architectures for brain- machine interfaces must therefore minimize size and power consumption, while maximizing the ability to compress data to be transmitted over limited-bandwidth wireless channels. Here we present a system of extremely low computational complexity, designed for real-time decoding of neural signals, and suited for highly scalable implantable systems. Our programmable architecture is an explicit implementation of a universal computing machine emulating the dynamics of a network of integrate-and-fire neurons; it requires no arithmetic operations except for counting, and decodes neural signals using only computationally inexpensive logic operations. The simplicity of this architecture does not compromise its ability to compress raw neural data by factors greater than [Formula: see text]. We describe a set of decoding algorithms based on this computational architecture, one designed to operate within an implanted system, minimizing its power consumption and data transmission bandwidth; and a complementary set of algorithms for learning, programming the decoder, and postprocessing the decoded output, designed to operate in an external, nonimplanted unit. The implementation of the implantable portion is estimated to require fewer than 5000 operations per second. A proof-of-concept, 32-channel field-programmable gate array (FPGA implementation of this portion

Preliminary Assessment of Two Alternative Core Design Concepts for the Special Purpose Reactor

Energy Technology Data Exchange (ETDEWEB)

Sterbentz, James W. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Werner, James E. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Hummel, Andrew J. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Kennedy, John C. [Idaho National Lab. (INL), Idaho Falls, ID (United States); O' Brien, Robert C. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Dion, Axel M. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Wright, Richard N. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Ananth, Krishnan P. [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2017-11-01

The Special Purpose Reactor (SPR) is a small 5 MWt, heat pipe-cooled, fast reactor based on the Los Alamos National Laboratory (LANL) Mega-Power concept. The LANL concept features a stainless steel monolithic core structure with drilled channels for UO2 pellet stacks and evaporator sections of the heat pipes. Two alternative active core designs are presented here that replace the monolithic core structure with simpler and easier to manufacture fuel elements. The two new core designs are simply referred to as Design A and Design B. In addition to ease of manufacturability, the fuel elements for both Design A and Design B can be individually fabricated, assembled, inspected, tested, and qualified prior to their installation into the reactor core leading to greater reactor system reliability and safety. Design A fuel elements will require the development of a new hexagonally-shaped UO2 fuel pellet. The Design A configuration will consist of an array of hexagonally-shaped fuel elements with each fuel element having a central heat pipe. This hexagonal fuel element configuration results in four radial gaps or thermal resistances per element. Neither the fuel element development, nor the radial gap issue are deemed to be serious and should not impact an aggressive reactor deployment schedule. Design B uses embedded arrays of heat pipes and fuel pins in a double-wall tank filled with liquid metal sodium. Sodium is used to thermally bond the heat pipes to the fuel pins, but its usage may create reactor transportation and regulatory challenges. An independent panel of U.S. manufacturing experts has preliminarily assessed the three SPR core designs and views Design A as simplest to manufacture. Herein are the results of a preliminary neutronic, thermal, mechanical, material, and manufacturing assessment of both Design A and Design B along with comparisons to the LANL concept (monolithic core structure). Despite the active core differences, all three reactor concepts behave

Parallelization of learning problems by artificial neural networks. Application in external radiotherapy

International Nuclear Information System (INIS)

Sauget, M.

2007-12-01

This research is about the application of neural networks used in the external radiotherapy domain. The goal is to elaborate a new evaluating system for the radiation dose distributions in heterogeneous environments. The al objective of this work is to build a complete tool kit to evaluate the optimal treatment planning. My st research point is about the conception of an incremental learning algorithm. The interest of my work is to combine different optimizations specialized in the function interpolation and to propose a new algorithm allowing to change the neural network architecture during the learning phase. This algorithm allows to minimise the al size of the neural network while keeping a good accuracy. The second part of my research is to parallelize the previous incremental learning algorithm. The goal of that work is to increase the speed of the learning step as well as the size of the learned dataset needed in a clinical case. For that, our incremental learning algorithm presents an original data decomposition with overlapping, together with a fault tolerance mechanism. My last research point is about a fast and accurate algorithm computing the radiation dose deposit in any heterogeneous environment. At the present time, the existing solutions used are not optimal. The fast solution are not accurate and do not give an optimal treatment planning. On the other hand, the accurate solutions are far too slow to be used in a clinical context. Our algorithm answers to this problem by bringing rapidity and accuracy. The concept is to use a neural network adequately learned together with a mechanism taking into account the environment changes. The advantages of this algorithm is to avoid the use of a complex physical code while keeping a good accuracy and reasonable computation times. (author)

The Thouless-Anderson-Palmer equation for an analogue neural network with temporally fluctuating white synaptic noise

International Nuclear Information System (INIS)

Ichiki, Akihisa; Shiino, Masatoshi

2007-01-01

Effects of synaptic noise on the retrieval process of associative memory neural networks are studied from the viewpoint of neurobiological and biophysical understanding of information processing in the brain. We investigate the statistical mechanical properties of stochastic analogue neural networks with temporally fluctuating synaptic noise, which is assumed to be white noise. Such networks, in general, defy the use of the replica method, since they have no energy concept. The self-consistent signal-to-noise analysis (SCSNA), which is an alternative to the replica method for deriving a set of order parameter equations, requires no energy concept and thus becomes available in studying networks without energy functions. Applying the SCSNA to stochastic networks requires the knowledge of the Thouless-Anderson-Palmer (TAP) equation which defines the deterministic networks equivalent to the original stochastic ones. The study of the TAP equation which is of particular interest for the case without energy concept is very less, while it is closely related to the SCSNA in the case with energy concept. This paper aims to derive the TAP equation for networks with synaptic noise together with a set of order parameter equations by a hybrid use of the cavity method and the SCSNA

Control the springback of metal sheets by using an artificial neural network

International Nuclear Information System (INIS)

Crina, Axinte

2007-01-01

One of the greatest challenges of manufacturing sheet metal parts is to obtain consistent parts dimensions. Springback is the major cause of variations and inconsistencies in the final part geometry. Obtaining a consistent and desirable amount of springback is extremely difficult due to the non-linear effects and interactions of process and material parameters. In this work, the ability of an artificial neural network model to predict optimum process parameters and tools geometry which allow to obtain minimum amount of springback is tested, in the case of a cylindrical deep-drawing process

The left inferior frontal gyrus: A neural crossroads between abstract and concrete knowledge.

Science.gov (United States)

Della Rosa, Pasquale Anthony; Catricalà, Eleonora; Canini, Matteo; Vigliocco, Gabriella; Cappa, Stefano F

2018-07-15

Evidence from both neuropsychology and neuroimaging suggests that different types of information are necessary for representing and processing concrete and abstract word meanings. Both abstract and concrete concepts, however, conjointly rely on perceptual, verbal and contextual knowledge, with abstract concepts characterized by low values of imageability (IMG) (low sensory-motor grounding) and low context availability (CA) (more difficult to contextualize). Imaging studies supporting differences between abstract and concrete concepts show a greater recruitment of the left inferior frontal gyrus (LIFG) for abstract concepts, which has been attributed either to the representation of abstract-specific semantic knowledge or to the request for more executive control than in the case of concrete concepts. We conducted an fMRI study on 27 participants, using a lexical decision task involving both abstract and concrete words, whose IMG and CA values were explicitly modelled in separate parametric analyses. The LIFG was significantly more activated for abstract than for concrete words, and a conjunction analysis showed a common activation for words with low IMG or low CA only in the LIFG, in the same area reported for abstract words. A regional template map of brain activations was then traced for words with low IMG or low CA, and BOLD regional time-series were extracted and correlated with the specific LIFG neural activity elicited for abstract words. The regions associated to low IMG, which were functionally correlated with LIFG, were mainly in the left hemisphere, while those associated with low CA were in the right hemisphere. Finally, in order to reveal which LIFG-related network increased its connectivity with decreases of IMG or CA, we conducted generalized psychophysiological interaction analyses. The connectivity strength values extracted from each region connected with the LIFG were correlated with specific LIFG neural activity for abstract words, and a regression

Identifying autism from neural representations of social interactions: neurocognitive markers of autism.

Science.gov (United States)

Just, Marcel Adam; Cherkassky, Vladimir L; Buchweitz, Augusto; Keller, Timothy A; Mitchell, Tom M

2014-01-01

Autism is a psychiatric/neurological condition in which alterations in social interaction (among other symptoms) are diagnosed by behavioral psychiatric methods. The main goal of this study was to determine how the neural representations and meanings of social concepts (such as to insult) are altered in autism. A second goal was to determine whether these alterations can serve as neurocognitive markers of autism. The approach is based on previous advances in fMRI analysis methods that permit (a) the identification of a concept, such as the thought of a physical object, from its fMRI pattern, and (b) the ability to assess the semantic content of a concept from its fMRI pattern. These factor analysis and machine learning methods were applied to the fMRI activation patterns of 17 adults with high-functioning autism and matched controls, scanned while thinking about 16 social interactions. One prominent neural representation factor that emerged (manifested mainly in posterior midline regions) was related to self-representation, but this factor was present only for the control participants, and was near-absent in the autism group. Moreover, machine learning algorithms classified individuals as autistic or control with 97% accuracy from their fMRI neurocognitive markers. The findings suggest that psychiatric alterations of thought can begin to be biologically understood by assessing the form and content of the altered thought's underlying brain activation patterns.

Development of a novel cold forging process to manufacture eccentric shafts

Science.gov (United States)

Pasler, Lukas; Liewald, Mathias

2018-05-01

Since the commercial usage of compact combustion engines, eccentric shafts have been used to transform translational into rotational motion. Over the years, several processes to manufacture these eccentric shafts or crankshafts have been developed. Especially for single-cylinder engines manufactured in small quantities, built crankshafts disclose advantages regarding tooling costs and performance. Those manufacturing processes do have one thing in common: They are all executed at elevated temperatures to enable the material to be formed to high forming degree. In this paper, a newly developed cold forging process is presented, which combines lateral extrusion and shifting for manufacturing a crank in one forming operation at room temperature. In comparison to the established upsetting and shifting methods to manufacture such components, the tool cavity or crank web thickness remains constant. Therefore, the developed new process presented in this paper consists of a combination of shifting and extrusion of the billet, which allows pushing material into the forming zone during shifting. In order to reduce the tensile stresses induced by the shifting process, compressive stresses are superimposed. It is expected that the process limits will be expanded regarding the horizontal displacement and form filling. In the following report, the simulation and design of the tooling concept are presented. Experiments were conducted and compared with corresponding simulation results afterwards.

Neural mechanisms of reinforcement learning in unmedicated patients with major depressive disorder.

Science.gov (United States)

Rothkirch, Marcus; Tonn, Jonas; Köhler, Stephan; Sterzer, Philipp

2017-04-01

According to current concepts, major depressive disorder is strongly related to dysfunctional neural processing of motivational information, entailing impairments in reinforcement learning. While computational modelling can reveal the precise nature of neural learning signals, it has not been used to study learning-related neural dysfunctions in unmedicated patients with major depressive disorder so far. We thus aimed at comparing the neural coding of reward and punishment prediction errors, representing indicators of neural learning-related processes, between unmedicated patients with major depressive disorder and healthy participants. To this end, a group of unmedicated patients with major depressive disorder (n = 28) and a group of age- and sex-matched healthy control participants (n = 30) completed an instrumental learning task involving monetary gains and losses during functional magnetic resonance imaging. The two groups did not differ in their learning performance. Patients and control participants showed the same level of prediction error-related activity in the ventral striatum and the anterior insula. In contrast, neural coding of reward prediction errors in the medial orbitofrontal cortex was reduced in patients. Moreover, neural reward prediction error signals in the medial orbitofrontal cortex and ventral striatum showed negative correlations with anhedonia severity. Using a standard instrumental learning paradigm we found no evidence for an overall impairment of reinforcement learning in medication-free patients with major depressive disorder. Importantly, however, the attenuated neural coding of reward in the medial orbitofrontal cortex and the relation between anhedonia and reduced reward prediction error-signalling in the medial orbitofrontal cortex and ventral striatum likely reflect an impairment in experiencing pleasure from rewarding events as a key mechanism of anhedonia in major depressive disorder. © The Author (2017). Published by Oxford

The development of neural stimulators: a review of preclinical safety and efficacy studies.

Science.gov (United States)

Shepherd, Robert K; Villalobos, Joel; Burns, Owen; Nayagam, David

2018-05-14

Given the rapid expansion of the field of neural stimulation and the rigorous regulatory approval requirements required before these devices can be applied clinically, it is important that there is clarity around conducting preclinical safety and efficacy studies required for the development of this technology. The present review examines basic design principles associated with the development of a safe neural stimulator and describes the suite of preclinical safety studies that need to be considered when taking a device to clinical trial. Neural stimulators are active implantable devices that provide therapeutic intervention, sensory feedback or improved motor control via electrical stimulation of neural or neuro-muscular tissue in response to trauma or disease. Because of their complexity, regulatory bodies classify these devices in the highest risk category (Class III), and they are therefore required to go through a rigorous regulatory approval process before progressing to market. The successful development of these devices is achieved through close collaboration across disciplines including engineers, scientists and a surgical/clinical team, and the adherence to clear design principles. Preclinical studies form one of several key components in the development pathway from concept to product release of neural stimulators. Importantly, these studies provide iterative feedback in order to optimise the final design of the device. Key components of any preclinical evaluation include: in vitro studies that are focussed on device reliability and include accelerated testing under highly controlled environments; in vivo studies using animal models of the disease or injury in order to assess safety and, given an appropriate animal model, the efficacy of the technology under both passive and electrically active conditions; and human cadaver and ex vivo studies designed to ensure the device's form factor conforms to human anatomy, to optimise the surgical approach and to

Synchronization stability of memristor-based complex-valued neural networks with time delays.

Science.gov (United States)

Liu, Dan; Zhu, Song; Ye, Er

2017-12-01

This paper focuses on the dynamical property of a class of memristor-based complex-valued neural networks (MCVNNs) with time delays. By constructing the appropriate Lyapunov functional and utilizing the inequality technique, sufficient conditions are proposed to guarantee exponential synchronization of the coupled systems based on drive-response concept. The proposed results are very easy to verify, and they also extend some previous related works on memristor-based real-valued neural networks. Meanwhile, the obtained sufficient conditions of this paper may be conducive to qualitative analysis of some complex-valued nonlinear delayed systems. A numerical example is given to demonstrate the effectiveness of our theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Manufacturing Renaissance : Return of manufacturing to western countries

OpenAIRE

Kianian, Babak; Larsson, Tobias; Tavassoli, Mohammad

2013-01-01

This chapter argues that the location of manufacturing is gradually shifting to the west again, exemplifying the ‘manufacturing renaissance’. Such a claim is based on the recent observed trend and the discussion is contextualized within the established theory that has been able to explain the location of manufacturing, that is, the product life cycle (PLC) model. Then the chapter identifies and discusses the four main drivers of this new phenomenon: (i) rising wage levels in emerging economie...

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

Science.gov (United States)

Williams-Hayes, Peggy S.

2004-01-01

The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

Online Scheduling in Manufacturing A Cumulative Delay Approach

CERN Document Server

Suwa, Haruhiko

2013-01-01

Online scheduling is recognized as the crucial decision-making process of production control at a phase of “being in production" according to the released shop floor schedule. Online scheduling can be also considered as one of key enablers to realize prompt capable-to-promise as well as available-to-promise to customers along with reducing production lead times under recent globalized competitive markets. Online Scheduling in Manufacturing introduces new approaches to online scheduling based on a concept of cumulative delay. The cumulative delay is regarded as consolidated information of uncertainties under a dynamic environment in manufacturing and can be collected constantly without much effort at any points in time during a schedule execution. In this approach, the cumulative delay of the schedule has the important role of a criterion for making a decision whether or not a schedule revision is carried out. The cumulative delay approach to trigger schedule revisions has the following capabilities for the ...

Quality by design approach: application of artificial intelligence techniques of tablets manufactured by direct compression.

Science.gov (United States)

Aksu, Buket; Paradkar, Anant; de Matas, Marcel; Ozer, Ozgen; Güneri, Tamer; York, Peter

2012-12-01

The publication of the International Conference of Harmonization (ICH) Q8, Q9, and Q10 guidelines paved the way for the standardization of quality after the Food and Drug Administration issued current Good Manufacturing Practices guidelines in 2003. "Quality by Design", mentioned in the ICH Q8 guideline, offers a better scientific understanding of critical process and product qualities using knowledge obtained during the life cycle of a product. In this scope, the "knowledge space" is a summary of all process knowledge obtained during product development, and the "design space" is the area in which a product can be manufactured within acceptable limits. To create the spaces, artificial neural networks (ANNs) can be used to emphasize the multidimensional interactions of input variables and to closely bind these variables to a design space. This helps guide the experimental design process to include interactions among the input variables, along with modeling and optimization of pharmaceutical formulations. The objective of this study was to develop an integrated multivariate approach to obtain a quality product based on an understanding of the cause-effect relationships between formulation ingredients and product properties with ANNs and genetic programming on the ramipril tablets prepared by the direct compression method. In this study, the data are generated through the systematic application of the design of experiments (DoE) principles and optimization studies using artificial neural networks and neurofuzzy logic programs.

Enabling Manufacturing Competitiveness and Economic Sustainability : Proceedings of the 4th International Conference on Changeable, Agile, Reconfigurable and Virtual production

CERN Document Server

2012-01-01

The changing manufacturing environment requires more responsive and adaptable manufacturing systems. The theme of the 4th International Conference on Changeable, Agile, Reconfigurable and Virtual production (CARV2011) is “Enabling Manufacturing Competitiveness and Economic Sustainability”. Leading edge research and best implementation practices and experiences, which address these important issues and challenges, are presented. The proceedings include advances in manufacturing systems design, planning, evaluation, control and evolving paradigms such as mass customization, personalization, changeability, re-configurability and flexibility. New and important concepts such as the dynamic product families and platforms, co-evolution of products and systems, and methods for enhancing manufacturing systems’ economic sustainability and prolonging their life to produce more than one product generation are treated. Enablers of change in manufacturing systems, production volume and capability scalability and man...

LEAN AND SIX SIGMA CONCEPTS APPLICATION IN PHARMACEUTICAL INDUSTRY

Directory of Open Access Journals (Sweden)

Katarina Pavlović

2012-03-01

Full Text Available LEAN thinking and Six Sigma have been utilized by manufacturing industries to decrease cost and improve quality and productivity by reducing variation and production defects [1]. Because of the dramatic successes in manufacturing, there is rising interest among companies in the pharmaceutical industry, which chooses to implement LEAN in order to accomplish such goals as decreased wait time to release product to the market, reduce production waste, and improve communication with end users and raize quality level both in the production and in testing laboratories. In this article, basics of LEAN and Six Sigma are presented and suggestion was given for application of their concepts in pharmaceutical industry together with harmonization with legal regulation represented by requirements Good Manufacturing Practice (cGMP, in order to work "smarter", more cost-effectively and avoid was ting time and other resources.

LEAN AND SIX SIGMA CONCEPTS - APPLICATION IN PHARMACEUTICAL INDUSTRY

Directory of Open Access Journals (Sweden)

Katarina Pavlović

2011-06-01

Full Text Available LEAN thinking and Six Sigma have been utilized by manufacturing industries to decrease cost and improve quality and productivity by reducing variation and production defects. Because of the dramatic successes in manufacturing, there is rising interest among companies in the pharmaceutical industry, which choose to implement LEAN in order to accomplish such goals as decreased wait time to release product to the market, reduce production waste, improve communication with end users and raise quality level both in the production and in testing laboratories. In this article, basics of LEAN and Six Sigma are presented and suggestion was given for application of their concepts in pharmaceutical industry together with harmonization with legal regulation represented by requirements Good Manufacturing Practice (cGMP, in order to work "smarter", more cost- effectively and avoid wasting time and other resources.

Design, Manufacturing and Characterization of Functionally Graded Flextensional Piezoelectric Actuators

International Nuclear Information System (INIS)

Amigo, R C R; Vatanabe, S L; Silva, E C N

2013-01-01

Previous works have been shown several advantages in using Functionally Graded Materials (FGMs) for the performance of flextensional devices, such as reduction of stress concentrations and gains in reliability. In this work, the FGM concept is explored in the design of graded devices by using the Topology Optimization Method (TOM), in order to determine optimal topologies and gradations of the coupled structures of piezoactuators. The graded pieces are manufactured by using the Spark Plasma Sintering (SPS) technique and are bonded to piezoelectric ceramics. The graded actuators are then tested by using a modular vibrometer system for measuring output displacements, in order to validate the numerical simulations. The technological path developed here represents the initial step toward the manufacturing of an integral piezoelectric device, constituted by piezoelectric and non-piezoelectric materials without bonding layers.

Fault detection and diagnosis for complex multivariable processes using neural networks

International Nuclear Information System (INIS)

Weerasinghe, M.

1998-06-01

Development of a reliable fault diagnosis method for large-scale industrial plants is laborious and often difficult to achieve due to the complexity of the targeted systems. The main objective of this thesis is to investigate the application of neural networks to the diagnosis of non-catastrophic faults in an industrial nuclear fuel processing plant. The proposed methods were initially developed by application to a simulated chemical process prior to further validation on real industrial data. The diagnosis of faults at a single operating point is first investigated. Statistical data conditioning methods of data scaling and principal component analysis are investigated to facilitate fault classification and reduce the complexity of neural networks. Successful fault diagnosis was achieved with significantly smaller networks than using all process variables as network inputs. Industrial processes often manufacture at various operating points, but demonstrated applications of neural networks for fault diagnosis usually only consider a single (primary) operating point. Developing a standard neural network scheme for fault diagnosis at all operating points would be usually impractical due to the unavailability of suitable training data for less frequently used (secondary) operating points. To overcome this problem, the application of a single neural network for the diagnosis of faults operating at different points is investigated. The data conditioning followed the same techniques as used for the fault diagnosis of a single operating point. The results showed that a single neural network could be successfully used to diagnose faults at operating points other than that it is trained for, and the data conditioning significantly improved the classification. Artificial neural networks have been shown to be an effective tool for process fault diagnosis. However, a main criticism is that details of the procedures taken to reach the fault diagnosis decisions are embedded in

The viability of neural network for modeling the impact of individual job satisfiers on work commitment in Indian manufacturing unit

Directory of Open Access Journals (Sweden)

Therasa Chandrasekar

2015-10-01

Full Text Available This paper provides an exposition about application of neural networks in the context of research to find out the contribution of individual job satisfiers towards work commitment. The purpose of the current study is to build a predictive model to estimate the normalized importance of individual job satisfiers towards work commitment of employees working in TVS Group, an Indian automobile company. The study is based on the tool developed by Spector (1985 and Sue Hayday (2003.The input variable of the study consists of nine independent individual job satisfiers which includes Pay, Promotion, Supervision, Benefits, Rewards, Operating procedures, Co-workers, Work-itself and Communication of Spector (1985 and dependent variable as work commitment of Sue Hayday (2003.The primary data has been collected using a closed-ended questionnaire based on simple random sampling approach. This study employed the multilayer Perceptron neural network model to envisage the level of job satisfiers towards work commitment. The result from the multilayer Perceptron neural network model displayed with four hidden layer with correct classification rate of 70% and 30% for training and testing data set. The normalized importance shows high value for coworkers, superior satisfaction and communication and which acts as most significant attributes of job satisfiers that predicts the overall work commitment of employees.

Use of the Inverse Approach for the Manufacture and Decoration of Food Cans

International Nuclear Information System (INIS)

Duffett, G.A.; Forgas, A.; Neamtu, L.; Naceur, H.; Batoz, J.L.; Guo, Y.Q.

2005-01-01

Innovation is a key objective in the metal packaging industry in order to produce new concepts, designs, shapes and printing. Simulation technology now allows both the can design as well as the manufacturing process to be carefully analysed before any physical prototypes or dies have been manufactured. These simulations are traditionally carried out using incremental simulation methodologies. However, much information may also be attained by using the inverse approach: the initial blank format for the can body as well as its lid may be optimised much faster, the actual decoration of the can may be evaluated and even calculated when deformation printing techniques are utilised. This paper presents some of the technical details relating to the inverse approach employed in Stampack to carry out simulations important for the manufacture of food cans that are shown via industrial

Neural networks

International Nuclear Information System (INIS)

Denby, Bruce; Lindsey, Clark; Lyons, Louis

1992-01-01

The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

The super-Turing computational power of plastic recurrent neural networks.

Science.gov (United States)

Cabessa, Jérémie; Siegelmann, Hava T

2014-12-01

We study the computational capabilities of a biologically inspired neural model where the synaptic weights, the connectivity pattern, and the number of neurons can evolve over time rather than stay static. Our study focuses on the mere concept of plasticity of the model so that the nature of the updates is assumed to be not constrained. In this context, we show that the so-called plastic recurrent neural networks (RNNs) are capable of the precise super-Turing computational power--as the static analog neural networks--irrespective of whether their synaptic weights are modeled by rational or real numbers, and moreover, irrespective of whether their patterns of plasticity are restricted to bi-valued updates or expressed by any other more general form of updating. Consequently, the incorporation of only bi-valued plastic capabilities in a basic model of RNNs suffices to break the Turing barrier and achieve the super-Turing level of computation. The consideration of more general mechanisms of architectural plasticity or of real synaptic weights does not further increase the capabilities of the networks. These results support the claim that the general mechanism of plasticity is crucially involved in the computational and dynamical capabilities of biological neural networks. They further show that the super-Turing level of computation reflects in a suitable way the capabilities of brain-like models of computation.

Neural Control and Adaptive Neural Forward Models for Insect-like, Energy-Efficient, and Adaptable Locomotion of Walking Machines

Directory of Open Access Journals (Sweden)

Poramate eManoonpong

2013-02-01

Full Text Available Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs and sensory feedback (afferent-based control but also on internal forward models (efference copies. They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.

Cloud manufacturing distributed computing technologies for global and sustainable manufacturing

CERN Document Server

Mehnen, Jörn

2013-01-01

Global networks, which are the primary pillars of the modern manufacturing industry and supply chains, can only cope with the new challenges, requirements and demands when supported by new computing and Internet-based technologies. Cloud Manufacturing: Distributed Computing Technologies for Global and Sustainable Manufacturing introduces a new paradigm for scalable service-oriented sustainable and globally distributed manufacturing systems.   The eleven chapters in this book provide an updated overview of the latest technological development and applications in relevant research areas.  Following an introduction to the essential features of Cloud Computing, chapters cover a range of methods and applications such as the factors that actually affect adoption of the Cloud Computing technology in manufacturing companies and new geometrical simplification method to stream 3-Dimensional design and manufacturing data via the Internet. This is further supported case studies and real life data for Waste Electrical ...

JIT Manufacturing: A Survey of Implementations in Small and Large U.S. Manufacturers

OpenAIRE

Richard E. White; John N. Pearson; Jeffrey R. Wilson

1999-01-01

Since the early 1980s, the diffusion of Just-In-Time (JIT) manufacturing from Japanese manufacturers to U.S. manufacturers has progressed at an accelerated rate. At this stage of the diffusion process, JIT implementations are more common and more advanced in large U.S. manufacturers than in small; consequently, U.S. businessmen's understanding of issues associated with JIT implementations in large manufacturers is more developed than that of small manufacturers. When small manufacturers repre...

Workshop Report on Additive Manufacturing for Large-Scale Metal Components - Development and Deployment of Metal Big-Area-Additive-Manufacturing (Large-Scale Metals AM) System

Energy Technology Data Exchange (ETDEWEB)

Babu, Sudarsanam Suresh [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Manufacturing Demonstration Facility; Love, Lonnie J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Manufacturing Demonstration Facility; Peter, William H. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Manufacturing Demonstration Facility; Dehoff, Ryan [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Manufacturing Demonstration Facility

2016-05-01

Additive manufacturing (AM) is considered an emerging technology that is expected to transform the way industry can make low-volume, high value complex structures. This disruptive technology promises to replace legacy manufacturing methods for the fabrication of existing components in addition to bringing new innovation for new components with increased functional and mechanical properties. This report outlines the outcome of a workshop on large-scale metal additive manufacturing held at Oak Ridge National Laboratory (ORNL) on March 11, 2016. The charter for the workshop was outlined by the Department of Energy (DOE) Advanced Manufacturing Office program manager. The status and impact of the Big Area Additive Manufacturing (BAAM) for polymer matrix composites was presented as the background motivation for the workshop. Following, the extension of underlying technology to low-cost metals was proposed with the following goals: (i) High deposition rates (approaching 100 lbs/h); (ii) Low cost (<$10/lbs) for steel, iron, aluminum, nickel, as well as, higher cost titanium, (iii) large components (major axis greater than 6 ft) and (iv) compliance of property requirements. The above concept was discussed in depth by representatives from different industrial sectors including welding, metal fabrication machinery, energy, construction, aerospace and heavy manufacturing. In addition, DOE’s newly launched High Performance Computing for Manufacturing (HPC4MFG) program was reviewed. This program will apply thermo-mechanical models to elucidate deeper understanding of the interactions between design, process, and materials during additive manufacturing. Following these presentations, all the attendees took part in a brainstorming session where everyone identified the top 10 challenges in large-scale metal AM from their own perspective. The feedback was analyzed and grouped in different categories including, (i) CAD to PART software, (ii) selection of energy source, (iii

Reconfigurable manufacturing system for agile mass customization manufacturing

CSIR Research Space (South Africa)

Xing, B

2006-07-01

Full Text Available Manufacturing companies are facing three challenges: low cost production of product, high quality standard and rapid responsiveness to customer requirements. These three goals are equally important for the manufacturing companies who want...

Manufacturing Initiative

Data.gov (United States)

National Aeronautics and Space Administration — The Advanced Manufacturing Technologies (AMT) Project supports multiple activities within the Administration's National Manufacturing Initiative. A key component of...

Accurate lithography simulation model based on convolutional neural networks

Science.gov (United States)

Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki

2017-07-01

Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.

The Impact of Trust on the Mode of Transaction Governance between Manufacturer and Distributor: Evidence from Georgia

OpenAIRE

George BERULAVA; David LEZHAVA

2008-01-01

The goal of the paper is to explore main determinants of the mode of transaction governance between manufacturers and distributors. We examine a number of types of transaction governance, viz., markets, relational transacting, and hierarchies. The model proposed in the paper integrates the concept of trust with key dimensions of transaction cost economics, being estimated with data from a sample of Georgian manufacturing industries. The main finding of the study is that trust along with tra...

Trial of Engineer Educating of Manufacturing Field in Kagoshima National College of Technology

Science.gov (United States)

Nakamura, Itaru; Hombu, Mitsuyuki; Kusuhara, Yoshito; Kashine, Kenji; Sakasegawa, Eiichi; Tashima, Daisuke; Fukidome, Hiromi

In Kagoshima National College of Technology, based on investigation with “the job boost measure investigation work in a power supply area” undertaken in the 2005 fiscal year, we accepted the trust from Kyushu Bureau of Economy, Trade and Industry, and undertook “the small-and-medium-sized-enterprises personnel educating work which utilized the technical college etc.” for three years from the 2006 fiscal year to the 2008 fiscal year. As the trial of engineer educating according to the electrical engineering concept to the manufacturing field based on a conventional result, we act as a professor of the base technique for applying alternative energy (a fuel cell and a solar cell) in which social needs are powerful these days, and aim at aiming at cultivation of the problem-solving type engineer who can contribute to a low carbon society through manufacturing, we undertook this work according to the manufacturing bearer educating work (personnel educating and secured work of the manufacturing field) in the 2009 fiscal year of National Federation of Small Business Associations.

Use artificial neural network to align biological ontologies.

Science.gov (United States)

Huang, Jingshan; Dang, Jiangbo; Huhns, Michael N; Zheng, W Jim

2008-09-16

Being formal, declarative knowledge representation models, ontologies help to address the problem of imprecise terminologies in biological and biomedical research. However, ontologies constructed under the auspices of the Open Biomedical Ontologies (OBO) group have exhibited a great deal of variety, because different parties can design ontologies according to their own conceptual views of the world. It is therefore becoming critical to align ontologies from different parties. During automated/semi-automated alignment across biological ontologies, different semantic aspects, i.e., concept name, concept properties, and concept relationships, contribute in different degrees to alignment results. Therefore, a vector of weights must be assigned to these semantic aspects. It is not trivial to determine what those weights should be, and current methodologies depend a lot on human heuristics. In this paper, we take an artificial neural network approach to learn and adjust these weights, and thereby support a new ontology alignment algorithm, customized for biological ontologies, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms. This approach has been evaluated by aligning two real-world biological ontologies, whose features include huge file size, very few instances, concept names in numerical strings, and others. The promising experiment results verify our proposed hypothesis, i.e., three weights for semantic aspects learned from a subset of concepts are representative of all concepts in the same ontology. Therefore, our method represents a large leap forward towards automating biological ontology alignment.

Technological assessment of local manufacturers for wind turbine blade manufacturing in Pakistan

Science.gov (United States)

Mahmood, Khurram; Haroon, General

2012-11-01

Composite materials manufacturing industry is one of the world's hi-tech industry. Manufacturing of wind turbine blades is one of the specialized fields requiring high degree of precision and composite manufacturing techniques. This paper identifies the industries specializing in the composite manufacturing and is able to manufacture wind turbines blades in Pakistan. In the second phase, their technology readiness level is determined, based on some factors and then a readiness level are assigned to them. The assigned technology readiness level will depict the absorptive capacity of each manufacturing unit and its capability to take on such projects. The individual readiness level of manufacturing unit will then be used to establish combined technology readiness level of Pakistan particularly for wind turbine blades manufacturing. The composite manufacturing industry provides many spin offs and a diverse range of products can be manufactured using this facility. This research will be helpful to categorize the strong points and flaws of local industry for the gap analysis. It can also be used as a prerequisite study before the evaluation of technologies and specialties to improve the industry of the country for the most favorable results. This will form a basic data base which can be used for the decision making related to transfer of technology, training of local skilled workers and general up-gradation of the local manufacturing units.

Working capital management : the case of government-owned, transitional, and privatised manufacturing firms in Eritrea

NARCIS (Netherlands)

Tewolde, S.

2002-01-01

In this book we go into the concepts of internal and external working capital management. The research focuses specifically at the government, transition and privatised manufacturing firms in Eritrea. The objective of this research is to study the working capital management practices of these firms