Model Optimization Identification Method Based on Closed-loop Operation Data and Process Characteristics Parameters

Directory of Open Access Journals (Sweden)

Zhiqiang GENG

2014-01-01

Full Text Available Output noise is strongly related to input in closed-loop control system, which makes model identification of closed-loop difficult, even unidentified in practice. The forward channel model is chosen to isolate disturbance from the output noise to input, and identified by optimization the dynamic characteristics of the process based on closed-loop operation data. The characteristics parameters of the process, such as dead time and time constant, are calculated and estimated based on the PI/PID controller parameters and closed-loop process input/output data. And those characteristics parameters are adopted to define the search space of the optimization identification algorithm. PSO-SQP optimization algorithm is applied to integrate the global search ability of PSO with the local search ability of SQP to identify the model parameters of forward channel. The validity of proposed method has been verified by the simulation. The practicability is checked with the PI/PID controller parameter turning based on identified forward channel model.

Warpage improvement on wheel caster by optimizing the process parameters using genetic algorithm (GA)

Science.gov (United States)

Safuan, N. S.; Fathullah, M.; Shayfull, Z.; Nasir, S. M.; Hazwan, M. H. M.

2017-09-01

In injection moulding process, the defects will always encountered and affected the final product shape and functionality. This study is concerning on minimizing warpage and optimizing the process parameter of injection moulding part. Apart from eliminating product wastes, this project also giving out best recommended parameters setting. This research studied on five parameters. The optimization showed that warpage have been improved 42.64% from 0.6524 mm to 0.30879 mm in Autodesk Moldflow Insight (AMI) simulation result and Genetic Algorithm (GA) respectively.

Optimization of process parameters in precipitation for consistent quality UO2 powder production

International Nuclear Information System (INIS)

Tiwari, S.K.; Reddy, A.L.V.; Venkataswamy, J.; Misra, M.; Setty, D.S.; Sheela, S.; Saibaba, N.

2013-01-01

Nuclear reactor grade natural uranium dioxide powder is being produced through precipitation route, which is further processed before converting into sintered pellets used in the fabrication of PHWR fuel assemblies of 220 and 540 MWe type reactors. The process of precipitating Uranyl Nitrate Pure Solution (UNPS) is an important step in the UO 2 powder production line, where in soluble uranium is transformed into solid form of Ammonium Uranate (AU), which in turn reflects and decides the powder characteristics. Precipitation of UNPS with vapour ammonia is being carried out in semi batch process and process parameters like ammonia flow rate, temperature, concentration of UNPS and free acidity of UNPS are very critical and decides the UO 2 powder quality. Variation in these critical parameters influences powder characteristics, which in turn influences the sinterability of UO 2 powder. In order to get consistent powder quality and sinterability the critical parameter like ammonia flow rate during precipitation is studied, optimized and validated. The critical process parameters are controlled through PLC based automated on-line data acquisition systems for achieving consistent powder quality with increased recovery and production. The present paper covers optimization of process parameters and powder characteristics. (author)

An analysis to optimize the process parameters of friction stir welded ...

African Journals Online (AJOL)

The friction stir welding (FSW) of steel is a challenging task. Experiments are conducted here, with a tool having a conical pin of 0.4mm clearance. The process parameters are optimized by using the Taguchi technique based on Taguchi's L9 orthogonal array. Experiments have been conducted based on three process ...

Optimization of process parameters for a quasi-continuous tablet coating system using design of experiments.

Science.gov (United States)

Cahyadi, Christine; Heng, Paul Wan Sia; Chan, Lai Wah

2011-03-01

The aim of this study was to identify and optimize the critical process parameters of the newly developed Supercell quasi-continuous coater for optimal tablet coat quality. Design of experiments, aided by multivariate analysis techniques, was used to quantify the effects of various coating process conditions and their interactions on the quality of film-coated tablets. The process parameters varied included batch size, inlet temperature, atomizing pressure, plenum pressure, spray rate and coating level. An initial screening stage was carried out using a 2(6-1(IV)) fractional factorial design. Following these preliminary experiments, optimization study was carried out using the Box-Behnken design. Main response variables measured included drug-loading efficiency, coat thickness variation, and the extent of tablet damage. Apparent optimum conditions were determined by using response surface plots. The process parameters exerted various effects on the different response variables. Hence, trade-offs between individual optima were necessary to obtain the best compromised set of conditions. The adequacy of the optimized process conditions in meeting the combined goals for all responses was indicated by the composite desirability value. By using response surface methodology and optimization, coating conditions which produced coated tablets of high drug-loading efficiency, low incidences of tablet damage and low coat thickness variation were defined. Optimal conditions were found to vary over a large spectrum when different responses were considered. Changes in processing parameters across the design space did not result in drastic changes to coat quality, thereby demonstrating robustness in the Supercell coating process. © 2010 American Association of Pharmaceutical Scientists

Laser dimpling process parameters selection and optimization using surrogate-driven process capability space

Science.gov (United States)

Ozkat, Erkan Caner; Franciosa, Pasquale; Ceglarek, Dariusz

2017-08-01

Remote laser welding technology offers opportunities for high production throughput at a competitive cost. However, the remote laser welding process of zinc-coated sheet metal parts in lap joint configuration poses a challenge due to the difference between the melting temperature of the steel (∼1500 °C) and the vapourizing temperature of the zinc (∼907 °C). In fact, the zinc layer at the faying surface is vapourized and the vapour might be trapped within the melting pool leading to weld defects. Various solutions have been proposed to overcome this problem over the years. Among them, laser dimpling has been adopted by manufacturers because of its flexibility and effectiveness along with its cost advantages. In essence, the dimple works as a spacer between the two sheets in lap joint and allows the zinc vapour escape during welding process, thereby preventing weld defects. However, there is a lack of comprehensive characterization of dimpling process for effective implementation in real manufacturing system taking into consideration inherent changes in variability of process parameters. This paper introduces a methodology to develop (i) surrogate model for dimpling process characterization considering multiple-inputs (i.e. key control characteristics) and multiple-outputs (i.e. key performance indicators) system by conducting physical experimentation and using multivariate adaptive regression splines; (ii) process capability space (Cp-Space) based on the developed surrogate model that allows the estimation of a desired process fallout rate in the case of violation of process requirements in the presence of stochastic variation; and, (iii) selection and optimization of the process parameters based on the process capability space. The proposed methodology provides a unique capability to: (i) simulate the effect of process variation as generated by manufacturing process; (ii) model quality requirements with multiple and coupled quality requirements; and (iii

Optimization of cutting parameters for machining time in turning process

Science.gov (United States)

Mavliutov, A. R.; Zlotnikov, E. G.

2018-03-01

This paper describes the most effective methods for nonlinear constraint optimization of cutting parameters in the turning process. Among them are Linearization Programming Method with Dual-Simplex algorithm, Interior Point method, and Augmented Lagrangian Genetic Algorithm (ALGA). Every each of them is tested on an actual example – the minimization of production rate in turning process. The computation was conducted in the MATLAB environment. The comparative results obtained from the application of these methods show: The optimal value of the linearized objective and the original function are the same. ALGA gives sufficiently accurate values, however, when the algorithm uses the Hybrid function with Interior Point algorithm, the resulted values have the maximal accuracy.

Application of dragonfly algorithm for optimal performance analysis of process parameters in turn-mill operations- A case study

Science.gov (United States)

Vikram, K. Arun; Ratnam, Ch; Lakshmi, VVK; Kumar, A. Sunny; Ramakanth, RT

2018-02-01

Meta-heuristic multi-response optimization methods are widely in use to solve multi-objective problems to obtain Pareto optimal solutions during optimization. This work focuses on optimal multi-response evaluation of process parameters in generating responses like surface roughness (Ra), surface hardness (H) and tool vibration displacement amplitude (Vib) while performing operations like tangential and orthogonal turn-mill processes on A-axis Computer Numerical Control vertical milling center. Process parameters like tool speed, feed rate and depth of cut are considered as process parameters machined over brass material under dry condition with high speed steel end milling cutters using Taguchi design of experiments (DOE). Meta-heuristic like Dragonfly algorithm is used to optimize the multi-objectives like ‘Ra’, ‘H’ and ‘Vib’ to identify the optimal multi-response process parameters combination. Later, the results thus obtained from multi-objective dragonfly algorithm (MODA) are compared with another multi-response optimization technique Viz. Grey relational analysis (GRA).

VISUALIZATION SOFTWARE DEVELOPMENT FOR PROCEDURE OF MULTI-DIMENSIONAL OPTIMIZATION OF TECHNOLOGICAL PROCESS FUNCTIONAL PARAMETERS

Directory of Open Access Journals (Sweden)

E. N. Ishakova

2016-05-01

Full Text Available A method for multi-criteria optimization of the design parameters for technological object is described. The existing optimization methods are overviewed, and works in the field of basic research and applied problems are analyzed. The problem is formulated, based on the process requirements, making it possible to choose the geometrical dimensions of machine tips and the flow rate of the process, so that the resulting technical and economical parameters were optimal. In the problem formulation application of the performance method adapted to a particular domain is described. Task implementation is shown; the method of characteristics creation for the studied object in view of some restrictions for parameters in both analytical and graphical representation. On the basis of theoretical research the software system is developed that gives the possibility to automate the discovery of optimal solutions for specific problems. Using available information sources, that characterize the object of study, it is possible to establish identifiers, add restrictions from the one side, and in the interval as well. Obtained result is a visual depiction of dependence of the main study parameters on the others, which may have an impact on both the flow of the process, and the quality of products. The resulting optimal area shows the use of different design options for technological object in an acceptable kinematic range that makes it possible for the researcher to choose the best design solution.

Grey fuzzy logic approach for the optimization of DLC thin film coating process parameters using PACVD technique

Science.gov (United States)

Ghadai, R. K.; Das, P. P.; Shivakoti, I.; Mondal, S. C.; Swain, B. P.

2017-07-01

Diamond-like carbon (DLC) coatings are widely used in medical, manufacturing and aerospace industries due to their excellent mechanical, biological, optical and tribological properties. The selection of optimal process parameters for efficient characteristics of DLC film is always a challenging issue for the materials science researchers. The optimal combination of the process parameters involved in the deposition of DLC films provide a better result, which subsequently help other researchers to choose the process parameters. In the present work Grey Relation Analysis (GRA) and Fuzzy-logic are being used for the optimization of process parameters in DLC film coating by using plasma assist chemical vapour deposition (PACVD) technique. The bias voltage, bias frequency, deposition pressure, gas composition are considered as input process parameters and hardness (GPa), Young's modulus (GPa), ratio between diamond to graphic fraction, (Id/Ig) ratio are considered as response parameters. The input parameters are optimized by grey fuzzy analysis. The contribution of individual input parameter is done by ANOVA. In this analysis found that bias voltage having the least influence and gas composition has highest influence in the PACVD deposited DLC films. The grey fuzzy analysis results indicated that optimum results for bias voltage, bias frequency, deposition pressure, gas composition for the DLC thin films are -50 V, 6 kHz, 4 μbar and 60:40 % respectively.

Optimization of process parameters in precipitation for consistent quality UO{sub 2} powder production

Energy Technology Data Exchange (ETDEWEB)

Tiwari, S.K.; Reddy, A.L.V.; Venkataswamy, J.; Misra, M.; Setty, D.S.; Sheela, S.; Saibaba, N., E-mail: misra@nfc.gov.in [Nuclear Fuel Complex, Hyderabad (India)

2013-07-01

Nuclear reactor grade natural uranium dioxide powder is being produced through precipitation route, which is further processed before converting into sintered pellets used in the fabrication of PHWR fuel assemblies of 220 and 540 MWe type reactors. The process of precipitating Uranyl Nitrate Pure Solution (UNPS) is an important step in the UO{sub 2} powder production line, where in soluble uranium is transformed into solid form of Ammonium Uranate (AU), which in turn reflects and decides the powder characteristics. Precipitation of UNPS with vapour ammonia is being carried out in semi batch process and process parameters like ammonia flow rate, temperature, concentration of UNPS and free acidity of UNPS are very critical and decides the UO{sub 2} powder quality. Variation in these critical parameters influences powder characteristics, which in turn influences the sinterability of UO{sub 2} powder. In order to get consistent powder quality and sinterability the critical parameter like ammonia flow rate during precipitation is studied, optimized and validated. The critical process parameters are controlled through PLC based automated on-line data acquisition systems for achieving consistent powder quality with increased recovery and production. The present paper covers optimization of process parameters and powder characteristics. (author)

Process parameter optimization based on principal components analysis during machining of hardened steel

Directory of Open Access Journals (Sweden)

Suryakant B. Chandgude

2015-09-01

Full Text Available The optimum selection of process parameters has played an important role for improving the surface finish, minimizing tool wear, increasing material removal rate and reducing machining time of any machining process. In this paper, optimum parameters while machining AISI D2 hardened steel using solid carbide TiAlN coated end mill has been investigated. For optimization of process parameters along with multiple quality characteristics, principal components analysis method has been adopted in this work. The confirmation experiments have revealed that to improve performance of cutting; principal components analysis method would be a useful tool.

Multi-objective Optimization of Process Parameters in Friction Stir Welding

DEFF Research Database (Denmark)

Tutum, Cem Celal; Hattel, Jesper Henri

The objective of this paper is to investigate optimum process parameters in Friction Stir Welding (FSW) to minimize residual stresses in the work piece and maximize production efficiency meanwhile satisfying process specific constraints as well. More specifically, the choices of tool rotational...... speed and traverse welding speed have been sought in order to achieve the goals mentioned above using an evolutionary multi-objective optimization (MOO) algorithm, i.e. non-dominated sorting genetic algorithm (NSGA-II), integrated with a transient, 2- dimensional sequentially coupled thermo...

Optimization of process parameters of ECM by RSM on AISI 202 steel

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P. Alex John Britto

2015-12-01

Full Text Available The machining of complex shaped designs was difficult earlier, but with the advent of the newer machining processes incorporating in it electrical, chemical & mechanical processes, manufacturing has redefined itself. Especially, the Electrochemical Machining (ECM process is used to machine the hard to cut materials without producing heat and friction. Hence, in this work, the ECM process has been chosen to machine SS AISI 202 steel. This study establishes the effect of process parameters such as voltage, current and concentration of electrolyte on the responses on material removal rate (MRR. In this work, second-order quadratic models were developed for MRR, considering the electrolyte concentration, voltage and current as the machining parameters, using central composite design. The developed models were used for Response Surface Methodology (RSM optimization by desirability function approach to determine the optimum machining parameters.

Process Parameters Optimization of 14nm MOSFET Using 2-D Analytical Modelling

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Noor Faizah Z.A.

2016-01-01

Full Text Available This paper presents the modeling and optimization of 14nm gate length CMOS transistor which is down-scaled from previous 32nm gate length. High-k metal gate material was used in this research utilizing Hafnium Dioxide (HfO2 as dielectric and Tungsten Silicide (WSi2 and Titanium Silicide (TiSi2 as a metal gate for NMOS and PMOS respectively. The devices are fabricated virtually using ATHENA module and characterized its performance evaluation via ATLAS module; both in Virtual Wafer Fabrication (VWF of Silvaco TCAD Tools. The devices were then optimized through a process parameters variability using L9 Taguchi Method. There were four process parameter with two noise factor of different values were used to analyze the factor effect. The results show that the optimal value for both transistors are well within ITRS 2013 prediction where VTH and IOFF are 0.236737V and 6.995705nA/um for NMOS device and 0.248635 V and 5.26nA/um for PMOS device respectively.

Parameters control in GAs for dynamic optimization

Directory of Open Access Journals (Sweden)

Khalid Jebari

2013-02-01

Full Text Available The Control of Genetic Algorithms parameters allows to optimize the search process and improves the performance of the algorithm. Moreover it releases the user to dive into a game process of trial and failure to find the optimal parameters.

Laser Welding Process Parameters Optimization Using Variable-Fidelity Metamodel and NSGA-II

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Wang Chaochao

2017-01-01

Full Text Available An optimization methodology based on variable-fidelity (VF metamodels and nondominated sorting genetic algorithm II (NSGA-II for laser bead-on-plate welding of stainless steel 316L is presented. The relationships between input process parameters (laser power, welding speed and laser focal position and output responses (weld width and weld depth are constructed by VF metamodels. In VF metamodels, the information from two levels fidelity models are integrated, in which the low-fidelity model (LF is finite element simulation model that is used to capture the general trend of the metamodels, and high-fidelity (HF model which from physical experiments is used to ensure the accuracy of metamodels. The accuracy of the VF metamodel is verified by actual experiments. To slove the optimization problem, NSGA-II is used to search for multi-objective Pareto optimal solutions. The results of verification experiments show that the obtained optimal parameters are effective and reliable.

Chickpea seeds germination rational parameters optimization

Science.gov (United States)

Safonova, Yu A.; Ivliev, M. N.; Lemeshkin, A. V.

2018-05-01

The paper presents the influence of chickpea seeds bioactivation parameters on their enzymatic activity experimental results. Optimal bioactivation process modes were obtained by regression-factor analysis: process temperature - 13.6 °C, process duration - 71.5 h. It was found that in the germination process, the proteolytic, amylolytic and lipolytic enzymes activity increased, and the urease enzyme activity is reduced. The dependences of enzyme activity on chickpea seeds germination conditions were obtained by mathematical processing of experimental data. The calculated data are in good agreement with the experimental ones. This confirms the optimization efficiency based on experiments mathematical planning in order to determine the enzymatic activity of chickpea seeds germination optimal parameters of bioactivated seeds.

Optimization of process parameters in drilling of fibre hybrid composite using Taguchi and grey relational analysis

Science.gov (United States)

Vijaya Ramnath, B.; Sharavanan, S.; Jeykrishnan, J.

2017-03-01

Nowadays quality plays a vital role in all the products. Hence, the development in manufacturing process focuses on the fabrication of composite with high dimensional accuracy and also incurring low manufacturing cost. In this work, an investigation on machining parameters has been performed on jute-flax hybrid composite. Here, the two important responses characteristics like surface roughness and material removal rate are optimized by employing 3 machining input parameters. The input variables considered are drill bit diameter, spindle speed and feed rate. Machining is done on CNC vertical drilling machine at different levels of drilling parameters. Taguchi’s L16 orthogonal array is used for optimizing individual tool parameters. Analysis Of Variance is used to find the significance of individual parameters. The simultaneous optimization of the process parameters is done by grey relational analysis. The results of this investigation shows that, spindle speed and drill bit diameter have most effect on material removal rate and surface roughness followed by feed rate.

Parameter extraction using global particle swarm optimization approach and the influence of polymer processing temperature on the solar cell parameters

Science.gov (United States)

Kumar, S.; Singh, A.; Dhar, A.

2017-08-01

The accurate estimation of the photovoltaic parameters is fundamental to gain an insight of the physical processes occurring inside a photovoltaic device and thereby to optimize its design, fabrication processes, and quality. A simulative approach of accurately determining the device parameters is crucial for cell array and module simulation when applied in practical on-field applications. In this work, we have developed a global particle swarm optimization (GPSO) approach to estimate the different solar cell parameters viz., ideality factor (η), short circuit current (Isc), open circuit voltage (Voc), shunt resistant (Rsh), and series resistance (Rs) with wide a search range of over ±100 % for each model parameter. After validating the accurateness and global search power of the proposed approach with synthetic and noisy data, we applied the technique to the extract the PV parameters of ZnO/PCDTBT based hybrid solar cells (HSCs) prepared under different annealing conditions. Further, we examine the variation of extracted model parameters to unveil the physical processes occurring when different annealing temperatures are employed during the device fabrication and establish the role of improved charge transport in polymer films from independent FET measurements. The evolution of surface morphology, optical absorption, and chemical compositional behaviour of PCDTBT co-polymer films as a function of processing temperature has also been captured in the study and correlated with the findings from the PV parameters extracted using GPSO approach.

Optimization of a Cu CMP process modeling parameters of nanometer integrated circuits

International Nuclear Information System (INIS)

Ruan Wenbiao; Chen Lan; Ma Tianyu; Fang Jingjing; Zhang He; Ye Tianchun

2012-01-01

A copper chemical mechanical polishing (Cu CMP) process is reviewed and analyzed from the view of chemical physics. Three steps Cu CMP process modeling is set up based on the actual process of manufacturing and pattern-density-step-height (PDSH) modeling from MIT. To catch the pattern dependency, a 65 nm testing chip is designed and processed in the foundry. Following the model parameter extraction procedure, the model parameters are extracted and verified by testing data from the 65 nm testing chip. A comparison of results between the model predictions and test data show that the former has the same trend as the latter and the largest deviation is less than 5 nm. Third party testing data gives further evidence to support the great performance of model parameter optimization. Since precise CMP process modeling is used for the design of manufacturability (DFM) checks, critical hotspots are displayed and eliminated, which will assure good yield and production capacity of IC. (semiconductor technology)

Multi-response optimization of process parameters in friction stir welded AM20 magnesium alloy by Taguchi grey relational analysis

Directory of Open Access Journals (Sweden)

Prakash Kumar Sahu

2015-03-01

Full Text Available The purpose of this paper is to optimize the process parameter to get the better mechanical properties of friction stir welded AM20 magnesium alloy using Taguchi Grey relational analysis (GRA. The considered process parameters are welding speed, tool rotation speed, shoulder diameter and plunging depth. The experiments were carried out by using Taguchi's L18 factorial design of experiment. The processes parameters were optimized and ranked the parameters based on the GRA. The percentage influence of each process parameter on the weld quality was also quantified. A validation experimental run was conducted using optimal process condition, which was obtained from the analysis, to show the improvement in mechanical properties of the joint. This study also shows the feasibility of the GRA with Taguchi technique for improvement in welding quality of magnesium alloy.

Optimizing supercritical antisolvent process parameters to minimize the particle size of paracetamol nanoencapsulated in L-polylactide

Directory of Open Access Journals (Sweden)

Kalani M

2011-05-01

Full Text Available Mahshid Kalani, Robiah Yunus, Norhafizah AbdullahChemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, Selangor Darul Ehsan, MalaysiaBackground: The aim of this study was to optimize the different process parameters including pressure, temperature, and polymer concentration, to produce fine small spherical particles with a narrow particle size distribution using a supercritical antisolvent method for drug encapsulation. The interaction between different process parameters was also investigated.Methods and results: The optimized process parameters resulted in production of nanoencapsulated paracetamol in L-polylactide with a mean diameter of approximately 300 nm at 120 bar, 30°C, and a polymer concentration of 16 ppm. Thermogravimetric analysis illustrated the thermal characteristics of the nanoparticles. The high electrical charge on the surface of the nanoparticles caused the particles to repel each other, with the high negative zeta potential preventing flocculation.Conclusion: Our results illustrate the effect of different process parameters on particle size and morphology, and validate results obtained via RSM statistical software. Furthermore, the in vitro drug-release profile is consistent with a Korsmeyer–Peppas kinetic model.Keywords: supercritical, antisolvent, encapsulation, nanoparticles, biodegradable polymer, optimization, drug delivery

Optimization of Robotic Spray Painting process Parameters using Taguchi Method

Science.gov (United States)

Chidhambara, K. V.; Latha Shankar, B.; Vijaykumar

2018-02-01

Automated spray painting process is gaining interest in industry and research recently due to extensive application of spray painting in automobile industries. Automating spray painting process has advantages of improved quality, productivity, reduced labor, clean environment and particularly cost effectiveness. This study investigates the performance characteristics of an industrial robot Fanuc 250ib for an automated painting process using statistical tool Taguchi’s Design of Experiment technique. The experiment is designed using Taguchi’s L25 orthogonal array by considering three factors and five levels for each factor. The objective of this work is to explore the major control parameters and to optimize the same for the improved quality of the paint coating measured in terms of Dry Film thickness(DFT), which also results in reduced rejection. Further Analysis of Variance (ANOVA) is performed to know the influence of individual factors on DFT. It is observed that shaping air and paint flow are the most influencing parameters. Multiple regression model is formulated for estimating predicted values of DFT. Confirmation test is then conducted and comparison results show that error is within acceptable level.

Optimization of process parameters in welding of dissimilar steels using robot TIG welding

Science.gov (United States)

Navaneeswar Reddy, G.; VenkataRamana, M.

2018-03-01

Robot TIG welding is a modern technique used for joining two work pieces with high precision. Design of Experiments is used to conduct experiments by varying weld parameters like current, wire feed and travelling speed. The welding parameters play important role in joining of dissimilar stainless steel SS 304L and SS430. In this work, influences of welding parameter on Robot TIG Welded specimens are investigated using Response Surface Methodology. The Micro Vickers hardness tests of the weldments are measured. The process parameters are optimized to maximize the hardness of the weldments.

Fault detection of feed water treatment process using PCA-WD with parameter optimization.

Science.gov (United States)

Zhang, Shirong; Tang, Qian; Lin, Yu; Tang, Yuling

2017-05-01

Feed water treatment process (FWTP) is an essential part of utility boilers; and fault detection is expected for its reliability improvement. Classical principal component analysis (PCA) has been applied to FWTPs in our previous work; however, the noises of T 2 and SPE statistics result in false detections and missed detections. In this paper, Wavelet denoise (WD) is combined with PCA to form a new algorithm, (PCA-WD), where WD is intentionally employed to deal with the noises. The parameter selection of PCA-WD is further formulated as an optimization problem; and PSO is employed for optimization solution. A FWTP, sustaining two 1000MW generation units in a coal-fired power plant, is taken as a study case. Its operation data is collected for following verification study. The results show that the optimized WD is effective to restrain the noises of T 2 and SPE statistics, so as to improve the performance of PCA-WD algorithm. And, the parameter optimization enables PCA-WD to get its optimal parameters in an automatic way rather than on individual experience. The optimized PCA-WD is further compared with classical PCA and sliding window PCA (SWPCA), in terms of four cases as bias fault, drift fault, broken line fault and normal condition, respectively. The advantages of the optimized PCA-WD, against classical PCA and SWPCA, is finally convinced with the results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Optimization of process parameter for graft copolymerization of glycidyl methacrylate onto delignified banana fibers

International Nuclear Information System (INIS)

Selambakkannu, S.; Nor Azillah Fatimah Othman; Siti Fatahiyah Mohamad

2016-01-01

This paper focused on pre-treated banana fibers as a trunk polymer for optimization of radiation-induced graft copolymerization process parameters. Pre-treated banana fiber was grafted with glycidyl methacrylate (GMA) via electron beam irradiation. Optimization of grafting parameters in term of grafting yield was analyzed at numerous radiation dose, monomer concentration and reaction time. Grafting yield had been calculated gravimetrically against all the process parameters. The grafting yield at 40 kGy had increases from 14 % to 22.5 % at 1 h and 24 h of reaction time respectively. Grafting yield at 1 % of GMA was about 58 % and it increases to 187 % at 3 % GMA. The grafting of GMA onto pre-treated banana fibers confirmed with the characterization using FTIR, SEM and TGA. Grafting of GMA onto pre-treated fibers was successfully carried out and it was confirmed by the results obtained via the characterization. (author)

Nanohydroxyapatite synthesis using optimized process parameters ...

Indian Academy of Sciences (India)

3Energy Research Group, School of Engineering, Taylor's University, 47500 ... influence of different ultrasonication parameters on the prop- ... to evaluate multiple process parameters and their interaction. ..... dent and dependent variables by a 3-D representation of .... The intensities of O–H functional groups are seen to.

Genetic Algorithm Optimizes Q-LAW Control Parameters

Science.gov (United States)

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

2008-01-01

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

Optimal process parameters for phosphorus spin-on-doping of germanium

Energy Technology Data Exchange (ETDEWEB)

Boldrini, Virginia [Dipartimento di Fisica e Astronomia, Università degli Studi di Padova, Via Marzolo 8, I-35131 Padova (Italy); INFN-LNL, Viale dell’Università 2, I-35020 Legnaro, Padova (Italy); Carturan, Sara Maria, E-mail: sara.carturan@lnl.infn.it [Dipartimento di Fisica e Astronomia, Università degli Studi di Padova, Via Marzolo 8, I-35131 Padova (Italy); INFN-LNL, Viale dell’Università 2, I-35020 Legnaro, Padova (Italy); Maggioni, Gianluigi; Napolitani, Enrico [Dipartimento di Fisica e Astronomia, Università degli Studi di Padova, Via Marzolo 8, I-35131 Padova (Italy); INFN-LNL, Viale dell’Università 2, I-35020 Legnaro, Padova (Italy); Napoli, Daniel Ricardo [INFN-LNL, Viale dell’Università 2, I-35020 Legnaro, Padova (Italy); Camattari, Riccardo [INFN Sezione di Ferrara, Dipartimento di Fisica, Università di Ferrara, Via Saragat 1, 44122, Ferrara (Italy); De Salvador, Davide [Dipartimento di Fisica e Astronomia, Università degli Studi di Padova, Via Marzolo 8, I-35131 Padova (Italy); INFN-LNL, Viale dell’Università 2, I-35020 Legnaro, Padova (Italy)

2017-01-15

Highlights: • Optimized protocol for the application of phosphorus spin-on-doping to Ge surface. • Homogeneous n-type Ge layers, fully electrically active, are obtained. • Crucial parameters for SOD curing are relative humidity, time and temperature. • Characterization of Ge loss from the surface into the SOD film by diffusion. • Spike annealing in standard tube chamber furnace are performed. - Abstract: The fabrication of homogeneously doped germanium layers characterized by total electrical activation is currently a hot topic in many fields, such as microelectronics, photovoltaics, optics and radiation detectors. Phosphorus spin-on-doping technique has been implemented on Ge wafers, by developing a protocol for the curing process and subsequent diffusion annealing for optimal doping. Parameters such as relative humidity and curing time turned out to affect the surface morphology, the degree of reticulation reached by the dopant source and the amount of dopant available for diffusion. After spike annealing in a conventional furnace, diffusion profiles and electrical properties have been measured. Ge loss from the surface during high-temperature annealing, due to diffusion into the source film, has been observed and quantified.

Infrared Drying Parameter Optimization

Science.gov (United States)

Jackson, Matthew R.

In recent years, much research has been done to explore direct printing methods, such as screen and inkjet printing, as alternatives to the traditional lithographic process. The primary motivation is reduction of the material costs associated with producing common electronic devices. Much of this research has focused on developing inkjet or screen paste formulations that can be printed on a variety of substrates, and which have similar conductivity performance to the materials currently used in the manufacturing of circuit boards and other electronic devices. Very little research has been done to develop a process that would use direct printing methods to manufacture electronic devices in high volumes. This study focuses on developing and optimizing a drying process for conductive copper ink in a high volume manufacturing setting. Using an infrared (IR) dryer, it was determined that conductive copper prints could be dried in seconds or minutes as opposed to tens of minutes or hours that it would take with other drying devices, such as a vacuum oven. In addition, this study also identifies significant parameters that can affect the conductivity of IR dried prints. Using designed experiments and statistical analysis; the dryer parameters were optimized to produce the best conductivity performance for a specific ink formulation and substrate combination. It was determined that for an ethylene glycol, butanol, 1-methoxy 2- propanol ink formulation printed on Kapton, the optimal drying parameters consisted of a dryer height of 4 inches, a temperature setting between 190 - 200°C, and a dry time of 50-65 seconds depending on the printed film thickness as determined by the number of print passes. It is important to note that these parameters are optimized specifically for the ink formulation and substrate used in this study. There is still much research that needs to be done into optimizing the IR dryer for different ink substrate combinations, as well as developing a

High Temperature Epoxy Foam: Optimization of Process Parameters

Directory of Open Access Journals (Sweden)

Samira El Gazzani

2016-06-01

Full Text Available For many years, reduction of fuel consumption has been a major aim in terms of both costs and environmental concerns. One option is to reduce the weight of fuel consumers. For this purpose, the use of a lightweight material based on rigid foams is a relevant choice. This paper deals with a new high temperature epoxy expanded material as substitution of phenolic resin, classified as potentially mutagenic by European directive Reach. The optimization of thermoset foam depends on two major parameters, the reticulation process and the expansion of the foaming agent. Controlling these two phenomena can lead to a fully expanded and cured material. The rheological behavior of epoxy resin is studied and gel time is determined at various temperatures. The expansion of foaming agent is investigated by thermomechanical analysis. Results are correlated and compared with samples foamed in the same temperature conditions. The ideal foaming/gelation temperature is then determined. The second part of this research concerns the optimization of curing cycle of a high temperature trifunctional epoxy resin. A two-step curing cycle was defined by considering the influence of different curing schedules on the glass transition temperature of the material. The final foamed material has a glass transition temperature of 270 °C.

Optimization of the Process Parameters for Controlling Residual Stress and Distortion in Friction Stir Welding

DEFF Research Database (Denmark)

Tutum, Cem Celal; Schmidt, Henrik Nikolaj Blicher; Hattel, Jesper Henri

2008-01-01

In the present paper, numerical optimization of the process parameters, i.e. tool rotation speed and traverse speed, aiming minimization of the two conflicting objectives, i.e. the residual stresses and welding time, subjected to process-specific thermal constraints in friction stir welding......, is investigated. The welding process is simulated in 2-dimensions with a sequentially coupled transient thermo-mechanical model using ANSYS. The numerical optimization problem is implemented in modeFRONTIER and solved using the Multi-Objective Genetic Algorithm (MOGA-II). An engineering-wise evaluation or ranking...

Multi-Objective Optimization of Friction Stir Welding Process Parameters of AA6061-T6 and AA7075-T6 Using a Biogeography Based Optimization Algorithm

Directory of Open Access Journals (Sweden)

Mehran Tamjidy

2017-05-01

Full Text Available The development of Friction Stir Welding (FSW has provided an alternative approach for producing high-quality welds, in a fast and reliable manner. This study focuses on the mechanical properties of the dissimilar friction stir welding of AA6061-T6 and AA7075-T6 aluminum alloys. The FSW process parameters such as tool rotational speed, tool traverse speed, tilt angle, and tool offset influence the mechanical properties of the friction stir welded joints significantly. A mathematical regression model is developed to determine the empirical relationship between the FSW process parameters and mechanical properties, and the results are validated. In order to obtain the optimal values of process parameters that simultaneously optimize the ultimate tensile strength, elongation, and minimum hardness in the heat affected zone (HAZ, a metaheuristic, multi objective algorithm based on biogeography based optimization is proposed. The Pareto optimal frontiers for triple and dual objective functions are obtained and the best optimal solution is selected through using two different decision making techniques, technique for order of preference by similarity to ideal solution (TOPSIS and Shannon’s entropy.

Multi-Objective Optimization of Friction Stir Welding Process Parameters of AA6061-T6 and AA7075-T6 Using a Biogeography Based Optimization Algorithm.

Science.gov (United States)

Tamjidy, Mehran; Baharudin, B T Hang Tuah; Paslar, Shahla; Matori, Khamirul Amin; Sulaiman, Shamsuddin; Fadaeifard, Firouz

2017-05-15

The development of Friction Stir Welding (FSW) has provided an alternative approach for producing high-quality welds, in a fast and reliable manner. This study focuses on the mechanical properties of the dissimilar friction stir welding of AA6061-T6 and AA7075-T6 aluminum alloys. The FSW process parameters such as tool rotational speed, tool traverse speed, tilt angle, and tool offset influence the mechanical properties of the friction stir welded joints significantly. A mathematical regression model is developed to determine the empirical relationship between the FSW process parameters and mechanical properties, and the results are validated. In order to obtain the optimal values of process parameters that simultaneously optimize the ultimate tensile strength, elongation, and minimum hardness in the heat affected zone (HAZ), a metaheuristic, multi objective algorithm based on biogeography based optimization is proposed. The Pareto optimal frontiers for triple and dual objective functions are obtained and the best optimal solution is selected through using two different decision making techniques, technique for order of preference by similarity to ideal solution (TOPSIS) and Shannon's entropy.

An intelligent approach to optimize the EDM process parameters using utility concept and QPSO algorithm

Directory of Open Access Journals (Sweden)

Chinmaya P. Mohanty

2017-04-01

Full Text Available Although significant research has gone into the field of electrical discharge machining (EDM, analysis related to the machining efficiency of the process with different electrodes has not been adequately made. Copper and brass are frequently used as electrode materials but graphite can be used as a potential electrode material due to its high melting point temperature and good electrical conductivity. In view of this, the present work attempts to compare the machinability of copper, graphite and brass electrodes while machining Inconel 718 super alloy. Taguchi’s L27 orthogonal array has been employed to collect data for the study and analyze effect of machining parameters on performance measures. The important performance measures selected for this study are material removal rate, tool wear rate, surface roughness and radial overcut. Machining parameters considered for analysis are open circuit voltage, discharge current, pulse-on-time, duty factor, flushing pressure and electrode material. From the experimental analysis, it is observed that electrode material, discharge current and pulse-on-time are the important parameters for all the performance measures. Utility concept has been implemented to transform a multiple performance characteristics into an equivalent performance characteristic. Non-linear regression analysis is carried out to develop a model relating process parameters and overall utility index. Finally, the quantum behaved particle swarm optimization (QPSO and particle swarm optimization (PSO algorithms have been used to compare the optimal level of cutting parameters. Results demonstrate the elegance of QPSO in terms of convergence and computational effort. The optimal parametric setting obtained through both the approaches is validated by conducting confirmation experiments.

Nanohydroxyapatite synthesis using optimized process parameters

Indian Academy of Sciences (India)

Nanohydroxyapatite; ultrasonication; response surface methodology; calcination; ... Three independent process parameters: temperature () (70, 80 and 90°C), ... Bangi, Selangor, Malaysia; Energy Research Group, School of Engineering, ...

Influence of emulsifiers on the optimization of processing parameters of refining milk chocolate in the ball mill

Directory of Open Access Journals (Sweden)

Pajin Biljana

2011-01-01

Full Text Available Chocolate manufacture is a complex process which includes a large number of technology operations. One of the obligatory phases is milling, called refining, which aims at obtaining the appropriate distribution of particle size, resulting in the chocolate with optimal physical and sensory characteristics. The aim of this work was to define and optimize the process parameters for the production of milk chocolate by a non-conventional procedure, using the ball mill. The quality of chocolate mass, produced on this way, is determined by measuring the following parameters: moisture, size of the largest cocoa particle, yield flow, and Casson plastic viscosity. A special consideration of this study is the optimization of the types and amounts of emulsifiers, which are responsible for achieving the appropriate rheological and physical characteristics of the chocolate mass. The obtained parameters are compared with those which are typical for the standard procedure.

Control parameter optimization for AP1000 reactor using Particle Swarm Optimization

International Nuclear Information System (INIS)

Wang, Pengfei; Wan, Jiashuang; Luo, Run; Zhao, Fuyu; Wei, Xinyu

2016-01-01

Highlights: • The PSO algorithm is applied for control parameter optimization of AP1000 reactor. • Key parameters of the MSHIM control system are optimized. • Optimization results are evaluated though simulations and quantitative analysis. - Abstract: The advanced mechanical shim (MSHIM) core control strategy is implemented in the AP1000 reactor for core reactivity and axial power distribution control simultaneously. The MSHIM core control system can provide superior reactor control capabilities via automatic rod control only. This enables the AP1000 to perform power change operations automatically without the soluble boron concentration adjustments. In this paper, the Particle Swarm Optimization (PSO) algorithm has been applied for the parameter optimization of the MSHIM control system to acquire better reactor control performance for AP1000. System requirements such as power control performance, control bank movement and AO control constraints are reflected in the objective function. Dynamic simulations are performed based on an AP1000 reactor simulation platform in each iteration of the optimization process to calculate the fitness values of particles in the swarm. The simulation platform is developed in Matlab/Simulink environment with implementation of a nodal core model and the MSHIM control strategy. Based on the simulation platform, the typical 10% step load decrease transient from 100% to 90% full power is simulated and the objective function used for control parameter tuning is directly incorporated in the simulation results. With successful implementation of the PSO algorithm in the control parameter optimization of AP1000 reactor, four key parameters of the MSHIM control system are optimized. It has been demonstrated by the calculation results that the optimized MSHIM control system parameters can improve the reactor power control capability and reduce the control rod movement without compromising AO control. Therefore, the PSO based optimization

Experiments for practical education in process parameter optimization for selective laser sintering to increase workpiece quality

Science.gov (United States)

Reutterer, Bernd; Traxler, Lukas; Bayer, Natascha; Drauschke, Andreas

2016-04-01

Selective Laser Sintering (SLS) is considered as one of the most important additive manufacturing processes due to component stability and its broad range of usable materials. However the influence of the different process parameters on mechanical workpiece properties is still poorly studied, leading to the fact that further optimization is necessary to increase workpiece quality. In order to investigate the impact of various process parameters, laboratory experiments are implemented to improve the understanding of the SLS limitations and advantages on an educational level. Experiments are based on two different workstations, used to teach students the fundamentals of SLS. First of all a 50 W CO2 laser workstation is used to investigate the interaction of the laser beam with the used material in accordance with varied process parameters to analyze a single-layered test piece. Second of all the FORMIGA P110 laser sintering system from EOS is used to print different 3D test pieces in dependence on various process parameters. Finally quality attributes are tested including warpage, dimension accuracy or tensile strength. For dimension measurements and evaluation of the surface structure a telecentric lens in combination with a camera is used. A tensile test machine allows testing of the tensile strength and the interpreting of stress-strain curves. The developed laboratory experiments are suitable to teach students the influence of processing parameters. In this context they will be able to optimize the input parameters depending on the component which has to be manufactured and to increase the overall quality of the final workpiece.

Optimization of parameters of special asynchronous electric drives

Science.gov (United States)

Karandey, V. Yu; Popov, B. K.; Popova, O. B.; Afanasyev, V. L.

2018-03-01

The article considers the solution of the problem of parameters optimization of special asynchronous electric drives. The solution of the problem will allow one to project and create special asynchronous electric drives for various industries. The created types of electric drives will have optimum mass-dimensional and power parameters. It will allow one to realize and fulfill the set characteristics of management of technological processes with optimum level of expenses of electric energy, time of completing the process or other set parameters. The received decision allows one not only to solve a certain optimizing problem, but also to construct dependences between the optimized parameters of special asynchronous electric drives, for example, with the change of power, current in a winding of the stator or rotor, induction in a gap or steel of magnetic conductors and other parameters. On the constructed dependences, it is possible to choose necessary optimum values of parameters of special asynchronous electric drives and their components without carrying out repeated calculations.

Experimental design approach to the process parameter optimization for laser welding of martensitic stainless steels in a constrained overlap configuration

Science.gov (United States)

Khan, M. M. A.; Romoli, L.; Fiaschi, M.; Dini, G.; Sarri, F.

2011-02-01

This paper presents an experimental design approach to process parameter optimization for the laser welding of martensitic AISI 416 and AISI 440FSe stainless steels in a constrained overlap configuration in which outer shell was 0.55 mm thick. To determine the optimal laser-welding parameters, a set of mathematical models were developed relating welding parameters to each of the weld characteristics. These were validated both statistically and experimentally. The quality criteria set for the weld to determine optimal parameters were the minimization of weld width and the maximization of weld penetration depth, resistance length and shearing force. Laser power and welding speed in the range 855-930 W and 4.50-4.65 m/min, respectively, with a fiber diameter of 300 μm were identified as the optimal set of process parameters. However, the laser power and welding speed can be reduced to 800-840 W and increased to 4.75-5.37 m/min, respectively, to obtain stronger and better welds.

Optimization of parameters of heat exchangers vehicles

Directory of Open Access Journals (Sweden)

Andrei MELEKHIN

2014-09-01

Full Text Available The relevance of the topic due to the decision of problems of the economy of resources in heating systems of vehicles. To solve this problem we have developed an integrated method of research, which allows to solve tasks on optimization of parameters of heat exchangers vehicles. This method decides multicriteria optimization problem with the program nonlinear optimization on the basis of software with the introduction of an array of temperatures obtained using thermography. The authors have developed a mathematical model of process of heat exchange in heat exchange surfaces of apparatuses with the solution of multicriteria optimization problem and check its adequacy to the experimental stand in the visualization of thermal fields, an optimal range of managed parameters influencing the process of heat exchange with minimal metal consumption and the maximum heat output fin heat exchanger, the regularities of heat exchange process with getting generalizing dependencies distribution of temperature on the heat-release surface of the heat exchanger vehicles, defined convergence of the results of research in the calculation on the basis of theoretical dependencies and solving mathematical model.

Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations

Science.gov (United States)

Saranya, Kunaparaju; John Rozario Jegaraj, J.; Ramesh Kumar, Katta; Venkateshwara Rao, Ghanta

2016-06-01

With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.

A bottom-up approach for optimization of friction stir processing parameters; a study on aluminium 2024-T3 alloy

International Nuclear Information System (INIS)

Nadammal, Naresh; Kailas, Satish V.; Suwas, Satyam

2015-01-01

Highlights: • An experimental bottom-up approach has been developed for optimizing the process parameters for friction stir processing. • Optimum parameter processed samples were tested and characterized in detail. • Ultimate tensile strength of 1.3 times the base metal strength was obtained. • Residual stresses on the processed surface were only 10% of the yield strength of base metal. • Microstructure observations revealed fine equi-axed grains with precipitate particles at the grain boundaries. - Abstract: Friction stir processing (FSP) is emerging as one of the most competent severe plastic deformation (SPD) method for producing bulk ultra-fine grained materials with improved properties. Optimizing the process parameters for a defect free process is one of the challenging aspects of FSP to mark its commercial use. For the commercial aluminium alloy 2024-T3 plate of 6 mm thickness, a bottom-up approach has been attempted to optimize major independent parameters of the process such as plunge depth, tool rotation speed and traverse speed. Tensile properties of the optimum friction stir processed sample were correlated with the microstructural characterization done using Scanning Electron Microscope (SEM) and Electron Back-Scattered Diffraction (EBSD). Optimum parameters from the bottom-up approach have led to a defect free FSP having a maximum strength of 93% the base material strength. Micro tensile testing of the samples taken from the center of processed zone has shown an increased strength of 1.3 times the base material. Measured maximum longitudinal residual stress on the processed surface was only 30 MPa which was attributed to the solid state nature of FSP. Microstructural observation reveals significant grain refinement with less variation in the grain size across the thickness and a large amount of grain boundary precipitation compared to the base metal. The proposed experimental bottom-up approach can be applied as an effective method for

Real-time parameter optimization based on neural network for smart injection molding

Science.gov (United States)

Lee, H.; Liau, Y.; Ryu, K.

2018-03-01

The manufacturing industry has been facing several challenges, including sustainability, performance and quality of production. Manufacturers attempt to enhance the competitiveness of companies by implementing CPS (Cyber-Physical Systems) through the convergence of IoT(Internet of Things) and ICT(Information & Communication Technology) in the manufacturing process level. Injection molding process has a short cycle time and high productivity. This features have been making it suitable for mass production. In addition, this process is used to produce precise parts in various industry fields such as automobiles, optics and medical devices. Injection molding process has a mixture of discrete and continuous variables. In order to optimized the quality, variables that is generated in the injection molding process must be considered. Furthermore, Optimal parameter setting is time-consuming work to predict the optimum quality of the product. Since the process parameter cannot be easily corrected during the process execution. In this research, we propose a neural network based real-time process parameter optimization methodology that sets optimal process parameters by using mold data, molding machine data, and response data. This paper is expected to have academic contribution as a novel study of parameter optimization during production compare with pre - production parameter optimization in typical studies.

Towards automatic parameter tuning of stream processing systems

KAUST Repository

Bilal, Muhammad; Canini, Marco

2017-01-01

for automating parameter tuning for stream-processing systems. Our framework supports standard black-box optimization algorithms as well as a novel gray-box optimization algorithm. We demonstrate the multiple benefits of automated parameter tuning in optimizing

A novel membrane-based process to isolate peroxidase from horseradish roots: optimization of operating parameters.

Science.gov (United States)

Liu, Jianguo; Yang, Bo; Chen, Changzhen

2013-02-01

The optimization of operating parameters for the isolation of peroxidase from horseradish (Armoracia rusticana) roots with ultrafiltration (UF) technology was systemically studied. The effects of UF operating conditions on the transmission of proteins were quantified using the parameter scanning UF. These conditions included solution pH, ionic strength, stirring speed and permeate flux. Under optimized conditions, the purity of horseradish peroxidase (HRP) obtained was greater than 84 % after a two-stage UF process and the recovery of HRP from the feedstock was close to 90 %. The resulting peroxidase product was then analysed by isoelectric focusing, SDS-PAGE and circular dichroism, to confirm its isoelectric point, molecular weight and molecular secondary structure. The effects of calcium ion on HRP specific activities were also experimentally determined.

Optimization of process parameters of pulsed TIG welded maraging steel C300

Science.gov (United States)

Deepak, P.; Jualeash, M. J.; Jishnu, J.; Srinivasan, P.; Arivarasu, M.; Padmanaban, R.; Thirumalini, S.

2016-09-01

Pulsed TIG welding technology provides excellent welding performance on thin sections which helps to increase productivity, enhance weld quality, minimize weld costs, and boost operator efficiency and this has drawn the attention of the welding society. Maraging C300 steel is extensively used in defence and aerospace industry and thus its welding becomes an area of paramount importance. In pulsed TIG welding, weld quality depends on the process parameters used. In this work, Pulsed TIG bead-on-plate welding is performed on a 5mm thick maraging C300 plate at different combinations of input parameters: peak current (Ip), base current (Ib) and pulsing frequency (HZ) as per box behnken design with three-levels for each factor. Response surface methodology is utilized for establishing a mathematical model for predicting the weld bead depth. The effect of Ip, Ib and HZ on the weld bead depth is investigated using the developed model. The weld bead depth is found to be affected by all the three parameters. Surface and contour plots developed from regression equation are used to optimize the processing parameters for maximizing the weld bead depth. Optimum values of Ip, Ib and HZ are obtained as 259 A, 120 A and 8 Hz respectively. Using this optimum condition, maximum bead depth of the weld is predicted to be 4.325 mm.

Parameters Determination of Yoshida Uemori Model Through Optimization Process of Cyclic Tension-Compression Test and V-Bending Springback

Directory of Open Access Journals (Sweden)

Serkan Toros

Full Text Available Abstract In recent years, the studies on the enhancement of the prediction capability of the sheet metal forming simulations have increased remarkably. Among the used models in the finite element simulations, the yield criteria and hardening models have a great importance for the prediction of the formability and springback. The required model parameters are determined by using the several test results, i.e. tensile, compression, biaxial stretching tests (bulge test and cyclic tests (tension-compression. In this study, the Yoshida-Uemori (combined isotropic and kinematic hardening model is used to determine the performance of the springback prediction. The model parameters are determined by the optimization processes of the cyclic test by finite element simulations. However, in the study besides the cyclic tests, the model parameters are also evaluated by the optimization process of both cyclic and V-die bending simulations. The springback angle predictions with the model parameters obtained by the optimization of both cyclic and V-die bending simulations are found to mimic the experimental results in a better way than those obtained from only cyclic tests. However, the cyclic simulation results are found to be close enough to the experimental results.

Multi-Response Optimization of WEDM Process Parameters Using Taguchi Based Desirability Function Analysis

Science.gov (United States)

Majumder, Himadri; Maity, Kalipada

2018-03-01

Shape memory alloy has a unique capability to return to its original shape after physical deformation by applying heat or thermo-mechanical or magnetic load. In this experimental investigation, desirability function analysis (DFA), a multi-attribute decision making was utilized to find out the optimum input parameter setting during wire electrical discharge machining (WEDM) of Ni-Ti shape memory alloy. Four critical machining parameters, namely pulse on time (TON), pulse off time (TOFF), wire feed (WF) and wire tension (WT) were taken as machining inputs for the experiments to optimize three interconnected responses like cutting speed, kerf width, and surface roughness. Input parameter combination TON = 120 μs., TOFF = 55 μs., WF = 3 m/min. and WT = 8 kg-F were found to produce the optimum results. The optimum process parameters for each desired response were also attained using Taguchi’s signal-to-noise ratio. Confirmation test has been done to validate the optimum machining parameter combination which affirmed DFA was a competent approach to select optimum input parameters for the ideal response quality for WEDM of Ni-Ti shape memory alloy.

[Temporal and spatial heterogeneity analysis of optimal value of sensitive parameters in ecological process model: The BIOME-BGC model as an example.

Science.gov (United States)

Li, Yi Zhe; Zhang, Ting Long; Liu, Qiu Yu; Li, Ying

2018-01-01

The ecological process models are powerful tools for studying terrestrial ecosystem water and carbon cycle at present. However, there are many parameters for these models, and weather the reasonable values of these parameters were taken, have important impact on the models simulation results. In the past, the sensitivity and the optimization of model parameters were analyzed and discussed in many researches. But the temporal and spatial heterogeneity of the optimal parameters is less concerned. In this paper, the BIOME-BGC model was used as an example. In the evergreen broad-leaved forest, deciduous broad-leaved forest and C3 grassland, the sensitive parameters of the model were selected by constructing the sensitivity judgment index with two experimental sites selected under each vegetation type. The objective function was constructed by using the simulated annealing algorithm combined with the flux data to obtain the monthly optimal values of the sensitive parameters at each site. Then we constructed the temporal heterogeneity judgment index, the spatial heterogeneity judgment index and the temporal and spatial heterogeneity judgment index to quantitatively analyze the temporal and spatial heterogeneity of the optimal values of the model sensitive parameters. The results showed that the sensitivity of BIOME-BGC model parameters was different under different vegetation types, but the selected sensitive parameters were mostly consistent. The optimal values of the sensitive parameters of BIOME-BGC model mostly presented time-space heterogeneity to different degrees which varied with vegetation types. The sensitive parameters related to vegetation physiology and ecology had relatively little temporal and spatial heterogeneity while those related to environment and phenology had generally larger temporal and spatial heterogeneity. In addition, the temporal heterogeneity of the optimal values of the model sensitive parameters showed a significant linear correlation

Optimization of process parameters in flash pyrolysis of waste tyres to liquid and gaseous fuel in a fluidized bed reactor

International Nuclear Information System (INIS)

Edwin Raj, R.; Robert Kennedy, Z.; Pillai, B.C.

2013-01-01

Highlights: ► Non-recyclable, hazards, under-utilized waste tyre was converted to useful fuel. ► Design of experiment was used to optimize the process parameters. ► Fuel compatibility for IC engines was tested by standard fuel testing procedures. ► Optimized process parameters were tested and the empirical model validated. - Abstract: Pyrolysis process offers solution to utilize huge quantity of worn out automobile tyres to produce fuel for energy needs. Shredded tyre wastes were subjected to pyrolysis at atmospheric pressure under inert gas atmosphere in a fluidized bed combustion setup. The shredded tyre particle size, the feed rate of the feed stock, and the pyrolysis temperature were varied systematically as per the designed experiment to study their influence on product yield. Maximizing the oil yield and subduing the gas and char yield is the objective to optimize the process parameters. A low pyrolysis temperature of 440 °C with low feed rate increases the residence time in the combustion reactor yielding maximum oil. The physical properties of raw pyrolysis oil, distilled oil and the evolved gases were done to find its suitability to utilize them as alternatives to the conventional fuels

OPTIMIZATION OF PROCESS PARAMETERS FOR ENHANCED MECHANICAL PROPERTIES OF POLYPROPYLENE TERNARY NANOCOMPOSITES

Directory of Open Access Journals (Sweden)

Oladipupo Olaosebikan Ogunleye

2015-02-01

Full Text Available Preparation of Polypropylene ternary nanocomposites (PPTN was accomplished by blending multiwall carbon nanotube (MWCNT in polypropylene/clay binary system using a melt intercalation method. The effects of MWCNT loadings (A, melting temperature (B and mixing speed (C were investigated and optimized using central composite design. The analysis of the fitted cubic model clearly indicated that A and B were the main factors influencing the tensile properties at a fixed value of C. However, the analysis of variance showed that the interactions between the process parameters, such as; AB, AC, AB2, A2B and ABC, were highly significant on both tensile strength and Young’s modulus enhancement, while no interaction is significant in all models considered for elongation. The established optimal conditions gave 0.17%, 165 °C, and 120 rpm for A, B and C, respectively. These conditions yielded a percentage increase of 57 and 63% for tensile strength and Young’s modulus respectively compared to the virgin Polypropylene used.

Optimization of electrospinning parameters for chitosan nanofibres

CSIR Research Space (South Africa)

Jacobs, V

2011-06-01

Full Text Available Electrospinning of chitosan, a naturally occurring polysaccharide biopolymer, has been investigated. In this paper, the authors report the optimization of electrospinning process and solution parameters using factorial design approach to obtain...

Process Parameter Optimization of Extrusion-Based 3D Metal Printing Utilizing PW-LDPE-SA Binder System.

Science.gov (United States)

Ren, Luquan; Zhou, Xueli; Song, Zhengyi; Zhao, Che; Liu, Qingping; Xue, Jingze; Li, Xiujuan

2017-03-16

Recently, with a broadening range of available materials and alteration of feeding processes, several extrusion-based 3D printing processes for metal materials have been developed. An emerging process is applicable for the fabrication of metal parts into electronics and composites. In this paper, some critical parameters of extrusion-based 3D printing processes were optimized by a series of experiments with a melting extrusion printer. The raw materials were copper powder and a thermoplastic organic binder system and the system included paraffin wax, low density polyethylene, and stearic acid (PW-LDPE-SA). The homogeneity and rheological behaviour of the raw materials, the strength of the green samples, and the hardness of the sintered samples were investigated. Moreover, the printing and sintering parameters were optimized with an orthogonal design method. The influence factors in regard to the ultimate tensile strength of the green samples can be described as follows: infill degree > raster angle > layer thickness. As for the sintering process, the major factor on hardness is sintering temperature, followed by holding time and heating rate. The highest hardness of the sintered samples was very close to the average hardness of commercially pure copper material. Generally, the extrusion-based printing process for producing metal materials is a promising strategy because it has some advantages over traditional approaches for cost, efficiency, and simplicity.

Process Parameter Optimization of Extrusion-Based 3D Metal Printing Utilizing PW–LDPE–SA Binder System

Directory of Open Access Journals (Sweden)

Luquan Ren

2017-03-01

Full Text Available Recently, with a broadening range of available materials and alteration of feeding processes, several extrusion-based 3D printing processes for metal materials have been developed. An emerging process is applicable for the fabrication of metal parts into electronics and composites. In this paper, some critical parameters of extrusion-based 3D printing processes were optimized by a series of experiments with a melting extrusion printer. The raw materials were copper powder and a thermoplastic organic binder system and the system included paraffin wax, low density polyethylene, and stearic acid (PW–LDPE–SA. The homogeneity and rheological behaviour of the raw materials, the strength of the green samples, and the hardness of the sintered samples were investigated. Moreover, the printing and sintering parameters were optimized with an orthogonal design method. The influence factors in regard to the ultimate tensile strength of the green samples can be described as follows: infill degree > raster angle > layer thickness. As for the sintering process, the major factor on hardness is sintering temperature, followed by holding time and heating rate. The highest hardness of the sintered samples was very close to the average hardness of commercially pure copper material. Generally, the extrusion-based printing process for producing metal materials is a promising strategy because it has some advantages over traditional approaches for cost, efficiency, and simplicity.

Process Parameter Optimization of Extrusion-Based 3D Metal Printing Utilizing PW–LDPE–SA Binder System

Science.gov (United States)

Ren, Luquan; Zhou, Xueli; Song, Zhengyi; Zhao, Che; Liu, Qingping; Xue, Jingze; Li, Xiujuan

2017-01-01

Recently, with a broadening range of available materials and alteration of feeding processes, several extrusion-based 3D printing processes for metal materials have been developed. An emerging process is applicable for the fabrication of metal parts into electronics and composites. In this paper, some critical parameters of extrusion-based 3D printing processes were optimized by a series of experiments with a melting extrusion printer. The raw materials were copper powder and a thermoplastic organic binder system and the system included paraffin wax, low density polyethylene, and stearic acid (PW–LDPE–SA). The homogeneity and rheological behaviour of the raw materials, the strength of the green samples, and the hardness of the sintered samples were investigated. Moreover, the printing and sintering parameters were optimized with an orthogonal design method. The influence factors in regard to the ultimate tensile strength of the green samples can be described as follows: infill degree > raster angle > layer thickness. As for the sintering process, the major factor on hardness is sintering temperature, followed by holding time and heating rate. The highest hardness of the sintered samples was very close to the average hardness of commercially pure copper material. Generally, the extrusion-based printing process for producing metal materials is a promising strategy because it has some advantages over traditional approaches for cost, efficiency, and simplicity. PMID:28772665

Optimization of machining parameters of turning operations based on multi performance criteria

Directory of Open Access Journals (Sweden)

N.K.Mandal

2013-01-01

Full Text Available The selection of optimum machining parameters plays a significant role to ensure quality of product, to reduce the manufacturing cost and to increase productivity in computer controlled manufacturing process. For many years, multi-objective optimization of turning based on inherent complexity of process is a competitive engineering issue. This study investigates multi-response optimization of turning process for an optimal parametric combination to yield the minimum power consumption, surface roughness and frequency of tool vibration using a combination of a Grey relational analysis (GRA. Confirmation test is conducted for the optimal machining parameters to validate the test result. Various turning parameters, such as spindle speed, feed and depth of cut are considered. Experiments are designed and conducted based on full factorial design of experiment.

An optimal generic model for multi-parameters and big data optimizing: a laboratory experimental study

Science.gov (United States)

Utama, D. N.; Ani, N.; Iqbal, M. M.

2018-03-01

Optimization is a process for finding parameter (parameters) that is (are) able to deliver an optimal value for an objective function. Seeking an optimal generic model for optimizing is a computer science study that has been being practically conducted by numerous researchers. Generic model is a model that can be technically operated to solve any varieties of optimization problem. By using an object-oriented method, the generic model for optimizing was constructed. Moreover, two types of optimization method, simulated-annealing and hill-climbing, were functioned in constructing the model and compared to find the most optimal one then. The result said that both methods gave the same result for a value of objective function and the hill-climbing based model consumed the shortest running time.

Parameter optimization for surface flux transport models

Science.gov (United States)

Whitbread, T.; Yeates, A. R.; Muñoz-Jaramillo, A.; Petrie, G. J. D.

2017-11-01

Accurate prediction of solar activity calls for precise calibration of solar cycle models. Consequently we aim to find optimal parameters for models which describe the physical processes on the solar surface, which in turn act as proxies for what occurs in the interior and provide source terms for coronal models. We use a genetic algorithm to optimize surface flux transport models using National Solar Observatory (NSO) magnetogram data for Solar Cycle 23. This is applied to both a 1D model that inserts new magnetic flux in the form of idealized bipolar magnetic regions, and also to a 2D model that assimilates specific shapes of real active regions. The genetic algorithm searches for parameter sets (meridional flow speed and profile, supergranular diffusivity, initial magnetic field, and radial decay time) that produce the best fit between observed and simulated butterfly diagrams, weighted by a latitude-dependent error structure which reflects uncertainty in observations. Due to the easily adaptable nature of the 2D model, the optimization process is repeated for Cycles 21, 22, and 24 in order to analyse cycle-to-cycle variation of the optimal solution. We find that the ranges and optimal solutions for the various regimes are in reasonable agreement with results from the literature, both theoretical and observational. The optimal meridional flow profiles for each regime are almost entirely within observational bounds determined by magnetic feature tracking, with the 2D model being able to accommodate the mean observed profile more successfully. Differences between models appear to be important in deciding values for the diffusive and decay terms. In like fashion, differences in the behaviours of different solar cycles lead to contrasts in parameters defining the meridional flow and initial field strength.

Process parameter optimization during EDM of AISI 316 LN stainless steel by using fuzzy based multi-objective PSO

Energy Technology Data Exchange (ETDEWEB)

Majumder, Arindam [National Institute of Technology Agartala, Tripura (India)

2013-07-15

The present contribution describes an application of a hybrid approach using fuzzy logic and particle swarm optimization (PSO) for optimizing the process parameters in the electric discharge machining (EDM) of AISI 316LN Stainless Steel. In this study, each experimentation was performed under different machining conditions of pulse current, pulse on-time, and pulse off-time. Machining performances such as MRR and EWR were evaluated. A Taguchi L9 orthogonal array was produced to plan the experimentation and the regression method was applied to model the relationship between the input factors and responses. A fuzzy model was employed to provide a fitness function to PSO by unifying the multiple responses. Finally, PSO was used to predict the optimal process parametric settings for the multi-performance optimization of the EDM operation. The experimental results confirm the feasibility of the strategy and are in good agreement with the predicted results over a wide range of machining conditions employed in the process.

Honing process optimization algorithms

Science.gov (United States)

Kadyrov, Ramil R.; Charikov, Pavel N.; Pryanichnikova, Valeria V.

2018-03-01

This article considers the relevance of honing processes for creating high-quality mechanical engineering products. The features of the honing process are revealed and such important concepts as the task for optimization of honing operations, the optimal structure of the honing working cycles, stepped and stepless honing cycles, simulation of processing and its purpose are emphasized. It is noted that the reliability of the mathematical model determines the quality parameters of the honing process control. An algorithm for continuous control of the honing process is proposed. The process model reliably describes the machining of a workpiece in a sufficiently wide area and can be used to operate the CNC machine CC743.

Experimental Verification of Statistically Optimized Parameters for Low-Pressure Cold Spray Coating of Titanium

Directory of Open Access Journals (Sweden)

Damilola Isaac Adebiyi

2016-06-01

Full Text Available The cold spray coating process involves many process parameters which make the process very complex, and highly dependent and sensitive to small changes in these parameters. This results in a small operational window of the parameters. Consequently, mathematical optimization of the process parameters is key, not only to achieving deposition but also improving the coating quality. This study focuses on the mathematical identification and experimental justification of the optimum process parameters for cold spray coating of titanium alloy with silicon carbide (SiC. The continuity, momentum and the energy equations governing the flow through the low-pressure cold spray nozzle were solved by introducing a constitutive equation to close the system. This was used to calculate the critical velocity for the deposition of SiC. In order to determine the input temperature that yields the calculated velocity, the distribution of velocity, temperature, and pressure in the cold spray nozzle were analyzed, and the exit values were predicted using the meshing tool of Solidworks. Coatings fabricated using the optimized parameters and some non-optimized parameters are compared. The coating of the CFD-optimized parameters yielded lower porosity and higher hardness.

Optimization of surface roughness parameters in dry turning

OpenAIRE

R.A. Mahdavinejad; H. Sharifi Bidgoli

2009-01-01

Purpose: The precision of machine tools on one hand and the input setup parameters on the other hand, are strongly influenced in main output machining parameters such as stock removal, toll wear ratio and surface roughnes.Design/methodology/approach: There are a lot of input parameters which are effective in the variations of these output parameters. In CNC machines, the optimization of machining process in order to predict surface roughness is very important.Findings: From this point of view...

Optimization of the Hot Forging Processing Parameters for Powder Metallurgy Fe-Cu-C Connecting Rods Based on Finite Element Simulation

Science.gov (United States)

Li, Fengxian; Yi, Jianhong; Eckert, Jürgen

2017-12-01

Powder forged connecting rods have the problem of non-uniform density distributions because of their complex geometric shape. The densification behaviors of powder metallurgy (PM) connecting rod preforms during hot forging processes play a significant role in optimizing the connecting rod quality. The deformation behaviors of a connecting rod preform, a Fe-3Cu-0.5C (wt pct) alloy compacted and sintered by the powder metallurgy route (PM Fe-Cu-C), were investigated using the finite element method, while damage and friction behaviors of the material were considered in the complicated forging process. The calculated results agree well with the experimental results. The relationship between the processing parameters of hot forging and the relative density of the connecting rod was revealed. The results showed that the relative density of the hot forged connecting rod at the central shank changed significantly compared with the relative density at the big end and at the small end. Moreover, the relative density of the connecting rod was sensitive to the processing parameters such as the forging velocity and the initial density of the preform. The optimum forging processing parameters were determined and presented by using an orthogonal design method. This work suggests that the processing parameters can be optimized to prepare a connecting rod with uniform density distribution and can help to better meet the requirements of the connecting rod industry.

Alanine-EPR dosimetry in 10 MeV electron beam to optimize process parameters for food irradiation

International Nuclear Information System (INIS)

Sanyal, B.; Kumar, S.; Kumar, M.; Mittal, K.C.; Sharma, A.

2011-01-01

Absorbed dose in a food product is determined and controlled by several components of the LINAC irradiation facility as well as the product. Standardization of the parameters characterizing the facility components, process load and the irradiation conditions collectively termed as 'process parameters' are of paramount importance for successful dose delivery to the food products. In the present study alanine-EPR dosimetry system was employed to optimize the process parameters of 10 MeV electron beam of a LINAC facility for commercial irradiation of food. Three sets of experiments were carried out with different food commodities namely, mango, potato and rawa with the available product conveying system of different irradiation geometry like one sided or both sided mode of irradiation. Three dimensional dose distributions into the process load for low dose requiring food commodities (0.25 to 1 kGy) were measured in each experiment. The actual depth dose profile in food product and useful scan width of the electron beam were found out to be satisfactory for commercial radiation processing of food. Finally a scaled up experiment with commercial food product (packets of Rawa) exhibited adequate dose uniformity ratio of 3 proving the feasibility of the facility for large scale radiation processing of food commodities. (author)

Estimation of fundamental kinetic parameters of polyhydroxybutyrate fermentation process of Azohydromonas australica using statistical approach of media optimization.

Science.gov (United States)

Gahlawat, Geeta; Srivastava, Ashok K

2012-11-01

Polyhydroxybutyrate or PHB is a biodegradable and biocompatible thermoplastic with many interesting applications in medicine, food packaging, and tissue engineering materials. The present study deals with the enhanced production of PHB by Azohydromonas australica using sucrose and the estimation of fundamental kinetic parameters of PHB fermentation process. The preliminary culture growth inhibition studies were followed by statistical optimization of medium recipe using response surface methodology to increase the PHB production. Later on batch cultivation in a 7-L bioreactor was attempted using optimum concentration of medium components (process variables) obtained from statistical design to identify the batch growth and product kinetics parameters of PHB fermentation. A. australica exhibited a maximum biomass and PHB concentration of 8.71 and 6.24 g/L, respectively in bioreactor with an overall PHB production rate of 0.75 g/h. Bioreactor cultivation studies demonstrated that the specific biomass and PHB yield on sucrose was 0.37 and 0.29 g/g, respectively. The kinetic parameters obtained in the present investigation would be used in the development of a batch kinetic mathematical model for PHB production which will serve as launching pad for further process optimization studies, e.g., design of several bioreactor cultivation strategies to further enhance the biopolymer production.

Optimization of Pre-Treatment Process Parameters to Generate Biodiesel from Microalga

Directory of Open Access Journals (Sweden)

Chukwuma Onumaegbu

2018-03-01

Full Text Available Cell disruption is an integral part of microalga production process, which improves the release of intracellular products that are essential for biofuel production. In this work, pre-treatment parameters that will enhance the efficiency of lipid production using high-pressure homogenizer on microalgae biomass will be investigated. The high-pressure homogenizer that is considered is a GYB40-10S/GY60-6S; with a pre-treatment pressure of 1000 psi, 2000 psi, and 3000 psi, the number of passes; 1, 2, and 3, a reaction time of 3, 3.5, and 4 h. Pressure and cavitation increase the efficiency of the pre-treatment process of the homogenizer. In addition, homogenization shear force and pressure are the basic significant factors that enhance the efficiency of microalgae cell rupture. Also, the use of modelling to simulate pre-treatment processes (Response Surface Methodology (RSM, Box-Behnken Designs (BBD, and design of experiment (DOE for process optimization will be adopted in this study. The results clearly demonstrate that high-pressure homogenization pre-treatment can effectively disrupt microalga cell walls to enhance lipid recovery efficiency, with a relatively short extraction time, both that are essential for maintaining a good quality of lipids for biofuel production. A maximum of 18% lipid yields were obtained after 3 h of HPH pre-treatment at 3000 psi.

Investigation and validation of optimal cutting parameters for least ...

African Journals Online (AJOL)

The cutting parameters were analyzed and optimized using Box Behnken procedure in the DESIGN EXPERT environment. The effect of process parameters with the output variable were predicted which indicates that the highest cutting speed has significant role in producing least surface roughness followed by feed and ...

Towards automatic parameter tuning of stream processing systems

KAUST Repository

Bilal, Muhammad

2017-09-27

Optimizing the performance of big-data streaming applications has become a daunting and time-consuming task: parameters may be tuned from a space of hundreds or even thousands of possible configurations. In this paper, we present a framework for automating parameter tuning for stream-processing systems. Our framework supports standard black-box optimization algorithms as well as a novel gray-box optimization algorithm. We demonstrate the multiple benefits of automated parameter tuning in optimizing three benchmark applications in Apache Storm. Our results show that a hill-climbing algorithm that uses a new heuristic sampling approach based on Latin Hypercube provides the best results. Our gray-box algorithm provides comparable results while being two to five times faster.

Optimization of Process Parameters of Pulsed Electro Deposition Technique for Nanocrystalline Nickel Coating Using Gray Relational Analysis (GRA)

Science.gov (United States)

Venkatesh, C.; Sundara Moorthy, N.; Venkatesan, R.; Aswinprasad, V.

The moving parts of any mechanism and machine parts are always subjected to a significant wear due to the development of friction. It is an utmost important aspect to address the wear problems in present environment. But the complexity goes on increasing to replace the worn out parts if they are very precise. Technology advancement in surface engineering ensures the minimum surface wear with the introduction of polycrystalline nano nickel coating. The enhanced tribological property of the nano nickel coating was achieved by the development of grain size and hardness of the surface. In this study, it has been decided to focus on the optimized parameters of the pulsed electro deposition to develop such a coating. Taguchi’s method coupled gray relational analysis was employed by considering the pulse frequency, average current density and duty cycle as the chief process parameters. The grain size and hardness were considered as responses. Totally, nine experiments were conducted as per L9 design of experiment. Additionally, response graph method has been applied to determine the most significant parameter to influence both the responses. In order to improve the degree of validation, confirmation test and predicted gray grade were carried out with the optimized parameters. It has been observed that there was significant improvement in gray grade for the optimal parameters.

Improved Artificial Fish Algorithm for Parameters Optimization of PID Neural Network

OpenAIRE

Jing Wang; Yourui Huang

2013-01-01

In order to solve problems such as initial weights are difficult to be determined, training results are easy to trap in local minima in optimization process of PID neural network parameters by traditional BP algorithm, this paper proposed a new method based on improved artificial fish algorithm for parameters optimization of PID neural network. This improved artificial fish algorithm uses a composite adaptive artificial fish algorithm based on optimal artificial fish and nearest artificial fi...

Meltlets(®) of soy isoflavones: process optimization and the effect of extrusion spheronization process parameters on antioxidant activity.

Science.gov (United States)

Deshmukh, Ketkee; Amin, Purnima

2013-07-01

In the current research work an attempt was made to develop "Melt in mouth pellets" (Meltlets(®)) containing 40% herbal extract of soy isoflavones that served to provide antioxidants activity in menopausal women. The process of extrusion-spheronization was optimized for extruder speed, extruder screen size, spheronization speed, and time. While doing so the herbal extract incorporated in the pellet matrix was subjected to various processing conditions such as the effect of the presence of other excipients, mixing or kneading to prepare wet mass, heat generated during the process of extrusion, spheronization, and drying. Thus, the work further investigates the effect of these processing parameters on the antioxidant activity of the soy isoflavone herbal extract incorporated in the formula. Thereby, the antioxidant activity of the soya bean herbal extract, Meltlets(®) and of the placebo pellets was evaluated using DPPH free radical scavenging assay and total reduction capacity.

Optimization of Surface Roughness Parameters of Al-6351 Alloy in EDC Process: A Taguchi Coupled Fuzzy Logic Approach

Science.gov (United States)

Kar, Siddhartha; Chakraborty, Sujoy; Dey, Vidyut; Ghosh, Subrata Kumar

2017-10-01

This paper investigates the application of Taguchi method with fuzzy logic for multi objective optimization of roughness parameters in electro discharge coating process of Al-6351 alloy with powder metallurgical compacted SiC/Cu tool. A Taguchi L16 orthogonal array was employed to investigate the roughness parameters by varying tool parameters like composition and compaction load and electro discharge machining parameters like pulse-on time and peak current. Crucial roughness parameters like Centre line average roughness, Average maximum height of the profile and Mean spacing of local peaks of the profile were measured on the coated specimen. The signal to noise ratios were fuzzified to optimize the roughness parameters through a single comprehensive output measure (COM). Best COM obtained with lower values of compaction load, pulse-on time and current and 30:70 (SiC:Cu) composition of tool. Analysis of variance is carried out and a significant COM model is observed with peak current yielding highest contribution followed by pulse-on time, compaction load and composition. The deposited layer is characterised by X-Ray Diffraction analysis which confirmed the presence of tool materials on the work piece surface.

Multi-response optimization of process parameters for TIG welding of Incoloy 800HT by Taguchi grey relational analysis

Directory of Open Access Journals (Sweden)

Arun Kumar Srirangan

2016-06-01

Full Text Available Incoloy 800HT which was selected as one of the prominent material for fourth generation power plant can exhibit appreciable strength, good resistance to corrosion and oxidation in high temperature environment. This study focuses on the multi-objective optimization using grey relational analysis for Incoloy 800HT welded with tungsten inert arc welding process with N82 filler wire of diameter 1.2 mm. The welding input parameters play a vital role in determining desired weld quality. The experiments were conducted according to L9 orthogonal array. The input parameter chosen were the welding current, Voltage and welding speed. The output response for quality targets chosen were the ultimate tensile strength and yield strength (at room temperature, 750 °C and impact toughness. Grey relational analysis was applied to optimize the input parameters simultaneously considering multiple output variables. The optimal parameters combination was determined as A2B1C2 i.e. welding current at 110 A, voltage at 10 V and welding speed at 1.5 mm/s. ANOVA method was used to assess the significance of factors on the overall quality of the weldment. The output of the mechanical properties for best and least grey relational grade was validated by the metallurgical characteristics:

A Comparative Analysis of Taguchi Methodology and Shainin System DoE in the Optimization of Injection Molding Process Parameters

Science.gov (United States)

Khavekar, Rajendra; Vasudevan, Hari, Dr.; Modi, Bhavik

2017-08-01

Two well-known Design of Experiments (DoE) methodologies, such as Taguchi Methods (TM) and Shainin Systems (SS) are compared and analyzed in this study through their implementation in a plastic injection molding unit. Experiments were performed at a perfume bottle cap manufacturing company (made by acrylic material) using TM and SS to find out the root cause of defects and to optimize the process parameters for minimum rejection. Experiments obtained the rejection rate to be 8.57% from 40% (appx.) during trial runs, which is quiet low, representing successful implementation of these DoE methods. The comparison showed that both methodologies gave same set of variables as critical for defect reduction, but with change in their significance order. Also, Taguchi methods require more number of experiments and consume more time compared to the Shainin System. Shainin system is less complicated and is easy to implement, whereas Taguchi methods is statistically more reliable for optimization of process parameters. Finally, experimentations implied that DoE methods are strong and reliable in implementation, as organizations attempt to improve the quality through optimization.

Application of an Evolutionary Algorithm for Parameter Optimization in a Gully Erosion Model

Energy Technology Data Exchange (ETDEWEB)

Rengers, Francis; Lunacek, Monte; Tucker, Gregory

2016-06-01

Herein we demonstrate how to use model optimization to determine a set of best-fit parameters for a landform model simulating gully incision and headcut retreat. To achieve this result we employed the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), an iterative process in which samples are created based on a distribution of parameter values that evolve over time to better fit an objective function. CMA-ES efficiently finds optimal parameters, even with high-dimensional objective functions that are non-convex, multimodal, and non-separable. We ran model instances in parallel on a high-performance cluster, and from hundreds of model runs we obtained the best parameter choices. This method is far superior to brute-force search algorithms, and has great potential for many applications in earth science modeling. We found that parameters representing boundary conditions tended to converge toward an optimal single value, whereas parameters controlling geomorphic processes are defined by a range of optimal values.

Optimization of process parameters and catalyst compositions in carbon dioxide oxidative coupling of methane over CaO-MnO/CeO{sub 2} catalyst using response surface methodology

Energy Technology Data Exchange (ETDEWEB)

Istadi,; Amin, Nor Aishah Saidina [Chemical Reaction Engineering Group (CREG), Faculty of Chemical and Natural Resources Engineering, Universiti Teknologi Malaysia, UTM Skudai, Johor Bahru (81310 Malaysia)

2006-05-15

The optimization of process parameters and catalyst compositions for the CO{sub 2} oxidative coupling of methane (CO{sub 2}-OCM) reaction over CaO-MnO/CeO{sub 2} catalyst was developed using Response Surface Methodology (RSM). The relationship between the responses, i.e. CH{sub 4} conversion, C{sub 2} hydrocarbons selectivity or yield, with four independent variables, i.e. CO{sub 2}/CH{sub 4} ratio, reactor temperature, wt.% CaO and wt.% MnO in the catalyst, were presented as empirical mathematical models. The maximum C{sub 2} hydrocarbons selectivity and yields of 82.62% and 3.93%, respectively, were achieved by the individual-response optimization at the corresponding optimal process parameters and catalyst compositions. However, the CH{sub 4} conversion was a saddle function and did not show a unique optimum as revealed by the canonical analysis. Moreover pertaining to simultaneous multi-responses optimization, the maximum C{sub 2} selectivity and yield of 76.56% and 3.74%, respectively, were obtained at a unique optimal process parameters and catalyst compositions. It may be deduced that both individual- and multi-responses optimizations are useful for the recommendation of optimal process parameters and catalyst compositions for the CO{sub 2}-OCM process. (author)

Machinability study of Carbon Fiber Reinforced Polymer in the longitudinal and transverse direction and optimization of process parameters using PSO–GSA

Directory of Open Access Journals (Sweden)

K. Shunmugesh

2016-09-01

Full Text Available Carbon Fiber Reinforced Polymer (CFRP composites are widely used in aerospace industry in lieu of its high strength to weight ratio. This study is an attempt to evaluate the machinability of Bi-Directional Carbon Fiber–Epoxy composite and optimize the process parameters of cutting speed, feed rate and drill tool material. Machining trials were carried using drill bits made of high speed steel, TiN and TiAlN at different cutting speeds and feed rates. Output parameters of thrust force and torque were monitored using Kistler multicomponent dynamometer 9257B and vibrations occurring during machining normal to the work surface were measured by a vibration sensor (Dytran 3055B. Linear regression analysis was carried out by using Response Surface Methodology (RSM, to correlate the input and output parameters in drilling of the composite in the longitudinal and transverse directions. The optimization of process parameters were attempted using Genetic Algorithm (GA and Particle Swarm Optimization–Gravitational Search Algorithm (PSO–GSA techniques.

Ring rolling process simulation for geometry optimization

Science.gov (United States)

Franchi, Rodolfo; Del Prete, Antonio; Donatiello, Iolanda; Calabrese, Maurizio

2017-10-01

Ring Rolling is a complex hot forming process where different rolls are involved in the production of seamless rings. Since each roll must be independently controlled, different speed laws must be set; usually, in the industrial environment, a milling curve is introduced to monitor the shape of the workpiece during the deformation in order to ensure the correct ring production. In the present paper a ring rolling process has been studied and optimized in order to obtain anular components to be used in aerospace applications. In particular, the influence of process input parameters (feed rate of the mandrel and angular speed of main roll) on geometrical features of the final ring has been evaluated. For this purpose, a three-dimensional finite element model for HRR (Hot Ring Rolling) has been implemented in SFTC DEFORM V11. The FEM model has been used to formulate a proper optimization problem. The optimization procedure has been implemented in the commercial software DS ISight in order to find the combination of process parameters which allows to minimize the percentage error of each obtained dimension with respect to its nominal value. The software allows to find the relationship between input and output parameters applying Response Surface Methodology (RSM), by using the exact values of output parameters in the control points of the design space explored through FEM simulation. Once this relationship is known, the values of the output parameters can be calculated for each combination of the input parameters. After the calculation of the response surfaces for the selected output parameters, an optimization procedure based on Genetic Algorithms has been applied. At the end, the error between each obtained dimension and its nominal value has been minimized. The constraints imposed were the maximum values of standard deviations of the dimensions obtained for the final ring.

Optimization of Process Parameters During Drilling of Glass-Fiber Polyester Reinforced Composites Using DOE and ANOVA

Directory of Open Access Journals (Sweden)

N.S. Mohan

2010-09-01

Full Text Available Polymer-based composite material possesses superior properties such as high strength-to-weight ratio, stiffness-to-weight ratio and good corrosive resistance and therefore, is attractive for high performance applications such as in aerospace, defense and sport goods industries. Drilling is one of the indispensable methods for building products with composite panels. Surface quality and dimensional accuracy play an important role in the performance of a machined component. In machining processes, however, the quality of the component is greatly influenced by the cutting conditions, tool geometry, tool material, machining process, chip formation, work piece material, tool wear and vibration during cutting. Drilling tests were conducted on glass fiber reinforced plastic composite [GFRP] laminates using an instrumented CNC milling center. A series of experiments are conducted using TRIAC VMC CNC machining center to correlate the cutting parameters and material parameters on the cutting thrust, torque and surface roughness. The measured results were collected and analyzed with the help of the commercial software packages MINITAB14 and Taly Profile. The surface roughness of the drilled holes was measured using Rank Taylor Hobson Surtronic 3+ instrument. The method could be useful in predicting thrust, torque and surface roughness parameters as a function of process variables. The main objective is to optimize the process parameters to achieve low cutting thrust, torque and good surface roughness. From the analysis it is evident that among all the significant parameters, speed and drill size have significant influence cutting thrust and drill size and specimen thickness on the torque and surface roughness. It was also found that feed rate does not have significant influence on the characteristic output of the drilling process.

Parameter optimization of electrolytic process of obtaining sodium hypochlorite for disinfection of water

Science.gov (United States)

Bogoslovskii, S. Yu; Kuznetsov, N. N.; Boldyrev, V. S.

2017-11-01

Electrochlorination parameters were optimized in flowing and non-flowing modes for a cell with a volume of 1 l. At a current density of 0.1 A/cm2 in the range of flow rates from 0.8 to 6.0 l/h with a temperature of the initial solution below 20°C the outlet temperature is maintained close to the optimal 40°C. The pH of the solution during electrolysis increases to 8.8 ÷ 9.4. There was studied a process in which a solution with a temperature of 7-8°C and a concentration of sodium chloride of 25 and 35 g/l in non-flowing cell was used. The dependence of the concentration of active chlorine on the electrolysis time varies with the concentration of the initial solution of sodium chloride. In case of chloride concentration of 25 g/l virtually linear relationship makes it easy to choose the time of electrolysis with the aim of obtaining the needed concentration of the product.

Multi-objective optimization of swash plate forging process parameters for the die wear/service life improvement

Science.gov (United States)

Hu, X. F.; Wang, L. G.; Wu, H.; Liu, S. S.

2017-12-01

For the forging process of the swash plate, the author designed a kind of multi-index orthogonal experiment. Based on the Archard wear model, the influences of billet temperature, die temperature, forming speed, top die hardness and friction coefficient on forming load and die wear were numerically simulated by DEFORM software. Through the analysis of experimental results, the best forging process parameters were optimized and determined, which could effectively reduce the die wear and prolong the die service life. It is significant to increase the practical production of enterprise, especially to reduce the production cost and to promote enterprise profit.

Optimization of pulsed TIG welding process parameters on mechanical properties of AA 5456 Aluminum alloy weldments

Energy Technology Data Exchange (ETDEWEB)

Kumar, A. [Department of Mechanical Engineering, National Institute of Technology, Warangal 506 004 (India)], E-mail: adepu_kumar7@yahoo.co.in; Sundarrajan, S. [Scientist ' G' , Defence Research and Development Laboratory, Hyderabad 500 028 (India)

2009-04-15

The present work pertains to the improvement of mechanical properties of AA 5456 Aluminum alloy welds through pulsed tungsten inert gas (TIG) welding process. Taguchi method was employed to optimize the pulsed TIG welding process parameters of AA 5456 Aluminum alloy welds for increasing the mechanical properties. Regression models were developed. Analysis of variance was employed to check the adequacy of the developed models. The effect of planishing on mechanical properties was also studied and observed that there was improvement in mechanical properties. Microstructures of all the welds were studied and correlated with the mechanical properties.

Optimization of pulsed TIG welding process parameters on mechanical properties of AA 5456 Aluminum alloy weldments

International Nuclear Information System (INIS)

Kumar, A.; Sundarrajan, S.

2009-01-01

The present work pertains to the improvement of mechanical properties of AA 5456 Aluminum alloy welds through pulsed tungsten inert gas (TIG) welding process. Taguchi method was employed to optimize the pulsed TIG welding process parameters of AA 5456 Aluminum alloy welds for increasing the mechanical properties. Regression models were developed. Analysis of variance was employed to check the adequacy of the developed models. The effect of planishing on mechanical properties was also studied and observed that there was improvement in mechanical properties. Microstructures of all the welds were studied and correlated with the mechanical properties

Development of mathematical models and optimization of the process parameters of laser surface hardened EN25 steel using elitist non-dominated sorting genetic algorithm

Science.gov (United States)

Vignesh, S.; Dinesh Babu, P.; Surya, G.; Dinesh, S.; Marimuthu, P.

2018-02-01

The ultimate goal of all production entities is to select the process parameters that would be of maximum strength, minimum wear and friction. The friction and wear are serious problems in most of the industries which are influenced by the working set of parameters, oxidation characteristics and mechanism involved in formation of wear. The experimental input parameters such as sliding distance, applied load, and temperature are utilized in finding out the optimized solution for achieving the desired output responses such as coefficient of friction, wear rate, and volume loss. The optimization is performed with the help of a novel method, Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) based on an evolutionary algorithm. The regression equations obtained using Response Surface Methodology (RSM) are used in determining the optimum process parameters. Further, the results achieved through desirability approach in RSM are compared with that of the optimized solution obtained through NSGA-II. The results conclude that proposed evolutionary technique is much effective and faster than the desirability approach.

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

Directory of Open Access Journals (Sweden)

Adel Taieb

2017-01-01

Full Text Available This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO Fuzzy Optimal Model Predictive Control (FOMPC using the Adaptive Particle Swarm Optimization (APSO algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR and Tank system, where the proposed approach provides better performances compared with other methods.

Experimental investigation and optimization of welding process parameters for various steel grades using NN tool and Taguchi method

Science.gov (United States)

Soni, Sourabh Kumar; Thomas, Benedict

2018-04-01

The term "weldability" has been used to describe a wide variety of characteristics when a material is subjected to welding. In our analysis we perform experimental investigation to estimate the tensile strength of welded joint strength and then optimization of welding process parameters by using taguchi method and Artificial Neural Network (ANN) tool in MINITAB and MATLAB software respectively. The study reveals the influence on weldability of steel by varying composition of steel by mechanical characterization. At first we prepare the samples of different grades of steel (EN8, EN 19, EN 24). The samples were welded together by metal inert gas welding process and then tensile testing on Universal testing machine (UTM) was conducted for the same to evaluate the tensile strength of the welded steel specimens. Further comparative study was performed to find the effects of welding parameter on quality of weld strength by employing Taguchi method and Neural Network tool. Finally we concluded that taguchi method and Neural Network Tool is much efficient technique for optimization.

Multi-Response Optimization and Regression Analysis of Process Parameters for Wire-EDMed HCHCr Steel Using Taguchi’s Technique

Directory of Open Access Journals (Sweden)

K. Srujay Varma

2017-04-01

Full Text Available In this study, effect of machining process parameters viz. pulse-on time, pulse-off time, current and servo-voltage for machining High Carbon High Chromium Steel (HCHCr using copper electrode in wire EDM was investigated. High Carbon High Chromium Steel is a difficult to machine alloy, which has many applications in low temperature manufacturing, and copper is chosen as electrode as it has good electrical conductivity and most frequently used electrode all over the world. Tool making culture of copper has made many shops in Europe and Japan to used copper electrode. Experiments were conducted according to Taguchi’s technique by varying the machining process parameters at three levels. Taguchi’s method based on L9 orthogonal array was followed and number of experiments was limited to 9. Experimental cost and time consumption was reduced by following this statistical technique. Targeted output parameters are Material Removal Rate (MRR, Vickers Hardness (HV and Surface Roughness (SR. Analysis of Variance (ANOVA and Regression Analysis was performed using Minitab 17 software to optimize the parameters and draw relationship between input and output process parameters. Regression models were developed relating input and output parameters. It was observed that most influential factor for MRR, Hardness and SR are Ton, Toff and SV.

Sensitivity study and parameter optimization of OCD tool for 14nm finFET process

Science.gov (United States)

Zhang, Zhensheng; Chen, Huiping; Cheng, Shiqiu; Zhan, Yunkun; Huang, Kun; Shi, Yaoming; Xu, Yiping

2016-03-01

Optical critical dimension (OCD) measurement has been widely demonstrated as an essential metrology method for monitoring advanced IC process in the technology node of 90 nm and beyond. However, the rapidly shrunk critical dimensions of the semiconductor devices and the increasing complexity of the manufacturing process bring more challenges to OCD. The measurement precision of OCD technology highly relies on the optical hardware configuration, spectral types, and inherently interactions between the incidence of light and various materials with various topological structures, therefore sensitivity analysis and parameter optimization are very critical in the OCD applications. This paper presents a method for seeking the optimum sensitive measurement configuration to enhance the metrology precision and reduce the noise impact to the greatest extent. In this work, the sensitivity of different types of spectra with a series of hardware configurations of incidence angles and azimuth angles were investigated. The optimum hardware measurement configuration and spectrum parameter can be identified. The FinFET structures in the technology node of 14 nm were constructed to validate the algorithm. This method provides guidance to estimate the measurement precision before measuring actual device features and will be beneficial for OCD hardware configuration.

Application study of evolutionary operation methods in optimization of process parameters for mosquito coils industry

Science.gov (United States)

Ginting, E.; Tambunanand, M. M.; Syahputri, K.

2018-02-01

Evolutionary Operation Methods (EVOP) is a method that is designed used in the process of running or operating routinely in the company to enables high productivity. Quality is one of the critical factors for a company to win the competition. Because of these conditions, the research for products quality has been done by gathering the production data of the company and make a direct observation to the factory floor especially the drying department to identify the problem which is the high water content in the mosquito incense coil. PT.X which is producing mosquito coils attempted to reduce product defects caused by the inaccuracy of operating conditions. One of the parameters of good quality insect repellent that is water content, that if the moisture content is too high then the product easy to mold and broken, and vice versa if it is too low the products are easily broken and burn shorter hours. Three factors that affect the value of the optimal water content, the stirring time, drying temperature and drying time. To obtain the required conditions Evolutionary Operation (EVOP) methods is used. Evolutionary Operation (EVOP) is used as an efficient technique for optimization of two or three variable experimental parameters using two-level factorial designs with center point. Optimal operating conditions in the experiment are stirring time performed for 20 minutes, drying temperature at 65°C, and drying time for 130 minutes. The results of the analysis based on the method of Evolutionary Operation (EVOP) value is the optimum water content of 6.90%, which indicates the value has approached the optimal in a production plant that is 7%.

Optimization the machining parameters by using VIKOR and Entropy Weight method during EDM process of Al–18% SiCp Metal matrix composit

Directory of Open Access Journals (Sweden)

Rajesh Kumar Bhuyan

2016-06-01

Full Text Available The objective of this paper is to optimize the process parameters by combined approach of VIKOR and Entropy weight measurement method during Electrical discharge machining (EDM process of Al-18wt.%SiCp metal matrix composite (MMC. The central composite design (CCD method is considered to evaluate the effect of three process parameters; namely pulse on time (Ton, peak current (Ip and flushing pressure (Fp on the responses like material removal rate (MRR, tool wear rate (TWR, Radial over cut (ROC and surface roughness (Ra. The Entropy weight measurement method evaluates the individual weights of each response and, using VIKOR method, the multi-objective responses are optimized to get a single numerical index known as VIKOR Index. Then the Analysis of Variance (ANOVA technique is used to determine the significance of the process parameters on the VIKOR Index. Finally, the result of the VIKOR Indexed is validated by conformation test using the liner mathematical model equation develop by responses surface methodology to identify the effectiveness of the proposed method.

Process Parameters Optimization for Friction Stir Welding of Pure Aluminium to Brass (CuZn30 using Taguchi Technique

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Elfar O. M. R.

2016-01-01

Full Text Available In this research, the friction stir welding of dissimilar commercial pure aluminium and brass (CuZn30 plates was investigated and the process parameters were optimized using Taguchi L9 orthogonal array. The considered process parameters were the rotational speed, traverse speed and pin offset. The optimum setting was determined with reference to ultimate tensile strength of the joint. The predicted optimum value of ultimate tensile strength was confirmed by experimental run using optimum parameters. Analysis of variance revealed that traverse speed is the most significant factor in controlling the joint tensile strength and pin offset also plays a significant role. In this investigation, the optimum tensile strength is 50% of aluminium base metal. Metallographic examination revealed that intermetallic compounds were formed in the interface of the optimum joint where the tensile failure was observed to take place.

Optimization of turning process parameters by using grey-Taguchi

African Journals Online (AJOL)

DR OKE

... India continue to choose the operating conditions solely on the basis of handbook values .... Surface Roughness Measuring instrument ... process control parameters like spindle speed, feed and depth of cut. ..... and Industrial Engineering.

Solar degradation of diclofenac using Eosin-Y-activated TiO2: cost estimation, process optimization and parameter interaction study.

Science.gov (United States)

Hashim, Noshin; Thakur, Shaila; Patang, Mouska; Crapulli, Ferdinando; Ray, Ajay K

2017-04-01

Diclofenac (DCF), a widely used non-steroidal anti-inflammatory drug, is a commonly detected substance that readily accumulates in tissues of aquatic fish and poses a threat to wildlife and freshwater quality. Advanced Oxidation Processes have been employed as an alternative due to the inadequacy of conventional treatment methods of trace contaminants. This study utilized an innovative method of solar-activation of TiO 2 using Eosin-Y dye for the degradation of DCF. Furthermore, the study incorporated a central composite design (CCD) to optimize the dye concentration and estimated the cost for the present process. Optimized parameters for light intensity (750 mW/cm 2 ), Eosin-Y dye concentration (2 mg/L), TiO 2 loading (37.5 mg/cm 2 ) and DCF concentration (25 mg/L) were determined through a CCD. The optimized parameters convey a DCF degradation rate of 40% and 49% for 2 ppm (low range) and 4 ppm (high range) dye concentrations, respectively, for a 5-minute reaction time. Cost estimation for the materials used was for the current process was also performed. It was determined that the additional cost of using 4 ppm instead of 2 ppm to achieve only 10% more DCF degradation is not warranted and would require additional treatment to remove subsequently formed halogenated compounds.

Estimating cellular parameters through optimization procedures: elementary principles and applications

Directory of Open Access Journals (Sweden)

Akatsuki eKimura

2015-03-01

Full Text Available Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE in a prediction or to maximize likelihood. A (local maximum of likelihood or (local minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge of parameter optimization, cell and developmental biologists can develop realistic models that reproduce their observations and thus, obtain mechanistic insights into phenomena of interest.

Multi-response optimization of process parameters using Taguchi method and grey relational analysis during turning AA 7075/SiC composite in dry and spray cooling environments

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P. C. Mishra

2015-09-01

Full Text Available Turning experiments were carried out on AA 7075/SiC composite workpiece in dry and spray cooling environments based on L16 Taguchi design of experiments. Multiple performance optimization of process parameters was performed using grey relational analysis. The performance characteristics considered were average surface roughness, cutting tool temperature and material removal rate. Uncoated carbide inserts were used for machining the workpiece in a high speed precision lathe. A grey relational grade obtained from grey relational analysis was used to optimize the process parameters. Optimal combination of process parameters was then determined by the Taguchi method using the grey relational grade as the performance index. Experimental results indicated that the turning in spray cooling environment was beneficial compared to that in dry environment for the quality response characteristics under consideration. Analysis of variance showed that feed was the most significant parameter for the multiple performance characteristics during turning in both the environments.

Optimization of process parameters of the activated tungsten inert gas welding for aspect ratio of UNS S32205 duplex stainless steel welds

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G. Magudeeswaran

2014-09-01

Full Text Available The activated TIG (ATIG welding process mainly focuses on increasing the depth of penetration and the reduction in the width of weld bead has not been paid much attention. The shape of a weld in terms of its width-to-depth ratio known as aspect ratio has a marked influence on its solidification cracking tendency. The major influencing ATIG welding parameters, such as electrode gap, travel speed, current and voltage, that aid in controlling the aspect ratio of DSS joints, must be optimized to obtain desirable aspect ratio for DSS joints. Hence in this study, the above parameters of ATIG welding for aspect ratio of ASTM/UNS S32205 DSS welds are optimized by using Taguchi orthogonal array (OA experimental design and other statistical tools such as Analysis of Variance (ANOVA and Pooled ANOVA techniques. The optimum process parameters are found to be 1 mm electrode gap, 130 mm/min travel speed, 140 A current and 12 V voltage. The aspect ratio and the ferrite content for the DSS joints fabricated using the optimized ATIG parameters are found to be well within the acceptable range and there is no macroscopically evident solidification cracking.

Beyond bixels: Generalizing the optimization parameters for intensity modulated radiation therapy

International Nuclear Information System (INIS)

Markman, Jerry; Low, Daniel A.; Beavis, Andrew W.; Deasy, Joseph O.

2002-01-01

optimization. These results indicate that significant reductions in optimization parameter sets can be accomplished with a negligible reduction in dose distribution quality. The decreased parameters can result in a reduced optimization time, or can be used to allow an improved and consequently more computation-intensive dose calculation for more accurate dose calculations during the optimization process. The basis functions may be generalized to model the accelerator motion for direct computation of the accelerator motion sequence, removing the need for developing an independent leaf sequence step

Optimization of Parameters of Asymptotically Stable Systems

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Anna Guerman

2011-01-01

Full Text Available This work deals with numerical methods of parameter optimization for asymptotically stable systems. We formulate a special mathematical programming problem that allows us to determine optimal parameters of a stabilizer. This problem involves solutions to a differential equation. We show how to chose the mesh in order to obtain discrete problem guaranteeing the necessary accuracy. The developed methodology is illustrated by an example concerning optimization of parameters for a satellite stabilization system.

Stellar atmospheric parameter estimation using Gaussian process regression

Science.gov (United States)

Bu, Yude; Pan, Jingchang

2015-02-01

As is well known, it is necessary to derive stellar parameters from massive amounts of spectral data automatically and efficiently. However, in traditional automatic methods such as artificial neural networks (ANNs) and kernel regression (KR), it is often difficult to optimize the algorithm structure and determine the optimal algorithm parameters. Gaussian process regression (GPR) is a recently developed method that has been proven to be capable of overcoming these difficulties. Here we apply GPR to derive stellar atmospheric parameters from spectra. Through evaluating the performance of GPR on Sloan Digital Sky Survey (SDSS) spectra, Medium resolution Isaac Newton Telescope Library of Empirical Spectra (MILES) spectra, ELODIE spectra and the spectra of member stars of galactic globular clusters, we conclude that GPR can derive stellar parameters accurately and precisely, especially when we use data preprocessed with principal component analysis (PCA). We then compare the performance of GPR with that of several widely used regression methods (ANNs, support-vector regression and KR) and find that with GPR it is easier to optimize structures and parameters and more efficient and accurate to extract atmospheric parameters.

Comparisons of criteria in the assessment model parameter optimizations

International Nuclear Information System (INIS)

Liu Xinhe; Zhang Yongxing

1993-01-01

Three criteria (chi square, relative chi square and correlation coefficient) used in model parameter optimization (MPO) process that aims at significant reduction of prediction uncertainties were discussed and compared to each other with the aid of a well-controlled tracer experiment

Optimization of process parameters for spark plasma sintering of nano structured SAF 2205 composite

Directory of Open Access Journals (Sweden)

Samuel Ranti Oke

2018-04-01

Full Text Available This research optimized spark plasma sintering (SPS process parameters in terms of sintering temperature, holding time and heating rate for the development of a nano-structured duplex stainless steel (SAF 2205 grade reinforced with titanium nitride (TiN. The mixed powders were sintered using an automated spark plasma sintering machine (model HHPD-25, FCT GmbH, Germany. Characterization was performed using X-ray diffraction and scanning electron microscopy. Density and hardness of the composites were investigated. The XRD result showed the formation of FeN0.068. SEM/EDS revealed the presence of nano ranged particles of TiN segregated at the grain boundaries of the duplex matrix. A decrease in hardness and densification was observed when sintering temperature and heating rate were 1200 °C and 150 °C/min respectively. The optimum properties were obtained in composites sintered at 1150 °C for 15 min and 100 °C/min. The composite grades irrespective of the process parameters exhibited similar shrinkage behavior, which is characterized by three distinctive peaks, which is an indication of good densification phenomena. Keywords: Spark plasma sintering, Duplex stainless steel (SAF 2205, Titanium nitride (TiN, Microstructure, Density, Hardness

PARAMETER ESTIMATION OF VALVE STICTION USING ANT COLONY OPTIMIZATION

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S. Kalaivani

2012-07-01

Full Text Available In this paper, a procedure for quantifying valve stiction in control loops based on ant colony optimization has been proposed. Pneumatic control valves are widely used in the process industry. The control valve contains non-linearities such as stiction, backlash, and deadband that in turn cause oscillations in the process output. Stiction is one of the long-standing problems and it is the most severe problem in the control valves. Thus the measurement data from an oscillating control loop can be used as a possible diagnostic signal to provide an estimate of the stiction magnitude. Quantification of control valve stiction is still a challenging issue. Prior to doing stiction detection and quantification, it is necessary to choose a suitable model structure to describe control-valve stiction. To understand the stiction phenomenon, the Stenman model is used. Ant Colony Optimization (ACO, an intelligent swarm algorithm, proves effective in various fields. The ACO algorithm is inspired from the natural trail following behaviour of ants. The parameters of the Stenman model are estimated using ant colony optimization, from the input-output data by minimizing the error between the actual stiction model output and the simulated stiction model output. Using ant colony optimization, Stenman model with known nonlinear structure and unknown parameters can be estimated.

Application of Factorial Design for Gas Parameter Optimization in CO2 Laser Welding

DEFF Research Database (Denmark)

Gong, Hui; Dragsted, Birgitte; Olsen, Flemming Ove

1997-01-01

The effect of different gas process parameters involved in CO2 laser welding has been studied by applying two-set of three-level complete factorial designs. In this work 5 gas parameters, gas type, gas flow rate, gas blowing angle, gas nozzle diameter, gas blowing point-offset, are optimized...... to be a very useful tool for parameter optimi-zation in laser welding process. Keywords: CO2 laser welding, gas parameters, factorial design, Analysis of Variance........ The bead-on-plate welding specimens are evaluated by a number of quality char-acteristics, such as the penetration depth and the seam width. The significance of the gas pa-rameters and their interactions are based on the data found by the Analysis of Variance-ANOVA. This statistic methodology is proven...

Investigation on the Effects of Process Parameters on Laser Percussion Drilling Using Finite Element Methodology; Statistical Modelling and Optimization

Directory of Open Access Journals (Sweden)

Mahmoud Moradi

Full Text Available Abstract In the present research, the simulation of the Nickel-base superalloy Inconel 718 fiber-laser drilling process with the thickness of 1mm is investigated through the Finite Element Method. In order to specify the appropriate Gaussian distribution of laser beam, the results of an experimental research on glass laser drilling were simulated using three types of Gaussian distribution. The DFLUX subroutine was used to implement the laser heat sources of the models using the Fortran language. After the appropriate Gaussian distribution was chosen, the model was validated with the experimental results of the Nickel-base superalloy Inconel 718 laser drilling process. The negligible error percentage among the experimental and simulation results demonstrates the high accuracy of this model. The experiments were performed based on the Response Surface Methodology (RSM as a statistical design of experiment (DOE approach to investigate the influence of process parameters on the responses, obtaining the mathematical regressions and predicting the new results. Four parameters i.e. laser pulse frequency (150 to 550 Hz, laser power (200 to 500 watts, laser focal plane position (-0.5 to +0.5 mm and the duty cycle (30 to 70% were considered to be the input variables in 5 levels and four external parameters i.e. the hole's entrance and exit diameters, hole taper angle and the weight of mass removed from the hole, were observed to be the process output responses of this central composite design. By performing the statistical analysis, the input and output parameters were found to have a direct relation with each other. By an increase in each of the input variables, the entrance and exit hole diameters, the hole taper angel, and the weight of mass removed from the hole increase. Finally, the results of the conducted simulations and statistical analyses having been used, the laser drilling process was optimized by means of the desire ability approach. Good

A Novel adaptative Discrete Cuckoo Search Algorithm for parameter optimization in computer vision

Directory of Open Access Journals (Sweden)

loubna benchikhi

2017-10-01

Full Text Available Computer vision applications require choosing operators and their parameters, in order to provide the best outcomes. Often, the users quarry on expert knowledge and must experiment many combinations to find manually the best one. As performance, time and accuracy are important, it is necessary to automate parameter optimization at least for crucial operators. In this paper, a novel approach based on an adaptive discrete cuckoo search algorithm (ADCS is proposed. It automates the process of algorithms’ setting and provides optimal parameters for vision applications. This work reconsiders a discretization problem to adapt the cuckoo search algorithm and presents the procedure of parameter optimization. Some experiments on real examples and comparisons to other metaheuristic-based approaches: particle swarm optimization (PSO, reinforcement learning (RL and ant colony optimization (ACO show the efficiency of this novel method.

Optimization of laser welding process parameters for super austenitic stainless steel using artificial neural networks and genetic algorithm

International Nuclear Information System (INIS)

Sathiya, P.; Panneerselvam, K.; Abdul Jaleel, M.Y.

2012-01-01

Highlights: ► Super austenitic stainless steel has successfully welded by laser welding with three different shielding gases. ► Among the three shielded joints, the helium shielded weld has more tensile strength. ► Neural network model was developed to predict the depth of penetration, bead width and tensile strength of the joints. ► The developed ANN model is suitably integrated with GA for optimization. -- Abstract: The laser welding input parameters play a very significant role in determining the quality of a weld joint. The quality of the joint can be defined in terms of properties such as weld bead geometry, mechanical properties and distortion. In particular mechanical properties should be controlled to obtain good welded joints. In this study, the weld bead geometry such as depth of penetration (DP), bead width (BW) and tensile strength (TS) of the laser welded butt joints made of AISI 904L super austenitic stainless steel are investigated. Full factorial design is used to carry out the experimental design. Artificial neural networks (ANNs) program was developed in MatLab software to establish the relationship between the laser welding input parameters like beam power, travel speed and focal position and the three responses DP, BW and TS in three different shielding gases (argon, helium and nitrogen). The established models are used for optimizing the process parameters using genetic algorithm (GA). Optimum solutions for the three different gases and their respective responses are obtained. Confirmation experiment has also been conducted to validate the optimized parameters obtained from GA.

Optimization and Simulation of Machining Parameters in Radial-axial Ring Rolling Process

Directory of Open Access Journals (Sweden)

Shuiyuan Tang

2011-05-01

Full Text Available Ring rolling is a complicated process, in which rolling parameters influence directly the quality of ring. It is a process method with high productivity and few waste of material, widely used in transportation industry including automotive, shipbuilding, aerospace etc. During the rolling process of large-sized parts, crinkle and hollows often appear on surface, due to inconsistence of rolling motions with the deformation of ring part. Based on radial-axial ring rolling system configuration, motions and forces in rolling process are analyzed, and a dynamic model is formulated. Error of ring's end flatness and roundness are defined as the characteristic parameters of ring quality. The relationship between core roller feed speed, drive roller speed, the upper taper roller feed speed, and quality of ring part are analyzed. The stress and strain of the part are simulated in the Finite Element Method by DEFORM software. The simulation results provide a reference for the definition of ring rolling process parameters. It is able to make the deformation of the part be consistent with the process parameters, and improve product quality considerably.

Network optimization including gas lift and network parameters under subsurface uncertainty

Energy Technology Data Exchange (ETDEWEB)

Schulze-Riegert, R.; Baffoe, J.; Pajonk, O. [SPT Group GmbH, Hamburg (Germany); Badalov, H.; Huseynov, S. [Technische Univ. Clausthal, Clausthal-Zellerfeld (Germany). ITE; Trick, M. [SPT Group, Calgary, AB (Canada)

2013-08-01

Optimization of oil and gas field production systems poses a great challenge to field development due to complex and multiple interactions between various operational design parameters and subsurface uncertainties. Conventional analytical methods are capable of finding local optima based on single deterministic models. They are less applicable for efficiently generating alternative design scenarios in a multi-objective context. Practical implementations of robust optimization workflows integrate the evaluation of alternative design scenarios and multiple realizations of subsurface uncertainty descriptions. Production or economic performance indicators such as NPV (Net Present Value) are linked to a risk-weighted objective function definition to guide the optimization processes. This work focuses on an integrated workflow using a reservoir-network simulator coupled to an optimization framework. The work will investigate the impact of design parameters while considering the physics of the reservoir, wells, and surface facilities. Subsurface uncertainties are described by well parameters such as inflow performance. Experimental design methods are used to investigate parameter sensitivities and interactions. Optimization methods are used to find optimal design parameter combinations which improve key performance indicators of the production network system. The proposed workflow will be applied to a representative oil reservoir coupled to a network which is modelled by an integrated reservoir-network simulator. Gas-lift will be included as an explicit measure to improve production. An objective function will be formulated for the net present value of the integrated system including production revenue and facility costs. Facility and gas lift design parameters are tuned to maximize NPV. Well inflow performance uncertainties are introduced with an impact on gas lift performance. Resulting variances on NPV are identified as a risk measure for the optimized system design. A

Optimization of process parameters for microwave pyrolysis of oil palm fiber (OPF) for hydrogen and biochar production

International Nuclear Information System (INIS)

Arafat Hossain, Md; Ganesan, P.; Jewaratnam, J.; Chinna, K.

2017-01-01

Highlights: • Microwave pyrolysis process parameters are optimized by response surface methodology. • Experimental values are well in agreement with the predicted values from model. • Correction coefficients (R 2 ) which had been found near to the 1, satisfied the model. • Errors are less than 10% between the optimized conditions and experimental values. • Higher carbon (%) and porosity have been found in the biochar. - Abstract: Response surface methodology (RSM) based on central composite design (CCD) is used to investigate the optimized experimental conditions for maximum H 2 and biochar yields from microwave pyrolysis of OPF. Input parameters (temperature, microwave power and N 2 flow rate) have been coded which suggest a complete summary of experimental design with a set of experiment for the two responses of H 2 and biochar. Quadratic model has been found fit for the optimization. This method significantly reduces the number of the experiments (Full factorial experiments). Actual vs. predicted plots clearly imply that experimental values are well in agreement with the predicted values for both H 2 and biochar yield. The perturbation plots indicate that H 2 and biochar yields are more sensitive for N 2 flow rate and temperature respectively. The software suggested three optimized experimental conditions for maximum H 2 yield, maximum biochar yield and for both maximum H 2 and biochar yields together. The software results were further validated by conducting relevant experiments. The error was less than 10%, suggesting that the software predictions are quite reliable. Proximate and ultimate analysis of the optimized biochars have showed a big percentage of carbon contents (More than 60 wt.%) and high heating value. SEM and BET analysis show some pores in the biochars which are effective for soil improvements.

Thermo-mechanical simulation and parameters optimization for beam blank continuous casting

International Nuclear Information System (INIS)

Chen, W.; Zhang, Y.Z.; Zhang, C.J.; Zhu, L.G.; Lu, W.G.; Wang, B.X.; Ma, J.H.

2009-01-01

The objective of this work is to optimize the process parameters of beam blank continuous casting in order to ensure high quality and productivity. A transient thermo-mechanical finite element model is developed to compute the temperature and stress profile in beam blank continuous casting. By comparing the calculated data with the metallurgical constraints, the key factors causing defects of beam blank can be found out. Then based on the subproblem approximation method, an optimization program is developed to search out the optimum cooling parameters. Those optimum parameters can make it possible to run the caster at its maximum productivity, minimum cost and to reduce the defects. Now, online verifying of this optimization project has been put in practice, which can prove that it is very useful to control the actual production

PARAMETER COORDINATION AND ROBUST OPTIMIZATION FOR MULTIDISCIPLINARY DESIGN

Institute of Scientific and Technical Information of China (English)

HU Jie; PENG Yinghong; XIONG Guangleng

2006-01-01

A new parameter coordination and robust optimization approach for multidisciplinary design is presented. Firstly, the constraints network model is established to support engineering change, coordination and optimization. In this model, interval boxes are adopted to describe the uncertainty of design parameters quantitatively to enhance the design robustness. Secondly, the parameter coordination method is presented to solve the constraints network model, monitor the potential conflicts due to engineering changes, and obtain the consistency solution space corresponding to the given product specifications. Finally, the robust parameter optimization model is established, and genetic arithmetic is used to obtain the robust optimization parameter. An example of bogie design is analyzed to show the scheme to be effective.

Optimisation of shock absorber process parameters using failure mode and effect analysis and genetic algorithm

Science.gov (United States)

Mariajayaprakash, Arokiasamy; Senthilvelan, Thiyagarajan; Vivekananthan, Krishnapillai Ponnambal

2013-07-01

The various process parameters affecting the quality characteristics of the shock absorber during the process were identified using the Ishikawa diagram and by failure mode and effect analysis. The identified process parameters are welding process parameters (squeeze, heat control, wheel speed, and air pressure), damper sealing process parameters (load, hydraulic pressure, air pressure, and fixture height), washing process parameters (total alkalinity, temperature, pH value of rinsing water, and timing), and painting process parameters (flowability, coating thickness, pointage, and temperature). In this paper, the process parameters, namely, painting and washing process parameters, are optimized by Taguchi method. Though the defects are reasonably minimized by Taguchi method, in order to achieve zero defects during the processes, genetic algorithm technique is applied on the optimized parameters obtained by Taguchi method.

Optimal Laser Phototherapy Parameters for Pain Relief.

Science.gov (United States)

Kate, Rohit J; Rubatt, Sarah; Enwemeka, Chukuka S; Huddleston, Wendy E

2018-03-27

Studies on laser phototherapy for pain relief have used parameters that vary widely and have reported varying outcomes. The purpose of this study was to determine the optimal parameter ranges of laser phototherapy for pain relief by analyzing data aggregated from existing primary literature. Original studies were gathered from available sources and were screened to meet the pre-established inclusion criteria. The included articles were then subjected to meta-analysis using Cohen's d statistic for determining treatment effect size. From these studies, ranges of the reported parameters that always resulted into large effect sizes were determined. These optimal ranges were evaluated for their accuracy using leave-one-article-out cross-validation procedure. A total of 96 articles met the inclusion criteria for meta-analysis and yielded 232 effect sizes. The average effect size was highly significant: d = +1.36 (confidence interval [95% CI] = 1.04-1.68). Among all the parameters, total energy was found to have the greatest effect on pain relief and had the most prominent optimal ranges of 120-162 and 15.36-20.16 J, which always resulted in large effect sizes. The cross-validation accuracy of the optimal ranges for total energy was 68.57% (95% CI = 53.19-83.97). Fewer and less-prominent optimal ranges were obtained for the energy density and duration parameters. None of the remaining parameters was found to be independently related to pain relief outcomes. The findings of meta-analysis indicate that laser phototherapy is highly effective for pain relief. Based on the analysis of parameters, total energy can be optimized to yield the largest effect on pain relief.

Optimization of process parameter and reformer configuration for hydrogen production from steam reforming of heavy hydrocarbons. Paper no. IGEC-1-079

International Nuclear Information System (INIS)

Chen, Z.; Elnashaie, S.E.H.

2005-01-01

The present optimization investigation is classified into reforming configuration optimization in one hand and parameter optimization of each configuration on the other hand. Heptane is used as a model component for heavy hydrocarbons. The proposed novel reforming process is basically a Circulating Fluidized-Bed Membrane Reformer (CFBMR) with continuous catalyst regeneration and gas-solid separation. Composite hydrogen selective membranes are used for removing the product hydrogen from the reacting gas mixture and therefore driving the reversible reactions beyond their thermodynamic equilibriums. Dense perovskite oxygen selective membranes are also used to introduce oxygen for the exothermic oxidation of hydrocarbons and carbon. Four configurations are investigated, two of them are with the catalyst regeneration before the gas-solid separation and the other two are with the catalyst regeneration after the gas-solid separation. The optimization of the performance of each configuration is carried out for a number of design and operating parameters as optimization parameters and under both non-autothermal and autothermal reforming conditions. Results show that the autothermal operation with direct contact between cold feeds (water and heptane) and hot circulating catalyst can be the best configuration for efficient hydrogen production with minimum energy consumption. The maximum net hydrogen yield is 16.732 moles of hydrogen per mole of heptane fed, which is 76.05% of the maximum theoretical hydrogen yield of 22. (author)

Optimization of dissolution process parameters for uranium ore concentrate powders

Energy Technology Data Exchange (ETDEWEB)

Misra, M.; Reddy, D.M.; Reddy, A.L.V.; Tiwari, S.K.; Venkataswamy, J.; Setty, D.S.; Sheela, S.; Saibaba, N. [Nuclear Fuel Complex, Hyderabad (India)

2013-07-01

Nuclear fuel complex processes Uranium Ore Concentrate (UOC) for producing uranium dioxide powder required for the fabrication of fuel assemblies for Pressurized Heavy Water Reactor (PHWR)s in India. UOC is dissolved in nitric acid and further purified by solvent extraction process for producing nuclear grade UO{sub 2} powder. Dissolution of UOC in nitric acid involves complex nitric oxide based reactions, since it is in the form of Uranium octa oxide (U{sub 3}O{sub 8}) or Uranium Dioxide (UO{sub 2}). The process kinetics of UOC dissolution is largely influenced by parameters like concentration and flow rate of nitric acid, temperature and air flow rate and found to have effect on recovery of nitric oxide as nitric acid. The plant scale dissolution of 2 MT batch in a single reactor is studied and observed excellent recovery of oxides of nitrogen (NO{sub x}) as nitric acid. The dissolution process is automated by PLC based Supervisory Control and Data Acquisition (SCADA) system for accurate control of process parameters and successfully dissolved around 200 Metric Tons of UOC. The paper covers complex chemistry involved in UOC dissolution process and also SCADA system. The solid and liquid reactions were studied along with multiple stoichiometry of nitrous oxide generated. (author)

Optimization of a centrifugal compressor impeller using CFD: the choice of simulation model parameters

Science.gov (United States)

Neverov, V. V.; Kozhukhov, Y. V.; Yablokov, A. M.; Lebedev, A. A.

2017-08-01

Nowadays the optimization using computational fluid dynamics (CFD) plays an important role in the design process of turbomachines. However, for the successful and productive optimization it is necessary to define a simulation model correctly and rationally. The article deals with the choice of a grid and computational domain parameters for optimization of centrifugal compressor impellers using computational fluid dynamics. Searching and applying optimal parameters of the grid model, the computational domain and solver settings allows engineers to carry out a high-accuracy modelling and to use computational capability effectively. The presented research was conducted using Numeca Fine/Turbo package with Spalart-Allmaras and Shear Stress Transport turbulence models. Two radial impellers was investigated: the high-pressure at ψT=0.71 and the low-pressure at ψT=0.43. The following parameters of the computational model were considered: the location of inlet and outlet boundaries, type of mesh topology, size of mesh and mesh parameter y+. Results of the investigation demonstrate that the choice of optimal parameters leads to the significant reduction of the computational time. Optimal parameters in comparison with non-optimal but visually similar parameters can reduce the calculation time up to 4 times. Besides, it is established that some parameters have a major impact on the result of modelling.

Affect of different ICT processing parameters to the quality of tomograms

International Nuclear Information System (INIS)

Zhou Jiang; Sun Lingxia; Ye Yunchang

2009-01-01

The quality of ICT tomograms is affected by detecting processing parameters and image processing methods besides the performances of ICT systems. Optimal processing parameters and image processing methods can promote not only the quality of tomogram but also the resolution. Some research work was carried out about processing parameters and image processing methods including choice of collimator, filter, false color composite image. And some examples were given in this paper, which can provide the ICT analyst with reference. (authors)

Influence of Optimization of Process Parameters on Threshold Voltage for Development of HfO2/TiSi2 18 nm PMOS

Directory of Open Access Journals (Sweden)

Atan N.

2016-01-01

Full Text Available Manufacturing a 18-nm transistor requires a variety of parameters, materials, temperatures, and methods. In this research, HfO2 was used as the gate dielectric ad TiO2 was used as the gate material. The transistor HfO2/TiSi2 18-nm PMOS was invented using SILVACO TCAD. Ion implantation was adopted in the fabrication process for the method’s practicality and ability to be used to suppress short channel effects. The study involved ion implantation methods: compensation implantation, halo implantation energy, halo tilt, and source–drain implantation. Taguchi method is the best optimization process for a threshold voltage of HfO2/TiSi2 18-nm PMOS. In this case, the method adopted was Taguchi orthogonal array L9. The process parameters (ion implantations and noise factors were evaluated by examining the Taguchi’s signal-to-noise ratio (SNR and nominal-the-best for the threshold voltage (VTH. After optimization, the result showed that the VTH value of the 18-nm PMOS device was -0.291339.

Application of quality by design approach to optimize process and formulation parameters of rizatriptan loaded chitosan nanoparticles

Directory of Open Access Journals (Sweden)

Ajinath Eknath Shirsat

2015-01-01

Full Text Available The purpose of present study was to optimize rizatriptan (RZT chitosan (CS nanoparticles using ionic gelation method by application of quality by design (QbD approach. Based on risk assessment, effect of three variables, that is CS %, tripolyphosphate % and stirring speed were studied on critical quality attributes (CQAs; particle size and entrapment efficiency. Central composite design (CCD was implemented for design of experimentation with 20 runs. RZT CS nanoparticles were characterized for particle size, polydispersity index, entrapment efficiency, in-vitro release study, differential scanning calorimetric, X-ray diffraction, scanning electron microscopy (SEM. Based on QbD approach, design space (DS was optimized with a combination of selected variables with entrapment efficiency > 50% w/w and a particle size between 400 and 600 nm. Validation of model was performed with 3 representative formulations from DS for which standard error of − 0.70-3.29 was observed between experimental and predicted values. In-vitro drug release followed initial burst release 20.26 ± 2.34% in 3-4 h with sustained drug release of 98.43 ± 2.45% in 60 h. Lower magnitude of standard error for CQAs confirms the validation of selected CCD model for optimization of RZT CS nanoparticles. In-vitro drug release followed dual mechanism via, diffusion and polymer erosion. RZT CS nanoparticles were prepared successfully using QbD approach with the understanding of the high risk process and formulation parameters involved and optimized DS with a multifactorial combination of critical parameters to obtain predetermined RZT loaded CS nanoparticle specifications.

Optimization of IBF parameters based on adaptive tool-path algorithm

Science.gov (United States)

Deng, Wen Hui; Chen, Xian Hua; Jin, Hui Liang; Zhong, Bo; Hou, Jin; Li, An Qi

2018-03-01

As a kind of Computer Controlled Optical Surfacing(CCOS) technology. Ion Beam Figuring(IBF) has obvious advantages in the control of surface accuracy, surface roughness and subsurface damage. The superiority and characteristics of IBF in optical component processing are analyzed from the point of view of removal mechanism. For getting more effective and automatic tool path with the information of dwell time, a novel algorithm is proposed in this thesis. Based on the removal functions made through our IBF equipment and the adaptive tool-path, optimized parameters are obtained through analysis the residual error that would be created in the polishing process. A Φ600 mm plane reflector element was used to be a simulation instance. The simulation result shows that after four combinations of processing, the surface accuracy of PV (Peak Valley) value and the RMS (Root Mean Square) value was reduced to 4.81 nm and 0.495 nm from 110.22 nm and 13.998 nm respectively in the 98% aperture. The result shows that the algorithm and optimized parameters provide a good theoretical for high precision processing of IBF.

Determining the optimum process parameter for grinding operations using robust process

International Nuclear Information System (INIS)

Neseli, Suley Man; Asilturk, Ilhan; Celik, Levent

2012-01-01

We applied combined response surface methodology (RSM) and Taguchi methodology (TM) to determine optimum parameters for minimum surface roughness (Ra) and vibration (Vb) in external cylindrical grinding. First, an experiment was conducted in a CNC cylindrical grinding machine. The TM using L 27 orthogonal array was applied to the design of the experiment. The three input parameters were workpiece revolution, feed rate and depth of cut; the outputs were vibrations and surface roughness. Second, to minimize wheel vibration and surface roughness, two optimized models were developed using computer aided single objective optimization. The experimental and statistical results revealed that the most significant grinding parameter for surface roughness and vibration is workpiece revolution followed by the depth of cut. The predicted values and measured values were fairly close, which indicates 2 ( 94.99 R 2Ra =and 2 92.73) R 2Vb =that the developed models can be effectively used to predict surface roughness and vibration in the grinding. The established model for determination of optimal operating conditions shows that a hybrid approach can lead to success of a robust process

Prediction and optimization of friction welding parameters for super duplex stainless steel (UNS S32760) joints

International Nuclear Information System (INIS)

Udayakumar, T.; Raja, K.; Afsal Husain, T.M.; Sathiya, P.

2014-01-01

Highlights: • Corrosion resistance and impact strength – predicted by response surface methodology. • Burn off length has highest significance on corrosion resistance. • Friction force is a strong determinant in changing impact strength. • Pareto front points generated by genetic algorithm aid to fix input control variable. • Pareto front will be a trade-off between corrosion resistance and impact strength. - Abstract: Friction welding finds widespread industrial use as a mass production process for joining materials. Friction welding process allows welding of several materials that are extremely difficult to fusion weld. Friction welding process parameters play a significant role in making good quality joints. To produce a good quality joint it is important to set up proper welding process parameters. This can be done by employing optimization techniques. This paper presents a multi objective optimization method for optimizing the process parameters during friction welding process. The proposed method combines the response surface methodology (RSM) with an intelligent optimization algorithm, i.e. genetic algorithm (GA). Corrosion resistance and impact strength of friction welded super duplex stainless steel (SDSS) (UNS S32760) joints were investigated considering three process parameters: friction force (F), upset force (U) and burn off length (B). Mathematical models were developed and the responses were adequately predicted. Direct and interaction effects of process parameters on responses were studied by plotting graphs. Burn off length has high significance on corrosion current followed by upset force and friction force. In the case of impact strength, friction force has high significance followed by upset force and burn off length. Multi objective optimization for maximizing the impact strength and minimizing the corrosion current (maximizing corrosion resistance) was carried out using GA with the RSM model. The optimization procedure resulted in

Zener Diode Compact Model Parameter Extraction Using Xyce-Dakota Optimization.

Energy Technology Data Exchange (ETDEWEB)

Buchheit, Thomas E. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Wilcox, Ian Zachary [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandoval, Andrew J [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Reza, Shahed [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-12-01

This report presents a detailed process for compact model parameter extraction for DC circuit Zener diodes. Following the traditional approach of Zener diode parameter extraction, circuit model representation is defined and then used to capture the different operational regions of a real diode's electrical behavior. The circuit model contains 9 parameters represented by resistors and characteristic diodes as circuit model elements. The process of initial parameter extraction, the identification of parameter values for the circuit model elements, is presented in a way that isolates the dependencies between certain electrical parameters and highlights both the empirical nature of the extraction and portions of the real diode physical behavior which of the parameters are intended to represent. Optimization of the parameters, a necessary part of a robost parameter extraction process, is demonstrated using a 'Xyce-Dakota' workflow, discussed in more detail in the report. Among other realizations during this systematic approach of electrical model parameter extraction, non-physical solutions are possible and can be difficult to avoid because of the interdependencies between the different parameters. The process steps described are fairly general and can be leveraged for other types of semiconductor device model extractions. Also included in the report are recommendations for experiment setups for generating optimum dataset for model extraction and the Parameter Identification and Ranking Table (PIRT) for Zener diodes.

Modelling and multi objective optimization of LM13 aluminium alloy squeeze cast process parameters using taguchi and genetic algorithm

Directory of Open Access Journals (Sweden)

S. Vellingiri

2018-01-01

Full Text Available This present investigation deals with squeeze casting process in order to produce a component with good mechanical properties such as micro-hardness(VH, tensile strength(Rm, and density(ρ on LM13 by varying squeeze pressure(P, molten temperature(Tm and die temperature(Td. Taguchi experimental design L9 orthogonal array was used to determine the signal to noise ratio. The results specified that the squeeze pressure and die preheat temperature are the most influencing parameters for mechanical properties improvement. Genetic algorithm (GA has been applied to optimize the casting parameters that simultaneously maximize the responses.

Simulative design and process optimization of the two-stage stretch-blow molding process

Energy Technology Data Exchange (ETDEWEB)

Hopmann, Ch.; Rasche, S.; Windeck, C. [Institute of Plastics Processing at RWTH Aachen University (IKV) Pontstraße 49, 52062 Aachen (Germany)

2015-05-22

The total production costs of PET bottles are significantly affected by the costs of raw material. Approximately 70 % of the total costs are spent for the raw material. Therefore, stretch-blow molding industry intends to reduce the total production costs by an optimized material efficiency. However, there is often a trade-off between an optimized material efficiency and required product properties. Due to a multitude of complex boundary conditions, the design process of new stretch-blow molded products is still a challenging task and is often based on empirical knowledge. Application of current CAE-tools supports the design process by reducing development time and costs. This paper describes an approach to determine optimized preform geometry and corresponding process parameters iteratively. The wall thickness distribution and the local stretch ratios of the blown bottle are calculated in a three-dimensional process simulation. Thereby, the wall thickness distribution is correlated with an objective function and preform geometry as well as process parameters are varied by an optimization algorithm. Taking into account the correlation between material usage, process history and resulting product properties, integrative coupled simulation steps, e.g. structural analyses or barrier simulations, are performed. The approach is applied on a 0.5 liter PET bottle of Krones AG, Neutraubling, Germany. The investigations point out that the design process can be supported by applying this simulative optimization approach. In an optimization study the total bottle weight is reduced from 18.5 g to 15.5 g. The validation of the computed results is in progress.

Simulative design and process optimization of the two-stage stretch-blow molding process

International Nuclear Information System (INIS)

Hopmann, Ch.; Rasche, S.; Windeck, C.

2015-01-01

The total production costs of PET bottles are significantly affected by the costs of raw material. Approximately 70 % of the total costs are spent for the raw material. Therefore, stretch-blow molding industry intends to reduce the total production costs by an optimized material efficiency. However, there is often a trade-off between an optimized material efficiency and required product properties. Due to a multitude of complex boundary conditions, the design process of new stretch-blow molded products is still a challenging task and is often based on empirical knowledge. Application of current CAE-tools supports the design process by reducing development time and costs. This paper describes an approach to determine optimized preform geometry and corresponding process parameters iteratively. The wall thickness distribution and the local stretch ratios of the blown bottle are calculated in a three-dimensional process simulation. Thereby, the wall thickness distribution is correlated with an objective function and preform geometry as well as process parameters are varied by an optimization algorithm. Taking into account the correlation between material usage, process history and resulting product properties, integrative coupled simulation steps, e.g. structural analyses or barrier simulations, are performed. The approach is applied on a 0.5 liter PET bottle of Krones AG, Neutraubling, Germany. The investigations point out that the design process can be supported by applying this simulative optimization approach. In an optimization study the total bottle weight is reduced from 18.5 g to 15.5 g. The validation of the computed results is in progress

Optimal parameters of the SVM for temperature prediction

Directory of Open Access Journals (Sweden)

X. Shi

2015-05-01

Full Text Available This paper established three different optimization models in order to predict the Foping station temperature value. The dimension was reduced to change multivariate climate factors into a few variables by principal component analysis (PCA. And the parameters of support vector machine (SVM were optimized with genetic algorithm (GA, particle swarm optimization (PSO and developed genetic algorithm. The most suitable method was applied for parameter optimization by comparing the results of three different models. The results are as follows: The developed genetic algorithm optimization parameters of the predicted values were closest to the measured value after the analog trend, and it is the most fitting measured value trends, and its homing speed is relatively fast.

Optimization of the tape placement process parameters for carbon–PPS composites

NARCIS (Netherlands)

Grouve, Wouter Johannes Bernardus; Warnet, Laurent; Rietman, B.; Visser, Roy; Akkerman, Remko

2013-01-01

The interrelation between process parameters, material properties and interlaminar bond strength is investigated for the laser assisted tape placement process. Unidirectionally carbon reinforced poly(phenylene sulfide) (PPS) tapes were welded onto carbon woven fabric reinforced PPS laminates. The

Sensitive parameters' optimization of the permanent magnet supporting mechanism

Energy Technology Data Exchange (ETDEWEB)

Liu, Yongguang; Gao, Xiaohui; Wang, Yixuan; Yang, Xiaowei [Beihang University, Beijing (China)

2014-07-15

The fast development of the ultra-high speed vertical rotor promotes the study and exploration for the supporting mechanism. It has become the focus of research that how to improve the speed and overcome the vibration when the rotors pass through the low-order critical frequencies. This paper introduces a kind of permanent magnet (PM) supporting mechanism and describes an optimization method of its sensitive parameters, which can make the vertical rotor system reach 80000 r/min smoothly. Firstly we find the sensitive parameters through analyzing the rotor's features in the process of achieving high-speed, then, study these sensitive parameters and summarize the regularities with the method of combining the experiment and the finite element method (FEM), at last, achieve the optimization method of these parameters. That will not only get a stable effect of raising speed and shorten the debugging time greatly, but also promote the extensive application of the PM supporting mechanism in the ultra-high speed vertical rotors.

Optimal design for laser beam butt welding process parameter using artificial neural networks and genetic algorithm for super austenitic stainless steel

Science.gov (United States)

Sathiya, P.; Panneerselvam, K.; Soundararajan, R.

2012-09-01

Laser welding input parameters play a very significant role in determining the quality of a weld joint. The joint quality can be defined in terms of properties such as weld bead geometry, mechanical properties and distortion. Therefore, mechanical properties should be controlled to obtain good welded joints. In this study, the weld bead geometry such as depth of penetration (DP), bead width (BW) and tensile strength (TS) of the laser welded butt joints made of AISI 904L super austenitic stainless steel were investigated. Full factorial design was used to carry out the experimental design. Artificial Neural networks (ANN) program was developed in MatLab software to establish the relationships between the laser welding input parameters like beam power, travel speed and focal position and the three responses DP, BW and TS in three different shielding gases (Argon, Helium and Nitrogen). The established models were used for optimizing the process parameters using Genetic Algorithm (GA). Optimum solutions for the three different gases and their respective responses were obtained. Confirmation experiment has also been conducted to validate the optimized parameters obtained from GA.

Optimization of process parameters for the inactivation of Lactobacillus sporogenes in tomato paste with ultrasound and {sup 60}Co-{gamma} irradiation using response surface methodology

Energy Technology Data Exchange (ETDEWEB)

Ye Shengying [College of Food Science, South China Agricultural University, Wushan, Guangzhou, GD 510640 (China)], E-mail: yesy@scau.edu.cn; Qiu Yuanxin; Song Xianliang; Luo Shucan [College of Food Science, South China Agricultural University, Wushan, Guangzhou, GD 510640 (China)

2009-03-15

The processing parameters for ultrasound and {sup 60}Co-{gamma} irradiation were optimized for their ability to inactivate Lactobacillus sporogenes in tomato paste using a systematic experimental design based on response surface methodology. Ultrasonic power, ultrasonic processing time and irradiation dose were explored and a central composite rotation design was adopted as the experimental plan, and a least-squares regression model was obtained. The significant influential factors for the inactivation rate of L. sporogenes were obtained from the quadratic model and the t-test analyses for each process parameter. Confirmation of the experimental results indicated that the proposed model was reasonably accurate and could be used to describe the efficacy of the treatments for inactivating L. sporogenes within the limits of the factors studied. The optimized processing parameters were found to be an ultrasonic power of 120 W with a processing time of 25 min and an irradiation dose of 6.5 kGy. These were measured under the constraints of parameter limitation, based on the Monte Carlo searching method and the quadratic model of the response surface methodology, including the a/b value of the Hunter color scale of tomato paste. Nevertheless, the ultrasound treatment prior to irradiation for the inactivation of L. sporogenes in tomato paste was unsuitable for reducing the irradiation dose.

Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization

Science.gov (United States)

Azmi, Nur Iffah Mohamed; Arifin Mat Piah, Kamal; Yusoff, Wan Azhar Wan; Romlay, Fadhlur Rahman Mohd

2018-03-01

Controller that uses PID parameters requires a good tuning method in order to improve the control system performance. Tuning PID control method is divided into two namely the classical methods and the methods of artificial intelligence. Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. This research aims to improve the PSO-PID tuning algorithms by integrating the tuning process with the Variable Weight Grey- Taguchi Design of Experiment (DOE) method. This is done by conducting the DOE on the two PSO optimizing parameters: the particle velocity limit and the weight distribution factor. Computer simulations and physical experiments were conducted by using the proposed PSO- PID with the Variable Weight Grey-Taguchi DOE and the classical Ziegler-Nichols methods. They are implemented on the hydraulic positioning system. Simulation results show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE has reduced the rise time by 48.13% and settling time by 48.57% compared to the Ziegler-Nichols method. Furthermore, the physical experiment results also show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE tuning method responds better than Ziegler-Nichols tuning. In conclusion, this research has improved the PSO-PID parameter by applying the PSO-PID algorithm together with the Variable Weight Grey-Taguchi DOE method as a tuning method in the hydraulic positioning system.

Parametric optimization of ultrasonic machining process using gravitational search and fireworks algorithms

Directory of Open Access Journals (Sweden)

Debkalpa Goswami

2015-03-01

Full Text Available Ultrasonic machining (USM is a mechanical material removal process used to erode holes and cavities in hard or brittle workpieces by using shaped tools, high-frequency mechanical motion and an abrasive slurry. Unlike other non-traditional machining processes, such as laser beam and electrical discharge machining, USM process does not thermally damage the workpiece or introduce significant levels of residual stress, which is important for survival of materials in service. For having enhanced machining performance and better machined job characteristics, it is often required to determine the optimal control parameter settings of an USM process. The earlier mathematical approaches for parametric optimization of USM processes have mostly yielded near optimal or sub-optimal solutions. In this paper, two almost unexplored non-conventional optimization techniques, i.e. gravitational search algorithm (GSA and fireworks algorithm (FWA are applied for parametric optimization of USM processes. The optimization performance of these two algorithms is compared with that of other popular population-based algorithms, and the effects of their algorithm parameters on the derived optimal solutions and computational speed are also investigated. It is observed that FWA provides the best optimal results for the considered USM processes.

Multi-objective optimization model of CNC machining to minimize processing time and environmental impact

Science.gov (United States)

Hamada, Aulia; Rosyidi, Cucuk Nur; Jauhari, Wakhid Ahmad

2017-11-01

Minimizing processing time in a production system can increase the efficiency of a manufacturing company. Processing time are influenced by application of modern technology and machining parameter. Application of modern technology can be apply by use of CNC machining, one of the machining process can be done with a CNC machining is turning. However, the machining parameters not only affect the processing time but also affect the environmental impact. Hence, optimization model is needed to optimize the machining parameters to minimize the processing time and environmental impact. This research developed a multi-objective optimization to minimize the processing time and environmental impact in CNC turning process which will result in optimal decision variables of cutting speed and feed rate. Environmental impact is converted from environmental burden through the use of eco-indicator 99. The model were solved by using OptQuest optimization software from Oracle Crystal Ball.

Mixed integer evolution strategies for parameter optimization.

Science.gov (United States)

Li, Rui; Emmerich, Michael T M; Eggermont, Jeroen; Bäck, Thomas; Schütz, M; Dijkstra, J; Reiber, J H C

2013-01-01

Evolution strategies (ESs) are powerful probabilistic search and optimization algorithms gleaned from biological evolution theory. They have been successfully applied to a wide range of real world applications. The modern ESs are mainly designed for solving continuous parameter optimization problems. Their ability to adapt the parameters of the multivariate normal distribution used for mutation during the optimization run makes them well suited for this domain. In this article we describe and study mixed integer evolution strategies (MIES), which are natural extensions of ES for mixed integer optimization problems. MIES can deal with parameter vectors consisting not only of continuous variables but also with nominal discrete and integer variables. Following the design principles of the canonical evolution strategies, they use specialized mutation operators tailored for the aforementioned mixed parameter classes. For each type of variable, the choice of mutation operators is governed by a natural metric for this variable type, maximal entropy, and symmetry considerations. All distributions used for mutation can be controlled in their shape by means of scaling parameters, allowing self-adaptation to be implemented. After introducing and motivating the conceptual design of the MIES, we study the optimality of the self-adaptation of step sizes and mutation rates on a generalized (weighted) sphere model. Moreover, we prove global convergence of the MIES on a very general class of problems. The remainder of the article is devoted to performance studies on artificial landscapes (barrier functions and mixed integer NK landscapes), and a case study in the optimization of medical image analysis systems. In addition, we show that with proper constraint handling techniques, MIES can also be applied to classical mixed integer nonlinear programming problems.

Optimization of process parameter variations on leakage current in in silicon-oninsulator vertical double gate mosfet device

Directory of Open Access Journals (Sweden)

K.E. Kaharudin

2015-12-01

Full Text Available This paper presents a study of optimizing input process parameters on leakage current (IOFF in silicon-on-insulator (SOI Vertical Double-Gate,Metal Oxide Field-Effect-Transistor (MOSFET by using L36 Taguchi method. The performance of SOI Vertical DG-MOSFET device is evaluated in terms of its lowest leakage current (IOFF value. An orthogonal array, main effects, signal-to-noise ratio (SNR and analysis of variance (ANOVA are utilized in order to analyze the effect of input process parameter variation on leakage current (IOFF. Based on the results, the minimum leakage current ((IOFF of SOI Vertical DG-MOSFET is observed to be 0.009 nA/µm or 9 ρA/µm while keeping the drive current (ION value at 434 µA/µm. Both the drive current (ION and leakage current (IOFF values yield a higher ION/IOFF ratio (48.22 x 106 for low power consumption application. Meanwhile, polysilicon doping tilt angle and polysilicon doping energy are recognized as the most dominant factors with each of the contributing factor effects percentage of 59% and 25%.

Determining the optimum process parameter for grinding operations using robust process

Energy Technology Data Exchange (ETDEWEB)

Neseli, Suley Man; Asilturk, Ilhan; Celik, Levent [Univ. of Selcuk, Konya (Turkmenistan)

2012-11-15

We applied combined response surface methodology (RSM) and Taguchi methodology (TM) to determine optimum parameters for minimum surface roughness (Ra) and vibration (Vb) in external cylindrical grinding. First, an experiment was conducted in a CNC cylindrical grinding machine. The TM using L{sup 27} orthogonal array was applied to the design of the experiment. The three input parameters were workpiece revolution, feed rate and depth of cut; the outputs were vibrations and surface roughness. Second, to minimize wheel vibration and surface roughness, two optimized models were developed using computer aided single objective optimization. The experimental and statistical results revealed that the most significant grinding parameter for surface roughness and vibration is workpiece revolution followed by the depth of cut. The predicted values and measured values were fairly close, which indicates 2 ( 94.99 R{sup 2Ra}=and 2 92.73) R{sup 2Vb}=that the developed models can be effectively used to predict surface roughness and vibration in the grinding. The established model for determination of optimal operating conditions shows that a hybrid approach can lead to success of a robust process.

Optimization of the dressing parameters in cylindrical grinding based on a generalized utility function

Science.gov (United States)

Aleksandrova, Irina

2016-01-01

The existing studies, concerning the dressing process, focus on the major influence of the dressing conditions on the grinding response variables. However, the choice of the dressing conditions is often made, based on the experience of the qualified staff or using data from reference books. The optimal dressing parameters, which are only valid for the particular methods and dressing and grinding conditions, are also used. The paper presents a methodology for optimization of the dressing parameters in cylindrical grinding. The generalized utility function has been chosen as an optimization parameter. It is a complex indicator determining the economic, dynamic and manufacturing characteristics of the grinding process. The developed methodology is implemented for the dressing of aluminium oxide grinding wheels by using experimental diamond roller dressers with different grit sizes made of medium- and high-strength synthetic diamonds type ??32 and ??80. To solve the optimization problem, a model of the generalized utility function is created which reflects the complex impact of dressing parameters. The model is built based on the results from the conducted complex study and modeling of the grinding wheel lifetime, cutting ability, production rate and cutting forces during grinding. They are closely related to the dressing conditions (dressing speed ratio, radial in-feed of the diamond roller dresser and dress-out time), the diamond roller dresser grit size/grinding wheel grit size ratio, the type of synthetic diamonds and the direction of dressing. Some dressing parameters are determined for which the generalized utility function has a maximum and which guarantee an optimum combination of the following: the lifetime and cutting ability of the abrasive wheels, the tangential cutting force magnitude and the production rate of the grinding process. The results obtained prove the possibility of control and optimization of grinding by selecting particular dressing

Energy optimization of bread baking process undergoing quality constraints

International Nuclear Information System (INIS)

Papasidero, Davide; Pierucci, Sauro; Manenti, Flavio

2016-01-01

International home energy rating regulations are forcing to use efficient cooking equipment and processes towards energy saving and sustainability. For this reason gas ovens are replaced by the electric ones, to get the highest energy rating. Due to this fact, the study of the technologies related to the energy efficiency in cooking is increasingly developing. Indeed, big industries are working to the energy optimization of their processes since decades, while there is still a lot of room in energy optimization of single household appliances. The achievement of a higher efficiency can have a big impact on the society only if the use of modern equipment gets widespread. The combination of several energy sources (e.g. forced convection, irradiation, microwave, etc.) and their optimization is an emerging target for oven manufacturers towards optimal oven design. In this work, an energy consumption analysis and optimization is applied to the case of bread baking. Each source of energy gets the due importance and the process conditions are compared. A basic quality standard is guaranteed by taking into account some quality markers, which are relevant based on a consumer viewpoint. - Highlights: • Energy optimization is based on a validated finite-element model for bread baking. • Quality parameters for the product acceptability are introduced as constraints. • Dynamic optimization leads to 20% energy saving compared to non-optimized case. • The approach is applicable to many products, quality parameters, thermal processes. • Other heating processes can be easily integrated in the presented model.

Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction

Directory of Open Access Journals (Sweden)

Jenessa Lancaster

2018-02-01

Full Text Available Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years we trained support vector machines to (i distinguish between young (<22 years and old (>50 years brains (classification and (ii predict chronological age (regression. We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years. Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm. For predicting chronological age, a mean absolute error (MAE of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm. This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian

Saturne II synchroton injector parameters operation and control: computerization and optimization

International Nuclear Information System (INIS)

Lagniel, J.M.

1983-01-01

The injector control system has been studied, aiming at the beam quality improvement, the increasing of the versatility, and a better machine availability. It has been choosen to realize the three following functions: - acquisition of the principal parameters of the process, so as to control them quickly and to be warned if one of them is wrong (monitoring); - the control of those parameters, one by one or by families (starting, operating point); - the research of an optimal control (on a model or on the process itself) [fr

Process Parameter Evaluation and Optimization for Advanced Material Development Final Report CRADA No. TC-1234-96

Energy Technology Data Exchange (ETDEWEB)

Hrubesh, L. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); McGann, T. W. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2017-10-19

This project was established as a three-year collaboration to produce and characterize · silica aerogels prepared by a Rapid Supercritical Extraction (RSCE) process to meet . BNA, Inc. application requirements. The objectives of this project were to study the parameters necessary to produce optimized aerogel parts with narrowly specified properties and establish the range and limits of the process for producing such aerogels. The project also included development of new aerogel materials useful for high temperature applications. The results of the project were expected to set the conditions necessary to produce quantities of aerogels having particular specifications such as size, shape, density, and mechanical strength. BNA, Inc. terminated the project on April 7, 1999, 10-months prior to the anticipated completion date, due to termination of corporate funding for the project. The technical accomplishments achieved are outlined in Paragraph C below.

Optimization of Agrobacterium -mediated transformation parameters ...

African Journals Online (AJOL)

Agrobacterium-mediated transformation factors for sweet potato embryogenic calli were optimized using -glucuronidase (GUS) as a reporter. The binary vector pTCK303 harboring the modified GUS gene driven by the CaMV 35S promoter was used. Transformation parameters were optimized including bacterial ...

Error propagation of partial least squares for parameters optimization in NIR modeling

Science.gov (United States)

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-01

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models.

Error propagation of partial least squares for parameters optimization in NIR modeling.

Science.gov (United States)

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-05

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models. Copyright © 2017. Published by Elsevier B.V.

Low-dose cone-beam CT via raw counts domain low-signal correction schemes: Performance assessment and task-based parameter optimization (Part II. Task-based parameter optimization).

Science.gov (United States)

Gomez-Cardona, Daniel; Hayes, John W; Zhang, Ran; Li, Ke; Cruz-Bastida, Juan Pablo; Chen, Guang-Hong

2018-05-01

Different low-signal correction (LSC) methods have been shown to efficiently reduce noise streaks and noise level in CT to provide acceptable images at low-radiation dose levels. These methods usually result in CT images with highly shift-variant and anisotropic spatial resolution and noise, which makes the parameter optimization process highly nontrivial. The purpose of this work was to develop a local task-based parameter optimization framework for LSC methods. Two well-known LSC methods, the adaptive trimmed mean (ATM) filter and the anisotropic diffusion (AD) filter, were used as examples to demonstrate how to use the task-based framework to optimize filter parameter selection. Two parameters, denoted by the set P, for each LSC method were included in the optimization problem. For the ATM filter, these parameters are the low- and high-signal threshold levels p l and p h ; for the AD filter, the parameters are the exponents δ and γ in the brightness gradient function. The detectability index d' under the non-prewhitening (NPW) mathematical observer model was selected as the metric for parameter optimization. The optimization problem was formulated as an unconstrained optimization problem that consisted of maximizing an objective function d'(P), where i and j correspond to the i-th imaging task and j-th spatial location, respectively. Since there is no explicit mathematical function to describe the dependence of d' on the set of parameters P for each LSC method, the optimization problem was solved via an experimentally measured d' map over a densely sampled parameter space. In this work, three high-contrast-high-frequency discrimination imaging tasks were defined to explore the parameter space of each of the LSC methods: a vertical bar pattern (task I), a horizontal bar pattern (task II), and a multidirectional feature (task III). Two spatial locations were considered for the analysis, a posterior region-of-interest (ROI) located within the noise streaks region

A Modified Penalty Parameter Approach for Optimal Estimation of UH with Simultaneous Estimation of Infiltration Parameters

Science.gov (United States)

Bhattacharjya, Rajib Kumar

2018-05-01

The unit hydrograph and the infiltration parameters of a watershed can be obtained from observed rainfall-runoff data by using inverse optimization technique. This is a two-stage optimization problem. In the first stage, the infiltration parameters are obtained and the unit hydrograph ordinates are estimated in the second stage. In order to combine this two-stage method into a single stage one, a modified penalty parameter approach is proposed for converting the constrained optimization problem to an unconstrained one. The proposed approach is designed in such a way that the model initially obtains the infiltration parameters and then searches the optimal unit hydrograph ordinates. The optimization model is solved using Genetic Algorithms. A reduction factor is used in the penalty parameter approach so that the obtained optimal infiltration parameters are not destroyed during subsequent generation of genetic algorithms, required for searching optimal unit hydrograph ordinates. The performance of the proposed methodology is evaluated by using two example problems. The evaluation shows that the model is superior, simple in concept and also has the potential for field application.

Electrocoagulation treatment of raw landfill leachate using iron-based electrodes: Effects of process parameters and optimization.

Science.gov (United States)

Huda, N; Raman, A A A; Bello, M M; Ramesh, S

2017-12-15

The main problem of landfill leachate is its diverse composition comprising many persistent organic pollutants which must be removed before being discharge into the environment. This study investigated the treatment of raw landfill leachate using electrocoagulation process. An electrocoagulation system was designed with iron as both the anode and cathode. The effects of inter-electrode distance, initial pH and electrolyte concentration on colour and COD removals were investigated. All these factors were found to have significant effects on the colour removal. On the other hand, electrolyte concentration was the most significant parameter affecting the COD removal. Numerical optimization was also conducted to obtain the optimum process performance. Under optimum conditions (initial pH: 7.73, inter-electrode distance: 1.16 cm, and electrolyte concentration (NaCl): 2.00 g/L), the process could remove up to 82.7% colour and 45.1% COD. The process can be applied as a pre-treatment for raw leachates before applying other appropriate treatment technologies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Tailored parameter optimization methods for ordinary differential equation models with steady-state constraints.

Science.gov (United States)

Fiedler, Anna; Raeth, Sebastian; Theis, Fabian J; Hausser, Angelika; Hasenauer, Jan

2016-08-22

Ordinary differential equation (ODE) models are widely used to describe (bio-)chemical and biological processes. To enhance the predictive power of these models, their unknown parameters are estimated from experimental data. These experimental data are mostly collected in perturbation experiments, in which the processes are pushed out of steady state by applying a stimulus. The information that the initial condition is a steady state of the unperturbed process provides valuable information, as it restricts the dynamics of the process and thereby the parameters. However, implementing steady-state constraints in the optimization often results in convergence problems. In this manuscript, we propose two new methods for solving optimization problems with steady-state constraints. The first method exploits ideas from optimization algorithms on manifolds and introduces a retraction operator, essentially reducing the dimension of the optimization problem. The second method is based on the continuous analogue of the optimization problem. This continuous analogue is an ODE whose equilibrium points are the optima of the constrained optimization problem. This equivalence enables the use of adaptive numerical methods for solving optimization problems with steady-state constraints. Both methods are tailored to the problem structure and exploit the local geometry of the steady-state manifold and its stability properties. A parameterization of the steady-state manifold is not required. The efficiency and reliability of the proposed methods is evaluated using one toy example and two applications. The first application example uses published data while the second uses a novel dataset for Raf/MEK/ERK signaling. The proposed methods demonstrated better convergence properties than state-of-the-art methods employed in systems and computational biology. Furthermore, the average computation time per converged start is significantly lower. In addition to the theoretical results, the

Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

Energy Technology Data Exchange (ETDEWEB)

Man, Jun [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Zhang, Jiangjiang [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Li, Weixuan [Pacific Northwest National Laboratory, Richland Washington USA; Zeng, Lingzao [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Wu, Laosheng [Department of Environmental Sciences, University of California, Riverside California USA

2016-10-01

The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.

Self-adaptive Green-Ampt infiltration parameters obtained from measured moisture processes

Directory of Open Access Journals (Sweden)

Long Xiang

2016-07-01

Full Text Available The Green-Ampt (G-A infiltration model (i.e., the G-A model is often used to characterize the infiltration process in hydrology. The parameters of the G-A model are critical in applications for the prediction of infiltration and associated rainfall-runoff processes. Previous approaches to determining the G-A parameters have depended on pedotransfer functions (PTFs or estimates from experimental results, usually without providing optimum values. In this study, rainfall simulators with soil moisture measurements were used to generate rainfall in various experimental plots. Observed runoff data and soil moisture dynamic data were jointly used to yield the infiltration processes, and an improved self-adaptive method was used to optimize the G-A parameters for various types of soil under different rainfall conditions. The two G-A parameters, i.e., the effective hydraulic conductivity and the effective capillary drive at the wetting front, were determined simultaneously to describe the relationships between rainfall, runoff, and infiltration processes. Through a designed experiment, the method for determining the G-A parameters was proved to be reliable in reflecting the effects of pedologic background in G-A type infiltration cases and deriving the optimum G-A parameters. Unlike PTF methods, this approach estimates the G-A parameters directly from infiltration curves obtained from rainfall simulation experiments so that it can be used to determine site-specific parameters. This study provides a self-adaptive method of optimizing the G-A parameters through designed field experiments. The parameters derived from field-measured rainfall-infiltration processes are more reliable and applicable to hydrological models.

Hybrid computer optimization of systems with random parameters

Science.gov (United States)

White, R. C., Jr.

1972-01-01

A hybrid computer Monte Carlo technique for the simulation and optimization of systems with random parameters is presented. The method is applied to the simultaneous optimization of the means and variances of two parameters in the radar-homing missile problem treated by McGhee and Levine.

Fine-Tuning ADAS Algorithm Parameters for Optimizing Traffic ...

Science.gov (United States)

With the development of the Connected Vehicle technology that facilitates wirelessly communication among vehicles and road-side infrastructure, the Advanced Driver Assistance Systems (ADAS) can be adopted as an effective tool for accelerating traffic safety and mobility optimization at various highway facilities. To this end, the traffic management centers identify the optimal ADAS algorithm parameter set that enables the maximum improvement of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. After adopting the optimal parameter set, the ADAS-equipped drivers become active agents in the traffic stream that work collectively and consistently to prevent traffic conflicts, lower the intensity of traffic disturbances, and suppress the development of traffic oscillations into heavy traffic jams. Successful implementation of this objective requires the analysis capability of capturing the impact of the ADAS on driving behaviors, and measuring traffic safety and mobility performance under the influence of the ADAS. To address this challenge, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through an optimization programming framework to enable th

Optimization of processing parameters on the controlled growth of c-axis oriented ZnO nanorod arrays

Energy Technology Data Exchange (ETDEWEB)

Malek, M. F., E-mail: mfmalek07@gmail.com; Rusop, M., E-mail: rusop@salam.uitm.my [NANO-ElecTronic Centre (NET), Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor (Malaysia); NANO-SciTech Centre (NST), Institute of Science (IOS), Universiti Teknologi MARA - UiTM, 40450 Shah Alam, Selangor (Malaysia); Mamat, M. H., E-mail: hafiz-030@yahoo.com [NANO-ElecTronic Centre (NET), Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor (Malaysia); Musa, M. Z., E-mail: musa948@gmail.com [NANO-ElecTronic Centre (NET), Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor (Malaysia); Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM) Pulau Pinang, Jalan Permatang Pauh, 13500 Permatang Pauh, Pulau Pinang (Malaysia); Saurdi, I., E-mail: saurdy788@gmail.com; Ishak, A., E-mail: ishak@sarawak.uitm.edu.my [NANO-ElecTronic Centre (NET), Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor (Malaysia); Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM) Sarawak, Kampus Kota Samarahan, Jalan Meranek, 94300 Kota Samarahan, Sarawak (Malaysia); Alrokayan, Salman A. H., E-mail: dr.salman@alrokayan.com; Khan, Haseeb A., E-mail: khan-haseeb@yahoo.com [Chair of Targeting and Treatment of Cancer Using Nanoparticles, Deanship of Scientific Research, King Saud University (KSU), Riyadh 11451 (Saudi Arabia)

2016-07-06

Optimization of the growth time parameter was conducted to synthesize high-quality c-axis ZnO nanorod arrays. The effects of the parameter on the crystal growth and properties were systematically investigated. Our studies confirmed that the growth time influence the properties of ZnO nanorods where the crystallite size of the structures was increased at higher deposition time. Field emission scanning electron microsope analysis confirmed the morphologies structure of the ZnO nanorods. The ZnO nanostructures prepared under the optimized growth conditions showed an intense XRD peak which reveal a higher c-axis oriented ZnO nanorod arrays thus demonstrating the formation of defect free structure.

A multicriteria framework with voxel-dependent parameters for radiotherapy treatment plan optimization

International Nuclear Information System (INIS)

Zarepisheh, Masoud; Uribe-Sanchez, Andres F.; Li, Nan; Jia, Xun; Jiang, Steve B.

2014-01-01

Purpose: To establish a new mathematical framework for radiotherapy treatment optimization with voxel-dependent optimization parameters. Methods: In the treatment plan optimization problem for radiotherapy, a clinically acceptable plan is usually generated by an optimization process with weighting factors or reference doses adjusted for a set of the objective functions associated to the organs. Recent discoveries indicate that adjusting parameters associated with each voxel may lead to better plan quality. However, it is still unclear regarding the mathematical reasons behind it. Furthermore, questions about the objective function selection and parameter adjustment to assure Pareto optimality as well as the relationship between the optimal solutions obtained from the organ-based and voxel-based models remain unanswered. To answer these questions, the authors establish in this work a new mathematical framework equipped with two theorems. Results: The new framework clarifies the different consequences of adjusting organ-dependent and voxel-dependent parameters for the treatment plan optimization of radiation therapy, as well as the impact of using different objective functions on plan qualities and Pareto surfaces. The main discoveries are threefold: (1) While in the organ-based model the selection of the objective function has an impact on the quality of the optimized plans, this is no longer an issue for the voxel-based model since the Pareto surface is independent of the objective function selection and the entire Pareto surface could be generated as long as the objective function satisfies certain mathematical conditions; (2) All Pareto solutions generated by the organ-based model with different objective functions are parts of a unique Pareto surface generated by the voxel-based model with any appropriate objective function; (3) A much larger Pareto surface is explored by adjusting voxel-dependent parameters than by adjusting organ-dependent parameters, possibly

Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area

Science.gov (United States)

Di, Zhenhua; Duan, Qingyun; Wang, Chen; Ye, Aizhong; Miao, Chiyuan; Gong, Wei

2018-03-01

Forecasting skills of the complex weather and climate models have been improved by tuning the sensitive parameters that exert the greatest impact on simulated results based on more effective optimization methods. However, whether the optimal parameter values are still work when the model simulation conditions vary, which is a scientific problem deserving of study. In this study, a highly-effective optimization method, adaptive surrogate model-based optimization (ASMO), was firstly used to tune nine sensitive parameters from four physical parameterization schemes of the Weather Research and Forecasting (WRF) model to obtain better summer precipitation forecasting over the Greater Beijing Area in China. Then, to assess the applicability of the optimal parameter values, simulation results from the WRF model with default and optimal parameter values were compared across precipitation events, boundary conditions, spatial scales, and physical processes in the Greater Beijing Area. The summer precipitation events from 6 years were used to calibrate and evaluate the optimal parameter values of WRF model. Three boundary data and two spatial resolutions were adopted to evaluate the superiority of the calibrated optimal parameters to default parameters under the WRF simulations with different boundary conditions and spatial resolutions, respectively. Physical interpretations of the optimal parameters indicating how to improve precipitation simulation results were also examined. All the results showed that the optimal parameters obtained by ASMO are superior to the default parameters for WRF simulations for predicting summer precipitation in the Greater Beijing Area because the optimal parameters are not constrained by specific precipitation events, boundary conditions, and spatial resolutions. The optimal values of the nine parameters were determined from 127 parameter samples using the ASMO method, which showed that the ASMO method is very highly-efficient for optimizing WRF

Optimal design criteria - prediction vs. parameter estimation

Science.gov (United States)

Waldl, Helmut

2014-05-01

G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.

A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design-Part I. Model development

Energy Technology Data Exchange (ETDEWEB)

He, L., E-mail: li.he@ryerson.ca [Department of Civil Engineering, Faculty of Engineering, Architecture and Science, Ryerson University, 350 Victoria Street, Toronto, Ontario, M5B 2K3 (Canada); Huang, G.H. [Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada); College of Urban Environmental Sciences, Peking University, Beijing 100871 (China); Lu, H.W. [Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada)

2010-04-15

Solving groundwater remediation optimization problems based on proxy simulators can usually yield optimal solutions differing from the 'true' ones of the problem. This study presents a new stochastic optimization model under modeling uncertainty and parameter certainty (SOMUM) and the associated solution method for simultaneously addressing modeling uncertainty associated with simulator residuals and optimizing groundwater remediation processes. This is a new attempt different from the previous modeling efforts. The previous ones focused on addressing uncertainty in physical parameters (i.e. soil porosity) while this one aims to deal with uncertainty in mathematical simulator (arising from model residuals). Compared to the existing modeling approaches (i.e. only parameter uncertainty is considered), the model has the advantages of providing mean-variance analysis for contaminant concentrations, mitigating the effects of modeling uncertainties on optimal remediation strategies, offering confidence level of optimal remediation strategies to system designers, and reducing computational cost in optimization processes.

Advanced Process Control Application and Optimization in Industrial Facilities

Directory of Open Access Journals (Sweden)

Howes S.

2015-01-01

Full Text Available This paper describes application of the new method and tool for system identification and PID tuning/advanced process control (APC optimization using the new 3G (geometric, gradient, gravity optimization method. It helps to design and implement control schemes directly inside the distributed control system (DCS or programmable logic controller (PLC. Also, the algorithm helps to identify process dynamics in closed-loop mode, optimizes controller parameters, and helps to develop adaptive control and model-based control (MBC. Application of the new 3G algorithm for designing and implementing APC schemes is presented. Optimization of primary and advanced control schemes stabilizes the process and allows the plant to run closer to process, equipment and economic constraints. This increases production rates, minimizes operating costs and improves product quality.

The Optimization of Process Parameters and Microstructural Characterization of Fiber Laser Welded Dissimilar HSLA and MART Steel Joints

Directory of Open Access Journals (Sweden)

Celalettin Yuce

2016-10-01

Full Text Available Nowadays, environmental impact, safety and fuel efficiency are fundamental issues for the automotive industry. These objectives are met by using a combination of different types of steels in the auto bodies. Therefore, it is important to have an understanding of how dissimilar materials behave when they are welded. This paper presents the process parameters’ optimization procedure of fiber laser welded dissimilar high strength low alloy (HSLA and martensitic steel (MART steel using a Taguchi approach. The influence of laser power, welding speed and focal position on the mechanical and microstructural properties of the joints was determined. The optimum parameters for the maximum tensile load-minimum heat input were predicted, and the individual significance of parameters on the response was evaluated by ANOVA results. The optimum levels of the process parameters were defined. Furthermore, microstructural examination and microhardness measurements of the selected welds were conducted. The samples of the dissimilar joints showed a remarkable microstructural change from nearly fully martensitic in the weld bead to the unchanged microstructure in the base metals. The heat affected zone (HAZ region of joints was divided into five subzones. The fusion zone resulted in an important hardness increase, but the formation of a soft zone in the HAZ region.

Intelligent methods for the process parameter determination of plastic injection molding

Science.gov (United States)

Gao, Huang; Zhang, Yun; Zhou, Xundao; Li, Dequn

2018-03-01

Injection molding is one of the most widely used material processing methods in producing plastic products with complex geometries and high precision. The determination of process parameters is important in obtaining qualified products and maintaining product quality. This article reviews the recent studies and developments of the intelligent methods applied in the process parameter determination of injection molding. These intelligent methods are classified into three categories: Case-based reasoning methods, expert system- based methods, and data fitting and optimization methods. A framework of process parameter determination is proposed after comprehensive discussions. Finally, the conclusions and future research topics are discussed.

Optimization of CO2 Laser Cutting Process using Taguchi and Dual Response Surface Methodology

Directory of Open Access Journals (Sweden)

M. Madić

2014-09-01

Full Text Available Selection of optimal cutting parameter settings for obtaining high cut quality in CO2 laser cutting process is of great importance. Among various analytical and experimental optimization methods, the application of Taguchi and response surface methodology is one of most commonly used for laser cutting process optimization. Although the concept of dual response surface methodology for process optimization has been used with success, till date, no experimental study has been reported in the field of laser cutting. In this paper an approach for optimization of CO2 laser cutting process using Taguchi and dual response surface methodology is presented. The goal was to determine the near optimal laser cutting parameter values in order to ensure robust condition for minimization of average surface roughness. To obtain experimental database for development of response surface models, Taguchi’s L25 orthogonal array was implemented for experimental plan. Three cutting parameters, the cutting speed (3, 4, 5, 6, 7 m/min, the laser power (0.7, 0.9, 1.1, 1.3, 1.5 kW, and the assist gas pressure (3, 4, 5, 6, 7 bar, were used in the experiment. To obtain near optimal cutting parameters settings, multi-stage Monte Carlo simulation procedure was performed on the developed response surface models.

APPLICATION OF GENETIC ALGORITHMS FOR ROBUST PARAMETER OPTIMIZATION

Directory of Open Access Journals (Sweden)

N. Belavendram

2010-12-01

Full Text Available Parameter optimization can be achieved by many methods such as Monte-Carlo, full, and fractional factorial designs. Genetic algorithms (GA are fairly recent in this respect but afford a novel method of parameter optimization. In GA, there is an initial pool of individuals each with its own specific phenotypic trait expressed as a ‘genetic chromosome’. Different genes enable individuals with different fitness levels to reproduce according to natural reproductive gene theory. This reproduction is established in terms of selection, crossover and mutation of reproducing genes. The resulting child generation of individuals has a better fitness level akin to natural selection, namely evolution. Populations evolve towards the fittest individuals. Such a mechanism has a parallel application in parameter optimization. Factors in a parameter design can be expressed as a genetic analogue in a pool of sub-optimal random solutions. Allowing this pool of sub-optimal solutions to evolve over several generations produces fitter generations converging to a pre-defined engineering optimum. In this paper, a genetic algorithm is used to study a seven factor non-linear equation for a Wheatstone bridge as the equation to be optimized. A comparison of the full factorial design against a GA method shows that the GA method is about 1200 times faster in finding a comparable solution.

Optimization of process parameters for ethanol production from sugar cane molasses by Zymomonas mobilis using response surface methodology and genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Maiti, Bodhisatta; Shekhawat, Mitali; Srivastava, Pradeep [Banaras Hindu Univ., Varanasi (India). School of Biochemical Engineering; Rathore, Ankita [Nizam College, Hyderabad (India). Dept. of Biotechnology; Srivastava, Saurav [National Institute of Technology, Durgapur (India). Dept. of Biotechnology

2011-04-15

Ethanol is a potential energy source and its production from renewable biomass has gained lot of popularity. There has been worldwide research to produce ethanol from regional inexpensive substrates. The present study deals with the optimization of process parameters (viz. temperature, pH, initial total reducing sugar (TRS) concentration in sugar cane molasses and fermentation time) for ethanol production from sugar cane molasses by Zymomonas mobilis using Box-Behnken experimental design and genetic algorithm (GA). An empirical model was developed through response surface methodology to analyze the effects of the process parameters on ethanol production. The data obtained after performing the experiments based on statistical design was utilized for regression analysis and analysis of variance studies. The regression equation obtained after regression analysis was used as a fitness function for the genetic algorithm. The GA optimization technique predicted a maximum ethanol yield of 59.59 g/L at temperature 31 C, pH 5.13, initial TRS concentration 216 g/L and fermentation time 44 h. The maximum experimental ethanol yield obtained after applying GA was 58.4 g/L, which was in close agreement with the predicted value. (orig.)

Intensification of the Reverse Cationic Flotation of Hematite Ores with Optimization of Process and Hydrodynamic Parameters of Flotation Cell

Science.gov (United States)

Poperechnikova, O. Yu; Filippov, L. O.; Shumskaya, E. N.; Filippova, I. V.

2017-07-01

The demand of high grade iron ore concentrates is a major issue due to the depletion of rich iron-bearing ores and high competitiveness in the iron ore market. Iron ore production is forced out to upgrade flowsheets to decrease the silica content in the pelettes. Different types of ore have different mineral composition and texture-structural features which require different mineral processing methods and technologies. The paper presents a comparative study of the cationic and anionic flotation routes to process a fine-grain oxidized iron ore. The modified carboxymethyl cellulose was found as the most efficient depressant in reverse cationic flotation. The results of flotation optimization of hematite ores using matrix of second-order center rotatable uniform design allowed to define the collector concentration, impeller rotation speed and air flowrate as the main flotation parameters impacting on the iron ore concentrate quality and iron recovery in a laboratory flotation machine. These parameters have been selected as independent during the experiments.

Multi-parameter optimization design of parabolic trough solar receiver

International Nuclear Information System (INIS)

Guo, Jiangfeng; Huai, Xiulan

2016-01-01

Highlights: • The optimal condition can be obtained by multi-parameter optimization. • Exergy and thermal efficiencies are employed as objective function. • Exergy efficiency increases at the expense of heat losses. • The heat obtained by working fluid increases as thermal efficiency grows. - Abstract: The design parameters of parabolic trough solar receiver are interrelated and interact with one another, so the optimal performance of solar receiver cannot be obtained by the convectional single-parameter optimization. To overcome the shortcoming of single-parameter optimization, a multi-parameter optimization of parabolic trough solar receiver is employed based on genetic algorithm in the present work. When the thermal efficiency is taken as the objective function, the heat obtained by working fluid increases while the average temperature of working fluid and wall temperatures of solar receiver decrease. The average temperature of working fluid and the wall temperatures of solar receiver increase while the heat obtained by working fluid decreases generally by taking the exergy efficiency as an objective function. Assuming that the solar radiation intensity remains constant, the exergy obtained by working fluid increases by taking exergy efficiency as the objective function, which comes at the expense of heat losses of solar receiver.

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

Science.gov (United States)

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme. PMID:27362762

A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design--part I. Model development.

Science.gov (United States)

He, L; Huang, G H; Lu, H W

2010-04-15

Solving groundwater remediation optimization problems based on proxy simulators can usually yield optimal solutions differing from the "true" ones of the problem. This study presents a new stochastic optimization model under modeling uncertainty and parameter certainty (SOMUM) and the associated solution method for simultaneously addressing modeling uncertainty associated with simulator residuals and optimizing groundwater remediation processes. This is a new attempt different from the previous modeling efforts. The previous ones focused on addressing uncertainty in physical parameters (i.e. soil porosity) while this one aims to deal with uncertainty in mathematical simulator (arising from model residuals). Compared to the existing modeling approaches (i.e. only parameter uncertainty is considered), the model has the advantages of providing mean-variance analysis for contaminant concentrations, mitigating the effects of modeling uncertainties on optimal remediation strategies, offering confidence level of optimal remediation strategies to system designers, and reducing computational cost in optimization processes. 2009 Elsevier B.V. All rights reserved.

Importance of design optimization of gamma processing plants

International Nuclear Information System (INIS)

George, Jain Reji

2014-01-01

Radiation processing of food commodities using ionizing radiations is well established world wide. In India too, novel designs are coming up for food irradiation as well as for multiproduct irradiation. It has been observed that though the designs of the product movement systems are excelling, the actual purpose for which the designs are made are failing in some. In such situations it is difficult to achieve an effective dose delivery by controlling the process parameters or even by modifying the source activity distribution without compromising some other aspects like throughput. It is very essential to arrive at an optimization in all components such as radiation source geometry, source product geometry and protective barriers of an irradiator system. Optimization of the various parameters can be done by modeling and analysis of the design

Optimal control of a CSTR process

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A. Soukkou

2008-12-01

Full Text Available Designing an effective criterion and learning algorithm for find the best structure is a major problem in the control design process. In this paper, the fuzzy optimal control methodology is applied to the design of the feedback loops of an Exothermic Continuous Stirred Tank Reactor system. The objective of design process is to find an optimal structure/gains of the Robust and Optimal Takagi Sugeno Fuzzy Controller (ROFLC. The control signal thus obtained will minimize a performance index, which is a function of the tracking/regulating errors, the quantity of the energy of the control signal applied to the system, and the number of fuzzy rules. The genetic learning is proposed for constructing the ROFLC. The chromosome genes are arranged into two parts, the binary-coded part contains the control genes and the real-coded part contains the genes parameters representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the ROFLC are compared to these found by the traditional PD controller with Genetic Optimization (PD_GO. Simulations demonstrate that the proposed ROFLC and PD_GO has successfully met the design specifications.

A New Method for Determining Optimal Regularization Parameter in Near-Field Acoustic Holography

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Yue Xiao

2018-01-01

Full Text Available Tikhonov regularization method is effective in stabilizing reconstruction process of the near-field acoustic holography (NAH based on the equivalent source method (ESM, and the selection of the optimal regularization parameter is a key problem that determines the regularization effect. In this work, a new method for determining the optimal regularization parameter is proposed. The transfer matrix relating the source strengths of the equivalent sources to the measured pressures on the hologram surface is augmented by adding a fictitious point source with zero strength. The minimization of the norm of this fictitious point source strength is as the criterion for choosing the optimal regularization parameter since the reconstructed value should tend to zero. The original inverse problem in calculating the source strengths is converted into a univariate optimization problem which is solved by a one-dimensional search technique. Two numerical simulations with a point driven simply supported plate and a pulsating sphere are investigated to validate the performance of the proposed method by comparison with the L-curve method. The results demonstrate that the proposed method can determine the regularization parameter correctly and effectively for the reconstruction in NAH.

Multi Objective Optimization of Weld Parameters of Boiler Steel Using Fuzzy Based Desirability Function

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M. Satheesh

2014-01-01

Full Text Available The high pressure differential across the wall of pressure vessels is potentially dangerous and has caused many fatal accidents in the history of their development and operation. For this reason the structural integrity of weldments is critical to the performance of pressure vessels. In recent years much research has been conducted to the study of variations in welding parameters and consumables on the mechanical properties of pressure vessel steel weldments to optimize weld integrity and ensure pressure vessels are safe. The quality of weld is a very important working aspect for the manufacturing and construction industries. Because of high quality and reliability, Submerged Arc Welding (SAW is one of the chief metal joining processes employed in industry. This paper addresses the application of desirability function approach combined with fuzzy logic analysis to optimize the multiple quality characteristics (bead reinforcement, bead width, bead penetration and dilution of submerged arc welding process parameters of SA 516 Grade 70 steels(boiler steel. Experiments were conducted using Taguchi’s L27 orthogonal array with varying the weld parameters of welding current, arc voltage, welding speed and electrode stickout. By analyzing the response table and response graph of the fuzzy reasoning grade, optimal parameters were obtained. Solutions from this method can be useful for pressure vessel manufacturers and operators to search an optimal solution of welding condition.

Optimization of friction stir welding process parameters to maximize tensile strength of stir cast AA6061-T6/AlNp composite

International Nuclear Information System (INIS)

Ashok Kumar, B.; Murugan, N.

2014-01-01

Highlights: • AA6061/AlN p cast composite was welded by FSW process. • Regression models were developed to predict UTS and elongation of the FS welded joint. • FS welded joint using the optimized parameters exhibited maximum UTS and joint efficiency. • Defect free weld joint was obtained with optimized parameters value. - Abstract: Aluminium Matrix Composites (AMCs) reinforced with particulate form of reinforcement has replaced monolithic alloys in many engineering industries due to its superior mechanical properties and tailorable thermal and electrical properties. As aluminium nitride (AlN) has high specific strength, high thermal conductivity, high electrical resistivity, low dielectric constant, low coefficient of thermal expansion and good compatibility with aluminium alloy, Al/AlN composite is extensively used in electronic packaging industries. Joining of AMCs is unavoidable in many engineering applications. Friction Stir Welding (FSW) is one of the most suitable welding process to weld the AMCs reinforced with particulate form of ceramics without deteriorating its superior mechanical properties. An attempt has been made to develop regression models to predict the Ultimate Tensile Strength (UTS) and Percent Elongation (PE) of the friction stir welded AA6061 matrix composite reinforced with aluminium nitride particles (AlN p ) by correlating the significant parameters such as tool rotational speed, welding speed, axial force and percentage of AlN p reinforcement in the AA6061 matrix. Statistical software SYSTAT 12 and statistical tools such as analysis of variance (ANOVA) and student’s t test, have been used to validate the developed models. It was observed from the investigation that these factors independently influenced the UTS and PE of the friction stir welded composite joints. The developed regression models were optimized to maximize UTS of friction stir welded AA6061/AlN p composite joints

Optimization of basic parameters of cyclic operation of underground gas storages

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Віктор Олександрович Заєць

2015-04-01

Full Text Available The problem of optimization of process parameters of cyclic operation of underground gas storages in gas mode is determined in the article. The target function is defined, expressing necessary capacity of compressor station for gas injection in the storage. Its minimization will find the necessary technological parameters, such as flow and reservoir pressure change over time. Limitations and target function are reduced to a linear form. Solution of problems is made by the simplex method

An Iterative Optimization Algorithm for Lens Distortion Correction Using Two-Parameter Models

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Daniel Santana-Cedrés

2016-12-01

Full Text Available We present a method for the automatic estimation of two-parameter radial distortion models, considering polynomial as well as division models. The method first detects the longest distorted lines within the image by applying the Hough transform enriched with a radial distortion parameter. From these lines, the first distortion parameter is estimated, then we initialize the second distortion parameter to zero and the two-parameter model is embedded into an iterative nonlinear optimization process to improve the estimation. This optimization aims at reducing the distance from the edge points to the lines, adjusting two distortion parameters as well as the coordinates of the center of distortion. Furthermore, this allows detecting more points belonging to the distorted lines, so that the Hough transform is iteratively repeated to extract a better set of lines until no improvement is achieved. We present some experiments on real images with significant distortion to show the ability of the proposed approach to automatically correct this type of distortion as well as a comparison between the polynomial and division models.

On the role of modeling parameters in IMRT plan optimization

International Nuclear Information System (INIS)

Krause, Michael; Scherrer, Alexander; Thieke, Christian

2008-01-01

The formulation of optimization problems in intensity-modulated radiotherapy (IMRT) planning comprises the choice of various values such as function-specific parameters or constraint bounds. In current inverse planning programs that yield a single treatment plan for each optimization, it is often unclear how strongly these modeling parameters affect the resulting plan. This work investigates the mathematical concepts of elasticity and sensitivity to deal with this problem. An artificial planning case with a horse-shoe formed target with different opening angles surrounding a circular risk structure is studied. As evaluation functions the generalized equivalent uniform dose (EUD) and the average underdosage below and average overdosage beyond certain dose thresholds are used. A single IMRT plan is calculated for an exemplary parameter configuration. The elasticity and sensitivity of each parameter are then calculated without re-optimization, and the results are numerically verified. The results show the following. (1) elasticity can quantify the influence of a modeling parameter on the optimization result in terms of how strongly the objective function value varies under modifications of the parameter value. It also can describe how strongly the geometry of the involved planning structures affects the optimization result. (2) Based on the current parameter settings and corresponding treatment plan, sensitivity analysis can predict the optimization result for modified parameter values without re-optimization, and it can estimate the value intervals in which such predictions are valid. In conclusion, elasticity and sensitivity can provide helpful tools in inverse IMRT planning to identify the most critical parameters of an individual planning problem and to modify their values in an appropriate way

Optimization of Blending Parameters and Fiber Size of Kenaf-Bast-Fiber-Reinforced the Thermoplastic Polyurethane Composites by Taguchi Method

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Y. A. El-Shekeil

2013-01-01

Full Text Available “Kenaf-fibers- (KF-” reinforced “thermoplastic polyurethane (TPU” composites were prepared by the melt-blending method followed by compression molding. Composite specimens were cut from the sheets that were prepared by compression molding. The criteria of optimization were testing the specimens by tensile test and comparing the ultimate tensile strength. The aim of this study is to optimize processing parameters (e.g., processing temperature, time, and speed and fiber size using the Taguchi approach. These four parameters were investigated in three levels each. The L9 orthogonal array was used based on the number of parameters and levels that has been selected. Furthermore, analysis of variance (ANOVA was used to determine the significance of different parameters. The results showed that the optimum values were 180°C, 50 rpm, 13 min, and 125–300 micron for processing temperature, processing speed, processing time, and fiber size, respectively. Using ANOVA, processing temperature showed the highest significance value followed by fiber size. Processing time and speed did not show any significance on the optimization of TPU/KF.

Trafficability Analysis at Traffic Crossing and Parameters Optimization Based on Particle Swarm Optimization Method

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Bin He

2014-01-01

Full Text Available In city traffic, it is important to improve transportation efficiency and the spacing of platoon should be shortened when crossing the street. The best method to deal with this problem is automatic control of vehicles. In this paper, a mathematical model is established for the platoon’s longitudinal movement. A systematic analysis of longitudinal control law is presented for the platoon of vehicles. However, the parameter calibration for the platoon model is relatively difficult because the platoon model is complex and the parameters are coupled with each other. In this paper, the particle swarm optimization method is introduced to effectively optimize the parameters of platoon. The proposed method effectively finds the optimal parameters based on simulations and makes the spacing of platoon shorter.

OPTIMIZATION OF FLOCCULATION PROCESS BY MICROBIAL COAGULANT IN RIVER WATER

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Fatin Nabilah Murad

2017-12-01

Full Text Available The existing process of coagulation and flocculation are using chemicals that known as cationic coagulant such as alum, ferric sulfate, calcium oxide, and organic polymers.  Thus, this study concentrates on optimizing of flocculation process by microbial coagulant in river water. Turbidity and suspended solids are the main constraints of river water quality in Malaysia. Hence, a study is proposed to produce microbial coagulants isolated locally for river water treatment. The chosen microbe used as the bioflocculant producer is Aspergillus niger. The parameters to optimization in the flocculation process were pH, bioflocculant dosage and effluent concentration. The research was done in the jar test process and the process parameters for maximum turbidity removal was validated. The highest flocculating activity was obtained on day seven of cultivation in the supernatant. The optimum pH and bioflocculant dosage for an optimize sedimentation process were between 4-5 and 2-3 mL for 0.3 g/L of effluent concentration respectively. The model was validated by using a river water sample from Sg. Pusu and the result showed that the model was acceptable to evaluate the bioflocculation process.

Multi objective optimization model for minimizing production cost and environmental impact in CNC turning process

Science.gov (United States)

Widhiarso, Wahyu; Rosyidi, Cucuk Nur

2018-02-01

Minimizing production cost in a manufacturing company will increase the profit of the company. The cutting parameters will affect total processing time which then will affect the production cost of machining process. Besides affecting the production cost and processing time, the cutting parameters will also affect the environment. An optimization model is needed to determine the optimum cutting parameters. In this paper, we develop an optimization model to minimize the production cost and the environmental impact in CNC turning process. The model is used a multi objective optimization. Cutting speed and feed rate are served as the decision variables. Constraints considered are cutting speed, feed rate, cutting force, output power, and surface roughness. The environmental impact is converted from the environmental burden by using eco-indicator 99. Numerical example is given to show the implementation of the model and solved using OptQuest of Oracle Crystal Ball software. The results of optimization indicate that the model can be used to optimize the cutting parameters to minimize the production cost and the environmental impact.

Multivariate optimization of ILC parameters

International Nuclear Information System (INIS)

Bazarov, I.V.; Padamsee, H.S.

2005-01-01

We present results of multiobjective optimization of the International Linear Collider (ILC) which seeks to maximize luminosity at each given total cost of the linac (capital and operating costs of cryomodules, refrigeration and RF). Evolutionary algorithms allow quick exploration of optimal sets of parameters in a complicated system such as ILC in the presence of realistic constraints as well as investigation of various what-if scenarios in potential performance. Among the parameters we varied there were accelerating gradient and Q of the cavities (in a coupled manner following a realistic Q vs. E curve), the number of particles per bunch, the bunch length, number of bunches in the train, etc. We find an optimum which decreases (relative to TESLA TDR baseline) the total linac cost by 22%, capital cost by 25% at the same luminosity of 3 x 10 38 m -2 s -1 . For this optimum the gradient is 35 MV/m, the final spot size is 3.6 nm, and the beam power is 15.9 MV/m. Changing the luminosity by 10 38 m -2 s -1 results in 10% change in the total linac cost and 4% in the capital cost. We have also explored the optimal fronts of luminosity vs. cost for several other scenarios using the same approach. (orig.)

Genetic Algorithm-Based Optimization for Surface Roughness in Cylindrically Grinding Process Using Helically Grooved Wheels

Science.gov (United States)

Çaydaş, Ulaş; Çelik, Mahmut

The present work is focused on the optimization of process parameters in cylindrical surface grinding of AISI 1050 steel with grooved wheels. Response surface methodology (RSM) and genetic algorithm (GA) techniques were merged to optimize the input variable parameters of grinding. The revolution speed of workpiece, depth of cut and number of grooves on the wheel were changed to explore their experimental effects on the surface roughness of machined bars. The mathematical models were established between the input parameters and response by using RSM. Then, the developed RSM model was used as objective functions on GA to optimize the process parameters.

Optimizing parameters of a technical system using quality function deployment method

Science.gov (United States)

Baczkowicz, M.; Gwiazda, A.

2015-11-01

The article shows the practical use of Quality Function Deployment (QFD) on the example of a mechanized mining support. Firstly it gives a short description of this method and shows how the designing process, from the constructor point of view, looks like. The proposed method allows optimizing construction parameters and comparing them as well as adapting to customer requirements. QFD helps to determine the full set of crucial construction parameters and then their importance and difficulty of their execution. Secondly it shows chosen technical system and presents its construction with figures of the existing and future optimized model. The construction parameters were selected from the designer point of view. The method helps to specify a complete set of construction parameters, from the point of view, of the designed technical system and customer requirements. The QFD matrix can be adjusted depending on designing needs and not every part of it has to be considered. Designers can choose which parts are the most important. Due to this QFD can be a very flexible tool. The most important is to define relationships occurring between parameters and that part cannot be eliminated from the analysis.

Optimization of the process of egg omelet production with fillings with extended storage period

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V. Sukmanov

2015-05-01

Full Text Available Introduction. Optimization of the egg omelets (EO production using high pressure (HP will allow to produce a minimum cost product during manufacturing and also to obtain a product with high consumer properties. Materialsand methods. The concerned product is -EO -a mixture of liquid egg with grated or chopped cheese, xanthan gum, water or milk and spices. The EO manufacturing process consisted of packing the mixture in an airtight container with heating and processing in the high pressure installation. The EO suitability for long-term storage was evaluated by the "water activity" term. The EO quality was evaluated by an expert. There was used the undetermined Lagrange multipliers method to obtain the optimal process parameters. Results. As a result of the central composite rotatabel plan there was developed optimization model allowed to obtain the optimal EO HP processing parameters: pressure – 690 МPа, temperature –1220С, treatment duration –7×60s, 14g of water on 100 g of melange, 13 g of dry milk on 100 g of melange, xanthan gum content -0,75% of the total mixture mass, 25 g of cheese on 100 g of melange. These indicators allow to obtain the EO process parameters with the next indicators: water activity -0.704 and comprehensive quality Score - 0.98 that characterize the product as a product with high quality indicators stable over a long period of storage. The developed model analysis with using of Student's t test, Fisher dyspepsia and predicted optimization values calculation errors confirmed the reliability of the optimization parameters obtained values and the optimization model reliability. The calculations results for the given optimization parameters are presented as confidence intervals, confirming that their experimental values do not exceed the respective intervals and thus confirm the results authenticity . Conclusions. These results have practical significance and were adopted as the basis for the technical documentation

Fractional Order Controller Designing with Firefly Algorithm and Parameter Optimization for Hydroturbine Governing System

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Li Junyi

2015-01-01

Full Text Available A fractional order PID (FOPID controller, which is suitable for control system designing for being insensitive to the variation in system parameter, is proposed for hydroturbine governing system in the paper. The simultaneous optimization for several parameters of controller, that is, Ki, Kd, Kp, λ, and μ, is done by a recently developed metaheuristic nature-inspired algorithm, namely, the firefly algorithm (FA, for the first time, where the selecting, moving, attractiveness behavior between fireflies and updating of brightness, and decision range are studied in detail to simulate the optimization process. Investigation clearly reveals the advantages of the FOPID controller over the integer controllers in terms of reduced oscillations and settling time. The present work also explores the superiority of FA based optimization technique in finding optimal parameters of the controller. Further, convergence characteristics of the FA are compared with optimum integer order PID (IOPID controller to justify its efficiency. What is more, analysis confirms the robustness of FOPID controller under isolated load operation conditions.

Optimization Design of Multi-Parameters in Rail Launcher System

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Yujiao Zhang

2014-05-01

Full Text Available Today the energy storage systems are still encumbering, therefore it is useful to think about the optimization of a railgun system in order to achieve the best performance with the lowest energy input. In this paper, an optimal design method considering 5 parameters is proposed to improve the energy conversion efficiency of a simple railgun. In order to avoid costly trials, the field- circuit method is employed to analyze the operations of different structural railguns with different parameters respectively. And the orthogonal test approach is used to guide the simulation for choosing the better parameter combinations, as well reduce the calculation cost. The research shows that the proposed method gives a better result in the energy efficiency of the system. To improve the energy conversion efficiency of electromagnetic rail launchers, the selection of more parameters must be considered in the design stage, such as the width, height and length of rail, the distance between rail pair, and pulse forming inductance. However, the relationship between these parameters and energy conversion efficiency cannot be directly described by one mathematical expression. So optimization methods must be applied to conduct design. In this paper, a rail launcher with five parameters was optimized by using orthogonal test method. According to the arrangement of orthogonal table, the better parameters’ combination can be obtained through less calculation. Under the condition of different parameters’ value, field and circuit simulation analysis were made. The results show that the energy conversion efficiency of the system is increased by 71.9 % after parameters optimization.

Information theoretic methods for image processing algorithm optimization

Science.gov (United States)

Prokushkin, Sergey F.; Galil, Erez

2015-01-01

Modern image processing pipelines (e.g., those used in digital cameras) are full of advanced, highly adaptive filters that often have a large number of tunable parameters (sometimes > 100). This makes the calibration procedure for these filters very complex, and the optimal results barely achievable in the manual calibration; thus an automated approach is a must. We will discuss an information theory based metric for evaluation of algorithm adaptive characteristics ("adaptivity criterion") using noise reduction algorithms as an example. The method allows finding an "orthogonal decomposition" of the filter parameter space into the "filter adaptivity" and "filter strength" directions. This metric can be used as a cost function in automatic filter optimization. Since it is a measure of a physical "information restoration" rather than perceived image quality, it helps to reduce the set of the filter parameters to a smaller subset that is easier for a human operator to tune and achieve a better subjective image quality. With appropriate adjustments, the criterion can be used for assessment of the whole imaging system (sensor plus post-processing).

Optimization of welding parameters using a genetic algorithm: A robotic arm–assisted implementation for recovery of Pelton turbine blades

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Luis Pérez Pozo

2015-11-01

Full Text Available This work presents the operational optimization of a welding operation involving using genetic algorithms. The welding curves correspond to the profile of a blade-shaped Pelton turbine. The procedure involved the development of a series of tests and observation of the parameters that will be controlled during the welding process. After the tests were performed, the samples were prepared for chemical attack, which allowed observation of the penetration, weld area, and dilution. After that, mathematical models were developed that correlate the controllable welding parameters with the aforementioned bead parameters. In those mathematical models, the optimization of the process parameters was performed using genetic algorithms. Specially programmed functions for mutation, reproduction, and initialization processes were written and used in the implemented model. After the optimization process was completed, the results were evaluated through new tests to verify whether the obtained objective functions properly describe the characteristics of the weld. The comparisons showed errors of less than 6%.

A procedure for multi-objective optimization of tire design parameters

OpenAIRE

Nikola Korunović; Miloš Madić; Miroslav Trajanović; Miroslav Radovanović

2015-01-01

The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zo...

Evaluation of Injection Molding Process Parameters for Manufacturing Polyethylene Terephthalate

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Marwah O.M.F.

2017-01-01

Full Text Available Quality control is an important aspect in manufacturing process. The quality of product in injection moulding is influenced by injection moulding process parameter. In this study, the effect of injection moulding parameter on defects quantity of PET preform was investigated. Optimizing the parameter of injection moulding process is critical to enhance productivity where parameters must operate at an optimum level for an acceptable performance. Design of Experiment (DOE by factorial design approach was used to find an optimum parameter setting and reduce the defects. In this case study, Minitab 17 software was used to analyses the data. The selected input parameters were mould hot runner temperature, water cooling chiller temperature 1 and water cooling chiller temperature 2. Meanwhile, the output for the process was defects quantity of the preform. The relationship between input and output of the process was analyzed using regression method and Analysis of Variance (ANOVA. In order to interpolate the experiment data, mathematical modeling was used which consists of different types of regression equation. Next, from the model, 95% confidence level (p-value was considered and the significant parameter was figured out. This study involved a collaboration with a preform injection moulding company which was Nilai Legasi Plastik Sdn Bhd. The collaboration enabled the researchers to collect the data and also help the company to improve the quality of its production. The results of the study showed that the optimum parameter setting that could reduce the defect quantity of preform was MHR= 88°C, CT1= 24°C and CT2= 27°C. The comparison defect quantity analysis between current companies setting with the optimum setting showed improvement by 21% reduction of defect quantity at the optimum setting. Finally, from the optimization plot, the validation error between the prediction value and experiment was 1.72%. The result proved that quality of products

Optimal nonlinear information processing capacity in delay-based reservoir computers

Science.gov (United States)

Grigoryeva, Lyudmila; Henriques, Julie; Larger, Laurent; Ortega, Juan-Pablo

2015-09-01

Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus in a particular kind of time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters. This article addresses the reservoir design problem, which remains the biggest challenge in the applicability of this information processing scheme. More specifically, we use the information available regarding the optimal reservoir working regimes to construct a functional link between the reservoir parameters and its performance. This function is used to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scannings used so far in the literature.

Finite element analysis and optimization of process parameters during stamp forming of composite materials

International Nuclear Information System (INIS)

Venkatesan, S; Kalyanasundaram, S

2010-01-01

In the manufacture of parts for high performance structures using composite materials, the quality and robustness of the parts is of utmost importance. The quality of the produced parts depends largely on the process parameters and manufacturing methodologies. This study presents the use of a temperature dependant orthotropic material for a coupled structural-thermal analysis of the stamp forming process. The study investigated the effects of process parameters such as pre-heat temperature, blank holder force and process time on the formability of composite materials. Temperature was found to be the dominant factor governing the formability of the composite material while higher blank holder forces were deemed to be important for achieving high quality of the parts manufactured. Finally, an optimum set of parameters was used to compare the simulations with experimental results using an optical strain measurement system.

Optimization and Simulation of SLM Process for High Density H13 Tool Steel Parts

Science.gov (United States)

Laakso, Petri; Riipinen, Tuomas; Laukkanen, Anssi; Andersson, Tom; Jokinen, Antero; Revuelta, Alejandro; Ruusuvuori, Kimmo

This paper demonstrates the successful printing and optimization of processing parameters of high-strength H13 tool steel by Selective Laser Melting (SLM). D-Optimal Design of Experiments (DOE) approach is used for parameter optimization of laser power, scanning speed and hatch width. With 50 test samples (1×1×1cm) we establish parameter windows for these three parameters in relation to part density. The calculated numerical model is found to be in good agreement with the density data obtained from the samples using image analysis. A thermomechanical finite element simulation model is constructed of the SLM process and validated by comparing the calculated densities retrieved from the model with the experimentally determined densities. With the simulation tool one can explore the effect of different parameters on density before making any printed samples. Establishing a parameter window provides the user with freedom for parameter selection such as choosing parameters that result in fastest print speed.

Optimization of Cutting Parameters on Delamination of Drilling Glass-Polyester Composites

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Majid Habeeb Faidh-Allah

2018-02-01

Full Text Available This paper attempted to study the effect of cutting parameters (spindle speed and feed rate on delamination phenomena during the drilling glass-polyester composites. Drilling process was done by CNC machine with 10 mm diameter of high-speed steel (HSS drill bit. Taguchi technique with L16 orthogonal layout was used to analyze the effective parameters on delamination factor. The optimal experiment was no. 13 with spindle speed 1273 rpm and feed 0.05 mm/rev with minimum delamination factor 1.28.

Optimal correction and design parameter search by modern methods of rigorous global optimization

International Nuclear Information System (INIS)

Makino, K.; Berz, M.

2011-01-01

Frequently the design of schemes for correction of aberrations or the determination of possible operating ranges for beamlines and cells in synchrotrons exhibit multitudes of possibilities for their correction, usually appearing in disconnected regions of parameter space which cannot be directly qualified by analytical means. In such cases, frequently an abundance of optimization runs are carried out, each of which determines a local minimum depending on the specific chosen initial conditions. Practical solutions are then obtained through an often extended interplay of experienced manual adjustment of certain suitable parameters and local searches by varying other parameters. However, in a formal sense this problem can be viewed as a global optimization problem, i.e. the determination of all solutions within a certain range of parameters that lead to a specific optimum. For example, it may be of interest to find all possible settings of multiple quadrupoles that can achieve imaging; or to find ahead of time all possible settings that achieve a particular tune; or to find all possible manners to adjust nonlinear parameters to achieve correction of high order aberrations. These tasks can easily be phrased in terms of such an optimization problem; but while mathematically this formulation is often straightforward, it has been common belief that it is of limited practical value since the resulting optimization problem cannot usually be solved. However, recent significant advances in modern methods of rigorous global optimization make these methods feasible for optics design for the first time. The key ideas of the method lie in an interplay of rigorous local underestimators of the objective functions, and by using the underestimators to rigorously iteratively eliminate regions that lie above already known upper bounds of the minima, in what is commonly known as a branch-and-bound approach. Recent enhancements of the Differential Algebraic methods used in particle

Taguchi design optimization of machining parameters on the CNC end milling process of halloysite nanotube with aluminium reinforced epoxy matrix (HNT/Al/Ep hybrid composite

Directory of Open Access Journals (Sweden)

J.S. Pang

2014-08-01

Full Text Available This paper introduces the application of Taguchi optimization methodology in optimizing the cutting parameters of end-milling process for machining the halloysite nanotubes (HNTs with aluminium reinforced epoxy hybrid composite material under dry condition. The machining parameters which are chosen to be evaluated in this study are the depth of cut (d, cutting speed (S and feed rate (f. While, the response factors to be measured are the surface roughness of the machined composite surface and the cutting force. An orthogonal array of the Taguchi method was set-up and used to analyse the effect of the milling parameters on the surface roughness and cutting force. The result from this study shows that the application of the Taguchi method can determine the best combination of machining parameters that can provide the optimal machining response conditions which are the lowest surface roughness and lowest cutting force value. For the best surface finish, A1–B3–C3 (d = 0.4 mm, S = 1500 rpm, f = 60 mmpm is found to be the optimized combination of levels for all the three control factors from the analysis. Meanwhile, the optimized combination of levels for all the three control factors from the analysis which provides the lowest cutting force was found to be A2–B2–C2 (d = 0.6 mm, S = 1000 rpm, f = 40 mmpm.

Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm

International Nuclear Information System (INIS)

Oliva, Diego; Abd El Aziz, Mohamed; Ella Hassanien, Aboul

2017-01-01

Highlights: •We modify the whale algorithm using chaotic maps. •We apply a chaotic algorithm to estimate parameter of photovoltaic cells. •We perform a study of chaos in whale algorithm. •Several comparisons and metrics support the experimental results. •We test the method with data from real solar cells. -- Abstract: The using of solar energy has been increased since it is a clean source of energy. In this way, the design of photovoltaic cells has attracted the attention of researchers over the world. There are two main problems in this field: having a useful model to characterize the solar cells and the absence of data about photovoltaic cells. This situation even affects the performance of the photovoltaic modules (panels). The characteristics of the current vs. voltage are used to describe the behavior of solar cells. Considering such values, the design problem involves the solution of the complex non-linear and multi-modal objective functions. Different algorithms have been proposed to identify the parameters of the photovoltaic cells and panels. Most of them commonly fail in finding the optimal solutions. This paper proposes the Chaotic Whale Optimization Algorithm (CWOA) for the parameters estimation of solar cells. The main advantage of the proposed approach is using the chaotic maps to compute and automatically adapt the internal parameters of the optimization algorithm. This situation is beneficial in complex problems, because along the iterative process, the proposed algorithm improves their capabilities to search for the best solution. The modified method is able to optimize complex and multimodal objective functions. For example, the function for the estimation of parameters of solar cells. To illustrate the capabilities of the proposed algorithm in the solar cell design, it is compared with other optimization methods over different datasets. Moreover, the experimental results support the improved performance of the proposed approach

SU-E-J-16: Automatic Image Contrast Enhancement Based On Automatic Parameter Optimization for Radiation Therapy Setup Verification

International Nuclear Information System (INIS)

Qiu, J; Li, H. Harlod; Zhang, T; Yang, D; Ma, F

2015-01-01

Purpose: In RT patient setup 2D images, tissues often cannot be seen well due to the lack of image contrast. Contrast enhancement features provided by image reviewing software, e.g. Mosaiq and ARIA, require manual selection of the image processing filters and parameters thus inefficient and cannot be automated. In this work, we developed a novel method to automatically enhance the 2D RT image contrast to allow automatic verification of patient daily setups as a prerequisite step of automatic patient safety assurance. Methods: The new method is based on contrast limited adaptive histogram equalization (CLAHE) and high-pass filtering algorithms. The most important innovation is to automatically select the optimal parameters by optimizing the image contrast. The image processing procedure includes the following steps: 1) background and noise removal, 2) hi-pass filtering by subtracting the Gaussian smoothed Result, and 3) histogram equalization using CLAHE algorithm. Three parameters were determined through an iterative optimization which was based on the interior-point constrained optimization algorithm: the Gaussian smoothing weighting factor, the CLAHE algorithm block size and clip limiting parameters. The goal of the optimization is to maximize the entropy of the processed Result. Results: A total 42 RT images were processed. The results were visually evaluated by RT physicians and physicists. About 48% of the images processed by the new method were ranked as excellent. In comparison, only 29% and 18% of the images processed by the basic CLAHE algorithm and by the basic window level adjustment process, were ranked as excellent. Conclusion: This new image contrast enhancement method is robust and automatic, and is able to significantly outperform the basic CLAHE algorithm and the manual window-level adjustment process that are currently used in clinical 2D image review software tools

SU-E-J-16: Automatic Image Contrast Enhancement Based On Automatic Parameter Optimization for Radiation Therapy Setup Verification

Energy Technology Data Exchange (ETDEWEB)

Qiu, J [Taishan Medical University, Taian, Shandong (China); Washington University in St Louis, St Louis, MO (United States); Li, H. Harlod; Zhang, T; Yang, D [Washington University in St Louis, St Louis, MO (United States); Ma, F [Taishan Medical University, Taian, Shandong (China)

2015-06-15

Purpose: In RT patient setup 2D images, tissues often cannot be seen well due to the lack of image contrast. Contrast enhancement features provided by image reviewing software, e.g. Mosaiq and ARIA, require manual selection of the image processing filters and parameters thus inefficient and cannot be automated. In this work, we developed a novel method to automatically enhance the 2D RT image contrast to allow automatic verification of patient daily setups as a prerequisite step of automatic patient safety assurance. Methods: The new method is based on contrast limited adaptive histogram equalization (CLAHE) and high-pass filtering algorithms. The most important innovation is to automatically select the optimal parameters by optimizing the image contrast. The image processing procedure includes the following steps: 1) background and noise removal, 2) hi-pass filtering by subtracting the Gaussian smoothed Result, and 3) histogram equalization using CLAHE algorithm. Three parameters were determined through an iterative optimization which was based on the interior-point constrained optimization algorithm: the Gaussian smoothing weighting factor, the CLAHE algorithm block size and clip limiting parameters. The goal of the optimization is to maximize the entropy of the processed Result. Results: A total 42 RT images were processed. The results were visually evaluated by RT physicians and physicists. About 48% of the images processed by the new method were ranked as excellent. In comparison, only 29% and 18% of the images processed by the basic CLAHE algorithm and by the basic window level adjustment process, were ranked as excellent. Conclusion: This new image contrast enhancement method is robust and automatic, and is able to significantly outperform the basic CLAHE algorithm and the manual window-level adjustment process that are currently used in clinical 2D image review software tools.

Analysis and optimization of machining parameters of laser cutting for polypropylene composite

Science.gov (United States)

Deepa, A.; Padmanabhan, K.; Kuppan, P.

2017-11-01

Present works explains about machining of self-reinforced Polypropylene composite fabricated using hot compaction method. The objective of the experiment is to find optimum machining parameters for Polypropylene (PP). Laser power and Machining speed were the parameters considered in response to tensile test and Flexure test. Taguchi method is used for experimentation. Grey Relational Analysis (GRA) is used for multiple process parameter optimization. ANOVA (Analysis of Variance) is used to find impact for process parameter. Polypropylene has got the great application in various fields like, it is used in the form of foam in model aircraft and other radio-controlled vehicles, thin sheets (∼2-20μm) used as a dielectric, PP is also used in piping system, it is also been used in hernia and pelvic organ repair or protect new herrnis in the same location.

Development of a parameter optimization technique for the design of automatic control systems

Science.gov (United States)

Whitaker, P. H.

1977-01-01

Parameter optimization techniques for the design of linear automatic control systems that are applicable to both continuous and digital systems are described. The model performance index is used as the optimization criterion because of the physical insight that can be attached to it. The design emphasis is to start with the simplest system configuration that experience indicates would be practical. Design parameters are specified, and a digital computer program is used to select that set of parameter values which minimizes the performance index. The resulting design is examined, and complexity, through the use of more complex information processing or more feedback paths, is added only if performance fails to meet operational specifications. System performance specifications are assumed to be such that the desired step function time response of the system can be inferred.

Forecasting optimal duration of a beer main fermentation process using the Kalman filter

OpenAIRE

Niyonsaba T.; Pavlov V.A.

2016-01-01

One of the most important processes of beer production is the main process of fermentation. In this process, the wort transforms into beer. The quality of beer depends on the dynamics of wort parameters. The main fermentation process continues for 10 days and requires high costs. Therefore, the main purpose of this article is to forecast the optimal duration of the beer main fermentation process and provide its optimal control. The Kalman filter can provide optimal control of the main ferment...

[Optimization of digital chest radiography image post-processing in diagnosis of pneumoconiosis].

Science.gov (United States)

Sheng, Bing-yong; Mao, Ling; Zhou, Shao-wei; Shi, Jin

2013-11-01

To establish the optimal image post-processing parameters for digital chest radiography as preliminary research for introducing digital radiography (DR) to pneumoconiosis diagnosis in China. A total of 204 pneumoconiosis patients and 31 dust-exposed workers were enrolled as the subjects in this research. Film-screen radiography (FSR) and DR images were taken for all subjects. DR films were printed after raw images were processed and parameters were altered using DR workstation (GE Healthcare, U.S.A.). Image gradations, lung textures, and the imaging of thoracic vertebra were evaluated by pneumoconiosis experts, and the optimal post-processing parameters were selected. Optical density was measured for both DR films and FSR films. For the DR machine used in this research, the contrast adjustment (CA) and brightness adjustment (BA) were the main parameters that determine the brightness and gray levels of images. The optimal ranges for CA and BA were 115%∼120% and 160%∼165%, respectively. The quality of DR chest films would be optimized when tissue contrast was adjusted to a maximum of 0.15, edge to a minimum of 1, and both noise reduction and tissue equalization to0.The failure rate of chest DR (0.4%) was significantly lower than that of chest FSR (17%) (P image post-processing on DR machine purchased from GE Healthcare, the DR chest films can meet all requirements for the quality of chest X-ray films in the Chinese diagnostic criteria for pneumoconiosis.

A procedure for multi-objective optimization of tire design parameters

Directory of Open Access Journals (Sweden)

Nikola Korunović

2015-04-01

Full Text Available The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zones inside the tire. It consists of four main stages: pre-analysis, design of experiment, mathematical modeling and multi-objective optimization. Advantage of the proposed procedure is reflected in the fact that multi-objective optimization is based on the Pareto concept, which enables design engineers to obtain a complete set of optimization solutions and choose a suitable tire design. Furthermore, modeling of the relationships between tire design parameters and objective functions based on multiple regression analysis minimizes computational and modeling effort. The adequacy of the proposed tire design multi-objective optimization procedure has been validated by performing experimental trials based on finite element method.

Parameters Selection for Electropolishing Process of Products Made of Copper and Its Alloys

Directory of Open Access Journals (Sweden)

Maciąg T.

2017-09-01

Full Text Available Electropolishing is electrochemical method used in metal working that has a vital role in production of medical apparatus, in food or electric industry. The purpose of this paper is to determine optimal current parameters and time required for conducting electropolishing process from the perspective of changes of surface microgeometry. Furthermore, effect of different types of mechanical working used before electropolishing on final surface state was evaluated by observation in changes of topography. Research was conducted on electrolytic copper and brass. Analysis of surface geometry and its parameters (Ra, Sa was used as criterion describing efficiency of chemical electropolishing. Results of the experiment allow for current parameter optimization of electrochemical polishing process for selected non-ferrous alloys with preliminary mechanical preparation of the surface.

Optimization of the parameter calculation the process of production historic by using Parallel Virtual Machine-PVM; Otimizacao do calculo de parametros no processo de ajuste de historicos de producao usando PVM

Energy Technology Data Exchange (ETDEWEB)

Vargas Cuervo, Carlos Hernan

1997-03-01

The main objective of this work is to develop a methodology to optimize the simultaneous computation of two parameters in the process of production history matching. This work describes a procedure to minimize an objective function established to find the values of the parameters which are modified in the process. The parameters are chosen after a sensibility analysis. Two optimization methods are tested: a Region Search Method (MBR) and Polytope Method. Both are based in direct search methods which do not require the function derivative. The software PVM (Parallel Virtual Machine) is used to parallelize the simulation runs, allowing the acceleration of the process and the search of multiple solutions. The validation of the methodology is applied to two reservoir models: one homogeneous and other heterogeneous. The advantages of each method and of the parallelization are also present. (author)

Drying of water based foundry coatings: Innovative test, process design and optimization methods

DEFF Research Database (Denmark)

Di Muoio, Giovanni Luca; Johansen, Bjørn Budolph

on real industrial cases. These tools have been developed in order to simulate and optimize the drying process and reduce drying time and power consumption as well as production process design time and cost of expensive drying equipment. Results show that test methods from other industries can be used...... capacity goals there is a need to understand how to design, control and optimize drying processes. The main focus of this project was on the critical parameters and properties to be controlled in production in order to achieve a stable and predictable drying process. We propose for each of these parameters...... of Denmark with the overall aim to optimize the drying process of water based foundry coatings. Drying of foundry coatings is a relatively new process in the foundry industry that followed the introduction of water as a solvent. In order to avoid moisture related quality problems and reach production...

Integral Optimization of Systematic Parameters of Flip-Flow Screens

Institute of Scientific and Technical Information of China (English)

翟宏新

2004-01-01

The synthetic index Ks for evaluating flip-flow screens is proposed and systematically optimized in view of the whole system. A series of optimized values of relevant parameters are found and then compared with those of the current industrial specifications. The results show that the optimized value Ks approaches the one of those famous flip-flow screens in the world. Some new findings on geometric and kinematics parameters are useful for improving the flip-flow screens with a low Ks value, which is helpful in developing clean coal technology.

Production of biodiesel from coastal macroalgae (Chara vulgaris) and optimization of process parameters using Box-Behnken design.

Science.gov (United States)

Siddiqua, Shaila; Mamun, Abdullah Al; Enayetul Babar, Sheikh Md

2015-01-01

Renewable biodiesels are needed as an alternative to petroleum-derived transport fuels, which contribute to global warming and are of limited availability. Algae biomass, are a potential source of renewable energy, and they can be converted into energy such as biofuels. This study introduces an integrated method for the production of biodiesel from Chara vulgaris algae collected from the coastal region of Bangladesh. The Box-Behnken design based on response surface methods (RSM) used as the statistical tool to optimize three variables for predicting the best performing conditions (calorific value and yield) of algae biodiesel. The three parameters for production condition were chloroform (X1), sodium chloride concentration (X2) and temperature (X3). Optimal conditions were estimated by the aid of statistical regression analysis and surface plot chart. The optimal condition of biodiesel production parameter for 12 g of dry algae biomass was observed to be 198 ml chloroform with 0.75 % sodium chloride at 65 °C temperature, where the calorific value of biodiesel is 9255.106 kcal/kg and yield 3.6 ml.

Modeling and optimization of CO2 capture processes by chemical absorption

International Nuclear Information System (INIS)

Neveux, Thibaut

2013-01-01

CO 2 capture processes by chemical absorption lead to a large energy penalty on efficiency of coal-fired power plants, establishing one of the main bottleneck to its industrial deployment. The objective of this thesis is the development and validation of a global methodology, allowing the precise evaluation of the potential of a given amine capture process. Characteristic phenomena of chemical absorption have been thoroughly studied and represented with state-of-the-art models. The e-UNIQUAC model has been used to describe vapor-liquid and chemical equilibria of electrolyte solutions and the model parameters have been identified for four solvents. A rate-based formulation has been adopted for the representation of chemically enhanced heat and mass transfer in columns. The absorption and stripping models have been successfully validated against experimental data from an industrial and a laboratory pilot plants. The influence of the numerous phenomena has been investigated in order to highlight the most limiting ones. A methodology has been proposed to evaluate the total energy penalty resulting from the implementation of a capture process on an advanced supercritical coal-fired power plant, including thermal and electric consumptions. Then, the simulation and process evaluation environments have been coupled with a non-linear optimization algorithm in order to find optimal operating and design parameters with respect to energetic and economic performances. This methodology has been applied to optimize five process flow schemes operating with an monoethanolamine aqueous solution at 30% by weight: the conventional flow scheme and four process modifications. The performance comparison showed that process modifications using a heat pump effect give the best gains. The use of technical-economic analysis as an evaluation criterion of a process performance, coupled with a optimization algorithm, has proved its capability to find values for the numerous operating and design

Mammalian Cell Culture Process for Monoclonal Antibody Production: Nonlinear Modelling and Parameter Estimation

Directory of Open Access Journals (Sweden)

Dan Selişteanu

2015-01-01

Full Text Available Monoclonal antibodies (mAbs are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies.

Selecting and optimizing eco-physiological parameters of Biome-BGC to reproduce observed woody and leaf biomass growth of Eucommia ulmoides plantation in China using Dakota optimizer

Science.gov (United States)

Miyauchi, T.; Machimura, T.

2013-12-01

In the simulation using an ecosystem process model, the adjustment of parameters is indispensable for improving the accuracy of prediction. This procedure, however, requires much time and effort for approaching the simulation results to the measurements on models consisting of various ecosystem processes. In this study, we tried to apply a general purpose optimization tool in the parameter optimization of an ecosystem model, and examined its validity by comparing the simulated and measured biomass growth of a woody plantation. A biometric survey of tree biomass growth was performed in 2009 in an 11-year old Eucommia ulmoides plantation in Henan Province, China. Climate of the site was dry temperate. Leaf, above- and below-ground woody biomass were measured from three cut trees and converted into carbon mass per area by measured carbon contents and stem density. Yearly woody biomass growth of the plantation was calculated according to allometric relationships determined by tree ring analysis of seven cut trees. We used Biome-BGC (Thornton, 2002) to reproduce biomass growth of the plantation. Air temperature and humidity from 1981 to 2010 was used as input climate condition. The plant functional type was deciduous broadleaf, and non-optimizing parameters were left default. 11-year long normal simulations were performed following a spin-up run. In order to select optimizing parameters, we analyzed the sensitivity of leaf, above- and below-ground woody biomass to eco-physiological parameters. Following the selection, optimization of parameters was performed by using the Dakota optimizer. Dakota is an optimizer developed by Sandia National Laboratories for providing a systematic and rapid means to obtain optimal designs using simulation based models. As the object function, we calculated the sum of relative errors between simulated and measured leaf, above- and below-ground woody carbon at each of eleven years. In an alternative run, errors at the last year (at the

Optimization of turning process through the analytic flank wear modelling

Science.gov (United States)

Del Prete, A.; Franchi, R.; De Lorenzis, D.

2018-05-01

In the present work, the approach used for the optimization of the process capabilities for Oil&Gas components machining will be described. These components are machined by turning of stainless steel castings workpieces. For this purpose, a proper Design Of Experiments (DOE) plan has been designed and executed: as output of the experimentation, data about tool wear have been collected. The DOE has been designed starting from the cutting speed and feed values recommended by the tools manufacturer; the depth of cut parameter has been maintained as a constant. Wear data has been obtained by means the observation of the tool flank wear under an optical microscope: the data acquisition has been carried out at regular intervals of working times. Through a statistical data and regression analysis, analytical models of the flank wear and the tool life have been obtained. The optimization approach used is a multi-objective optimization, which minimizes the production time and the number of cutting tools used, under the constraint on a defined flank wear level. The technique used to solve the optimization problem is a Multi Objective Particle Swarm Optimization (MOPS). The optimization results, validated by the execution of a further experimental campaign, highlighted the reliability of the work and confirmed the usability of the optimized process parameters and the potential benefit for the company.

Parameters Optimization and Application to Glutamate Fermentation Model Using SVM

OpenAIRE

Zhang, Xiangsheng; Pan, Feng

2015-01-01

Aimed at the parameters optimization in support vector machine (SVM) for glutamate fermentation modelling, a new method is developed. It optimizes the SVM parameters via an improved particle swarm optimization (IPSO) algorithm which has better global searching ability. The algorithm includes detecting and handling the local convergence and exhibits strong ability to avoid being trapped in local minima. The material step of the method was shown. Simulation experiments demonstrate the effective...

Optimisation Of Cutting Parameters Of Composite Material Laser Cutting Process By Taguchi Method

Science.gov (United States)

Lokesh, S.; Niresh, J.; Neelakrishnan, S.; Rahul, S. P. Deepak

2018-03-01

The aim of this work is to develop a laser cutting process model that can predict the relationship between the process input parameters and resultant surface roughness, kerf width characteristics. The research conduct is based on the Design of Experiment (DOE) analysis. Response Surface Methodology (RSM) is used in this work. It is one of the most practical and most effective techniques to develop a process model. Even though RSM has been used for the optimization of the laser process, this research investigates laser cutting of materials like Composite wood (veneer)to be best circumstances of laser cutting using RSM process. The input parameters evaluated are focal length, power supply and cutting speed, the output responses being kerf width, surface roughness, temperature. To efficiently optimize and customize the kerf width and surface roughness characteristics, a machine laser cutting process model using Taguchi L9 orthogonal methodology was proposed.

Optimization of cladding parameters for resisting corrosion on low carbon steels using simulated annealing algorithm

Science.gov (United States)

Balan, A. V.; Shivasankaran, N.; Magibalan, S.

2018-04-01

Low carbon steels used in chemical industries are frequently affected by corrosion. Cladding is a surfacing process used for depositing a thick layer of filler metal in a highly corrosive materials to achieve corrosion resistance. Flux cored arc welding (FCAW) is preferred in cladding process due to its augmented efficiency and higher deposition rate. In this cladding process, the effect of corrosion can be minimized by controlling the output responses such as minimizing dilution, penetration and maximizing bead width, reinforcement and ferrite number. This paper deals with the multi-objective optimization of flux cored arc welding responses by controlling the process parameters such as wire feed rate, welding speed, Nozzle to plate distance, welding gun angle for super duplex stainless steel material using simulated annealing technique. Regression equation has been developed and validated using ANOVA technique. The multi-objective optimization of weld bead parameters was carried out using simulated annealing to obtain optimum bead geometry for reducing corrosion. The potentiodynamic polarization test reveals the balanced formation of fine particles of ferrite and autenite content with desensitized nature of the microstructure in the optimized clad bead.

Application of the Taguchi analytical method for optimization of effective parameters of the chemical vapor deposition process controlling the production of nanotubes/nanobeads.

Science.gov (United States)

Sharon, Maheshwar; Apte, P R; Purandare, S C; Zacharia, Renju

2005-02-01

Seven variable parameters of the chemical vapor deposition system have been optimized with the help of the Taguchi analytical method for getting a desired product, e.g., carbon nanotubes or carbon nanobeads. It is observed that almost all selected parameters influence the growth of carbon nanotubes. However, among them, the nature of precursor (racemic, R or Technical grade camphor) and the carrier gas (hydrogen, argon and mixture of argon/hydrogen) seem to be more important parameters affecting the growth of carbon nanotubes. Whereas, for the growth of nanobeads, out of seven parameters, only two, i.e., catalyst (powder of iron, cobalt, and nickel) and temperature (1023 K, 1123 K, and 1273 K), are the most influential parameters. Systematic defects or islands on the substrate surface enhance nucleation of novel carbon materials. Quantitative contributions of process parameters as well as optimum factor levels are obtained by performing analysis of variance (ANOVA) and analysis of mean (ANOM), respectively.

Combination of Compensations and Multi-Parameter Coil for Efficiency Optimization of Inductive Power Transfer System

Directory of Open Access Journals (Sweden)

Guozhen Hu

2017-12-01

Full Text Available A loosely coupled inductive power transfer (IPT system for industrial track applications has been researched in this paper. The IPT converter using primary Inductor-Capacitor-Inductor (LCL network and secondary parallel-compensations is analyzed combined coil design for optimal operating efficiency. Accurate mathematical analytical model and expressions of self-inductance and mutual inductance are proposed to achieve coil parameters. Furthermore, the optimization process is performed combined with the proposed resonant compensations and coil parameters. The results are evaluated and discussed using finite element analysis (FEA. Finally, an experimental prototype is constructed to verify the proposed approach and the experimental results show that the optimization can be better applied to industrial track distributed IPT system.

Optimization of Squeeze Casting Parameters for 2017 A Wrought Al Alloy Using Taguchi Method

Directory of Open Access Journals (Sweden)

Najib Souissi

2014-04-01

Full Text Available This study applies the Taguchi method to investigate the relationship between the ultimate tensile strength, hardness and process variables in a squeeze casting 2017 A wrought aluminium alloy. The effects of various casting parameters including squeeze pressure, melt temperature and die temperature were studied. Therefore, the objectives of the Taguchi method for the squeeze casting process are to establish the optimal combination of process parameters and to reduce the variation in quality between only a few experiments. The experimental results show that the squeeze pressure significantly affects the microstructure and the mechanical properties of 2017 A Al alloy.

Optimization of process parameters during carbonization for improved carbon fibre strength

Science.gov (United States)

Köhler, T.; Pursche, F.; Burscheidt, P.; Seide, G.; Gries, T.

2017-10-01

Based on their extraordinary properties, carbon fibres nowadays play a significant role in modern industries. In the last years carbon fibres are increasingly used for lightweight constructions in the energy or the transportation industry. However, a bigger market penetration of carbon fibres is still hindered by high prices (~ 22 /kg) [3]. One crucial step in carbon fibre production is the process of carbonization of stabilized fibres. However, the cause effect relationships of carbonization are nowadays not fully understood. Therefore, the main goal of this research work is the quantification of the cause-effect relationships of process parameters like temperature and residence time on carbon fibre strength.

Multi-objective optimization in quantum parameter estimation

Science.gov (United States)

Gong, BeiLi; Cui, Wei

2018-04-01

We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.

Design and Optimization of Sheet Hydroforming Process for Manufacturing Oil tank

International Nuclear Information System (INIS)

Prakash, C.; Narasimhan, K.

2005-01-01

The need for reduction of weight is an important issue in sheet metal forming industry. The hydroforming process has become an effective manufacturing process, as it can be adapted for the manufacturing of complex structural components with high structural stiffness. The process parameters and material properties are important factors that influence the quality of final product. In this paper, an optimized window of process parameters is obtained for successful sheet hydroforming of Oil tank. The simulation of hydroforming process is performed by using a Finite Element Method based Commercial code

Selection of parameters for advanced machining processes using firefly algorithm

Directory of Open Access Journals (Sweden)

Rajkamal Shukla

2017-02-01

Full Text Available Advanced machining processes (AMPs are widely utilized in industries for machining complex geometries and intricate profiles. In this paper, two significant processes such as electric discharge machining (EDM and abrasive water jet machining (AWJM are considered to get the optimum values of responses for the given range of process parameters. The firefly algorithm (FA is attempted to the considered processes to obtain optimized parameters and the results obtained are compared with the results given by previous researchers. The variation of process parameters with respect to the responses are plotted to confirm the optimum results obtained using FA. In EDM process, the performance parameter “MRR” is increased from 159.70 gm/min to 181.6723 gm/min, while “Ra” and “REWR” are decreased from 6.21 μm to 3.6767 μm and 6.21% to 6.324 × 10−5% respectively. In AWJM process, the value of the “kerf” and “Ra” are decreased from 0.858 mm to 0.3704 mm and 5.41 mm to 4.443 mm respectively. In both the processes, the obtained results show a significant improvement in the responses.

Optimization of hydraulic turbine governor parameters based on WPA

Science.gov (United States)

Gao, Chunyang; Yu, Xiangyang; Zhu, Yong; Feng, Baohao

2018-01-01

The parameters of hydraulic turbine governor directly affect the dynamic characteristics of the hydraulic unit, thus affecting the regulation capacity and the power quality of power grid. The governor of conventional hydropower unit is mainly PID governor with three adjustable parameters, which are difficult to set up. In order to optimize the hydraulic turbine governor, this paper proposes wolf pack algorithm (WPA) for intelligent tuning since the good global optimization capability of WPA. Compared with the traditional optimization method and PSO algorithm, the results show that the PID controller designed by WPA achieves a dynamic quality of hydraulic system and inhibits overshoot.

Optimization of Loudspeaker Part Design Parameters by Air Viscosity Damping Effect

OpenAIRE

Yue Hu; Xilu Zhao; Takao Yamaguchi; Manabu Sasajima; Yoshio Koike; Akira Hara

2016-01-01

This study optimized the design parameters of a cone loudspeaker as an example of high flexibility of the product design. We developed an acoustic analysis software program that considers the impact of damping caused by air viscosity. In sound reproduction, it is difficult to optimize each parameter of the loudspeaker design. To overcome the limitation of the design problem in practice, this study presents an acoustic analysis algorithm to optimize the design parameters of the loudspeaker. Th...

Influence analysis of sewage sludge methane fermentation parameters on process efficiency

OpenAIRE

Катерина Борисівна Сорокіна

2016-01-01

The efficiency dependence of sewage sludge organic matter decomposition from organization and conditions of the process is analyzed. Support of the optimal values of several parameters ensures to provide completeness of the sludge fermentation process and obtain biogas in calculated amount. Biogas utilization reduces costs for reactor heating and provides additional obtaining of other types of energy

Parameters Optimization and Application to Glutamate Fermentation Model Using SVM

Directory of Open Access Journals (Sweden)

Xiangsheng Zhang

2015-01-01

Full Text Available Aimed at the parameters optimization in support vector machine (SVM for glutamate fermentation modelling, a new method is developed. It optimizes the SVM parameters via an improved particle swarm optimization (IPSO algorithm which has better global searching ability. The algorithm includes detecting and handling the local convergence and exhibits strong ability to avoid being trapped in local minima. The material step of the method was shown. Simulation experiments demonstrate the effectiveness of the proposed algorithm.

Optimization of MIS/IL solar cells parameters using genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Ahmed, K.A.; Mohamed, E.A.; Alaa, S.H. [Faculty of Engineering, Alexandria Univ. (Egypt); Motaz, M.S. [Institute of Graduate Studies and Research, Alexandria Univ. (Egypt)

2004-07-01

This paper presents a genetic algorithm optimization for MIS/IL solar cell parameters including doping concentration NA, metal work function {phi}m, oxide thickness d{sub ox}, mobile charge density N{sub m}, fixed oxide charge density N{sub ox} and the external back bias applied to the inversion grid V. The optimization results are compared with theoretical optimization and shows that the genetic algorithm can be used for determining the optimum parameters of the cell. (orig.)

Optimizing incomplete sample designs for item response model parameters

NARCIS (Netherlands)

van der Linden, Willem J.

Several models for optimizing incomplete sample designs with respect to information on the item parameters are presented. The following cases are considered: (1) known ability parameters; (2) unknown ability parameters; (3) item sets with multiple ability scales; and (4) response models with

Statistical optimization of process parameters for the production of tannase by Aspergillus flavus under submerged fermentation.

Science.gov (United States)

Mohan, S K; Viruthagiri, T; Arunkumar, C

2014-04-01

Production of tannase by Aspergillus flavus (MTCC 3783) using tamarind seed powder as substrate was studied in submerged fermentation. Plackett-Burman design was applied for the screening of 12 medium nutrients. From the results, the significant nutrients were identified as tannic acid, magnesium sulfate, ferrous sulfate and ammonium sulfate. Further the optimization of process parameters was carried out using response surface methodology (RSM). RSM has been applied for designing of experiments to evaluate the interactive effects through a full 31 factorial design. The optimum conditions were tannic acid concentration, 3.22 %; fermentation period, 96 h; temperature, 35.1 °C; and pH 5.4. Higher value of the regression coefficient (R 2  = 0.9638) indicates excellent evaluation of experimental data by second-order polynomial regression model. The RSM revealed that a maximum tannase production of 139.3 U/ml was obtained at the optimum conditions.

Nonlinearity Analysis and Parameters Optimization for an Inductive Angle Sensor

Directory of Open Access Journals (Sweden)

Lin Ye

2014-02-01

Full Text Available Using the finite element method (FEM and particle swarm optimization (PSO, a nonlinearity analysis based on parameter optimization is proposed to design an inductive angle sensor. Due to the structure complexity of the sensor, understanding the influences of structure parameters on the nonlinearity errors is a critical step in designing an effective sensor. Key parameters are selected for the design based on the parameters’ effects on the nonlinearity errors. The finite element method and particle swarm optimization are combined for the sensor design to get the minimal nonlinearity error. In the simulation, the nonlinearity error of the optimized sensor is 0.053% in the angle range from −60° to 60°. A prototype sensor is manufactured and measured experimentally, and the experimental nonlinearity error is 0.081% in the angle range from −60° to 60°.

Optimal parameters uncoupling vibration modes of oscillators

Science.gov (United States)

Le, K. C.; Pieper, A.

2017-07-01

This paper proposes a novel optimization concept for an oscillator with two degrees of freedom. By using specially defined motion ratios, we control the action of springs to each degree of freedom of the oscillator. We aim at showing that, if the potential action of the springs in one period of vibration, used as the payoff function for the conservative oscillator, is maximized among all admissible parameters and motions satisfying Lagrange's equations, then the optimal motion ratios uncouple vibration modes. A similar result holds true for the dissipative oscillator having dampers. The application to optimal design of vehicle suspension is discussed.

Optimizing a Laser Process for Making Carbon Nanotubes

Science.gov (United States)

Arepalli, Sivaram; Nikolaev, Pavel; Holmes, William

2010-01-01

A systematic experimental study has been performed to determine the effects of each of the operating conditions in a double-pulse laser ablation process that is used to produce single-wall carbon nanotubes (SWCNTs). The comprehensive data compiled in this study have been analyzed to recommend conditions for optimizing the process and scaling up the process for mass production. The double-pulse laser ablation process for making SWCNTs was developed by Rice University researchers. Of all currently known nanotube-synthesizing processes (arc and chemical vapor deposition), this process yields the greatest proportion of SWCNTs in the product material. The aforementioned process conditions are important for optimizing the production of SWCNTs and scaling up production. Reports of previous research (mostly at Rice University) toward optimization of process conditions mention effects of oven temperature and briefly mention effects of flow conditions, but no systematic, comprehensive study of the effects of process conditions was done prior to the study described here. This was a parametric study, in which several production runs were carried out, changing one operating condition for each run. The study involved variation of a total of nine parameters: the sequence of the laser pulses, pulse-separation time, laser pulse energy density, buffer gas (helium or nitrogen instead of argon), oven temperature, pressure, flow speed, inner diameter of the flow tube, and flow-tube material.

Optimization Design of Multi-Parameters in Rail Launcher System

OpenAIRE

Yujiao Zhang; Weinan Qin; Junpeng Liao; Jiangjun Ruan

2014-01-01

Today the energy storage systems are still encumbering, therefore it is useful to think about the optimization of a railgun system in order to achieve the best performance with the lowest energy input. In this paper, an optimal design method considering 5 parameters is proposed to improve the energy conversion efficiency of a simple railgun. In order to avoid costly trials, the field- circuit method is employed to analyze the operations of different structural railguns with different paramete...

Influence analysis of sewage sludge methane fermentation parameters on process efficiency

Directory of Open Access Journals (Sweden)

Катерина Борисівна Сорокіна

2016-12-01

Full Text Available The efficiency dependence of sewage sludge organic matter decomposition from organization and conditions of the process is analyzed. Support of the optimal values of several parameters ensures to provide completeness of the sludge fermentation process and obtain biogas in calculated amount. Biogas utilization reduces costs for reactor heating and provides additional obtaining of other types of energy

Safer operating conditions and optimal scaling-up process for cyclohexanone peroxide reaction

International Nuclear Information System (INIS)

Zang, Na; Qian, Xin-Ming; Liu, Zhen-Yi; Shu, Chi-Min

2015-01-01

Highlights: • Thermal hazard of cyclohexanone peroxide reaction was measured by experimental techniques. • Levenberg–Marquardt algorithm was adopted to evaluate kinetic parameters. • Safer operating conditions at laboratory scale were acquired by BDs and TDs. • The verified safer operating conditions were used to obtain the optimal scale-up parameters applied in industrial plants. - Abstract: The cyclohexanone peroxide reaction process, one of the eighteen hazardous chemical processes identified in China, is performed in indirectly cooled semibatch reactors. The peroxide reaction is added to a mixture of hydrogen peroxide and nitric acid, which form heterogeneous liquid–liquid systems. A simple and general procedure for building boundary and temperature diagrams of peroxide process is given here to account for the overall kinetic expressions. Such a procedure has been validated by comparison with experimental data. Thermally safer operating parameters were obtained at laboratory scale, and the scaled-up procedure was performed to give the minimum dosing time in an industrial plant, which is in favor of maximizing industrial reactor productivity. The results are of great significance for governing the peroxide reaction process apart from the thermal runaway region. It also greatly aids in determining optimization on operating parameters in industrial plants.

Multiobjective Optimization of ELID Grinding Process Using Grey Relational Analysis Coupled with Principal Component Analysis

Directory of Open Access Journals (Sweden)

S. Prabhu

2014-06-01

Full Text Available Carbon nanotube (CNT mixed grinding wheel has been used in the electrolytic in-process dressing (ELID grinding process to analyze the surface characteristics of AISI D2 Tool steel material. CNT grinding wheel is having an excellent thermal conductivity and good mechanical property which is used to improve the surface finish of the work piece. The multiobjective optimization of grey relational analysis coupled with principal component analysis has been used to optimize the process parameters of ELID grinding process. Based on the Taguchi design of experiments, an L9 orthogonal array table was chosen for the experiments. The confirmation experiment verifies the proposed that grey-based Taguchi method has the ability to find out the optimal process parameters with multiple quality characteristics of surface roughness and metal removal rate. Analysis of variance (ANOVA has been used to verify and validate the model. Empirical model for the prediction of output parameters has been developed using regression analysis and the results were compared for with and without using CNT grinding wheel in ELID grinding process.

Die design and process optimization of plastic gear extrusion

Science.gov (United States)

Zhang, Lei; Fu, Zhihong; Yao, Chen; Zang, Gongzheng; Wan, Yue

2018-01-01

The flow velocity of the melt in the extruder was simulated by using software Polyflow, and the size of the die channel with the best flow uniformity was obtained. The die profile shape is obtained by reverse design. The length of the shaping section is determined by Ansys transient thermal analysis. According to the simulation results, the design and manufacture of extrusion die of plastic gear and vacuum cooling setting were obtained. The influence of the five process parameters on the precision of the plastic gear were studied by the single factor analysis method, such as the die temperature T, the screw speed R, the die spacing S, the vacuum degree M and the hauling speed V. The optimal combination of process parameters was obtained by using the neural network particle swarm optimization algorithm(T = 197.05 °C, R = 9.04rpm, S = 67mm, M = -0.0194MPa). The tooth profile deviation of the extruded plastic gear can reach 9 level of accuracy.

Optimization of process parameters for WEDM of Inconel 825 using grey relational analysis

Directory of Open Access Journals (Sweden)

Pawan Kuma

2018-09-01

Full Text Available Inconel 825 is high nickel-chromium-based superalloy which retains its mechanical properties and exhibits good corrosion and oxidation resistance at elevated temperature. Inconel 825 is extensively used for making aircraft engine parts like combustor casing and turbine blades in aero space industry. This research proposed the Response Surface Methodology with GRA to optimize multiple responses during Wire-cut EDM of Inconel 825. At optimum combination of input parameters i.e. A4B1C1D5E4F2, increase in MRR from 36.13 mm2/min to 41.822 mm2/min, decrease in SR from 2.842μm to 2.445μm and decrease in WWR from 0.01832 to 0.01758 was obtained. Experimental results showed that pulse-on time, wire feed, pulse-off time, and peak current significantly affected the MRR, and surface integrity of specimen and electrode with the formation of craters, pockmarks, debris, micro cracks, and recast layer. The optimal parametric combination obtained from the present study will be advantageous for working on high strength; high thermal conductivity and low melting point materials like nickel alloys.

Investigation, sensitivity analysis, and multi-objective optimization of effective parameters on temperature and force in robotic drilling cortical bone.

Science.gov (United States)

Tahmasbi, Vahid; Ghoreishi, Majid; Zolfaghari, Mojtaba

2017-11-01

The bone drilling process is very prominent in orthopedic surgeries and in the repair of bone fractures. It is also very common in dentistry and bone sampling operations. Due to the complexity of bone and the sensitivity of the process, bone drilling is one of the most important and sensitive processes in biomedical engineering. Orthopedic surgeries can be improved using robotic systems and mechatronic tools. The most crucial problem during drilling is an unwanted increase in process temperature (higher than 47 °C), which causes thermal osteonecrosis or cell death and local burning of the bone tissue. Moreover, imposing higher forces to the bone may lead to breaking or cracking and consequently cause serious damage. In this study, a mathematical second-order linear regression model as a function of tool drilling speed, feed rate, tool diameter, and their effective interactions is introduced to predict temperature and force during the bone drilling process. This model can determine the maximum speed of surgery that remains within an acceptable temperature range. Moreover, for the first time, using designed experiments, the bone drilling process was modeled, and the drilling speed, feed rate, and tool diameter were optimized. Then, using response surface methodology and applying a multi-objective optimization, drilling force was minimized to sustain an acceptable temperature range without damaging the bone or the surrounding tissue. In addition, for the first time, Sobol statistical sensitivity analysis is used to ascertain the effect of process input parameters on process temperature and force. The results show that among all effective input parameters, tool rotational speed, feed rate, and tool diameter have the highest influence on process temperature and force, respectively. The behavior of each output parameters with variation in each input parameter is further investigated. Finally, a multi-objective optimization has been performed considering all the

Method of optimization of the natural gas refining process

Energy Technology Data Exchange (ETDEWEB)

Sadykh-Zade, E.S.; Bagirov, A.A.; Mardakhayev, I.M.; Razamat, M.S.; Tagiyev, V.G.

1980-01-01

The SATUM (automatic control system of technical operations) system introduced at the Shatlyk field should assure good quality of gas refining. In order to optimize the natural gas refining processes and experimental-analytical method is used in compiling the mathematical descriptions. The program, compiled in Fortran language, in addition to parameters of optimal conditions gives information on the yield of concentrate and water, concentration and consumption of DEG, composition and characteristics of the gas and condensate. The algorithm for calculating optimum engineering conditions of gas refining is proposed to be used in ''advice'' mode, and also for monitoring progress of the gas refining process.

Study on feed forward neural network convex optimization for LiFePO4 battery parameters

Science.gov (United States)

Liu, Xuepeng; Zhao, Dongmei

2017-08-01

Based on the modern facility agriculture automatic walking equipment LiFePO4 Battery, the parameter identification of LiFePO4 Battery is analyzed. An improved method for the process model of li battery is proposed, and the on-line estimation algorithm is presented. The parameters of the battery are identified using feed forward network neural convex optimization algorithm.

Intracellular response to process optimization and impact on productivity and product aggregates for a high-titer CHO cell process.

Science.gov (United States)

Handlogten, Michael W; Lee-O'Brien, Allison; Roy, Gargi; Levitskaya, Sophia V; Venkat, Raghavan; Singh, Shailendra; Ahuja, Sanjeev

2018-01-01

A key goal in process development for antibodies is to increase productivity while maintaining or improving product quality. During process development of an antibody, titers were increased from 4 to 10 g/L while simultaneously decreasing aggregates. Process development involved optimization of media and feed formulations, feed strategy, and process parameters including pH and temperature. To better understand how CHO cells respond to process changes, the changes were implemented in a stepwise manner. The first change was an optimization of the feed formulation, the second was an optimization of the medium, and the third was an optimization of process parameters. Multiple process outputs were evaluated including cell growth, osmolality, lactate production, ammonium concentration, antibody production, and aggregate levels. Additionally, detailed assessment of oxygen uptake, nutrient and amino acid consumption, extracellular and intracellular redox environment, oxidative stress, activation of the unfolded protein response (UPR) pathway, protein disulfide isomerase (PDI) expression, and heavy and light chain mRNA expression provided an in-depth understanding of the cellular response to process changes. The results demonstrate that mRNA expression and UPR activation were unaffected by process changes, and that increased PDI expression and optimized nutrient supplementation are required for higher productivity processes. Furthermore, our findings demonstrate the role of extra- and intracellular redox environment on productivity and antibody aggregation. Processes using the optimized medium, with increased concentrations of redox modifying agents, had the highest overall specific productivity, reduced aggregate levels, and helped cells better withstand the high levels of oxidative stress associated with increased productivity. Specific productivities of different processes positively correlated to average intracellular values of total glutathione. Additionally

Optimization of injection moulding process parameters in the ...

African Journals Online (AJOL)

In this study, optimal injection moulding conditions for minimum shrinkage during moulding of High Density Polyethylene (HDPE) were obtained by Taguchi method. The result showed that melting temperature of 190OC, injection pressure of 55 MPa, refilling pressure of 85 MPa and cooling time of 11 seconds gave ...

Search Parameter Optimization for Discrete, Bayesian, and Continuous Search Algorithms

Science.gov (United States)

2017-09-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CONTINUOUS SEARCH ALGORITHMS by...to 09-22-2017 4. TITLE AND SUBTITLE SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CON- TINUOUS SEARCH ALGORITHMS 5. FUNDING NUMBERS 6...simple search and rescue acts to prosecuting aerial/surface/submersible targets on mission. This research looks at varying the known discrete and

Optimization on Turning Parameters of 15-5PH Stainless Steel Using Taguchi Based Grey Approach and Topsis

Directory of Open Access Journals (Sweden)

Palanisamy D.

2016-09-01

Full Text Available The machinability and the process parameter optimization of turning operation for 15-5 Precipitation Hardening (PH stainless steel have been investigated based on the Taguchi based grey approach and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS. An L27 orthogonal array was selected for planning the experiment. Cutting speed, depth of cut and feed rate were considered as input process parameters. Cutting force (Fz and surface roughness (Ra were considered as the performance measures. These performance measures were optimized for the improvement of machinability quality of product. A comparison is made between the multi-criteria decision making tools. Grey Relational Analysis (GRA and TOPSIS are used to confirm and prove the similarity. To determine the influence of process parameters, Analysis of Variance (ANOVA is employed. The end results of experimental investigation proved that the machining performance can be enhanced effectively with the assistance of the proposed approaches.

Cosmological parameter estimation using particle swarm optimization

Science.gov (United States)

Prasad, Jayanti; Souradeep, Tarun

2012-06-01

Constraining theoretical models, which are represented by a set of parameters, using observational data is an important exercise in cosmology. In Bayesian framework this is done by finding the probability distribution of parameters which best fits to the observational data using sampling based methods like Markov chain Monte Carlo (MCMC). It has been argued that MCMC may not be the best option in certain problems in which the target function (likelihood) poses local maxima or have very high dimensionality. Apart from this, there may be examples in which we are mainly interested to find the point in the parameter space at which the probability distribution has the largest value. In this situation the problem of parameter estimation becomes an optimization problem. In the present work we show that particle swarm optimization (PSO), which is an artificial intelligence inspired population based search procedure, can also be used for cosmological parameter estimation. Using PSO we were able to recover the best-fit Λ cold dark matter (LCDM) model parameters from the WMAP seven year data without using any prior guess value or any other property of the probability distribution of parameters like standard deviation, as is common in MCMC. We also report the results of an exercise in which we consider a binned primordial power spectrum (to increase the dimensionality of problem) and find that a power spectrum with features gives lower chi square than the standard power law. Since PSO does not sample the likelihood surface in a fair way, we follow a fitting procedure to find the spread of likelihood function around the best-fit point.

Optimization of Minimum Quantity Lubricant Conditions and Cutting Parameters in Hard Milling of AISI H13 Steel

OpenAIRE

The-Vinh Do; Quang-Cherng Hsu

2016-01-01

As a successful solution applied to hard machining, the minimum quantity lubricant (MQL) has already been established as an alternative to flood coolant processing. The optimization of MQL parameters and cutting parameters under MQL condition are essential and pressing. The study was divided into two parts. In the first part of this study, the Taguchi method was applied to find the optimal values of MQL condition in the hard milling of AISI H13 with consideration of reduced surface roughness....

Statistical optimization of process parameters for the simultaneous adsorption of Cr(VI) and phenol onto Fe-treated tea waste biomass

Science.gov (United States)

Gupta, Ankur; Balomajumder, Chandrajit

2017-12-01

In this study, simultaneous removal of Cr(VI) and phenol from binary solution was carried out using Fe-treated tea waste biomass. The effect of process parameters such as adsorbent dose, pH, initial concentration of Cr(VI) (mg/L), and initial concentration of phenol (mg/L) was optimized. The analysis of variance of the quadratic model demonstrates that the experimental results are in good agreement with the predicted values. Based on experimental design at an initial concentration of 55 mg/L of Cr(VI), 27.50 mg/L of phenol, pH 2.0, 15 g/L adsorbent dose, 99.99% removal of Cr(VI), and phenol was achieved.

Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters

Science.gov (United States)

Gowtham, K. N.; Vasudevan, M.; Maduraimuthu, V.; Jayakumar, T.

2011-04-01

Modified 9Cr-1Mo ferritic steel is used as a structural material for steam generator components of power plants. Generally, tungsten inert gas (TIG) welding is preferred for welding of these steels in which the depth of penetration achievable during autogenous welding is limited. Therefore, activated flux TIG (A-TIG) welding, a novel welding technique, has been developed in-house to increase the depth of penetration. In modified 9Cr-1Mo steel joints produced by the A-TIG welding process, weld bead width, depth of penetration, and heat-affected zone (HAZ) width play an important role in determining the mechanical properties as well as the performance of the weld joints during service. To obtain the desired weld bead geometry and HAZ width, it becomes important to set the welding process parameters. In this work, adaptative neuro fuzzy inference system is used to develop independent models correlating the welding process parameters like current, voltage, and torch speed with weld bead shape parameters like depth of penetration, bead width, and HAZ width. Then a genetic algorithm is employed to determine the optimum A-TIG welding process parameters to obtain the desired weld bead shape parameters and HAZ width.

Optimum design of forging process parameters and preform shape under uncertainties

International Nuclear Information System (INIS)

Repalle, Jalaja; Grandhi, Ramana V.

2004-01-01

Forging is a highly complex non-linear process that is vulnerable to various uncertainties, such as variations in billet geometry, die temperature, material properties, workpiece and forging equipment positional errors and process parameters. A combination of these uncertainties could induce heavy manufacturing losses through premature die failure, final part geometric distortion and production risk. Identifying the sources of uncertainties, quantifying and controlling them will reduce risk in the manufacturing environment, which will minimize the overall cost of production. In this paper, various uncertainties that affect forging tool life and preform design are identified, and their cumulative effect on the forging process is evaluated. Since the forging process simulation is computationally intensive, the response surface approach is used to reduce time by establishing a relationship between the system performance and the critical process design parameters. Variability in system performance due to randomness in the parameters is computed by applying Monte Carlo Simulations (MCS) on generated Response Surface Models (RSM). Finally, a Robust Methodology is developed to optimize forging process parameters and preform shape. The developed method is demonstrated by applying it to an axisymmetric H-cross section disk forging to improve the product quality and robustness

Optimization of ohmic heating parameters for polyphenoloxidase inactivation in not-from-concentrate elstar apple juice using RSM

DEFF Research Database (Denmark)

Abedelmaksoud, Tarek; Mohsen, Sobhy Mohamed; Duedahl-Olesen, Lene

2018-01-01

In this study, optimization of ohmic heating (OH) process parameters (temperature and voltage gradient) to inactivate polyphenoloxidase (PPO) of not-from-concentrate (NFC) apple juice was conducted. Response surface methodology was used for optimization of OH parameters, where the voltage gradient...... and temperature on the PPO activity in the NFC apple juice was evaluated. Then the optimized condition was used to produce the NFC apple juice and the quality parameters were evaluated and compared to NFC apple juice prepared by conventional heating (CH). The studied parameters were: PPO activity, total phenolic......, total carotenoids, ascorbic acid, cloud value, color as well as physical properties (i.e., TSS, acidity, electric conductivity and viscosity). The reduction of PPO activities was 97 and 91% for OH (at 40 V/cm and 80 °C) and CH (at 90 °C and 60 s), respectively. The reduction of the ascorbic acid...

Optimal Design of Shock Tube Experiments for Parameter Inference

KAUST Repository

Bisetti, Fabrizio; Knio, Omar

2014-01-01

We develop a Bayesian framework for the optimal experimental design of the shock tube experiments which are being carried out at the KAUST Clean Combustion Research Center. The unknown parameters are the pre-exponential parameters and the activation

Joint optimization of collimator and reconstruction parameters in SPECT imaging for lesion quantification

International Nuclear Information System (INIS)

McQuaid, Sarah J; Southekal, Sudeepti; Kijewski, Marie Foley; Moore, Stephen C

2011-01-01

Obtaining the best possible task performance using reconstructed SPECT images requires optimization of both the collimator and reconstruction parameters. The goal of this study is to determine how to perform this optimization, namely whether the collimator parameters can be optimized solely from projection data, or whether reconstruction parameters should also be considered. In order to answer this question, and to determine the optimal collimation, a digital phantom representing a human torso with 16 mm diameter hot lesions (activity ratio 8:1) was generated and used to simulate clinical SPECT studies with parallel-hole collimation. Two approaches to optimizing the SPECT system were then compared in a lesion quantification task: sequential optimization, where collimation was optimized on projection data using the Cramer–Rao bound, and joint optimization, which simultaneously optimized collimator and reconstruction parameters. For every condition, quantification performance in reconstructed images was evaluated using the root-mean-squared-error of 400 estimates of lesion activity. Compared to the joint-optimization approach, the sequential-optimization approach favoured a poorer resolution collimator, which, under some conditions, resulted in sub-optimal estimation performance. This implies that inclusion of the reconstruction parameters in the optimization procedure is important in obtaining the best possible task performance; in this study, this was achieved with a collimator resolution similar to that of a general-purpose (LEGP) collimator. This collimator was found to outperform the more commonly used high-resolution (LEHR) collimator, in agreement with other task-based studies, using both quantification and detection tasks.

Castor Oil: Properties, Uses, and Optimization of Processing Parameters in Commercial Production.

Science.gov (United States)

Patel, Vinay R; Dumancas, Gerard G; Kasi Viswanath, Lakshmi C; Maples, Randall; Subong, Bryan John J

2016-01-01

Castor oil, produced from castor beans, has long been considered to be of important commercial value primarily for the manufacturing of soaps, lubricants, and coatings, among others. Global castor oil production is concentrated primarily in a small geographic region of Gujarat in Western India. This region is favorable due to its labor-intensive cultivation method and subtropical climate conditions. Entrepreneurs and castor processors in the United States and South America also cultivate castor beans but are faced with the challenge of achieving high castor oil production efficiency, as well as obtaining the desired oil quality. In this manuscript, we provide a detailed analysis of novel processing methods involved in castor oil production. We discuss novel processing methods by explaining specific processing parameters involved in castor oil production.

Determination of Optimal Parameters for Diffusion Bonding of Semi-Solid Casting Aluminium Alloy by Response Surface Methodology

Directory of Open Access Journals (Sweden)

Kaewploy Somsak

2015-01-01

Full Text Available Liquid state welding techniques available are prone to gas porosity problems. To avoid this solid state bonding is usually an alternative of preference. Among solid state bonding techniques, diffusion bonding is often employed in aluminium alloy automotive parts welding in order to enhance their mechanical properties. However, there has been no standard procedure nor has there been any definitive criterion for judicious welding parameters setting. It is thus a matter of importance to find the set of optimal parameters for effective diffusion bonding. This work proposes the use of response surface methodology in determining such a set of optimal parameters. Response surface methodology is more efficient in dealing with complex process compared with other techniques available. There are two variations of response surface methodology. The one adopted in this work is the central composite design approach. This is because when the initial upper and lower bounds of the desired parameters are exceeded the central composite design approach is still capable of yielding the optimal values of the parameters that appear to be out of the initially preset range. Results from the experiments show that the pressing pressure and the holding time affect the tensile strength of jointing. The data obtained from the experiment fits well to a quadratic equation with high coefficient of determination (R2 = 94.21%. It is found that the optimal parameters in the process of jointing semi-solid casting aluminium alloy by using diffusion bonding are the pressing pressure of 2.06 MPa and 214 minutes of the holding time in order to achieve the highest tensile strength of 142.65 MPa

Parameters optimization for magnetic resonance coupling wireless power transmission.

Science.gov (United States)

Li, Changsheng; Zhang, He; Jiang, Xiaohua

2014-01-01

Taking maximum power transmission and power stable transmission as research objectives, optimal design for the wireless power transmission system based on magnetic resonance coupling is carried out in this paper. Firstly, based on the mutual coupling model, mathematical expressions of optimal coupling coefficients for the maximum power transmission target are deduced. Whereafter, methods of enhancing power transmission stability based on parameters optimal design are investigated. It is found that the sensitivity of the load power to the transmission parameters can be reduced and the power transmission stability can be enhanced by improving the system resonance frequency or coupling coefficient between the driving/pick-up coil and the transmission/receiving coil. Experiment results are well conformed to the theoretical analysis conclusions.

Q-Learning Multi-Objective Sequential Optimal Sensor Parameter Weights

Directory of Open Access Journals (Sweden)

Raquel Cohen

2016-04-01

Full Text Available The goal of our solution is to deliver trustworthy decision making analysis tools which evaluate situations and potential impacts of such decisions through acquired information and add efficiency for continuing mission operations and analyst information.We discuss the use of cooperation in modeling and simulation and show quantitative results for design choices to resource allocation. The key contribution of our paper is to combine remote sensing decision making with Nash Equilibrium for sensor parameter weighting optimization. By calculating all Nash Equilibrium possibilities per period, optimization of sensor allocation is achieved for overall higher system efficiency. Our tool provides insight into what are the most important or optimal weights for sensor parameters and can be used to efficiently tune those weights.

Hydrologic Process-oriented Optimization of Electrical Resistivity Tomography

Science.gov (United States)

Hinnell, A.; Bechtold, M.; Ferre, T. A.; van der Kruk, J.

2010-12-01

Electrical resistivity tomography (ERT) is commonly used in hydrologic investigations. Advances in joint and coupled hydrogeophysical inversion have enhanced the quantitative use of ERT to construct and condition hydrologic models (i.e. identify hydrologic structure and estimate hydrologic parameters). However the selection of which electrical resistivity data to collect and use is often determined by a combination of data requirements for geophysical analysis, intuition on the part of the hydrogeophysicist and logistical constraints of the laboratory or field site. One of the advantages of coupled hydrogeophysical inversion is the direct link between the hydrologic model and the individual geophysical data used to condition the model. That is, there is no requirement to collect geophysical data suitable for independent geophysical inversion. The geophysical measurements collected can be optimized for estimation of hydrologic model parameters rather than to develop a geophysical model. Using a synthetic model of drip irrigation we evaluate the value of individual resistivity measurements to describe the soil hydraulic properties and then use this information to build a data set optimized for characterizing hydrologic processes. We then compare the information content in the optimized data set with the information content in a data set optimized using a Jacobian sensitivity analysis.

Cosmological parameter estimation using Particle Swarm Optimization

Science.gov (United States)

Prasad, J.; Souradeep, T.

2014-03-01

Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.

Cosmological parameter estimation using Particle Swarm Optimization

International Nuclear Information System (INIS)

Prasad, J; Souradeep, T

2014-01-01

Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite

Optimization Of PVDF-TrFE Processing Conditions For The Fabrication Of Organic MEMS Resonators.

Science.gov (United States)

Ducrot, Pierre-Henri; Dufour, Isabelle; Ayela, Cédric

2016-01-21

This paper reports a systematic optimization of processing conditions of PVDF-TrFE piezoelectric thin films, used as integrated transducers in organic MEMS resonators. Indeed, despite data on electromechanical properties of PVDF found in the literature, optimized processing conditions that lead to these properties remain only partially described. In this work, a rigorous optimization of parameters enabling state-of-the-art piezoelectric properties of PVDF-TrFE thin films has been performed via the evaluation of the actuation performance of MEMS resonators. Conditions such as annealing duration, poling field and poling duration have been optimized and repeatability of the process has been demonstrated.

Optimization of Tape Winding Process Parameters to Enhance the Performance of Solid Rocket Nozzle Throat Back Up Liners using Taguchi's Robust Design Methodology

Science.gov (United States)

Nath, Nayani Kishore

2017-08-01

The throat back up liners is used to protect the nozzle structural members from the severe thermal environment in solid rocket nozzles. The throat back up liners is made with E-glass phenolic prepregs by tape winding process. The objective of this work is to demonstrate the optimization of process parameters of tape winding process to achieve better insulative resistance using Taguchi's robust design methodology. In this method four control factors machine speed, roller pressure, tape tension, tape temperature that were investigated for the tape winding process. The presented work was to study the cogency and acceptability of Taguchi's methodology in manufacturing of throat back up liners. The quality characteristic identified was Back wall temperature. Experiments carried out using L 9 ' (34) orthogonal array with three levels of four different control factors. The test results were analyzed using smaller the better criteria for Signal to Noise ratio in order to optimize the process. The experimental results were analyzed conformed and successfully used to achieve the minimum back wall temperature of the throat back up liners. The enhancement in performance of the throat back up liners was observed by carrying out the oxy-acetylene tests. The influence of back wall temperature on the performance of throat back up liners was verified by ground firing test.

Optimizing sonication parameters for dispersion of single-walled carbon nanotubes

Energy Technology Data Exchange (ETDEWEB)

Yu, Haibo [Fraunhofer Institute for Electronic Nano Systems (Fraunhofer ENAS), 09126 Chemnitz (Germany); Graduate University of the Chinese Academy of Sciences, Beijing (China); State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016 Shenyang (China); Hermann, Sascha, E-mail: sascha.hermann@zfm.tu-chemnitz.de [Center for Microtechnologies (ZfM), Chemnitz University of Technology, 09126 Chemnitz (Germany); Schulz, Stefan E.; Gessner, Thomas [Fraunhofer Institute for Electronic Nano Systems (Fraunhofer ENAS), 09126 Chemnitz (Germany); Center for Microtechnologies (ZfM), Chemnitz University of Technology, 09126 Chemnitz (Germany); Dong, Zaili [State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016 Shenyang (China); Li, Wen J., E-mail: wenjungli@gmail.com [State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016 Shenyang (China); Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong SAR (China)

2012-10-26

Graphical abstract: We study the dispersing behavior of SWCNTs based on the surfactant and the optimization of sonication parameters including the sonication power and running time. Highlights: Black-Right-Pointing-Pointer We study the optimization of sonication for the surfactant-based dispersion of SWCNTs. Black-Right-Pointing-Pointer The absorption spectrum of SWCNT solution strongly depend on the sonication conditions. Black-Right-Pointing-Pointer The sonication process has an important influence on the average length and diameters of SWCNTs in solution. Black-Right-Pointing-Pointer Centrifugation mainly contributes to the decrease of nonresonant absorption background. Black-Right-Pointing-Pointer Under the same sonication parameters, the large-diameter tip performs dispersion of SWCNTs better than the small-diameter tip. -- Abstract: Non-covalent functionalization based on surfactants has become one of the most common methods for dispersing of single-walled carbon nanotubes (SWCNTs). Previously, efforts have mainly been focused on experimenting with different surfactant systems, varying their concentrations and solvents. However sonication plays a very important role during the surfactant-based dispersion process for SWCNTs. The sonication treatment enables the surfactant molecules to adsorb onto the surface of SWCNTs by overcoming the interactions induced by the hydrophobic, electrostatic and van der Waals forces. This work describes a systematic study of the influence of the sonication power and time on the dispersion of SWCNTs. UV-vis-NIR absorption spectra is used to analyze and to evaluate the dispersion of SWCNTs in an aqueous solution of 1 w/v% sodium deoxycholate (DOC) showing that the resonant and nonresonant background absorption strongly depends on the sonication conditions. Furthermore, the diameter and length of SWCNTs under different sonication parameters are investigated using atomic force microscopy (AFM).

An optimization method for parameters in reactor nuclear physics

International Nuclear Information System (INIS)

Jachic, J.

1982-01-01

An optimization method for two basic problems of Reactor Physics was developed. The first is the optimization of a plutonium critical mass and the bruding ratio for fast reactors in function of the radial enrichment distribution of the fuel used as control parameter. The second is the maximization of the generation and the plutonium burnup by an optimization of power temporal distribution. (E.G.) [pt

Oyster Creek cycle 10 nodal model parameter optimization study using PSMS

International Nuclear Information System (INIS)

Dougher, J.D.

1987-01-01

The power shape monitoring system (PSMS) is an on-line core monitoring system that uses a three-dimensional nodal code (NODE-B) to perform nodal power calculations and compute thermal margins. The PSMS contains a parameter optimization function that improves the ability of NODE-B to accurately monitor core power distributions. This functions iterates on the model normalization parameters (albedos and mixing factors) to obtain the best agreement between predicted and measured traversing in-core probe (TIP) reading on a statepoint-by-statepoint basis. Following several statepoint optimization runs, an average set of optimized normalization parameters can be determined and can be implemented into the current or subsequent cycle core model for on-line core monitoring. A statistical analysis of 19 high-power steady-state state-points throughout Oyster Creek cycle 10 operation has shown a consistently poor virgin model performance. The normalization parameters used in the cycle 10 NODE-B model were based on a cycle 8 study, which evaluated only Exxon fuel types. The introduction of General Electric (GE) fuel into cycle 10 (172 assemblies) was a significant fuel/core design change that could have altered the optimum set of normalization parameters. Based on the need to evaluate a potential change in the model normalization parameters for cycle 11 and in an attempt to account for the poor cycle 10 model performance, a parameter optimization study was performed

Optimization of the fiber laser parameters for local high-temperature impact on metal

Science.gov (United States)

Yatsko, Dmitrii S.; Polonik, Marina V.; Dudko, Olga V.

2016-11-01

This paper presents the local laser heating process of surface layer of the metal sample. The aim is to create the molten pool with the required depth by laser thermal treatment. During the heating the metal temperature at any point of the molten zone should not reach the boiling point of the main material. The laser power, exposure time and the spot size of a laser beam are selected as the variable parameters. The mathematical model for heat transfer in a semi-infinite body, applicable to finite slab, is used for preliminary theoretical estimation of acceptable parameters values of the laser thermal treatment. The optimization problem is solved by using an algorithm based on the scanning method of the search space (the zero-order method of conditional optimization). The calculated values of the parameters (the optimal set of "laser radiation power - exposure time - spot radius") are used to conduct a series of natural experiments to obtain a molten pool with the required depth. A two-stage experiment consists of: a local laser treatment of metal plate (steel) and then the examination of the microsection of the laser irradiated region. According to the experimental results, we can judge the adequacy of the ongoing calculations within the selected models.

GEMSFITS: Code package for optimization of geochemical model parameters and inverse modeling

International Nuclear Information System (INIS)

Miron, George D.; Kulik, Dmitrii A.; Dmytrieva, Svitlana V.; Wagner, Thomas

2015-01-01

Highlights: • Tool for generating consistent parameters against various types of experiments. • Handles a large number of experimental data and parameters (is parallelized). • Has a graphical interface and can perform statistical analysis on the parameters. • Tested on fitting the standard state Gibbs free energies of aqueous Al species. • Example on fitting interaction parameters of mixing models and thermobarometry. - Abstract: GEMSFITS is a new code package for fitting internally consistent input parameters of GEM (Gibbs Energy Minimization) geochemical–thermodynamic models against various types of experimental or geochemical data, and for performing inverse modeling tasks. It consists of the gemsfit2 (parameter optimizer) and gfshell2 (graphical user interface) programs both accessing a NoSQL database, all developed with flexibility, generality, efficiency, and user friendliness in mind. The parameter optimizer gemsfit2 includes the GEMS3K chemical speciation solver ( (http://gems.web.psi.ch/GEMS3K)), which features a comprehensive suite of non-ideal activity- and equation-of-state models of solution phases (aqueous electrolyte, gas and fluid mixtures, solid solutions, (ad)sorption. The gemsfit2 code uses the robust open-source NLopt library for parameter fitting, which provides a selection between several nonlinear optimization algorithms (global, local, gradient-based), and supports large-scale parallelization. The gemsfit2 code can also perform comprehensive statistical analysis of the fitted parameters (basic statistics, sensitivity, Monte Carlo confidence intervals), thus supporting the user with powerful tools for evaluating the quality of the fits and the physical significance of the model parameters. The gfshell2 code provides menu-driven setup of optimization options (data selection, properties to fit and their constraints, measured properties to compare with computed counterparts, and statistics). The practical utility, efficiency, and

Identification of strategy parameters for particle swarm optimizer through Taguchi method

Institute of Scientific and Technical Information of China (English)

KHOSLA Arun; KUMAR Shakti; AGGARWAL K.K.

2006-01-01

Particle swarm optimization (PSO), like other evolutionary algorithms is a population-based stochastic algorithm inspired from the metaphor of social interaction in birds, insects, wasps, etc. It has been used for finding promising solutions in complex search space through the interaction of particles in a swarm. It is a well recognized fact that the performance of evolutionary algorithms to a great extent depends on the choice of appropriate strategy/operating parameters like population size,crossover rate, mutation rate, crossover operator, etc. Generally, these parameters are selected through hit and trial process, which is very unsystematic and requires rigorous experimentation. This paper proposes a systematic based on Taguchi method reasoning scheme for rapidly identifying the strategy parameters for the PSO algorithm. The Taguchi method is a robust design approach using fractional factorial design to study a large number of parameters with small number of experiments. Computer simulations have been performed on two benchmark functions-Rosenbrock function and Griewank function-to validate the approach.

STATISTICAL APPROACH FOR MULTI CRITERIA OPTIMIZATION OF CUTTING PARAMETERS OF TURNING ON HEAT TREATED BERYLLIUM COPPER ALLOY

Directory of Open Access Journals (Sweden)

K. DEVAKI DEVI

2017-08-01

Full Text Available In machining operations, achieving desired performance features of the machined product, is really a challenging job. Because, these quality features are highly correlated and are expected to be influenced directly or indirectly by the direct effect of process parameters or their interactive effects. This paper presents effective method and to determine optimal machining parameters in a turning operation on heat treated Beryllium copper alloy to minimize the surface roughness, cutting forces and work tool interface temperature along with the maximization of metal removal rate. The scope of this work is extended to Multi Objective Optimization. Response Surface Methodology is opted for preparing the design matrix, generating ANOVA, and optimization. A powerful model would be obtained with high accuracy to analyse the effect of each parameter on the output. The input parameters considered in this work are cutting speed, feed, depth of cut, work material (Annealed and Hardened and tool material (CBN and HSS.

Statistical analysis of process parameters to eliminate hot cracking of fiber laser welded aluminum alloy

Science.gov (United States)

Wang, Jin; Wang, Hui-Ping; Wang, Xiaojie; Cui, Haichao; Lu, Fenggui

2015-03-01

This paper investigates hot cracking rate in Al fiber laser welding under various process conditions and performs corresponding process optimization. First, effects of welding process parameters such as distance between welding center line and its closest trim edge, laser power and welding speed on hot cracking rate were investigated experimentally with response surface methodology (RSM). The hot cracking rate in the paper is defined as ratio of hot cracking length over the total weld seam length. Based on the experimental results following Box-Behnken design, a prediction model for the hot cracking rate was developed using a second order polynomial function considering only two factor interaction. The initial prediction result indicated that the established model could predict the hot cracking rate adequately within the range of welding parameters being used. The model was then used to optimize welding parameters to achieve cracking-free welds.

Effect of fermentation parameters on bio-alcohols production from glycerol using immobilized Clostridium pasteurianum: an optimization study.

Science.gov (United States)

Khanna, Swati; Goyal, Arun; Moholkar, Vijayanand S

2013-01-01

This article addresses the issue of effect of fermentation parameters for conversion of glycerol (in both pure and crude form) into three value-added products, namely, ethanol, butanol, and 1,3-propanediol (1,3-PDO), by immobilized Clostridium pasteurianum and thereby addresses the statistical optimization of this process. The analysis of effect of different process parameters such as agitation rate, fermentation temperature, medium pH, and initial glycerol concentration indicated that medium pH was the most critical factor for total alcohols production in case of pure glycerol as fermentation substrate. On the other hand, initial glycerol concentration was the most significant factor for fermentation with crude glycerol. An interesting observation was that the optimized set of fermentation parameters was found to be independent of the type of glycerol (either pure or crude) used. At optimum conditions of agitation rate (200 rpm), initial glycerol concentration (25 g/L), fermentation temperature (30°C), and medium pH (7.0), the total alcohols production was almost equal in anaerobic shake flasks and 2-L bioreactor. This essentially means that at optimum process parameters, the scale of operation does not affect the output of the process. The immobilized cells could be reused for multiple cycles for both pure and crude glycerol fermentation.

Statistical optimization of process parameters for inulinase production from Tithonia weed by Arthrobacter mysorens strain no.1.

Science.gov (United States)

Kamble, Prajakta P; Kore, Maheshkumar V; Patil, Sushama A; Jadhav, Jyoti P; Attar, Yasmin C

2018-06-01

Tithonia rotundifolia is an easily available and abundant inulin rich weed reported to be competitive and allelopathic. This weed inulin is hydrolyzed by inulinase into fructose. Response surface methodology was employed to optimize culture conditions for the inulinase production from Arthrobacter mysorens strain no.1 isolated from rhizospheric area of Tithonia weed. Initially, Plackett- Burman design was used for screening 11 nutritional parameters for inulinase production including inulin containing weeds as cost effective substrate. The experiment shows that amongst the 11 parameters studied, K 2 HPO 4 , Inulin, Agave sisalana extract and Tithonia rotundifolia were the most significant variables for inulinase production. Quantitative effects of these 4 factors were further investigated using Box Behnken design. The medium having 0.27% K 2 HPO 4 , 2.54% Inulin, 6.57% Agave sisalana extract and 7.27% Tithonia rotundifolia extract were found to be optimum for maximum inulinase production. The optimization strategies used showed 2.12 fold increase in inulinase yield (1669.45 EU/ml) compared to non-optimized medium (787 EU/ml). Fructose produced by the action of inulinase was further confirmed by spectrophotometer, osazone, HPTLC and FTIR methods. Thus Tithonia rotundifolia can be used as an eco-friendly, economically feasible and promising alternative substrate for commercial inulinase production yielding fructose from Arthrobacter mysorens strain no.1. Copyright © 2018 Elsevier B.V. All rights reserved.

Combustion Model and Control Parameter Optimization Methods for Single Cylinder Diesel Engine

Directory of Open Access Journals (Sweden)

Bambang Wahono

2014-01-01

Full Text Available This research presents a method to construct a combustion model and a method to optimize some control parameters of diesel engine in order to develop a model-based control system. The construction purpose of the model is to appropriately manage some control parameters to obtain the values of fuel consumption and emission as the engine output objectives. Stepwise method considering multicollinearity was applied to construct combustion model with the polynomial model. Using the experimental data of a single cylinder diesel engine, the model of power, BSFC, NOx, and soot on multiple injection diesel engines was built. The proposed method succesfully developed the model that describes control parameters in relation to the engine outputs. Although many control devices can be mounted to diesel engine, optimization technique is required to utilize this method in finding optimal engine operating conditions efficiently beside the existing development of individual emission control methods. Particle swarm optimization (PSO was used to calculate control parameters to optimize fuel consumption and emission based on the model. The proposed method is able to calculate control parameters efficiently to optimize evaluation item based on the model. Finally, the model which added PSO then was compiled in a microcontroller.

METAHEURISTIC OPTIMIZATION METHODS FOR PARAMETERS ESTIMATION OF DYNAMIC SYSTEMS

Directory of Open Access Journals (Sweden)

V. Panteleev Andrei

2017-01-01

Full Text Available The article considers the usage of metaheuristic methods of constrained global optimization: “Big Bang - Big Crunch”, “Fireworks Algorithm”, “Grenade Explosion Method” in parameters of dynamic systems estimation, described with algebraic-differential equations. Parameters estimation is based upon the observation results from mathematical model behavior. Their values are derived after criterion minimization, which describes the total squared error of state vector coordinates from the deduced ones with precise values observation at different periods of time. Paral- lelepiped type restriction is imposed on the parameters values. Used for solving problems, metaheuristic methods of constrained global extremum don’t guarantee the result, but allow to get a solution of a rather good quality in accepta- ble amount of time. The algorithm of using metaheuristic methods is given. Alongside with the obvious methods for solving algebraic-differential equation systems, it is convenient to use implicit methods for solving ordinary differen- tial equation systems. Two ways of solving the problem of parameters evaluation are given, those parameters differ in their mathematical model. In the first example, a linear mathematical model describes the chemical action parameters change, and in the second one, a nonlinear mathematical model describes predator-prey dynamics, which characterize the changes in both kinds’ population. For each of the observed examples there are calculation results from all the three methods of optimization, there are also some recommendations for how to choose methods parameters. The obtained numerical results have demonstrated the efficiency of the proposed approach. The deduced parameters ap- proximate points slightly differ from the best known solutions, which were deduced differently. To refine the results one should apply hybrid schemes that combine classical methods of optimization of zero, first and second orders and

Analysis of process parameters in surface grinding using single objective Taguchi and multi-objective grey relational grade

Directory of Open Access Journals (Sweden)

Prashant J. Patil

2016-09-01

Full Text Available Close tolerance and good surface finish are achieved by means of grinding process. This study was carried out for multi-objective optimization of MQL grinding process parameters. Water based Al2O3 and CuO nanofluids of various concentrations are used as lubricant for MQL system. Grinding experiments were carried out on instrumented surface grinding machine. For experimentation purpose Taguchi's method was used. Important process parameters that affect the G ratio and surface finish in MQL grinding are depth of cut, type of lubricant, feed rate, grinding wheel speed, coolant flow rate, and nanoparticle size. Grinding performance was calculated by the measurement G ratio and surface finish. For improvement of grinding process a multi-objective process parameter optimization is performed by use of Taguchi based grey relational analysis. To identify most significant factor of process analysis of variance (ANOVA has been used.

Optimal Machining Parameters for Achieving the Desired Surface Roughness in Turning of Steel

Directory of Open Access Journals (Sweden)

LB Abhang

2012-06-01

Full Text Available Due to the widespread use of highly automated machine tools in the metal cutting industry, manufacturing requires highly reliable models and methods for the prediction of output performance in the machining process. The prediction of optimal manufacturing conditions for good surface finish and dimensional accuracy plays a very important role in process planning. In the steel turning process the tool geometry and cutting conditions determine the time and cost of production which ultimately affect the quality of the final product. In the present work, experimental investigations have been conducted to determine the effect of the tool geometry (effective tool nose radius and metal cutting conditions (cutting speed, feed rate and depth of cut on surface finish during the turning of EN-31 steel. First and second order mathematical models are developed in terms of machining parameters by using the response surface methodology on the basis of the experimental results. The surface roughness prediction model has been optimized to obtain the surface roughness values by using LINGO solver programs. LINGO is a mathematical modeling language which is used in linear and nonlinear optimization to formulate large problems concisely, solve them, and analyze the solution in engineering sciences, operation research etc. The LINGO solver program is global optimization software. It gives minimum values of surface roughness and their respective optimal conditions.

Artificial neural network modeling studies to predict the friction welding process parameters of Incoloy 800H joints

Directory of Open Access Journals (Sweden)

K. Anand

2015-09-01

Full Text Available The present study focuses on friction welding process parameter optimization using a hybrid technique of ANN and different optimization algorithms. This optimization techniques are not only for the effective process modelling, but also to illustrate the correlation between the input and output responses of the friction welding of Incoloy 800H. In addition the focus is also to obtain optimal strength and hardness of joints with minimum burn off length. ANN based approaches could model this welding process of INCOLOY 800H in both forward and reverse directions efficiently, which are required for the automation of the same. Five different training algorithms were used to train ANN for both forward and reverse mapping and ANN tuned force approach was used for optimization. The paper makes a robust comparison of the performances of the five algorithms employing standard statistical indices. The results showed that GANN with 4-9-3 for forward and 4-7-3 for reverse mapping arrangement could outperform the other four approaches in most of the cases but not in all. Experiments on tensile strength (TS, microhardness (H and burn off length (BOL of the joints were performed with optimised parameter. It is concluded that this ANN model with genetic algorithm may provide good ability to predict the friction welding process parameters to weld Incoloy 800H.

Optimal construction parameters of electrosprayed trilayer organic photovoltaic devices

International Nuclear Information System (INIS)

Shah, S K; Ali, M; Gunnella, R; Abbas, M; Hirsch, L

2014-01-01

A detailed investigation of the optimal set of parameters employed in multilayer device fabrication obtained through successive electrospray deposited layers is reported. In this scheme, the donor/acceptor (D/A) bulk heterojunction layer is sandwiched between two thin stacked layers of individual donor and acceptor materials. The stacked layers geometry with optimal thicknesses plays a decisive role in improving operation characteristics. Among the parameters of the multilayer organic photovoltaics device, the D/A concentration ratio, blend thickness and stacking layers thicknesses are optimized. Other parameters, such as thermal annealing and the role of top metal contacts, are also discussed. Internal photon to current efficiency is found to attain a strong response in the 500 nm optical region for the most efficient device architectures. Such an observation indicates a clear interplay between photon harvesting of active layers and transport by ancillary stacking layers, opening up the possibility to engineer both the material fine structure and the device architecture to obtain the best photovoltaic response from a complex organic heterostructure. (paper)

Analyses of Methods and Algorithms for Modelling and Optimization of Biotechnological Processes

Directory of Open Access Journals (Sweden)

Stoyan Stoyanov

2009-08-01

Full Text Available A review of the problems in modeling, optimization and control of biotechnological processes and systems is given in this paper. An analysis of existing and some new practical optimization methods for searching global optimum based on various advanced strategies - heuristic, stochastic, genetic and combined are presented in the paper. Methods based on the sensitivity theory, stochastic and mix strategies for optimization with partial knowledge about kinetic, technical and economic parameters in optimization problems are discussed. Several approaches for the multi-criteria optimization tasks are analyzed. The problems concerning optimal controls of biotechnological systems are also discussed.

Unraveling the Processing Parameters in Friction Stir Welding

Science.gov (United States)

Schneider, Judy; Nunes, Arthur C., Jr.

2005-01-01

In friction stir welding (FSW), a rotating threaded pin tool is translated along a weld seam, literally stirring the edges of the seam together. To determine optimal processing parameters for producing a defect free weld, a better understanding of the resulting metal deformation flow path or paths is required. In this study, various markers are used to trace the flow paths of the metal. X-ray radiographs record the segmentation and position of the wire. Several variations in the trajectories can be differentiated within the weld zone.

Influence of processing parameters on PZT thick films

International Nuclear Information System (INIS)

Huang, Oliver; Bandyopadhyay, Amit; Bose, Susmita

2005-01-01

We have studied influence of processing parameters on the microstructure and ferroelectric properties of lead zirconate titanate (PZT)-based thick films in the range of 5-25 μm. PZT and 2% La-doped PZT thick films were processed using a modified sol-gel process. In this process, PZT- and La-doped PZT powders were first prepared via sol-gel. These powders were calcined and then used with respective sols to form a slurry. Slurry composition was optimized to spin-coat thick films on platinized Si substrate (Si/SiO 2 /Ti/Pt). Spinning rate, acceleration and slurry deposition techniques were optimized to form thick films with uniform thickness and without any cracking. Increasing solids loading was found to enhance the surface smoothness of the film and decrease porosity. Films were tested for their electrical properties and ferroelectric fatigue response. The maximum polarization obtained was 40 μC/cm 2 at 250 kV/cm for PZT thick film and 30 μC/cm 2 at 450 kV/cm for La-doped PZT thick film. After 10 9 cycles of fatiguing at 35 kHz, La-doped PZT showed better resistance for ferroelectric fatigue compared with un-doped PZT films

Optimization of Cutting Parameters of the Haynes 718 Nickel Alloy With Gas CO2 Laser

Directory of Open Access Journals (Sweden)

Jana PETRŮ

2011-06-01

Full Text Available This article deals with the application of laser technology and the optimization of parameters in the area of nickel alloy laser cutting intended for application in the aircraft industry. The main goal is to outline possibilities of use of the laser technology, primarily its application in the area of 3D material cutting. This experiment is focused on the optimization of cutting parameters of the Haynes 718 alloy with a gas CO2 laser. Originating cuts are evaluated primarily from the point of view of cut quality and accompanying undesirable phenomena occurring in the process of cutting. In conclusion the results achieved in the metallographic laboratory are described and analyzed.

Comparative study for different statistical models to optimize cutting parameters of CNC end milling machines

International Nuclear Information System (INIS)

El-Berry, A.; El-Berry, A.; Al-Bossly, A.

2010-01-01

In machining operation, the quality of surface finish is an important requirement for many work pieces. Thus, that is very important to optimize cutting parameters for controlling the required manufacturing quality. Surface roughness parameter (Ra) in mechanical parts depends on turning parameters during the turning process. In the development of predictive models, cutting parameters of feed, cutting speed, depth of cut, are considered as model variables. For this purpose, this study focuses on comparing various machining experiments which using CNC vertical machining center, work pieces was aluminum 6061. Multiple regression models are used to predict the surface roughness at different experiments.

A process insight repository supporting process optimization

OpenAIRE

Vetlugin, Andrey

2012-01-01

Existing solutions for analysis and optimization of manufacturing processes, such as online analysis processing or statistical calculations, have shortcomings that limit continuous process improvements. In particular, they lack means of storing and integrating the results of analysis. This makes the valuable information that can be used for process optimizations used only once and then disposed. The goal of the Advanced Manufacturing Analytics (AdMA) research project is to design an integrate...

Complicated problem solution techniques in optimal parameter searching

International Nuclear Information System (INIS)

Gergel', V.P.; Grishagin, V.A.; Rogatneva, E.A.; Strongin, R.G.; Vysotskaya, I.N.; Kukhtin, V.V.

1992-01-01

An algorithm is presented of a global search for numerical solution of multidimentional multiextremal multicriteria optimization problems with complicated constraints. A boundedness of object characteristic changes is assumed at restricted changes of its parameters (Lipschitz condition). The algorithm was realized as a computer code. The algorithm was realized as a computer code. The programme was used to solve in practice the different applied optimization problems. 10 refs.; 3 figs

Benchmarking of radiological departments. Starting point for successful process optimization

International Nuclear Information System (INIS)

Busch, Hans-Peter

2010-01-01

Continuous optimization of the process of organization and medical treatment is part of the successful management of radiological departments. The focus of this optimization can be cost units such as CT and MRI or the radiological parts of total patient treatment. Key performance indicators for process optimization are cost- effectiveness, service quality and quality of medical treatment. The potential for improvements can be seen by comparison (benchmark) with other hospitals and radiological departments. Clear definitions of key data and criteria are absolutely necessary for comparability. There is currently little information in the literature regarding the methodology and application of benchmarks especially from the perspective of radiological departments and case-based lump sums, even though benchmarking has frequently been applied to radiological departments by hospital management. The aim of this article is to describe and discuss systematic benchmarking as an effective starting point for successful process optimization. This includes the description of the methodology, recommendation of key parameters and discussion of the potential for cost-effectiveness analysis. The main focus of this article is cost-effectiveness (efficiency and effectiveness) with respect to cost units and treatment processes. (orig.)

Structural parameter optimization design for Halbach permanent maglev rail

International Nuclear Information System (INIS)

Guo, F.; Tang, Y.; Ren, L.; Li, J.

2010-01-01

Maglev rail is an important part of the magnetic levitation launch system. Reducing the manufacturing cost of magnetic levitation rail is the key problem for the development of magnetic levitation launch system. The Halbach permanent array has an advantage that the fundamental spatial field is cancelled on one side of the array while the field on the other side is enhanced. So this array used in the design of high temperature superconducting permanent maglev rail could improve the surface magnetic field and the levitation force. In order to make the best use of Nd-Fe-B (NdFeB) material and reduce the cost of maglev rail, the effect of the rail's structural parameters on levitation force and the utilization rate of NdFeB material are analyzed. The optimal ranges of these structural parameters are obtained. The mutual impact of these parameters is also discussed. The optimization method of these structure parameters is proposed at the end of this paper.

Structural parameter optimization design for Halbach permanent maglev rail

Energy Technology Data Exchange (ETDEWEB)

Guo, F., E-mail: guofang19830119@163.co [R and D Center of Applied Superconductivity, Huazhong University of Science and Technology, Wuhan 430074 (China); Tang, Y.; Ren, L.; Li, J. [R and D Center of Applied Superconductivity, Huazhong University of Science and Technology, Wuhan 430074 (China)

2010-11-01

Maglev rail is an important part of the magnetic levitation launch system. Reducing the manufacturing cost of magnetic levitation rail is the key problem for the development of magnetic levitation launch system. The Halbach permanent array has an advantage that the fundamental spatial field is cancelled on one side of the array while the field on the other side is enhanced. So this array used in the design of high temperature superconducting permanent maglev rail could improve the surface magnetic field and the levitation force. In order to make the best use of Nd-Fe-B (NdFeB) material and reduce the cost of maglev rail, the effect of the rail's structural parameters on levitation force and the utilization rate of NdFeB material are analyzed. The optimal ranges of these structural parameters are obtained. The mutual impact of these parameters is also discussed. The optimization method of these structure parameters is proposed at the end of this paper.

Optimal Design of Shock Tube Experiments for Parameter Inference

KAUST Repository

Bisetti, Fabrizio

2014-01-06

We develop a Bayesian framework for the optimal experimental design of the shock tube experiments which are being carried out at the KAUST Clean Combustion Research Center. The unknown parameters are the pre-exponential parameters and the activation energies in the reaction rate expressions. The control parameters are the initial mixture composition and the temperature. The approach is based on first building a polynomial based surrogate model for the observables relevant to the shock tube experiments. Based on these surrogates, a novel MAP based approach is used to estimate the expected information gain in the proposed experiments, and to select the best experimental set-ups yielding the optimal expected information gains. The validity of the approach is tested using synthetic data generated by sampling the PC surrogate. We finally outline a methodology for validation using actual laboratory experiments, and extending experimental design methodology to the cases where the control parameters are noisy.

On the design of experimental separation processes for maximum accuracy in the estimation of their parameters

International Nuclear Information System (INIS)

Volkman, Y.

1980-07-01

The optimal design of experimental separation processes for maximum accuracy in the estimation of process parameters is discussed. The sensitivity factor correlates the inaccuracy of the analytical methods with the inaccuracy of the estimation of the enrichment ratio. It is minimized according to the design parameters of the experiment and the characteristics of the analytical method

Bioprocessing of Proximally Analyzed Wheat Straw for Enhanced Cellulase Production through Process Optimization with Trichodermaviride under SSF

OpenAIRE

Ishtiaq Ahmed; Muhammad Anjum Zia; Hafiz Muhammad Nasir Iqbal

2010-01-01

The purpose of the present work was to study the production and process parameters optimization for the synthesis of cellulase from Trichoderma viride in solid state fermentation (SSF) using an agricultural wheat straw as substrates; as fungal conversion of lignocellulosic biomass for cellulase production is one among the major increasing demand for various biotechnological applications. An optimization of process parameters is a necessary step to get higher yield of prod...

Data Mining Process Optimization in Computational Multi-agent Systems

OpenAIRE

Kazík, O.; Neruda, R. (Roman)

2015-01-01

In this paper, we present an agent-based solution of metalearning problem which focuses on optimization of data mining processes. We exploit the framework of computational multi-agent systems in which various meta-learning problems have been already studied, e.g. parameter-space search or simple method recommendation. In this paper, we examine the effect of data preprocessing for machine learning problems. We perform the set of experiments in the search-space of data mining processes which is...

Optimization of Key Parameters of Energy Management Strategy for Hybrid Electric Vehicle Using DIRECT Algorithm

Directory of Open Access Journals (Sweden)

Jingxian Hao

2016-11-01

Full Text Available The rule-based logic threshold control strategy has been frequently used in energy management strategies for hybrid electric vehicles (HEVs owing to its convenience in adjusting parameters, real-time performance, stability, and robustness. However, the logic threshold control parameters cannot usually ensure the best vehicle performance at different driving cycles and conditions. For this reason, the optimization of key parameters is important to improve the fuel economy, dynamic performance, and drivability. In principle, this is a multiparameter nonlinear optimization problem. The logic threshold energy management strategy for an all-wheel-drive HEV is comprehensively analyzed and developed in this study. Seven key parameters to be optimized are extracted. The optimization model of key parameters is proposed from the perspective of fuel economy. The global optimization method, DIRECT algorithm, which has good real-time performance, low computational burden, rapid convergence, is selected to optimize the extracted key parameters globally. The results show that with the optimized parameters, the engine operates more at the high efficiency range resulting into a fuel savings of 7% compared with non-optimized parameters. The proposed method can provide guidance for calibrating the parameters of the vehicle energy management strategy from the perspective of fuel economy.

The study of optimization on process parameters of high-accuracy computerized numerical control polishing

Science.gov (United States)

Huang, Wei-Ren; Huang, Shih-Pu; Tsai, Tsung-Yueh; Lin, Yi-Jyun; Yu, Zong-Ru; Kuo, Ching-Hsiang; Hsu, Wei-Yao; Young, Hong-Tsu

2017-09-01

Spherical lenses lead to forming spherical aberration and reduced optical performance. Consequently, in practice optical system shall apply a combination of spherical lenses for aberration correction. Thus, the volume of the optical system increased. In modern optical systems, aspherical lenses have been widely used because of their high optical performance with less optical components. However, aspherical surfaces cannot be fabricated by traditional full aperture polishing process due to their varying curvature. Sub-aperture computer numerical control (CNC) polishing is adopted for aspherical surface fabrication in recent years. By using CNC polishing process, mid-spatial frequency (MSF) error is normally accompanied during this process. And the MSF surface texture of optics decreases the optical performance for high precision optical system, especially for short-wavelength applications. Based on a bonnet polishing CNC machine, this study focuses on the relationship between MSF surface texture and CNC polishing parameters, which include feed rate, head speed, track spacing and path direction. The power spectral density (PSD) analysis is used to judge the MSF level caused by those polishing parameters. The test results show that controlling the removal depth of single polishing path, through the feed rate, and without same direction polishing path for higher total removal depth can efficiently reduce the MSF error. To verify the optical polishing parameters, we divided a correction polishing process to several polishing runs with different direction polishing paths. Compare to one shot polishing run, multi-direction path polishing plan could produce better surface quality on the optics.

Setting of the Optimal Parameters of Melted Glass

Czech Academy of Sciences Publication Activity Database

Luptáková, Natália; Matejíčka, L.; Krečmer, N.

2015-01-01

Roč. 10, č. 1 (2015), s. 73-79 ISSN 1802-2308 Institutional support: RVO:68081723 Keywords : Striae * Glass * Glass melting * Regression * Optimal parameters Subject RIV: JH - Ceramics, Fire-Resistant Materials and Glass

Sensitivity of Calibrated Parameters and Water Resource Estimates on Different Objective Functions and Optimization Algorithms

Directory of Open Access Journals (Sweden)

Delaram Houshmand Kouchi

2017-05-01

Full Text Available The successful application of hydrological models relies on careful calibration and uncertainty analysis. However, there are many different calibration/uncertainty analysis algorithms, and each could be run with different objective functions. In this paper, we highlight the fact that each combination of optimization algorithm-objective functions may lead to a different set of optimum parameters, while having the same performance; this makes the interpretation of dominant hydrological processes in a watershed highly uncertain. We used three different optimization algorithms (SUFI-2, GLUE, and PSO, and eight different objective functions (R2, bR2, NSE, MNS, RSR, SSQR, KGE, and PBIAS in a SWAT model to calibrate the monthly discharges in two watersheds in Iran. The results show that all three algorithms, using the same objective function, produced acceptable calibration results; however, with significantly different parameter ranges. Similarly, an algorithm using different objective functions also produced acceptable calibration results, but with different parameter ranges. The different calibrated parameter ranges consequently resulted in significantly different water resource estimates. Hence, the parameters and the outputs that they produce in a calibrated model are “conditioned” on the choices of the optimization algorithm and objective function. This adds another level of non-negligible uncertainty to watershed models, calling for more attention and investigation in this area.

Variation of physicochemical parameters during a composting process

International Nuclear Information System (INIS)

Faria C, D.M.; Ballesteros, M.I.; Bendeck, M.

1999-01-01

Two composting processes were carried out; they lasted for about 165 days. In one of the processes the decomposition of the material was performed only by microorganisms only (direct composting) and in the other one, by microorganisms and earthworms -Eisenla foetida- (indirect composting). The first one was carried out in a composting system called c amas a nd the indirect one was carried out in its initial phase in a system of p anelas , then the wastes were transferred to a c ama . The materials were treated in both processes with lime, ammonium nitrate and microorganisms. Periodical samples were taken from different places of the pile and a temperature control was made weekly. The following physicochemical parameters were analyzed in each sample: Humidity, color, pH soil : water in ratios of 1:5 and 1:10, ash, organic matter, CIC, contents of carbon and nitrogen and C/N ratio. In the aqueous extract, C/N ratio and percentage of hydro solubles were analyzed. It was also made a germination assay taking measurements of the percentage of garden cress seeds (Lepidium sativum) that germinated in the aqueous extract. The parameters variation in each process let us to establish that the greatest changes in the material happened in the initial phases of the process (thermophilic and mesophilic phases); the presence of microorganisms was the limiting factor in the dynamic of the process; on the other hand, the earthworm addition did not accelerate the mineralization of organic matter. The results let us to establish that the color determination is not an effective parameter in order to evaluate the degree of maturity of the compost. Other parameters such as temperature and germination percentage can be made as routine test to determine the process rate. Determination of CIC, ash and hydro solubles content are recommended to evaluate the optimal maturity degree in the material. It is proposed changes such as to reduce the composting time to a maximum of 100 days and to

Kinetics parameters of a slurry remediation process in rotating drum bioreactors

International Nuclear Information System (INIS)

Esquivel-Rios, I.; Rodriguez-Meza, M. A.; Barrera-Cortes, J.

2009-01-01

The knowledge of biotransformation pollution dynamics in any systems is important for design and optimization purposes of biochemical processes involved. this is focus to the determination of kinetics parameters such as the maximum specific growth rate (μMAX), saturation constant (Ks), biomass yield (YX/S; X: biomass, S: substrate) and oxygen consumption (YO 2 /S; O 2 : oxygen). Several approximations, based on Monod equation, have been developed for estimating kinetics parameters in terms of concentration and type of substrate, bioprocess type and microflora available. (Author)

Optimization of Minimum Quantity Lubricant Conditions and Cutting Parameters in Hard Milling of AISI H13 Steel

Directory of Open Access Journals (Sweden)

The-Vinh Do

2016-03-01

Full Text Available As a successful solution applied to hard machining, the minimum quantity lubricant (MQL has already been established as an alternative to flood coolant processing. The optimization of MQL parameters and cutting parameters under MQL condition are essential and pressing. The study was divided into two parts. In the first part of this study, the Taguchi method was applied to find the optimal values of MQL condition in the hard milling of AISI H13 with consideration of reduced surface roughness. The L9 orthogonal array, the signal-to-noise (S/N ratio and analysis of variance (ANOVA were employed to analyze the effect of the performance characteristics of MQL parameters (i.e., cutting fluid type, pressure, and fluid flow on good surface finish. In the results section, lubricant and pressure of MQL condition are determined to be the most influential factors which give a statistically significant effect on machined surfaces. A verifiable experiment was conducted to demonstrate the reliability of the results. In the second section, the optimized MQL parameters were applied in a series of experiments to find out cutting parameters of hard milling. The Taguchi method was also used to optimize the cutting parameters in order to obtain the best surface roughness. The design of the experiment (DOE was implemented by using the L27 orthogonal array. Based on an analysis of the signal-to-noise response and ANOVA, the optimal values of cutting parameters (i.e., cutting speed, feed rate, depth-of-cut and hardness of workpiece were introduced. The results of the present work indicate feed rate is the factor having the most effect on surface roughness.

Experimental Investigation and Optimization of TIG Welding Parameters on Aluminum 6061 Alloy Using Firefly Algorithm

Science.gov (United States)

Kumar, Rishi; Mevada, N. Ramesh; Rathore, Santosh; Agarwal, Nitin; Rajput, Vinod; Sinh Barad, AjayPal

2017-08-01

To improve Welding quality of aluminum (Al) plate, the TIG Welding system has been prepared, by which Welding current, Shielding gas flow rate and Current polarity can be controlled during Welding process. In the present work, an attempt has been made to study the effect of Welding current, current polarity, and shielding gas flow rate on the tensile strength of the weld joint. Based on the number of parameters and their levels, the Response Surface Methodology technique has been selected as the Design of Experiment. For understanding the influence of input parameters on Ultimate tensile strength of weldment, ANOVA analysis has been carried out. Also to describe and optimize TIG Welding using a new metaheuristic Nature - inspired algorithm which is called as Firefly algorithm which was developed by Dr. Xin-She Yang at Cambridge University in 2007. A general formulation of firefly algorithm is presented together with an analytical, mathematical modeling to optimize the TIG Welding process by a single equivalent objective function.

Parameter assessment for virtual Stackelberg game in aerodynamic shape optimization

Science.gov (United States)

Wang, Jing; Xie, Fangfang; Zheng, Yao; Zhang, Jifa

2018-05-01

In this paper, parametric studies of virtual Stackelberg game (VSG) are conducted to assess the impact of critical parameters on aerodynamic shape optimization, including design cycle, split of design variables and role assignment. Typical numerical cases, including the inverse design and drag reduction design of airfoil, have been carried out. The numerical results confirm the effectiveness and efficiency of VSG. Furthermore, the most significant parameters are identified, e.g. the increase of design cycle can improve the optimization results but it will also add computational burden. These studies will maximize the productivity of the effort in aerodynamic optimization for more complicated engineering problems, such as the multi-element airfoil and wing-body configurations.

Laser cutting: industrial relevance, process optimization, and laser safety

Science.gov (United States)

Haferkamp, Heinz; Goede, Martin; von Busse, Alexander; Thuerk, Oliver

1998-09-01

Compared to other technological relevant laser machining processes, up to now laser cutting is the application most frequently used. With respect to the large amount of possible fields of application and the variety of different materials that can be machined, this technology has reached a stable position within the world market of material processing. Reachable machining quality for laser beam cutting is influenced by various laser and process parameters. Process integrated quality techniques have to be applied to ensure high-quality products and a cost effective use of the laser manufacturing plant. Therefore, rugged and versatile online process monitoring techniques at an affordable price would be desirable. Methods for the characterization of single plant components (e.g. laser source and optical path) have to be substituted by an omnivalent control system, capable of process data acquisition and analysis as well as the automatic adaptation of machining and laser parameters to changes in process and ambient conditions. At the Laser Zentrum Hannover eV, locally highly resolved thermographic measurements of the temperature distribution within the processing zone using cost effective measuring devices are performed. Characteristic values for cutting quality and plunge control as well as for the optimization of the surface roughness at the cutting edges can be deducted from the spatial distribution of the temperature field and the measured temperature gradients. Main influencing parameters on the temperature characteristic within the cutting zone are the laser beam intensity and pulse duration in pulse operation mode. For continuous operation mode, the temperature distribution is mainly determined by the laser output power related to the cutting velocity. With higher cutting velocities temperatures at the cutting front increase, reaching their maximum at the optimum cutting velocity. Here absorption of the incident laser radiation is drastically increased due to

Parameter estimation for chaotic systems with a Drift Particle Swarm Optimization method

International Nuclear Information System (INIS)

Sun Jun; Zhao Ji; Wu Xiaojun; Fang Wei; Cai Yujie; Xu Wenbo

2010-01-01

Inspired by the motion of electrons in metal conductors in an electric field, we propose a variant of Particle Swarm Optimization (PSO), called Drift Particle Swarm Optimization (DPSO) algorithm, and apply it in estimating the unknown parameters of chaotic dynamic systems. The principle and procedure of DPSO are presented, and the algorithm is used to identify Lorenz system and Chen system. The experiment results show that for the given parameter configurations, DPSO can identify the parameters of the systems accurately and effectively, and it may be a promising tool for chaotic system identification as well as other numerical optimization problems in physics.

Multivariate optimization of process parameters in the synthesis of calcined Ca‒Al (NO3) LDH for defluoridation using 3(3) factorial, central composite and Box-Behnken design.

Science.gov (United States)

Ghosal, Partha S; Gupta, Ashok K; Sulaiman, Ayoob

2016-01-01

Response surface methodology was applied for the first time in the optimization of the preparation of layered double hydroxide (LDH) for defluoridation. The influence of three vital process parameters (viz. pH, molar ratio and calcination temperature) in the synthesis of the adsorbent 'Calcined Ca‒Al (NO3) LDH' was thoroughly examined to maximize its fluoride scavenging potential. The process parameters were optimized using the 3(3) factorial, face centered central composite and Box-Behnken designs and a comparative assessment of the methods was conducted. The maximum fluoride removal efficiency was achieved at a calcination temperature of approximately 500ºC; however, the efficiency decreased with increasing pH and molar ratio. The outcome of the comparative assessment clearly delineates the case specific nature of the models. A better predictability over the entire experimental domain was obtained with the 3(3) factorial method, whereas the Box-Behnken design was found to be the most efficient model with lesser number of experimental runs. The desirability function technique was performed for optimizing the response, wherein face centered central composite design exhibited a maximum desirability. The calcined Ca‒Al (NO3) LDH, synthesized under the optimum conditions, demonstrated the removal efficiencies of 95% and 99% for the doses of 3 g L(-1) and 5 g L(-1), respectively.

Dynamic Optimization of UV Flash Processes

DEFF Research Database (Denmark)

Ritschel, Tobias Kasper Skovborg; Capolei, Andrea; Jørgensen, John Bagterp

2017-01-01

UV ash processes, also referred to as isoenergetic-isochoric ash processes, occur for dynamic simulation and optimization of vapor-liquid equilibrium processes. Dynamic optimization and nonlinear model predictive control of distillation columns, certain two-phase ow problems, as well as oil reser...... that the optimization solver, the compiler, and high-performance linear algebra software are all important for e_cient dynamic optimization of UV ash processes....

Synthesis of Optimal Processing Pathway for Microalgae-based Biorefinery under Uncertainty

DEFF Research Database (Denmark)

Rizwan, Muhammad; Lee, Jay H.; Gani, Rafiqul

2015-01-01

decision making, we propose a systematic framework for the synthesis and optimal design of microalgae-based processing network under uncertainty. By incorporating major uncertainties into the biorefinery superstructure model we developed previously, a stochastic mixed integer nonlinear programming (s......The research in the field of microalgae-based biofuels and chemicals is in early phase of the development, and therefore a wide range of uncertainties exist due to inconsistencies among and shortage of technical information. In order to handle and address these uncertainties to ensure robust......MINLP) problem is formulated for determining the optimal biorefinery structure under given parameter uncertainties modelled as sampled scenarios. The solution to the sMINLP problem determines the optimal decisions with respect to processing technologies, material flows, and product portfolio in the presence...

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

Directory of Open Access Journals (Sweden)

MadhuSudana Rao Nalluri

2017-01-01

Full Text Available With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM and multilayer perceptron (MLP technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs. Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.

The Process of Optimizing Mechanical Sound Quality in Product Design

DEFF Research Database (Denmark)

Eriksen, Kaare; Holst, Thomas

2011-01-01

The research field concerning optimizing product sound quality is a relatively unexplored area, and may become difficult for designers to operate in. To some degree, sound is a highly subjective parameter, which is normally targeted sound specialists. This paper describes the theoretical...... and practical background for managing a process of optimizing the mechanical sound quality in a product design by using simple tools and workshops systematically. The procedure is illustrated by a case study of a computer navigation tool (computer mouse or mouse). The process is divided into 4 phases, which...... clarify the importance of product sound, defining perceptive demands identified by users, and, finally, how to suggest mechanical principles for modification of an existing sound design. The optimized mechanical sound design is followed by tests on users of the product in its use context. The result...

Hydrogen production by onboard gasoline processing – Process simulation and optimization

Energy Technology Data Exchange (ETDEWEB)

Bisaria, Vega; Smith, R.J. Byron,

2013-12-15

Highlights: • Process flow sheet for an onboard fuel processor for 100 kW fuel cell output was simulated. • Gasoline fuel requirement was found to be 30.55 kg/hr. • The fuel processor efficiency was found to be 95.98%. • An heat integrated optimum flow sheet was developed. - Abstract: Fuel cell vehicles have reached the commercialization stage and hybrid vehicles are already on the road. While hydrogen storage and infrastructure remain critical issues in stand alone commercialization of the technology, researchers are developing onboard fuel processors, which can convert a variety of fuels into hydrogen to power these fuel cell vehicles. The feasibility study of a 100 kW on board fuel processor based on gasoline fuel is carried out using process simulation. The steady state model has been developed with the help of Aspen HYSYS to analyze the fuel processor and total system performance. The components of the fuel processor are the fuel reforming unit, CO clean-up unit and auxiliary units. Optimization studies were carried out by analyzing the influence of various operating parameters such as oxygen to carbon ratio, steam to carbon ratio, temperature and pressure on the process equipments. From the steady state model optimization using Aspen HYSYS, an optimized reaction composition in terms of hydrogen production and carbon monoxide concentration corresponds to: oxygen to carbon ratio of 0.5 and steam to carbon ratio of 0.5. The fuel processor efficiency of 95.98% is obtained under these optimized conditions. The heat integration of the system using the composite curve, grand composite curve and utility composite curve were studied for the system. The most appropriate heat exchanger network from the generated ones was chosen and that was incorporated into the optimized flow sheet of the100 kW fuel processor. A completely heat integrated 100 kW fuel processor flow sheet using gasoline as fuel was thus successfully simulated and optimized.

Hydrogen production by onboard gasoline processing – Process simulation and optimization

International Nuclear Information System (INIS)

Bisaria, Vega; Smith, R.J. Byron

2013-01-01

Highlights: • Process flow sheet for an onboard fuel processor for 100 kW fuel cell output was simulated. • Gasoline fuel requirement was found to be 30.55 kg/hr. • The fuel processor efficiency was found to be 95.98%. • An heat integrated optimum flow sheet was developed. - Abstract: Fuel cell vehicles have reached the commercialization stage and hybrid vehicles are already on the road. While hydrogen storage and infrastructure remain critical issues in stand alone commercialization of the technology, researchers are developing onboard fuel processors, which can convert a variety of fuels into hydrogen to power these fuel cell vehicles. The feasibility study of a 100 kW on board fuel processor based on gasoline fuel is carried out using process simulation. The steady state model has been developed with the help of Aspen HYSYS to analyze the fuel processor and total system performance. The components of the fuel processor are the fuel reforming unit, CO clean-up unit and auxiliary units. Optimization studies were carried out by analyzing the influence of various operating parameters such as oxygen to carbon ratio, steam to carbon ratio, temperature and pressure on the process equipments. From the steady state model optimization using Aspen HYSYS, an optimized reaction composition in terms of hydrogen production and carbon monoxide concentration corresponds to: oxygen to carbon ratio of 0.5 and steam to carbon ratio of 0.5. The fuel processor efficiency of 95.98% is obtained under these optimized conditions. The heat integration of the system using the composite curve, grand composite curve and utility composite curve were studied for the system. The most appropriate heat exchanger network from the generated ones was chosen and that was incorporated into the optimized flow sheet of the100 kW fuel processor. A completely heat integrated 100 kW fuel processor flow sheet using gasoline as fuel was thus successfully simulated and optimized

Multi-scale Modeling Approach for Design and Optimization of Oleochemical Processes

DEFF Research Database (Denmark)

Jones, Mark Nicholas; Forero-Hernandez, Hector Alexander; Sarup, Bent

2017-01-01

The primary goal of this work is to present a systematic methodology and software frameworkfor a multi-level approach ranging from process synthesis and modeling throughproperty prediction, to sensitivity analysis, property parameter tuning and optimization.This framework is applied to the follow...

Analysis and modeling of safety parameters in the selection of optimal routes for emergency evacuation after the earthquake (Case study: 13 Aban neighborhood of Tehran

Directory of Open Access Journals (Sweden)

Sajad Ganjehi

2013-08-01

Full Text Available Introduction : Earthquakes are imminent threats to urban areas of Iran, especially Tehran. They can cause extensive destructions and lead to heavy casualties. One of the most important aspects of disaster management after earthquake is the rapid transfer of casualties to emergency shelters. To expedite emergency evacuation process the optimal safe path method should be considered. To examine the safety of road networks and to determine the most optimal route at pre-earthquake phase, a series of parameters should be taken into account.   Methods : In this study, we employed a multi-criteria decision-making approach to determine and evaluate the effective safety parameters for selection of optimal routes in emergency evacuation after an earthquake.   Results: The relationship between the parameters was analyzed and the effect of each parameter was listed. A process model was described and a case study was implemented in the 13th Aban neighborhood ( Tehran’s 20th municipal district . Then, an optimal path to safe places in an emergency evacuation after an earthquake in the 13th Aban neighborhood was selected.   Conclusion : Analytic hierarchy process (AHP, as the main model, was employed. Each parameter of the model was described. Also, the capabilities of GIS software such as layer coverage were used.     Keywords: Earthquake, emergency evacuation, Analytic Hierarchy Process (AHP, crisis management, optimization, 13th Aban neighborhood of Tehran

Intelligent Mechatronics Systems for Transport Climate Parameters Optimization Using Fuzzy Logic Control

OpenAIRE

Beinarts, I; Ļevčenkovs, A; Kuņicina, N

2007-01-01

In article interest is concentrated on the climate parameters optimization in passengers’ salon of public electric transportation vehicles. The article presents mathematical problem for using intelligent agents in mechatronics problems for climate parameters optimal control. Idea is to use fuzzy logic and intelligent algorithms to create coordination mechanism for climate parameters control to save electrical energy, and it increases the level of comfort for passengers. A special interest for...

Use of multilevel modeling for determining optimal parameters of heat supply systems

Science.gov (United States)

Stennikov, V. A.; Barakhtenko, E. A.; Sokolov, D. V.

2017-07-01

The problem of finding optimal parameters of a heat-supply system (HSS) is in ensuring the required throughput capacity of a heat network by determining pipeline diameters and characteristics and location of pumping stations. Effective methods for solving this problem, i.e., the method of stepwise optimization based on the concept of dynamic programming and the method of multicircuit optimization, were proposed in the context of the hydraulic circuit theory developed at Melentiev Energy Systems Institute (Siberian Branch, Russian Academy of Sciences). These methods enable us to determine optimal parameters of various types of piping systems due to flexible adaptability of the calculation procedure to intricate nonlinear mathematical models describing features of used equipment items and methods of their construction and operation. The new and most significant results achieved in developing methodological support and software for finding optimal parameters of complex heat supply systems are presented: a new procedure for solving the problem based on multilevel decomposition of a heat network model that makes it possible to proceed from the initial problem to a set of interrelated, less cumbersome subproblems with reduced dimensionality; a new algorithm implementing the method of multicircuit optimization and focused on the calculation of a hierarchical model of a heat supply system; the SOSNA software system for determining optimum parameters of intricate heat-supply systems and implementing the developed methodological foundation. The proposed procedure and algorithm enable us to solve engineering problems of finding the optimal parameters of multicircuit heat supply systems having large (real) dimensionality, and are applied in solving urgent problems related to the optimal development and reconstruction of these systems. The developed methodological foundation and software can be used for designing heat supply systems in the Central and the Admiralty regions in

Optimizing Methods of Obtaining Stellar Parameters for the H3 Survey

Science.gov (United States)

Ivory, KeShawn; Conroy, Charlie; Cargile, Phillip

2018-01-01

The Stellar Halo at High Resolution with Hectochelle Survey (H3) is in the process of observing and collecting stellar parameters for stars in the Milky Way's halo. With a goal of measuring radial velocities for fainter stars, it is crucial that we have optimal methods of obtaining this and other parameters from the data from these stars.The method currently developed is The Payne, named after Cecilia Payne-Gaposchkin, a code that uses neural networks and Markov Chain Monte Carlo methods to utilize both spectra and photometry to obtain values for stellar parameters. This project was to investigate the benefit of fitting both spectra and spectral energy distributions (SED). Mock spectra using the parameters of the Sun were created and noise was inserted at various signal to noise values. The Payne then fit each mock spectrum with and without a mock SED also generated from solar parameters. The result was that at high signal to noise, the spectrum dominated and the effect of fitting the SED was minimal. But at low signal to noise, the addition of the SED greatly decreased the standard deviation of the data and resulted in more accurate values for temperature and metallicity.

Robust fluence map optimization via alternating direction method of multipliers with empirical parameter optimization

International Nuclear Information System (INIS)

Gao, Hao

2016-01-01

For the treatment planning during intensity modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT), beam fluence maps can be first optimized via fluence map optimization (FMO) under the given dose prescriptions and constraints to conformally deliver the radiation dose to the targets while sparing the organs-at-risk, and then segmented into deliverable MLC apertures via leaf or arc sequencing algorithms. This work is to develop an efficient algorithm for FMO based on alternating direction method of multipliers (ADMM). Here we consider FMO with the least-square cost function and non-negative fluence constraints, and its solution algorithm is based on ADMM, which is efficient and simple-to-implement. In addition, an empirical method for optimizing the ADMM parameter is developed to improve the robustness of the ADMM algorithm. The ADMM based FMO solver was benchmarked with the quadratic programming method based on the interior-point (IP) method using the CORT dataset. The comparison results suggested the ADMM solver had a similar plan quality with slightly smaller total objective function value than IP. A simple-to-implement ADMM based FMO solver with empirical parameter optimization is proposed for IMRT or VMAT. (paper)

Optimization of operating parameters in polysilicon chemical vapor deposition reactor with response surface methodology

Science.gov (United States)

An, Li-sha; Liu, Chun-jiao; Liu, Ying-wen

2018-05-01

In the polysilicon chemical vapor deposition reactor, the operating parameters are complex to affect the polysilicon's output. Therefore, it is very important to address the coupling problem of multiple parameters and solve the optimization in a computationally efficient manner. Here, we adopted Response Surface Methodology (RSM) to analyze the complex coupling effects of different operating parameters on silicon deposition rate (R) and further achieve effective optimization of the silicon CVD system. Based on finite numerical experiments, an accurate RSM regression model is obtained and applied to predict the R with different operating parameters, including temperature (T), pressure (P), inlet velocity (V), and inlet mole fraction of H2 (M). The analysis of variance is conducted to describe the rationality of regression model and examine the statistical significance of each factor. Consequently, the optimum combination of operating parameters for the silicon CVD reactor is: T = 1400 K, P = 3.82 atm, V = 3.41 m/s, M = 0.91. The validation tests and optimum solution show that the results are in good agreement with those from CFD model and the deviations of the predicted values are less than 4.19%. This work provides a theoretical guidance to operate the polysilicon CVD process.

Maximising municipal solid waste--legume trimming residue mixture degradation in composting by control parameters optimization.

Science.gov (United States)

Cabeza, I O; López, R; Ruiz-Montoya, M; Díaz, M J

2013-10-15

Composting is one of the most successful biological processes for the treatment of the residues enriched in putrescible materials. The optimization of parameters which have an influence on the stability of the products is necessary in order to maximize recycling and recovery of waste components. The influence of the composting process parameters (aeration, moisture, C/N ratio, and time) on the stability parameters (organic matter, N-losses, chemical oxygen demand, nitrate, biodegradability coefficient) of the compost was studied. The composting experiment was carried out using Municipal Solid Waste (MSW) and Legume Trimming Residues (LTR) in 200 L isolated acrylic barrels following a Box-Behnken central composite experimental design. Second-order polynomial models were found for each of the studied compost stability parameter, which accurately described the relationship between the parameters. The differences among the experimental values and those estimated by using the equations never exceeded 10% of the former. Results of the modelling showed that excluding the time, the C/N ratio is the strongest variable influencing almost all the stability parameters studied in this case, with the exception of N-losses which is strongly dependent on moisture. Moreover, an optimized ratio MSW/LTR of 1/1 (w/w), moisture content in the range of 40-55% and moderate to low aeration rate (0.05-0.175 Lair kg(-)(1) min(-1)) is recommended to maximise degradation and to obtain a stable product during co-composting of MSW and LTR. Copyright © 2013 Elsevier Ltd. All rights reserved.

Parameters in selective laser melting for processing metallic powders

Science.gov (United States)

Kurzynowski, Tomasz; Chlebus, Edward; Kuźnicka, Bogumiła; Reiner, Jacek

2012-03-01

The paper presents results of studies on Selective Laser Melting. SLM is an additive manufacturing technology which may be used to process almost all metallic materials in the form of powder. Types of energy emission sources, mainly fiber lasers and/or Nd:YAG laser with similar characteristics and the wavelength of 1,06 - 1,08 microns, are provided primarily for processing metallic powder materials with high absorption of laser radiation. The paper presents results of selected variable parameters (laser power, scanning time, scanning strategy) and fixed parameters such as the protective atmosphere (argon, nitrogen, helium), temperature, type and shape of the powder material. The thematic scope is very broad, so the work was focused on optimizing the process of selective laser micrometallurgy for producing fully dense parts. The density is closely linked with other two conditions: discontinuity of the microstructure (microcracks) and stability (repeatability) of the process. Materials used for the research were stainless steel 316L (AISI), tool steel H13 (AISI), and titanium alloy Ti6Al7Nb (ISO 5832-11). Studies were performed with a scanning electron microscope, a light microscopes, a confocal microscope and a μCT scanner.

Optimal Inversion Parameters for Full Waveform Inversion using OBS Data Set

Science.gov (United States)

Kim, S.; Chung, W.; Shin, S.; Kim, D.; Lee, D.

2017-12-01

In recent years, full Waveform Inversion (FWI) has been the most researched technique in seismic data processing. It uses the residuals between observed and modeled data as an objective function; thereafter, the final subsurface velocity model is generated through a series of iterations meant to minimize the residuals.Research on FWI has expanded from acoustic media to elastic media. In acoustic media, the subsurface property is defined by P-velocity; however, in elastic media, properties are defined by multiple parameters, such as P-velocity, S-velocity, and density. Further, the elastic media can also be defined by Lamé constants, density or impedance PI, SI; consequently, research is being carried out to ascertain the optimal parameters.From results of advanced exploration equipment and Ocean Bottom Seismic (OBS) survey, it is now possible to obtain multi-component seismic data. However, to perform FWI on these data and generate an accurate subsurface model, it is important to determine optimal inversion parameters among (Vp, Vs, ρ), (λ, μ, ρ), and (PI, SI) in elastic media. In this study, staggered grid finite difference method was applied to simulate OBS survey. As in inversion, l2-norm was set as objective function. Further, the accurate computation of gradient direction was performed using the back-propagation technique and its scaling was done using the Pseudo-hessian matrix.In acoustic media, only Vp is used as the inversion parameter. In contrast, various sets of parameters, such as (Vp, Vs, ρ) and (λ, μ, ρ) can be used to define inversion in elastic media. Therefore, it is important to ascertain the parameter that gives the most accurate result for inversion with OBS data set.In this study, we generated Vp and Vs subsurface models by using (λ, μ, ρ) and (Vp, Vs, ρ) as inversion parameters in every iteration, and compared the final two FWI results.This research was supported by the Basic Research Project(17-3312) of the Korea Institute of

Optimization of Multiple Responses of Ultrasonic Machining (USM Process: A Comparative Study

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Rina Chakravorty

2013-04-01

Full Text Available Ultrasonic machining (USM process has multiple performance measures, e.g. material removal rate (MRR, tool wear rate (TWR, surface roughness (SR etc., which are affected by several process parameters. The researchers commonly attempted to optimize USM process with respect to individual responses, separately. In the recent past, several systematic procedures for dealing with the multi-response optimization problems have been proposed in the literature. Although most of these methods use complex mathematics or statistics, there are some simple methods, which can be comprehended and implemented by the engineers to optimize the multiple responses of USM processes. However, the relative optimization performance of these approaches is unknown because the effectiveness of different methods has been demonstrated using different sets of process data. In this paper, the computational requirements for four simple methods are presented, and two sets of past experimental data on USM processes are analysed using these methods. The relative performances of these methods are then compared. The results show that weighted signal-to-noise (WSN ratio method and utility theory (UT method usually give better overall optimisation performance for the USM process than the other approaches.

Tuning of PID controller using optimization techniques for a MIMO process

Science.gov (United States)

Thulasi dharan, S.; Kavyarasan, K.; Bagyaveereswaran, V.

2017-11-01

In this paper, two processes were considered one is Quadruple tank process and the other is CSTR (Continuous Stirred Tank Reactor) process. These are majorly used in many industrial applications for various domains, especially, CSTR in chemical plants.At first mathematical model of both the process is to be done followed by linearization of the system due to MIMO process and controllers are the major part to control the whole process to our desired point as per the applications so the tuning of the controller plays a major role among the whole process. For tuning of parameters we use two optimizations techniques like Particle Swarm Optimization, Genetic Algorithm. The above techniques are majorly used in different applications to obtain which gives the best among all, we use these techniques to obtain the best tuned values among many. Finally, we will compare the performance of the each process with both the techniques.

A Bayesian optimal design for degradation tests based on the inverse Gaussian process

Energy Technology Data Exchange (ETDEWEB)

Peng, Weiwen; Liu, Yu; Li, Yan Feng; Zhu, Shun Peng; Huang, Hong Zhong [University of Electronic Science and Technology of China, Chengdu (China)

2014-10-15

The inverse Gaussian process is recently introduced as an attractive and flexible stochastic process for degradation modeling. This process has been demonstrated as a valuable complement for models that are developed on the basis of the Wiener and gamma processes. We investigate the optimal design of the degradation tests on the basis of the inverse Gaussian process. In addition to an optimal design with pre-estimated planning values of model parameters, we also address the issue of uncertainty in the planning values by using the Bayesian method. An average pre-posterior variance of reliability is used as the optimization criterion. A trade-off between sample size and number of degradation observations is investigated in the degradation test planning. The effects of priors on the optimal designs and on the value of prior information are also investigated and quantified. The degradation test planning of a GaAs Laser device is performed to demonstrate the proposed method.

Quality by design approach in the optimization of the spray-drying process.

Science.gov (United States)

Baldinger, Arnaud; Clerdent, Lucas; Rantanen, Jukka; Yang, Mingshi; Grohganz, Holger

2012-01-01

The aim of this study was to illustrate the influence of the processing parameters, inlet temperature, atomization air flow rate and feed flow rate, on critical quality attributes of spray-dried powders using design of experiments (DoE). Spray-dried powders were characterized by laser diffraction, X-ray powder diffraction (XRPD) and near-infrared spectroscopy (NIR). Multivariate analysis of two different experimental designs was performed to elucidate the optimal process conditions. XRPD revealed that the spray-dried powders consisted of crystalline β-mannitol and amorphous trehalose. Non-invasive NIR measurement was successfully used for correlating the critical quality attribute particle size with size determined by laser diffraction. The full factorial design proved to be unsuitable due to the non-linear influence of factors. The composite face-centered design improved the quality of the models and showed both linear and non-linear influence of the parameters on the outcomes. A model explaining the influence of the factors on all quality attributes showed similar results as the models optimized for a single response. This study showed the applicability of DoE for the investigation of spray-dried powders. The knowledge of the interplay between process parameters and quality attributes will enable rational process design to achieve a desired outcome.

Optimization of squeeze casting parameters for non symmetrical AC2A aluminium alloy castings through Taguchi method

International Nuclear Information System (INIS)

Senthil, P.; Amirthagadeswaran, K. S.

2012-01-01

This paper reports a research in which an attempt was made to prepare AC2A aluminium alloy castings of a non symmetrical component through squeeze casting process. The primary objective was to investigate the influence of process parameters on mechanical properties of the castings. Experiments were conducted based on orthogonal array suggested in Taguchi's offline quality control concept. The experimental results showed that squeeze pressure, die preheating temperature and compression holding time were the parameters making significant improvement in mechanical properties. The optimal squeeze casting condition was found and mathematical models were also developed for the process

Optimization of plasma flow parameters of the magnetoplasma compressor

International Nuclear Information System (INIS)

Dojcinovic, I P; Kuraica, M M; Obradovc, B M; Cvetanovic, N; Puric, J

2007-01-01

Optimization of the working conditions of the magnetoplasma compressor (MPC) has been performed through analysing discharge and compression plasma flow parameters in hydrogen, nitrogen and argon at different pressures. Energy conversion rate, volt-ampere curve exponent and plasma flow velocities have been studied to optimize the efficiency of energy transfer from the supply source to the plasma. It has been found that the most effective energy transfer from the supply to the plasma is in hydrogen as a working gas at 1000 Pa pressure. It was found that the accelerating regime exists for hydrogen up to 3000 Pa pressures, in nitrogen up to 2000 Pa and in argon up to 1000 Pa pressure. At higher pressures MPC in all the gases works in the decelerating regime. At pressures lower than 200 Pa, high cathode erosion is observed. MPC plasma flow parameter optimization is very important because this plasma accelerating system may be of special interest for solid surface modification and other technology applications

Mixed-integer evolution strategies for parameter optimization and their applications to medical image analysis

NARCIS (Netherlands)

Li, Rui

2009-01-01

The target of this work is to extend the canonical Evolution Strategies (ES) from traditional real-valued parameter optimization domain to mixed-integer parameter optimization domain. This is necessary because there exist numerous practical optimization problems from industry in which the set of

Optimal Reinsurance-Investment Problem for an Insurer and a Reinsurer with Jump-Diffusion Process

Directory of Open Access Journals (Sweden)

Hanlei Hu

2018-01-01

Full Text Available The optimal reinsurance-investment strategies considering the interests of both the insurer and reinsurer are investigated. The surplus process is assumed to follow a jump-diffusion process and the insurer is permitted to purchase proportional reinsurance from the reinsurer. Applying dynamic programming approach and dual theory, the corresponding Hamilton-Jacobi-Bellman equations are derived and the optimal strategies for exponential utility function are obtained. In addition, several sensitivity analyses and numerical illustrations in the case with exponential claiming distributions are presented to analyze the effects of parameters about the optimal strategies.

Parameter optimization method for longitudinal vibration absorber of ship shaft system

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LIU Jinlin

2017-05-01

Full Text Available The longitudinal vibration of the ship shaft system is the one of the most important factors of hull stern vibration, and it can be effectively minimized by installing a longitudinal vibration absorber. In this way, the vibration and noise of ships can be brought under control. However, the parameters of longitudinal vibration absorbers have a great influence on the vibration characteristics of the shaft system. As such, a certain shafting testing platform was studied as the object on which a finite model was built, and the relationship between longitudinal stiffness and longitudinal vibration in the shaft system was analyzed in a straight alignment state. Furthermore, a longitudinal damping model of the shaft system was built in which the parameters of the vibration absorber were non-dimensionalized, the weight of the vibration absorber was set as a constant, and an optimizing algorithm was used to calculate the optimized stiffness and damping coefficient of the vibration absorber. Finally, the longitudinal vibration frequency response of the shafting testing platform before and after optimizing the parameters of the longitudinal vibration absorber were compared, and the results indicated that the longitudinal vibration of the shafting testing platform was decreased effectively, which suggests that it could provide a theoretical foundation for the parameter optimization of longitudinal vibration absorbers.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

Science.gov (United States)

Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

2017-03-01

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

Directory of Open Access Journals (Sweden)

Huanqing Cui

2017-03-01

Full Text Available Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

Harmony search optimization in dimensional accuracy of die sinking EDM process using SS316L stainless steel

Science.gov (United States)

Deris, A. M.; Zain, A. M.; Sallehuddin, R.; Sharif, S.

2017-09-01

Electric discharge machine (EDM) is one of the widely used nonconventional machining processes for hard and difficult to machine materials. Due to the large number of machining parameters in EDM and its complicated structural, the selection of the optimal solution of machining parameters for obtaining minimum machining performance is remain as a challenging task to the researchers. This paper proposed experimental investigation and optimization of machining parameters for EDM process on stainless steel 316L work piece using Harmony Search (HS) algorithm. The mathematical model was developed based on regression approach with four input parameters which are pulse on time, peak current, servo voltage and servo speed to the output response which is dimensional accuracy (DA). The optimal result of HS approach was compared with regression analysis and it was found HS gave better result y giving the most minimum DA value compared with regression approach.

Parameter Optimization for Quantitative Signal-Concentration Mapping Using Spoiled Gradient Echo MRI

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Gasser Hathout

2012-01-01

Full Text Available Rationale and Objectives. Accurate signal to tracer concentration maps are critical to quantitative MRI. The purpose of this study was to evaluate and optimize spoiled gradient echo (SPGR MR sequences for the use of gadolinium (Gd-DTPA as a kinetic tracer. Methods. Water-gadolinium phantoms were constructed for a physiologic range of gadolinium concentrations. Observed and calculated SPGR signal to concentration curves were generated. Using a percentage error determination, optimal pulse parameters for signal to concentration mapping were obtained. Results. The accuracy of the SPGR equation is a function of the chosen MR pulse parameters, particularly the time to repetition (TR and the flip angle (FA. At all experimental values of TR, increasing FA decreases the ratio between observed and calculated signals. Conversely, for a constant FA, increasing TR increases this ratio. Using optimized pulse parameter sets, it is possible to achieve excellent accuracy (approximately 5% over a physiologic range of concentration tracer concentrations. Conclusion. Optimal pulse parameter sets exist and their use is essential for deriving accurate signal to concentration curves in quantitative MRI.

An optimization methodology for identifying robust process integration investments under uncertainty