Calibration of diagnostic x-ray machines using radiation exposure and radiographic parameters

International Nuclear Information System (INIS)

Agba, E.H.; Uloko, P. I.; Tyovenda, A. A.

2011-01-01

Calibration of diagnostic x-ray machines using radiation exposure and radiographic parameters of the x-ray machines has been carried out. Three phase diagnostic x-ray machines situated at Federal Medical Centre, Makurdi, General Hospital, Otukpo and Christian Hospital, Mkar were used for the calibration work. The radiation meter was used to measure x-ray radiation exposure. The result of this work demonstrates mR/mAs=C(KV p ) that there exist a power law relation of the form between the radiation exposure and the radiographic parameters of diagnostic x-ray machines, which can be used to estimate patient exposure during routine x-ray diagnostic examinations for wide range of operating parameters. The values of the power exponent n, constant c and total filtrations of the diagnostic x-ray machines have been estimated. These values for the diagnostic x-ray machines at the Federal Medical Centre, Makurdi are: 2.14, 0.88 and 2.77 respectively, for the one at the General Hospital, Otukpo are: 2.07, 0.76 and 2.68 respectively and that of the Christian Hospital, Mkar are: 2.01,0.69 and 2.61 respectively.

Precision Parameter Estimation and Machine Learning

Science.gov (United States)

Wandelt, Benjamin D.

2008-12-01

I discuss the strategy of ``Acceleration by Parallel Precomputation and Learning'' (AP-PLe) that can vastly accelerate parameter estimation in high-dimensional parameter spaces and costly likelihood functions, using trivially parallel computing to speed up sequential exploration of parameter space. This strategy combines the power of distributed computing with machine learning and Markov-Chain Monte Carlo techniques efficiently to explore a likelihood function, posterior distribution or χ2-surface. This strategy is particularly successful in cases where computing the likelihood is costly and the number of parameters is moderate or large. We apply this technique to two central problems in cosmology: the solution of the cosmological parameter estimation problem with sufficient accuracy for the Planck data using PICo; and the detailed calculation of cosmological helium and hydrogen recombination with RICO. Since the APPLe approach is designed to be able to use massively parallel resources to speed up problems that are inherently serial, we can bring the power of distributed computing to bear on parameter estimation problems. We have demonstrated this with the CosmologyatHome project.

Optimization of the Machining parameter of LM6 Alminium alloy in CNC Turning using Taguchi method

Science.gov (United States)

Arunkumar, S.; Muthuraman, V.; Baskaralal, V. P. M.

2017-03-01

Due to widespread use of highly automated machine tools in the industry, manufacturing requires reliable models and methods for the prediction of output performance of machining process. In machining of parts, surface quality is one of the most specified customer requirements. In order for manufactures to maximize their gains from utilizing CNC turning, accurate predictive models for surface roughness must be constructed. The prediction of optimum machining conditions for good surface finish plays an important role in process planning. This work deals with the study and development of a surface roughness prediction model for machining LM6 aluminum alloy. Two important tools used in parameter design are Taguchi orthogonal arrays and signal to noise ratio (S/N). Speed, feed, depth of cut and coolant are taken as process parameter at three levels. Taguchi’s parameters design is employed here to perform the experiments based on the various level of the chosen parameter. The statistical analysis results in optimum parameter combination of speed, feed, depth of cut and coolant as the best for obtaining good roughness for the cylindrical components. The result obtained through Taguchi is confirmed with real time experimental work.

AC machine control : robust and sensorless control by parameter independency

Energy Technology Data Exchange (ETDEWEB)

Samuelsen, Dag Andreas Hals

2009-06-15

In this thesis it is first presented how robust control can be used to give AC motor drive systems competitive dynamic performance under parameter variations. These variations are common to all AC machines, and are a result of temperature change in the machine, and imperfect machine models. This robust control is, however, dependent on sensor operation in the sense that the rotor position is needed in the control loop. Elimination of this control loop has been for many years, and still is, a main research area of AC machines control systems. An integrated PWM modulator and sampler unit has been developed and tested. The sampler unit is able to give current and voltage measurements with a reduced noise component. It is further used to give the true derivative of currents and voltages in the machine and the power converter, as an average over a PWM period, and as separate values for all states of the power converter. In this way, it can give measurements of the currents as well as the derivative of the currents, at the start and at the end of a single power inverter state. This gave a large degree of freedom in parameter and state identification during uninterrupted operation of the induction machine. The special measurement scheme of the system achieved three main goals: By avoiding the time frame where the transistors commutate and the noise in the measurement of the current is large, filtering of the current measurement is no longer needed. The true derivative of the current in the machine is can be measured with far less noise components. This was extended to give any separate derivative in all three switching states of the power converter. Using the computational resources of the FPGA, more advanced information was supplied to the control system, in order to facilitate sensor less operation, with low computational demands on the DSP. As shown in the papers, this extra information was first used to estimate some of the states of the machine, in some or all of the

Multi criteria decision making of machining parameters for Die Sinking EDM Process

Directory of Open Access Journals (Sweden)

G. K. Bose

2015-04-01

Full Text Available Electrical Discharge Machining (EDM is one of the most basic non-conventional machining processes for production of complex geometries and process of hard materials, which are difficult to machine by conventional process. It is capable of machining geometrically complex or hard material components, that are precise and difficult-to-machine such as heat-treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels etc. The present study is focusing on the die sinking electric discharge machining (EDM of AISI H 13, W.-Nr. 1.2344 Grade: Ovar Supreme for finding out the effect of machining parameters such as discharge current (GI, pulse on time (POT, pulse off time (POF and spark gap (SG on performance response like Material removal rate (MRR, Surface Roughness (Ra & Overcut (OC using Square-shaped Cu tool with Lateral flushing. A well-designed experimental scheme is used to reduce the total number of experiments. Parts of the experiment are conducted with the L9 orthogonal array based on the Taguchi methodology and significant process parameters are identified using Analysis of Variance (ANOVA. It is found that MRR is affected by gap current & Ra is affected by pulse on time. Moreover, the signal-to-noise ratios associated with the observed values in the experiments are determined by which factor is most affected by the responses of MRR, Ra and OC. These experimental data are further investigated using Grey Relational Analysis to optimize multiple performances in which different levels combination of the factors are ranked based on grey relational grade. The analysis reveals that substantial improvement in machining performance takes place following this technique.

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.

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.

Comparative analysis of partial imaging performance parameters of home and imported X-ray machines

International Nuclear Information System (INIS)

Cao Yunxi; Wang Xianyun; Liu Huiqin; Guo Yongxin

2002-01-01

Objective: To compare and analyze the performance indexes and the imaging quality of the home and imported X-ray machines through testing their partial imaging performance parameters. Methods: By separate sampling from 10 home and 10 imported X-ray machines, the parameters including tube current, time of exposure, machine total exposure, and repeatability were tested, and the imaging performance was evaluated according to the national standard. Results: All the performance indexes met the standard of GB4505-84. The first sampling tests showed the maximum changing coefficient of imaging performance repeatability of the home X-ray machines was Δmax1 = 0.025, while that of the imported X-ray machine was Δmax1 = 0.016. In the second sampling tests, the maximum changing coefficients of the two were Δmax2 = 0.048 and Δmax2 = 0.022, respectively. Conclusion: The 2 years' follow-up tests indicate that there is no significant difference between the above-mentioned parameters of the elaborately adjusted home X-ray machines and imported ones, but the home X-ray machines are no better than the imported X-ray machines in stability and consistency

Characterization of cutting parameters in the minimum quantity lubricant (MQL) machining process of a gearbox

OpenAIRE

Travieso Rodriguez, Jose Antonio; Gómez Gras, David; García Vilana, Silvia; Mainau Noguer, Ferran; Jerez Mesa, Ramón

2015-01-01

This paper aims to find the key process parameters for machining different parts of an automobile gearbox, commissioned by a company that needs to replace with the MQL lubrication system their current machining process based on cutting fluids. It particularly focuses on the definition of appropriate cutting parameters for machining under the MQL condition through a statistical method of Design of Experiments (DOE). Using a combination of recommended parameters, significant improvements in the...

Machine-learned and codified synthesis parameters of oxide materials

Science.gov (United States)

Kim, Edward; Huang, Kevin; Tomala, Alex; Matthews, Sara; Strubell, Emma; Saunders, Adam; McCallum, Andrew; Olivetti, Elsa

2017-09-01

Predictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed. In this work, we present a collection of aggregated synthesis parameters computed using the text contained within over 640,000 journal articles using state-of-the-art natural language processing and machine learning techniques. We provide a dataset of synthesis parameters, compiled autonomously across 30 different oxide systems, in a format optimized for planning novel syntheses of materials.

Influence of Wire Electrical Discharge Machining (WEDM) process parameters on surface roughness

Science.gov (United States)

Yeakub Ali, Mohammad; Banu, Asfana; Abu Bakar, Mazilah

2018-01-01

In obtaining the best quality of engineering components, the quality of machined parts surface plays an important role. It improves the fatigue strength, wear resistance, and corrosion of workpiece. This paper investigates the effects of wire electrical discharge machining (WEDM) process parameters on surface roughness of stainless steel using distilled water as dielectric fluid and brass wire as tool electrode. The parameters selected are voltage open, wire speed, wire tension, voltage gap, and off time. Empirical model was developed for the estimation of surface roughness. The analysis revealed that off time has a major influence on surface roughness. The optimum machining parameters for minimum surface roughness were found to be at a 10 V open voltage, 2.84 μs off time, 12 m/min wire speed, 6.3 N wire tension, and 54.91 V voltage gap.

Review of the Tandem Mirror Experiment-Upgrade (TMX-U) machine-parameter-instrumentation system

International Nuclear Information System (INIS)

Kane, R.J.; Coffield, F.E.; Coutts, G.W.; Hornady, R.S.

1983-01-01

The Tandem Mirror Experiment-Upgrade (TMX-U) machine consists of seven major machine subsystems: magnet system, neutral beam system, microwave heating (ECRH), ion heating (ICRH), gas fueling, stream guns, and vacuum system. Satisfactory performance of these subsystems is necessary to achieve the experimental objectives planned for TMX-U operations. Since the performance quality of the subsystem is important and can greatly affect plasma parameters, a 233-channel instrumentation system has been installed. Data from the instrumentation system are acquired and stored with the plasma diagnostic information. Thus, the details of the machine performance are available during post-shot analysis. This paper describes all the machine-parameter-instrumentation hardware, presents some typical data, and outlines how the data are used

Pre-segmented 2-Step IMRT with subsequent direct machine parameter optimisation – a planning study

International Nuclear Information System (INIS)

Bratengeier, Klaus; Meyer, Jürgen; Flentje, Michael

2008-01-01

Modern intensity modulated radiotherapy (IMRT) mostly uses iterative optimisation methods. The integration of machine parameters into the optimisation process of step and shoot leaf positions has been shown to be successful. For IMRT segmentation algorithms based on the analysis of the geometrical structure of the planning target volumes (PTV) and the organs at risk (OAR), the potential of such procedures has not yet been fully explored. In this work, 2-Step IMRT was combined with subsequent direct machine parameter optimisation (DMPO-Raysearch Laboratories, Sweden) to investigate this potential. In a planning study DMPO on a commercial planning system was compared with manual primary 2-Step IMRT segment generation followed by DMPO optimisation. 15 clinical cases and the ESTRO Quasimodo phantom were employed. Both the same number of optimisation steps and the same set of objective values were used. The plans were compared with a clinical DMPO reference plan and a traditional IMRT plan based on fluence optimisation and consequent segmentation. The composite objective value (the weighted sum of quadratic deviations of the objective values and the related points in the dose volume histogram) was used as a measure for the plan quality. Additionally, a more extended set of parameters was used for the breast cases to compare the plans. The plans with segments pre-defined with 2-Step IMRT were slightly superior to DMPO alone in the majority of cases. The composite objective value tended to be even lower for a smaller number of segments. The total number of monitor units was slightly higher than for the DMPO-plans. Traditional IMRT fluence optimisation with subsequent segmentation could not compete. 2-Step IMRT segmentation is suitable as starting point for further DMPO optimisation and, in general, results in less complex plans which are equal or superior to plans generated by DMPO alone

Evaluating the electrical discharge machining (EDM) parameters with using carbon nanotubes

Science.gov (United States)

Sari, M. M.; Noordin, M. Y.; Brusa, E.

2012-09-01

Electrical discharge machining (EDM) is one of the most accurate non traditional manufacturing processes available for creating tiny apertures, complex or simple shapes and geometries within parts and assemblies. Performance of the EDM process is usually evaluated in terms of surface roughness, existence of cracks, voids and recast layer on the surface of product, after machining. Unfortunately, the high heat generated on the electrically discharged material during the EDM process decreases the quality of products. Carbon nanotubes display unexpected strength and unique electrical and thermal properties. Multi-wall carbon nanotubes are therefore on purpose added to the dielectric used in the EDM process to improve its performance when machining the AISI H13 tool steel, by means of copper electrodes. Some EDM parameters such as material removal rate, electrode wear rate, surface roughness and recast layer are here first evaluated, then compared to the outcome of EDM performed without using nanotubes mixed to the dielectric. Independent variables investigated are pulse on time, peak current and interval time. Experimental evidences show that EDM process operated by mixing multi-wall carbon nanotubes within the dielectric looks more efficient, particularly if machining parameters are set at low pulse of energy.

Evaluating the electrical discharge machining (EDM) parameters with using carbon nanotubes

International Nuclear Information System (INIS)

Sari, M M; Brusa, E; Noordin, M Y

2012-01-01

Electrical discharge machining (EDM) is one of the most accurate non traditional manufacturing processes available for creating tiny apertures, complex or simple shapes and geometries within parts and assemblies. Performance of the EDM process is usually evaluated in terms of surface roughness, existence of cracks, voids and recast layer on the surface of product, after machining. Unfortunately, the high heat generated on the electrically discharged material during the EDM process decreases the quality of products. Carbon nanotubes display unexpected strength and unique electrical and thermal properties. Multi-wall carbon nanotubes are therefore on purpose added to the dielectric used in the EDM process to improve its performance when machining the AISI H13 tool steel, by means of copper electrodes. Some EDM parameters such as material removal rate, electrode wear rate, surface roughness and recast layer are here first evaluated, then compared to the outcome of EDM performed without using nanotubes mixed to the dielectric. Independent variables investigated are pulse on time, peak current and interval time. Experimental evidences show that EDM process operated by mixing multi-wall carbon nanotubes within the dielectric looks more efficient, particularly if machining parameters are set at low pulse of energy.

Grinding, Machining Morphological Studies on C/SiC Composites

Science.gov (United States)

Xiao, Chun-fang; Han, Bing

2018-05-01

C/SiC composite is a typical material difficult to machine. It is hard and brittle. In machining, the cutting force is large, the material removal rate is low, the edge is prone to collapse, and the tool wear is serious. In this paper, the grinding of C/Si composites material along the direction of fiber distribution is studied respectively. The surface microstructure and mechanical properties of C/SiC composites processed by ultrasonic machining were evaluated. The change of surface quality with the change of processing parameters has also been studied. By comparing the performances of conventional grinding and ultrasonic grinding, the surface roughness and functional characteristics of the material can be improved by optimizing the processing parameters.

Kernel methods for interpretable machine learning of order parameters

Science.gov (United States)

Ponte, Pedro; Melko, Roger G.

2017-11-01

Machine learning is capable of discriminating phases of matter, and finding associated phase transitions, directly from large data sets of raw state configurations. In the context of condensed matter physics, most progress in the field of supervised learning has come from employing neural networks as classifiers. Although very powerful, such algorithms suffer from a lack of interpretability, which is usually desired in scientific applications in order to associate learned features with physical phenomena. In this paper, we explore support vector machines (SVMs), which are a class of supervised kernel methods that provide interpretable decision functions. We find that SVMs can learn the mathematical form of physical discriminators, such as order parameters and Hamiltonian constraints, for a set of two-dimensional spin models: the ferromagnetic Ising model, a conserved-order-parameter Ising model, and the Ising gauge theory. The ability of SVMs to provide interpretable classification highlights their potential for automating feature detection in both synthetic and experimental data sets for condensed matter and other many-body systems.

Influence of TiB2 particles on machinability and machining parameter optimization of TiB2/Al MMCs

Directory of Open Access Journals (Sweden)

Ruisong JIANG

2018-01-01

Full Text Available In situ formed TiB2 particle reinforced aluminum matrix composites (TiB2/Al MMCs have some extraordinary properties which make them be a promising material for high performance aero-engine blade. Due to the influence of TiB2 particles, the machinability is still a problem which restricts the application of TiB2/Al MMCs. In order to meet the industrial requirements, the influence of TiB2 particles on the machinability of TiB2/Al MMCs was investigated experimentally. Moreover, the optimal machining conditions for this kind of MMCs were investigated in this study. The major conclusions are: (1 the machining force of TiB2/Al MMCs is bigger than that of non-reinforced alloy and mainly controlled by feed rate; (2 the residual stress of TiB2/Al MMCs is compressive while that of non-reinforced alloy is nearly neutral; (3 the surface roughness of TiB2/Al MMCs is smaller than that of non-reinforced alloy under the same cutting speed, but reverse result was observed when the feed rate increased; (4 a multi-objective optimization model for surface roughness and material removal rate (MRR was established, and a set of optimal parameter combinations of the machining was obtained. The results show a great difference from SiC particle reinforced MMCs and provide a useful guide for a better control of machining process of this material.

Machine learning action parameters in lattice quantum chromodynamics

Science.gov (United States)

Shanahan, Phiala E.; Trewartha, Daniel; Detmold, William

2018-05-01

Numerical lattice quantum chromodynamics studies of the strong interaction are important in many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. The high information content and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.

Beam interlock system and safe machine parameters system 2010 and beyond

CERN Document Server

Todd, B

2010-01-01

The Beam Interlock System (BIS) and Safe Machine Parameters (SMP) system are central to the protection of the Large Hadron Collider (LHC) machine. The BIS has been critical for the safe operation of LHC from the first day of operation. It has been installed and commissioned, only minor enhancements are required in order to accommodate all future LHC machine protection requirements. At reduced intensity, the SMP system is less critical for LHC operation. As such, the current system satisfies the 2010 operational requirements. Further developments are required, both at the SMP Controller level, and at the system level, in order to accommodate the requirements of the LHC beyond 2010.

Effect of cutting parameters on sustainable machining performance of coated carbide tool in dry turning process of stainless steel 316

Science.gov (United States)

Bagaber, Salem A.; Yusoff, Ahmed Razlan

2017-04-01

The manufacturing industry aims to produce many products of high quality with relatively less cost and time. Different cutting parameters affect the machining performance of surface roughness, cutting force, and material removal rate. Nevertheless, a few studies reported on the effects of sustainable factors such as power consumed, cycle time during machining, and tool life on the dry turning of AISI 316. The present study aims to evaluate the machining performance of coated carbide in the machining of hard steel AISI 316 under the dry turning process. The influence of cutting parameters of cutting speed, feed rate, and depth of cut with their five (5) levels is established by a central composite design. Highly significant parameters were determined by analysis of variance (ANOVA), and the main effects of power consumed and time during machining, surface roughness, and tool wear were observed. Results showed that the cutting speed was proportional to power consumption and tool wear. Meanwhile, insignificant to surface roughness, feed rate most significantly affected surface roughness and power consumption followed by depth of cut.

A Comparative Experimental Study on the Use of Machine Learning Approaches for Automated Valve Monitoring Based on Acoustic Emission Parameters

Science.gov (United States)

Ali, Salah M.; Hui, K. H.; Hee, L. M.; Salman Leong, M.; Al-Obaidi, M. A.; Ali, Y. H.; Abdelrhman, Ahmed M.

2018-03-01

Acoustic emission (AE) analysis has become a vital tool for initiating the maintenance tasks in many industries. However, the analysis process and interpretation has been found to be highly dependent on the experts. Therefore, an automated monitoring method would be required to reduce the cost and time consumed in the interpretation of AE signal. This paper investigates the application of two of the most common machine learning approaches namely artificial neural network (ANN) and support vector machine (SVM) to automate the diagnosis of valve faults in reciprocating compressor based on AE signal parameters. Since the accuracy is an essential factor in any automated diagnostic system, this paper also provides a comparative study based on predictive performance of ANN and SVM. AE parameters data was acquired from single stage reciprocating air compressor with different operational and valve conditions. ANN and SVM diagnosis models were subsequently devised by combining AE parameters of different conditions. Results demonstrate that ANN and SVM models have the same results in term of prediction accuracy. However, SVM model is recommended to automate diagnose the valve condition in due to the ability of handling a high number of input features with low sampling data sets.

Relationship of goat milk flow emission variables with milking routine, milking parameters, milking machine characteristics and goat physiology.

Science.gov (United States)

Romero, G; Panzalis, R; Ruegg, P

2017-11-01

The aim of this paper was to study the relationship between milk flow emission variables recorded during milking of dairy goats with variables related to milking routine, goat physiology, milking parameters and milking machine characteristics, to determine the variables affecting milking performance and help the goat industry pinpoint farm and milking practices that improve milking performance. In total, 19 farms were visited once during the evening milking. Milking parameters (vacuum level (VL), pulsation ratio and pulsation rate, vacuum drop), milk emission flow variables (milking time, milk yield, maximum milk flow (MMF), average milk flow (AVMF), time until 500 g/min milk flow is established (TS500)), doe characteristics of 8 to 10 goats/farm (breed, days in milk and parity), milking practices (overmilking, overstripping, pre-lag time) and milking machine characteristics (line height, presence of claw) were recorded on every farm. The relationships between recorded variables and farm were analysed by a one-way ANOVA analysis. The relationships of milk yield, MMF, milking time and TS500 with goat physiology, milking routine, milking parameters and milking machine design were analysed using a linear mixed model, considering the farm as the random effect. Farm was significant (Pfarms, being similar to those recommended in scientific studies. Few milking parameters and milking machine characteristics affected the tested variables: average vacuum level only showed tendency on MMF, and milk pipeline height on TS500. Milk yield (MY) was mainly affected by parity, as the interaction of days in milk with parity was also significant. Milking time was mainly affected by milk yield and breed. Also significant were parity, the interaction of days in milk with parity and overstripping, whereas overmilking showed a slight tendency. We concluded that most of the studied variables were mainly related to goat physiology characteristics, as the effects of milking parameters and

Effect of processing parameters of rotary ultrasonic machining on surface integrity of potassium dihydrogen phosphate crystals

Directory of Open Access Journals (Sweden)

Jianfu Zhang

2015-09-01

Full Text Available Potassium dihydrogen phosphate is an important optical crystal. However, high-precision processing of large potassium dihydrogen phosphate crystal workpieces is difficult. In this article, surface roughness and subsurface damage characteristics of a (001 potassium dihydrogen phosphate crystal surface produced by traditional and rotary ultrasonic machining are studied. The influence of process parameters, including spindle speed, feed speed, type and size of sintered diamond wheel, ultrasonic power, and selection of cutting fluid on potassium dihydrogen phosphate crystal surface integrity, was analyzed. The surface integrity, especially the subsurface damage depth, was affected significantly by the ultrasonic power. Metal-sintered diamond tools with high granularity were most suitable for machining potassium dihydrogen phosphate crystal. Cutting fluid played a key role in potassium dihydrogen phosphate crystal machining. A more precise surface can be obtained in machining with a higher spindle speed, lower feed speed, and using kerosene as cutting fluid. Based on the provided optimized process parameters for machining potassium dihydrogen phosphate crystal, a processed surface quality with Ra value of 33 nm and subsurface damage depth value of 6.38 μm was achieved.

SU-F-T-521: Flattening-Filter-Free Beam Parameters Comparison From Different Linac Machine Types

Energy Technology Data Exchange (ETDEWEB)

Hussain, A [King Faisal Specialist Hospital, Riyadh, Saudi Arabia, Arkansas Cancer Institute, Pine Bluff, AR (Saudi Arabia)

2016-06-15

Purpose: Novel linac machines, TrueBeam (TB) and Elekta Versa have updated head designing and software control system, include flattening-filter-free (FFF) photon and electron beams. Later on FFF beams were also introduced on C-Series machines. In this work FFF beams for same energy 6MV but from different machine versions were studied with reference to beam data parameters. Methods: The 6MV-FFF percent depth doses, profile symmetry and flatness, dose rate tables, and multi-leaf collimator (MLC) transmission factors were measured during commissioning process of both C-series and Truebeam machines. The scanning and dosimetric data for 6MV-FFF beam from Truebeam and C-Series linacs was compared. A correlation of 6MV-FFF beam from Elekta Versa with that of Varian linacs was also found. Results: The scanning files were plotted for both qualitative and quantitative analysis. The dosimetric leaf gap (DLG) for C-Series 6MV-FFF beam is 1.1 mm. Published values for Truebeam dosimetric leaf gap is 1.16 mm. 6MV MLC transmission factor varies between 1.3 % and 1.4 % in two separate measurements and measured DLG values vary between 1.32 mm and 1.33 mm on C-Series machine. MLC transmission factor from C-Series machine varies between 1.5 % and 1.6 %. Some of the measured data values from C-Series FFF beam are compared with Truebeam representative data. 6MV-FFF beam parameter values like dmax, OP factors, beam symmetry and flatness and additional parameters for C-Series and Truebeam liancs will be presented and compared in graphical form and tabular data form if selected. Conclusion: The 6MV flattening filter (FF) beam data from C-Series & Truebeam and 6MV-FFF beam data from Truebeam has already presented. This particular analysis to compare 6MV-FFF beam from C-Series and Truebeam provides opportunity to better elaborate FFF mode on novel machines. It was found that C-Series and Truebeam 6MV-FFF dosimetric and beam data was quite similar.

Reliability of measuring abductor hallucis muscle parameters using two different diagnostic ultrasound machines

Directory of Open Access Journals (Sweden)

Cameron Alyse FM

2009-11-01

Full Text Available Abstract Background Diagnostic ultrasound provides a method of analysing soft tissue structures of the musculoskeletal system effectively and reliably. The aim of this study was to evaluate within and between session reliability of measuring muscle dorso-plantar thickness, medio-lateral length and cross-sectional area, of the abductor hallucis muscle using two different ultrasound machines, a higher end Philips HD11 Ultrasound machine and clinically orientated Chison 8300 Deluxe Digital Portable Ultrasound System. Methods The abductor hallucis muscle of both the left and right feet of thirty asymptomatic participants was imaged and then measured using both ultrasound machines. Interclass correlation coefficients (ICC with 95% confidence intervals (CI were used to calculate both within and between session intra-tester reliability. Standard error of the measurement (SEM calculations were undertaken to assess difference between the actual measured score across trials and the smallest real difference (SRD was calculated from the SEM to indicate the degree of change that would exceed the expected trial to trial variability. Results The ICCs, SEM and SRD for dorso-plantar thickness and medial-lateral length were shown to have excellent to high within and between-session reliability for both ultrasound machines. The between-session reliability indices for cross-sectional area were acceptable for both ultrasound machines. Conclusion The results of the current study suggest that regardless of the type ultrasound machine, intra-tester reliability for the measurement the abductor hallucis muscle parameters is very high.

Optimizing friction stir weld parameters of aluminum and copper using conventional milling machine

Science.gov (United States)

Manisegaran, Lohappriya V.; Ahmad, Nurainaa Ayuni; Nazri, Nurnadhirah; Noor, Amirul Syafiq Mohd; Ramachandran, Vignesh; Ismail, Muhammad Tarmizizulfika; Ahmad, Ku Zarina Ku; Daruis, Dian Darina Indah

2018-05-01

The joining of two of any particular materials through friction stir welding (FSW) are done by a rotating tool and the work piece material that generates heat which causes the region near the FSW tool to soften. This in return will mechanically intermix the work pieces. The first objective of this study is to join aluminum plates and copper plates by means of friction stir welding process using self-fabricated tools and conventional milling machine. This study also aims to investigate the optimum process parameters to produce the optimum mechanical properties of the welding joints for Aluminum plates and Copper plates. A suitable tool bit and a fixture is to be fabricated for the welding process. A conventional milling machine will be used to weld the aluminum and copper. The most important parameters to enable the process are speed and pressure of the tool (or tool design and alignment of the tool onto the work piece). The study showed that the best surface finish was produced from speed of 1150 rpm and tool bit tilted to 3°. For a 200mm × 100mm Aluminum 6061 with plate thickness of 2 mm at a speed of 1 mm/s, the time taken to complete the welding is only 200 seconds or equivalent to 3 minutes and 20 seconds. The Copper plates was successfully welded using FSW with tool rotation speed of 500 rpm, 700 rpm, 900 rpm, 1150 rpm and 1440 rpm and with welding traverse rate of 30 mm/min, 60 mm/min and 90 mm/min. As the conclusion, FSW using milling machine can be done on both Aluminum and Copper plates, however the weld parameters are different for the two types of plates.

Analyzing the effect of cutting parameters on surface roughness and tool wear when machining nickel based hastelloy - 276

International Nuclear Information System (INIS)

Khidhir, Basim A; Mohamed, Bashir

2011-01-01

Machining parameters has an important factor on tool wear and surface finish, for that the manufacturers need to obtain optimal operating parameters with a minimum set of experiments as well as minimizing the simulations in order to reduce machining set up costs. The cutting speed is one of the most important cutting parameter to evaluate, it clearly most influences on one hand, tool life, tool stability, and cutting process quality, and on the other hand controls production flow. Due to more demanding manufacturing systems, the requirements for reliable technological information have increased. For a reliable analysis in cutting, the cutting zone (tip insert-workpiece-chip system) as the mechanics of cutting in this area are very complicated, the chip is formed in the shear plane (entrance the shear zone) and is shape in the sliding plane. The temperature contributed in the primary shear, chamfer and sticking, sliding zones are expressed as a function of unknown shear angle on the rake face and temperature modified flow stress in each zone. The experiments were carried out on a CNC lathe and surface finish and tool tip wear are measured in process. Machining experiments are conducted. Reasonable agreement is observed under turning with high depth of cut. Results of this research help to guide the design of new cutting tool materials and the studies on evaluation of machining parameters to further advance the productivity of nickel based alloy Hastelloy - 276 machining.

Study of the stiffness for predicting the accuracy of machine tools

International Nuclear Information System (INIS)

Ortega, N.; Campa, F.J.; Fernandez Valdivielso, A.; Alonso, U.; Olvera, D.; Compean, F.I.

2010-01-01

Machining processes are frequently faced with the challenge of achieving more and more precision and surface qualities. These requirements are usually attained taking into account some process variables, including the cutting parameters and the use or not of refrigerant, leaving aside the mechanical aspects associated with the influence of machine tool itself. There are many sources of error linked with machine-workpiece interaction, but, in general, we can summarize them into two types of error: quasi-static and dynamic. This paper shows the influence of quasi-static error caused by low machine rigidity on the accuracy applied on two very different processes: turning and grinding. For the study of the static stiffness of these two machines, two different methods are proposed, both of them equally valid. The first one is based on separated parameters and the second one on finite elements. (Author).

Parameter identification and optimization of slide guide joint of CNC machine tools

Science.gov (United States)

Zhou, S.; Sun, B. B.

2017-11-01

The joint surface has an important influence on the performance of CNC machine tools. In order to identify the dynamic parameters of slide guide joint, the parametric finite element model of the joint is established and optimum design method is used based on the finite element simulation and modal test. Then the mode that has the most influence on the dynamics of slip joint is found through harmonic response analysis. Take the frequency of this mode as objective, the sensitivity analysis of the stiffness of each joint surface is carried out using Latin Hypercube Sampling and Monte Carlo Simulation. The result shows that the vertical stiffness of slip joint surface constituted by the bed and the slide plate has the most obvious influence on the structure. Therefore, this stiffness is taken as the optimization variable and the optimal value is obtained through studying the relationship between structural dynamic performance and stiffness. Take the stiffness values before and after optimization into the FEM of machine tool, and it is found that the dynamic performance of the machine tool is improved.

QSPR studies for predicting polarity parameter of organic compounds in methanol using support vector machine and enhanced replacement method.

Science.gov (United States)

Golmohammadi, H; Dashtbozorgi, Z

2016-12-01

In the present work, enhanced replacement method (ERM) and support vector machine (SVM) were used for quantitative structure-property relationship (QSPR) studies of polarity parameter (p) of various organic compounds in methanol in reversed phase liquid chromatography based on molecular descriptors calculated from the optimized structures. Diverse kinds of molecular descriptors were calculated to encode the molecular structures of compounds, such as geometric, thermodynamic, electrostatic and quantum mechanical descriptors. The variable selection method of ERM was employed to select an optimum subset of descriptors. The five descriptors selected using ERM were used as inputs of SVM to predict the polarity parameter of organic compounds in methanol. The coefficient of determination, r 2 , between experimental and predicted polarity parameters for the prediction set by ERM and SVM were 0.952 and 0.982, respectively. Acceptable results specified that the ERM approach is a very effective method for variable selection and the predictive aptitude of the SVM model is superior to those obtained by ERM. The obtained results demonstrate that SVM can be used as a substitute influential modeling tool for QSPR studies.

Identification of Technological Parameters of Ni-Alloys When Machining by Monolithic Ceramic Milling Tool

Science.gov (United States)

Czán, Andrej; Kubala, Ondrej; Danis, Igor; Czánová, Tatiana; Holubják, Jozef; Mikloš, Matej

2017-12-01

The ever-increasing production and the usage of hard-to-machine progressive materials are the main cause of continual finding of new ways and methods of machining. One of these ways is the ceramic milling tool, which combines the pros of conventional ceramic cutting materials and pros of conventional coating steel-based insert. These properties allow to improve cutting conditions and so increase the productivity with preserved quality known from conventional tools usage. In this paper, there is made the identification of properties and possibilities of this tool when machining of hard-to-machine materials such as nickel alloys using in airplanes engines. This article is focused on the analysis and evaluation ordinary technological parameters and surface quality, mainly roughness of surface and quality of machined surface and tool wearing.

Effect of different parameters on machining of SiC/SiC composites via pico-second laser

Energy Technology Data Exchange (ETDEWEB)

Li, Weinan; Zhang, Ruoheng [State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, Shaanxi 10068 (China); Liu, Yongsheng, E-mail: yongshengliu@nwpu.edu.cn [Science and technology on Thermostructure Composite Materials Laboratory, Northwestern Polytechnical University, Xi’an, Shaanxi 710072 (China); Wang, Chunhui; Wang, Jing [Science and technology on Thermostructure Composite Materials Laboratory, Northwestern Polytechnical University, Xi’an, Shaanxi 710072 (China); Yang, Xiaojun [State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, Shaanxi 10068 (China); Cheng, Laifei [Science and technology on Thermostructure Composite Materials Laboratory, Northwestern Polytechnical University, Xi’an, Shaanxi 710072 (China)

2016-02-28

Graphical abstract: - Highlights: • The highlights of the manuscript include the following two aspects. • First, we found that the different machining modes (helical line scanning and single ring line scanning) and processing power of machining have remarkable effect on the surface morphology of the machined area, such as the shape, depth and the formation of different surface structures. • Secondly, we investigated that the debris consisted of C, Si and O was observed on the machined surface. • Some of the Si–C bonds of the SiC matrix and fibers would be transformed into Si–O bonds after machined, depending on the processing power. - Abstract: Pico-second laser plays an important role in modern machining technology, especially in machining high hardness materials. In this article, pico-second laser was utilized for irradiation on SiC/SiC composites, and effects of different processing parameters including the machining modes and laser power were discussed in detail. The results indicated that the machining modes and laser power had great effect on machining of SiC/SiC composites. Different types of surface morphology and structure were observed under helical line scanning and single ring line scanning, and the analysis of their formulation was discussed in detail. It was believed that the machining modes would be responsible to the different shapes of machining results at the same parameters. The processing power shall also influence the surface morphology and quality of machining results. In micro-hole drilling process, large amount of debris and fragments were observed within the micro-holes, and XPS analysis showed that there existed Si–O bonds and Si–C bonds, indicating that the oxidation during processing was incomplete. Other surface morphology, such as pores and pits were discussed as well.

Machine learning of the reactor core loading pattern critical parameters

International Nuclear Information System (INIS)

Trontl, K.; Pevec, D.; Smuc, T.

2007-01-01

The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employed a recently introduced machine learning technique, Support Vector Regression (SVR), which has a strong theoretical background in statistical learning theory. Superior empirical performance of the method has been reported on difficult regression problems in different fields of science and technology. SVR is a data driven, kernel based, nonlinear modelling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modelling. The starting set of experimental data for training and testing of the machine learning algorithm was obtained using a two-dimensional diffusion theory reactor physics computer code. We illustrate the performance of the solution and discuss its applicability, i.e., complexity, speed and accuracy, with a projection to a more realistic scenario involving machine learning from the results of more accurate and time consuming three-dimensional core modelling code. (author)

Experimental study on Response Parameters of Ni-rich NiTi Shape Memory Alloy during Wire Electric Discharge Machining

Science.gov (United States)

Bisaria, Himanshu; Shandilya, Pragya

2018-03-01

Nowadays NiTi SMAs are gaining more prominence due to their unique properties such as superelasticity, shape memory effect, high fatigue strength and many other enriched physical and mechanical properties. The current studies explore the effect of machining parameters namely, peak current (Ip), pulse off time (TOFF), and pulse on time (TON) on wire wear ratio (WWR), and dimensional deviation (DD) in WEDM. It was found that high discharge energy was mainly ascribed to high WWR and DD. The WWR and DD increased with the increase in pulse on time and peak current whereas high pulse off time was favourable for low WWR and DD.

Stellar Parameters in an Instant with Machine Learning

Directory of Open Access Journals (Sweden)

Bellinger Earl P.

2017-01-01

Full Text Available With the advent of dedicated photometric space missions, the ability to rapidly process huge catalogues of stars has become paramount. Bellinger and Angelou et al. [1] recently introduced a new method based on machine learning for inferring the stellar parameters of main-sequence stars exhibiting solar-like oscillations. The method makes precise predictions that are consistent with other methods, but with the advantages of being able to explore many more parameters while costing practically no time. Here we apply the method to 52 so-called “LEGACY“ main-sequence stars observed by the Kepler space mission. For each star, we present estimates and uncertainties of mass, age, radius, luminosity, core hydrogen abundance, surface helium abundance, surface gravity, initial helium abundance, and initial metallicity as well as estimates of their evolutionary model parameters of mixing length, overshooting coeffcient, and diffusion multiplication factor. We obtain median uncertainties in stellar age, mass, and radius of 14.8%, 3.6%, and 1.7%, respectively. The source code for all analyses and for all figures appearing in this manuscript can be found electronically at https://github.com/earlbellinger/asteroseismology

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

Science.gov (United States)

Manurung, Jonson; Mawengkang, Herman; Zamzami, Elviawaty

2017-12-01

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

Characteristics of laser assisted machining for silicon nitride ceramic according to machining parameters

International Nuclear Information System (INIS)

Kim, Jong Do; Lee, Su Jin; Suh, Jeong

2011-01-01

This paper describes the Laser Assisted Machining (LAM) that cuts and removes softened parts by locally heating the ceramic with laser. Silicon nitride ceramics can be machined with general machining tools as well, because YSiAlON, which was made up ceramics, is soften at about 1,000 .deg. C. In particular, the laser, which concentrates on highly dense energy, can locally heat materials and very effectively control the temperature of the heated part of specimen. Therefore, this paper intends to propose an efficient machining method of ceramic by deducing the machining governing factors of laser assisted machining and understanding its mechanism. While laser power is the machining factor that controls the temperature, the CBN cutting tool could cut the material more easily as the material gets deteriorated from the temperature increase by increasing the laser power, but excessive oxidation can negatively affect the quality of the material surface after machining. As the feed rate and cutting depth increase, the cutting force increases and tool lifespan decreases, but surface oxidation also decreases. In this experiment, the material can be cut to 3 mm of cutting depth. And based on the results of the experiment, the laser assisted machining mechanism is clarified

Study on the machinability characteristics of superalloy Inconel 718 during high speed turning

International Nuclear Information System (INIS)

Thakur, D.G.; Ramamoorthy, B.; Vijayaraghavan, L.

2009-01-01

The present paper is an attempt of an experimental investigation on the machinability of superalloy, Inconel 718 during high speed turning using tungsten carbide insert (K20) tool. The effect of machining parameters on the cutting force, specific cutting pressure, cutting temperature, tool wear and surface finish criteria were investigated during the experimentation. The machining parameters have been optimized by measuring forces. The effect of machining parameters on the tool wear was examined through SEM micrographs. During high speed turning acoustic emission signal were collected and analyzed to understand the effect of cutting parameters during online. The research work findings will also provide useful economic machining solution by utilizing economical tungsten carbide tooling during high speed processing of Inconel 718, which is otherwise usually machined by costly PCD or CBN tools. The present approach and results will be helpful for understanding the machinability of Inconel 718 during high speed turning for the manufacturing engineers

Parameter Optimization of Black Tea Fermentation Machine Based on RSM and BP-AdaBoost-GA

DEFF Research Database (Denmark)

Dong, Chunwang; Zhao, Jiewen; Zhu, Hongkai

2017-01-01

Fermentation is the key procedure in processing of congou black tea, which directly decides the quality and flavor of tea products. Fermentation experiments were conducted on a novel drum-type fermentation machine as the platform, the performance parameters of fermentation machine were clarified...... of black tea, moderate rotation and mixing material can enhance the quality of black tea and shorten the fermentation time....

PARAMETER DETERMINATION FOR ADDITIONAL OPERATING FORCE MECHANISM IN DEVICE FOR PNEUMO-CENTRIFUGAL MACHINING OF BALL-SHAPED WORKPIECES

Directory of Open Access Journals (Sweden)

A. A. Sukhotsky

2014-01-01

Full Text Available The paper describes development of the methodology for optimization of parameters for an additional operating force mechanism in a device for pneumo-centrifugal machining of glass balls. Specific feature in manufacturing glass balls for micro-optics in accordance with technological process for obtaining ball-shaped workpieces is grinding and polishing of spherical surface in a free state. In this case component billets of future balls are made in the form of cubes and the billets are given preliminary a form of ball with the help of rough grinding. An advanced method for obtaining ball-shaped work-pieces from brittle materials is a pneumocentrifugal machining. This method presupposes an application of two conic rings with abrasive working surfaces which are set coaxially with large diameters to each other and the billets are rolled along these rings. Rotation of the billets is conveyed by means of pressure medium.The present devices for pneumo-centrifugal machining are suitable for obtaining balls up to 6 mm. Machining of the work-pieces with full spherical surfaces and large diameter is non-productive due to impossibility to ensure a sufficient force on the billet in the working zone. For this reason the paper proposes a modified device where an additional force on the machined billet is created by upper working disc that is making a reciprocating motion along an axis of abrasive conic rings. The motion is realized with the help of a cylindrical camshaft mechanism in the form of a ring with a profile working end face and the purpose of present paper is to optimize parameters of the proposed device.The paper presents expressions for calculation of constitutive parameters of the additional operating force mechanism including parameters of loading element motion, main dimensions of the additional operating force mechanism and parameters of a profile element in the additional operating force mechanism.Investigation method is a mathematical

Effect of Machining Velocity in Nanoscale Machining Operations

International Nuclear Information System (INIS)

Islam, Sumaiya; Khondoker, Noman; Ibrahim, Raafat

2015-01-01

The aim of this study is to investigate the generated forces and deformations of single crystal Cu with (100), (110) and (111) crystallographic orientations at nanoscale machining operation. A nanoindenter equipped with nanoscratching attachment was used for machining operations and in-situ observation of a nano scale groove. As a machining parameter, the machining velocity was varied to measure the normal and cutting forces. At a fixed machining velocity, different levels of normal and cutting forces were generated due to different crystallographic orientations of the specimens. Moreover, after machining operation percentage of elastic recovery was measured and it was found that both the elastic and plastic deformations were responsible for producing a nano scale groove within the range of machining velocities from 250-1000 nm/s. (paper)

Correlating neutron yield and reliability for selecting experimental parameters for a plasma focus machine

International Nuclear Information System (INIS)

Pross, G.

Possibilities of optimizing focus machines with a given energy content in the sense of high neutron yield and high reliability of the discharges are investigated experimentally. For this purpose, a focus machine of the Mather type with an energy content of 12 kJ was constructed. The following experimental parameters were varied: the material of the insulator in the ignition zone, the structure of the outside electrode, the length of the inside electrode, the filling pressure and the amount and polarity of the battery voltage. An important part of the diagnostic program consists of measurements of the azimuthal and axial current distribution in the accelerator, correlated with short-term photographs of the luminous front as a function of time. The results are given. A functional schematic has been drafted for focus discharge as an aid in extensive optimization of focus machines, combining findings from theory and experiments. The schematic takes into account the multiparameter character of the discharge and clarifies relationships between the experimental parameters and the target variables neutron yield and reliability

Machine Learning of the Reactor Core Loading Pattern Critical Parameters

Directory of Open Access Journals (Sweden)

Krešimir Trontl

2008-01-01

Full Text Available The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper, we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employ a recently introduced machine learning technique, support vector regression (SVR, which is a data driven, kernel based, nonlinear modeling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modeling. We illustrate the performance of the solution and discuss its applicability, that is, complexity, speed, and accuracy.

Machine Learning of the Reactor Core Loading Pattern Critical Parameters

International Nuclear Information System (INIS)

Trontl, K.; Pevec, D.; Smuc, T.

2008-01-01

The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper, we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employ a recently introduced machine learning technique, support vector regression (SVR), which is a data driven, kernel based, nonlinear modeling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modeling. We illustrate the performance of the solution and discuss its applicability, that is, complexity, speed, and accuracy

EXPERIMENTAL STUDY OF THE DYNAMICS OF CENTRIFUGAL CASTING MACHINES FOR PRODUCTION OF MILL ROLLS

Directory of Open Access Journals (Sweden)

P. G. Anofriev

2017-06-01

Full Text Available Purpose. The main purpose of experimental studies is to establish the adequacy of the developed mathematical models of machine fluctuations and the actual parameters of machine vibration. Almost all casting machines for the production of mill rolls have a unique design and performances. The additional aim of this work is to compare the vibration level of the casting machine with the requirements of the current vibration standards for new technological machines. Frequency analysis of the oscillations allows establishing defects in workmanship, errors of rotating parts installation and their influence on the dynamics of the machine. Methodology. Measurement of vibration parameters was performed on the moving parts of roller bearings of the machine. To measure the amplitudes of accelerations in three mutually perpendicular directions piezoelectric sensors with magnetic mount were used. Electrical signals from the sensors were recorded on magnetic tape. Further analysis of the oscillations was carried out and visualized using specialized frequency analyzer. The frequency analyzer implements the algorithm of fast Fourier transformation and/or integration of sensor input signal. After the first integration the data for plotting the vibration velocity spectrogram were obtained and as a result of the second integration there are the data of vibration displacements spectrogram of the machine supports. Findings. The results of experimental studies of centrifugal casting machine vibrations for the production of two-layer rolls were presented. There were obtained and analyzed the spectrograms of accelerations, velocities and displacements of moving parts of the upper and lower roller supports. The work of the machine is associated with the calculated values passing of critical frequencies and the short-term development of resonance oscillations of the rotor and roller bearings. Originality. For the first time the author obtained the frequency spectra of

Exploring the influence of constitutive models and associated parameters for the orthogonal machining of Ti6Al4V

Science.gov (United States)

Pervaiz, S.; Anwar, S.; Kannan, S.; Almarfadi, A.

2018-04-01

Ti6Al4V is known as difficult-to-cut material due to its inherent properties such as high hot hardness, low thermal conductivity and high chemical reactivity. Though, Ti6Al4V is utilized by industrial sectors such as aeronautics, energy generation, petrochemical and bio-medical etc. For the metal cutting community, competent and cost-effective machining of Ti6Al4V is a challenging task. To optimize cost and machining performance for the machining of Ti6Al4V, finite element based cutting simulation can be a very useful tool. The aim of this paper is to develop a finite element machining model for the simulation of Ti6Al4V machining process. The study incorporates material constitutive models namely Power Law (PL) and Johnson – Cook (JC) material models to mimic the mechanical behaviour of Ti6Al4V. The study investigates cutting temperatures, cutting forces, stresses, and plastic strains with respect to different PL and JC material models with associated parameters. In addition, the numerical study also integrates different cutting tool rake angles in the machining simulations. The simulated results will be beneficial to draw conclusions for improving the overall machining performance of Ti6Al4V.

Parameters optimization for wavelet denoising based on normalized spectral angle and threshold constraint machine learning

Science.gov (United States)

Li, Hao; Ma, Yong; Liang, Kun; Tian, Yong; Wang, Rui

2012-01-01

Wavelet parameters (e.g., wavelet type, level of decomposition) affect the performance of the wavelet denoising algorithm in hyperspectral applications. Current studies select the best wavelet parameters for a single spectral curve by comparing similarity criteria such as spectral angle (SA). However, the method to find the best parameters for a spectral library that contains multiple spectra has not been studied. In this paper, a criterion named normalized spectral angle (NSA) is proposed. By comparing NSA, the best combination of parameters for a spectral library can be selected. Moreover, a fast algorithm based on threshold constraint and machine learning is developed to reduce the time of a full search. After several iterations of learning, the combination of parameters that constantly surpasses a threshold is selected. The experiments proved that by using the NSA criterion, the SA values decreased significantly, and the fast algorithm could save 80% time consumption, while the denoising performance was not obviously impaired.

Field tests and machine learning approaches for refining algorithms and correlations of driver's model parameters.

Science.gov (United States)

Tango, Fabio; Minin, Luca; Tesauri, Francesco; Montanari, Roberto

2010-03-01

This paper describes the field tests on a driving simulator carried out to validate the algorithms and the correlations of dynamic parameters, specifically driving task demand and drivers' distraction, able to predict drivers' intentions. These parameters belong to the driver's model developed by AIDE (Adaptive Integrated Driver-vehicle InterfacE) European Integrated Project. Drivers' behavioural data have been collected from the simulator tests to model and validate these parameters using machine learning techniques, specifically the adaptive neuro fuzzy inference systems (ANFIS) and the artificial neural network (ANN). Two models of task demand and distraction have been developed, one for each adopted technique. The paper provides an overview of the driver's model, the description of the task demand and distraction modelling and the tests conducted for the validation of these parameters. A test comparing predicted and expected outcomes of the modelled parameters for each machine learning technique has been carried out: for distraction, in particular, promising results (low prediction errors) have been obtained by adopting an artificial neural network.

Predictive Models for Different Roughness Parameters During Machining Process of Peek Composites Using Response Surface Methodology

Directory of Open Access Journals (Sweden)

Mata-Cabrera Francisco

2013-10-01

Full Text Available Polyetheretherketone (PEEK composite belongs to a group of high performance thermoplastic polymers and is widely used in structural components. To improve the mechanical and tribological properties, short fibers are added as reinforcement to the material. Due to its functional properties and potential applications, it’s impor- tant to investigate the machinability of non-reinforced PEEK (PEEK, PEEK rein- forced with 30% of carbon fibers (PEEK CF30, and reinforced PEEK with 30% glass fibers (PEEK GF30 to determine the optimal conditions for the manufacture of the parts. The present study establishes the relationship between the cutting con- ditions (cutting speed and feed rate and the roughness (Ra , Rt , Rq , Rp , by develop- ing second order mathematical models. The experiments were planned as per full factorial design of experiments and an analysis of variance has been performed to check the adequacy of the models. These state the adequacy of the derived models to obtain predictions for roughness parameters within ranges of parameters that have been investigated during the experiments. The experimental results show that the most influence of the cutting parameters is the feed rate, furthermore, proved that glass fiber reinforcements produce a worse machinability.

A comparative study on optimization of machining parameters by turning aerospace materials according to Taguchi method

Directory of Open Access Journals (Sweden)

Altin Abdullah

2017-01-01

Full Text Available The effects of cutting tool coating material and cutting speed on cutting forces and surface roughness were investigated by Taguchi experimental design. Main cutting force, Fz is considered as a criterion. The effects of machining parameters were investigated using Taguchi L18 orthogonal array. Optimal cutting conditions were determined using the signal-to-noise (S/N ratio which is calculated for average surface roughness and cutting force according to the “the smaller is better” approach. Using results of analysis of variance (ANOVA and signal-to-noise (S/N ratio, effects of parameters on both average surface roughness and cutting forces were statistically investigated. It was observed that feed rate and cutting speed had higher effect on cutting force in Hastelloy X, while the feed rate and cutting tool had higher effect on cutting force in Inconel 625. According to average surface roughness the cutting tool and feed rate had higher effect in Hastelloy X and Inconel 625.

Effects of cutting parameters and machining environments on surface roughness in hard turning using design of experiment

Science.gov (United States)

Mia, Mozammel; Bashir, Mahmood Al; Dhar, Nikhil Ranjan

2016-07-01

Hard turning is gradually replacing the time consuming conventional turning process, which is typically followed by grinding, by producing surface quality compatible to grinding. The hard turned surface roughness depends on the cutting parameters, machining environments and tool insert configurations. In this article the variation of the surface roughness of the produced surfaces with the changes in tool insert configuration, use of coolant and different cutting parameters (cutting speed, feed rate) has been investigated. This investigation was performed in machining AISI 1060 steel, hardened to 56 HRC by heat treatment, using coated carbide inserts under two different machining environments. The depth of cut, fluid pressure and material hardness were kept constant. The Design of Experiment (DOE) was performed to determine the number and combination sets of different cutting parameters. A full factorial analysis has been performed to examine the effect of main factors as well as interaction effect of factors on surface roughness. A statistical analysis of variance (ANOVA) was employed to determine the combined effect of cutting parameters, environment and tool configuration. The result of this analysis reveals that environment has the most significant impact on surface roughness followed by feed rate and tool configuration respectively.

Machine parameters and characteristic features

International Nuclear Information System (INIS)

Le Duff, J.

1979-01-01

The design and operating characteristics of LEP are presented. Its probable performance, possible improvements and cost are discussed and some comparisons are drawn with machines currently in operation. (W.D.L.)

Machinability of nickel based alloys using electrical discharge machining process

Science.gov (United States)

Khan, M. Adam; Gokul, A. K.; Bharani Dharan, M. P.; Jeevakarthikeyan, R. V. S.; Uthayakumar, M.; Thirumalai Kumaran, S.; Duraiselvam, M.

2018-04-01

The high temperature materials such as nickel based alloys and austenitic steel are frequently used for manufacturing critical aero engine turbine components. Literature on conventional and unconventional machining of steel materials is abundant over the past three decades. However the machining studies on superalloy is still a challenging task due to its inherent property and quality. Thus this material is difficult to be cut in conventional processes. Study on unconventional machining process for nickel alloys is focused in this proposed research. Inconel718 and Monel 400 are the two different candidate materials used for electrical discharge machining (EDM) process. Investigation is to prepare a blind hole using copper electrode of 6mm diameter. Electrical parameters are varied to produce plasma spark for diffusion process and machining time is made constant to calculate the experimental results of both the material. Influence of process parameters on tool wear mechanism and material removal are considered from the proposed experimental design. While machining the tool has prone to discharge more materials due to production of high energy plasma spark and eddy current effect. The surface morphology of the machined surface were observed with high resolution FE SEM. Fused electrode found to be a spherical structure over the machined surface as clumps. Surface roughness were also measured with surface profile using profilometer. It is confirmed that there is no deviation and precise roundness of drilling is maintained.

Design of Parameter Independent, High Performance Sensorless Controllers for Permanent Magnet Synchronous Machines

DEFF Research Database (Denmark)

Xie, Ge

. The transient fluctuation of the estimated rotor position error is around 20 degrees with a step load torque change from 0% to 100% of the rated torque. The position error in steady state is within ±2 electrical degrees for the best case. The proposed method may also be used for e.g. online machine parameter......The Permanent Magnet Synchronous Machine (PMSM) has become an attractive candidate for various industrial applications due to its high efficiency and torque density. In the PMSM drive system, simple and robust control methods play an important role in achieving satisfactory drive performances....... For reducing the cost and increasing the reliability of the drive system, eliminating the mechanical sensor brings a lot advantages to the PMSM drive system. Therefore, sensorless control was developed and has been increasingly used in different PMSM drive systems in the last 20 years. However, machine...

Pavement Subgrade Performance Study in the Danish Road Testing Machine

DEFF Research Database (Denmark)

Ullidtz, Per; Ertman Larsen, Hans Jørgen

1997-01-01

Most existing pavement subgrade criteria are based on the AASHO Road Test, where only one material was tested and for only one climatic condition. To study the validity of these criteria and to refine the criteria a co-operative research program entitled the "International Pavement Subgrade...... Performance Study" was sponsored by the FHWA with American, Finnish and Danish partners. This paper describes the first test series which was carried out in the Danish Road Testing Machine (RTM).The first step in this program is a full scale test on an instrumented pavement in the Danish Road Testing Machine....... Pressure gauges and strain cells were installed in the upper part of the subgrade, for measuring stresses and strains in all three directions. During and after construction FWD testing was carried out to evaluate the elastic parameters of the materials. These parameters were then used with the theory...

A Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting

Directory of Open Access Journals (Sweden)

Jian Chai

2015-01-01

Full Text Available This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine model to analyze the CSI 300 index. A WD-LSSVM (wavelet denoising least squares support machine is also proposed as a benchmark to compare with the performance of EMD-LSSVM. Since parameters selection is vital to the performance of the model, different optimization methods are used, including simplex, GS (grid search, PSO (particle swarm optimization, and GA (genetic algorithm. Experimental results show that the EMD-LSSVM model with GS algorithm outperforms other methods in predicting stock market movement direction.

Electron-beam welding of 21-6-9 (Cr--Ni--Mn) stainless steel: effect of machine parameters on weldability

International Nuclear Information System (INIS)

Casey, H.

1975-04-01

The high-manganese, nitrogen-strengthened 21-6-9 (Cr--Ni--Mn) austenitic stainless steel has a weldability rating similar to that of more common austenitic stainless steels in terms of cracking, porosity, etc. However, weld pool disruption problems may occur with this alloy that can be related to instability within the molten weld pool. Selection of machine parameters is critical to achieving weld pool quiescence as this report confirms from recent tests. Test samples came from heats of air-melted, vacuum-arc remelted, and electroslag remelted material. Low- and high-voltage machine parameters are discussed, and effects of parameter variation on weld pool behavior are given. Data relate weld pool behavior to weld fusion-zone geometry. Various weld parameters are recommended for the 21-6-9 alloy, regardless of its source or chemistry. (auth)

Determining Machining Parameters of Corn Byproduct Filled Plastics

Science.gov (United States)

In a collaborative project between the USDA and Northern Illinois University, the use of corn ethanol processing byproducts (i.e., DDGS) as bio-filler materials in the compression molding of phenolic plastics has been studied. This paper reports on the results of a machinability study in the milling...

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

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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.

Machine performance assessment and enhancement for a hexapod machine

Energy Technology Data Exchange (ETDEWEB)

Mou, J.I. [Arizona State Univ., Tempe, AZ (United States); King, C. [Sandia National Labs., Livermore, CA (United States). Integrated Manufacturing Systems Center

1998-03-19

The focus of this study is to develop a sensor fused process modeling and control methodology to model, assess, and then enhance the performance of a hexapod machine for precision product realization. Deterministic modeling technique was used to derive models for machine performance assessment and enhancement. Sensor fusion methodology was adopted to identify the parameters of the derived models. Empirical models and computational algorithms were also derived and implemented to model, assess, and then enhance the machine performance. The developed sensor fusion algorithms can be implemented on a PC-based open architecture controller to receive information from various sensors, assess the status of the process, determine the proper action, and deliver the command to actuators for task execution. This will enhance a hexapod machine`s capability to produce workpieces within the imposed dimensional tolerances.

Application of a virtual coordinate measuring machine for measurement uncertainty estimation of aspherical lens parameters

International Nuclear Information System (INIS)

Küng, Alain; Meli, Felix; Nicolet, Anaïs; Thalmann, Rudolf

2014-01-01

Tactile ultra-precise coordinate measuring machines (CMMs) are very attractive for accurately measuring optical components with high slopes, such as aspheres. The METAS µ-CMM, which exhibits a single point measurement repeatability of a few nanometres, is routinely used for measurement services of microparts, including optical lenses. However, estimating the measurement uncertainty is very demanding. Because of the many combined influencing factors, an analytic determination of the uncertainty of parameters that are obtained by numerical fitting of the measured surface points is almost impossible. The application of numerical simulation (Monte Carlo methods) using a parametric fitting algorithm coupled with a virtual CMM based on a realistic model of the machine errors offers an ideal solution to this complex problem: to each measurement data point, a simulated measurement variation calculated from the numerical model of the METAS µ-CMM is added. Repeated several hundred times, these virtual measurements deliver the statistical data for calculating the probability density function, and thus the measurement uncertainty for each parameter. Additionally, the eventual cross-correlation between parameters can be analyzed. This method can be applied for the calibration and uncertainty estimation of any parameter of the equation representing a geometric element. In this article, we present the numerical simulation model of the METAS µ-CMM and the application of a Monte Carlo method for the uncertainty estimation of measured asphere parameters. (paper)

Improving Machining Accuracy of CNC Machines with Innovative Design Methods

Science.gov (United States)

Yemelyanov, N. V.; Yemelyanova, I. V.; Zubenko, V. L.

2018-03-01

The article considers achieving the machining accuracy of CNC machines by applying innovative methods in modelling and design of machining systems, drives and machine processes. The topological method of analysis involves visualizing the system as matrices of block graphs with a varying degree of detail between the upper and lower hierarchy levels. This approach combines the advantages of graph theory and the efficiency of decomposition methods, it also has visual clarity, which is inherent in both topological models and structural matrices, as well as the resiliency of linear algebra as part of the matrix-based research. The focus of the study is on the design of automated machine workstations, systems, machines and units, which can be broken into interrelated parts and presented as algebraic, topological and set-theoretical models. Every model can be transformed into a model of another type, and, as a result, can be interpreted as a system of linear and non-linear equations which solutions determine the system parameters. This paper analyses the dynamic parameters of the 1716PF4 machine at the stages of design and exploitation. Having researched the impact of the system dynamics on the component quality, the authors have developed a range of practical recommendations which have enabled one to reduce considerably the amplitude of relative motion, exclude some resonance zones within the spindle speed range of 0...6000 min-1 and improve machining accuracy.

EXPERIMENTAL EVALUATION OF WEDM MACHINED SURFACE WAVINESS

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Katerina Mouralova

2016-10-01

Full Text Available Wire Electrical Discharge Machining (WEDM an unconventional machining technology which has become indispensable in many industries. The typical morphology of a surface machined using the electrical discharge technology is characterized with a large number of craters caused by electro-spark discharges produced during the machining process. The study deals with an evaluation of the machine parameter setting on the profile parameters of surface waviness on samples made of two metal materials Al 99.5 and Ti-6Al-4V. Attention was also paid to an evaluation of the surface morphology using 3D colour filtered and non-filtered images.

OPTIMIZATION OF MACHINING PARAMETERS USING TAGUCHI APPROACH DURING HARD TURNING OF ALLOY STEEL WITH UNCOATED CARBIDE UNDER DRY CUTTING ENVIRONMENT

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A. Das

2015-12-01

Full Text Available In today’s world of manufacturing by machining process two things are very important, one is productivity and the other one is quality. Quality of a product generally depends upon the surface finish and dimensional deviations. The productivity can be seen as a key economic indicator of innovation in terms of higher material removal rate with a less time and cost in machining industries. Taguchi method is a popular statistical technique for optimization of input parameters to get the best output results. Dry machining is a popular methodology for machining hard material and it has been accepted by many researchers to a great extent because of its low cost and safety. Many scientists have taken various input parameters and studied their effects on different output responses. In the present paper an attempt has been made to study the effect of input parameters such as cutting speed, feed rate and depth of cut on Surface roughness, Tool wear, Power consumption and Chip reduction co-efficient under dry condition using uncoated carbide insert. Signal to noise ratio has been used to select the optimal condition for various output responses. ANOVA table has been drawn for each output responses and finally mathematical model of multiple regression analysis has been prepared and authenticity of the statistical model have been checked by normal probability plot. It has been found from the experimental result that the power consumption and flank wear both were minimum at the cutting speed of 250 rpm and 400 rpm respectively. Chip reduction coefficient has been found minimum at a depth of cut of 0.3 mm and surface roughness was minimum at 0.1 mm/rev. feed rate.

A 3D Dynamic Lumped Parameter Thermal Network of Air-Cooled YASA Axial Flux Permanent Magnet Synchronous Machine

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Abdalla Hussein Mohamed

2018-03-01

Full Text Available To find the temperature rise for high power density yokeless and segmented armature (YASA axial flux permanent magnet synchronous (AFPMSM machines quickly and accurately, a 3D lumped parameter thermal model is developed and validated experimentally and by finite element (FE simulations on a 4 kW YASA machine. Additionally, to get insight in the thermal transient response of the machine, the model accounts for the thermal capacitance of different machine components. The model considers the stator, bearing, and windage losses, as well as eddy current losses in the magnets on the rotors. The new contribution of this work is that the thermal model takes cooling via air channels between the magnets on the rotor discs into account. The model is parametrized with respect to the permanent magnet (PM angle ratio, the PM thickness ratio, the air gap length, and the rotor speed. The effect of the channels is incorporated via convection equations based on many computational fluid dynamics (CFD computations. The model accuracy is validated at different values of parameters by FE simulations in both transient and steady state. The model takes less than 1 s to solve for the temperature distribution.

Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning

Science.gov (United States)

Chaney, Nathaniel W.; Herman, Jonathan D.; Ek, Michael B.; Wood, Eric F.

2016-11-01

With their origins in numerical weather prediction and climate modeling, land surface models aim to accurately partition the surface energy balance. An overlooked challenge in these schemes is the role of model parameter uncertainty, particularly at unmonitored sites. This study provides global parameter estimates for the Noah land surface model using 85 eddy covariance sites in the global FLUXNET network. The at-site parameters are first calibrated using a Latin Hypercube-based ensemble of the most sensitive parameters, determined by the Sobol method, to be the minimum stomatal resistance (rs,min), the Zilitinkevich empirical constant (Czil), and the bare soil evaporation exponent (fxexp). Calibration leads to an increase in the mean Kling-Gupta Efficiency performance metric from 0.54 to 0.71. These calibrated parameter sets are then related to local environmental characteristics using the Extra-Trees machine learning algorithm. The fitted Extra-Trees model is used to map the optimal parameter sets over the globe at a 5 km spatial resolution. The leave-one-out cross validation of the mapped parameters using the Noah land surface model suggests that there is the potential to skillfully relate calibrated model parameter sets to local environmental characteristics. The results demonstrate the potential to use FLUXNET to tune the parameterizations of surface fluxes in land surface models and to provide improved parameter estimates over the globe.

Machine learning of parameters for accurate semiempirical quantum chemical calculations

International Nuclear Information System (INIS)

Dral, Pavlo O.; Lilienfeld, O. Anatole von; Thiel, Walter

2015-01-01

We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempirical OM2 method using a set of 6095 constitutional isomers C 7 H 10 O 2 , for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules

Determination of Machining Parameters of Corn Byproduct Filled Plastics

Science.gov (United States)

In a collaborative project between the USDA and Northern Illinois University, the use of ethanol corn processing by-products as bio-filler materials in the compression molding of phenolic plastics has been studied. This paper reports on the results of a machinability study in the milling of various ...

Evaluation of parameter sensitivities for flux-switching permanent magnet machines based on simplified equivalent magnetic circuit

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Gan Zhang

2017-05-01

Full Text Available Most of the published papers regarding the design of flux-switching permanent magnet machines are focused on the analysis and optimization of electromagnetic or mechanical behaviors, however, the evaluate of the parameter sensitivities have not been covered, which contrarily, is the main contribution of this paper. Based on the finite element analysis (FEA and simplified equivalent magnetic circuit, the method proposed in this paper enables the influences of parameters on the electromagnetic performances, i.e. the parameter sensitivities, to be given by equations. The FEA results are also validated by experimental measurements.

COMPARISON OF STATISTICALLY CONTROLLED MACHINING SOLUTIONS OF TITANIUM ALLOYS USING USM

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R. Singh

2010-06-01

Full Text Available The purpose of the present investigation is to compare the statistically controlled machining solution of titanium alloys using ultrasonic machining (USM. In this study, the previously developed Taguchi model for USM of titanium and its alloys has been investigated and compared. Relationships between the material removal rate, tool wear rate, surface roughness and other controllable machining parameters (power rating, tool type, slurry concentration, slurry type, slurry temperature and slurry size have been deduced. The results of this study suggest that at the best settings of controllable machining parameters for titanium alloys (based upon the Taguchi design, the machining solution with USM is statistically controlled, which is not observed for other settings of input parameters on USM.

The effect of machining parameters on surface roughness during turning of stainless steel

International Nuclear Information System (INIS)

El-Belazi, Khalid M.

1991-03-01

Surface roughness is a direct consequence of the cutting tool action, its assessment and control represent an effective way by which the machining process can be studied. The control of surface roughness has become increasingly important during the last thirty years, because the quality of surface is extremely important for machined components that have been designed to stand to static and cyclic loads. This work has two major goals. The first is to develop a new theoretical model based on the assumption that the shape of the cutting tool nose is elliptical to evaluate the surface roughness parameters. The second is to investigate the effect of cutting speed, feed rate, overhang length, tool nose radius (circular sharp), and depth of cut on surface roughness of turned surfaces of austenitic stainless steel grade 12X18H10T. It was found from the theoretical part that the surface roughness values obtained from the elliptical model are much better than those obtained from the other models. It was found from the experimental work that the surface roughness values increase by increasing cutting speed, feed rate, depth of cut, and overhang length, and fluctuates when using cutting tools with various nose radii, during turning of the above mentioned steel by using a brazed carbide cutting tool. (author)

The Influence Study of Ultrasonic honing parameters to workpiece surface temperature

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Zhang Xiaoqiang

2016-01-01

Full Text Available Ultrasonic vibration honing(UVH, a machine technology, has a lot of advantages. Lower grinding temperature is a significant character and is beneficial for both processing and workpiece surface. But the high temperature caused by big honing pressure becomes the main factor to produce workpiece heat damage in grinding zone. In various honing parameter combinations, the showing effect is different. Based on the thermodynamics classical theory, established the heat transfer equation for grinding zone, simplified the model and obtained the two-dimenssion temperature field expression for workpiece, then simulated the temperature changing trend in a variety of conditions. It is shown that themain temp is in a range of 700K to 1200K. In addition, the variation is huge for every parameter. The study provides a theoretical basis for deeply seeking reasonable machining parameter and obtaining better workpiece quality.

A Fault Alarm and Diagnosis Method Based on Sensitive Parameters and Support Vector Machine

Science.gov (United States)

Zhang, Jinjie; Yao, Ziyun; Lv, Zhiquan; Zhu, Qunxiong; Xu, Fengtian; Jiang, Zhinong

2015-08-01

Study on the extraction of fault feature and the diagnostic technique of reciprocating compressor is one of the hot research topics in the field of reciprocating machinery fault diagnosis at present. A large number of feature extraction and classification methods have been widely applied in the related research, but the practical fault alarm and the accuracy of diagnosis have not been effectively improved. Developing feature extraction and classification methods to meet the requirements of typical fault alarm and automatic diagnosis in practical engineering is urgent task. The typical mechanical faults of reciprocating compressor are presented in the paper, and the existing data of online monitoring system is used to extract fault feature parameters within 15 types in total; the inner sensitive connection between faults and the feature parameters has been made clear by using the distance evaluation technique, also sensitive characteristic parameters of different faults have been obtained. On this basis, a method based on fault feature parameters and support vector machine (SVM) is developed, which will be applied to practical fault diagnosis. A better ability of early fault warning has been proved by the experiment and the practical fault cases. Automatic classification by using the SVM to the data of fault alarm has obtained better diagnostic accuracy.

Using Perturbed Physics Ensembles and Machine Learning to Select Parameters for Reducing Regional Biases in a Global Climate Model

Science.gov (United States)

Li, S.; Rupp, D. E.; Hawkins, L.; Mote, P.; McNeall, D. J.; Sarah, S.; Wallom, D.; Betts, R. A.

2017-12-01

This study investigates the potential to reduce known summer hot/dry biases over Pacific Northwest in the UK Met Office's atmospheric model (HadAM3P) by simultaneously varying multiple model parameters. The bias-reduction process is done through a series of steps: 1) Generation of perturbed physics ensemble (PPE) through the volunteer computing network weather@home; 2) Using machine learning to train "cheap" and fast statistical emulators of climate model, to rule out regions of parameter spaces that lead to model variants that do not satisfy observational constraints, where the observational constraints (e.g., top-of-atmosphere energy flux, magnitude of annual temperature cycle, summer/winter temperature and precipitation) are introduced sequentially; 3) Designing a new PPE by "pre-filtering" using the emulator results. Steps 1) through 3) are repeated until results are considered to be satisfactory (3 times in our case). The process includes a sensitivity analysis to find dominant parameters for various model output metrics, which reduces the number of parameters to be perturbed with each new PPE. Relative to observational uncertainty, we achieve regional improvements without introducing large biases in other parts of the globe. Our results illustrate the potential of using machine learning to train cheap and fast statistical emulators of climate model, in combination with PPEs in systematic model improvement.

DETERMINATION AND ANALYSIS OF CHANGE POWER CHARACTER AND POWER PARAMETERS OF EARTHMOVING- TRANSPORT WORKING PROCESS MACHINES OF CYCLIC ACTION

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KHMARA L. A.

2017-05-01

Full Text Available Summary. Raising of problem. Efficiency of implementation working process an earthmoving-transport machine on digging of soil depends on complete realization of power equipment and hauling properties working equipment during implementation this operation. Most effective will be the mode of digging when from his beginning to the final stage a power equipment will realize nominal power, and working equipment maximal KKD at that skidding of mover does not exceed the defined possible value. However, for the traditional constructions of earthmoving-transport machines cyclic action, for such, as a drag shovel, bulldozer, realizing these terms is heavy. The feature of process digging consists in the increase of resistance to digging soil from the ego of the initial stage to eventual when hauling possibilities of machine will be maximally realized. Therefore the calculation of power equipment takes into account the power indexes of machine on the final stage of digging. Thus the unstationarity of working process results in the under exploitation of power equipment machine and hereupon appearance her bits and pieces. The size of bits and pieces power depends on the stage digging of soil, his physical and mechanical properties, terms cooperation of working equipment with the surface of motion. One of methods realization surplus power, this use it for the drive intensifiers working process of earthmoving-transport machines. Therefore for the effective choice parameters of intensifier, his office hours it is necessary to know the size of bits and pieces of power and character her change during digging of soil. The purpose of the article. Development of methodology determination remaining power equipment an earthmoving-transport machine on the example self-propelled drags hovel, character her change at digging of soil taking into account physical and mechanical properties of soil and terms cooperation working equipment with the surface of motion. Conclusion

Automated valve fault detection based on acoustic emission parameters and support vector machine

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Salah M. Ali

2018-03-01

Full Text Available Reciprocating compressors are one of the most used types of compressors with wide applications in industry. The most common failure in reciprocating compressors is always related to the valves. Therefore, a reliable condition monitoring method is required to avoid the unplanned shutdown in this category of machines. Acoustic emission (AE technique is one of the effective recent methods in the field of valve condition monitoring. However, a major challenge is related to the analysis of AE signal which perhaps only depends on the experience and knowledge of technicians. This paper proposes automated fault detection method using support vector machine (SVM and AE parameters in an attempt to reduce human intervention in the process. Experiments were conducted on a single stage reciprocating air compressor by combining healthy and faulty valve conditions to acquire the AE signals. Valve functioning was identified through AE waveform analysis. SVM faults detection model was subsequently devised and validated based on training and testing samples respectively. The results demonstrated automatic valve fault detection model with accuracy exceeding 98%. It is believed that valve faults can be detected efficiently without human intervention by employing the proposed model for a single stage reciprocating compressor. Keywords: Condition monitoring, Faults detection, Signal analysis, Acoustic emission, Support vector machine

Preliminary study on rotary ultrasonic machining of Bk-7 optical glass rod

International Nuclear Information System (INIS)

Hamzah, E.; Izman, S.; Khoo, C.Y.; Zainal Abidin, N.N.

2007-01-01

This paper presents an experimental observation on rotary ultrasonic machining (RUM) of BK7 optical glass rod. BK7 is a common technical optical glass for high quality optical components due to its high linear optical transmission in the visible range and is chemically stable. RUM is a hybrid machining process that combines the material removal mechanisms of diamond grinding and ultrasonic machining (USM) and it is non-thermal, non-chemical, creates no change in the microstructure, chemical or physical properties of the work piece. In the RUM, a controlled static load is applied to the rotating core drill with metal bonded diamond abrasive and is ultrasonically vibrated in the axial direction. A water-soluble coolant was used to cool the tool and sample during machining processes. By using DOE (Design of Experiment) approach, the effect of spindle speed and feed rate to the ultrasonic machinability had been developed. The main effects and two-factor interactions of process parameters (spindle speed) and feed rate) on output variables (MRR, surface roughness, opaqueness, chipping thickness and chipping size) are studied. (author)

Identification of constitutive theory parameters using a tensile machine for deposited filaments of microcrystalline ink by the direct-write method

International Nuclear Information System (INIS)

Lourdel, N; Therriault, D; Lévesque, M

2009-01-01

A custom-designed tensile machine is developed to characterize the mechanical properties of ink micro-filaments deposited by the direct-write method. The direct-write method has been used for the fabrication of a wide variety of micro-systems such as microvascular networks, chaotic mixers and laboratory on chips. The tensile machine was used to measure the induced force in ink filaments during tensile and tension-relaxation tests as a function of the applied strain rate, the ink composition and the filament diameter. Experimental data were fitted by a linearly viscoelastic model using a data reduction procedure in order to identify the constitutive theory parameters of the deposited ink filaments. The model predictions based on the linearly viscoelastic model and the defined constitutive theory parameters give a close approximation of all experimental data generated in this study. Such models will be useful for the development and optimization of future 3D complex structures made by the direct-write method

SU-G-TeP4-09: Development of a Plan Data Aggregator for Time Efficient Physics Second-Checks of Machine Parameters for External Beam Treatment Plans

Energy Technology Data Exchange (ETDEWEB)

Belley, M; Schmidt, M; Knutson, N [Rhode Island Hospital, Providence RI (United States); University of Rhode Island, Kingston, RI (United States); Price, M [Rhode Island Hospital, Providence RI (United States); University of Rhode Island, Kingston, RI (United States); Alpert Medical School of Brown University, Providence, RI (United States)

2016-06-15

Purpose: Physics second-checks for external beam radiation therapy are performed, in-part, to verify that the machine parameters in the Record-and-Verify (R&V) system that will ultimately be sent to the LINAC exactly match the values initially calculated by the Treatment Planning System (TPS). While performing the second-check, a large portion of the physicists’ time is spent navigating and arranging display windows to locate and compare the relevant numerical values (MLC position, collimator rotation, field size, MU, etc.). Here, we describe the development of a software tool that guides the physicist by aggregating and succinctly displaying machine parameter data relevant to the physics second-check process. Methods: A data retrieval software tool was developed using Python to aggregate data and generate a list of machine parameters that are commonly verified during the physics second-check process. This software tool imported values from (i) the TPS RT Plan DICOM file and (ii) the MOSAIQ (R&V) Structured Query Language (SQL) database. The machine parameters aggregated for this study included: MLC positions, X&Y jaw positions, collimator rotation, gantry rotation, MU, dose rate, wedges and accessories, cumulative dose, energy, machine name, couch angle, and more. Results: A GUI interface was developed to generate a side-by-side display of the aggregated machine parameter values for each field, and presented to the physicist for direct visual comparison. This software tool was tested for 3D conformal, static IMRT, sliding window IMRT, and VMAT treatment plans. Conclusion: This software tool facilitated the data collection process needed in order for the physicist to conduct a second-check, thus yielding an optimized second-check workflow that was both more user friendly and time-efficient. Utilizing this software tool, the physicist was able to spend less time searching through the TPS PDF plan document and the R&V system and focus the second-check efforts on

Machine assembly with a new material handling mechanism in the sewing machine

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Umarova Z.M.

2017-05-01

Full Text Available the paper presents the dynamic model of the machine assembly with a recommended mechanism for moving material and the definition of the law of rails motion under various system parameters. The author has suggested the solution implemented by the system of differential equations numerically on the PC and the system describing the motion of the machine set. Recommended values ​​of the parameters of elastic links of material transfer mechanism have been obtained. The researcher has developed the methods of kinematic and dynamic analysis of the material transfer mechanism with elastic elements of the sewing machine and has approved the parameters and development of the design.

Machining of Machine Elements Made of Polymer Composite Materials

Science.gov (United States)

Baurova, N. I.; Makarov, K. A.

2017-12-01

The machining of the machine elements that are made of polymer composite materials (PCMs) or are repaired using them is considered. Turning, milling, and drilling are shown to be most widely used among all methods of cutting PCMs. Cutting conditions for the machining of PCMs are presented. The factors that most strongly affect the roughness parameters and the accuracy of cutting PCMs are considered.

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

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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.

Comparison of effects of machine performance parameters and energy indices of soybean production in conservation and conventional tillage systems

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A Sharifi

2016-09-01

Full Text Available Introduction Nowadays, agricultural systems are seeking economic, ecological and bioenvironmental goals for production of agricultural crops with protection and sustainability of the environment. Therefore, there is need to extend sustainable agricultural systems such as conservation agriculture. One of the principles of conservation agriculture is conservation tillage. Conservation tillage is a kind of tillage that retains crop residues on the soil surface or mixes it with soil using related machines. It could also affect on machine performance parameters. Energy consumption for producing one kilogram crop could be studied for conservation tillage. Several researchers have conducted studies on this issue for production of different crops including wheat, sunflower and forage crops. This study conducted to assess machine performance parameters and energy indices of conservation tillage systems for soybean cultivation in Golestan province. Materials and Methods This study was conducted to investigate the effects of conservation tillage systems on machine performance and energy indices in soybean production at the Gorgan research station of Golestan Agricultural and Natural Resource Research Center in 2012. The precipitation was 450 mm. Soil texture was silty clay loam. Treatments were four tillage methods, including no-till using row crop direct planter, no-till using grain direct drill, conventional tillage usin a disk harrow with working depth of 10-15 cm and minimum tillage using chisel packer with a working depth of 20 cm. Machine performance parameters and energy indices studied in a farm covered by wheat residues in a randomized complete block design (RCBD with four treatments and four replications. Machine performance parameters consisted of field efficiency, field capacity, total field capacity and planting uniformity index were measured. Energy indices such as energy ratio, energy productivity, energy intensity and net energy gain were

Machine protection systems

CERN Document Server

Macpherson, A L

2010-01-01

A summary of the Machine Protection System of the LHC is given, with particular attention given to the outstanding issues to be addressed, rather than the successes of the machine protection system from the 2009 run. In particular, the issues of Safe Machine Parameter system, collimation and beam cleaning, the beam dump system and abort gap cleaning, injection and dump protection, and the overall machine protection program for the upcoming run are summarised.

Advances in three-dimensional field analysis and evaluation of performance parameters of electrical machines

Science.gov (United States)

Sivasubramaniam, Kiruba

This thesis makes advances in three dimensional finite element analysis of electrical machines and the quantification of their parameters and performance. The principal objectives of the thesis are: (1)the development of a stable and accurate method of nonlinear three-dimensional field computation and application to electrical machinery and devices; and (2)improvement in the accuracy of determination of performance parameters, particularly forces and torque computed from finite elements. Contributions are made in two general areas: a more efficient formulation for three dimensional finite element analysis which saves time and improves accuracy, and new post-processing techniques to calculate flux density values from a given finite element solution. A novel three-dimensional magnetostatic solution based on a modified scalar potential method is implemented. This method has significant advantages over the traditional total scalar, reduced scalar or vector potential methods. The new method is applied to a 3D geometry of an iron core inductor and a permanent magnet motor. The results obtained are compared with those obtained from traditional methods, in terms of accuracy and speed of computation. A technique which has been observed to improve force computation in two dimensional analysis using a local solution of Laplace's equation in the airgap of machines is investigated and a similar method is implemented in the three dimensional analysis of electromagnetic devices. A new integral formulation to improve force calculation from a smoother flux-density profile is also explored and implemented. Comparisons are made and conclusions drawn as to how much improvement is obtained and at what cost. This thesis also demonstrates the use of finite element analysis to analyze torque ripples due to rotor eccentricity in permanent magnet BLDC motors. A new method for analyzing torque harmonics based on data obtained from a time stepping finite element analysis of the machine is

Asynchronized synchronous machines

CERN Document Server

Botvinnik, M M

1964-01-01

Asynchronized Synchronous Machines focuses on the theoretical research on asynchronized synchronous (AS) machines, which are "hybrids” of synchronous and induction machines that can operate with slip. Topics covered in this book include the initial equations; vector diagram of an AS machine; regulation in cases of deviation from the law of full compensation; parameters of the excitation system; and schematic diagram of an excitation regulator. The possible applications of AS machines and its calculations in certain cases are also discussed. This publication is beneficial for students and indiv

Study on spectral parameters and the support vector machine in surface enhanced Raman spectroscopy of serum for the detection of colon cancer

International Nuclear Information System (INIS)

Li, Xiaozhou; Yang, Tianyue; Yao, Jun; Wang, Deli; Li, Siqi; Song, Youtao; Ding, Jianhua

2015-01-01

Surface enhanced Raman spectroscopy (SERS) has been recognized as an effective tool for the analysis of tissue samples and biofluids. In this work, a total of 27 spectral parameters were chosen and compared using SERS. Four parameters with the highest prediction ability were selected for further support vector machine (SVM) analysis. As a comparison, principal component analysis (PCA) was used on the same dataset for feature extraction. SVM was used with the above two data reduction methods separately to differentiate colon cancer and the control groups. Serum taken from 52 colon cancer patients and 60 healthy volunteers were collected and tested by SERS. The accuracy for Parameter-SVM was 95.0%, the sensitivity was 96.2%, and the specificity was 95.5%, which was much higher than the results using only one parameter, while for PCA-SVM, the results are 93.3%, 92.3%, and 92.9%, respectively. These results demonstrate that the SERS analysis method can be used to identify serum differences between colon cancer patients and normal people. (letter)

Beam homogeneity and dependence of a Thomson ionic microetching machine on the variation in the working parameters

International Nuclear Information System (INIS)

Pere, J.F.

1974-01-01

The specific use of ion beam machining in selective etching of superposed thin films requires a rigourous control of the process. This control entails the two following requirements: a perfect etching uniformity on a large duty surface, an excellent reproducibility in etching velocities. Such performance can be obtained only from a systematic optimization study for the various discharge and neutralization parameters. Results obtained give information on the role and criticality of each of them, the mutual influences appearing at use, as well as improvements to be done [fr

Machining of Molybdenum by EDM-EP and EDC Processes

Science.gov (United States)

Wu, K. L.; Chen, H. J.; Lee, H. M.; Lo, J. S.

2017-12-01

Molybdenum metal (Mo) can be machined with conventional tools and equipment, however, its refractory propertytends to chip when being machined. In this study, the nonconventional processes of electrical discharge machining (EDM) and electro-polishing (EP) have been conducted to investigate the machining of Mo metal and fabrication of Mo grid. Satisfactory surface quality was obtained using appropriate EDM parameters of Ip ≦ 3A and Ton EDMed Mo metal. Experimental results proved that the appropriate parameters of Ip = 5A and Ton = 50μs at Toff = 10μs can obtain the deposit with about 60μm thickness. The major phase of deposit on machined Mo surface was SiC ceramic, while the minor phases included MoSi2 and/or SiO2 with the presence of free Si due to improper discharging parameters and the use of silicone oil as the dielectric fluid.

Study of on-machine error identification and compensation methods for micro machine tools

International Nuclear Information System (INIS)

Wang, Shih-Ming; Yu, Han-Jen; Lee, Chun-Yi; Chiu, Hung-Sheng

2016-01-01

Micro machining plays an important role in the manufacturing of miniature products which are made of various materials with complex 3D shapes and tight machining tolerance. To further improve the accuracy of a micro machining process without increasing the manufacturing cost of a micro machine tool, an effective machining error measurement method and a software-based compensation method are essential. To avoid introducing additional errors caused by the re-installment of the workpiece, the measurement and compensation method should be on-machine conducted. In addition, because the contour of a miniature workpiece machined with a micro machining process is very tiny, the measurement method should be non-contact. By integrating the image re-constructive method, camera pixel correction, coordinate transformation, the error identification algorithm, and trajectory auto-correction method, a vision-based error measurement and compensation method that can on-machine inspect the micro machining errors and automatically generate an error-corrected numerical control (NC) program for error compensation was developed in this study. With the use of the Canny edge detection algorithm and camera pixel calibration, the edges of the contour of a machined workpiece were identified and used to re-construct the actual contour of the work piece. The actual contour was then mapped to the theoretical contour to identify the actual cutting points and compute the machining errors. With the use of a moving matching window and calculation of the similarity between the actual and theoretical contour, the errors between the actual cutting points and theoretical cutting points were calculated and used to correct the NC program. With the use of the error-corrected NC program, the accuracy of a micro machining process can be effectively improved. To prove the feasibility and effectiveness of the proposed methods, micro-milling experiments on a micro machine tool were conducted, and the results

Optimization of pocket machining strategy in HSM

OpenAIRE

Msaddek, El Bechir; Bouaziz, Zoubeir; Dessein, Gilles; Baili, Maher

2012-01-01

International audience; Our two major concerns, which should be taken into consideration as soon as we start the selecting the machining parameters, are the minimization of the machining time and the maintaining of the high-speed machining machine in good state. The manufacturing strategy is one of the parameters which practically influences the time of the different geometrical forms manufacturing, as well as the machine itself. In this article, we propose an optimization methodology of the ...

Experimental Investigation – Magnetic Assisted Electro Discharge Machining

Science.gov (United States)

Kesava Reddy, Chirra; Manzoor Hussain, M.; Satyanarayana, S.; Krishna, M. V. S. Murali

2018-04-01

Emerging technology needs advanced machined parts with high strength and temperature resistance, high fatigue life at low production cost with good surface quality to fit into various industrial applications. Electro discharge machine is one of the extensively used machines to manufacture advanced machined parts which cannot be machined by other traditional machine with high precision and accuracy. Machining of DIN 17350-1.2080 (High Carbon High Chromium steel), using electro discharge machining has been discussed in this paper. In the present investigation an effort is made to use permanent magnet at various positions near the spark zone to improve surface quality of the machined surface. Taguchi methodology is used to obtain optimal choice for each machining parameter such as peak current, pulse duration, gap voltage and Servo reference voltage etc. Process parameters have significant influence on machining characteristics and surface finish. Improvement in surface finish is observed when process parameters are set at optimum condition under the influence of magnetic field at various positions.

Machinability of IPS Empress 2 framework ceramic.

Science.gov (United States)

Schmidt, C; Weigl, P

2000-01-01

Using ceramic materials for an automatic production of ceramic dentures by CAD/CAM is a challenge, because many technological, medical, and optical demands must be considered. The IPS Empress 2 framework ceramic meets most of them. This study shows the possibilities for machining this ceramic with economical parameters. The long life-time requirement for ceramic dentures requires a ductile machined surface to avoid the well-known subsurface damages of brittle materials caused by machining. Slow and rapid damage propagation begins at break outs and cracks, and limits life-time significantly. Therefore, ductile machined surfaces are an important demand for machine dental ceramics. The machining tests were performed with various parameters such as tool grain size and feed speed. Denture ceramics were machined by jig grinding on a 5-axis CNC milling machine (Maho HGF 500) with a high-speed spindle up to 120,000 rpm. The results of the wear test indicate low tool wear. With one tool, you can machine eight occlusal surfaces including roughing and finishing. One occlusal surface takes about 60 min machining time. Recommended parameters for roughing are middle diamond grain size (D107), cutting speed v(c) = 4.7 m/s, feed speed v(ft) = 1000 mm/min, depth of cut a(e) = 0.06 mm, width of contact a(p) = 0.8 mm, and for finishing ultra fine diamond grain size (D46), cutting speed v(c) = 4.7 m/s, feed speed v(ft) = 100 mm/min, depth of cut a(e) = 0.02 mm, width of contact a(p) = 0.8 mm. The results of the machining tests give a reference for using IPS Empress(R) 2 framework ceramic in CAD/CAM systems. Copyright 2000 John Wiley & Sons, Inc.

FSW of Aluminum Tailor Welded Blanks across Machine Platforms

Energy Technology Data Exchange (ETDEWEB)

Hovanski, Yuri; Upadhyay, Piyush; Carlson, Blair; Szymanski, Robert; Luzanski, Tom; Marshall, Dustin

2015-02-16

Development and characterization of friction stir welded aluminum tailor welded blanks was successfully carried out on three separate machine platforms. Each was a commercially available, gantry style, multi-axis machine designed specifically for friction stir welding. Weld parameters were developed to support high volume production of dissimilar thickness aluminum tailor welded blanks at speeds of 3 m/min and greater. Parameters originally developed on an ultra-high stiffness servo driven machine where first transferred to a high stiffness servo-hydraulic friction stir welding machine, and subsequently transferred to a purpose built machine designed to accommodate thin sheet aluminum welding. The inherent beam stiffness, bearing compliance, and control system for each machine were distinctly unique, which posed specific challenges in transferring welding parameters across machine platforms. This work documents the challenges imposed by successfully transferring weld parameters from machine to machine, produced from different manufacturers and with unique control systems and interfaces.

Fully automatic CNC machining production system

Directory of Open Access Journals (Sweden)

Lee Jeng-Dao

2017-01-01

Full Text Available Customized manufacturing is increasing years by years. The consumption habits change has been cause the shorter of product life cycle. Therefore, many countries view industry 4.0 as a target to achieve more efficient and more flexible automated production. To develop an automatic loading and unloading CNC machining system via vision inspection is the first step in industrial upgrading. CNC controller is adopted as the main controller to command to the robot, conveyor, and other equipment in this study. Moreover, machine vision systems are used to detect position of material on the conveyor and the edge of the machining material. In addition, Open CNC and SCADA software will be utilized to make real-time monitor, remote system of control, alarm email notification, and parameters collection. Furthermore, RFID has been added to employee classification and management. The machine handshaking has been successfully proposed to achieve automatic vision detect, edge tracing measurement, machining and system parameters collection for data analysis to accomplish industrial automation system integration with real-time monitor.

Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm

Institute of Scientific and Technical Information of China (English)

无

2005-01-01

In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear statistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two representative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method performs well in selecting genes and achieves high classification accuracies with these genes.

Variable geometry Darrieus wind machine

Science.gov (United States)

Pytlinski, J. T.; Serrano, D.

1983-08-01

A variable geometry Darrieus wind machine is proposed. The lower attachment of the blades to the rotor can move freely up and down the axle allowing the blades of change shape during rotation. Experimental data for a 17 m. diameter Darrieus rotor and a theoretical model for multiple streamtube performance prediction were used to develop a computer simulation program for studying parameters that affect the machine's performance. This new variable geometry concept is described and interrelated with multiple streamtube theory through aerodynamic parameters. The computer simulation study shows that governor behavior of a Darrieus turbine can not be attained by a standard turbine operating within normally occurring rotational velocity limits. A second generation variable geometry Darrieus wind turbine which uses a telescopic blade is proposed as a potential improvement on the studied concept.

Toolpath Strategy and Optimum Combination of Machining Parameter during Pocket Mill Process of Plastic Mold Steels Material

Science.gov (United States)

Wibowo, Y. T.; Baskoro, S. Y.; Manurung, V. A. T.

2018-02-01

Plastic based products spread all over the world in many aspects of life. The ability to substitute other materials is getting stronger and wider. The use of plastic materials increases and become unavoidable. Plastic based mass production requires injection process as well Mold. The milling process of plastic mold steel material was done using HSS End Mill cutting tool that is widely used in a small and medium enterprise for the reason of its ability to be re sharpened and relatively inexpensive. Study on the effect of the geometry tool states that it has an important effect on the quality improvement. Cutting speed, feed rate, depth of cut and radii are input parameters beside to the tool path strategy. This paper aims to investigate input parameter and cutting tools behaviors within some different tool path strategy. For the reason of experiments efficiency Taguchi method and ANOVA were used. Response studied is surface roughness and cutting behaviors. By achieving the expected quality, no more additional process is required. Finally, the optimal combination of machining parameters will deliver the expected roughness and of course totally reduced cutting time. However actually, SMEs do not optimally use this data for cost reduction.

Machining of uranium and uranium alloys

International Nuclear Information System (INIS)

Morris, T.O.

1981-01-01

Uranium and uranium alloys can be readily machined by conventional methods in the standard machine shop when proper safety and operating techniques are used. Material properties that affect machining processes and recommended machining parameters are discussed. Safety procedures and precautions necessary in machining uranium and uranium alloys are also covered. 30 figures

Machine Learning and Inverse Problem in Geodynamics

Science.gov (United States)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R.

2017-12-01

During the past few decades numerical modeling and traditional HPC have been widely deployed in many diverse fields for problem solutions. However, in recent years the rapid emergence of machine learning (ML), a subfield of the artificial intelligence (AI), in many fields of sciences, engineering, and finance seems to mark a turning point in the replacement of traditional modeling procedures with artificial intelligence-based techniques. The study of the circulation in the interior of Earth relies on the study of high pressure mineral physics, geochemistry, and petrology where the number of the mantle parameters is large and the thermoelastic parameters are highly pressure- and temperature-dependent. More complexity arises from the fact that many of these parameters that are incorporated in the numerical models as input parameters are not yet well established. In such complex systems the application of machine learning algorithms can play a valuable role. Our focus in this study is the application of supervised machine learning (SML) algorithms in predicting mantle properties with the emphasis on SML techniques in solving the inverse problem. As a sample problem we focus on the spin transition in ferropericlase and perovskite that may cause slab and plume stagnation at mid-mantle depths. The degree of the stagnation depends on the degree of negative density anomaly at the spin transition zone. The training and testing samples for the machine learning models are produced by the numerical convection models with known magnitudes of density anomaly (as the class labels of the samples). The volume fractions of the stagnated slabs and plumes which can be considered as measures for the degree of stagnation are assigned as sample features. The machine learning models can determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at mid-mantle depths. Employing support vector machine (SVM) algorithms we show that SML techniques

A Study of the Interaction between Batting Cage Baseballs and Pitching Machine

Directory of Open Access Journals (Sweden)

Patrick Drane

2018-02-01

Full Text Available Batting cage pitching machines are widely used across the sports of baseball and softball for training and recreation purposes. The balls are specifically designed for the machines and for the environment to ensure high durability and typically do not have seams. Polymeric foam balls are widely used in these automated pitching machines for batting practice in a cage environment and are similar in weight and size compared with the regulation balls used in leagues. The primary objective of this paper is to characterize the polymeric balls and their interaction with the pitching machine. The paper will present measured ball properties and measured relationships between various pitching machine parameters such as wheel speed, and the ratio of wheel speeds on the ball exit velocity and rotation. This paper will also characterize some of the effects of wear on the baseballs and wheels from their prolonged use.

Study on electromagnetism force of CARR control rod drive mechanism experimental machine

International Nuclear Information System (INIS)

Zhu Xuewei; Zhen Jianxiao; Wang Yulin; Jia Yueguang; Yang Kun; Yin Haozhe

2015-01-01

With the aim of acquiring electromagnetic force and electromagnetic field distributions of control rod drive mechanism (CRDM) in China Advanced Research Reactor (CARR), the force analysis on the CRDM was taken. Manufacturing the experimental machine, the electromagnetic force experiment was taken on it. The electromagnetic field and electromagnetic force simulation analyses of experimental machine were taken, working out distribution data of electromagnetic force and magnetic induction intensity distribution curve, and the effects of permanent magnetic field on electromagnetic field and structure parameters on electromagnetic force. The simulation value is accord with experiment value, the research results provide a reference to electromagnetic force study on CRDM in CARR, and also provide a reference to design of the same type CRDM. (authors)

Estimates of the atmospheric parameters of M-type stars: a machine-learning perspective

Science.gov (United States)

Sarro, L. M.; Ordieres-Meré, J.; Bello-García, A.; González-Marcos, A.; Solano, E.

2018-05-01

Estimating the atmospheric parameters of M-type stars has been a difficult task due to the lack of simple diagnostics in the stellar spectra. We aim at uncovering good sets of predictive features of stellar atmospheric parameters (Teff, log (g), [M/H]) in spectra of M-type stars. We define two types of potential features (equivalent widths and integrated flux ratios) able to explain the atmospheric physical parameters. We search the space of feature sets using a genetic algorithm that evaluates solutions by their prediction performance in the framework of the BT-Settl library of stellar spectra. Thereafter, we construct eight regression models using different machine-learning techniques and compare their performances with those obtained using the classical χ2 approach and independent component analysis (ICA) coefficients. Finally, we validate the various alternatives using two sets of real spectra from the NASA Infrared Telescope Facility (IRTF) and Dwarf Archives collections. We find that the cross-validation errors are poor measures of the performance of regression models in the context of physical parameter prediction in M-type stars. For R ˜ 2000 spectra with signal-to-noise ratios typical of the IRTF and Dwarf Archives, feature selection with genetic algorithms or alternative techniques produces only marginal advantages with respect to representation spaces that are unconstrained in wavelength (full spectrum or ICA). We make available the atmospheric parameters for the two collections of observed spectra as online material.

Operation and machine studies

International Nuclear Information System (INIS)

1992-01-01

This annual report describes the GANIL (Grand accelerateur national d'ions lourds, Caen, France) operation and the machine studies realized in 1992. Metallic ions have been accelerated during 36 pc of the time; some were produced for the first time at GANIL: 125 Te, 52 Cr with ECR3, 181 Ta with ECR4. The various machine studies are: comparison of lifetimes of carbon sheets, charge exchange of very heavy ions in carbon foils and in the residual gas of the Ganil cyclotrons, commissioning of the new high intensity axial injection system for Ganil, tantalum acceleration with the new injector, a cyclotron as a mass spectrometer; other studies concerned: implementing the new control system, gettering flux measurement, energy deposited by neutrons and gamma rays in the cryogenic system of SISSI; latest developments on multicharged ECR ion sources, and an on-line isotopic separator test bench at Ganil

Prediction of tunnel boring machine performance using machine and rock mass data

International Nuclear Information System (INIS)

Dastgir, G.

2012-01-01

Performance of the tunnel boring machine and its prediction by different methods has been a hot issue since the first TBM came into being. For the sake of safe and sound transport, improvement of hydro-power, mining, civil and many other tunneling projects that cannot be driven efficiently and economically by conventional drill and blast, TBMs are quite frequently used. TBM parameters and rock mass properties, which heavily influence machine performance, should be estimated or known before choice of TBM-type and start of excavation. By applying linear regression analysis (SPSS19), fuzzy logic tools and a special Math-Lab code on actual field data collected from seven TBM driven tunnels (Hieflau expansion, Queen water tunnel, Vereina, Hemerwald, Maen, Pieve and Varzo tunnel), an attempt was made to provide prediction of rock mass class (RMC), rock fracture class (RFC), penetration rate (PR) and advance rate (AR). For detailed analysis of TBM performance, machine parameters (thrust, machine rpm, torque, power etc.), machine types and specification and rock mass properties (UCS, discontinuity in rock mass, RMC, RFC, RMR, etc.) were analyzed by 3-D surface plotting using statistical software R. Correlations between machine parameters and rock mass properties which effectively influence prediction models, are presented as well. In Hieflau expansion tunnel AR linearly decreases with increase of thrust due to high dependence of machine advance rate upon rock strength. For Hieflau expansion tunnel three types of data (TBM, rock mass and seismic data e.g. amplitude, pseudo velocity etc.) were coupled and simultaneously analyzed by plotting 3-D surfaces. No appreciable correlation between seismic data (Amplitude and Pseudo velocity) and rock mass properties and machine parameters could be found. Tool wear as a function of TBM operational parameters was analyzed which revealed that tool wear is minimum if applied thrust is moderate and that tool wear is high when thrust is

Running and machine studies in 1990

International Nuclear Information System (INIS)

1991-03-01

This annual report described the GANIL performance and machine studies. During the year 1990, the machine has been operated for 36 weeks divided into periods of 5, 6 or 7 weeks; consequently the number of beam setting up has been reduced. From 5682 hours of scheduled beam 3239 hours have been delivered on target. Very heavy ions (Pb, U) are now accelerated owing to the OAE modification. Many experiments have been completed with the new medium energy beam facility. The machine studies were devoted to the development ot the following items: production of 157 Gd 19+ ions, acceleration of 238 U 59+ at 24 MeV/u, SSC1 orbit precession, charge state distribution and energy spread after stripping [fr

Effect of electrical discharge machining on surface characteristics and machining damage of AISI D2 tool steel

International Nuclear Information System (INIS)

Guu, Y.H.; Hocheng, H.; Chou, C.Y.; Deng, C.S.

2003-01-01

In this work the electrical discharge machining (EDM) of AISI D2 tool steel was investigated. The surface characteristics and machining damage caused by EDM were studied in terms of machining parameters. Based on the experimental data, an empirical model of the tool steel was also proposed. A new damage variable was used to study the EDM damage. The workpiece surface and re-solidified layers were examined by a scanning electron microscopy. Surface roughness was determined with a surface profilometer. The residual stress acting on the EDM specimen was measured by the X-ray diffraction technique. Experimental results indicate that the thickness of the recast layer, and surface roughness are proportional to the power input. The EDM process introduces tensile residual stress on the machined surface. The EDM damage leads to strength degradation

Effect of surface machining on corrosion behavior of SA182-304 in simulated

International Nuclear Information System (INIS)

Zhang Zhiming; Wang Jianqiu; Han Enhou; Ke Wei

2015-01-01

Different machining processes of mechanical parts can cause surface damage layers with different levels. The surface deformed layer can affect the corrosion behavior and service life of these mechanical parts a lot during the following service process. As a result, it is a key issue for the fabrication of the mechanical parts with long life that the selection of proper machining parameters and the removal of surface damage. The purpose of this study is to study the influence of different turning parameters on the corrosion behavior of nuclear grade SA182-304 stainless steel widely used in the advanced pressured water reactors (PWRs). 6 kinds of samples with different surface state are prepared by a lathe with different machining parameters, such as the feed, cutting speed and back engagement of the cutting edge. The high temperature and high pressure immersion test of these samples in the simulated PWR primary watershows that machining processes can affect the microstructure and chemical composition of the formed surface oxide scales a lot. According to the experimental results, the proper machining parameters for the studied SA182-304 are suggested. (authors)

Optimization of electrical parameters of windings used in axial flux electrical machines

International Nuclear Information System (INIS)

Uhrik, M.

2012-01-01

This paper deals with shape optimization of windings used in electrical machines with disc type construction. These machines have short axial length what makes them suitable for use in small wind-power turbines or in-wheel traction drives. Disc type construction of stator offers more possibilities for winding arrangements than are available in classical machines with cylindrical construction. To find out the best winding arrangement for the novel disc type machine construction a series of analytical calculations, simulations and experimental measurements were performed. (Authors)

Machining of bone: Analysis of cutting force and surface roughness by turning process.

Science.gov (United States)

Noordin, M Y; Jiawkok, N; Ndaruhadi, P Y M W; Kurniawan, D

2015-11-01

There are millions of orthopedic surgeries and dental implantation procedures performed every year globally. Most of them involve machining of bones and cartilage. However, theoretical and analytical study on bone machining is lagging behind its practice and implementation. This study views bone machining as a machining process with bovine bone as the workpiece material. Turning process which makes the basis of the actually used drilling process was experimented. The focus is on evaluating the effects of three machining parameters, that is, cutting speed, feed, and depth of cut, to machining responses, that is, cutting forces and surface roughness resulted by the turning process. Response surface methodology was used to quantify the relation between the machining parameters and the machining responses. The turning process was done at various cutting speeds (29-156 m/min), depths of cut (0.03 -0.37 mm), and feeds (0.023-0.11 mm/rev). Empirical models of the resulted cutting force and surface roughness as the functions of cutting speed, depth of cut, and feed were developed. Observation using the developed empirical models found that within the range of machining parameters evaluated, the most influential machining parameter to the cutting force is depth of cut, followed by feed and cutting speed. The lowest cutting force was obtained at the lowest cutting speed, lowest depth of cut, and highest feed setting. For surface roughness, feed is the most significant machining condition, followed by cutting speed, and with depth of cut showed no effect. The finest surface finish was obtained at the lowest cutting speed and feed setting. © IMechE 2015.

MATHEMATICAL MODEL FOR THE STUDY AND DESIGN OF A ROTARY-VANE GAS REFRIGERATION MACHINE

Directory of Open Access Journals (Sweden)

V. V. Trandafilov

2016-08-01

Full Text Available This paper presents a mathematical model of calculating the main parameters the operating cycle, rotary-vane gas refrigerating machine that affect installation, machine control and working processes occurring in it at the specified criteria. A procedure and a graphical method for the rotary-vane gas refrigerating machine (RVGRM are proposed. A parametric study of the main geometric variables and temperature variables on the thermal behavior of the system is analyzed. The model considers polytrope index for the compression and expansion in the chamber. Graphs of the pressure and temperature in the chamber of the angle of rotation of the output shaft are received. The possibility of inclusion in the cycle regenerative heat exchanger is appreciated. The change of the coefficient of performance machine after turning the cycle regenerative heat exchanger is analyzed. It is shown that the installation of a regenerator RVGRM cycle results in increased COP more than 30%. The simulation results show that the proposed model can be used to design and optimize gas refrigerator Stirling.

Machinability of advanced materials

CERN Document Server

Davim, J Paulo

2014-01-01

Machinability of Advanced Materials addresses the level of difficulty involved in machining a material, or multiple materials, with the appropriate tooling and cutting parameters.  A variety of factors determine a material's machinability, including tool life rate, cutting forces and power consumption, surface integrity, limiting rate of metal removal, and chip shape. These topics, among others, and multiple examples comprise this research resource for engineering students, academics, and practitioners.

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

Science.gov (United States)

Hussain, Lal

2018-06-01

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

Momentum transport studies from multi-machine comparisons

International Nuclear Information System (INIS)

Yoshida, M.; Kamada, Y.; Sakamoto, Y.; Kaye, S.; Solomon, W.; Bell, R.E.; Rice, J.; Podpaly, Y.; Reinke, M.L.; Tala, T.; Salmi, A.; Burrell, K.H.; Ferreira, J.; McDonald, D.; Mantica, P.

2012-01-01

A database of toroidal momentum transport on five tokamaks, Alcator C-Mod, DIII-D, JET, NSTX and JT-60U, has been constructed under a wide range of conditions in order to understand the characteristics of toroidal momentum transport coefficients, namely the toroidal momentum diffusivity (χ φ ) and the pinch velocity (V pinch ). Through an inter-machine comparison, the similarities and differences in the properties of χ φ and V pinch among the machines have been clarified. Parametric dependences of these momentum transport coefficients have been investigated over a wide range of plasma parameters taking advantage of the different operation regimes in machines. The approach offers insights into the parametric dependences as follows. The toroidal momentum diffusivity (χ φ ) generally increases with increasing heat diffusivity (χ i ). The correlation is observed over a wide range of χ φ , covering roughly two orders of magnitude, and within each of the machines over the whole radius. Through the inter-machine comparison, it is found that χ φ becomes larger in the outer region of the plasma. Also observed is a general trend for V pinch in tokamaks; the inward pinch velocity (−V pinch ) increases with increasing χ φ . The results that are commonly observed in machines will support a toroidal rotation prediction in future devices. On the other hand, differences among machines have been observed. The toroidal momentum diffusivity, χ φ , is larger than or equal to χ i in JET and JT-60U; on the other hand, χ φ is smaller than or equal to χ i in NSTX, DIII-D and Alcator C-Mod. In DIII-D, the ratio −RV pinch /χ φ at r/a = 0.5–0.6 is about 2, which is small compared with that in other tokamaks (−RV pinch /χ φ ≈ 5). Based on these different observations, parametric dependences of χ φ /χ i , RV pinch /χ φ and χ φ have been investigated in H-mode plasmas. Across the dataset from all machines, the ratio χ φ /χ i tends to be larger in low �

Identification of Mechanical parameters for Resistance Welding Machines

DEFF Research Database (Denmark)

Wu, Pei; Zhang, Wenqi; Bay, Niels

2003-01-01

Mechanical dynamic responses of resistance welding machine have a significant influence on weld quality and electrode service life, it must be considered when the real welding production is carried out or the welding process is simulated. The mathematical models for characterizing the mechanical...

Research of influence of technological parameters on the noise characteristics of the machine for grinding meat

Directory of Open Access Journals (Sweden)

A. K. Pil’nenko

2016-01-01

Full Text Available Noise characteristics (NC machine is one of the main indicators of its quality and competitiveness on the world markets. Scientific and technical work to improve the noise characteristics are relevant and modern. Work focuses on the study of the emergence of the technological equipment of acoustic phenomena. Was selected method of determination and equipment, according to the international standards ISO “Acoustics” taking into account the acoustic properties of the surrounding space. Been established NC machines for grinding meat and fish under operating conditions the in various modes. The maximum value for the characteristic A sound power level (SPL machines produced at idling 79,7 dBA. When the machine comes with the product decline USM on the characteristics A 7.3 dB. It was found exceeding the maximum allowable sound power level at medium frequencies on 2 dB. Impact the components of machines on its NC depending on variables technological factors - the module of elasticity of the product and the effort on the pushrod. Increase modulus of elasticity SPL machines decreases and increase efforts on the pusher Machines USM increases. It was found negative impact construction machines part sat USM. Should be increased rigidity design of the machine.

Parameter Identification of Ship Maneuvering Models Using Recursive Least Square Method Based on Support Vector Machines

Directory of Open Access Journals (Sweden)

Man Zhu

2017-03-01

Full Text Available Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS, are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM, is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.

Machine and plasma diagnostic instrumentation systems for the Tandem Mirror Experiment Upgrade

International Nuclear Information System (INIS)

Coutts, G.W.; Coffield, F.E.; Lang, D.D.; Hornady, R.S.

1981-01-01

To evaluate performance of a second generation Tandem Mirror Machine, an extensive instrumentation system is being designed and installed as part of the major device fabrication. The systems listed will be operational during the start-up phase of the TMX Upgrade machine and provide bench marks for future performance data. In addition to plasma diagnostic instrumentation, machine parameter monitoring systems will be installed prior to machine operation. Simultaneous recording of machine parameters will permit evaluation of plasma parameters sensitive to machine conditions

Summary of the snowmass working group on machine-detector interface

International Nuclear Information System (INIS)

Bharadwaj, V.; Colestock, P.; Cooper, J.; Goderre, G.; Holt, J.

1993-01-01

From the detector point of view, what experimenters need is an outline of the EXPECTED machine parameters tempered with some indication of the POSSIBLE machine parameters. Given guidance from accelerator physicists on the machine, experimenters may get the germ of an idea of how to exploit a particular machine property. Similarly, given some indication of what is important to the experimenters, accelerator physicists may have ideas of how to modify the machine appropriately. The authors discuss a list of machine parameters from the perspective of experimentalists. These include: luminosity; number of bunches/bunch spacing; beam energy; luminosity lifetime; β * /longitudinal emittance; transverse emittance. They also include a summary of tevatron performance - past, present, and projected

A study on ultra-precision machining technique for Al6061-T6 to fabricate space infrared optics

Science.gov (United States)

Ryu, Geun-man; Lee, Gil-jae; Hyun, Sang-won; Sung, Ha-yeong; Chung, Euisik; Kim, Geon-hee

2014-08-01

In this paper, analysis of variance on designed experiments with full factorial design was applied to determine the optimized machining parameters for ultra-precision fabrication of the secondary aspheric mirror, which is one of the key elements of the space cryogenic infrared optics. A single point diamond turning machine (SPDTM, Nanotech 4μpL Moore) was adopted to fabricate the material, AL6061-T6, and the three machining parameters of cutting speed, feed rate and depth of cut were selected. With several randomly assigned experimental conditions, surface roughness of each condition was measured by a non-contact optical profiler (NT2000; Vecco). As a result of analysis using Minitab, the optimum cutting condition was determined as following; cutting speed: 122 m/min, feed rate: 3 mm/min and depth of cut: 1 μm. Finally, a 120 mm diameter aspheric secondary mirror was attached to a particularly designed jig by using mixture of paraffin and wax and successfully fabricated under the optimum machining parameters. The profile of machined surface was measured by a high-accuracy 3-D profilometer(UA3P; Panasonic) and we obtained the geometrical errors of 30.6 nm(RMS) and 262.4 nm(PV), which satisfy the requirements of the space cryogenic infrared optics.

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...

Effects of cutting parameters on machinability characteristics of Ni-based superalloys: a review

Directory of Open Access Journals (Sweden)

Kaya Eren

2017-12-01

Full Text Available Nickel based superalloys offer high strength, corrosion resistance, thermal stability and superb thermal fatigue properties. However, they have been one of the most difficult materials to machine due to these properties. Although we are witnessing improved machining strategies with the developing machining, tooling and inspection technologies, machining of nickel based superalloys is still a challenging task due to in-process strains and post process part quality demands.

Studying depression using imaging and machine learning methods.

Science.gov (United States)

Patel, Meenal J; Khalaf, Alexander; Aizenstein, Howard J

2016-01-01

Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

Effect of operational conditions of electroerosion machining on the surface microgeometry parameters of steels and alloys

International Nuclear Information System (INIS)

Foteev, N.K.

1976-01-01

Studies the influence of pulse duration and a series of operating conditions of a ShGI-40-440 spark-machining generator on changes in the basic surface microgeometry characteristics of components of stainless steel 1Kh18N10T, steel St 45 and hard alloy T14K8. The microgeometry characteristics of spark-machined surfaces differ significantly from the corresponding characteristics of surfaces machined by cutting and vibro-rolling

Study of the stiffness for predicting the accuracy of machine tools; Estudio de la rigidez para la prediccion de la precision de las maquinas-herramientas

Energy Technology Data Exchange (ETDEWEB)

Ortega, N.; Campa, F.J.; Fernandez Valdivielso, A.; Alonso, U.; Olvera, D.; Compean, F.I.

2010-07-01

Machining processes are frequently faced with the challenge of achieving more and more precision and surface qualities. These requirements are usually attained taking into account some process variables, including the cutting parameters and the use or not of refrigerant, leaving aside the mechanical aspects associated with the influence of machine tool itself. There are many sources of error linked with machine-workpiece interaction, but, in general, we can summarize them into two types of error: quasi-static and dynamic. This paper shows the influence of quasi-static error caused by low machine rigidity on the accuracy applied on two very different processes: turning and grinding. For the study of the static stiffness of these two machines, two different methods are proposed, both of them equally valid. The first one is based on separated parameters and the second one on finite elements. (Author).

Vector control of induction machines

CERN Document Server

Robyns, Benoit

2012-01-01

After a brief introduction to the main law of physics and fundamental concepts inherent in electromechanical conversion, ""Vector Control of Induction Machines"" introduces the standard mathematical models for induction machines - whichever rotor technology is used - as well as several squirrel-cage induction machine vector-control strategies. The use of causal ordering graphs allows systematization of the design stage, as well as standardization of the structure of control devices. ""Vector Control of Induction Machines"" suggests a unique approach aimed at reducing parameter sensitivity for

Study on the Optimization and Process Modeling of the Rotary Ultrasonic Machining of Zerodur Glass-Ceramic

Science.gov (United States)

Pitts, James Daniel

Rotary ultrasonic machining (RUM), a hybrid process combining ultrasonic machining and diamond grinding, was created to increase material removal rates for the fabrication of hard and brittle workpieces. The objective of this research was to experimentally derive empirical equations for the prediction of multiple machined surface roughness parameters for helically pocketed rotary ultrasonic machined Zerodur glass-ceramic workpieces by means of a systematic statistical experimental approach. A Taguchi parametric screening design of experiments was employed to systematically determine the RUM process parameters with the largest effect on mean surface roughness. Next empirically determined equations for the seven common surface quality metrics were developed via Box-Behnken surface response experimental trials. Validation trials were conducted resulting in predicted and experimental surface roughness in varying levels of agreement. The reductions in cutting force and tool wear associated with RUM, reported by previous researchers, was experimentally verified to also extended to helical pocketing of Zerodur glass-ceramic.

Revisit of Machine Learning Supported Biological and Biomedical Studies.

Science.gov (United States)

Yu, Xiang-Tian; Wang, Lu; Zeng, Tao

2018-01-01

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.

Study of Material Densification of In718 in the Higher Throughput Parameter Regime

Science.gov (United States)

Cordner, Samuel

2016-01-01

Selective Laser Melting (SLM) is a powder bed fusion additive manufacturing process used increasingly in the aerospace industry to reduce the cost, weight, and fabrication time for complex propulsion components. Previous optimization studies for SLM using the Concept Laser M1 and M2 machines at NASA Marshall Space Flight Center have centered on machine default parameters. The objective of this project is to characterize how heat treatment affects density and porosity from a microscopic point of view. This is performs using higher throughput parameters (a previously unexplored region of the manufacturing operating envelope for this application) on material consolidation. Density blocks were analyzed to explore the relationship between build parameters (laser power, scan speed, and hatch spacing) and material consolidation (assessed in terms of density and porosity). The study also considers the impact of post-processing, specifically hot isostatic pressing and heat treatment, as well as deposition pattern on material consolidation in the higher energy parameter regime. Metallurgical evaluation of specimens will also be presented. This work will contribute to creating a knowledge base (understanding material behavior in all ranges of the AM equipment operating envelope) that is critical to transitioning AM from the custom low rate production sphere it currently occupies to the world of mass high rate production, where parts are fabricated at a rapid rate with confidence that they will meet or exceed all stringent functional requirements for spaceflight hardware. These studies will also provide important data on the sensitivity of material consolidation to process parameters that will inform the design and development of future flight articles using SLM.

Parameter Estimation of the Thermal Network Model of a Machine Tool Spindle by Self-made Bluetooth Temperature Sensor Module

Directory of Open Access Journals (Sweden)

Yuan-Chieh Lo

2018-02-01

Full Text Available Thermal characteristic analysis is essential for machine tool spindles because sudden failures may occur due to unexpected thermal issue. This article presents a lumped-parameter Thermal Network Model (TNM and its parameter estimation scheme, including hardware and software, in order to characterize both the steady-state and transient thermal behavior of machine tool spindles. For the hardware, the authors develop a Bluetooth Temperature Sensor Module (BTSM which accompanying with three types of temperature-sensing probes (magnetic, screw, and probe. Its specification, through experimental test, achieves to the precision ±(0.1 + 0.0029|t| °C, resolution 0.00489 °C, power consumption 7 mW, and size Ø40 mm × 27 mm. For the software, the heat transfer characteristics of the machine tool spindle correlative to rotating speed are derived based on the theory of heat transfer and empirical formula. The predictive TNM of spindles was developed by grey-box estimation and experimental results. Even under such complicated operating conditions as various speeds and different initial conditions, the experiments validate that the present modeling methodology provides a robust and reliable tool for the temperature prediction with normalized mean square error of 99.5% agreement, and the present approach is transferable to the other spindles with a similar structure. For realizing the edge computing in smart manufacturing, a reduced-order TNM is constructed by Model Order Reduction (MOR technique and implemented into the real-time embedded system.

Optimization of machining parameters in dry EDM of EN31 steel

Science.gov (United States)

Brar, G. S.

2018-03-01

Dry electric discharge machining (Dry EDM) is one of the novel EDM technology in which gases namely helium, argon, oxygen, nitrogen etc. are used as a dielectric medium at high pressure instead of oil based liquid dielectric. The present study investigates dry electric discharge machining (with rotary tool) of EN-31 steel to achieve lower tool wear rate (TWR) and better surface roughness (Ra) by performing a set of exploratory experiments with oxygen gas as dielectric. The effect of polarity, discharge current, gas flow pressure, pulse-on time, R.P.M. and gap voltage on the MRR, TWR and surface roughness (Ra) in dry EDM was studied with copper as rotary tool. The significant factors affecting MRR are discharge current and pulse on time. The significant factors affecting TWR are gas flow pressure, pulse on time and R.P.M. TWR was found close to zero in most of the experiments. The significant factors affecting Ra are pulse on time, gas flow pressure and R.P.M. It was found that polarity has nearly zero effect on all the three output variables.

Effect of machining parameters on surface integrity of silicon carbide ceramic using end electric discharge milling and mechanical grinding hybrid machining

International Nuclear Information System (INIS)

Ji, Renjie; Liu, Yonghong; Zhang, Yanzhen; Cai, Baoping; Li, Xiaopeng; Zheng, Chao

2013-01-01

A novel hybrid process that integrates end electric discharge (ED) milling and mechanical grinding is proposed. The process is able to effectively machine a large surface area on SiC ceramic with good surface quality and fine working environmental practice. The polarity, pulse on-time, and peak current are varied to explore their effects on the surface integrity, such as surface morphology, surface roughness, micro-cracks, and composition on the machined surface. The results show that positive tool polarity, short pulse on-time, and low peak current cause a fine surface finish. During the hybrid machining of SiC ceramic, the material is mainly removed by end ED milling at rough machining mode, whereas it is mainly removed by mechanical grinding at finish machining mode. Moreover, the material from the tool can transfer to the workpiece, and a combination reaction takes place during machining.

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

Directory of Open Access Journals (Sweden)

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.

Studying depression using imaging and machine learning methods

Directory of Open Access Journals (Sweden)

Meenal J. Patel

2016-01-01

Full Text Available Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1 presents a background on depression, imaging, and machine learning methodologies; (2 reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3 suggests directions for future depression-related studies.

Investigation of permanent magnet machines for downhole applications: Design, prototype and testing of a flux-switching permanent magnet machine

Energy Technology Data Exchange (ETDEWEB)

Chen, Anyuan

2011-01-15

downhole applications. Flux-switching PM (FSPM) machines, which have the PMs located on the stator and are therefore more reliable, can theoretically also exhibit high torque density and relatively high efficiency. This thesis has put an emphasis on studying this type of machine. Two FSPM machines have been investigated in detail and compared by analytical method, FEM simulation and prototype measurements. Their operating principle and important design parameters are also presented. A lumped parameter magnetic circuit model for designing a high-torque FSPM machine is newly introduced and the designed machine is verified by FEM simulations. A prototype machine with an outer diameter of 100 mm and an axial length of 200 mm is built in the laboratory and tested at room temperature. Based on that, the machine performance at an ambient temperature of 150 C is also predicted. The results show that the FSPM machine can provide a high torque density with slight compromise of efficiency and power factor. Choosing a proper machine type is significantly dependent on the application specifications. The presented results in this thesis can be used as a reference for selecting the best machine type for a specific downhole case. (Author)

Identifying product order with restricted Boltzmann machines

Science.gov (United States)

Rao, Wen-Jia; Li, Zhenyu; Zhu, Qiong; Luo, Mingxing; Wan, Xin

2018-03-01

Unsupervised machine learning via a restricted Boltzmann machine is a useful tool in distinguishing an ordered phase from a disordered phase. Here we study its application on the two-dimensional Ashkin-Teller model, which features a partially ordered product phase. We train the neural network with spin configuration data generated by Monte Carlo simulations and show that distinct features of the product phase can be learned from nonergodic samples resulting from symmetry breaking. Careful analysis of the weight matrices inspires us to define a nontrivial machine-learning motivated quantity of the product form, which resembles the conventional product order parameter.

Disruption Warning Database Development and Exploratory Machine Learning Studies on Alcator C-Mod

Science.gov (United States)

Montes, Kevin; Rea, Cristina; Granetz, Robert

2017-10-01

A database of about 1800 shots from the 2015 campaign on the Alcator C-Mod tokamak is assembled, including disruptive and non-disruptive discharges. The database consists of 40 relevant plasma parameters with data taken from 160k time slices. In order to investigate the possibility of developing a robust disruption prediction algorithm that is tokamak-independent, we focused machine learning studies on a subset of dimensionless parameters such as βp, n /nG , etc. The Random Forests machine learning algorithm provides insight on the available data set by ranking the relative importance of the input features. Its application on the C-Mod database, however, reveals that virtually no one parameter has more importance than any other, and that its classification algorithm has a low rate of successfully predicted samples, as well as poor false positive and false negative rates. Comparing the analysis of this algorithm on the C-Mod database with its application to a similar database on DIII-D, we conclude that disruption prediction may not be feasible on C-Mod. This conclusion is supported by empirical observations that most C-Mod disruptions are caused by radiative collapse due to molybdenum from the first wall, which happens on just a 1-2ms timescale. Supported by the US Dept. of Energy under DE-FC02-99ER54512 and DE-FC02-04ER54698.

Machine Protection with a 700 MJ Beam

Science.gov (United States)

Baer, T.; Schmidt, R.; Wenninger, J.; Wollmann, D.; Zerlauth, M.

After the high luminosity upgrade of the LHC, the stored energy per proton beam will increase by a factor of two as compared to the nominal LHC. Therefore, many damage studies need to be revisited to ensure a safe machine operation with the new beam parameters. Furthermore, new accelerator equipment like crab cavities might cause new failure modes, which are not sufficiently covered by the current machine protection system of the LHC. These failure modes have to be carefully studied and mitigated by new protection systems. Finally the ambitious goals for integrated luminosity delivered to the experiments during the era of HL-LHC require an increase of the machine availability without jeopardizing equipment protection.

Human-machine interface upgrade

International Nuclear Information System (INIS)

Kropik, M.; Matejka, K.; Sklenka, L.; Chab, V.

2002-01-01

The article describes a new human-machine interface that was installed at the VR-1 training reactor. The human-machine interface upgrade was completed in the summer 2001. The interface was designed with respect to functional, ergonomic and aesthetic requirements. The interface is based on a personal computer equipped with two displays. One display enables alphanumeric communication between the reactor operator and the nuclear reactor I and C. The second display is a graphical one. It presents the status of the reactor, principal parameters (as power, period), control rods positions, course of the reactor power. Furthermore, it is possible to set parameters, to show the active core configuration, to perform reactivity calculations, etc. The software for the new human-machine interface was produced with the InTouch developing tool of the Wonder-Ware Company. It is possible to switch the language of the interface between Czech and English because of many foreign students and visitors to the reactor. Microcomputer based communication units with proper software were developed to connect the new human-machine interface with the present reactor I and C. The new human-machine interface at the VR-1 training reactor improves the comfort and safety of the reactor utilisation, facilitates experiments and training, and provides better support for foreign visitors. (orig.)

Study on effect of tool electrodes on surface finish during electrical discharge machining of Nitinol

Science.gov (United States)

Sahu, Anshuman Kumar; Chatterjee, Suman; Nayak, Praveen Kumar; Sankar Mahapatra, Siba

2018-03-01

Electrical discharge machining (EDM) is a non-traditional machining process which is widely used in machining of difficult-to-machine materials. EDM process can produce complex and intrinsic shaped component made of difficult-to-machine materials, largely applied in aerospace, biomedical, die and mold making industries. To meet the required applications, the EDMed components need to possess high accuracy and excellent surface finish. In this work, EDM process is performed using Nitinol as work piece material and AlSiMg prepared by selective laser sintering (SLS) as tool electrode along with conventional copper and graphite electrodes. The SLS is a rapid prototyping (RP) method to produce complex metallic parts by additive manufacturing (AM) process. Experiments have been carried out varying different process parameters like open circuit voltage (V), discharge current (Ip), duty cycle (τ), pulse-on-time (Ton) and tool material. The surface roughness parameter like average roughness (Ra), maximum height of the profile (Rt) and average height of the profile (Rz) are measured using surface roughness measuring instrument (Talysurf). To reduce the number of experiments, design of experiment (DOE) approach like Taguchi’s L27 orthogonal array has been chosen. The surface properties of the EDM specimen are optimized by desirability function approach and the best parametric setting is reported for the EDM process. Type of tool happens to be the most significant parameter followed by interaction of tool type and duty cycle, duty cycle, discharge current and voltage. Better surface finish of EDMed specimen can be obtained with low value of voltage (V), discharge current (Ip), duty cycle (τ) and pulse on time (Ton) along with the use of AlSiMg RP electrode.

Feasibility Studies of Alpha-Channeling in Mirror Machines

International Nuclear Information System (INIS)

Zhmoginov, A.I.; Fisch, N.J.

2010-01-01

The linear magnetic trap is an attractive concept both for fusion reactors and for other plasma applications due to its relative engineering simplicity and high-beta operation. Applying the α-channeling technique to linear traps, such as mirror machines, can benefit this concept by efficiently redirecting α particle energy to fuel ion heating or by otherwise sustaining plasma confinement, thus increasing the effective fusion reactivity. To identify waves suitable for α-channeling a rough optimization of the energy extraction rate with respect to the wave parameters is performed. After the optimal regime is identified, a systematic search for modes with similar parameters in mirror plasmas is performed, assuming quasi-longitudinal or quasi-transverse wave propagation. Several modes suitable for α particle energy extraction are identified for both reactor designs and for proof- of-principle experiments.

A reliability-based preventive maintenance methodology for the projection spot welding machine

Directory of Open Access Journals (Sweden)

Fayzimatov Ulugbek

2018-06-01

Full Text Available An effective operations of a projection spot welding (PSW machine is closely related to the effec-tiveness of the maintenance. Timely maintenance can prevent failures and improve reliability and maintainability of the machine. Therefore, establishing the maintenance frequency for the welding machine is one of the most important tasks for plant engineers. In this regard, reliability analysis of the welding machine can be used to establish preventive maintenance intervals (PMI and to identify the critical parts of the system. In this reliability and maintainability study, analysis of the PSW machine was carried out. The failure and repair data for analysis were obtained from automobile manufacturing company located in Uzbekistan. The machine was divided into three main sub-systems: electrical, pneumatic and hydraulic. Different distributions functions for all sub-systems was tested and their parameters tabulated. Based on estimated parameters of the analyzed distribu-tions, PMI for the PSW machines sub-systems at different reliability levels was calculated. Finally, preventive measures for enhancing the reliability of the PSW machine sub-systems are suggested.

Numerical modeling and optimization of machining duplex stainless steels

Directory of Open Access Journals (Sweden)

Rastee D. Koyee

2015-01-01

Full Text Available The shortcomings of the machining analytical and empirical models in combination with the industry demands have to be fulfilled. A three-dimensional finite element modeling (FEM introduces an attractive alternative to bridge the gap between pure empirical and fundamental scientific quantities, and fulfill the industry needs. However, the challenging aspects which hinder the successful adoption of FEM in the machining sector of manufacturing industry have to be solved first. One of the greatest challenges is the identification of the correct set of machining simulation input parameters. This study presents a new methodology to inversely calculate the input parameters when simulating the machining of standard duplex EN 1.4462 and super duplex EN 1.4410 stainless steels. JMatPro software is first used to model elastic–viscoplastic and physical work material behavior. In order to effectively obtain an optimum set of inversely identified friction coefficients, thermal contact conductance, Cockcroft–Latham critical damage value, percentage reduction in flow stress, and Taylor–Quinney coefficient, Taguchi-VIKOR coupled with Firefly Algorithm Neural Network System is applied. The optimization procedure effectively minimizes the overall differences between the experimentally measured performances such as cutting forces, tool nose temperature and chip thickness, and the numerically obtained ones at any specified cutting condition. The optimum set of input parameter is verified and used for the next step of 3D-FEM application. In the next stage of the study, design of experiments, numerical simulations, and fuzzy rule modeling approaches are employed to optimize types of chip breaker, insert shapes, process conditions, cutting parameters, and tool orientation angles based on many important performances. Through this study, not only a new methodology in defining the optimal set of controllable parameters for turning simulations is introduced, but also

Development of Web-based Virtual Training Environment for Machining

Science.gov (United States)

Yang, Zhixin; Wong, S. F.

2010-05-01

With the booming in the manufacturing sector of shoe, garments and toy, etc. in pearl region, training the usage of various facilities and design the facility layout become crucial for the success of industry companies. There is evidence that the use of virtual training may provide benefits in improving the effect of learning and reducing risk in the physical work environment. This paper proposed an advanced web-based training environment that could demonstrate the usage of a CNC machine in terms of working condition and parameters selection. The developed virtual environment could provide training at junior level and advanced level. Junior level training is to explain machining knowledge including safety factors, machine parameters (ex. material, speed, feed rate). Advanced level training enables interactive programming of NG coding and effect simulation. Operation sequence was used to assist the user to choose the appropriate machining condition. Several case studies were also carried out with animation of milling and turning operations.

Application of target costing in machining

Science.gov (United States)

Gopalakrishnan, Bhaskaran; Kokatnur, Ameet; Gupta, Deepak P.

2004-11-01

In today's intensely competitive and highly volatile business environment, consistent development of low cost and high quality products meeting the functionality requirements is a key to a company's survival. Companies continuously strive to reduce the costs while still producing quality products to stay ahead in the competition. Many companies have turned to target costing to achieve this objective. Target costing is a structured approach to determine the cost at which a proposed product, meeting the quality and functionality requirements, must be produced in order to generate the desired profits. It subtracts the desired profit margin from the company's selling price to establish the manufacturing cost of the product. Extensive literature review revealed that companies in automotive, electronic and process industries have reaped the benefits of target costing. However target costing approach has not been applied in the machining industry, but other techniques based on Geometric Programming, Goal Programming, and Lagrange Multiplier have been proposed for application in this industry. These models follow a forward approach, by first selecting a set of machining parameters, and then determining the machining cost. Hence in this study we have developed an algorithm to apply the concepts of target costing, which is a backward approach that selects the machining parameters based on the required machining costs, and is therefore more suitable for practical applications in process improvement and cost reduction. A target costing model was developed for turning operation and was successfully validated using practical data.

The Impact Of Surface Shape Of Chip-Breaker On Machined Surface

Science.gov (United States)

Šajgalík, Michal; Czán, Andrej; Martinček, Juraj; Varga, Daniel; Hemžský, Pavel; Pitela, David

2015-12-01

Machined surface is one of the most used indicators of workpiece quality. But machined surface is influenced by several factors such as cutting parameters, cutting material, shape of cutting tool or cutting insert, micro-structure of machined material and other known as technological parameters. By improving of these parameters, we can improve machined surface. In the machining, there is important to identify the characteristics of main product of these processes - workpiece, but also the byproduct - the chip. Size and shape of chip has impact on lifetime of cutting tools and its inappropriate form can influence the machine functionality and lifetime, too. This article deals with elimination of long chip created when machining of shaft in automotive industry and with impact of shape of chip-breaker on shape of chip in various cutting conditions based on production requirements.

Consequences of heavy machining vis à vis the machine structure – typical applications

International Nuclear Information System (INIS)

Leuch, M

2011-01-01

StarragHeckert has built 5 axis machines since the middle of the 80s for heavy duty milling. The STC-Centres are predominantly utilised in the aerospace industry, especially for milling structural workpieces, casings or Impellers made out of titanium and steel. StarragHeckert has a history of building machines for high performance milling. The machining of these components includes high forces thus spreading the wheat from the chaff. Although FEM calculations and multi-body simulations are carried out in the early stages of development, this paper will illustrate how the real process stability with modal analysis and cutting trials is determined. The experiment observes chatter stability to identify if the machine devices are adequate for the application or if the design has to be improved. Machining parameters of industrial applications are demonstrating the process stability for five axis heavy duties milling of StarragHeckert machine.

Study of discharge in quiescent plasma machine of the INPE

International Nuclear Information System (INIS)

Ferreira, J.G.; Ferreira, J.L.; Ludwig, G.O.; Maciel, H.S.

1988-12-01

Measurements of principal plasma parameters produced by quiescent plasma machine of the Instituto de Pesquisas Espaciais (INPE) for current of 500 mA and several values of pressure and discharge power are presented. A qualitative interpretation for obtained results is done and a simple model for plasma density is compared with experimental values. The conditions of cathode operation are also investigated. (M.C.K.)

Comparative Study of White Layer Characteristics for Static and Rotating Workpiece during Electric Discharge Machining

Directory of Open Access Journals (Sweden)

SHAHID MEHMOOD

2017-10-01

Full Text Available EDMed (Electric Discharge Machined surfaces are unique in their appearance and metallurgical characteristics, which depend on different parameter such as electric parameters, flushing method, and dielectric type. Conventionally, in static workpiece method the EDM (Electric Discharge Machining is performed by submerging both of the tool and workpiece in dielectric liquid and side flushing is provided by impinging pressurized dielectric liquid into the gap. Another flushing method has been investigated in this study, in which, instead of side flushing the rotation motion is provided to the workpiece. Surface characteristics for both flushing methods are determined and compared in this study. The investigated surface characteristics are: surface roughness, crater size, surface morphology, white layer thickness and composition. These investigations are performed using optical and SEM (Scanning Electron Microscope. Statistical confidence limits are determined for scattered data of surface roughness. It is found that the white layer thickness and surface roughness are directly proportional to discharge current for both flushing methods. The comparison has shown that the side flushing of statics workpiece gives thicker white layer and lower surface finish as compared to the flushing caused by the rotation of workpiece

Comparative study of white layer characteristics for static and rotating workpiece during electric discharge machining

International Nuclear Information System (INIS)

Mehmood, S.; Shah, M.; Anjum, N.A.

2017-01-01

EDMed (Electric Discharge Machined) surfaces are unique in their appearance and metallurgical characteristics, which depend on different parameter such as electric parameters, flushing method, and dielectric type. Conventionally, in static workpiece method the EDM (Electric Discharge Machining) is performed by submerging both of the tool and workpiece in dielectric liquid and side flushing is provided by impinging pressurized dielectric liquid into the gap. Another flushing method has been investigated in this study, in which, instead of side flushing the rotation motion is provided to the workpiece. Surface characteristics for both flushing methods are determined and compared in this study. The investigated surface characteristics are: surface roughness, crater size, surface morphology, white layer thickness and composition. These investigations are performed using optical and SEM (Scanning Electron Microscope). Statistical confidence limits are determined for scattered data of surface roughness. It is found that the white layer thickness and surface roughness are directly proportional to discharge current for both flushing methods. The comparison has shown that the side flushing of statics workpiece gives thicker white layer and lower surface finish as compared to the flushing caused by the rotation of workpiece. (author)

The phaco machine: analysing new technology.

Science.gov (United States)

Fishkind, William J

2013-01-01

The phaco machine is frequently overlooked as the crucial surgical instrument it is. Understanding how to set parameters is initiated by understanding fundamental concepts of machine function. This study analyses the critical concepts of partial occlusion phaco, occlusion phaco and pump technology. In addition, phaco energy categories as well as variations of phaco energy production are explored. Contemporary power modulations and pump controls allow for the enhancement of partial occlusion phacoemulsification. These significant changes in the anterior chamber dynamics produce a balanced environment for phaco; less complications; and improved patient outcomes.

Characterisation of Dynamic Mechanical Properties of Resistance Welding Machines

DEFF Research Database (Denmark)

Wu, Pei; Zhang, Wenqi; Bay, Niels

2005-01-01

characterizing the dynamic mechanical characteristics of resistance welding machines is suggested, and a test set-up is designed determining the basic, independent machine parameters required in the model. The model is verified by performing a series of mechanical tests as well as real projection welds.......The dynamic mechanical properties of a resistance welding machine have significant influence on weld quality, which must be considered when simulating the welding process numerically. However, due to the complexity of the machine structure and the mutual coupling of components of the machine system......, it is very difficult to measure or calculate the basic, independent machine parameters required in a mathematical model of the machine dynamics, and no test method has so far been presented in literature, which can be applied directly in an industrial environment. In this paper, a mathematical model...

MLBCD: a machine learning tool for big clinical data.

Science.gov (United States)

Luo, Gang

2015-01-01

Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. The paper describes MLBCD's design in detail. By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

DESIGN ANALYSIS OF ELECTRICAL MACHINES THROUGH INTEGRATED NUMERICAL APPROACH

Directory of Open Access Journals (Sweden)

ARAVIND C.V.

2016-02-01

Full Text Available An integrated design platform for the newer type of machines is presented in this work. The machine parameters are evaluated out using developed modelling tool. With the machine parameters, the machine is modelled using computer aided tool. The designed machine is brought to simulation tool to perform electromagnetic and electromechanical analysis. In the simulation, conditions setting are performed to setup the materials, meshes, rotational speed and the excitation circuit. Electromagnetic analysis is carried out to predict the behavior of the machine based on the movement of flux in the machines. Besides, electromechanical analysis is carried out to analyse the speed-torque characteristic, the current-torque characteristic and the phase angle-torque characteristic. After all the results are analysed, the designed machine is used to generate S block function that is compatible with MATLAB/SIMULINK tool for the dynamic operational characteristics. This allows the integration of existing drive system into the new machines designed in the modelling tool. An example of the machine design is presented to validate the usage of such a tool.

Virtual machining considering dimensional, geometrical and tool deflection errors in three-axis CNC milling machines

OpenAIRE

Soori, Mohsen; Arezoo, Behrooz; Habibi, Mohsen

2014-01-01

Virtual manufacturing systems can provide useful means for products to be manufactured without the need of physical testing on the shop floor. As a result, the time and cost of part production can be decreased. There are different error sources in machine tools such as tool deflection, geometrical deviations of moving axis and thermal distortions of machine tool structures. Some of these errors can be decreased by controlling the machining process and environmental parameters. However other e...

Virtual machining considering dimensional, geometrical and tool deflection errors in three-axis CNC milling machines

OpenAIRE

Soori, Mohsen; Arezoo, Behrooz; Habibi, Mohsen

2016-01-01

Virtual manufacturing systems can provide useful means for products to be manufactured without the need of physical testing on the shop floor. As a result, the time and cost of part production can be decreased. There are different error sources in machine tools such as tool deflection, geometrical deviations of moving axis and thermal distortions of machine tool structures. Some of these errors can be decreased by controlling the machining process and environmental parameters. However other e...

Surface quality analysis of die steels in powder-mixed electrical discharge machining using titan powder in fine machining

Directory of Open Access Journals (Sweden)

Banh Tien Long

2016-06-01

Full Text Available Improving the quality of surface molds after electrical discharge machining is still being considered by many researchers. Powder-mixed dielectric in electrical discharge machining showed that it is one of the processing methods with high efficiency. This article reports on the results of surface quality of mold steels after powder-mixed electrical discharge machining using titanium powder in fine machining. The process parameters such as electrode material, workpiece material, electrode polarity, pulse on-time, pulse off-time, current, and titanium powder concentration were considered in the research. These materials are most commonly used with die-sinking electrical discharge machining in the manufacture of molds and has been selected as the subject of research: workpiece materials were SKD61, SKT4, and SKD11 mold steels, and electrode materials were copper and graphite. Taguchi’s method is used to design experiments. The influence of the parameters on surface roughness was evaluated through the average value and ratio (S/N. Results showed that the parameters such as electrical current, electrode material, pulse on-time, electrode polarity, and interaction between the electrode materials with concentration powder mostly influence surface roughness and surface roughness at optimal parameters SRopt = 1.73 ± 0.39 µm. Analysis of the surface layer after powder-mixed electrical discharge machining using titanium powder in optimal conditions has shown that the white layer with more uniform thickness and increased hardness (≈861.0 HV, and amount and size of microscopic cracks, is reduced. This significantly leads to the increase in the quality of the surface layer.

Effect of changing polarity of graphite tool/ Hadfield steel workpiece couple on machining performances in die sinking EDM

Directory of Open Access Journals (Sweden)

Özerkan Haci Bekir

2017-01-01

Full Text Available In this study, machining performance ouput parameters such as machined surface roughness (SR, material removal rate (MRR, tool wear rate (TWR, were experimentally examined and analyzed with the diversifying and changing machining parameters in (EDM. The processing parameters (input par. of this research are stated as tool material, peak current (I, pulse duration (ton and pulse interval (toff. The experimental machinings were put into practice by using Hadfield steel workpiece (prismatic and cylindrical graphite electrodes with kerosene dielectric at different machining current, polarity and pulse time settings. The experiments have shown that the type of tool material, polarity (direct polarity forms higher MRR, SR and TWR, current (high current lowers TWR and enhances MRR, TWR and pulse on time (ton=48□s is critical threshold value for MRR and TWR were influential on machining performance in electrical discharge machining.

Characterization and modeling of 2D-glass micro-machining by spark-assisted chemical engraving (SACE) with constant velocity

International Nuclear Information System (INIS)

Didar, Tohid Fatanat; Dolatabadi, Ali; Wüthrich, Rolf

2008-01-01

Spark-assisted chemical engraving (SACE) is an unconventional micro-machining technology based on electrochemical discharge used for micro-machining nonconductive materials. SACE 2D micro-machining with constant speed was used to machine micro-channels in glass. Parameters affecting the quality and geometry of the micro-channels machined by SACE technology with constant velocity were presented and the effect of each of the parameters was assessed. The effect of chemical etching on the geometry of micro-channels under different machining conditions has been studied, and a model is proposed for characterization of the micro-channels as a function of machining voltage and applied speed

Machining of glass fiber reinforced polyamide

Directory of Open Access Journals (Sweden)

2007-12-01

Full Text Available The machinability of a 30 wt% glass fiber reinforced polyamide (PA was investigated by means of drilling tests. A disk was cut from an extruded rod and drilled on the flat surface: thrust was acquired during drilling at different drilling speed, feed rate and drill diameter. Differential scanning calorimetry (DSC and indentation were used to characterize PA so as to evaluate the intrinsic lack of homogeneity of the extruded material. In conclusion, it was observed that the chip formation mechanism affects the thrust dependence on the machining parameters. A traditional modeling approach is able to predict thrust only in presence of a continuous chip. In some conditions, thrust increases as drilling speed increases and feed rate decreases; this evidence suggests not to consider the general scientific approach which deals the machining of plastics in analogy with metals. Moreover, the thrust can be significantly affected by the workpiece fabrication effect, as well as by the machining parameters; therefore, the fabrication effect is not negligible in the definition of an optimum for the machining process.

Materials study for reacting plasma machine

International Nuclear Information System (INIS)

Kamada, Kohji; Hamada, Yasuji

1982-01-01

A new reacting plasma machine is designed, and will be constructed at the Institute of Plasma Physics, Nagoya University. It is important to avoid the activation of the materials for the machine, accordingly, aluminum alloy has been considered as the material since the induced activity of aluminum due to 14 MeV neutrons is small. The vacuum chamber of the new machine consists of four modules, and the remote control of each module is considered. However, the cost of the remote control of modules is expensive. To minimize the dependence on the remote control, the use of aluminum alloy is considered as the first step. The low electrical resistivity, over-ageing, weak mechanical strength and eddy current characteristics of aluminum alloy must be improved. The physical and electrical properties of various aluminum alloys have been investigated. Permeability of hydrogen through aluminum, the recycling characteristics and surface coating materials have been also studied. (Kato, T.)

A MACHINE-LEARNING METHOD TO INFER FUNDAMENTAL STELLAR PARAMETERS FROM PHOTOMETRIC LIGHT CURVES

International Nuclear Information System (INIS)

Miller, A. A.; Bloom, J. S.; Richards, J. W.; Starr, D. L.; Lee, Y. S.; Butler, N. R.; Tokarz, S.; Smith, N.; Eisner, J. A.

2015-01-01

A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are >10 9 photometrically cataloged sources, yet modern spectroscopic surveys are limited to ∼few× 10 6 targets. As we approach the Large Synoptic Survey Telescope era, new algorithmic solutions are required to cope with the data deluge. Here we report the development of a machine-learning framework capable of inferring fundamental stellar parameters (T eff , log g, and [Fe/H]) using photometric-brightness variations and color alone. A training set is constructed from a systematic spectroscopic survey of variables with Hectospec/Multi-Mirror Telescope. In sum, the training set includes ∼9000 spectra, for which stellar parameters are measured using the SEGUE Stellar Parameters Pipeline (SSPP). We employed the random forest algorithm to perform a non-parametric regression that predicts T eff , log g, and [Fe/H] from photometric time-domain observations. Our final optimized model produces a cross-validated rms error (RMSE) of 165 K, 0.39 dex, and 0.33 dex for T eff , log g, and [Fe/H], respectively. Examining the subset of sources for which the SSPP measurements are most reliable, the RMSE reduces to 125 K, 0.37 dex, and 0.27 dex, respectively, comparable to what is achievable via low-resolution spectroscopy. For variable stars this represents a ≈12%-20% improvement in RMSE relative to models trained with single-epoch photometric colors. As an application of our method, we estimate stellar parameters for ∼54,000 known variables. We argue that this method may convert photometric time-domain surveys into pseudo-spectrographic engines, enabling the construction of extremely detailed maps of the Milky Way, its structure, and history

A review on feasibility study of ultrasonic assisted machining on aircraft component manufacturing

Science.gov (United States)

Hafiz, M. S. A.; Kawaz, M. H. A.; Mohamad, W. N. F.; Kasim, M. S.; Izamshah, R.; Saedon, J. B.; Mohamed, S. B.

2017-12-01

Inconel 718 has been widely used in aerospace because of its excellent mechanical properties such as good corrosion resistance, strong creep resistance and high fatigue strength. However, these excellent properties also lead to heavy tool damage and high cutting force in the milling process. There is no reported investigation on ultrasonic assisted machining (UAM) of Inconel 718 parts. In this paper, UAM is proposed as the potential technique to reduce tool damage and the cutting force of Inconel 718 parts. This review paper provides an overview of UAM to investigate the relationship between the tool wear and the cutting force with ultrasonic vibration compared to without ultrasonic vibration assisted. Throughout the study, the UAM scopes are related to the tool life of coated carbide insert, the force generated during the cutting process and also the final surface finish of the workpiece by using various parameters during the machining activity.

[A new machinability test machine and the machinability of composite resins for core built-up].

Science.gov (United States)

Iwasaki, N

2001-06-01

A new machinability test machine especially for dental materials was contrived. The purpose of this study was to evaluate the effects of grinding conditions on machinability of core built-up resins using this machine, and to confirm the relationship between machinability and other properties of composite resins. The experimental machinability test machine consisted of a dental air-turbine handpiece, a control weight unit, a driving unit of the stage fixing the test specimen, and so on. The machinability was evaluated as the change in volume after grinding using a diamond point. Five kinds of core built-up resins and human teeth were used in this study. The machinabilities of these composite resins increased with an increasing load during grinding, and decreased with repeated grinding. There was no obvious correlation between the machinability and Vickers' hardness; however, a negative correlation was observed between machinability and scratch width.

Testing and Modeling of Mechanical Characteristics of Resistance Welding Machines

DEFF Research Database (Denmark)

Wu, Pei; Zhang, Wenqi; Bay, Niels

2003-01-01

for both upper and lower electrode systems. This has laid a foundation for modeling the welding process and selecting the welding parameters considering the machine factors. The method is straightforward and easy to be applied in industry since the whole procedure is based on tests with no requirements......The dynamic mechanical response of resistance welding machine is very important to the weld quality in resistance welding especially in projection welding when collapse or deformation of work piece occurs. It is mainly governed by the mechanical parameters of machine. In this paper, a mathematical...... model for characterizing the dynamic mechanical responses of machine and a special test set-up called breaking test set-up are developed. Based on the model and the test results, the mechanical parameters of machine are determined, including the equivalent mass, damping coefficient, and stiffness...

Superconducting machines. Chapter 4

International Nuclear Information System (INIS)

Appleton, A.D.

1977-01-01

A brief account is given of the principles of superconductivity and superconductors. The properties of Nb-Ti superconductors and the method of flux stabilization are described. The basic features of superconducting d.c. machines are illustrated by the use of these machines for ship propulsion, steel-mill drives, industrial drives, aluminium production, and other d.c. power supplies. Superconducting a.c. generators and their design parameters are discussed. (U.K.)

Modeling and simulation of five-axis virtual machine based on NX

Science.gov (United States)

Li, Xiaoda; Zhan, Xianghui

2018-04-01

Virtual technology in the machinery manufacturing industry has shown the role of growing. In this paper, the Siemens NX software is used to model the virtual CNC machine tool, and the parameters of the virtual machine are defined according to the actual parameters of the machine tool so that the virtual simulation can be carried out without loss of the accuracy of the simulation. How to use the machine builder of the CAM module to define the kinematic chain and machine components of the machine is described. The simulation of virtual machine can provide alarm information of tool collision and over cutting during the process to users, and can evaluate and forecast the rationality of the technological process.

Case study of virtual reality in CNC machine tool exhibition

Directory of Open Access Journals (Sweden)

Kao Yung-Chou

2017-01-01

Full Text Available Exhibition and demonstration are generally used in the promotion and sale-assistance of manufactured products. However, the transportation cost of the real goods from the vender factory to the exposition venue is generally expensive for huge and heavy commodity. With the advancement of computing, graphics, mobile apps, and mobile hardware the 3D visibility technology is getting more and more popular to be adopted in visual-assisted communication such as amusement games. Virtual reality (VR technology has therefore being paid great attention in emulating expensive small and/or huge and heavy equipment. Virtual reality can be characterized as 3D extension with Immersion, Interaction and Imagination. This paper was then be focused on the study of virtual reality in the assistance of CNC machine tool demonstration and exhibition. A commercial CNC machine tool was used in this study to illustrate the effectiveness and usability of using virtual reality for an exhibition. The adopted CNC machine tool is a large and heavy mill-turn machine with the width up to eleven meters and weighted about 35 tons. A head-mounted display (HMD was attached to the developed VR CNC machine tool for the immersion viewing. A user can see around the 3D scene of the large mill-turn machine and the operation of the virtual CNC machine can be actuated by bare hand. Coolant was added to demonstrate more realistic operation while collision detection function was also added to remind the operator. The developed VR demonstration system has been presented in the 2017 Taipei International Machine Tool Show (TIMTOS 2017. This case study has shown that young engineers and/or students are very impressed by the VR-based demonstration while elder persons could not adapt themselves easily to the VR-based scene because of eyesight issues. However, virtual reality has successfully being adopted and integrated with the CNC machine tool in an international show. Another machine tool on

Depth analysis of mechanically machined flaws on steam generator tubings using multi-parameter algorithm

International Nuclear Information System (INIS)

Nam Gung, Chan; Lee, Yoon Sang; Hwang, Seong Sik; Kim, Hong Pyo

2004-01-01

The eddy current testing (ECT) is a nondestructive technique. It is used for evaluation of material's integrity, especially, steam generator (SG) tubing in nuclear plants, due to their rapid inspection, safe and easy operation. For depth measurement of defects, we prepared Electro Discharge Machined (EDM) notches that have several of defects and applied multi-parameter (MP) algorithm. It is a crack shape estimation program developed in Argonne National Laboratory (ANL). To evaluate the MP algorithm, we compared defect profile with fractography of the defects. In the following sections, we described the basic structure of a computer-aided data analysis algorithm used as means of more accurate and efficient processing of ECT data, and explained the specification of a standard calibration. Finally, we discussed the accuracy of estimated depth profile compared with conventional ECT method

Analysis of the Chip Geometry in Dry Machining of Aeronautical Aluminum Alloys

Directory of Open Access Journals (Sweden)

Francisco Javier Trujillo Vilches

2017-01-01

Full Text Available Aluminum alloys are widely used in the manufacturing of structural parts for aircraft, frequently in combination with other materials such as CFRP (Carbon Fiber Reinforced Polymer, to form FML (Fiber Metal Laminates structures (CFRP/Al. The dry machining of these structures presents several problems, some of which are related to chip evacuation, either when machining aluminum alloys as an isotropic material, or during hybridization with composites. In this work, a study of the way in which cutting parameters influence the chip morphology in the dry machining of UNS A97075-T6 (Al-Zn and UNS A92024-T3 (Al-Cu alloys, is performed. Thus, different geometric parameters of the chip morphology have been obtained, and their evolution with feed has been analysed. Finally, the different relationships which occur between these geometric parameters and feed, have been obtained. These relationships allow a prediction of the evolution of some of the geometric parameters of the chip, as a function of feed.

Study on ultra-fine w-EDM with on-machine measurement-assisted

International Nuclear Information System (INIS)

Chen Shuntong; Yang Hongye

2011-01-01

The purpose of this study was to develop the on-machine measurement techniques so as to precisely fabricate micro intricate part using ultra-fine w-EDM. The measurement-assisted approach which employs an automatic optical inspection (AOI) is incorporated to ultra-fine w-EDM process to on-machine detect the machining error for next re-machining. The AOI acquires the image through a high resolution CCD device from the contour of the workpiece after roughing in order to further process and recognize the image for determining the residual. This facilitates the on-machine error detection and compensation re-machining. The micro workpiece and electrode are not repositioned during machining. A fabrication for a micro probe of 30-μm diameter is rapidly machined and verified successfully. Based on the proposed technique, on-machine measurement with AOI has been realized satisfactorily.

Optimization of AVR Parameters of a Multi-machine Power System ...

African Journals Online (AJOL)

user1

Keywords: multi-machine power system stability, AVR system, power system stabilizer, PID controller ... The proposed controller was a fuzzy-logic-based stabilizer that has the capability to ..... Computer methods in power system analysis.

Modelling and Simulation of a Synchronous Machine with Power Electronic Systems

DEFF Research Database (Denmark)

Chen, Zhe; Blaabjerg, Frede

2005-01-01

is modelled in SIMULINK as well. The resulting model can more accurately represent non-idea situations such as non-symmetrical parameters of the electrical machines and unbalance conditions. The model may be used for both steady state and large-signal dynamic analysis. This is particularly useful......This paper reports the modeling and simulation of a synchronous machine with a power electronic interface in direct phase model. The implementation of a direct phase model of synchronous machines in MATLAB/SIMULINK is presented .The power electronic system associated with the synchronous machine...... in the systems where a detailed study is needed in order to assess the overall system stability. Simulation studies are performed under various operation conditions. It is shown that the developed model could be used for studies of various applications of synchronous machines such as in renewable and DG...

Wire Electrical Discharge Machining of a Hybrid Composite: Evaluation of Kerf Width and Surface Roughness

Directory of Open Access Journals (Sweden)

Abdil KUŞ

2016-06-01

Full Text Available In this study, the machinability characteristics of Al/B4C-Gr hybrid composite were investigated using wire electrical discharge machining (WEDM. In the experiments, the machining parameters of wire speed, pulse-on time and pulse-off time were varied in order to explaiın their effects on machining performance, including the width of slit (kerf and surface roughness values (Rz and Rt. According to the Taguchi quality design concept, a L18 (21×32 orthogonal array was used to determine the S/N ratio, and analysis of variance (ANOVA and the F-test were used to indicate the significant machining parameters affecting the machining performance. From the ANOVA and F-test results, the significant factors were determined for each of the machining performance criteria of kerf, Rz and Rt. The variations of kerf, Rz and Rt with the machining parameters were statistically modeled via the regression analysis method. The optimum levels of the control factors for kerf, Rz and Rt were specified as A1B1C1, A1B1C2 and A1B1C2, respectively. The correlation coefficients of the predictive equations developed for kerf, Rz and Rt were calculated as 0.98, 0.828 and 0.855, respectively.

Comparative Study of Powdered Ginger Drink Processed by Different Method:Traditional and using Evaporation Machine

Science.gov (United States)

Apriyana, Wuri; Taufika Rosyida, Vita; Nur Hayati, Septi; Darsih, Cici; Dewi Poeloengasih, Crescentiana

2017-12-01

Ginger drink is one of the traditional beverage that became one of the products of interest by consumers in Indonesia. This drink is believed to have excellent properties for the health of the body. In this study, we have compared the moisture content, ash content, metal content and the identified compound of product which processed with traditional technique and using an evaporator machine. The results show that both of products fulfilled some parameters of the Indonesian National Standard for the traditional powdered drink. GC-MS analysis data showed the identified compound of both product. The major of hydrocarbon groups that influenced the flavor such as zingiberene, camphene, beta-phelladrine, beta-sesquepelladrine, curcumene, and beta-bisabolene were found higher in ginger drink powder treated with a machine than those processed traditionally.

Comparation of Some Values of Measured Parameteres between Old and New X-Ray Machines

International Nuclear Information System (INIS)

Kocic, B.; Marinkovic, J.; Praskalo, J.

2013-01-01

Checking the change of values of parameters like specific value of kerma, repeatability of expositions, linearity of exposition, stability of high voltage, time of exposition and HVL of X-ray machines that are in use in medical centres of Republic of Serbia give us an idea of comparing those values for two groups of X-ray machines. One is in use since 70's in last century and is still in use, and the other group is quite new, and is controlled for last 5 years. Checking the results of controlled parameters within those two groups of X-ray machines we can compare stability and quality of old and new machines, and the influence of 'aging' of machines to the quality and stability of measured parameters. Some of conclusions are that old machines are showing better stability and repeatability of values of measured parameters during the 'aging' than new machines.(author)

A Study of the Resolution of Dental Intraoral X-Ray Machines

International Nuclear Information System (INIS)

Kim, Seon Ju; Chung, Hyon De

1990-01-01

The purpose of this study was to assess the resolution and focal spot size of dental X-ray machines. Fifty dental X-ray machines were selected for measuring resolution and focal spot size. These machines were used in general dental clinics. The time on installation of the X-ray machine varies from 1 years to 10 years. The resolution of these machines was measured with the test pattern. The focal spot size of these machines was measured with the star test pattern. The following results were obtained: 1. The resolution of dental intraoral X-ray machines was not significantly changed in ten years. 2. The focal spot size of dental intraoral X-ray machines was not significantly increased in ten years. The statistical analysis between the mean focal spot size and nominal focal spot size was significant at the 0.05 level about the more than 3 years used machines.

Evaluation influence of machining parameters on shape form errors in turning of machine parts clamped in the chuck with adaptive jaws

Directory of Open Access Journals (Sweden)

I.V. Lutsiv

2017-12-01

Full Text Available The paper deals with the derivation problem of the dependence of machine part geometric form deviation in cross section area on clamping diameter as well as cutting speed, feed and cutting depth in semi finish machining. The analysis of single factor circular deviation dependences on machining conditions values is performed. Using the special software application package the laboratory conditions experiment results are analyzed. The dispersion analysis including options for main linear and quadratic effects evaluation is given and the simplification model of experiment results is obtained. It presents the evaluation empiric dependence of cutting conditions and clamping diameter influence on shape error forming (dynamic error. It is found that to obtain the necessary form accuracy in machining with lathe chuck equipped with the adaptive clamping jaws it is desirable to control the most statistically significant factors that actually are the cutting depth and feed.

Developing Parametric Models for the Assembly of Machine Fixtures for Virtual Multiaxial CNC Machining Centers

Science.gov (United States)

Balaykin, A. V.; Bezsonov, K. A.; Nekhoroshev, M. V.; Shulepov, A. P.

2018-01-01

This paper dwells upon a variance parameterization method. Variance or dimensional parameterization is based on sketching, with various parametric links superimposed on the sketch objects and user-imposed constraints in the form of an equation system that determines the parametric dependencies. This method is fully integrated in a top-down design methodology to enable the creation of multi-variant and flexible fixture assembly models, as all the modeling operations are hierarchically linked in the built tree. In this research the authors consider a parameterization method of machine tooling used for manufacturing parts using multiaxial CNC machining centers in the real manufacturing process. The developed method allows to significantly reduce tooling design time when making changes of a part’s geometric parameters. The method can also reduce time for designing and engineering preproduction, in particular, for development of control programs for CNC equipment and control and measuring machines, automate the release of design and engineering documentation. Variance parameterization helps to optimize construction of parts as well as machine tooling using integrated CAE systems. In the framework of this study, the authors demonstrate a comprehensive approach to parametric modeling of machine tooling in the CAD package used in the real manufacturing process of aircraft engines.

Experimental investigation of the tip based micro/nano machining

Science.gov (United States)

Guo, Z.; Tian, Y.; Liu, X.; Wang, F.; Zhou, C.; Zhang, D.

2017-12-01

Based on the self-developed three dimensional micro/nano machining system, the effects of machining parameters and sample material on micro/nano machining are investigated. The micro/nano machining system is mainly composed of the probe system and micro/nano positioning stage. The former is applied to control the normal load and the latter is utilized to realize high precision motion in the xy plane. A sample examination method is firstly introduced to estimate whether the sample is placed horizontally. The machining parameters include scratching direction, speed, cycles, normal load and feed. According to the experimental results, the scratching depth is significantly affected by the normal load in all four defined scratching directions but is rarely influenced by the scratching speed. The increase of scratching cycle number can increase the scratching depth as well as smooth the groove wall. In addition, the scratching tests of silicon and copper attest that the harder material is easier to be removed. In the scratching with different feed amount, the machining results indicate that the machined depth increases as the feed reduces. Further, a cubic polynomial is used to fit the experimental results to predict the scratching depth. With the selected machining parameters of scratching direction d3/d4, scratching speed 5 μm/s and feed 0.06 μm, some more micro structures including stair, sinusoidal groove, Chinese character '田', 'TJU' and Chinese panda have been fabricated on the silicon substrate.

Study of a variable mass Atwood's machine using a smartphone

Science.gov (United States)

Lopez, Dany; Caprile, Isidora; Corvacho, Fernando; Reyes, Orfa

2018-03-01

The Atwood machine was invented in 1784 by George Atwood and this system has been widely studied both theoretically and experimentally over the years. Nowadays, it is commonplace that many experimental physics courses include both Atwood's machine and variable mass to introduce more complex concepts in physics. To study the dynamics of the masses that compose the variable Atwood's machine, laboratories typically use a smart pulley. Now, the first work that introduced a smartphone as data acquisition equipment to study the acceleration in the Atwood's machine was the one by M. Monteiro et al. Since then, there has been no further information available on the usage of smartphones in variable mass systems. This prompted us to do a study of this kind of system by means of data obtained with a smartphone and to show the practicality of using smartphones in complex experimental situations.

The effects of machine parameters on residual stress determined using micro-Raman spectroscopy

Energy Technology Data Exchange (ETDEWEB)

Sparks, R.G.; Enloe, W.S.; Paesler, M.A.

1988-12-01

The effects of machine parameters on residual stresses in single point diamond turned silicon and germanium have been investigated using micro-Raman spectroscopy. Residual stresses were sampled across ductile feed cuts in < 100 > silicon and germanium which were single point diamond turned using a variety of feed rates, rake angles and clearance angles. High spatial resolution micro-Raman spectra (1{mu}m spot) were obtained in regions of ductile cutting where no visible surface damage was present. The use of both 514-5nm and 488.0nm excitation wavelengths, by virtue of their differing characteristic penetration depths in the materials, allowed determinations of stress profiles as a function of depth into the sample. Previous discussions have demonstrated that such Raman spectra will exhibit asymmetrically broadened peaks which are characteristic of the superposition of a continuum of Raman scatterers from the various depths probed. Depth profiles of residual stress were obtained using computer deconvolution of the resulting asymmetrically broadened raman spectra.

GA based CNC turning center exploitation process parameters optimization

Directory of Open Access Journals (Sweden)

Z. Car

2009-01-01

Full Text Available This paper presents machining parameters (turning process optimization based on the use of artificial intelligence. To obtain greater efficiency and productivity of the machine tool, optimal cutting parameters have to be obtained. In order to find optimal cutting parameters, the genetic algorithm (GA has been used as an optimal solution finder. Optimization has to yield minimum machining time and minimum production cost, while considering technological and material constrains.

FRICTION - WELDING MACHINE AUTOMATIC CONTROL CIRCUIT DESIGN AND APPLICATION

OpenAIRE

Hakan ATEŞ; Ramazan BAYINDIR

2003-01-01

In this work, automatic controllability of a laboratory-sized friction-welding machine has been investigated. The laboratory-sized friction-welding machine was composed of motor, brake, rotary and constant samples late pliers, and hydraulic unit. In automatic method, welding parameters such as friction time, friction pressure, forge time and forge pressure can be applied sensitively using time relays and contactors. At the end of the experimental study it's observed that automatic control sys...

Machinability studies of infrared window materials and metals

International Nuclear Information System (INIS)

Arnold, J.B.; Morris, T.O.; Sladky, R.E.; Steger, P.J.

1976-01-01

Diamond machining of materials for optical applications is becoming an important fabrication process. Development work in material-removal technology to better understand the mechanics of the diamond-turning process, its limitations, and applications is described. The technique is presently limited to a select group of metals, most of which are of a face-center-cubic crystal structure. Machinability studies were done which were designed to better understand diamond compatibility and thus expand the range of applicable materials. Nonconventional methods such as ultrasonic tool stimulation were investigated. Work done to determine the machinability of infrared window materials indicates that this is a viable fabrication technique for many materials, although additional effort is needed to optimize the process for particular materials

Effect of dispersion hardening process on machinability of EN AB-AlSi9Mg silumin

Directory of Open Access Journals (Sweden)

J. Pezda

2009-07-01

Full Text Available Nowadays, aluminum and its alloys found their application in any type design structures, many’s the time being an alternative for a ferrous alloys due to their technological properties like low density, ductility, high strength and good corrosion resistance. Among different fabrication processes the machining stage has a significant importance considering fabrication costs and processing time. Therefore, optimization of the process parameters that affect machining stages such as, tool wear, alloy machinability, machining effort and cutting speed becomes an area of constant development and study. To the most important factors having impact on machining properties belong: initial condition of machined material, which depends on a method and conditions of material preparation. In the paper are presented initial tests of machining properties of the EN AB-AlSi9Mg silumin subjected to heat treatment. Machinability measurements of the investigated alloy were performed with use of reboring method with constant force of feed. It enabled determination of an effect of heat treatment on machining properties of the investigated alloy. A further investigation shall be connected with determination of optimal parameters of solutionizing and ageing treatments in aspects of improvement of both mechanical properties and its machinability.

An Expectation-Maximization Method for Calibrating Synchronous Machine Models

Energy Technology Data Exchange (ETDEWEB)

Meng, Da; Zhou, Ning; Lu, Shuai; Lin, Guang

2013-07-21

The accuracy of a power system dynamic model is essential to its secure and efficient operation. Lower confidence in model accuracy usually leads to conservative operation and lowers asset usage. To improve model accuracy, this paper proposes an expectation-maximization (EM) method to calibrate the synchronous machine model using phasor measurement unit (PMU) data. First, an extended Kalman filter (EKF) is applied to estimate the dynamic states using measurement data. Then, the parameters are calculated based on the estimated states using maximum likelihood estimation (MLE) method. The EM method iterates over the preceding two steps to improve estimation accuracy. The proposed EM method’s performance is evaluated using a single-machine infinite bus system and compared with a method where both state and parameters are estimated using an EKF method. Sensitivity studies of the parameter calibration using EM method are also presented to show the robustness of the proposed method for different levels of measurement noise and initial parameter uncertainty.

Technique for Increasing Accuracy of Positioning System of Machine Tools

Directory of Open Access Journals (Sweden)

Sh. Ji

2014-01-01

Full Text Available The aim of research is to improve the accuracy of positioning and processing system using a technique for optimization of pressure diagrams of guides in machine tools. The machining quality is directly related to its accuracy, which characterizes an impact degree of various errors of machines. The accuracy of the positioning system is one of the most significant machining characteristics, which allow accuracy evaluation of processed parts.The literature describes that the working area of the machine layout is rather informative to characterize the effect of the positioning system on the macro-geometry of the part surfaces to be processed. To enhance the static accuracy of the studied machine, in principle, two groups of measures are possible. One of them points toward a decrease of the cutting force component, which overturns the slider moments. Another group of measures is related to the changing sizes of the guide facets, which may lead to their profile change.The study was based on mathematical modeling and optimization of the cutting zone coordinates. And we find the formula to determine the surface pressure of the guides. The selected parameters of optimization are vectors of the cutting force and values of slides and guides. Obtained results show that a technique for optimization of coordinates in the cutting zone was necessary to increase a processing accuracy.The research has established that to define the optimal coordinates of the cutting zone we have to change the sizes of slides, value and coordinates of applied forces, reaching the pressure equalization and improving the accuracy of positioning system of machine tools. In different points of the workspace a vector of forces is applied, pressure diagrams are found, which take into account the changes in the parameters of positioning system, and the pressure diagram equalization to provide the most accuracy of machine tools is achieved.

“Investigations on the machinability of Waspaloy under dry environment”

Science.gov (United States)

Deepu, J.; Kuppan, P.; SBalan, A. S.; Oyyaravelu, R.

2016-09-01

Nickel based superalloy, Waspaloy is extensively used in gas turbine, aerospace and automobile industries because of their unique combination of properties like high strength at elevated temperatures, resistance to chemical degradation and excellent wear resistance in many hostile environments. It is considered as one of the difficult to machine superalloy due to excessive tool wear and poor surface finish. The present paper is an attempt for removing cutting fluids from turning process of Waspaloy and to make the processes environmentally safe. For this purpose, the effect of machining parameters such as cutting speed and feed rate on the cutting force, cutting temperature, surface finish and tool wear were investigated barrier. Consequently, the strength and tool wear resistance and tool life increased significantly. Response Surface Methodology (RSM) has been used for developing and analyzing a mathematical model which describes the relationship between machining parameters and output variables. Subsequently ANOVA was used to check the adequacy of the regression model as well as each machining variables. The optimal cutting parameters were determined based on multi-response optimizations by composite desirability approach in order to minimize cutting force, average surface roughness and maximum flank wear. The results obtained from the experiments shown that machining of Waspaloy using coated carbide tool with special ranges of parameters, cutting fluid could be completely removed from machining process

The Effect of Operational Cutting Parameters on Nitinol-60 in Wire Electrodischarge Machining

Directory of Open Access Journals (Sweden)

Ali Akbar LotfiNeyestanak

2013-01-01

Full Text Available Shape memory alloys are a kind of active materials, which have significant characteristics in comparison with other alloys. Since these materials are applicable in different fields such as aerospace, automobile industry, medicine, and dentistry, the effects of wire electrodischarge machining on the properties of these alloys have been studied. In this paper, changes in the shape recovery ability and microhardness of the machined surface of Nitonol-60 shape memory alloy have been studied considering recasting and formation of resolidificated layer on the shape memory alloy surface. XRD and EDXA analyses of the surface layer of the sample besides a microscopic study of the shape memory alloy layer by SEM and a study of the changes in mechanical properties of the surface layer were done by performing microhardness and tension tests on the work piece surface. Considering the surface layer, reversible strain has been studied according to the shape recovery percentage of Nitinol-60 shape memory alloy. Results show that the surface layer formed on the surface of the samples has caused changes in both physical and mechanical properties of the cut surface because of the penetration of the separated materials in comparison with deeper layers of the piece.

Dynamic Performances of Asynchronous Machines | Ubeku ...

African Journals Online (AJOL)

The per-phase parameters of a 1.5 hp, 380 V, 50 Hz, 4 poles, 3 phase asynchronous machine used in the simulation were computed with reading obtained from a dc, no-load and blocked rotor tests carried out on the machine in the laboratory. The results obtained from the computer simulations confirmed the capabilities ...

A theory of operation of a hydraulic drilling machine with a distributor

Energy Technology Data Exchange (ETDEWEB)

Totev, S Kh; Tsvetkov, Kh K

1982-01-01

The moment of impact of a striker piston against a drilling instrument is studied. The basic parameters of the drilling machine are identified, including cycle length, piston travel, impact velocity and impact energy. Formulas are acquired for rating the cited parameters.

Optimisation of milling parameters using neural network

Directory of Open Access Journals (Sweden)

Lipski Jerzy

2017-01-01

Full Text Available The purpose of this study was to design and test an intelligent computer software developed with the purpose of increasing average productivity of milling not compromising the design features of the final product. The developed system generates optimal milling parameters based on the extent of tool wear. The introduced optimisation algorithm employs a multilayer model of a milling process developed in the artificial neural network. The input parameters for model training are the following: cutting speed vc, feed per tooth fz and the degree of tool wear measured by means of localised flank wear (VB3. The output parameter is the surface roughness of a machined surface Ra. Since the model in the neural network exhibits good approximation of functional relationships, it was applied to determine optimal milling parameters in changeable tool wear conditions (VB3 and stabilisation of surface roughness parameter Ra. Our solution enables constant control over surface roughness parameters and productivity of milling process after each assessment of tool condition. The recommended parameters, i.e. those which applied in milling ensure desired surface roughness and maximal productivity, are selected from all the parameters generated by the model. The developed software may constitute an expert system supporting a milling machine operator. In addition, the application may be installed on a mobile device (smartphone, connected to a tool wear diagnostics instrument and the machine tool controller in order to supply updated optimal parameters of milling. The presented solution facilitates tool life optimisation and decreasing tool change costs, particularly during prolonged operation.

A Fast and On-Machine Measuring System Using the Laser Displacement Sensor for the Contour Parameters of the Drill Pipe Thread

Directory of Open Access Journals (Sweden)

Zhixu Dong

2018-04-01

Full Text Available The inconvenient loading and unloading of a long and heavy drill pipe gives rise to the difficulty in measuring the contour parameters of its threads at both ends. To solve this problem, in this paper we take the SCK230 drill pipe thread-repairing machine tool as a carrier to design and achieve a fast and on-machine measuring system based on a laser probe. This system drives a laser displacement sensor to acquire the contour data of a certain axial section of the thread by using the servo function of a CNC machine tool. To correct the sensor’s measurement errors caused by the measuring point inclination angle, an inclination error model is built to compensate data in real time. To better suppress random error interference and ensure real contour information, a new wavelet threshold function is proposed to process data through the wavelet threshold denoising. Discrete data after denoising is segmented according to the geometrical characteristics of the drill pipe thread, and the regression model of the contour data in each section is fitted by using the method of weighted total least squares (WTLS. Then, the thread parameters are calculated in real time to judge the processing quality. Inclination error experiments show that the proposed compensation model is accurate and effective, and it can improve the data acquisition accuracy of a sensor. Simulation results indicate that the improved threshold function is of better continuity and self-adaptability, which makes sure that denoising effects are guaranteed, and, meanwhile, the complete elimination of real data distorted in random errors is avoided. Additionally, NC50 thread-testing experiments show that the proposed on-machine measuring system can complete the measurement of a 25 mm thread in 7.8 s, with a measurement accuracy of ±8 μm and repeatability limit ≤ 4 μm (high repeatability, and hence the accuracy and efficiency of measurement are both improved.

Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

Science.gov (United States)

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

Optimization of cryogenic cooled EDM process parameters using grey relational analysis

International Nuclear Information System (INIS)

Kumar, S Vinoth; Kumar, M Pradeep

2014-01-01

This paper presents an experimental investigation on cryogenic cooling of liquid nitrogen (LN 2 ) copper electrode in the electrical discharge machining (EDM) process. The optimization of the EDM process parameters, such as the electrode environment (conventional electrode and cryogenically cooled electrode in EDM), discharge current, pulse on time, gap voltage on material removal rate, electrode wear, and surface roughness on machining of AlSiCp metal matrix composite using multiple performance characteristics on grey relational analysis was investigated. The L 18 orthogonal array was utilized to examine the process parameters, and the optimal levels of the process parameters were identified through grey relational analysis. Experimental data were analyzed through analysis of variance. Scanning electron microscopy analysis was conducted to study the characteristics of the machined surface.

Collider baseline parameters: Milestone M1.5

CERN Document Server

Schulte, Daniel

2016-01-01

The deliverable D1.1 provided a preliminary specification of the layout and target operation parameters for the FCC-hh hadron collider concept. It serves as the basis for the studies in all work packages. Tis milestone summarises the outcome of the first studies of this design. The goal of the FCC hadron collider is to provide proton-proton collisions at a centre-of-mass energy of 100 TeV. The machine is compatible with ion beam operation. Assuming a nominal dipole field of 16 T, such a machine is based on a perimeter of 100 km. The machine is designed to accommodate two main proton experiments that are operated simultaneously. The machine delivers a peak luminosity of 5-30 x 1034 cm-2s-1. The layout allows for two additional special-purpose experiments.

Metrological Aspects of Surface Topographies Produced by Different Machining Operations Regarding Their Potential Functionality

Directory of Open Access Journals (Sweden)

Żak Krzysztof

2017-06-01

Full Text Available This paper presents a comprehensive methodology for measuring and characterizing the surface topographies on machined steel parts produced by precision machining operations. The performed case studies concern a wide spectrum of topographic features of surfaces with different geometrical structures but the same values of the arithmetic mean height Sa. The tested machining operations included hard turning operations performed with CBN tools, grinding operations with Al2O3 ceramic and CBN wheels and superfinish using ceramic stones. As a result, several characteristic surface textures with the Sa roughness parameter value of about 0.2 μm were thoroughly characterized and compared regarding their potential functional capabilities. Apart from the standard 2D and 3D roughness parameters, the fractal, motif and frequency parameters were taken in the consideration.

An experimental result of surface roughness machining performance in deep hole drilling

Directory of Open Access Journals (Sweden)

Mohamad Azizah

2016-01-01

Full Text Available This study presents an experimental result of a deep hole drilling process for Steel material at different machining parameters which are feed rate (f, spindle speed (s, the depth of the hole (d and MQL, number of drops (m on surface roughness, Ra. The experiment was designed using two level full factorial design of experiment (DoE with centre points to collect surface roughness, Ra values. The signal to noise (S/N ratio analysis was used to discover the optimum level for each machining parameters in the experiment.

Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes - ELSA-Brasil: accuracy study.

Science.gov (United States)

Olivera, André Rodrigues; Roesler, Valter; Iochpe, Cirano; Schmidt, Maria Inês; Vigo, Álvaro; Barreto, Sandhi Maria; Duncan, Bruce Bartholow

2017-01-01

Type 2 diabetes is a chronic disease associated with a wide range of serious health complications that have a major impact on overall health. The aims here were to develop and validate predictive models for detecting undiagnosed diabetes using data from the Longitudinal Study of Adult Health (ELSA-Brasil) and to compare the performance of different machine-learning algorithms in this task. Comparison of machine-learning algorithms to develop predictive models using data from ELSA-Brasil. After selecting a subset of 27 candidate variables from the literature, models were built and validated in four sequential steps: (i) parameter tuning with tenfold cross-validation, repeated three times; (ii) automatic variable selection using forward selection, a wrapper strategy with four different machine-learning algorithms and tenfold cross-validation (repeated three times), to evaluate each subset of variables; (iii) error estimation of model parameters with tenfold cross-validation, repeated ten times; and (iv) generalization testing on an independent dataset. The models were created with the following machine-learning algorithms: logistic regression, artificial neural network, naïve Bayes, K-nearest neighbor and random forest. The best models were created using artificial neural networks and logistic regression. -These achieved mean areas under the curve of, respectively, 75.24% and 74.98% in the error estimation step and 74.17% and 74.41% in the generalization testing step. Most of the predictive models produced similar results, and demonstrated the feasibility of identifying individuals with highest probability of having undiagnosed diabetes, through easily-obtained clinical data.

Study of Environmental Data Complexity using Extreme Learning Machine

Science.gov (United States)

Leuenberger, Michael; Kanevski, Mikhail

2017-04-01

The main goals of environmental data science using machine learning algorithm deal, in a broad sense, around the calibration, the prediction and the visualization of hidden relationship between input and output variables. In order to optimize the models and to understand the phenomenon under study, the characterization of the complexity (at different levels) should be taken into account. Therefore, the identification of the linear or non-linear behavior between input and output variables adds valuable information for the knowledge of the phenomenon complexity. The present research highlights and investigates the different issues that can occur when identifying the complexity (linear/non-linear) of environmental data using machine learning algorithm. In particular, the main attention is paid to the description of a self-consistent methodology for the use of Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. By applying two ELM models (with linear and non-linear activation functions) and by comparing their efficiency, quantification of the linearity can be evaluated. The considered approach is accompanied by simulated and real high dimensional and multivariate data case studies. In conclusion, the current challenges and future development in complexity quantification using environmental data mining are discussed. References - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

TEA CO2 laser machining of CFRP composite

Science.gov (United States)

Salama, A.; Li, L.; Mativenga, P.; Whitehead, D.

2016-05-01

Carbon fibre-reinforced polymer (CFRP) composites have found wide applications in the aerospace, marine, sports and automotive industries owing to their lightweight and acceptable mechanical properties compared to the commonly used metallic materials. Machining of CFRP composites using lasers can be challenging due to inhomogeneity in the material properties and structures, which can lead to thermal damages during laser processing. In the previous studies, Nd:YAG, diode-pumped solid-state, CO2 (continuous wave), disc and fibre lasers were used in cutting CFRP composites and the control of damages such as the size of heat-affected zones (HAZs) remains a challenge. In this paper, a short-pulsed (8 μs) transversely excited atmospheric pressure CO2 laser was used, for the first time, to machine CFRP composites. The laser has high peak powers (up to 250 kW) and excellent absorption by both the carbon fibre and the epoxy binder. Design of experiment and statistical modelling, based on response surface methodology, was used to understand the interactions between the process parameters such as laser fluence, repetition rate and cutting speed and their effects on the cut quality characteristics including size of HAZ, machining depth and material removal rate (MRR). Based on this study, process parameter optimization was carried out to minimize the HAZ and maximize the MRR. A discussion is given on the potential applications and comparisons to other lasers in machining CFRP.

ANN-PSO Integrated Optimization Methodology for Intelligent Control of MMC Machining

Science.gov (United States)

Chandrasekaran, Muthumari; Tamang, Santosh

2017-08-01

Metal Matrix Composites (MMC) show improved properties in comparison with non-reinforced alloys and have found increased application in automotive and aerospace industries. The selection of optimum machining parameters to produce components of desired surface roughness is of great concern considering the quality and economy of manufacturing process. In this study, a surface roughness prediction model for turning Al-SiCp MMC is developed using Artificial Neural Network (ANN). Three turning parameters viz., spindle speed ( N), feed rate ( f) and depth of cut ( d) were considered as input neurons and surface roughness was an output neuron. ANN architecture having 3 -5 -1 is found to be optimum and the model predicts with an average percentage error of 7.72 %. Particle Swarm Optimization (PSO) technique is used for optimizing parameters to minimize machining time. The innovative aspect of this work is the development of an integrated ANN-PSO optimization method for intelligent control of MMC machining process applicable to manufacturing industries. The robustness of the method shows its superiority for obtaining optimum cutting parameters satisfying desired surface roughness. The method has better convergent capability with minimum number of iterations.

Session 2: Machine studies

International Nuclear Information System (INIS)

Assmann, R.W.; Papotti, G.

2012-01-01

This document summarizes the talks and discussion that took place in the second session of the Chamonix 2012 workshop concerning results from machine studies performed in 2011. The session consisted of the following presentations: -) LHC experience with different bunch spacings by G. Rumolo; -) Observations of beam-beam effects in MDs in 2011 by W. Herr; -) Beam-induced heating/ bunch length/RF and lessons for 2012 by E. Metral; -) Lessons in beam diagnostics by R. Jones; -) Quench margins by M. Sapinski; and -) First demonstration with beam of the Achromatic Telescopic Squeeze (ATS) by S. Fartoukh. (authors)

On-machine measurement of a slow slide servo diamond-machined 3D microstructure with a curved substrate

International Nuclear Information System (INIS)

Zhu, Wu-Le; Yang, Shunyao; Ju, Bing-Feng; Jiang, Jiacheng; Sun, Anyu

2015-01-01

A scanning tunneling microscope-based multi-axis measuring system is specially developed for the on-machine measurement of three-dimensional (3D) microstructures, to address the quality control difficulty with the traditional off-line measurement process. A typical 3D microstructure of the curved compound eye was diamond-machined by the slow slide servo technique, and then the whole surface was on-machine scanned three-dimensionally based on the tip-tracking strategy by utilizing a spindle, two linear motion stages, and an additional rotary stage. The machined surface profile and its shape deviation were accurately measured on-machine. The distortion of imaged ommatidia on the curved substrate was distinctively evaluated based on the characterized points extracted from the measured surface. Furthermore, the machining errors were investigated in connection with the on-machine measured surface and its characteristic parameters. Through experiments, the proposed measurement system is demonstrated to feature versatile on-machine measurement of 3D microstructures with a curved substrate, which is highly meaningful for quality control in the fabrication field. (paper)

Humanizing machines: Anthropomorphization of slot machines increases gambling.

Science.gov (United States)

Riva, Paolo; Sacchi, Simona; Brambilla, Marco

2015-12-01

Do people gamble more on slot machines if they think that they are playing against humanlike minds rather than mathematical algorithms? Research has shown that people have a strong cognitive tendency to imbue humanlike mental states to nonhuman entities (i.e., anthropomorphism). The present research tested whether anthropomorphizing slot machines would increase gambling. Four studies manipulated slot machine anthropomorphization and found that exposing people to an anthropomorphized description of a slot machine increased gambling behavior and reduced gambling outcomes. Such findings emerged using tasks that focused on gambling behavior (Studies 1 to 3) as well as in experimental paradigms that included gambling outcomes (Studies 2 to 4). We found that gambling outcomes decrease because participants primed with the anthropomorphic slot machine gambled more (Study 4). Furthermore, we found that high-arousal positive emotions (e.g., feeling excited) played a role in the effect of anthropomorphism on gambling behavior (Studies 3 and 4). Our research indicates that the psychological process of gambling-machine anthropomorphism can be advantageous for the gaming industry; however, this may come at great expense for gamblers' (and their families') economic resources and psychological well-being. (c) 2015 APA, all rights reserved).

Machinability study of Al-TiC metal matrix composite

Directory of Open Access Journals (Sweden)

Siddappa P. N.

2018-01-01

Full Text Available Aluminum Metal Matrix Composites have emerged as an advanced class of structural materials have a combination of different, superior properties compared to an unreinforced matrix, which can result in a number of service benefits such as increased strength, higher elastic moduli, higher service temperature, low CTE, improved wear resistance, high toughness, etc. The excellent mechanical properties of these materials together with weight saving makes them very attractive for a variety of engineering applications in aerospace, automotive, electronic industries, etc. Hence, these materials provide as alternative substitutes for conventional engineering materials when specific mechanical properties necessary for required applications. In this work an attempt is made to study the machining parameters of Al6061/TiC MMC. The composite is developed by reinforcing TiC particles in varying proportions of 3, 6, 9 and 12 % weight fractions to the Al6061 matric alloy through stir casting technique. Cutting forces were measured by varying cutting speed and feed rate with constant depth of cut for different % weight fractions. The results showed that the cutting force increases with the increase of feed rate and decreases with the increase of cutting speed for all the weight fractions. Cutting parameters were optimized using Taguchi technique.

Combination of Machining Parameters to Optimize Surface Roughness and Chip Thickness during End Milling Process on Aluminium 6351-T6 Alloy Using Taguchi Design Method

Directory of Open Access Journals (Sweden)

Reddy Sreenivasulu

2016-12-01

Full Text Available In any machining operations, quality is the important conflicting objective. In order to give assurance for high productivity, some extent of quality has to be compromised. Similarly productivity will be decreased while the efforts are channelized to enhance quality. In this study,  the experiments were carried out on a CNC vertical machining center  to perform 10mm slots on Al 6351-T6 alloy work piece by K10 carbide, four flute end milling cutter. Furthermore the cutting speed, the feed rate and depth of cut are regulated in this experiment. Each experiment was conducted three times and the surface roughness and chip thickness was measured by a surface analyser of Surf Test-211 series (Mitutoyo and Digital Micrometer (Mitutoyo with least count 0.001 mm respectively. The selection of orthogonal array is concerned with the total degree of freedom of process parameters. Total degree of freedom (DOF associated with three parameters is equal to 6 (3X2.The degree of freedom for the orthogonal array should be greater than or at least equal to that of the process parameters. There by, a L9 orthogonal array having degree of freedom equal to (9-1= 8 8 has been considered .But in present case each experiment is conducted three times, therefore total degree of freedom (9X3-1=26 26 has been considered. Finally, confirmation test (ANOVA was conducted to compare the predicted values with the experimental values confirm its effectiveness in the analysis of surface roughness and chip thickness. Surface Roughness (Ra is greatly reduced from 0.145 µm to 0.1326 µm and the chip thickness (Ct is slightly reduced from 0.1 mm to 0.085 mm, because of in the measurement collected the chips after machining of every experiment, from that randomly selected a few chips for measuring of their thickness using digital micrometer.

Parameters identification of the compound cage rotor induction machine based on linearized Kalman filtering

Institute of Scientific and Technical Information of China (English)

王铁成; 李伟力; 孙建伟

2003-01-01

A mathematical model has been built up for compound cage rotor induction machine with the rotor re-sistance and leakage inductance in the model identified through Kalman filtering method. Using the identifiedparameters, simulation studies are performed, and simulation results are compared with testing results.

Testing of machinability of mould steel 40CrMnMo7 using genetic algorithm

OpenAIRE

Stoić, Antun; Cukor, Goran; Kopač, Janez

2015-01-01

This paper deals with testing of hard materials machinability by high speed turning process and influence of cutting parameters on machinability rates. Todetermine machinability rates, surface roughness, tool wear and cutting force components were measured. In accordance with expected influence of certain parameter on machinability, experiments were designed and performed todetermine mathematical models of the measured values over full response range. Obtained mathematical models were used fo...

Application of support vector machines to breast cancer screening using mammogram and clinical history data

Science.gov (United States)

Land, Walker H., Jr.; McKee, Dan; Velazquez, Roberto; Wong, Lut; Lo, Joseph Y.; Anderson, Francis R.

2003-05-01

The objectives of this paper are to discuss: (1) the development and testing of a new Evolutionary Programming (EP) method to optimally configure Support Vector Machine (SVM) parameters for facilitating the diagnosis of breast cancer; (2) evaluation of EP derived learning machines when the number of BI-RADS and clinical history discriminators are reduced from 16 to 7; (3) establishing system performance for several SVM kernels in addition to the EP/Adaptive Boosting (EP/AB) hybrid using the Digital Database for Screening Mammography, University of South Florida (DDSM USF) and Duke data sets; and (4) obtaining a preliminary evaluation of the measurement of SVM learning machine inter-institutional generalization capability using BI-RADS data. Measuring performance of the SVM designs and EP/AB hybrid against these objectives will provide quantative evidence that the software packages described can generalize to larger patient data sets from different institutions. Most iterative methods currently in use to optimize learning machine parameters are time consuming processes, which sometimes yield sub-optimal values resulting in performance degradation. SVMs are new machine intelligence paradigms, which use the Structural Risk Minimization (SRM) concept to develop learning machines. These learning machines can always be trained to provide global minima, given that the machine parameters are optimally computed. In addition, several system performance studies are described which include EP derived SVM performance as a function of: (a) population and generation size as well as a method for generating initial populations and (b) iteratively derived versus EP derived learning machine parameters. Finally, the authors describe a set of experiments providing preliminary evidence that both the EP/AB hybrid and SVM Computer Aided Diagnostic C++ software packages will work across a large population of patients, based on a data set of approximately 2,500 samples from five different

State machine operation of the MICE cooling channel

International Nuclear Information System (INIS)

Hanlet, Pierrick

2014-01-01

The Muon Ionization Cooling Experiment (MICE) is a demonstration experiment to prove the feasibility of cooling a beam of muons for use in a Neutrino Factory and/or Muon Collider. The MICE cooling channel is a section of a modified Study II cooling channel which will provide a 10% reduction in beam emittance. In order to ensure a reliable measurement, MICE will measure the beam emittance before and after the cooling channel at the level of 1%, a relative measurement of 0.001. This renders MICE a precision experiment which requires strict controls and monitoring of all experimental parameters in order to control systematic errors. The MICE Controls and Monitoring system is based on EPICS and integrates with the DAQ, Data monitoring systems, and a configuration database. The cooling channel for MICE has between 12 and 18 superconductnig solenoid coils in 3 to 7 magnets, depending on the staged development of the experiment. The magnets are coaxial and in close proximity which requires coordinated operation of the magnets when ramping, responding to quench conditions, and quench recovery. To reliably manage the operation of the magnets, MICE is implementing state machines for each magnet and an over-arching state machine for the magnets integrated in the cooling channel. The state machine transitions and operating parameters are stored/restored to/from the configuration database and coupled with MICE Run Control. Proper implementation of the state machines will not only ensure safe operation of the magnets, but will help ensure reliable data quality. A description of MICE, details of the state machines, and lessons learned from use of the state machines in recent magnet training tests will be discussed.

FRICTION - WELDING MACHINE AUTOMATIC CONTROL CIRCUIT DESIGN AND APPLICATION

Directory of Open Access Journals (Sweden)

Hakan ATEŞ

2003-02-01

Full Text Available In this work, automatic controllability of a laboratory-sized friction-welding machine has been investigated. The laboratory-sized friction-welding machine was composed of motor, brake, rotary and constant samples late pliers, and hydraulic unit. In automatic method, welding parameters such as friction time, friction pressure, forge time and forge pressure can be applied sensitively using time relays and contactors. At the end of the experimental study it's observed that automatic control system has been worked successfully.

Machinability Evaluation in Hard Milling of AISI D2 Steel

OpenAIRE

Gaitonde, Vinayak Neelakanth; Karnik, Sulse Ramesh; Maciel, Caio Henrique Alves; Rubio, Juan Carlos Campos; Abrão, Alexandre Mendes

2016-01-01

Milling of hardened steel components provides considerable benefits in terms of reduced manufacturing cost and time compared to traditional machining. Temperature variation in milling is an important factor affecting the wear of cutting tools. The poor selection of milling parameters may cause excessive tool wear and increased work surface roughness. Hence, there is a need to study the machinability aspects during milling of hardened steel components. In the present work, influence of cutting...

Efficient tuning in supervised machine learning

NARCIS (Netherlands)

Koch, Patrick

2013-01-01

The tuning of learning algorithm parameters has become more and more important during the last years. With the fast growth of computational power and available memory databases have grown dramatically. This is very challenging for the tuning of parameters arising in machine learning, since the

Development of Health Parameter Model for Risk Prediction of CVD Using SVM

Directory of Open Access Journals (Sweden)

P. Unnikrishnan

2016-01-01

Full Text Available Current methods of cardiovascular risk assessment are performed using health factors which are often based on the Framingham study. However, these methods have significant limitations due to their poor sensitivity and specificity. We have compared the parameters from the Framingham equation with linear regression analysis to establish the effect of training of the model for the local database. Support vector machine was used to determine the effectiveness of machine learning approach with the Framingham health parameters for risk assessment of cardiovascular disease (CVD. The result shows that while linear model trained using local database was an improvement on Framingham model, SVM based risk assessment model had high sensitivity and specificity of prediction of CVD. This indicates that using the health parameters identified using Framingham study, machine learning approach overcomes the low sensitivity and specificity of Framingham model.

Parametric optimization for the production of nanostructure in high carbon steel chips via machining

Directory of Open Access Journals (Sweden)

M. Ilangkumaran

2015-09-01

Full Text Available Nano crystalline materials are an area of interest for the researchers all over the world due to its superior mechanical properties such as high strength and high hardness. But the cost of nano-crystals is high because of the complexity and cost incurred during its production. This paper focuses on the application of Taguchi method with Fuzzy logic for optimizing the machining parameters of nano-crystalline structured chips production in High Carbon Steel (HCS through machining. An orthogonal array, multi-response performance index, signals to noise ratio and analysis of variance are used to study the machining process with multi-response performance characteristics. The machining parameters namely rake angle, depth of cut, heat treatment, feed and cutting velocity are optimized with considerations of the multi-response performance characteristics. Using the Taguchi and Fuzzy logic method optimum cutting conditions are identified in order to obtain the smallest nanocrystalline structure via machining.

Evaluation of Surface Roughness and Power Consumption in Machining FCD 450 Cast Iron using Coated and Uncoated Irregular Milling Tools

International Nuclear Information System (INIS)

Yusoff, Ahmad Razlan; Arsyad, Fitriyanti

2016-01-01

In this project, the effects of different cutting parameters on surface roughness and power consumption when machining FCD450 cast iron were studied using coated and uncoated irregular milling tool geometry of variable helix and pitch. Their responses on roughness and power consumption were evaluated based on the spindle speed, feed rate, and depth of cut, machining length and machining time. Results showed that except spindle speed and machining length, other parameters such as feed rate, axial and radial depth of cut and also machining time proportionate with surface roughness. The power consumption proportionately increase for all cutting parameters except feedrate. It is showed that the average decrement 27.92 percent for surface roughness and average decrement 9.32 percent for power consumption by using coated compared to uncoated tool. Optimum cutting parameters for both minimum surface roughness and power consumption can be determined. The coated tools performed better than uncoated milling tools for responses of surface roughness and power consumption to increase machining productivity and profit. (paper)

Discrete Model Reference Adaptive Control System for Automatic Profiling Machine

Directory of Open Access Journals (Sweden)

Peng Song

2012-01-01

Full Text Available Automatic profiling machine is a movement system that has a high degree of parameter variation and high frequency of transient process, and it requires an accurate control in time. In this paper, the discrete model reference adaptive control system of automatic profiling machine is discussed. Firstly, the model of automatic profiling machine is presented according to the parameters of DC motor. Then the design of the discrete model reference adaptive control is proposed, and the control rules are proven. The results of simulation show that adaptive control system has favorable dynamic performances.

Using Phun to Study ``Perpetual Motion'' Machines

Science.gov (United States)

Koreš, Jaroslav

2012-05-01

The concept of "perpetual motion" has a long history. The Indian astronomer and mathematician Bhaskara II (12th century) was the first person to describe a perpetual motion (PM) machine. An example of a 13th- century PM machine is shown in Fig. 1. Although the law of conservation of energy clearly implies the impossibility of PM construction, over the centuries numerous proposals for PM have been made, involving ever more elements of modern science in their construction. It is possible to test a variety of PM machines in the classroom using a program called Phun2 or its commercial version Algodoo.3 The programs are designed to simulate physical processes and we can easily simulate mechanical machines using them. They provide an intuitive graphical environment controlled with a mouse; a programming language is not needed. This paper describes simulations of four different (supposed) PM machines.4

Machine learning techniques applied to system characterization and equalization

DEFF Research Database (Denmark)

Zibar, Darko; Thrane, Jakob; Wass, Jesper

2016-01-01

Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals.......Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals....

Study of axial protections of unloading machines of graphite piles

International Nuclear Information System (INIS)

Duco, Jacques; Pepin, Pierre; Cabaret, Guy; Dubor, Monique

1969-10-01

As previous studies resulted in the development of a simple calculation formula based on experimental results for the calculation of neutron protection thicknesses for loading machines, this study aimed at determining axial protections of these machines which represent a specific problem: scattering of delayed neutrons in the machine inner cavity may result in an important neutron leakage through the upper part, at the level of the winch enclosure. In an experimental part, this study comprises the measurement of the neutron dose in a 2.60 m long and 54 cm diameter cylindrical cavity, and in the thickness of the surrounding concrete protection. In the second part, the authors present a calculation method which uses the Zeus and Mercure codes to interpret the results [fr

Chip formation and surface integrity in high-speed machining of hardened steel

Science.gov (United States)

Kishawy, Hossam Eldeen A.

Increasing demands for high production rates as well as cost reduction have emphasized the potential for the industrial application of hard turning technology during the past few years. Machining instead of grinding hardened steel components reduces the machining sequence, the machining time, and the specific cutting energy. Hard turning Is characterized by the generation of high temperatures, the formation of saw toothed chips, and the high ratio of thrust to tangential cutting force components. Although a large volume of literature exists on hard turning, the change in machined surface physical properties represents a major challenge. Thus, a better understanding of the cutting mechanism in hard turning is still required. In particular, the chip formation process and the surface integrity of the machined surface are important issues which require further research. In this thesis, a mechanistic model for saw toothed chip formation is presented. This model is based on the concept of crack initiation on the free surface of the workpiece. The model presented explains the mechanism of chip formation. In addition, experimental investigation is conducted in order to study the chip morphology. The effect of process parameters, including edge preparation and tool wear on the chip morphology, is studied using Scanning Electron Microscopy (SEM). The dynamics of chip formation are also investigated. The surface integrity of the machined parts is also investigated. This investigation focusses on residual stresses as well as surface and sub-surface deformation. A three dimensional thermo-elasto-plastic finite element model is developed to predict the machining residual stresses. The effect of flank wear is introduced during the analysis. Although residual stresses have complicated origins and are introduced by many factors, in this model only the thermal and mechanical factors are considered. The finite element analysis demonstrates the significant effect of the heat generated

Contribution to the study, modelling and optimisation of a variable reluctance machine excited by permanent magnets; Contribution a l'etude, la modelisation et l'optimisation d'une machine a reluctance variable excitee par des aimants permanents

Energy Technology Data Exchange (ETDEWEB)

Haouara, I.

1998-07-01

Variable reluctance machines (VRM) with double cogs are interesting solutions for gearing mechanisms requiring a strong torque and a low speed. In the case of wind to electricity power conversion they can represent an improvement with the removal of the speed multiplier. A brief classification of VRMs has been made first and then, a double cogs synchronous structure with an excitation system made of permanent magnets included in the rotor has been chosen for this application. Then, using the dimensioning principles of synchronous machines, an approach is proposed to perform its pre-dimensioning. A finite-elements modeling of the machine and a model of equivalent electrical circuit have been used to evaluate its performances. An analytical approach based on the calculation of the air-gap permeation and of the magnetic scalar potential is proposed to identify the parameters of the equivalent electrical circuit (inductances and electromotive force). After validation, this model has been retained for the study of the performances of the generator-rectifier system in real conditions of operation (converter with a high frequency). Finally the optimization of the geometrical and magnetic parameters of the structure is analyzed in order to maximize its power. A conjugate experiment plans and fields calculation method has been used. It allows to take into consideration the real conditions of operation (saturation and dynamical functioning of magnets) and the interaction between the different parameters. The study of an optimized prototype has shown an improvement for all operation modes. (J.S.)

Learning Machine Learning: A Case Study

Science.gov (United States)

Lavesson, N.

2010-01-01

This correspondence reports on a case study conducted in the Master's-level Machine Learning (ML) course at Blekinge Institute of Technology, Sweden. The students participated in a self-assessment test and a diagnostic test of prerequisite subjects, and their results on these tests are correlated with their achievement of the course's learning…

Can Machines Learn Respiratory Virus Epidemiology?: A Comparative Study of Likelihood-Free Methods for the Estimation of Epidemiological Dynamics

Directory of Open Access Journals (Sweden)

Heidi L. Tessmer

2018-03-01

Full Text Available To estimate and predict the transmission dynamics of respiratory viruses, the estimation of the basic reproduction number, R0, is essential. Recently, approximate Bayesian computation methods have been used as likelihood free methods to estimate epidemiological model parameters, particularly R0. In this paper, we explore various machine learning approaches, the multi-layer perceptron, convolutional neural network, and long-short term memory, to learn and estimate the parameters. Further, we compare the accuracy of the estimates and time requirements for machine learning and the approximate Bayesian computation methods on both simulated and real-world epidemiological data from outbreaks of influenza A(H1N1pdm09, mumps, and measles. We find that the machine learning approaches can be verified and tested faster than the approximate Bayesian computation method, but that the approximate Bayesian computation method is more robust across different datasets.

ESTIMATION OF THE SPECIFIC ENERGY OF TUNNEL BORING MACHINE USING POST-FAILURE BEHAVIOUR OF ROCK MASS.CASE STUDY: KARAJ-TEHRAN WATER CONVEYANCE TUNNEL IN IRAN

Directory of Open Access Journals (Sweden)

MAJID MIRAHMADI

2017-09-01

Full Text Available Performance prediction of tunnel boring machines (TBM is the most important factor for successful tunnel excavation projects. The specific energy (SE of TBM, defined as the amount of energy required to excavate a unit volume of rock, is one of the critical parameters used for performance prediction of these machines. Estimation of SE is very useful to design the drilling project because it is a function of many parameters such as rock mass behaviour, machine properties and project parameters. Several methods are used to estimate this parameter, such as experimental, empirical and numerical. The aim of this study is to estimate the SE considering the postfailure behaviour of rock mass. For this reason, based on the actual data from Karaj-Tehran water conveyance tunnel, a new empirical method is proposed to estimate the SE using the drop-to-deformation modulus ratio (λ. Based on the statistical analysis, the relation between the SE and λ is estimated. It is clear that the amplitude of λ, is high and to increase the correlation between mentioned parameters, the classification of data is performed. All data is classified in three classes as very weak (GSI75. Also a statistical analysis is performed to estimate the SE using the mentioned parameter (λ in any class. The result shows that there is a direct relation between both parameters and the best correlation is achieved. So, the best equations are proposed to estimate SE using λ, considering the post failure behaviour of rock mass.

Ultrasonic horn design for ultrasonic machining technologies

Directory of Open Access Journals (Sweden)

Naď M.

2010-07-01

Full Text Available Many of industrial applications and production technologies are based on the application of ultrasound. In many cases, the phenomenon of ultrasound is also applied in technological processes of the machining of materials. The main element of equipments that use the effects of ultrasound for machining technology is the ultrasonic horn – so called sonotrode. The performance of ultrasonic equipment, respectively ultrasonic machining technologies depends on properly designed of sonotrode shape. The dynamical properties of different geometrical shapes of ultrasonic horns are presented in this paper. Dependence of fundamental modal properties (natural frequencies, mode shapes of various sonotrode shapes for various geometrical parameters is analyzed. Modal analyses of the models are determined by the numerical simulation using finite element method (FEM design procedures. The mutual comparisons of the comparable parameters of the various sonotrode shapes are presented.

Preliminary Development of Real Time Usage-Phase Monitoring System for CNC Machine Tools with a Case Study on CNC Machine VMC 250

Science.gov (United States)

Budi Harja, Herman; Prakosa, Tri; Raharno, Sri; Yuwana Martawirya, Yatna; Nurhadi, Indra; Setyo Nogroho, Alamsyah

2018-03-01

The production characteristic of job-shop industry at which products have wide variety but small amounts causes every machine tool will be shared to conduct production process with dynamic load. Its dynamic condition operation directly affects machine tools component reliability. Hence, determination of maintenance schedule for every component should be calculated based on actual usage of machine tools component. This paper describes study on development of monitoring system to obtaining information about each CNC machine tool component usage in real time approached by component grouping based on its operation phase. A special device has been developed for monitoring machine tool component usage by utilizing usage phase activity data taken from certain electronics components within CNC machine. The components are adaptor, servo driver and spindle driver, as well as some additional components such as microcontroller and relays. The obtained data are utilized for detecting machine utilization phases such as power on state, machine ready state or spindle running state. Experimental result have shown that the developed CNC machine tool monitoring system is capable of obtaining phase information of machine tool usage as well as its duration and displays the information at the user interface application.

The Effect of Machining Conditions on the Forces in the Process of Roller Brush Machining

Directory of Open Access Journals (Sweden)

Jakub Matuszak

2017-12-01

Full Text Available Because of its advantages, brushing processing has many uses. The main ones include the removal of corrosion products, surface cleaning, deburring and shaping the properties of the surface layer. The intensity of these processes depends on the degree of impact of brush fibres on the work surface. In the case of tools, in which the resilient fibres are the working elements, forces in the brushing process, apart from the machining parameters, depend on the characteristics and overall dimensions of individual fibres. The paper presents the results of studies of the influence of technological parameters and type of fibres on the radial force in the brushing process.

Material Choice for spindle of machine tools

Science.gov (United States)

Gouasmi, S.; Merzoug, B.; Abba, G.; Kherredine, L.

2012-02-01

The requirements of contemporary industry and the flashing development of modern sciences impose restrictions on the majority of the elements of machines; the resulting financial constraints can be satisfied by a better output of the production equipment. As for those concerning the design, the resistance and the correct operation of the product, these require the development of increasingly precise parts, therefore the use of increasingly powerful tools [5]. The precision of machining and the output of the machine tools are generally determined by the precision of rotation of the spindle, indeed, more this one is large more the dimensions to obtain are in the zone of tolerance and the defects of shape are minimized. During the development of the machine tool, the spindle which by definition is a rotating shaft receiving and transmitting to the work piece or the cutting tool the rotational movement, must be designed according to certain optimal parameters to be able to ensure the precision required. This study will be devoted to the choice of the material of the spindle fulfilling the imposed requirements of precision.

Material Choice for spindle of machine tools

International Nuclear Information System (INIS)

Gouasmi, S; Merzoug, B; Kherredine, L; Abba, G

2012-01-01

The requirements of contemporary industry and the flashing development of modern sciences impose restrictions on the majority of the elements of machines; the resulting financial constraints can be satisfied by a better output of the production equipment. As for those concerning the design, the resistance and the correct operation of the product, these require the development of increasingly precise parts, therefore the use of increasingly powerful tools [5]. The precision of machining and the output of the machine tools are generally determined by the precision of rotation of the spindle, indeed, more this one is large more the dimensions to obtain are in the zone of tolerance and the defects of shape are minimized. During the development of the machine tool, the spindle which by definition is a rotating shaft receiving and transmitting to the work piece or the cutting tool the rotational movement, must be designed according to certain optimal parameters to be able to ensure the precision required. This study will be devoted to the choice of the material of the spindle fulfilling the imposed requirements of precision.

Research on intrusion detection based on Kohonen network and support vector machine

Science.gov (United States)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

Trends and developments in industrial machine vision: 2013

Science.gov (United States)

Niel, Kurt; Heinzl, Christoph

2014-03-01

When following current advancements and implementations in the field of machine vision there seems to be no borders for future developments: Calculating power constantly increases, and new ideas are spreading and previously challenging approaches are introduced in to mass market. Within the past decades these advances have had dramatic impacts on our lives. Consumer electronics, e.g. computers or telephones, which once occupied large volumes, now fit in the palm of a hand. To note just a few examples e.g. face recognition was adopted by the consumer market, 3D capturing became cheap, due to the huge community SW-coding got easier using sophisticated development platforms. However, still there is a remaining gap between consumer and industrial applications. While the first ones have to be entertaining, the second have to be reliable. Recent studies (e.g. VDMA [1], Germany) show a moderately increasing market for machine vision in industry. Asking industry regarding their needs the main challenges for industrial machine vision are simple usage and reliability for the process, quick support, full automation, self/easy adjustment at changing process parameters, "forget it in the line". Furthermore a big challenge is to support quality control: Nowadays the operator has to accurately define the tested features for checking the probes. There is an upcoming development also to let automated machine vision applications find out essential parameters in a more abstract level (top down). In this work we focus on three current and future topics for industrial machine vision: Metrology supporting automation, quality control (inline/atline/offline) as well as visualization and analysis of datasets with steadily growing sizes. Finally the general trend of the pixel orientated towards object orientated evaluation is addressed. We do not directly address the field of robotics taking advances from machine vision. This is actually a fast changing area which is worth an own

Inverse Problems in Geodynamics Using Machine Learning Algorithms

Science.gov (United States)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R. N.

2018-01-01

During the past few decades numerical studies have been widely employed to explore the style of circulation and mixing in the mantle of Earth and other planets. However, in geodynamical studies there are many properties from mineral physics, geochemistry, and petrology in these numerical models. Machine learning, as a computational statistic-related technique and a subfield of artificial intelligence, has rapidly emerged recently in many fields of sciences and engineering. We focus here on the application of supervised machine learning (SML) algorithms in predictions of mantle flow processes. Specifically, we emphasize on estimating mantle properties by employing machine learning techniques in solving an inverse problem. Using snapshots of numerical convection models as training samples, we enable machine learning models to determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at midmantle depths. Employing support vector machine algorithms, we show that SML techniques can successfully predict the magnitude of mantle density anomalies and can also be used in characterizing mantle flow patterns. The technique can be extended to more complex geodynamic problems in mantle dynamics by employing deep learning algorithms for putting constraints on properties such as viscosity, elastic parameters, and the nature of thermal and chemical anomalies.

The Effects of Operational Parameters on a Mono-wire Cutting System: Efficiency in Marble Processing

Science.gov (United States)

Yilmazkaya, Emre; Ozcelik, Yilmaz

2016-02-01

Mono-wire block cutting machines that cut with a diamond wire can be used for squaring natural stone blocks and the slab-cutting process. The efficient use of these machines reduces operating costs by ensuring less diamond wire wear and longer wire life at high speeds. The high investment costs of these machines will lead to their efficient use and reduce production costs by increasing plant efficiency. Therefore, there is a need to investigate the cutting performance parameters of mono-wire cutting machines in terms of rock properties and operating parameters. This study aims to investigate the effects of the wire rotational speed (peripheral speed) and wire descending speed (cutting speed), which are the operating parameters of a mono-wire cutting machine, on unit wear and unit energy, which are the performance parameters in mono-wire cutting. By using the obtained results, cuttability charts for each natural stone were created on the basis of unit wear and unit energy values, cutting optimizations were performed, and the relationships between some physical and mechanical properties of rocks and the optimum cutting parameters obtained as a result of the optimization were investigated.

Reducing surface roughness by optimising the turning parameters

Directory of Open Access Journals (Sweden)

Senthil Kumar, K.

2013-08-01

Full Text Available Modern manufacturers worldwide look for the cheapest quality-manufactured machined components to compete in the market. Good surface quality is desired for the proper functioning of the parts produced. The surface quality is influenced by the cutting speed, feed rate, depth of cut, and many other parameters. In this paper, the Taguchi method a powerful tool to design optimisation for quality is used to find the optimal machining parameters for the turning operation. An orthogonal array, the signal-to-noise (S/N ratio, and the analysis of variance (ANOVA are employed to investigate the machining characteristics of super duplex stainless steel bars using uncoated carbide cutting tools. The effect of machining parameters on surface roughness was discovered. Confirmation tests were conducted at optimal conditions to compare the experimental results with the predicted values.

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.

High-speed micro-electro-discharge machining.

Energy Technology Data Exchange (ETDEWEB)

Chandrasekar, Srinivasan Dr. (.School of Industrial Engineering, West Lafayette, IN); Moylan, Shawn P. (School of Industrial Engineering, West Lafayette, IN); Benavides, Gilbert Lawrence

2005-09-01

When two electrodes are in close proximity in a dielectric liquid, application of a voltage pulse can produce a spark discharge between them, resulting in a small amount of material removal from both electrodes. Pulsed application of the voltage at discharge energies in the range of micro-Joules results in the continuous material removal process known as micro-electro-discharge machining (micro-EDM). Spark erosion by micro-EDM provides significant opportunities for producing small features and micro-components such as nozzle holes, slots, shafts and gears in virtually any conductive material. If the speed and precision of micro-EDM processes can be significantly enhanced, then they have the potential to be used for a wide variety of micro-machining applications including fabrication of microelectromechanical system (MEMS) components. Toward this end, a better understanding of the impacts the various machining parameters have on material removal has been established through a single discharge study of micro-EDM and a parametric study of small hole making by micro-EDM. The main avenues for improving the speed and efficiency of the micro-EDM process are in the areas of more controlled pulse generation in the power supply and more controlled positioning of the tool electrode during the machining process. Further investigation of the micro-EDM process in three dimensions leads to important design rules, specifically the smallest feature size attainable by the process.

Predicting Complexation Thermodynamic Parameters of β-Cyclodextrin with Chiral Guests by Using Swarm Intelligence and Support Vector Machines

Directory of Open Access Journals (Sweden)

Luckhana Lawtrakul

2009-05-01

Full Text Available The Particle Swarm Optimization (PSO and Support Vector Machines (SVMs approaches are used for predicting the thermodynamic parameters for the 1:1 inclusion complexation of chiral guests with β-cyclodextrin. A PSO is adopted for descriptor selection in the quantitative structure-property relationships (QSPR of a dataset of 74 chiral guests due to its simplicity, speed, and consistency. The modified PSO is then combined with SVMs for its good approximating properties, to generate a QSPR model with the selected features. Linear, polynomial, and Gaussian radial basis functions are used as kernels in SVMs. All models have demonstrated an impressive performance with R2 higher than 0.8.

Application of Fuzzy TOPSIS for evaluating machining techniques using sustainability metrics

Science.gov (United States)

Digalwar, Abhijeet K.

2018-04-01

Sustainable processes and techniques are getting increased attention over the last few decades due to rising concerns over the environment, improved focus on productivity and stringency in environmental as well as occupational health and safety norms. The present work analyzes the research on sustainable machining techniques and identifies techniques and parameters on which sustainability of a process is evaluated. Based on the analysis these parameters are then adopted as criteria’s to evaluate different sustainable machining techniques such as Cryogenic Machining, Dry Machining, Minimum Quantity Lubrication (MQL) and High Pressure Jet Assisted Machining (HPJAM) using a fuzzy TOPSIS framework. In order to facilitate easy arithmetic, the linguistic variables represented by fuzzy numbers are transformed into crisp numbers based on graded mean representation. Cryogenic machining was found to be the best alternative sustainable technique as per the fuzzy TOPSIS framework adopted. The paper provides a method to deal with multi criteria decision making problems in a complex and linguistic environment.

Investigation on Surface Roughness of Inconel 718 in Photochemical Machining

Directory of Open Access Journals (Sweden)

Nitin D. Misal

2017-01-01

Full Text Available The present work is focused on estimating the optimal machining parameters required for photochemical machining (PCM of an Inconel 718 and effects of these parameters on surface topology. An experimental analysis was carried out to identify optimal values of parameters using ferric chloride (FeCl3 as an etchant. The parameters considered in this analysis are concentration of etchant, etching time, and etchant temperature. The experimental analysis shows that etching performance as well as surface topology improved by appropriate selection of etching process parameters. Temperature of the etchant found to be dominant parameter in the PCM of Inconel 718 for surface roughness. At optimal etching conditions, surface roughness was found to be 0.201 μm.

Machining of {gamma}-TiAl

Energy Technology Data Exchange (ETDEWEB)

Aust, E.; Niemann, H.-R. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Werkstofforschung

1999-09-01

Knowledge of the machining parameters for titanium aluminides of the type {gamma}-TiAl is essential for the acceptance and application of this new heat-resistant light-weight material for high performance components in automobile and aircraft engines. This work evaluates drilling, turning, sawing, milling, electroerosion, grinding, and high-pressure water-jetting of primary castings. The results indicate that there is a potential for each machining process, but a high quality of surface finish can only be achieved by some of the processes. (orig.)

Book review: A first course in Machine Learning

DEFF Research Database (Denmark)

Ortiz-Arroyo, Daniel

2016-01-01

"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background...... to change models and parameter values to make [it] easier to understand and apply these models in real applications. The authors [also] introduce more advanced, state-of-the-art machine learning methods, such as Gaussian process models and advanced mixture models, which are used across machine learning....... This makes the book interesting not only to students with little or no background in machine learning but also to more advanced graduate students interested in statistical approaches to machine learning." —Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark...

Optimization of process parameters through GRA, TOPSIS and RSA models

Directory of Open Access Journals (Sweden)

Suresh Nipanikar

2018-01-01

Full Text Available This article investigates the effect of cutting parameters on the surface roughness and flank wear during machining of titanium alloy Ti-6Al-4V ELI( Extra Low Interstitial in minimum quantity lubrication environment by using PVD TiAlN insert. Full factorial design of experiment was used for the machining 2 factors 3 levels and 2 factors 2 levels. Turning parameters studied were cutting speed (50, 65, 80 m/min, feed (0.08, 0.15, 0.2 mm/rev and depth of cut 0.5 mm constant. The results show that 44.61 % contribution of feed and 43.57 % contribution of cutting speed on surface roughness also 53.16 % contribution of cutting tool and 26.47 % contribution of cutting speed on tool flank wear. Grey relational analysis and TOPSIS method suggest the optimum combinations of machining parameters as cutting speed: 50 m/min, feed: 0.8 mm/rev., cutting tool: PVD TiAlN, cutting fluid: Palm oi

Comparison of direct machine parameter optimization versus fluence optimization with sequential sequencing in IMRT of hypopharyngeal carcinoma

International Nuclear Information System (INIS)

Dobler, Barbara; Pohl, Fabian; Bogner, Ludwig; Koelbl, Oliver

2007-01-01

To evaluate the effects of direct machine parameter optimization in the treatment planning of intensity-modulated radiation therapy (IMRT) for hypopharyngeal cancer as compared to subsequent leaf sequencing in Oncentra Masterplan v1.5. For 10 hypopharyngeal cancer patients IMRT plans were generated in Oncentra Masterplan v1.5 (Nucletron BV, Veenendal, the Netherlands) for a Siemens Primus linear accelerator. For optimization the dose volume objectives (DVO) for the planning target volume (PTV) were set to 53 Gy minimum dose and 59 Gy maximum dose, in order to reach a dose of 56 Gy to the average of the PTV. For the parotids a median dose of 22 Gy was allowed and for the spinal cord a maximum dose of 35 Gy. The maximum DVO to the external contour of the patient was set to 59 Gy. The treatment plans were optimized with the direct machine parameter optimization ('Direct Step & Shoot', DSS, Raysearch Laboratories, Sweden) newly implemented in Masterplan v1.5 and the fluence modulation technique ('Intensity Modulation', IM) which was available in previous versions of Masterplan already. The two techniques were compared with regard to compliance to the DVO, plan quality, and number of monitor units (MU) required per fraction dose. The plans optimized with the DSS technique met the DVO for the PTV significantly better than the plans optimized with IM (p = 0.007 for the min DVO and p < 0.0005 for the max DVO). No significant difference could be observed for compliance to the DVO for the organs at risk (OAR) (p > 0.05). Plan quality, target coverage and dose homogeneity inside the PTV were superior for the plans optimized with DSS for similar dose to the spinal cord and lower dose to the normal tissue. The mean dose to the parotids was lower for the plans optimized with IM. Treatment plan efficiency was higher for the DSS plans with (901 ± 160) MU compared to (1151 ± 157) MU for IM (p-value < 0.05). Renormalization of the IM plans to the mean of the

Cutting temperature measurement and material machinability

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Nedić Bogdan P.

2014-01-01

Full Text Available Cutting temperature is very important parameter of cutting process. Around 90% of heat generated during cutting process is then away by sawdust, and the rest is transferred to the tool and workpiece. In this research cutting temperature was measured with artificial thermocouples and question of investigation of metal machinability from aspect of cutting temperature was analyzed. For investigation of material machinability during turning artificial thermocouple was placed just below the cutting top of insert, and for drilling thermocouples were placed through screw holes on the face surface. In this way was obtained simple, reliable, economic and accurate method for investigation of cutting machinability.

An experimental study on a superconducting generator with dual machine shield system

International Nuclear Information System (INIS)

Ishigohka, T.; Ninomiya, A.; Okada, T.; Nitta, T.; Shintani, T.; Mukai, E.

1988-01-01

The authors have studied the optimal machine shield system through experiments on a 20kVa superconducting generator. The first experiment is carried out on a fully iron-less aluminum-shield machine which has only an aluminum eddy current machine shield in the stator. The second experiment is carried out on a generator with a dual-shield system which has both an aluminum eddy current shield and an iron magnetic shield. From the first one, the authors have got an experimental result that the aluminum-shield machine exhibits so large eddy current loss in the shield that it would be difficult to operate the machine continuously. On the other hand, the second experiment shows that the dual-shield machine exhibits much smaller loss in the shielding system, and that it has higher output power than the aluminum-shield machine. From these experiments, it becomes clear that insertion of a very thin iron shield between the armature winding and the eddy current shield can improve the machine performance eminently without large weight increase even if the iron shield were saturated

Machine-Vision Systems Selection for Agricultural Vehicles: A Guide

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Gonzalo Pajares

2016-11-01

Full Text Available Machine vision systems are becoming increasingly common onboard agricultural vehicles (autonomous and non-autonomous for different tasks. This paper provides guidelines for selecting machine-vision systems for optimum performance, considering the adverse conditions on these outdoor environments with high variability on the illumination, irregular terrain conditions or different plant growth states, among others. In this regard, three main topics have been conveniently addressed for the best selection: (a spectral bands (visible and infrared; (b imaging sensors and optical systems (including intrinsic parameters and (c geometric visual system arrangement (considering extrinsic parameters and stereovision systems. A general overview, with detailed description and technical support, is provided for each topic with illustrative examples focused on specific applications in agriculture, although they could be applied in different contexts other than agricultural. A case study is provided as a result of research in the RHEA (Robot Fleets for Highly Effective Agriculture and Forestry Management project for effective weed control in maize fields (wide-rows crops, funded by the European Union, where the machine vision system onboard the autonomous vehicles was the most important part of the full perception system, where machine vision was the most relevant. Details and results about crop row detection, weed patches identification, autonomous vehicle guidance and obstacle detection are provided together with a review of methods and approaches on these topics.

Factors affecting the laser processing of wood, 2: Effects of material parameters on machinability

International Nuclear Information System (INIS)

Arai, T.; Hayashi, D.

1994-01-01

Material parameters of wood were investigated. Factors relating to the workpiece include cutting direction, specific gravity, and components of the wood such as resin-like materials. Also studies of the effects of irregular tissue on machinability were made. The interactions between laser beam and materials are often greatly complex. They depend on the characteristics of the laser beam, the thermal constants of the woods, and the optical surface properties of the woods. Therefore, high quality beam mode and carefully selected materials were used. The following laser cutting properties became clear after studying the experimental results. Slow speed cutting and softwoods make slight differences, regarding cutting section and fiber direction. However, it can beconsidered that there is not very much change except in cross-section. Because of the high power density of laser, cutting speed makes no big difference. The irregular tissue of wood cannot maintain normal cutting speed and accuracy. The factor of genuine density eta, which is thought to be entirely independent of each specific gravity, is definedas the concept of density in general. It can be obtained by applying a simple rule, that is, the eta is the ratio of r(u)/rho(s) where rho(s) is the wood substance as the characteristic value of wood, and r(u)is specific gravity. An experimental formula shows that the depth of cut decreases in proportion to the value of eta. However, in the strict sense of the word, data of wood material as a natural resources mustbe treated qualitatively, because there are deviations from regularity due to various reasons. (author)

A study of wear in refrigerating machines using thin layer activation

International Nuclear Information System (INIS)

Hammer, P.; Eichhorn, K.; Eifrig, C.

1986-01-01

Wear is studied in a ball-and-socket joint of a newly developed refrigerating machine. Using deuteron activation a 15 μm deep Co-57 layer is generated at the ring-shaped friction area in the steel socket of the joint. The measurement of the Co-57 intensity of the wear particles held back on an oil filter provides information about the wear rate of the socket during machine operation. The measurement of the Co-57 contaminations occuring in the individual parts of the machine at the end of the test gives information about the distribution of the wear particles in the machine and about the material transfer in the ball-and-socket joint. (author)

Experimental Research into Technology of Abrasive Flow Machining Nonlinear Tube Runner

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Junye Li

2014-06-01

Full Text Available In the fields of military and civil uses, some special passages exist in many major parts, such as non-linear tubes. The overall performance is usually decided by the surface quality. Abrasive flow machining (AFM technology can effectively improve the surface quality of the parts. In order to discuss the mechanism and technology of abrasive flow machining nonlinear tube, the nozzle is picked up as the researching object, and the self-designed polishing liquid is employed to make research on the key technological parameters of abrasive flow machining linear tube. Technological parameters’ impact on surface quality of the parts through the nozzle surface topography and scanning electron microscopy (SEM map is explored. It is experimentally confirmed that abrasive flow machining can significantly improve surface quality of nonlinear runner, and experimental results can provide technical reference to optimizing study of abrasive flow machining theory.

Research on machine learning framework based on random forest algorithm

Science.gov (United States)

Ren, Qiong; Cheng, Hui; Han, Hai

2017-03-01

With the continuous development of machine learning, industry and academia have released a lot of machine learning frameworks based on distributed computing platform, and have been widely used. However, the existing framework of machine learning is limited by the limitations of machine learning algorithm itself, such as the choice of parameters and the interference of noises, the high using threshold and so on. This paper introduces the research background of machine learning framework, and combined with the commonly used random forest algorithm in machine learning classification algorithm, puts forward the research objectives and content, proposes an improved adaptive random forest algorithm (referred to as ARF), and on the basis of ARF, designs and implements the machine learning framework.

In vivo and in vitro performance of a China-made hemodialysis machine: a multi-center prospective controlled study.

Science.gov (United States)

Wang, Yong; Chen, Xiang-Mei; Cai, Guang-Yan; Li, Wen-Ge; Zhang, Ai-Hua; Hao, Li-Rong; Shi, Ming; Wang, Rong; Jiang, Hong-Li; Luo, Hui-Min; Zhang, Dong; Sun, Xue-Feng

2017-08-02

To evaluate the in vivo and in vitro performance of a China-made dialysis machine (SWS-4000). This was a multi-center prospective controlled study consisting of both long-term in vitro evaluations and cross-over in vivo tests in 132 patients. The China-made SWS-4000 dialysis machine was compared with a German-made dialysis machine (Fresenius 4008) with regard to Kt/V values, URR values, and dialysis-related adverse reactions in patients on maintenance hemodialysis, as well as the ultrafiltration rate, the concentration of electrolytes in the proportioned dialysate, the rate of heparin injection, the flow rate of the blood pump, and the rate of malfunction. The Kt/V and URR values at the 1st and 4th weeks of dialysis as well as the incidence of adverse effects did not differ between the two groups in cross-over in vivo tests (P > 0.05). There were no significant differences between the two groups in the error values of the ultrafiltration rate, the rate of heparin injection or the concentrations of electrolytes in the proportioned dialysate at different time points under different parameter settings. At weeks 2 and 24, with the flow rate of the blood pump set at 300 mL/min, the actual error of the SWS-4000 dialysis machine was significantly higher than that of the Fresenius 4008 dialysis machine (P  0.05). The malfunction rate was higher in the SWS-4000 group than in the Fresenius 4008 group (P Fresenius 4008 dialysis machine; however, the malfunction rate of the former is higher than that of the latter in in vitro tests. The stability and long-term accuracy of the SWS-4000 dialysis machine remain to be improved.

Machinability of Al 6061 Deposited with Cold Spray Additive Manufacturing

Science.gov (United States)

Aldwell, Barry; Kelly, Elaine; Wall, Ronan; Amaldi, Andrea; O'Donnell, Garret E.; Lupoi, Rocco

2017-10-01

Additive manufacturing techniques such as cold spray are translating from research laboratories into more mainstream high-end production systems. Similar to many additive processes, finishing still depends on removal processes. This research presents the results from investigations into aspects of the machinability of aluminum 6061 tubes manufactured with cold spray. Through the analysis of cutting forces and observations on chip formation and surface morphology, the effect of cutting speed, feed rate, and heat treatment was quantified, for both cold-sprayed and bulk aluminum 6061. High-speed video of chip formation shows changes in chip form for varying material and heat treatment, which is supported by the force data and quantitative imaging of the machined surface. The results shown in this paper demonstrate that parameters involved in cold spray directly impact on machinability and therefore have implications for machining parameters and strategy.

Photoelectron studies of machined brass surfaces

Science.gov (United States)

Potts, A. W.; Merrison, J. P.; Tournas, A. D.; Yacoot, A.

UV photoelectron spectroscopy has been used to determine the surface composition of machined brass. The results show a considerable change between the photoelectron surface composition and the bulk composition of the same sample determined by energy-dispersive X-ray fluorescence. On the surface the lead composition is increased by ˜900 G. This is consistent with the important part that lead is believed to play in improving the machinability of this alloy.

A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation.

Science.gov (United States)

Wang, Hongxun; Zhang, Weifang; Sun, Fuqiang; Zhang, Wei

2017-05-18

The relationships between the fatigue crack growth rate ( d a / d N ) and stress intensity factor range ( Δ K ) are not always linear even in the Paris region. The stress ratio effects on fatigue crack growth rate are diverse in different materials. However, most existing fatigue crack growth models cannot handle these nonlinearities appropriately. The machine learning method provides a flexible approach to the modeling of fatigue crack growth because of its excellent nonlinear approximation and multivariable learning ability. In this paper, a fatigue crack growth calculation method is proposed based on three different machine learning algorithms (MLAs): extreme learning machine (ELM), radial basis function network (RBFN) and genetic algorithms optimized back propagation network (GABP). The MLA based method is validated using testing data of different materials. The three MLAs are compared with each other as well as the classical two-parameter model ( K * approach). The results show that the predictions of MLAs are superior to those of K * approach in accuracy and effectiveness, and the ELM based algorithms show overall the best agreement with the experimental data out of the three MLAs, for its global optimization and extrapolation ability.

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.

Deep learning: Using machine learning to study biological vision

OpenAIRE

Majaj, Najib; Pelli, Denis

2017-01-01

Today most vision-science presentations mention machine learning. Many neuroscientists use machine learning to decode neural responses. Many perception scientists try to understand recognition by living organisms. To them, machine learning offers a reference of attainable performance based on learned stimuli. This brief overview of the use of machine learning in biological vision touches on its strengths, weaknesses, milestones, controversies, and current directions.

Machine learning modelling for predicting soil liquefaction susceptibility

Directory of Open Access Journals (Sweden)

P. Samui

2011-01-01

Full Text Available This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN based on multi-layer perceptions (MLP that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N₁₆₀] and cyclic stress ratio (CSR. Further, an attempt has been made to simplify the models, requiring only the two parameters [(N₁₆₀ and peck ground acceleration (a_max/g], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Modelling open pit shovel-truck systems using the Machine Repair Model

Energy Technology Data Exchange (ETDEWEB)

Krause, A.; Musingwini, C. [CBH Resources Ltd., Sydney, NSW (Australia). Endeaver Mine

2007-08-15

Shovel-truck systems for loading and hauling material in open pit mines are now routinely analysed using simulation models or off-the-shelf simulation software packages, which can be very expensive for once-off or occasional use. The simulation models invariably produce different estimations of fleet sizes due to their differing estimations of cycle time. No single model or package can accurately estimate the required fleet size because the fleet operating parameters are characteristically random and dynamic. In order to improve confidence in sizing the fleet for a mining project, at least two estimation models should be used. This paper demonstrates that the Machine Repair Model can be modified and used as a model for estimating truck fleet size in an open pit shovel-truck system. The modified Machine Repair Model is first applied to a virtual open pit mine case study. The results compare favourably to output from other estimation models using the same input parameters for the virtual mine. The modified Machine Repair Model is further applied to an existing open pit coal operation, the Kwagga Section of Optimum Colliery as a case study. Again the results confirm those obtained from the virtual mine case study. It is concluded that the Machine Repair Model can be an affordable model compared to off-the-shelf generic software because it is easily modelled in Microsoft Excel, a software platform that most mines already use.

RAM analysis of earth pressure balance tunnel boring machines: A case study

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Hasel Amini Khoshalan

2015-12-01

Full Text Available Earth pressure balance tunnel boring machines (EPB-TBMs are favorably applied in urban tunneling projects. Despite their numerous advantages, considerable delays and high maintenance cost are the main disadvantages these machines suffer from. Reliability, availability, and maintainability (RAM analysis is a practical technique that uses failure and repair dataset obtained over a reasonable time for dealing with proper machine operation, maintenance scheduling, cost control, and improving the availability and performance of such machines. In the present study, a database of failures and repairs of an EBP-TBM was collected in line 1 of Tabriz subway project over a 26-month interval of machine operation. In order to model the reliability of the TBM, this machine was divided into five distinct subsystems including mechanical, electrical, hydraulic, pneumatic, and water systems in a series configuration. According to trend and serial correlation tests, the renewal processes were applied, for analysis of all subsystems. After calculating the reliability and maintainability functions for all subsystems, it was revealed that the mechanical subsystem with the highest failure frequency has the lowest reliability and maintainability. Similarly, estimating the availability of all subsystems indicated that the mechanical subsystem has a relatively low availability level of 52.6%, while other subsystems have acceptable availability level of 97%. Finally, the overall availability of studied machine was calculated as 48.3%.

Machinability of structural steels with calcium addition

International Nuclear Information System (INIS)

Pytel, S.; Zadecki, M.

2003-01-01

The machinability of the plain carbon and low alloy structural steels with carbon content of 0.1-0.6% is briefly discussed in the first part of the paper. In the experimental part a dependence between the addition of calcium and some changes in sulphide and oxide inclusions morphology is presented. The Volvo test for measurement of machinability index B i has been applied. Using the multiple regression methods two relationships between machinability index B i and stereological parameters of non-metallic inclusions as well as hardness of the steels have been calculated. The authors have reached the conclusion that owing to the changes in inclusion chemical composition and geometry as result of calcium addition the machinability index of the steel can be higher. (author)

Cutting Zone Temperature Identification During Machining of Nickel Alloy Inconel 718

Science.gov (United States)

Czán, Andrej; Daniš, Igor; Holubják, Jozef; Zaušková, Lucia; Czánová, Tatiana; Mikloš, Matej; Martikáň, Pavol

2017-12-01

Quality of machined surface is affected by quality of cutting process. There are many parameters, which influence on the quality of the cutting process. The cutting temperature is one of most important parameters that influence the tool life and the quality of machined surfaces. Its identification and determination is key objective in specialized machining processes such as dry machining of hard-to-machine materials. It is well known that maximum temperature is obtained in the tool rake face at the vicinity of the cutting edge. A moderate level of cutting edge temperature and a low thermal shock reduce the tool wear phenomena, and a low temperature gradient in the machined sublayer reduces the risk of high tensile residual stresses. The thermocouple method was used to measure the temperature directly in the cutting zone. An original thermocouple was specially developed for measuring of temperature in the cutting zone, surface and subsurface layers of machined surface. This paper deals with identification of temperature and temperature gradient during dry peripheral milling of Inconel 718. The measurements were used to identification the temperature gradients and to reconstruct the thermal distribution in cutting zone with various cutting conditions.

Self-commissioning of permanent magnet synchronous machine drives using hybrid approach

DEFF Research Database (Denmark)

Basar, Mehmet Sertug

2016-01-01

Self-commissioning of permanent-magnet (PM) synchronous machines (PMSMs) is of prime importance in an industrial drive system because control performance and system stability depend heavily on the accurate machine parameter information. This article focuses on a combination of offline and online...... parameter estimation for a non-salient pole PMSM which eliminates the need for any prior knowledge on machine parameters. Stator resistance and inductance are first identified at standstill utilising fundamental and high-frequency excitation signals, respectively. A novel method has been developed...... and employed for inductance estimation. Then, stator resistance, inductance and PM flux are updated online using a recursive least-squares (RLS) algorithm. The proposed controllers are designed using MATLAB/Simulink® and implemented on d-Space® real-time system incorporating a commercially available PMSM drive....

A COMPUTERIZED DIAGNOSTIC COMPLEX FOR RELIABILITY TESTING OF ELECTRIC MACHINES

Directory of Open Access Journals (Sweden)

O.О. Somka

2015-06-01

Full Text Available Purpose. To develop a diagnostic complex meeting the criteria and requirements for carrying out accelerated reliability test and realizing the basic modes of electric machines operation and performance of the posed problems necessary in the process of such test. Methodology. To determine and forecast the indices of electric machines reliability in accordance with the statistic data of repair plants we have conditionally divided them into structural parts that are most likely to fail. We have preliminarily assessed the state of each of these parts, which includes revelation of faults and deviations of technical and geometric parameters. We have determined the analyzed electric machine controlled parameters used for assessment of quantitative characteristics of reliability of these parts and electric machines on the whole. Results. As a result of the research, we have substantiated the structure of a computerized complex for electric machines reliability test. It allows us to change thermal and vibration actions without violation of the physics of the processes of aging and wearing of the basic structural parts and elements material. The above mentioned makes it possible to considerably reduce time spent on carrying out electric machines reliability tests and improve trustworthiness of the data obtained as a result of their performance. Originality. A special feature of determination of the controlled parameters consists in removal of vibration components in the idle mode and after disconnection of the analyzed electric machine from the power supply with the aim of singling out the vibration electromagnetic component, fixing the degree of sparking and bend of the shaft by means of phototechnique and local determination of structural parts temperature provided by corresponding location of thermal sensors. Practical value. We have offered a scheme of location of thermal and vibration sensors, which allows improvement of parameters measuring accuracy

Impact of the HEALTHY study on vending machine offerings in middle schools

Science.gov (United States)

The purpose of this study is to report the impact of the three-year middle school-based HEALTHY study on intervention school vending machine offerings. There were two goals for the vending machines: serve only dessert/snack foods with 200 kilocalories or less per single serving package, and eliminat...

Defining difficult laryngoscopy findings by using multiple parameters: A machine learning approach

Directory of Open Access Journals (Sweden)

Moustafa Abdelaziz Moustafa

2017-04-01

Conclusion: “Alex Difficult Laryngoscopy Software” (ADLS is a machine learning program for prediction of difficult laryngoscopy. New cases can be entered to the training set thus improving the accuracy of the software.

CAT-PUMA: CME Arrival Time Prediction Using Machine learning Algorithms

Science.gov (United States)

Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

2018-04-01

CAT-PUMA (CME Arrival Time Prediction Using Machine learning Algorithms) quickly and accurately predicts the arrival of Coronal Mass Ejections (CMEs) of CME arrival time. The software was trained via detailed analysis of CME features and solar wind parameters using 182 previously observed geo-effective partial-/full-halo CMEs and uses algorithms of the Support Vector Machine (SVM) to make its predictions, which can be made within minutes of providing the necessary input parameters of a CME.

Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

Directory of Open Access Journals (Sweden)

C. V. Subbulakshmi

2015-01-01

Full Text Available Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO algorithm with the extreme learning machine (ELM classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN, proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

Identification of Synchronous Generator Electric Parameters Connected to the Distribution Grid

Directory of Open Access Journals (Sweden)

Frolov M. Yu.

2017-04-01

Full Text Available According to modern trends, the power grids with distributed generation will have an open system architecture. It means that active consumers, owners of distributed power units, including mobile units, must have free access to the grid, like when using internet, so it is necessary to have plug and play technologies. Thanks to them, the system will be able to identify the unit type and the unit parameters. Therefore, the main aim of research, described in the paper, was to develop and research a new method of electric parameters identification of synchronous generator. The main feature of the proposed method is that parameter identification is performed while the generator to the grid, so it fits in the technological process of operation of the machine and does not influence on the connection time of the machine. For the implementation of the method, it is not necessary to create dangerous operation modes for the machine or to have additional expensive equipment and it can be used for salient pole machines and round rotor machines. The parameter identification accuracy can be achieved by more accurate account of electromechanical transient process, and making of overdetermined system with many more numbers of equations. Parameter identification will be made with each generator connection to the grid. Comparing data obtained from each connection, the middle values can be find by numerical method, and thus, each subsequent identification will accurate the machine parameters.

Identifying predictive features in drug response using machine learning: opportunities and challenges.

Science.gov (United States)

Vidyasagar, Mathukumalli

2015-01-01

This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

A single-phase axially-magnetized permanent-magnet oscillating machine for miniature aerospace power sources

Directory of Open Access Journals (Sweden)

Yi Sui

2017-05-01

Full Text Available A single-phase axially-magnetized permanent-magnet (PM oscillating machine which can be integrated with a free-piston Stirling engine to generate electric power, is investigated for miniature aerospace power sources. Machine structure, operating principle and detent force characteristic are elaborately studied. With the sinusoidal speed characteristic of the mover considered, the proposed machine is designed by 2D finite-element analysis (FEA, and some main structural parameters such as air gap diameter, dimensions of PMs, pole pitches of both stator and mover, and the pole-pitch combinations, etc., are optimized to improve both the power density and force capability. Compared with the three-phase PM linear machines, the proposed single-phase machine features less PM use, simple control and low controller cost. The power density of the proposed machine is higher than that of the three-phase radially-magnetized PM linear machine, but lower than the three-phase axially-magnetized PM linear machine.

A single-phase axially-magnetized permanent-magnet oscillating machine for miniature aerospace power sources

Science.gov (United States)

Sui, Yi; Zheng, Ping; Cheng, Luming; Wang, Weinan; Liu, Jiaqi

2017-05-01

A single-phase axially-magnetized permanent-magnet (PM) oscillating machine which can be integrated with a free-piston Stirling engine to generate electric power, is investigated for miniature aerospace power sources. Machine structure, operating principle and detent force characteristic are elaborately studied. With the sinusoidal speed characteristic of the mover considered, the proposed machine is designed by 2D finite-element analysis (FEA), and some main structural parameters such as air gap diameter, dimensions of PMs, pole pitches of both stator and mover, and the pole-pitch combinations, etc., are optimized to improve both the power density and force capability. Compared with the three-phase PM linear machines, the proposed single-phase machine features less PM use, simple control and low controller cost. The power density of the proposed machine is higher than that of the three-phase radially-magnetized PM linear machine, but lower than the three-phase axially-magnetized PM linear machine.

A self-centering active probing technique for kinematic parameter identification and verification of articulated arm coordinate measuring machines

International Nuclear Information System (INIS)

Santolaria, J; Brau, A; Velázquez, J; Aguilar, J J

2010-01-01

A crucial task in the procedure of identifying the parameters of a kinematic model of an articulated arm coordinate measuring machine (AACMM) or robot arm is the process of capturing data. In this paper a capturing data method is analyzed using a self-centering active probe, which drastically reduces the capture time and the required number of positions of the gauge as compared to the usual standard and manufacturer methods. The mathematical models of the self-centering active probe and AACMM are explained, as well as the mathematical model that links the AACMM global reference system to the probe reference system. We present a self-calibration method that will allow us to determine a homogeneous transformation matrix that relates the probe's reference system to the AACMM last reference system from the probing of a single sphere. In addition, a comparison between a self-centering passive probe and self-centering active probe is carried out to show the advantages of the latter in the procedures of kinematic parameter identification and verification of the AACMM

Analysis and prediction of dimensions and cost of laser micro-machining internal channel fabrication process

Directory of Open Access Journals (Sweden)

Brabazon D.

2010-06-01

Full Text Available This paper presents the utilisation of Response Surface Methodology (RSM as the prediction tool for the laser micro-machining process. Laser internal microchannels machined using pulsed Nd:YVO4 laser in polycarbonate were investigated. The experiments were carried out according to 33 factorial Design of Experiment (DoE. In this work the three input process set as control parameters were laser power, P; pulse repetition frequency, PRF; and sample translation speed, U. Measured responses were the channel width and the micro-machining operating cost per metre of produced microchannels. The responses were sufficiently predicted within the set micro-machining parameters limits. Two factorial interaction (2FI and quadratic polynomial regression equations for both responses were constructed. It is proposed that the developed prediction equations can be used to find locally optimal micro-machining process parameters under experimental and operational conditions.

Engineered Surface Properties of Porous Tungsten from Cryogenic Machining

Science.gov (United States)

Schoop, Julius Malte

Porous tungsten is used to manufacture dispenser cathodes due to it refractory properties. Surface porosity is critical to functional performance of dispenser cathodes because it allows for an impregnated ceramic compound to migrate to the emitting surface, lowering its work function. Likewise, surface roughness is important because it is necessary to ensure uniform wetting of the molten impregnate during high temperature service. Current industry practice to achieve surface roughness and surface porosity requirements involves the use of a plastic infiltrant during machining. After machining, the infiltrant is baked and the cathode pellet is impregnated. In this context, cryogenic machining is investigated as a substitutionary process for the current plastic infiltration process. Along with significant reductions in cycle time and resource use, surface quality of cryogenically machined un-infiltrated (as-sintered) porous tungsten has been shown to significantly outperform dry machining. The present study is focused on examining the relationship between machining parameters and cooling condition on the as-machined surface integrity of porous tungsten. The effects of cryogenic pre-cooling, rake angle, cutting speed, depth of cut and feed are all taken into consideration with respect to machining-induced surface morphology. Cermet and Polycrystalline diamond (PCD) cutting tools are used to develop high performance cryogenic machining of porous tungsten. Dry and pre-heated machining were investigated as a means to allow for ductile mode machining, yet severe tool-wear and undesirable smearing limited the feasibility of these approaches. By using modified PCD cutting tools, high speed machining of porous tungsten at cutting speeds up to 400 m/min is achieved for the first time. Beyond a critical speed, brittle fracture and built-up edge are eliminated as the result of a brittle to ductile transition. A model of critical chip thickness ( hc ) effects based on cutting

Reducing the uncertainty in robotic machining by modal analysis

Science.gov (United States)

Alberdi, Iñigo; Pelegay, Jose Angel; Arrazola, Pedro Jose; Ørskov, Klaus Bonde

2017-10-01

The use of industrial robots for machining could lead to high cost and energy savings for the manufacturing industry. Machining robots offer several advantages respect to CNC machines such as flexibility, wide working space, adaptability and relatively low cost. However, there are some drawbacks that are preventing a widespread adoption of robotic solutions namely lower stiffness, vibration/chatter problems and lower accuracy and repeatability. Normally due to these issues conservative cutting parameters are chosen, resulting in a low material removal rate (MRR). In this article, an example of a modal analysis of a robot is presented. For that purpose the Tap-testing technology is introduced, which aims at maximizing productivity, reducing the uncertainty in the selection of cutting parameters and offering a stable process free from chatter vibrations.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

Science.gov (United States)

Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

Science.gov (United States)

Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Artificial neural network applying for justification of tractors undercarriages parameters

Directory of Open Access Journals (Sweden)

V. A. Kuz’Min

2017-01-01

Full Text Available One of the most important properties that determine undercarriage layout on design stage is the soil compaction effect. Existing domestic standards of undercarriages impact to soil do not meet modern agricultural requirements completely. The authors justify the need for analysis of traction and transportation machines travel systems and recommendations for these parameters applied to machines that are on design or modernization stage. The database of crawler agricultural tractors particularly in such parameters as traction class and basic operational weight, engine power rating, average ground pressure, square of track basic branch surface area was modeled. Meanwhile the considered machines were divided into two groups by producing countries: Europe/North America and Russian Federation/CIS. The main graphical dependences for every group of machines are plotted, and the conforming analytical dependences within the ranges with greatest concentration of machines are generated. To make the procedure of obtaining parameters of the soil panning by tractors easier it is expedient to use the program tool - artificial neural network (or perceptron. It is necessary to apply to the solution of this task multilayered perceptron - neutron network of direct distribution of signals (without feedback. To carry out the analysis of parameters of running systems taking into account parameters of the soil panning by them and to recommend the choice of these parameters for newly created machines. The program code of artificial neural network is developed. On the basis of the created base of tractors the artificial neural network was created and tested. Accumulated error was not more than 5 percent. These data indicate the results accuracy and tool reliability. It is possible by operating initial design-data base and using the designed artificial neural network to define missing parameters.

Study of Material Consolidation at Higher Throughput Parameters in Selective Laser Melting of Inconel 718

Science.gov (United States)

Prater, Tracie

2016-01-01

Selective Laser Melting (SLM) is a powder bed fusion additive manufacturing process used increasingly in the aerospace industry to reduce the cost, weight, and fabrication time for complex propulsion components. SLM stands poised to revolutionize propulsion manufacturing, but there are a number of technical questions that must be addressed in order to achieve rapid, efficient fabrication and ensure adequate performance of parts manufactured using this process in safety-critical flight applications. Previous optimization studies for SLM using the Concept Laser M1 and M2 machines at NASA Marshall Space Flight Center have centered on machine default parameters. The objective of this work is to characterize the impact of higher throughput parameters (a previously unexplored region of the manufacturing operating envelope for this application) on material consolidation. In phase I of this work, density blocks were analyzed to explore the relationship between build parameters (laser power, scan speed, hatch spacing, and layer thickness) and material consolidation (assessed in terms of as-built density and porosity). Phase II additionally considers the impact of post-processing, specifically hot isostatic pressing and heat treatment, as well as deposition pattern on material consolidation in the same higher energy parameter regime considered in the phase I work. Density and microstructure represent the "first-gate" metrics for determining the adequacy of the SLM process in this parameter range and, as a critical initial indicator of material quality, will factor into a follow-on DOE that assesses the impact of these parameters on mechanical properties. This work will contribute to creating a knowledge base (understanding material behavior in all ranges of the AM equipment operating envelope) that is critical to transitioning AM from the custom low rate production sphere it currently occupies to the world of mass high rate production, where parts are fabricated at a rapid

Beam Dynamics Studies in Recirculating Machines

CERN Document Server

Pellegrini, Dario; Latina, A

The LHeC and the CLIC Drive Beam share not only the high-current beams that make them prone to show instabilities, but also unconventional lattice topologies and operational schemes in which the time sequence of the bunches varies along the machine. In order to asses the feasibility of these projects, realistic simulations taking into account the most worrisome effects and their interplays, are crucial. These include linear and non-linear optics with time dependent elements, incoherent and coherent synchrotron radiation, short and long-range wakefields, beam-beam effect and ion cloud. In order to investigate multi-bunch effects in recirculating machines, a new version of the tracking code PLACET has been developed from scratch. PLACET2, already integrates most of the effects mentioned before and can easily receive additional physics. Its innovative design allows to describe complex lattices and track one or more bunches accordingly to the machine operation, reproducing the bunch train splitting and recombinat...

An Artificial Neural Network Modeling for Force Control System of a Robotic Pruning Machine

Directory of Open Access Journals (Sweden)

Ali Hashemi

2014-06-01

Full Text Available Nowadays, there has been an increasing application of pruning robots for planted forests due to the growing concern on the efficiency and safety issues. Power consumption and working time of agricultural machines have become important issues due to the high value of energy in modern world. In this study, different multi-layer back-propagation networks were utilized for mapping the complex and highly interactive of pruning process parameters and to predict power consumption and cutting time of a force control equipped robotic pruning machine by knowing input parameters such as: rotation speed, stalk diameter, and sensitivity coefficient. Results showed significant effects of all input parameters on output parameters except rotational speed on cutting time. Therefore, for reducing the wear of cutting system, a less rotational speed in every sensitivity coefficient should be selected.

Evaluation of Empirical and Machine Learning Algorithms for Estimation of Coastal Water Quality Parameters

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Majid Nazeer

2017-11-01

Full Text Available Coastal waters are one of the most vulnerable resources that require effective monitoring programs. One of the key factors for effective coastal monitoring is the use of remote sensing technologies that significantly capture the spatiotemporal variability of coastal waters. Optical properties of coastal waters are strongly linked to components, such as colored dissolved organic matter (CDOM, chlorophyll-a (Chl-a, and suspended solids (SS concentrations, which are essential for the survival of a coastal ecosystem and usually independent of each other. Thus, developing effective remote sensing models to estimate these important water components based on optical properties of coastal waters is mandatory for a successful coastal monitoring program. This study attempted to evaluate the performance of empirical predictive models (EPM and neural networks (NN-based algorithms to estimate Chl-a and SS concentrations, in the coastal area of Hong Kong. Remotely-sensed data over a 13-year period was used to develop regional and local models to estimate Chl-a and SS over the entire Hong Kong waters and for each water class within the study area, respectively. The accuracy of regional models derived from EPM and NN in estimating Chl-a and SS was 83%, 93%, 78%, and 97%, respectively, whereas the accuracy of local models in estimating Chl-a and SS ranged from 60–94% and 81–94%, respectively. Both the regional and local NN models exhibited a higher performance than those models derived from empirical analysis. Thus, this study suggests using machine learning methods (i.e., NN for the more accurate and efficient routine monitoring of coastal water quality parameters (i.e., Chl-a and SS concentrations over the complex coastal area of Hong Kong and other similar coastal environments.

Modeling and Analysis of CNC Milling Process Parameters on Al3030 based Composite

Science.gov (United States)

Gupta, Anand; Soni, P. K.; Krishna, C. M.

2018-04-01

The machining of Al3030 based composites on Computer Numerical Control (CNC) high speed milling machine have assumed importance because of their wide application in aerospace industries, marine industries and automotive industries etc. Industries mainly focus on surface irregularities; material removal rate (MRR) and tool wear rate (TWR) which usually depends on input process parameters namely cutting speed, feed in mm/min, depth of cut and step over ratio. Many researchers have carried out researches in this area but very few have taken step over ratio or radial depth of cut also as one of the input variables. In this research work, the study of characteristics of Al3030 is carried out at high speed CNC milling machine over the speed range of 3000 to 5000 r.p.m. Step over ratio, depth of cut and feed rate are other input variables taken into consideration in this research work. A total nine experiments are conducted according to Taguchi L9 orthogonal array. The machining is carried out on high speed CNC milling machine using flat end mill of diameter 10mm. Flatness, MRR and TWR are taken as output parameters. Flatness has been measured using portable Coordinate Measuring Machine (CMM). Linear regression models have been developed using Minitab 18 software and result are validated by conducting selected additional set of experiments. Selection of input process parameters in order to get best machining outputs is the key contributions of this research work.

Numerical Study of Compact Plate-Fin Heat Exchanger for Rotary-Vane Gas Refrigeration Machine

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V. V. Trandafilov

2017-10-01

Full Text Available Plate-fin heat exchangers are widely used in refrigeration technique. They are popular because of their compactness and excellent heat transfer performance. Here we present a numerical model for the development, research and optimization of a plate-fin heat exchanger for a rotary-vane gas refrigeration machine. The method of analysis by graphic method of plate - fin heat exchanger is proposed. The model describes the effects of secondary parameters such as axial thermal conductivity through a metal matrix of the heat exchanger. The influence of geometric parameters and heat transfer coefficient is studied. Graphs of dependences of length, efficiency of a fin and pressure drop in a heat exchanger on the thickness of the fin and the number of fins per meter are obtained. To analyze the results of numerical simulation, the heat exchanger was designed in the Aspen HYSYS program. The simulation results show that the total deviation from the proposed numerical model is not more than 15%. 

Study of a Variable Mass Atwood's Machine Using a Smartphone

Science.gov (United States)

Lopez, Dany; Caprile, Isidora; Corvacho, Fernando; Reyes, Orfa

2018-01-01

The Atwood machine was invented in 1784 by George Atwood and this system has been widely studied both theoretically and experimentally over the years. Nowadays, it is commonplace that many experimental physics courses include both Atwood's machine and variable mass to introduce more complex concepts in physics. To study the dynamics of the masses…

Impact of the HEALTHY Study on Vending Machine Offerings in Middle Schools

Science.gov (United States)

Hartstein, Jill; Cullen, Karen W.; Virus, Amy; El Ghormli, Laure; Volpe, Stella L.; Staten, Myrlene A.; Bridgman, Jessica C.; Stadler, Diane D.; Gillis, Bonnie; McCormick, Sarah B.; Mobley, Connie C.

2011-01-01

Purpose/Objectives: The purpose of this study is to report the impact of the three-year middle school-based HEALTHY study on intervention school vending machine offerings. There were two goals for the vending machines: serve only dessert/snack foods with 200 kilocalories or less per single serving package, and eliminate 100% fruit juice and…

Experimental works in plasma developed in INPE (Brazil). 1. Double plasma machine for longitudinal wave study. 2. Plasma centrifuge

International Nuclear Information System (INIS)

Ferreira, J.L.; Ludwig, G.O.; Del Bosco, E.

1982-01-01

This work describes some experiments done at the Plasma Physics Laboratory at INPE. In the first part, the double plasma machine used for the study of ion acoustic wave propagation is described, and the results obtained so far are shown. The second part consists in the description of a plasma centrifuge project. It contains some basic parameters of our apparatus used for isotope separation, throuth electromagtnetic rotation of the plasma. (Author) [pt

Analysis of machining and machine tools

CERN Document Server

Liang, Steven Y

2016-01-01

This book delivers the fundamental science and mechanics of machining and machine tools by presenting systematic and quantitative knowledge in the form of process mechanics and physics. It gives readers a solid command of machining science and engineering, and familiarizes them with the geometry and functionality requirements of creating parts and components in today’s markets. The authors address traditional machining topics, such as: single and multiple point cutting processes grinding components accuracy and metrology shear stress in cutting cutting temperature and analysis chatter They also address non-traditional machining, such as: electrical discharge machining electrochemical machining laser and electron beam machining A chapter on biomedical machining is also included. This book is appropriate for advanced undergraduate and graduate mechani cal engineering students, manufacturing engineers, and researchers. Each chapter contains examples, exercises and their solutions, and homework problems that re...

Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

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Kuan-Cheng Lin

2015-01-01

Full Text Available Rapid advances in information and communication technology have made ubiquitous computing and the Internet of Things popular and practicable. These applications create enormous volumes of data, which are available for analysis and classification as an aid to decision-making. Among the classification methods used to deal with big data, feature selection has proven particularly effective. One common approach involves searching through a subset of the features that are the most relevant to the topic or represent the most accurate description of the dataset. Unfortunately, searching through this kind of subset is a combinatorial problem that can be very time consuming. Meaheuristic algorithms are commonly used to facilitate the selection of features. The artificial fish swarm algorithm (AFSA employs the intelligence underlying fish swarming behavior as a means to overcome optimization of combinatorial problems. AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA to improve feature selection and parameter optimization for support vector machine classifiers. Experiment results demonstrate the superiority of MAFSA in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original FASA.

Electrochemical Machining – Special Equipment and Applications in Aircraft Industry

Directory of Open Access Journals (Sweden)

Ruszaj Adam

2016-06-01

Full Text Available Electrochemical machining is an unique method of shaping in which, for optimal parameters tool has no wear, surface layer properties after machining are similar to the core material and surface quality and accuracy increase together with material removal rate increase. Such advantages of electrochemical machining, besides of some ecological problems, create industry interest in the range of manufacturing elements made of materials with special properties (i.e. turbine blades of flow aircrafts engines. In the paper the nowadays possibilities and recent practical application of electrochemical machining in aircraft have been presented.

Intellectual Property and Machine Learning: An exploratory study

OpenAIRE

Øverlier, Lasse

2017-01-01

Our research makes a contribution by exemplifying what controls the freedom-to-operate for a company operating in the area of machine learning. Through interviews we demonstrate the industry’s alternating viewpoints to whether copyrighted data used as input to machine learning systems should be viewed differently than copying the data for storage or reproduction. In addition we show that unauthorized use of copyrighted data in machine learning systems is hard to detect with the burden of proo...

Stability Enhancement of Multi machine AC Systems by Synchronverter HVDC control

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Raouia Aouini

2016-06-01

Full Text Available This paper investigates the impact of the Synchronverter based HVDC control on power system stability. The study considers multi machine power systems, with realistic parameters. A specific tuning method of the parameters of the regulators is used. The proposed control scheme is based on the sensitivity of the poles of the HVDC neighbor zone to the control parameters, and next, on their placement using residues. The transient stability of the HVDC neighbor zone is a priori taken into account at the design stage. The new tuning method is evaluated in comparison with the standard vector control via simulation tests. Extensive tests are performed using Matlab/Simulink implementation of the IEEE 9 bus/3 machines test system. The results prove the superiority of the proposed control to the classic vector control. The synchronverter control allows to improve not only the local performances of the HVDC link, but also the overall transient stability of the AC zone in which the HVDC is inserted. (where

Using Phun to Study "Perpetual Motion" Machines

Science.gov (United States)

Kores, Jaroslav

2012-01-01

The concept of "perpetual motion" has a long history. The Indian astronomer and mathematician Bhaskara II (12th century) was the first person to describe a perpetual motion (PM) machine. An example of a 13th-century PM machine is shown in Fig. 1. Although the law of conservation of energy clearly implies the impossibility of PM construction, over…

An analysis of switching and non-switching slot machine player behaviour.

Science.gov (United States)

Coates, Ewan; Blaszczynski, Alex

2013-12-01

Learning theory predicts that, given the repeated choice to bet between two concurrently available slot machines, gamblers will learn to bet more money on the machine with higher expected return (payback percentage) or higher win probability per spin (volatility). The purpose of this study was to investigate whether this occurs when the two machines vary orthogonally on payback percentage and volatility. The sample comprised 52 first year psychology students (mean age = 20.3 years, 20 females, 32 males) who had played a gaming machine at least once in the previous 12 months. Participants were administered a battery of questionnaires designed to assess level of knowledge on the characteristics and operation of poker machines, frequency of poker machine play in the past 12 months, personality traits of impulsivity and capacity for cognitive reflection, and gambling beliefs. For the experimental task, participants were instructed to play on two PC-simulated electronic gaming machines (EGMs or slot machines) that differed on payback percentage and volatility, with the option of freely switching between EGMs after a practice phase. Results indicated that participants were able to easily discriminate between machines and manifested a preference to play machines offering higher payback or volatility. These findings diverged from previous findings of no preference for play on higher payback/volatility machines, potentially due to of the current study's absence of the option to make multi-line and multi-credit bets. It was concluded that return rate parameters like payback percentage and volatility strongly influenced slot machine preference in the absence of betting options like multi-line bets, though more research is needed to determine the effects of such betting options on player distribution of money between multiple EGMs.

Principle of maximum entropy for reliability analysis in the design of machine components

Science.gov (United States)

Zhang, Yimin

2018-03-01

We studied the reliability of machine components with parameters that follow an arbitrary statistical distribution using the principle of maximum entropy (PME). We used PME to select the statistical distribution that best fits the available information. We also established a probability density function (PDF) and a failure probability model for the parameters of mechanical components using the concept of entropy and the PME. We obtained the first four moments of the state function for reliability analysis and design. Furthermore, we attained an estimate of the PDF with the fewest human bias factors using the PME. This function was used to calculate the reliability of the machine components, including a connecting rod, a vehicle half-shaft, a front axle, a rear axle housing, and a leaf spring, which have parameters that typically follow a non-normal distribution. Simulations were conducted for comparison. This study provides a design methodology for the reliability of mechanical components for practical engineering projects.

Parametric study of an absorption refrigeration machine using advanced exergy analysis

International Nuclear Information System (INIS)

Gong, Sunyoung; Goni Boulama, Kiari

2014-01-01

An advanced exergy analysis of a water–lithium bromide absorption refrigeration machine was conducted. For each component of the machine, the proposed analysis quantified the irreversibility that can be avoided and the irreversibility that is unavoidable. It also identified the irreversibility originating from inefficiencies within the component and the irreversibility that does not originate from the operation of the considered component. It was observed that the desorber and absorber concentrated most of the exergy destruction. Furthermore, the exergy destruction at these components was found to be dominantly endogenous and unavoidable. A parametrical study has been presented discussing the sensitivity of the different performance indicators to the temperature at which the heat source is available, the temperature of the refrigerated environment, and the temperature of the cooling medium used at the condenser and absorber. It was observed that the endogenous avoidable exergy destruction at the desorber, i.e. the portion of the desorber irreversibility that could be avoided by improving the design and operation of the desorber, decreased when the heat source or the temperature at which the cooling effect was generated increased, and it decreased when the heat sink temperature increased. The endogenous avoidable exergy destruction at the absorber displayed the same variations, though it was observed to be less affected by the heat source temperature. Contrary to the aforementioned two components, the exergy destruction at the evaporator and condenser were dominantly endogenous and avoidable, with little sensitivity to the cycle operating parameters. - Highlights: • Endogenous, exogenous, avoidable and unavoidable irreversibilities were calculated for a water–LiBr absorption machine. • Overall, desorber and absorber concentrated most of the exergy destruction of the cycle. • The exergy destruction was mainly endogenous and unavoidable for the desorber and

Classifying smoking urges via machine learning.

Science.gov (United States)

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-12-01

Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights

Machine learning techniques in optical communication

DEFF Research Database (Denmark)

Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

2016-01-01

Machine learning techniques relevant for nonlinearity mitigation, carrier recovery, and nanoscale device characterization are reviewed and employed. Markov Chain Monte Carlo in combination with Bayesian filtering is employed within the nonlinear state-space framework and demonstrated for parameter...

Micro Electro Discharge Machining of Electrically Nonconductive Ceramics

International Nuclear Information System (INIS)

Schubert, A.; Zeidler, H.; Hackert, M.; Wolf, N.

2011-01-01

EDM is a known process for machining of hard and brittle materials. Due to its noncontact and nearly forceless behaviour, it has been introduced into micro manufacturing and through constant development it is now an important means for producing high-precision micro geometries. One restriction of EDM is its limitation to electrically conducting materials.Today many applications, especially in the biomedical field, make use of the benefits of ceramic materials, such as high strength, very low wear and biocompatibility. Common ceramic materials such as Zirconium dioxide are, due to their hardness in the sintered state, difficult to machine with conventional cutting techniques. A demand for the introduction of EDM to these materials could so far not be satisfied because of their nonconductive nature.At the Chemnitz University of Technology and the Fraunhofer IWU, investigations in the applicability of micro-EDM for the machining of nonconductive ceramics are being conducted. Tests are undertaken using micro-EDM drilling with Tungsten carbide tool electrodes and ZrO 2 ceramic workpieces. A starting layer, in literature often referred to as 'assisting electrode' is used to set up a closed electric circuit to start the EDM process. Combining carbon hydride based dielectric and a specially designed low-frequency vibration setup to excite the workpiece, the process environment can be held within parameters to allow for a constant EDM process even after the starting layer is machined. In the experiments a cylindrical 120 μm diameter Tungsten carbide tool electrode and Y 2 O 3 - and MgO- stabilized ZrO 2 worpieces are used. The current and voltage signals of the discharges within the different stages of the process (machining of the starting layer, machining of the base material, transition stage) are recorded and their characteristics compared to discharges in metallic material. Additionally, the electrode feed is monitored. The influences of the process parameters are

Output-only modal parameter estimator of linear time-varying structural systems based on vector TAR model and least squares support vector machine

Science.gov (United States)

Zhou, Si-Da; Ma, Yuan-Chen; Liu, Li; Kang, Jie; Ma, Zhi-Sai; Yu, Lei

2018-01-01

Identification of time-varying modal parameters contributes to the structural health monitoring, fault detection, vibration control, etc. of the operational time-varying structural systems. However, it is a challenging task because there is not more information for the identification of the time-varying systems than that of the time-invariant systems. This paper presents a vector time-dependent autoregressive model and least squares support vector machine based modal parameter estimator for linear time-varying structural systems in case of output-only measurements. To reduce the computational cost, a Wendland's compactly supported radial basis function is used to achieve the sparsity of the Gram matrix. A Gamma-test-based non-parametric approach of selecting the regularization factor is adapted for the proposed estimator to replace the time-consuming n-fold cross validation. A series of numerical examples have illustrated the advantages of the proposed modal parameter estimator on the suppression of the overestimate and the short data. A laboratory experiment has further validated the proposed estimator.

Micro Electro Discharge Machining for Nonconductive Ceramic Materials

Directory of Open Access Journals (Sweden)

Mohammad Yeakub Ali

2018-03-01

Full Text Available In micro-electro discharge machining (micro-EDM of nonconductive ceramics, material is removed mainly by spalling due to the dominance of alternating thermal load. The established micro-EDM models established for single spark erosion are not applicable for nonconductive ceramics because of random spalling. Moreover, it is difficult to create single spark on a nonconductive ceramic workpiece when the spark is initiated by the assisting electrode. In this paper, theoretical model of material removal rate (MRR as the function of capacitance and voltage is developed for micro-EDM of nonconductive zirconium oxide (ZrO2. It is shown that the charging and discharging duration depend on the capacitance and resistances of the circuit. The number of sparks per unit time is estimated from the single spark duration s derived from heat transfer fundamentals. The model showed that both the capacitance and voltage are significant process parameters where any increase of capacitance and voltage increases the MRR. However, capacitance was found to be the dominating parameter over voltage. As in case of higher capacitances, the creation of a conductive carbonic layer on the machined surface was not stable; the effective window of machining 101 - 103 pF capacitance and 80 - 100 V gap voltage or 10 - 470 pF capacitance and 80 - 110 V gap voltage. This fact was confirmed EDX analysis where the presence of high carbon content was evident. Conversely, the spark was found to be inconsistent using parameters beyond these ranges and consequently insignificant MRR. Nevertheless, the effective number of sparks per second were close to the predicted numbers when machining conductive copper material. In addition, higher percentage of ineffective pulses was observed during the machining which eventually reduced the MRR. In case of validation, average deviations between the predicted and experimental values were found to be around 10%. Finally, micro-channels were machined on

Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

Science.gov (United States)

Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

2016-01-01

Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

Supervised Machine Learning for Regionalization of Environmental Data: Distribution of Uranium in Groundwater in Ukraine

Science.gov (United States)

Govorov, Michael; Gienko, Gennady; Putrenko, Viktor

2018-05-01

In this paper, several supervised machine learning algorithms were explored to define homogeneous regions of con-centration of uranium in surface waters in Ukraine using multiple environmental parameters. The previous study was focused on finding the primary environmental parameters related to uranium in ground waters using several methods of spatial statistics and unsupervised classification. At this step, we refined the regionalization using Artifi-cial Neural Networks (ANN) techniques including Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Convolutional Neural Network (CNN). The study is focused on building local ANN models which may significantly improve the prediction results of machine learning algorithms by taking into considerations non-stationarity and autocorrelation in spatial data.

A Method for Identifying the Mechanical Parameters in Resistance Spot Welding Machines

DEFF Research Database (Denmark)

Wu, Pei; Zhang, Wenqi; Bay, Niels

2003-01-01

Mechanical dynamic responses of resistance welding machine have a significant influence on weld quality and electrode service life, it must be considered when the real welding production is carried out or the welding process is stimulated. The mathematical models for characterizing the mechanical...

Novel jet observables from machine learning

Science.gov (United States)

Datta, Kaustuv; Larkoski, Andrew J.

2018-03-01

Previous studies have demonstrated the utility and applicability of machine learning techniques to jet physics. In this paper, we construct new observables for the discrimination of jets from different originating particles exclusively from information identified by the machine. The approach we propose is to first organize information in the jet by resolved phase space and determine the effective N -body phase space at which discrimination power saturates. This then allows for the construction of a discrimination observable from the N -body phase space coordinates. A general form of this observable can be expressed with numerous parameters that are chosen so that the observable maximizes the signal vs. background likelihood. Here, we illustrate this technique applied to discrimination of H\\to b\\overline{b} decays from massive g\\to b\\overline{b} splittings. We show that for a simple parametrization, we can construct an observable that has discrimination power comparable to, or better than, widely-used observables motivated from theory considerations. For the case of jets on which modified mass-drop tagger grooming is applied, the observable that the machine learns is essentially the angle of the dominant gluon emission off of the b\\overline{b} pair.

Analysis of Effects of Cutting Parameters of Wire Electrical Discharge Machining on Material Removal Rate and Surface Integrity

Science.gov (United States)

Tonday, H. R.; Tigga, A. M.

2016-02-01

As wire electrical discharge machining is pioneered as a vigorous, efficient and precise and complex nontraditional machining technique, research is needed in this area for efficient machining. In this paper, the influence of various input factors of wire electrical discharge machining (WEDM) on output variable has been analyzed by using Taguchi technique and analysis of variance. The design of experiments has been done and by applying L8 orthogonal arrays method and experiments have been conducted and collected required data. The objectives of the research are to maximize the material removal rate and to minimize the surface roughness value (Ra). Surface morphology of machined workpiece has been obtained and examined by employing scanning electron microscopy (SEM) technique.

Analysis of Effects of Cutting Parameters of Wire Electrical Discharge Machining on Material Removal Rate and Surface Integrity

International Nuclear Information System (INIS)

Tonday, H. R.; Tigga, A. M.

2016-01-01

As wire electrical discharge machining is pioneered as a vigorous, efficient and precise and complex nontraditional machining technique, research is needed in this area for efficient machining. In this paper, the influence of various input factors of wire electrical discharge machining (WEDM) on output variable has been analyzed by using Taguchi technique and analysis of variance. The design of experiments has been done and by applying L8 orthogonal arrays method and experiments have been conducted and collected required data. The objectives of the research are to maximize the material removal rate and to minimize the surface roughness value (Ra). Surface morphology of machined workpiece has been obtained and examined by employing scanning electron microscopy (SEM) technique. (paper)

Possibilities for Automatic Control of Hydro-Mechanical Transmission and Birotating Electric Machine

Directory of Open Access Journals (Sweden)

V. V. Mikhailov

2014-01-01

Full Text Available The paper presents mathematical models and results of virtual investigations pertaining to the selected motion parameters of a mobile machine equipped with hydro mechanical and modernized transmissions. The machine has been tested in similar technological cycles and it has been equipped with a universal automatic control system. Changes in structure and type of power transmission have been obtained with the help of a control algorithm including an extra reversible electric machine which is switched in at some operational modes.Implementation of the proposed  concept makes it possible to obtain and check the improved C-code of the control system and enhance operational parameters of the transmission and machine efficiency, reduce slippage and tire wear while using braking energy for its later beneficial use which is usually considered as a consumable element.

A comparative study of machine learning models for ethnicity classification

Science.gov (United States)

Trivedi, Advait; Bessie Amali, D. Geraldine

2017-11-01

This paper endeavours to adopt a machine learning approach to solve the problem of ethnicity recognition. Ethnicity identification is an important vision problem with its use cases being extended to various domains. Despite the multitude of complexity involved, ethnicity identification comes naturally to humans. This meta information can be leveraged to make several decisions, be it in target marketing or security. With the recent development of intelligent systems a sub module to efficiently capture ethnicity would be useful in several use cases. Several attempts to identify an ideal learning model to represent a multi-ethnic dataset have been recorded. A comparative study of classifiers such as support vector machines, logistic regression has been documented. Experimental results indicate that the logical classifier provides a much accurate classification than the support vector machine.

Tooth-coil permanent magnet synchronous machine design for special applications

Energy Technology Data Exchange (ETDEWEB)

Ponomarev, P.

2013-11-01

This doctoral thesis presents a study on the design of tooth-coil permanent magnet synchronous machines. The electromagnetic properties of concentrated non-overlapping winding permanent magnet synchronous machines, or simply tooth-coil permanent magnet synchronous machines (TC-PMSMs), are studied in details. It is shown that current linkage harmonics play the deterministic role in the behavior of this type of machines. Important contributions are presented as regards of calculation of parameters of TC-PMSMs,particularly the estimation of inductances. The current linkage harmonics essentially define the air-gap harmonic leakage inductance, rotor losses and localized temporal inductance variation. It is proven by FEM analysis that inductance variation caused by the local temporal harmonic saturation results in considerable torque ripple, and can influence on sensorless control capabilities. Example case studies an integrated application of TC-IPMSMs in hybrid off-highway working vehicles. A methodology for increasing the efficiency of working vehicles is introduced. It comprises several approaches - hybridization, working operations optimization, component optimization and integration. As a result of component optimization and integration, a novel integrated electro-hydraulic energy converter (IEHEC) for off-highway working vehicles is designed. The IEHEC can considerably increase the operational efficiency of a hybrid working vehicle. The energy converter consists of an axial-piston hydraulic machine and an integrated TCIPMSM being built on the same shaft. The compact assembly of the electrical and hydraulic machines enhances the ability to find applications for such a device in the mobile environment of working vehicles.Usage of hydraulic fluid, typically used in working actuators, enables direct-immersion oil cooling of designed electrical machine, and further increases the torque- and power- densities of the whole device. (orig.)

Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine.

Science.gov (United States)

Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam Sm, Jahangir

2017-04-19

As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems.

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

Science.gov (United States)

Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-07-18

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

Study on the effect of thermal property of metals in ultrasonic-assisted laser machining

International Nuclear Information System (INIS)

Lee, Hu Seung; Kim, Gun Woo; Park, Jong Eun; Cho, Sung Hak; Yang, Min Yang; Park, Jong Kweon

2015-01-01

The laser machining process has been proposed as an advanced process for the selective fabrication of electrodes without a mask. In this study, we adapt laser machining to metals that have different thermal properties. Based on the results, the metals exhibit a different surface morphology, heat-affected zone (HAZ), and a recast layer around the machined surface according to their thermal conductivity, boiling point, and thermal diffusivity. Then, we apply ultrasonic-assisted laser machining to remove the recast layer. The ultrasonic-assisted laser machining exhibits a better surface quality in metals with higher diffusivity than those having lower diffusivity

Experimental verification and analytical calculation of unbalanced magnetic force in permanent magnet machines

Directory of Open Access Journals (Sweden)

Kyung-Hun Shin

2017-05-01

Full Text Available In this study, an exact analytical solution based on Fourier analysis is proposed to compute the unbalanced magnetic force in a permanent magnet machine. The magnetic field solutions are obtained by using a magnetic vector potential and by selecting the appropriate boundary conditions. Based on these field solutions, the force characteristics are also determined analytically. All analytical results were extensively validated with nonlinear two-dimensional finite element analysis and experimental results. Using proposed method, we investigated the influence on the UMF according to machine parameters. Therefore, the proposed method should be very useful in initial design and optimization process of PM machines for UMF analysis.

Revisiting Boltzmann learning: parameter estimation in Markov random fields

DEFF Research Database (Denmark)

Hansen, Lars Kai; Andersen, Lars Nonboe; Kjems, Ulrik

1996-01-01

This article presents a generalization of the Boltzmann machine that allows us to use the learning rule for a much wider class of maximum likelihood and maximum a posteriori problems, including both supervised and unsupervised learning. Furthermore, the approach allows us to discuss regularization...... and generalization in the context of Boltzmann machines. We provide an illustrative example concerning parameter estimation in an inhomogeneous Markov field. The regularized adaptation produces a parameter set that closely resembles the “teacher” parameters, hence, will produce segmentations that closely reproduce...

Justification for parameters of a dynamic stabilizer of the experimental stand mobile unit in studying of active rotational working tools of tiller machines

Directory of Open Access Journals (Sweden)

Vladimir F. Kupryashkin

2017-03-01

Full Text Available Introduction: The article deals with design options and technological modes of the dynamic stabilizer of the experimental stand mobile unit for studying tillage machine active rotating work tools. Based on theoretical and experimental studies, the possibility the movable module instability was discovered. This negatively affects on implementing the experiment program trough the especific method. The need in engineering solutions for the defect correction is shown. In addition, the authors consider the structural features and characteristics of the used devices for providing the stabilization of the movable module in the study of active rotating work tools of tillage machines. An electromagnetic brake dynamic stabilizer in the structure of the existing rolling module was proposed as an engineering device. Materials and Methods: A theoretical study of rolling module stability, based on synthesis of basic regulations and laws of mechanics related to active rotating work tools was conducted. As a result of the theoretical research, a design scheme of movable module loading was created. This scheme includes the design features and structural power factors. Results: A database representing the settings of power specification in the motion stability determining the mobile unit was created. Further use of the database values allow supporting the most optimal location of the electromagnetic brake with its design options. Discussion and Conclusions: The research of the electromagnetic brake in a mobile unit promoted stabilizing the unit movement, increased the frequency of its use and provided data that are more precise during experiments.

Study on Dynamic Characteristics of Heavy Machine Tool-Composite Pile Foundation-Soil

Directory of Open Access Journals (Sweden)

CAI Li-Gang

2014-09-01

Full Text Available Heavy duty computer numerical control machine tools have characteristics of large self-weight, load and. The insufficiency of foundation bearing capacity leads to deformation of lathe bed, which effects machining accuracy. A combined-layer foundation model is created to describe the pile group foundation of multi-soil layer in this paper. Considering piles and soil in pile group as transversely isotropic material, equivalent constitutive relationship of composite foundation is constructed. A mathematical model is established by the introduction of boundary conditions, which is based on heavy duty computer numerical control machine tools-composite pile foundation-soil interaction system. And then, the response of different soil and pile depth is studied by a case. The model improves motion accuracy of machine tools.

Binary pressure-sensitive paint measurements using miniaturised, colour, machine vision cameras

Science.gov (United States)

Quinn, Mark Kenneth

2018-05-01

Recent advances in machine vision technology and capability have led to machine vision cameras becoming applicable for scientific imaging. This study aims to demonstrate the applicability of machine vision colour cameras for the measurement of dual-component pressure-sensitive paint (PSP). The presence of a second luminophore component in the PSP mixture significantly reduces its inherent temperature sensitivity, increasing its applicability at low speeds. All of the devices tested are smaller than the cooled CCD cameras traditionally used and most are of significantly lower cost, thereby increasing the accessibility of such technology and techniques. Comparisons between three machine vision cameras, a three CCD camera, and a commercially available specialist PSP camera are made on a range of parameters, and a detailed PSP calibration is conducted in a static calibration chamber. The findings demonstrate that colour machine vision cameras can be used for quantitative, dual-component, pressure measurements. These results give rise to the possibility of performing on-board dual-component PSP measurements in wind tunnels or on real flight/road vehicles.

Machine Imperfection Studies of the RAON Superconducting Linac

Science.gov (United States)

Jeon, D.; Jang, J.-H.; Jin, H.

2018-05-01

Studies of the machine imperfections in the RAON superconducting linac (SCL) that employs normal conducting (NC) quadrupoles were done to assess the tolerable error budgets of the machine imperfections that ensure operation of the beam. The studies show that the beam loss requirement is met even before the orbit correction and that the beam loss requirement is met even without the MHB (multi-harmonic buncher) and VE (velocity equalizer) thanks to the RAON's radio-frequency quadrupole (RFQ) design feature. For the low energy section of the linac (SCL3), a comparison is made between the two superconducting linac lattice types: one lattice that employs NC quadrupoles and the other that employs SC solenoids. The studies show that both lattices meet the beam loss requirement after the orbit correction. However, before the orbit correction, the lattice employing SC solenoids does not meet the beam loss requirement and can cause a significant beam loss, while the lattice employing NC quadrupoles meets the requirement. For the lattice employing SC solenoids, care must be taken during the beam commissioning.

Machine learning algorithms to classify spinal muscular atrophy subtypes.

Science.gov (United States)

Srivastava, Tuhin; Darras, Basil T; Wu, Jim S; Rutkove, Seward B

2012-07-24

The development of better biomarkers for disease assessment remains an ongoing effort across the spectrum of neurologic illnesses. One approach for refining biomarkers is based on the concept of machine learning, in which individual, unrelated biomarkers are simultaneously evaluated. In this cross-sectional study, we assess the possibility of using machine learning, incorporating both quantitative muscle ultrasound (QMU) and electrical impedance myography (EIM) data, for classification of muscles affected by spinal muscular atrophy (SMA). Twenty-one normal subjects, 15 subjects with SMA type 2, and 10 subjects with SMA type 3 underwent EIM and QMU measurements of unilateral biceps, wrist extensors, quadriceps, and tibialis anterior. EIM and QMU parameters were then applied in combination using a support vector machine (SVM), a type of machine learning, in an attempt to accurately categorize 165 individual muscles. For all 3 classification problems, normal vs SMA, normal vs SMA 3, and SMA 2 vs SMA 3, use of SVM provided the greatest accuracy in discrimination, surpassing both EIM and QMU individually. For example, the accuracy, as measured by the receiver operating characteristic area under the curve (ROC-AUC) for the SVM discriminating SMA 2 muscles from SMA 3 muscles was 0.928; in comparison, the ROC-AUCs for EIM and QMU parameters alone were only 0.877 (p < 0.05) and 0.627 (p < 0.05), respectively. Combining EIM and QMU data categorizes individual SMA-affected muscles with very high accuracy. Further investigation of this approach for classifying and for following the progression of neuromuscular illness is warranted.

Impact of Clinical Parameters in the Intrahost Evolution of HIV-1 Subtype B in Pediatric Patients: A Machine Learning Approach

Science.gov (United States)

Rojas Sánchez, Patricia; Cobos, Alberto; Navaro, Marisa; Ramos, José Tomas; Pagán, Israel

2017-01-01

Abstract Determining the factors modulating the genetic diversity of HIV-1 populations is essential to understand viral evolution. This study analyzes the relative importance of clinical factors in the intrahost HIV-1 subtype B (HIV-1B) evolution and in the fixation of drug resistance mutations (DRM) during longitudinal pediatric HIV-1 infection. We recovered 162 partial HIV-1B pol sequences (from 3 to 24 per patient) from 24 perinatally infected patients from the Madrid Cohort of HIV-1 infected children and adolescents in a time interval ranging from 2.2 to 20.3 years. We applied machine learning classification methods to analyze the relative importance of 28 clinical/epidemiological/virological factors in the HIV-1B evolution to predict HIV-1B genetic diversity (d), nonsynonymous and synonymous mutations (dN, dS) and DRM presence. Most of the 24 HIV-1B infected pediatric patients were Spanish (91.7%), diagnosed before 2000 (83.3%), and all were antiretroviral therapy experienced. They had from 0.3 to 18.8 years of HIV-1 exposure at sampling time. Most sequences presented DRM. The best-predictor variables for HIV-1B evolutionary parameters were the age of HIV-1 diagnosis for d, the age at first antiretroviral treatment for dN and the year of HIV-1 diagnosis for ds. The year of infection (birth year) and year of sampling seemed to be relevant for fixation of both DRM at large and, considering drug families, to protease inhibitors (PI). This study identifies, for the first time using machine learning, the factors affecting more HIV-1B pol evolution and those affecting DRM fixation in HIV-1B infected pediatric patients. PMID:29044435

Influence of shear cutting parameters on the electromagnetic properties of non-oriented electrical steel sheets

Energy Technology Data Exchange (ETDEWEB)

Weiss, H.A., E-mail: hw@utg.de [Institute of Metal Forming and Casting, Technical University of Munich, Garching, D-85748 Germany (Germany); Leuning, N.; Steentjes, S.; Hameyer, K. [Institute of Electrical Machines, RWTH Aachen University, Aachen, D-52062 Germany (Germany); Andorfer, T.; Jenner, S.; Volk, W. [Institute of Metal Forming and Casting, Technical University of Munich, Garching, D-85748 Germany (Germany)

2017-01-01

Mechanical stress occurring during the manufacturing process of electrical machines detrimentally alters the magnetic properties (iron losses and magnetizability). This affects the efficiency and performance of the machine. Improvement of the manufacturing process in terms of reduced magnetic property deterioration enables the full potential of the magnetic materials to be exploited, and as a result, the performance of the machine to be improved. A high quantity of electrical machine components is needed, with shear cutting (punching, blanking) being the most efficient manufacturing technology. The cutting process leads to residual stresses inside the non-oriented electrical sheet metal, resulting in increased iron losses. This paper studies the residual stresses induced by punching with different shear cutting parameters, taking a qualitative approach using finite element analysis. In order to calibrate the finite element analysis, shear cutting experiments are performed. A single sheet tester analysis of the cut blanks allows the correlation between residual stresses, micro hardness measurements, cutting surface parameters and magnetic properties to be studied.

Predictive Surface Roughness Model for End Milling of Machinable Glass Ceramic

Energy Technology Data Exchange (ETDEWEB)

Reddy, M Mohan; Gorin, Alexander [School of Engineering and Science, Curtin University of Technology, Sarawak (Malaysia); Abou-El-Hossein, K A, E-mail: mohan.m@curtin.edu.my [Mechanical and Aeronautical Department, Nelson Mandela Metropolitan University, Port Elegebeth, 6031 (South Africa)

2011-02-15

Advanced ceramics of Machinable glass ceramic is attractive material to produce high accuracy miniaturized components for many applications in various industries such as aerospace, electronics, biomedical, automotive and environmental communications due to their wear resistance, high hardness, high compressive strength, good corrosion resistance and excellent high temperature properties. Many research works have been conducted in the last few years to investigate the performance of different machining operations when processing various advanced ceramics. Micro end-milling is one of the machining methods to meet the demand of micro parts. Selecting proper machining parameters are important to obtain good surface finish during machining of Machinable glass ceramic. Therefore, this paper describes the development of predictive model for the surface roughness of Machinable glass ceramic in terms of speed, feed rate by using micro end-milling operation.

Predictive Surface Roughness Model for End Milling of Machinable Glass Ceramic

International Nuclear Information System (INIS)

Reddy, M Mohan; Gorin, Alexander; Abou-El-Hossein, K A

2011-01-01

Advanced ceramics of Machinable glass ceramic is attractive material to produce high accuracy miniaturized components for many applications in various industries such as aerospace, electronics, biomedical, automotive and environmental communications due to their wear resistance, high hardness, high compressive strength, good corrosion resistance and excellent high temperature properties. Many research works have been conducted in the last few years to investigate the performance of different machining operations when processing various advanced ceramics. Micro end-milling is one of the machining methods to meet the demand of micro parts. Selecting proper machining parameters are important to obtain good surface finish during machining of Machinable glass ceramic. Therefore, this paper describes the development of predictive model for the surface roughness of Machinable glass ceramic in terms of speed, feed rate by using micro end-milling operation.

Cleaning and outgassing studies of machinable tungsten for beamline safety shutters

International Nuclear Information System (INIS)

Liu, C.; Ryding, D.; Nielsen, R.W.; Kruy, T.L.; Kuzay, T.M.

1996-01-01

Machinable tungsten blocks are used as safety shutters in the front ends and the beamlines at the Advanced Photon Source (APS). The machinable tungsten used is characterized as a UHV-compatible metal by the vendor and was developed through a joint research effort with the APS. However, because of the inherent porosity in the sintered tungsten metal, it may present a vacuum problem and has to be subjected to strict vacuum testing before it is put on the beamlines. We have chosen specially heat-treated machinable tungsten with a density of 18 g/cm 3 for safety shutters. In-house-developed, environmentally friendly vacuum cleaning procedures were used. In this paper, we present results of thermal outgassing tests for machinable tungsten safety shutter sets. Each set consists of five blocks and has a total area of 4500 cm 2 . A cleaning procedure using alkaline detergent ultrasonic washes, deionized water rinses, and a 500 degree C vacuum furnace baking was used before outgassing measurements. Outgassing rates 10 hours after initial pump down at room temperature reached ∼1.60x10 -10 Torr·l·s -1 ·cm -2 for machinable tungsten and ∼1.56x10 -10 Torr·l·s -1 ·cm -2 for the stainless steel vacuum chamber. The outgassing rate of machinable tungsten 24 hours after an in situ 48 h bake at 160 degree C is also comparable to that of the stainless steel vacuum chamber. The importance of a 500 degree C vacuum furnace baking has been confirmed by outgassing studies for machinable tungsten sets that were not subject to vacuum furnace baking. copyright 1996 American Institute of Physics

Optimisation of wire-cut EDM process parameter by Grey-based response surface methodology

Science.gov (United States)

Kumar, Amit; Soota, Tarun; Kumar, Jitendra

2018-03-01

Wire electric discharge machining (WEDM) is one of the advanced machining processes. Response surface methodology coupled with Grey relation analysis method has been proposed and used to optimise the machining parameters of WEDM. A face centred cubic design is used for conducting experiments on high speed steel (HSS) M2 grade workpiece material. The regression model of significant factors such as pulse-on time, pulse-off time, peak current, and wire feed is considered for optimising the responses variables material removal rate (MRR), surface roughness and Kerf width. The optimal condition of the machining parameter was obtained using the Grey relation grade. ANOVA is applied to determine significance of the input parameters for optimising the Grey relation grade.

Academic Training: The LHC machine /experiment interface

CERN Multimedia

Françoise Benz

2005-01-01

2004-2005 ACADEMIC TRAINING PROGRAMME LECTURE SERIES 18, 19, 20, 21 & 22 April from 11.00 to 12.00 hrs - Main Auditorium, bldg. 500 The LHC machine /experiment interface S. TAPPROGGE, Univ. of Mainz, D, R. ASSMANN, CERN-AB E. TSESMELIS and D. MACINA, CERN-TS This series of lectures will cover some of the major issues at the boundary between the LHC machine and the experiments: 1) The physics motivation and expectations of the experiments regarding the machine operation. This will include an overview of the LHC physics programme (in pp and PbPb collisions), of the experimental signatures (from high pT objects to leading nucleons) and of the expected trigger rates as well as the data sets needed for specific measurements. Furthermore, issues related to various modes of operation of the machine (e.g. bunch spacings of 25 ns. vs. 75 ns.) and special requirements of the detectors for their commissioning will be described. 2) The LHC machine aspects: introduction of the main LHC parameters and discu...

Academic Training: The LHC machine /experiment interface

CERN Multimedia

Françoise Benz

2005-01-01

2004-2005 ACADEMIC TRAINING PROGRAMME LECTURE SERIES 18, 19, 20, 21 & 22 April from 11.00 to 12.00 hrs - Main Auditorium, bldg. 500 The LHC machine /experiment interface S. TAPPROGGE, Univ. of Mainz, D, R. ASSMANN, CERN-AB E. TSESMELIS and D. MACINA, CERN-TS This series of lectures will cover some of the major issues at the boundary between the LHC machine and the experiments: 1) The physics motivation and expectations of the experiments regarding the machine operation. This will include an overview of the LHC physics programme (in pp and PbPb collisions), of the experimental signatures (from high pT objects to leading nucleons) and of the expected trigger rates as well as the data sets needed for specific measurements. Furthermore, issues related to various modes of operation of the machine (e.g. bunch spacings of 25 ns. vs. 75 ns.) and special requirements of the detectors for their commissioning will be described. 2) The LHC machine aspects: introduction of the main LHC parameters and disc...

Impact of Cutting Forces and Chip Microstructure in High Speed Machining of Carbon Fiber – Epoxy Composite Tube

Directory of Open Access Journals (Sweden)

Roy Y. Allwin

2017-09-01

Full Text Available Carbon fiber reinforced polymeric (CFRP composite materials are widely used in aerospace, automobile and biomedical industries due to their high strength to weight ratio, corrosion resistance and durability. High speed machining (HSM of CFRP material is needed to study the impact of cutting parameters on cutting forces and chip microstructure which offer vital inputs to the machinability and deformation characteristics of the material. In this work, the orthogonal machining of CFRP was conducted by varying the cutting parameters such as cutting speed and feed rate at high cutting speed/feed rate ranges up to 346 m/min/ 0.446 mm/rev. The impact of the cutting parameters on cutting forces (principal cutting, feed and thrust forces and chip microstructure were analyzed. A significant impact on thrust forces and chip segmentation pattern was seen at higher feed rates and low cutting speeds.

Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters

Directory of Open Access Journals (Sweden)

Jan Kleine Deters

2017-01-01

Full Text Available Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM2.5. Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM2.5 from wind (speed and direction and precipitation levels. The results of the classification model show a high reliability in the classification of low (25 µg/m3 and low (<10 µg/m3 versus moderate (10–25 µg/m3 concentrations of PM2.5. A regression analysis suggests a better prediction of PM2.5 when the climatic conditions are getting more extreme (strong winds or high levels of precipitation. The high correlation between estimated and real data for a time series analysis during the wet season confirms this finding. The study demonstrates that the use of statistical models based on machine learning is relevant to predict PM2.5 concentrations from meteorological data.

Empirical Studies On Machine Learning Based Text Classification Algorithms

OpenAIRE

Shweta C. Dharmadhikari; Maya Ingle; Parag Kulkarni

2011-01-01

Automatic classification of text documents has become an important research issue now days. Properclassification of text documents requires information retrieval, machine learning and Natural languageprocessing (NLP) techniques. Our aim is to focus on important approaches to automatic textclassification based on machine learning techniques viz. supervised, unsupervised and semi supervised.In this paper we present a review of various text classification approaches under machine learningparadig...

Gesture-controlled interfaces for self-service machines and other applications

Science.gov (United States)

Cohen, Charles J. (Inventor); Beach, Glenn (Inventor); Cavell, Brook (Inventor); Foulk, Gene (Inventor); Jacobus, Charles J. (Inventor); Obermark, Jay (Inventor); Paul, George (Inventor)

2004-01-01

A gesture recognition interface for use in controlling self-service machines and other devices is disclosed. A gesture is defined as motions and kinematic poses generated by humans, animals, or machines. Specific body features are tracked, and static and motion gestures are interpreted. Motion gestures are defined as a family of parametrically delimited oscillatory motions, modeled as a linear-in-parameters dynamic system with added geometric constraints to allow for real-time recognition using a small amount of memory and processing time. A linear least squares method is preferably used to determine the parameters which represent each gesture. Feature position measure is used in conjunction with a bank of predictor bins seeded with the gesture parameters, and the system determines which bin best fits the observed motion. Recognizing static pose gestures is preferably performed by localizing the body/object from the rest of the image, describing that object, and identifying that description. The disclosure details methods for gesture recognition, as well as the overall architecture for using gesture recognition to control of devices, including self-service machines.

Machine rates for selected forest harvesting machines

Science.gov (United States)

R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

2002-01-01

Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...

An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms.

Science.gov (United States)

Amaral, Jorge L M; Lopes, Agnaldo J; Jansen, José M; Faria, Alvaro C D; Melo, Pedro L

2013-12-01

The purpose of this study was to develop an automatic classifier to increase the accuracy of the forced oscillation technique (FOT) for diagnosing early respiratory abnormalities in smoking patients. The data consisted of FOT parameters obtained from 56 volunteers, 28 healthy and 28 smokers with low tobacco consumption. Many supervised learning techniques were investigated, including logistic linear classifiers, k nearest neighbor (KNN), neural networks and support vector machines (SVM). To evaluate performance, the ROC curve of the most accurate parameter was established as baseline. To determine the best input features and classifier parameters, we used genetic algorithms and a 10-fold cross-validation using the average area under the ROC curve (AUC). In the first experiment, the original FOT parameters were used as input. We observed a significant improvement in accuracy (KNN=0.89 and SVM=0.87) compared with the baseline (0.77). The second experiment performed a feature selection on the original FOT parameters. This selection did not cause any significant improvement in accuracy, but it was useful in identifying more adequate FOT parameters. In the third experiment, we performed a feature selection on the cross products of the FOT parameters. This selection resulted in a further increase in AUC (KNN=SVM=0.91), which allows for high diagnostic accuracy. In conclusion, machine learning classifiers can help identify early smoking-induced respiratory alterations. The use of FOT cross products and the search for the best features and classifier parameters can markedly improve the performance of machine learning classifiers. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Machine translation

Energy Technology Data Exchange (ETDEWEB)

Nagao, M

1982-04-01

Each language has its own structure. In translating one language into another one, language attributes and grammatical interpretation must be defined in an unambiguous form. In order to parse a sentence, it is necessary to recognize its structure. A so-called context-free grammar can help in this respect for machine translation and machine-aided translation. Problems to be solved in studying machine translation are taken up in the paper, which discusses subjects for semantics and for syntactic analysis and translation software. 14 references.

Online Sequential Projection Vector Machine with Adaptive Data Mean Update.

Science.gov (United States)

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM.

Online Sequential Projection Vector Machine with Adaptive Data Mean Update

Directory of Open Access Journals (Sweden)

Lin Chen

2016-01-01

Full Text Available We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1 the projection vectors for dimension reduction, (2 the input weights, biases, and output weights, and (3 the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD approach, adaptive multihyperplane machine (AMM, primal estimated subgradient solver (Pegasos, online sequential extreme learning machine (OSELM, and SVD + OSELM (feature selection based on SVD is performed before OSELM. The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM.

Machining of AISI D2 Tool Steel with Multiple Hole Electrodes by EDM Process

Science.gov (United States)

Prasad Prathipati, R.; Devuri, Venkateswarlu; Cheepu, Muralimohan; Gudimetla, Kondaiah; Uzwal Kiran, R.

2018-03-01

In recent years, with the increasing of technology the demand for machining processes is increasing for the newly developed materials. The conventional machining processes are not adequate to meet the accuracy of the machining of these materials. The non-conventional machining processes of electrical discharge machining is one of the most efficient machining processes is being widely used to machining of high accuracy products of various industries. The optimum selection of process parameters is very important in machining processes as that of an electrical discharge machining as they determine surface quality and dimensional precision of the obtained parts, even though time consumption rate is higher for machining of large dimension features. In this work, D2 high carbon and chromium tool steel has been machined using electrical discharge machining with the multiple hole electrode technique. The D2 steel has several applications such as forming dies, extrusion dies and thread rolling. But the machining of this tool steel is very hard because of it shard alloyed elements of V, Cr and Mo which enhance its strength and wear properties. However, the machining is possible by using electrical discharge machining process and the present study implemented a new technique to reduce the machining time using a multiple hole copper electrode. In this technique, while machining with multiple holes electrode, fin like projections are obtained, which can be removed easily by chipping. Then the finishing is done by using solid electrode. The machining time is reduced to around 50% while using multiple hole electrode technique for electrical discharge machining.

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.

Testing machine for fatigue crack kinetic investigation in specimens under bending

International Nuclear Information System (INIS)

Panasyuk, V.V.; Ratych, L.V.; Dmytrakh, I.N.

1978-01-01

A kinematic diagram of testing mashine for the investigation of fatigue crack kinetics in prismatic specimens, subjected to pure bending is described. Suggested is a technique of choosing an optimum ratio of the parameters of ''the testing machine-specimen'' system, which provide the stabilization of the stress intensity coefficient for a certain region of crack development under hard loading. On the example of the 40KhS and 15Kh2MFA steel specimens the pliability of the machine constructed according to the described diagram and designed for the 30ONxm maximum bending moment. The results obtained can be used in designing of the testing machines for studying pure bending under hard loading and in choosing the sizes of specimens with rectangular cross sections for investigations into the kinetics of the fatigue crack

Analysis and application of two recursive parametric estimation algorithms for an asynchronous machine

International Nuclear Information System (INIS)

Damek, Nawel; Kamoun, Samira

2011-01-01

In this communication, two recursive parametric estimation algorithms are analyzed and applied to an squirrelcage asynchronous machine located at the research ''Unit of Automatic Control'' (UCA) at ENIS. The first algorithm which, use the transfer matrix mathematical model, is based on the gradient principle. The second algorithm, which use the state-space mathematical model, is based on the minimization of the estimation error. These algorithms are applied as a key technique to estimate asynchronous machine with unknown, but constant or timevarying parameters. Stator voltage and current are used as measured data. The proposed recursive parametric estimation algorithms are validated on the experimental data of an asynchronous machine under normal operating condition as full load. The results show that these algorithms can estimate effectively the machine parameters with reliability.

Investigation of High-Speed Cryogenic Machining Based on Finite Element Approach

Directory of Open Access Journals (Sweden)

Pooyan Vahidi Pashaki

Full Text Available Abstract The simulation of cryogenic machining process because of using a three-dimensional model and high process duration time in the finite element method, have been studied rarely. In this study, to overcome this limitation, a 2.5D finite element model using the commercial finite element software ABAQUS has been developed for the cryogenic machining process and by considering more realistic assumptions, the chip formation procedure investigated. In the proposed method, the liquid nitrogen has been used as a coolant. At the modeling of friction during the interaction of tools - chip, the Coulomb law has been used. In order to simulate the behavior of plasticity and failure criterion, Johnson-Cook model was used, and unlike previous investigations, thermal and mechanical properties of materials as a function of temperature were applied to the software. After examining accuracy of the model with present experimental data, the effect of parameters such as rake angle and the cutting speed as well as dry machining of aluminum alloy by the use of coupled dynamic temperature solution has been studied. Results indicated that at the cutting velocity of 10 m/s, cryogenic cooling has caused into decreasing 60 percent of tools temperature in comparison with the dry cooling. Furthermore, a chip which has been made by cryogenic machining were connected and without fracture in contrast to dry machining.

Face Recognition in Humans and Machines

Science.gov (United States)

O'Toole, Alice; Tistarelli, Massimo

The study of human face recognition by psychologists and neuroscientists has run parallel to the development of automatic face recognition technologies by computer scientists and engineers. In both cases, there are analogous steps of data acquisition, image processing, and the formation of representations that can support the complex and diverse tasks we accomplish with faces. These processes can be understood and compared in the context of their neural and computational implementations. In this chapter, we present the essential elements of face recognition by humans and machines, taking a perspective that spans psychological, neural, and computational approaches. From the human side, we overview the methods and techniques used in the neurobiology of face recognition, the underlying neural architecture of the system, the role of visual attention, and the nature of the representations that emerges. From the computational side, we discuss face recognition technologies and the strategies they use to overcome challenges to robust operation over viewing parameters. Finally, we conclude the chapter with a look at some recent studies that compare human and machine performances at face recognition.

Laser machining micro-structures on diamond surface with a sub-nanosecond pulsed laser

Science.gov (United States)

Wu, Mingtao; Guo, Bing; Zhao, Qingliang

2018-02-01

Micro-structure surface on diamond material is widely used in a series of industrial and scientific applications, such as micro-electromechanical systems (MEMS), nanoelectromechanical systems (NEMS), microelectronics, textured or micro-structured diamond machining tools. The efficient machining of micro-structure on diamond surface is urgently demanded in engineering. In this paper, laser machining square micro-structure on diamond surface was studied with a sub-nanosecond pulsed laser. The influences of laser machining parameters, including the laser power, scanning speed, defocusing quantity and scanning pitch, were researched in view of the ablation depth, material removal rate and machined surface topography. Both the ablation depth and material removal rate increased with average laser power. A reduction of the growth rate of the two parameters was induced by the absorption of the laser plasma plume at high laser power. The ablation depth non-linearly decreased with the increasing of the scanning speed while the material removal rate showed an opposite tendency. The increasing of the defocusing quantity induced complex variation of the ablation depth and the material removal rate. The maximum ablation depth and material removal rate were achieved at a defocusing position. The ablation depth and material removal rate oppositely varied about the scanning pitch. A high overlap ratio was meaningful for achieving a smooth micro-structure surface topography. Laser machining with a large defocusing quantity, high laser power and small scanning pitch was helpful for acquiring the desired micro-structure which had a large depth and smooth micro-structure surface topography.

Study of the ion sputter-machining, 1

International Nuclear Information System (INIS)

Miyamoto, Iwao; Taniguchi, Norio

1979-01-01

A lattice disordering of the surface of single crystal silicon due to ion bombardment of Ar + was investigated by the high energy electron diffraction method, with the incident angle of 1.7 0 and 2.8 0 . By this measuring system, the degree of disorders of the sputter-machined surface layer of Si single crystal in the depth of 50 A and 30 A has been determined, under the working conditions of the ion energy ranging from 0.2 keV to 1.5 keV and the incident angle of ion ranging from 0 0 to 75 0 . Moreover, the recovery of lattice disorder of sputter-machined surface layer of Si single crystal by means of the isochronal thermal annealing has been also confirmed by the same method. From the above experiments, the following conclusions are obtained. (1) The layers of sputter-machined surface of Si single crystal workpiece are highly disordered like amorphous, under the working conditions of ion energy ranging from 0.2 keV to 1.5 keV for the vertical ion incident angle. (2) Under the working conditions of ion incident angle larger than 60 0 , using the ion beam with a lower energy under 300 eV, the surface of the workpiece is not disordered. Therefore, a sputter-machined surface of Si single crystal with highly ordered structure can be obtained under this working condition. (3) The recovery of disorder of sputter-machined surface is completed by the heat-treatment of workpiece under isochronal annealing for 1 hour at 800 0 C. However, it is not clear whether this recovery of lattice point or the dispersion of interstitially located argon atoms from the surface to the outside. (author)

A Study of Synchronous Machine Model Implementations in Matlab/Simulink Simulations for New and Renewable Energy Systems

DEFF Research Database (Denmark)

Chen, Zhe; Blaabjerg, Frede; Iov, Florin

2005-01-01

A direct phase model of synchronous machines implemented in MA TLAB/SIMULINK is presented. The effects of the machine saturation have been included. Simulation studies are performed under various conditions. It has been demonstrated that the MATLAB/SIMULINK is an effective tool to study the compl...... synchronous machine and the implemented model could be used for studies of various applications of synchronous machines including in renewable and DG generation systems....

Support vector machine to predict diesel engine performance and emission parameters fueled with nano-particles additive to diesel fuel

Science.gov (United States)

Ghanbari, M.; Najafi, G.; Ghobadian, B.; Mamat, R.; Noor, M. M.; Moosavian, A.

2015-12-01

This paper studies the use of adaptive Support Vector Machine (SVM) to predict the performance parameters and exhaust emissions of a diesel engine operating on nanodiesel blended fuels. In order to predict the engine parameters, the whole experimental data were randomly divided into training and testing data. For SVM modelling, different values for radial basis function (RBF) kernel width and penalty parameters (C) were considered and the optimum values were then found. The results demonstrate that SVM is capable of predicting the diesel engine performance and emissions. In the experimental step, Carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) with nanostructure were prepared and added as additive to the diesel fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel fuel, increased diesel engine power and torque output. For nano-diesel it was found that the brake specific fuel consumption (bsfc) was decreased compared to the net diesel fuel. The results proved that with increase of nano particles concentrations (from 40 ppm to 120 ppm) in diesel fuel, CO2 emission increased. CO emission in diesel fuel with nano-particles was lower significantly compared to pure diesel fuel. UHC emission with silver nano-diesel blended fuel decreased while with fuels that contains CNT nano particles increased. The trend of NOx emission was inverse compared to the UHC emission. With adding nano particles to the blended fuels, NOx increased compared to the net diesel fuel. The tests revealed that silver & CNT nano particles can be used as additive in diesel fuel to improve complete combustion of the fuel and reduce the exhaust emissions significantly.

Machinability of titanium metal matrix composites (Ti-MMCs)

Science.gov (United States)

Aramesh, Maryam

Titanium metal matrix composites (Ti-MMCs), as a new generation of materials, have various potential applications in aerospace and automotive industries. The presence of ceramic particles enhances the physical and mechanical properties of the alloy matrix. However, the hard and abrasive nature of these particles causes various issues in the field of their machinability. Severe tool wear and short tool life are the most important drawbacks of machining this class of materials. There is very limited work in the literature regarding the machinability of this class of materials especially in the area of tool life estimation and tool wear. By far, polycrystalline diamond (PCD) tools appear to be the best choice for machining MMCs from researchers' point of view. However, due to their high cost, economical alternatives are sought. Cubic boron nitride (CBN) inserts, as the second hardest available tools, show superior characteristics such as great wear resistance, high hardness at elevated temperatures, a low coefficient of friction and a high melting point. Yet, so far CBN tools have not been studied during machining of Ti-MMCs. In this study, a comprehensive study has been performed to explore the tool wear mechanisms of CBN inserts during turning of Ti-MMCs. The unique morphology of the worn faces of the tools was investigated for the first time, which led to new insights in the identification of chemical wear mechanisms during machining of Ti-MMCs. Utilizing the full tool life capacity of cutting tools is also very crucial, due to the considerable costs associated with suboptimal replacement of tools. This strongly motivates development of a reliable model for tool life estimation under any cutting conditions. In this study, a novel model based on the survival analysis methodology is developed to estimate the progressive states of tool wear under any cutting conditions during machining of Ti-MMCs. This statistical model takes into account the machining time in

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.

Testing of Anesthesia Machines and Defibrillators in Healthcare Institutions.

Science.gov (United States)

Gurbeta, Lejla; Dzemic, Zijad; Bego, Tamer; Sejdic, Ervin; Badnjevic, Almir

2017-09-01

To improve the quality of patient treatment by improving the functionality of medical devices in healthcare institutions. To present the results of the safety and performance inspection of patient-relevant output parameters of anesthesia machines and defibrillators defined by legal metrology. This study covered 130 anesthesia machines and 161 defibrillators used in public and private healthcare institutions, during a period of two years. Testing procedures were carried out according to international standards and legal metrology legislative procedures in Bosnia and Herzegovina. The results show that in 13.84% of tested anesthesia machine and 14.91% of defibrillators device performance is not in accordance with requirements and should either have its results be verified, or the device removed from use or scheduled for corrective maintenance. Research emphasizes importance of independent safety and performance inspections, and gives recommendations for the frequency of inspection based on measurements. Results offer implications for adequacy of preventive and corrective maintenance performed in healthcare institutions. Based on collected data, the first digital electronical database of anesthesia machines and defibrillators used in healthcare institutions in Bosnia and Herzegovina is created. This database is a useful tool for tracking each device's performance over time.

The survey of the surface doses of the dental x-ray machines

International Nuclear Information System (INIS)

Lee, Jae Seo; Kang, Byung Cheol; Yoon, Suk Ja

2005-01-01

The purpose of this study was to investigate variability of doses with same exposure parameters and evaluate radiographic density according to the variability of doses. Twenty-eight MAX-GLS (Shinhung Co, Seoul, Korea), twenty-one D-60-S (DongSeo Med, Seoul, Korea), and eleven REX-601 (Yoshida Dental MFG, Tokyo, Japan) dental x-ray machines were selected for this study. Surface doses were measured under selected combinations of tube voltage, tube current, exposure time, and constant distance 42 cm from the focal spot to the surface of the Multi-O-meter (Unfors Instrument, Billdal, Sweden). Radiographic densities were measured on the films at maximum, minimum and mean surface doses of each brand of x-ray units. With MAX-GLS, the maximum surface doses were thirteen to fourteen times as much as the minimum surfaces doses. With D-60-S, the maximum surface doses were three to eight times as much as the minimum surface doses. With REX-601, the maximum surface doses were six to ten times as much as the minimum surface doses. The differences in radiographic densities among maximum, mean, and minimum doses were significant (p<0.01). The surface exposure doses of each x-ray machine at the same exposure parameters were different within the same manufacturer's machines.

Study on Damage Evaluation and Machinability of UD-CFRP for the Orthogonal Cutting Operation Using Scanning Acoustic Microscopy and the Finite Element Method

Directory of Open Access Journals (Sweden)

Dongyao Wang

2017-02-01

Full Text Available Owing to high specific strength and designability, unidirectional carbon fiber reinforced polymer (UD-CFRP has been utilized in numerous fields to replace conventional metal materials. Post machining processes are always required for UD-CFRP to achieve dimensional tolerance and assembly specifications. Due to inhomogeneity and anisotropy, UD-CFRP differs greatly from metal materials in machining and failure mechanism. To improve the efficiency and avoid machining-induced damage, this paper undertook to study the correlations between cutting parameters, fiber orientation angle, cutting forces, and cutting-induced damage for UD-CFRP laminate. Scanning acoustic microscopy (SAM was employed and one-/two-dimensional damage factors were then created to quantitatively characterize the damage of the laminate workpieces. According to the 3D Hashin’s criteria a numerical model was further proposed in terms of the finite element method (FEM. A good agreement between simulation and experimental results was validated for the prediction and structural optimization of the UD-CFRP.

Study on Damage Evaluation and Machinability of UD-CFRP for the Orthogonal Cutting Operation Using Scanning Acoustic Microscopy and the Finite Element Method.

Science.gov (United States)

Wang, Dongyao; He, Xiaodong; Xu, Zhonghai; Jiao, Weicheng; Yang, Fan; Jiang, Long; Li, Linlin; Liu, Wenbo; Wang, Rongguo

2017-02-20

Owing to high specific strength and designability, unidirectional carbon fiber reinforced polymer (UD-CFRP) has been utilized in numerous fields to replace conventional metal materials. Post machining processes are always required for UD-CFRP to achieve dimensional tolerance and assembly specifications. Due to inhomogeneity and anisotropy, UD-CFRP differs greatly from metal materials in machining and failure mechanism. To improve the efficiency and avoid machining-induced damage, this paper undertook to study the correlations between cutting parameters, fiber orientation angle, cutting forces, and cutting-induced damage for UD-CFRP laminate. Scanning acoustic microscopy (SAM) was employed and one-/two-dimensional damage factors were then created to quantitatively characterize the damage of the laminate workpieces. According to the 3D Hashin's criteria a numerical model was further proposed in terms of the finite element method (FEM). A good agreement between simulation and experimental results was validated for the prediction and structural optimization of the UD-CFRP.

Survey of mirror machine reactors

International Nuclear Information System (INIS)

Condit, W.C.

1978-01-01

The Magnetic Mirror Fusion Program is one of the two main-line fusion efforts in the United States. Starting from the simple axisymmetric mirror concept in the 1950's, the program has successfully overcome gross flute-type instabilities (using minimum-B magnetic fields), and the most serious of the micro-instabilities which plagued it (the drift-cyclotron loss-cone mode). Dense plasmas approaching the temperature range of interest for fusion have been created (n/sub p/ = 10 14 /cc at 10 to 12 keV). At the same time, rather extensive conceptual design studies of possible mirror configurations have led to three principle designs of interest: the standard mirror fission-fusion hybrid, tandem mirror, and the field-reversed mirror. The lectures will discuss these three concepts in turn. There will be no discussion of diagnostics for the mirror machine in these lectures, but typical plasma parameters will be given for each type of machine, and the diagnostic requirements will be apparent. In a working fusion reactor, diagnostics will be required for operational control, and remarks will be made on this subject

The parameter spreadsheets and their applications

International Nuclear Information System (INIS)

Schwitters, R.; Chao, A.; Chou, W.; Peterson, J.

1993-01-01

This paper is to announce that a set of parameter spreadsheets, using the Microsoft EXCEL software, has been developed for the SSC (and also for the LHC). In this program, the input (or control) parameters and the derived parameters are linked by equations that express the accelerator physics involved. A subgroup of parameters that are considered critical, or possible bottlenecks, has been highlighted under the category of open-quotes Flagsclose quotes. Given certain performance goals, one can use this program to open-quotes tuneclose quotes the input parameters in such a way that the flagged parameters do not exceed their acceptable range. During the past years, this program has been employed for the following purposes: (a) To guide the machine designs for various operation scenarios, (b) To generate a parameter list that is self-consistent and, (c) To study the impact of some proposed parameter changes (e.g., different choices of the rf frequency and bunch spacing)

Engineering of the 'PCAST machine'

International Nuclear Information System (INIS)

Sinnis, J.; Brooks, A.; Brown, T.

1996-01-01

The President's Committee of Advisors on Science and Technology (PCAST) has suggested that a device with a mission of ignition and moderate burn time could address the physics of burning plasmas at a lesser cost than ITER with its more comprehensive physics and technology mission. The Department of Energy commissioned a study to explore this PCAST suggestion. This paper describes the results of the engineering portion of the study of this 'PCAST Machine;' physics is covered in a companion paper authored by G.H. Neilson, et al; and the costs are covered in a companion paper by R.T. Simmons, et al. Both are published in the proceedings of this conference. The study was undertaken by a team under the direction of Bruce Montgomery that included representatives from MIT, PPPL, ORNL, LLNL, GA, Northrup-Grumman, and Stone and Webster. The performance requirements for the PCAST machine are to form and sustain a burning plasma for three helium accumulation times. The philosophy adopted for this design was to achieve the required performance at lower cost by decreasing the major radius to five meters, increasing the toroidal field to 7 tesla, and using stronger shaping. The major device parameters are given. 4 refs., 4 figs., 1 tab

Some new machine projects studied at LNS

International Nuclear Information System (INIS)

Tkatchenko, A.

1983-01-01

In front of the increasing interest for the synchrotron radiation uses, the electron storage rings have been gradually transformed in light sources. Yet, those machines had not been optimized for this use. So, in the last 10 years, numerous optimized machines have been defined and destinated to sole synchrotron light production up to the X domain. In the French domain, several projects have been elaborated, to satisfy the national needs in far UV and X radiation. - SUPER-ACO project (0,8 GeV) from Orsay. Its realisation is in progress. - SIREM project (1,2 GeV) from Grenoble CEN. - European ESRF project (5 GeV) optimized for X radiation, and for which a work group has been installed at CERN. - A X radiation national machine project (3 or 4 GeV) derived from the ESRF one. - At last, the Mars project, concerning a X radiation source storage ring aimed to a industrial use: the microlithographie [fr

An observational study investigating the association of ultrasonographically assessed machine milking-induced changes in teat condition and teat-end shape in dairy cows.

Science.gov (United States)

Wieland, M; Virkler, P D; Borkowski, A H; Älveby, N; Wood, P; Nydam, D V

2018-06-21

Mechanical forces during machine milking induce changes in teat condition which can be differentiated into short-term and long-term changes. Machine milking-induced short-term changes in teat condition (STC) are defined as tissue responses to a single milking and have been associated with the risk of new intramammary infection. Albeit, their association with teat characteristics, such as teat-end shape, has not been investigated by rigorous methods. The primary objective was to determine the association of STC, as measured by ultrasonography, with teat-end shape. The second objective was to describe possible differences in the recovery time of teat tissue after machine milking among teats with different teat-end shapes. Holstein cows (n=128) were enrolled in an observational study, housed in free-stall pens with sand bedding and milked three times a day. Ultrasonography of the left front and right hind teat was performed after teat preparation before milking (t-1), immediately after milking (t 0) and 1, 3, 5 and 7 h after milking (t 1, t 3, t 5, t 7). The teat tissue parameters measured from ultrasound scans were teat canal length, teat-end diameter, teat-end diameter at the midpoint between the distal and proximal end of the teat canal, teat wall thickness, and teat cistern width. Teat-end shape was assessed visually and classified into three categories: pointed, flat and round. Multivariable linear regression analyses showed differences in the relative change of teat tissue parameters (compared with t-1) at t 0 among teats with different teat-end shapes, with most parameters showing the largest change for round teats. The premilking values were reached (recovery time) after 7 h in teats with a pointed teat-end shape, whereas recovery time was greater than 7 h in teats with flat and round teat-end shapes. Under the same liner and milking machine conditions, teats with a round teat-end shape had the most severe short-term changes. The results of this observational

Method of control of machining accuracy of low-rigidity elastic-deformable shafts

Directory of Open Access Journals (Sweden)

Antoni Świć

Full Text Available The paper presents an analysis of the possibility of increasing the accuracy and stability of machining of low-rigidity shafts while ensuring high efficiency and economy of their machining. An effective way of improving the accuracy of machining of shafts is increasing their rigidity as a result of oriented change of the elastic-deformable state through the application of a tensile force which, combined with the machining force, forms longitudinal-lateral strains. The paper also presents mathematical models describing the changes of the elastic-deformable state resulting from the application of the tensile force. It presents the results of experimental studies on the deformation of elastic low-rigidity shafts, performed on a special test stand developed on the basis of a lathe. An estimation was made of the effectiveness of the method of control of the elastic-deformable state with the use, as the regulating effects, the tensile force and eccentricity. It was demonstrated that controlling the two parameters: tensile force and eccentricity, one can improve the accuracy of machining, and thus achieve a theoretically assumed level of accuracy.

Experimental and Mathematical Modeling for Prediction of Tool Wear on the Machining of Aluminium 6061 Alloy by High Speed Steel Tools

Directory of Open Access Journals (Sweden)

Okokpujie Imhade Princess

2017-12-01

Full Text Available In recent machining operation, tool life is one of the most demanding tasks in production process, especially in the automotive industry. The aim of this paper is to study tool wear on HSS in end milling of aluminium 6061 alloy. The experiments were carried out to investigate tool wear with the machined parameters and to developed mathematical model using response surface methodology. The various machining parameters selected for the experiment are spindle speed (N, feed rate (f, axial depth of cut (a and radial depth of cut (r. The experiment was designed using central composite design (CCD in which 31 samples were run on SIEG 3/10/0010 CNC end milling machine. After each experiment the cutting tool was measured using scanning electron microscope (SEM. The obtained optimum machining parameter combination are spindle speed of 2500 rpm, feed rate of 200 mm/min, axial depth of cut of 20 mm, and radial depth of cut 1.0mm was found out to achieved the minimum tool wear as 0.213 mm. The mathematical model developed predicted the tool wear with 99.7% which is within the acceptable accuracy range for tool wear prediction.

Experimental and Mathematical Modeling for Prediction of Tool Wear on the Machining of Aluminium 6061 Alloy by High Speed Steel Tools

Science.gov (United States)

Okokpujie, Imhade Princess; Ikumapayi, Omolayo M.; Okonkwo, Ugochukwu C.; Salawu, Enesi Y.; Afolalu, Sunday A.; Dirisu, Joseph O.; Nwoke, Obinna N.; Ajayi, Oluseyi O.

2017-12-01

In recent machining operation, tool life is one of the most demanding tasks in production process, especially in the automotive industry. The aim of this paper is to study tool wear on HSS in end milling of aluminium 6061 alloy. The experiments were carried out to investigate tool wear with the machined parameters and to developed mathematical model using response surface methodology. The various machining parameters selected for the experiment are spindle speed (N), feed rate (f), axial depth of cut (a) and radial depth of cut (r). The experiment was designed using central composite design (CCD) in which 31 samples were run on SIEG 3/10/0010 CNC end milling machine. After each experiment the cutting tool was measured using scanning electron microscope (SEM). The obtained optimum machining parameter combination are spindle speed of 2500 rpm, feed rate of 200 mm/min, axial depth of cut of 20 mm, and radial depth of cut 1.0mm was found out to achieved the minimum tool wear as 0.213 mm. The mathematical model developed predicted the tool wear with 99.7% which is within the acceptable accuracy range for tool wear prediction.

MRR and TWR evaluation on electrical discharge machining of Ti-6Al-4V using tungsten : copper composite electrode

Science.gov (United States)

Prasanna, J.; Rajamanickam, S.; Amith Kumar, O.; Karthick Raj, G.; Sathya Narayanan, P. V. V.

2017-05-01

In this paper Ti-6Al-4V used as workpiece material and it is keenly seen in variety of field including medical, chemical, marine, automotive, aerospace, aviation, electronic industries, nuclear reactor, consumer products etc., The conventional machining of Ti-6Al-4V is very difficult due to its distinctive properties. The Electrical Discharge Machining (EDM) is right choice of machining this material. The tungsten copper composite material is employed as tool material. The gap voltage, peak current, pulse on time and duty factor is considered as the machining parameter to analyze the machining characteristics Material Removal Rate (MRR) and Tool Wear Rate (TWR). The Taguchi method is provided to work for finding the significant parameter of EDM. It is found that for MRR significant parameters rated in the following order Gap Voltage, Pulse On-Time, Peak Current and Duty Factor. On the other hand for TWR significant parameters are listed in line of Gap Voltage, Duty Factor, Peak Current and Pulse On-Time.

Measuring Diagnostic Stand for Experimental Researches in Technology Machining

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A. E. Dreval'

2014-01-01

Full Text Available The paper reviews applied techniques, methods, and structure of the control and measuring means to conduct experimental and scientific researches of cutting processes. Existing research methods in cutting the metals are divided by features, such as essence of methods, the number of records of physical indicators, the number of studied factors, duration of tests. The groups of methods are briefly characterized.The chair "Tool Engineering and Technologies" of BMSTU developed and made a diagnostic stand of control and measurements for conducting research activities in the field of materials processing technology by cutting to define rational technological decisions, when machining, and carry out an analysis of efficiency and economic feasibility of made decisions. The diagnostic stand contains modern the electronic equipment. Record of measuring parameters is made in real time with a possibility for visual representation of read results and mathematical and statistical processing of measurement results. The stand can be used in research laboratories of machine-building enterprises, laboratories of higher education institutions, and other scientific divisions.The paper presents a justification that the stand is reasonable to use for the following: completion and choice of rational cutting modes, workability assessment of new constructional materials, technical and operational characteristics of the processed surfaces, and operational properties of the cutting tools of various producers, choice of optimum geometrical parameters of the cutting tools and brands of the lubricant cooling technological means, as well as the energy consumption for the chosen machining process. The stand allows us to make an assessment of wear resistance and tribology-technical characteristics of tool materials, as well as an accuracy, rigidity, vibration stability of machines, both new and being in operation.

Effect of microstructure and cutting speed on machining behavior of Ti6Al4V alloy

Energy Technology Data Exchange (ETDEWEB)

Telrandhe, Sagar V.; Mishra, Sushil; Saxena, Ashish K. [Indian Institute of Technology Bombay, Mumbai (India)

2017-05-15

Machining of aerospace and biomedical grade titanium alloys has always been a challenge because of their low conductivity and elastic modulus. Different machining methods and parameters have been adopted for high precision machining of titanium alloys. Machining of titanium alloys can be improved by microstructure optimization. The present study focuses on the effect of microstructure on ma- chinability of Ti6Al4V alloys at different cutting speeds. Samples were subjected to different annealing conditions resulting in different grain sizes and local micro-strains (misorientation). Cutting forces were significantly reduced after annealing; consequently, sub-surface residual stresses were reduced. Deformation twinning was also observed on samples annealed at a higher temperature due to larger grain size. Initial strain free grains and deformation twinning during machining reduces the cutting force at higher cutting speed.

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

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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.

Main studies results for introduction of EB machine to Vietnam and for its application

International Nuclear Information System (INIS)

Tran, Khac An; Nguyen, Quoc Hien; Le, Hai

2004-01-01

Upon the national program on utilization of EB machine for research and development purposes and the FNCA project on application of electron accelerator, as a counterpart the Research and Development Center for Radiation Technology (VINAGAMMA) is preparing technical, manpower and financial conditions for introduction of an EB machine for R and D purposes. The paper offers main studied results in the field of Radiation Processing aimed at putting applications of EB technology into Vietnam and studies on selection of EB machine for R and D purposes in Vietnam. (author)

Investigation of approximate models of experimental temperature characteristics of machines

Science.gov (United States)

Parfenov, I. V.; Polyakov, A. N.

2018-05-01

This work is devoted to the investigation of various approaches to the approximation of experimental data and the creation of simulation mathematical models of thermal processes in machines with the aim of finding ways to reduce the time of their field tests and reducing the temperature error of the treatments. The main methods of research which the authors used in this work are: the full-scale thermal testing of machines; realization of various approaches at approximation of experimental temperature characteristics of machine tools by polynomial models; analysis and evaluation of modelling results (model quality) of the temperature characteristics of machines and their derivatives up to the third order in time. As a result of the performed researches, rational methods, type, parameters and complexity of simulation mathematical models of thermal processes in machine tools are proposed.

Improvement of human operator vibroprotection system in the utility machine

Science.gov (United States)

Korchagin, P. A.; Teterina, I. A.; Rahuba, L. F.

2018-01-01

The article is devoted to an urgent problem of improving efficiency of road-building utility machines in terms of improving human operator vibroprotection system by determining acceptable values of the rigidity coefficients and resistance coefficients of operator’s cab suspension system elements and those of operator’s seat. Negative effects of vibration result in labour productivity decrease and occupational diseases. Besides, structure vibrations have a damaging impact on the machine units and mechanisms, which leads to reducing an overall service life of the machine. Results of experimental and theoretical research of operator vibroprotection system in the road-building utility machine are presented. An algorithm for the program to calculate dynamic impacts on the operator in terms of different structural and performance parameters of the machine and considering combination of external pertrubation influences was proposed.

Dynamic identification of plasma magnetic contour in fusion machines

International Nuclear Information System (INIS)

Bettini, P.; Trevisan, F.; Cavinato, M.

2005-01-01

The paper presents a method to identify the plasma magnetic contour in fusion machines, when eddy currents are present in the conducting structures surrounding the plasma. The approach presented is based on the integration of an electromagnetic model of the plasma with a lumped parameters model of the conducting structures around the plasma. This approach has been validated against experimental data from RFX, a reversed field pinch machine. (author)

Self-Improving CNC Milling Machine

OpenAIRE

Spilling, Torjus

2014-01-01

This thesis is a study of the ability of a CNC milling machine to create parts for itself, and an evaluation of whether or not the machine is able to improve itself by creating new machine parts. This will be explored by using off-the-shelf parts to build an initial machine, using 3D printing/rapid prototyping to create any special parts needed for the initial build. After an initial working machine is completed, the design of the machine parts will be adjusted so that the machine can start p...

comparative study of moore and mealy machine models adaptation

African Journals Online (AJOL)

user

automata model was developed for ABS manufacturing process using Moore and Mealy Finite State Machines. Simulation ... The simulation results showed that the Mealy Machine is faster than the Moore ..... random numbers from MATLAB.

Surface roughness optimization in machining of AZ31 magnesium alloy using ABC algorithm

Directory of Open Access Journals (Sweden)

Abhijith

2018-01-01

Full Text Available Magnesium alloys serve as excellent substitutes for materials traditionally used for engine block heads in automobiles and gear housings in aircraft industries. AZ31 is a magnesium alloy finds its applications in orthopedic implants and cardiovascular stents. Surface roughness is an important parameter in the present manufacturing sector. In this work optimization techniques namely firefly algorithm (FA, particle swarm optimization (PSO and artificial bee colony algorithm (ABC which are based on swarm intelligence techniques, have been implemented to optimize the machining parameters namely cutting speed, feed rate and depth of cut in order to achieve minimum surface roughness. The parameter Ra has been considered for evaluating the surface roughness. Comparing the performance of ABC algorithm with FA and PSO algorithm, which is a widely used optimization algorithm in machining studies, the results conclude that ABC produces better optimization when compared to FA and PSO for optimizing surface roughness of AZ 31.

Optimasi Parameter Permesinan Terhadap Waktu Proses Pada Pemrograman Cnc Milling Dengan Berbasis Cad/cam

OpenAIRE

Yudhyadi, IGNK; Rachmanto, Tri; Ramadan, Adnan Dedy

2016-01-01

The milling process is one of many machining processes for manufacturing component. The length of time in the process of milling machining is influenced by selection and design of machining parameters including cutting speed, feed rate and depth of cut. The purpose of this study to know the influence of cutting speed, feed rate and depth of cut as independent variables versus operation time at CNC milling process as dependent variables. Each independent variable consists of three level of fac...

An investigative study towards constructing anthropocentric Man-Machine System design evaluation methodology

International Nuclear Information System (INIS)

Yoshikawa, H.; Gofuku, A.; Itoh, T.; Sasaki, K.

1992-01-01

A methodological investigation has been conducted for evaluating the reliability of man-machine interaction in the total Man-Machine System (MMS) from the view-point of safety maintenance for emergent situations of nuclear power plant. Basic considerations in our study are: (i) what are the MMS design data to be evaluated, (ii) how are those MMS design data should be treated, and (iii) how the introduction effects of various operator support tools can be evaluated. The methods of both qualitative and quantitative MMS design evaluation are summarized in this paper, with the system architecture based on man-machine interaction simulation and the related cognitive human error factor analysis. (author)

electrical-thermal coupling of induction machine for improved

African Journals Online (AJOL)

user

parameter method was used in the thermal model of the machine. The system of ... Thermal modeling is important for design purpose, fault detection ... dependent problems are challenging both in software development ... numerical solution.

Early failure analysis of machining centers: a case study

International Nuclear Information System (INIS)

Wang Yiqiang; Jia Yazhou; Jiang Weiwei

2001-01-01

To eliminate the early failures and improve the reliability, nine ex-factory machining centers are traced under field conditions in workshops. Their early failure information throughout the ex-factory run-in test is collected. The field early failure database is constructed based on the collection of field early failure data and the codification of data. Early failure mode and effects analysis is performed to indicate the weak subsystem of a machining center or the troublemaker. The distribution of the time between early failures is analyzed and the optimal ex-factory run-in test time for machining center that may expose sufficiently the early failures and cost minimum is discussed. Suggestions how to arrange ex-factory run-in test and how to take actions to reduce early failures for machining center is proposed

Comparitive study of the influence of harmonic voltage distortion on the efficiency of induction machines versus line start permanent magnet machines

OpenAIRE

Debruyne, Colin; Derammelaere, Stijn; Desmet, Jan; Vandevelde, Lieven

2012-01-01

Induction machines have nearly reached their maximal efficiency. In order to further increase the efficiency the use of permanent magnets in combination with the robust design of the induction machine is being extensively researched. These so-called line start permanent magnet machines have an increased efficiency in sine wave conditions in respect to standard induction machines, however the efficiency of these machines is less researched under distorted voltage conditions. This paper compare...

Logical Evaluation of Consciousness: For Incorporating Consciousness into Machine Architecture

OpenAIRE

Padhy, C. N.; Panda, R. R.

2010-01-01

Machine Consciousness is the study of consciousness in a biological, philosophical, mathematical and physical perspective and designing a model that can fit into a programmable system architecture. Prime objective of the study is to make the system architecture behave consciously like a biological model does. Present work has developed a feasible definition of consciousness, that characterizes consciousness with four parameters i.e., parasitic, symbiotic, self referral and reproduction. Prese...

Optimization of control parameters for SR in EDM injection flushing type on stainless steel 304 workpiece

International Nuclear Information System (INIS)

Reza, M S; Yusoff, A R; Shaharun, M A

2012-01-01

The operating control parameters of injection flushing type of electrical discharge machining process on stainless steel 304 workpiece with copper tools are being optimized according to its individual machining characteristic i.e. surface roughness (SR). Higher SR during EDM machining process results for poor surface integrity of the workpiece. Hence, the quality characteristic for SR is set to lower-the-better to achieve the optimum surface integrity. Taguchi method has been used for the construction, layout and analysis of the experiment for each of the machining characteristic for the SR. The use of Taguchi method in the experiment saves a lot of time and cost of machining the experiment samples. Therefore, an L18 Orthogonal array which was the fundamental component in the statistical design of experiments has been used to plan the experiments and Analysis of Variance (ANOVA) is used to determine the optimum machining parameters for this machining characteristic. The control parameters selected for this optimization experiments are polarity, pulse on duration, discharge current, discharge voltage, machining depth, machining diameter and dielectric liquid pressure. The result had shown that the lower the machining diameter, the lower will be the SR.

Sensitivity analysis of machine-learning models of hydrologic time series

Science.gov (United States)

O'Reilly, A. M.

2017-12-01

Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

Optimization of design parameters for bulk micromachined silicon membranes for piezoresistive pressure sensing application

International Nuclear Information System (INIS)

Belwanshi, Vinod; Topkar, Anita

2016-01-01

Finite element analysis study has been carried out to optimize the design parameters for bulk micro-machined silicon membranes for piezoresistive pressure sensing applications. The design is targeted for measurement of pressure up to 200 bar for nuclear reactor applications. The mechanical behavior of bulk micro-machined silicon membranes in terms of deflection and stress generation has been simulated. Based on the simulation results, optimization of the membrane design parameters in terms of length, width and thickness has been carried out. Subsequent to optimization of membrane geometrical parameters, the dimensions and location of the high stress concentration region for implantation of piezoresistors have been obtained for sensing of pressure using piezoresistive sensing technique.

Optimization of design parameters for bulk micromachined silicon membranes for piezoresistive pressure sensing application

Science.gov (United States)

Belwanshi, Vinod; Topkar, Anita

2016-05-01

Finite element analysis study has been carried out to optimize the design parameters for bulk micro-machined silicon membranes for piezoresistive pressure sensing applications. The design is targeted for measurement of pressure up to 200 bar for nuclear reactor applications. The mechanical behavior of bulk micro-machined silicon membranes in terms of deflection and stress generation has been simulated. Based on the simulation results, optimization of the membrane design parameters in terms of length, width and thickness has been carried out. Subsequent to optimization of membrane geometrical parameters, the dimensions and location of the high stress concentration region for implantation of piezoresistors have been obtained for sensing of pressure using piezoresistive sensing technique.

LINEAR KERNEL SUPPORT VECTOR MACHINES FOR MODELING PORE-WATER PRESSURE RESPONSES

Directory of Open Access Journals (Sweden)

KHAMARUZAMAN W. YUSOF

2017-08-01

Full Text Available Pore-water pressure responses are vital in many aspects of slope management, design and monitoring. Its measurement however, is difficult, expensive and time consuming. Studies on its predictions are lacking. Support vector machines with linear kernel was used here to predict the responses of pore-water pressure to rainfall. Pore-water pressure response data was collected from slope instrumentation program. Support vector machine meta-parameter calibration and model development was carried out using grid search and k-fold cross validation. The mean square error for the model on scaled test data is 0.0015 and the coefficient of determination is 0.9321. Although pore-water pressure response to rainfall is a complex nonlinear process, the use of linear kernel support vector machine can be employed where high accuracy can be sacrificed for computational ease and time.

An improved barium plasma source for q-machines

International Nuclear Information System (INIS)