Search Results - (( developing deposition based algorithm ) OR ( java implication force algorithm ))
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Numerical study of suspension filtration model in porous medium with modified deposition kinetics
Published 2020“…To solve the problem, a stable, effective and simple numerical algorithm is proposed based on the finite difference method. …”
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Development of group method of data handling based on genetic algorithm to predict incipient motion in rigid rectangular storm water channel
Published 2017“…Also, a sensitivityanalysis is presented to study the performance of each input combination in predictingincipient motion (15) Development of Group Method of Data Handling based on Genetic Algorithm to predict incipient motion in rigid rectangular storm water channel. …”
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Development of drift conversion algorithm for ISFET based pH sensor for continuous measurement system
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Development of group method of data handling based on genetic algorithm to predict incipient motion in rigid rectangular storm water channel
Published 2017“…This study utilizes a novel hybrid method based on Group Method of Data Handling (GMDH) and Genetic Algorithm (GA) to design GMDH structural (GMDH-GA). …”
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A deep bed filtration model of two-component suspension in dual-zone porous medium
Published 2020“…To solve the problem, a numerical algorithm for computer experimentation is developed on the basis of finite difference method. …”
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Modeling And Optimization Of Physical Vapour Deposition Coating Process Parameters For Tin Grain Size Using Combined Genetic Algorithms With Response Surface Methodology
Published 2015“…Additionally,analysis of variance(ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters, genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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Numerical simulation and experimental verification on distortions induced by wire-arc additive manufacturing components and costing analysis / Keval Priapratama Prajadhiana
Published 2024“…On analysing the distortion effect by means of numerical computation method, a commercial specialized simulation software Simufact.Welding was used in the development of the numerical model. The development of numerical simulation model started by the geometrical modelling followed by material modelling based on real scanned material using JMATPRO. …”
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Intelligence Integration Of Particle Swarm Optimization And Physical Vapour Deposition For Tin Grain Size Coating Process Parameters
Published 2016“…Additionally,analysis of variance (ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters,genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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Trajectory planning and simulation for 3d printing process
Published 2022“…The output of slicing algorithm will be used as input of infill generator to generate infill vertices based on the infill density and infill pattern that decided by users. …”
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