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Multi Objective Optimization of Surface Grinding Process by Combination of Response Surface Methodology and Enhanced Non-dominated Sorting Genetic Algorithm

by Dayananda Pai, Shrikantha S. Rao, Rio D'Souza
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 36 - Number 3
Year of Publication: 2011
Authors: Dayananda Pai, Shrikantha S. Rao, Rio D'Souza
10.5120/4471-6267

Dayananda Pai, Shrikantha S. Rao, Rio D'Souza . Multi Objective Optimization of Surface Grinding Process by Combination of Response Surface Methodology and Enhanced Non-dominated Sorting Genetic Algorithm. International Journal of Computer Applications. 36, 3 ( December 2011), 19-24. DOI=10.5120/4471-6267

@article{ 10.5120/4471-6267,
author = { Dayananda Pai, Shrikantha S. Rao, Rio D'Souza },
title = { Multi Objective Optimization of Surface Grinding Process by Combination of Response Surface Methodology and Enhanced Non-dominated Sorting Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { December 2011 },
volume = { 36 },
number = { 3 },
month = { December },
year = { 2011 },
issn = { 0975-8887 },
pages = { 19-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume36/number3/4471-6267/ },
doi = { 10.5120/4471-6267 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:22:10.549284+05:30
%A Dayananda Pai
%A Shrikantha S. Rao
%A Rio D'Souza
%T Multi Objective Optimization of Surface Grinding Process by Combination of Response Surface Methodology and Enhanced Non-dominated Sorting Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 36
%N 3
%P 19-24
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The present study is focused on the multi-objective optimization of performance parameters such as specific energy (u), metal removal rate (MRR) and surface roughness(Ra) obtained in grinding of Al-SiC35P composites. The enhanced elitist non-dominated sorting genetic algorithm (NSGA -II) is used to solve this multi-objective optimization problem. Al-SiC specimens containing 8 vol. %, 10 vol. % and 12 vol. % of silicon carbide particles of mean diameter 35µm, feed and depth of cut were chosen as process variables. A mathematical predictive model for each of the performance parameters was developed using response surface methodology (RSM). Further, an enhanced NSGA-II algorithm is used to optimize the model developed by RSM. Finally, the experiments were carried out to validate the results obtained from RSM and enhanced NSGA-II. The results obtained were in close agreement, which indicates that the developed model can be effectively used for the prediction.

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Index Terms

Computer Science
Information Sciences

Keywords

Discontinuously reinforced aluminium composites (DRACs) Surface grinding Central composite design (CCD) Response surface methodology (RSM) Enhanced non-dominated Sorting Genetic algorithm (NSGA-II) Multi objective optimization

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