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Evolutionary techniques in optimizing machining parameters: review and recent applications (2007-2011)

In highly competitive manufacturing industries nowadays, the manufactures ultimate goals are to produce high quality product with less cost and time constraints. To achieve these goals, one of the considerations is by optimizing the machining process parameters such as the cutting speed, depth of cu...

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Main Authors: Mohd. Zain, Azlan, Yusup, Norfadzlan, Mohd. Hashim, Siti Zaiton
Format: Article
Published: 2012
Subjects:
QA76 Computer software
Online Access:http://eprints.utm.my/id/eprint/46947/
http://dx.doi.org/10.1016/j.eswa.2012.02.109
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spelling my.utm.469472017-09-27T04:05:30Z http://eprints.utm.my/id/eprint/46947/ Evolutionary techniques in optimizing machining parameters: review and recent applications (2007-2011) Mohd. Zain, Azlan Yusup, Norfadzlan Mohd. Hashim, Siti Zaiton QA76 Computer software In highly competitive manufacturing industries nowadays, the manufactures ultimate goals are to produce high quality product with less cost and time constraints. To achieve these goals, one of the considerations is by optimizing the machining process parameters such as the cutting speed, depth of cut, radial rake angle. Recently, alternative to conventional techniques, evolutionary optimization techniques are the new trend for optimization of the machining process parameters. This paper gives an overview and the comparison of the latest five year researches from 2007 to 2011 that used evolutionary optimization techniques to optimize machining process parameter of both traditional and modern machining. Five techniques are considered, namely genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), ant colony optimization (ACO) and artificial bee colony (ABC) algorithm. Literature found that GA was widely applied by researchers to optimize the machining process parameters. Multi-pass turning was the largest machining operation that deals with GA optimization. In terms of machining performance, surface roughness was mostly studied with GA, SA, PSO, ACO and ABC evolutionary techniques. 2012 Article PeerReviewed Mohd. Zain, Azlan and Yusup, Norfadzlan and Mohd. Hashim, Siti Zaiton (2012) Evolutionary techniques in optimizing machining parameters: review and recent applications (2007-2011). Expert Systems With Applications, 39 (10). pp. 9909-9927. ISSN 0957-4174 http://dx.doi.org/10.1016/j.eswa.2012.02.109
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA76 Computer software
spellingShingle QA76 Computer software
Mohd. Zain, Azlan
Yusup, Norfadzlan
Mohd. Hashim, Siti Zaiton
Evolutionary techniques in optimizing machining parameters: review and recent applications (2007-2011)
description In highly competitive manufacturing industries nowadays, the manufactures ultimate goals are to produce high quality product with less cost and time constraints. To achieve these goals, one of the considerations is by optimizing the machining process parameters such as the cutting speed, depth of cut, radial rake angle. Recently, alternative to conventional techniques, evolutionary optimization techniques are the new trend for optimization of the machining process parameters. This paper gives an overview and the comparison of the latest five year researches from 2007 to 2011 that used evolutionary optimization techniques to optimize machining process parameter of both traditional and modern machining. Five techniques are considered, namely genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), ant colony optimization (ACO) and artificial bee colony (ABC) algorithm. Literature found that GA was widely applied by researchers to optimize the machining process parameters. Multi-pass turning was the largest machining operation that deals with GA optimization. In terms of machining performance, surface roughness was mostly studied with GA, SA, PSO, ACO and ABC evolutionary techniques.
format Article
author Mohd. Zain, Azlan
Yusup, Norfadzlan
Mohd. Hashim, Siti Zaiton
author_facet Mohd. Zain, Azlan
Yusup, Norfadzlan
Mohd. Hashim, Siti Zaiton
author_sort Mohd. Zain, Azlan
title Evolutionary techniques in optimizing machining parameters: review and recent applications (2007-2011)
title_short Evolutionary techniques in optimizing machining parameters: review and recent applications (2007-2011)
title_full Evolutionary techniques in optimizing machining parameters: review and recent applications (2007-2011)
title_fullStr Evolutionary techniques in optimizing machining parameters: review and recent applications (2007-2011)
title_full_unstemmed Evolutionary techniques in optimizing machining parameters: review and recent applications (2007-2011)
title_sort evolutionary techniques in optimizing machining parameters: review and recent applications (2007-2011)
publishDate 2012
url http://eprints.utm.my/id/eprint/46947/
http://dx.doi.org/10.1016/j.eswa.2012.02.109
_version_ 1643652188989292544
score 13.223943

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