A levy flight particle swarm optimizer for machining performances optimization

Machining processes has been used widely in manufacturing industry and manufacturers have realized the important of these processes to improve the machining performance that would lead to an increase in production. However, one of the problems identified is how to minimize the values of machining pe...

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Main Author:	Kamaruzaman, Anis Farhan
Format:	Thesis
Language:	English
Published:	2014
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/48029/25/AnisFarhanKamaruzamanMFC2014.pdf
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id	my-utm-ep.48029
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spelling	my-utm-ep.480292017-08-12T01:48:23Z A levy flight particle swarm optimizer for machining performances optimization 2014-04 Kamaruzaman, Anis Farhan QA75 Electronic computers. Computer science Machining processes has been used widely in manufacturing industry and manufacturers have realized the important of these processes to improve the machining performance that would lead to an increase in production. However, one of the problems identified is how to minimize the values of machining performance in terms of surface roughness (Ra), tool wear (VB) and power consumption (Pm). To provide better machining performance, it is essential to optimize cutting parameters which are cutting speed (V), feed rate (f) and cutting time (T). This research has developed a hybridization technique using particle swarm optimization (PSO) and Levy flight labeled as Levy flight particle swarm optimizer (LPSO) aimed at optimizing the cutting parameters to obtain minimum values of machining performance for a specific machining performance such as turning process. The simulation results obtained were compared with particle swarm optimization (PSO), regression analysis (RA), response surface method (RSM), artificial neural network (ANN) and support vector regression (SVR) and validated using regression model, analysis of variance (ANOVA) and determination of optimum level for each machining performance. The results showed that the LPSO could minimize the values of Ra, VB and Pm nearly 95% in comparison to the other research techniques listed in this research. The LPSO technique could minimize the values of machining performance substantially for the manufacturing industry. 2014-04 Thesis http://eprints.utm.my/id/eprint/48029/ http://eprints.utm.my/id/eprint/48029/25/AnisFarhanKamaruzamanMFC2014.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Kamaruzaman, Anis Farhan A levy flight particle swarm optimizer for machining performances optimization
description	Machining processes has been used widely in manufacturing industry and manufacturers have realized the important of these processes to improve the machining performance that would lead to an increase in production. However, one of the problems identified is how to minimize the values of machining performance in terms of surface roughness (Ra), tool wear (VB) and power consumption (Pm). To provide better machining performance, it is essential to optimize cutting parameters which are cutting speed (V), feed rate (f) and cutting time (T). This research has developed a hybridization technique using particle swarm optimization (PSO) and Levy flight labeled as Levy flight particle swarm optimizer (LPSO) aimed at optimizing the cutting parameters to obtain minimum values of machining performance for a specific machining performance such as turning process. The simulation results obtained were compared with particle swarm optimization (PSO), regression analysis (RA), response surface method (RSM), artificial neural network (ANN) and support vector regression (SVR) and validated using regression model, analysis of variance (ANOVA) and determination of optimum level for each machining performance. The results showed that the LPSO could minimize the values of Ra, VB and Pm nearly 95% in comparison to the other research techniques listed in this research. The LPSO technique could minimize the values of machining performance substantially for the manufacturing industry.
format	Thesis
qualification_level	Master's degree
author	Kamaruzaman, Anis Farhan
author_facet	Kamaruzaman, Anis Farhan
author_sort	Kamaruzaman, Anis Farhan
title	A levy flight particle swarm optimizer for machining performances optimization
title_short	A levy flight particle swarm optimizer for machining performances optimization
title_full	A levy flight particle swarm optimizer for machining performances optimization
title_fullStr	A levy flight particle swarm optimizer for machining performances optimization
title_full_unstemmed	A levy flight particle swarm optimizer for machining performances optimization
title_sort	levy flight particle swarm optimizer for machining performances optimization
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
url	http://eprints.utm.my/id/eprint/48029/25/AnisFarhanKamaruzamanMFC2014.pdf
_version_	1747817290472095744

A levy flight particle swarm optimizer for machining performances optimization

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