Hybrid taguchi glowworm optimization algorithm for optimization of cutting parameters

Quality of machining products is generally associated with the surface roughness (Ra) and is one of the important aspects that could affect machining performance. In traditional and modern machining operations, optimization of reasonable cutting parameters is a requirement for providing better quali...

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Main Author:	Zainal, Nurezayana
Format:	Thesis
Published:	2014
Subjects:	QA Mathematics
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spelling	my-utm-ep.484722017-08-08T04:28:43Z Hybrid taguchi glowworm optimization algorithm for optimization of cutting parameters 2014 Zainal, Nurezayana QA Mathematics Quality of machining products is generally associated with the surface roughness (Ra) and is one of the important aspects that could affect machining performance. In traditional and modern machining operations, optimization of reasonable cutting parameters is a requirement for providing better quality products. This research employs and enhanced the Glowworm Swarm Optimization (GSO) algorithm to optimize cutting parameters to obtain minimum Ra values. GSO is a new method of swarm intelligent based algorithm to search for global extremes of multi-modal optimization problems. The algorithm is employed in this study to approximate optimum cutting parameters to obtain improved values of Ra in end milling and abrasive water jet (AWJ) processes. The cutting parameters considered for end milling are cutting speed (v), feed rate (f) and depth of cut (d) whereas traverse speed (V), water jet pressure (P), standoff distance (h), abrasive grit size (D) and abrasive flow rate (m) are considered for AWJ. Following that, to improve further the Ra values, this study proposed hybridization of GSO and Taguchi method known as HTGSO. HTGSO simulation results were compared to experimental and GSO results. In AWJ machining process, HTGSO reduced the Ra value by 13% and 25% compared to those obtain from both experimental and GSO. Whilst, HTGSO is found has outperformed both experimental and GSO in end milling by decreasing the Ra value up to 25% and 40% respectively. Therefore, HTGSO produced the best Ra with the lowest values. This performance indicates HTGSO significantly improved the Ra during the machining process that lead to higher quality of machining product 2014 Thesis http://eprints.utm.my/id/eprint/48472/ masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
topic	QA Mathematics
spellingShingle	QA Mathematics Zainal, Nurezayana Hybrid taguchi glowworm optimization algorithm for optimization of cutting parameters
description	Quality of machining products is generally associated with the surface roughness (Ra) and is one of the important aspects that could affect machining performance. In traditional and modern machining operations, optimization of reasonable cutting parameters is a requirement for providing better quality products. This research employs and enhanced the Glowworm Swarm Optimization (GSO) algorithm to optimize cutting parameters to obtain minimum Ra values. GSO is a new method of swarm intelligent based algorithm to search for global extremes of multi-modal optimization problems. The algorithm is employed in this study to approximate optimum cutting parameters to obtain improved values of Ra in end milling and abrasive water jet (AWJ) processes. The cutting parameters considered for end milling are cutting speed (v), feed rate (f) and depth of cut (d) whereas traverse speed (V), water jet pressure (P), standoff distance (h), abrasive grit size (D) and abrasive flow rate (m) are considered for AWJ. Following that, to improve further the Ra values, this study proposed hybridization of GSO and Taguchi method known as HTGSO. HTGSO simulation results were compared to experimental and GSO results. In AWJ machining process, HTGSO reduced the Ra value by 13% and 25% compared to those obtain from both experimental and GSO. Whilst, HTGSO is found has outperformed both experimental and GSO in end milling by decreasing the Ra value up to 25% and 40% respectively. Therefore, HTGSO produced the best Ra with the lowest values. This performance indicates HTGSO significantly improved the Ra during the machining process that lead to higher quality of machining product
format	Thesis
qualification_level	Master's degree
author	Zainal, Nurezayana
author_facet	Zainal, Nurezayana
author_sort	Zainal, Nurezayana
title	Hybrid taguchi glowworm optimization algorithm for optimization of cutting parameters
title_short	Hybrid taguchi glowworm optimization algorithm for optimization of cutting parameters
title_full	Hybrid taguchi glowworm optimization algorithm for optimization of cutting parameters
title_fullStr	Hybrid taguchi glowworm optimization algorithm for optimization of cutting parameters
title_full_unstemmed	Hybrid taguchi glowworm optimization algorithm for optimization of cutting parameters
title_sort	hybrid taguchi glowworm optimization algorithm for optimization of cutting parameters
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
_version_	1747817398666264576

Hybrid taguchi glowworm optimization algorithm for optimization of cutting parameters

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