Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm

The selection of optimal cutting parameters has always presented a critical quality concern in the micromachining process. This study examines the effects of three process parameters which are spindle speed, feed rate and depth of cut on the process outputs. The outputs are the surface area roughne...

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Main Author:	Golshan, Abolfazl
Format:	Thesis
Language:	English
Published:	2013
Subjects:	Titanium Genetic algorithms
Online Access:	http://psasir.upm.edu.my/id/eprint/47576/1/FK%202013%2049R.pdf
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id	my-upm-ir.47576
record_format	uketd_dc
spelling	my-upm-ir.475762016-07-22T02:12:02Z Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm 2013-11 Golshan, Abolfazl The selection of optimal cutting parameters has always presented a critical quality concern in the micromachining process. This study examines the effects of three process parameters which are spindle speed, feed rate and depth of cut on the process outputs. The outputs are the surface area roughness and burr formation in micro-end milling of Ti-6Al-4V titanium alloy. Response surface methodology was utilized to develop mathematical models of the process outputs. In addition, analysis of variance and confirmation runs were employed to verify the precision of the mathematical models. Finally, non-dominated sorting genetic algorithm-II as evolutionary optimization approach was used for multi-objective optimization of the micro-end milling process. The optimization results demonstrate the high performance of this method to obtain the Pareto optimal set of solutions in the micro-end milling process. With the optimal parameter sets, an operator can select a suitable combination of variables to obtain a better surface finish or lower burr formation. Optimal machining parameters were the spindle speed of 40000 rpm, the feed rate of 61-75 mm/min, and the depth of cut of 86-92 μm. Titanium Genetic algorithms 2013-11 Thesis http://psasir.upm.edu.my/id/eprint/47576/ http://psasir.upm.edu.my/id/eprint/47576/1/FK%202013%2049R.pdf application/pdf en public masters Universiti Putra Malaysia Titanium Genetic algorithms
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Titanium Genetic algorithms
spellingShingle	Titanium Genetic algorithms Golshan, Abolfazl Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm
description	The selection of optimal cutting parameters has always presented a critical quality concern in the micromachining process. This study examines the effects of three process parameters which are spindle speed, feed rate and depth of cut on the process outputs. The outputs are the surface area roughness and burr formation in micro-end milling of Ti-6Al-4V titanium alloy. Response surface methodology was utilized to develop mathematical models of the process outputs. In addition, analysis of variance and confirmation runs were employed to verify the precision of the mathematical models. Finally, non-dominated sorting genetic algorithm-II as evolutionary optimization approach was used for multi-objective optimization of the micro-end milling process. The optimization results demonstrate the high performance of this method to obtain the Pareto optimal set of solutions in the micro-end milling process. With the optimal parameter sets, an operator can select a suitable combination of variables to obtain a better surface finish or lower burr formation. Optimal machining parameters were the spindle speed of 40000 rpm, the feed rate of 61-75 mm/min, and the depth of cut of 86-92 μm.
format	Thesis
qualification_level	Master's degree
author	Golshan, Abolfazl
author_facet	Golshan, Abolfazl
author_sort	Golshan, Abolfazl
title	Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm
title_short	Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm
title_full	Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm
title_fullStr	Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm
title_full_unstemmed	Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm
title_sort	optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm
granting_institution	Universiti Putra Malaysia
publishDate	2013
url	http://psasir.upm.edu.my/id/eprint/47576/1/FK%202013%2049R.pdf
_version_	1747811942829916160

Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm

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