Optimization of milling parameters using ant colony optimization

In process planning of conventional milling, selecting reasonable milling parameters is necessary to satisfy requirements involving machining economics, quality and safety. This study is to develop optimization procedures based on the Ant Colony Optimization (ACO). This method was demonstrated for t...

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Main Author:	Mohd Saupi, Mohd Sauki
Format:	Undergraduates Project Papers
Language:	English
Published:	2008
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/260/ http://umpir.ump.edu.my/id/eprint/260/ http://umpir.ump.edu.my/id/eprint/260/1/Fadzil_Faisae.pdf

id	ump-260
recordtype	eprints
spelling	ump-2602015-03-03T06:16:06Z http://umpir.ump.edu.my/id/eprint/260/ Optimization of milling parameters using ant colony optimization Mohd Saupi, Mohd Sauki TJ Mechanical engineering and machinery In process planning of conventional milling, selecting reasonable milling parameters is necessary to satisfy requirements involving machining economics, quality and safety. This study is to develop optimization procedures based on the Ant Colony Optimization (ACO). This method was demonstrated for the optimization of machining parameters for milling operation. The machining parameters in milling operations consist of cutting speed, feed rate and depth of cut. These machining parameters significantly impact on the cost, productivity and quality of machining parts. The developed strategy based on the maximize production rate criterion. This study describes development and utilization of an optimization system, which determines optimum machining parameters for milling operations. The ACO simulation is develop to achieve the objective to optimize milling parameters to maximize the production rate in milling operation. The Matlab software will be use to develop the ACO simulation. All the references are taken from related articles, journals and books. An example to apply the Ant Colony Algorithm to the problem has been presented at the end of the paper to give clear picture from the application of the system and its efficiency. The result obtained from this simulation will compare with another method like Genetic Algorithm (GA) and Linear Programming Technique (LPT) to validation. The simulation based on ACO algorithm are successful develop and the optimization of parameters values is to maximize the production rate is obtain from the simulation. 2008-11 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/260/1/Fadzil_Faisae.pdf Mohd Saupi, Mohd Sauki (2008) Optimization of milling parameters using ant colony optimization. Faculty of Mechanical Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:39796&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Mohd Saupi, Mohd Sauki Optimization of milling parameters using ant colony optimization
description	In process planning of conventional milling, selecting reasonable milling parameters is necessary to satisfy requirements involving machining economics, quality and safety. This study is to develop optimization procedures based on the Ant Colony Optimization (ACO). This method was demonstrated for the optimization of machining parameters for milling operation. The machining parameters in milling operations consist of cutting speed, feed rate and depth of cut. These machining parameters significantly impact on the cost, productivity and quality of machining parts. The developed strategy based on the maximize production rate criterion. This study describes development and utilization of an optimization system, which determines optimum machining parameters for milling operations. The ACO simulation is develop to achieve the objective to optimize milling parameters to maximize the production rate in milling operation. The Matlab software will be use to develop the ACO simulation. All the references are taken from related articles, journals and books. An example to apply the Ant Colony Algorithm to the problem has been presented at the end of the paper to give clear picture from the application of the system and its efficiency. The result obtained from this simulation will compare with another method like Genetic Algorithm (GA) and Linear Programming Technique (LPT) to validation. The simulation based on ACO algorithm are successful develop and the optimization of parameters values is to maximize the production rate is obtain from the simulation.
format	Undergraduates Project Papers
author	Mohd Saupi, Mohd Sauki
author_facet	Mohd Saupi, Mohd Sauki
author_sort	Mohd Saupi, Mohd Sauki
title	Optimization of milling parameters using ant colony optimization
title_short	Optimization of milling parameters using ant colony optimization
title_full	Optimization of milling parameters using ant colony optimization
title_fullStr	Optimization of milling parameters using ant colony optimization
title_full_unstemmed	Optimization of milling parameters using ant colony optimization
title_sort	optimization of milling parameters using ant colony optimization
publishDate	2008
url	http://umpir.ump.edu.my/id/eprint/260/ http://umpir.ump.edu.my/id/eprint/260/ http://umpir.ump.edu.my/id/eprint/260/1/Fadzil_Faisae.pdf
first_indexed	2023-09-18T21:52:16Z
last_indexed	2023-09-18T21:52:16Z
_version_	1777413847673470976

Optimization of milling parameters using ant colony optimization

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