Surface roughness optimization in end milling using the multi objective genetic algorithm approach

In finishing end milling, not only good accuracy but also good roughness levels must be achieved. Therefore, determining the optimum cutting levels to achieve the minimum surface roughness is important for it is economical and mechanical issues. This paper presents the optimization of machining para...

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Bibliographic Details
Main Authors: Al Hazza, Muataz Hazza Faizi, Adesta, Erry Yulian Triblas, Riza, Muhammad, Mohammad Yuhan, Suprianto
Format: Article
Language:English
English
Published: Trans Tech Publications Inc. 2012
Subjects:
Online Access:http://irep.iium.edu.my/55357/1/10.1.1.971.7225.pdf
http://irep.iium.edu.my/55357/7/55357_Surface%20Roughness%20Optimization_SCOPUS.pdf
http://irep.iium.edu.my/55357/
https://www.scientific.net/AMR.576.103
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Institution: Universiti Islam Antarabangsa Malaysia
Language: English
English
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Summary:In finishing end milling, not only good accuracy but also good roughness levels must be achieved. Therefore, determining the optimum cutting levels to achieve the minimum surface roughness is important for it is economical and mechanical issues. This paper presents the optimization of machining parameters in end milling processes by integrating the genetic algorithm (GA) with the statistical approach. Two objectives have been considered, minimum arithmetic mean roughness (Ra) and minimum Root-mean-square roughness (Rq). The mathematical models for the surface roughness parameters have been developed, in terms of cutting speed, feed rate, and axial depth of cut by using Response Methodology Method (RSM). Due to complexity of this machining optimization problem, a multi objective genetic algorithm (MOGA) has been applied to resolve the problem, and the results have been analyzed.