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Advances in Materials Science and Engineering
Volume 2016 (2016), Article ID 6429160, 8 pages
http://dx.doi.org/10.1155/2016/6429160
Research Article

Multiobjective Optimization of Turning Cutting Parameters for J-Steel Material

1Department of Mechanical Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
2Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48105, USA
3Department of Mechanical Design, American University in Cairo, New Cairo, Cairo 11835, Egypt

Received 11 January 2016; Revised 20 March 2016; Accepted 23 March 2016

Academic Editor: Wenbin Yi

Copyright © 2016 Adel T. Abbas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a multiobjective optimization study of cutting parameters in turning operation for a heat-treated alloy steel material (J-Steel) with Vickers hardness in the range of HV 365–395 using uncoated, unlubricated Tungsten-Carbide tools. The primary aim is to identify proper settings of the cutting parameters (cutting speed, feed rate, and depth of cut) that lead to reasonable compromises between good surface quality and high material removal rate. Thorough exploration of the range of cutting parameters was conducted via a five-level full-factorial experimental matrix of samples and the Pareto trade-off frontier is identified. The trade-off among the objectives was observed to have a “knee” shape, in which certain settings for the cutting parameters can achieve both good surface quality and high material removal rate within certain limits. However, improving one of the objectives beyond these limits can only happen at the expense of a large compromise in the other objective. An alternative approach for identifying the trade-off frontier was also tested via multiobjective implementation of the Efficient Global Optimization (m-EGO) algorithm. The m-EGO algorithm was successful in identifying two points within the good range of the trade-off frontier with 36% fewer experimental samples.