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Modelling and Simulation in Engineering
Volume 2017, Article ID 1940635, 10 pages
Research Article

Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm

Laboratory of Mechanical Engineering, Faculty of Sciences and Techniques, University of Sidi Mohamed Ben Abdellah, Fes, Morocco

Correspondence should be addressed to Abdelouahhab Jabri; moc.liamg@irbaj.bahhauoledba

Received 4 March 2017; Accepted 8 May 2017; Published 21 June 2017

Academic Editor: Enmin Feng

Copyright © 2017 Abdelouahhab Jabri 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.


For years, there has been increasing attention placed on the metal removal processes such as turning and milling operations; researchers from different areas focused on cutting conditions optimization. Cutting conditions optimization is a crucial step in Computer Aided Process Planning (CAPP); it aims to select optimal cutting parameters (such as cutting speed, feed rate, depth of cut, and number of passes) since these parameters affect production cost as well as production deadline. This paper deals with multipass turning operation optimization using a proposed Hybrid Genetic Simulated Annealing Algorithm (HSAGA). The SA-based local search is properly embedded into a GA search mechanism in order to move the GA away from being closed within local optima. The unit production cost is considered in this work as objective function to minimize under different practical and operational constraints. Taguchi method is then used to calibrate the parameters of proposed optimization approach. Finally, different results obtained by various optimization algorithms are compared to the obtained solution and the proposed hybrid evolutionary technique optimization has proved its effectiveness over other algorithms.