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U. Zuperl, F. Cus, B. Mursecb and T. Ploj, “A Hybrid Analytical-Neural Network Approach to the Determination of Optimal Cutting Conditions,” Journal of Materials Processing Technology, Vol. 157-158, 2004, pp. 82-90. doi:10.1016/j.jmatprotec.2004.09.019

has been cited by the following article:

• TITLE: Multi-Objective Optimization Using Genetic Algorithms of Multi-Pass Turning Process

  AUTHORS: Abdelouahhab Jabri, Abdellah El Barkany, Ahmed El Khalfi

  KEYWORDS: Genetic Algorithms; Mutli-Objective Optimization; Turning Process; Machining

  JOURNAL NAME: Engineering, Vol.5 No.7, July 15, 2013

  ABSTRACT: In this paper we present a multi-optimization technique based on genetic algorithms to search optimal cuttings parameters such as cutting depth, feed rate and cutting speed of multi-pass turning processes. Tow objective functions are simultaneously optimized under a set of practical of machining constraints, the first objective function is cutting cost and the second one is the used tool life time. The proposed model deals multi-pass turning processes where the cutting operations are divided into multi-pass rough machining and finish machining. Results obtained from Genetic Algorithms method are presented in Pareto frontier graphic; this technique helps us in decision making process. An example is presented to illustrate the procedure of this technique.