In a manufacturing industry, machining process is to shape the metal parts by removing unwanted material. During the machining process of any part given quality specifications such as surface finish, accuracy with minimum production cost or machining time are to be considered. Economy of machining operation plays a key role in competitiveness in the market. This paper presents a multi-objective optimization technique, based on genetic algorithms, to optimize the cutting parameters in turning processes: cutting depth, feed and speed. Optimization of cutting parameters is one of the most important elements in any process planning of metal parts. In this paper the three objective functions, minimum operating time and minimum production cost and minimum tool wear are simultaneously optimized. The proposed model uses a genetic algorithm in order to obtain the non dominated sorting genetic algorithm (NSGA-II) and build the Pareto front graph. An application sample is developed and its results are analyzed for several different production conditions. This paper also remarks the advantages of multi objective optimization approach over the single-objective one.
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