Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes

Determination of optimal cutting parameters is one of the most important elements in any process planning of metal parts. 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. Two conflicting objectives, tool life and operation time, are simultaneously optimized. The proposed model uses a microgenetic algorithm in order to obtain the non-dominated points and build the Pareto front graph. An application sample is developed and its results are analysed for several different production conditions. This paper also remarks the advantages of multi-objective optimization approach over the single-objective one.