A comparison of optimization methods in cutting parameters using Non-Dominated Sorting Genetic Algorithm (Nsga-Ii) and Micro Genetic Algorithm (Mga)

Abstract

Since cutting conditions have an influence on reducing the production cost and time and deciding thequality of a final product the determination of optimal cutting parameters such as cutting speed, feedrate, depth of cut and tool geometry is one of vital modules in process planning of metal parts. Withuse of experimental results and subsequently, with exploitation of main effects plot, importance ofeach parameter is studied. In this investigation these parameters was considered as input in order tooptimized the surface finish and tool life criteria, two conflicting objectives, as the processperformance simultaneously. In this study, micro genetic algorithm (MGA) and Non-dominated SortingGenetic Algorithm (NSGA-II) were compared with each other proving the superiority of Non-dominated Sorting Genetic Algorithm over micro genetic since Non-dominated Sorting GeneticAlgorithm results were more satisfactory than micro genetic algorithm in terms of optimizingmachining parameters.