Abstract
Economic profit of machining is essentially based on the optimal selection of cutting parameters. In this paper, a multi-objective particle swarm optimization approach is introduced to optimize the cutting parameters in turning processes: cutting speed, feed rate and cutting depth. The proposed model presents the problem in form of a multi-objective problem with production rate and used tool life objectives and has a set of constraints that represent the important limitations to be satisfied. To obtain the non dominated solutions and build the Pareto front graph, a modified dynamic neighborhood particle swarm optimization (DNPSO) technique is used. In addition, a fuzzy-based mechanism is employed to extract the best compromise solution. The results on an illustrative sample reveal the capabilities of the proposed DNPSO approach to generate well-distributed Pareto optimal solutions. Comparison with multi-objective deterministic approach (Min–Max) shows the superiority of the proposed approach and confirms its potential for solving multi-objective problems.
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