New approach for robust multi-objective optimization of turning parameters using probabilistic genetic algorithm

Abstract

In this paper, a contribution to the determination of reliable cutting parameters is presented, which is minimizing the expected machining cost and maximizing the expected production rate, with taking into account the uncertainties of uncontrollable factors. The concept of failure probability of stochastic production limitations is integrated into constrained and unconstrained formulations of multi-objective optimiza- tion problems. New probabilistic version of the nondominated sorting genetic algorithm P-NSGA-II, which incorporates the Monte Carlo simulations for accurate assessment of cumula- tive distribution functions, was developed and applied in two numerical examples based on similar and anterior work. In the first case, it is a question of the search space that is completely ‘ closed ’ by high natural variability related to the multi-pass roughing operation: in this case, the failure risk of technolog- ical limitations are considered as objectives to minimize with economic objectives. The second case is related to deformed search space due to the uncertainties specific to finishing op- eration; therefore, the economic objectives are minimized un- der imposed maximum probabilities of failure. In both situa- tions, the efficiency and robustness of optimal solutions