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

• Published in: International journal of advanced manufacturing technology Vol. 83; no. 5-8; pp. 1265 - 1279
• Main Authors: Sahali, M. A., Belaidi, I., Serra, R.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2016; Springer Nature B.V; Springer Verlag
• Subjects: CAE) and Design; Classification; Computer simulation; Computer-Aided Engineering (CAD; Cutting parameters; Deformation; Distribution functions; Engineering; Engineering Sciences; Failure; Formulations; Genetic algorithms; Industrial and Production Engineering; Mechanical Engineering; Media Management; Multiple objective analysis; Objectives; Optimization; Original Article; Probability theory; Robustness (mathematics); Sorting algorithms; Statistical analysis; Turning (machining); Uncertainty; Pareto optimal solutions; Reliable machining parameters; NSGA-II; Monte Carlo simulations; Optimization; Failure probability
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-015-7526-z

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 optimization problems. New probabilistic version of the nondominated sorting genetic algorithm P-NSGA-II, which incorporates the Monte Carlo simulations for accurate assessment of cumulative 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 technological 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 operation; therefore, the economic objectives are minimized under imposed maximum probabilities of failure. In both situations, the efficiency and robustness of optimal solutions generated by the P-NSGA-II algorithm are analysed, discussed and compared with sequence of unconstrained minimization technique (SUMT) method.