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

Content Provider	Springer Nature Link
Author	Sahali, M. A. Belaidi, I. Serra, R.
Copyright Year	2015
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 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.
Starting Page	1265
Ending Page	1279
Page Count	15
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	83
Issue Number	5-8
e-ISSN	14333015
Language	English
Publisher	Springer London
Publisher Date	2015-08-08
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Failure probability Monte Carlo simulations Pareto optimal solutions Optimization NSGA-II Reliable machining parameters Industrial and Production Engineering Production Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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