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Titre: | New approach for robust multi-objective optimization of turning parameters using probabilistic genetic algorithm |
Auteur(s): | Sahali, M. A. Belaidi, Idir Serra, R. |
Mots-clés: | Failure probability Monte Carlo simulations Pareto optimal solutions Optimization NSGA-II Reliable machining parameters |
Date de publication: | 2015 |
Editeur: | Springer |
Collection/Numéro: | International Journal of Advanced Manufacturing Technology;PP. 1-15 |
Résumé: | 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 |
URI/URL: | http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/2250 |
ISSN: | 0268-3768 |
Collection(s) : | Communications Internationales
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