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Reliable and Robust Optimization of the Planetary Gear Train Using Particle Swarm Optimization and Monte Carlo Simulation
Journal Article
05-15-01-0003
ISSN: 1946-3979, e-ISSN: 1946-3987
Sector:
Citation:
Ziat, A., Zaghar, H., Ait Taleb, A., and Sallaou, M., "Reliable and Robust Optimization of the Planetary Gear Train Using Particle Swarm Optimization and Monte Carlo Simulation," SAE Int. J. Mater. Manf. 15(1):21-33, 2022, https://doi.org/10.4271/05-15-01-0003.
Language:
English
Abstract:
Uncertainties in design represent a considerable industrial stake. Controlling
the reliability and robustness of a mechanical system at the level of design has
become necessary in order to control these uncertainties. Using the theory of
probabilistic design optimization, the present work reports on the application
of the concept of reliability-based robustness on minimizing the weight of a
planetary gear train (PGT). The optimum combination of reliability and
robustness for the minimum weight of the PGT was found using an optimization
algorithm based on Particle Swarm Optimization (PSO) and Monte Carlo Simulation
(MCS). The algorithm was developed by combining the propagation of uncertainties
with the optimization of the function objective within a single probabilistic
model. The results show that a reliability-based robust design offers a better
alternative to the traditional deterministic design models. An equivalence
between the proposed method and the deterministic method is established and
approved.