Robust optimization for multiple response using stochastic model

Authors: Dong, S., Yang, X., Tang, Z. and Zhang, J.

Journal: Lecture Notes in Mechanical Engineering

Pages: 396-405

eISSN: 2195-4364

ISSN: 2195-4356

DOI: 10.1007/978-3-030-53669-5_29

Abstract:

Due to a lot of uncertainties in the robust optimization process, especially in multiple response problems, many random factors can cost doubt on results. The aim of this paper is to propose a robust optimization method for multiple response considering the random factors in the robust optimization design to solve the aforementioned problem. In this paper, we research the multi-response robustness optimization of the anti-rolling torsion bar using a stochastic model. First, the quality loss function of the anti-rolling torsion bar is determined as the optimization object, and the diameters of the anti-rolling torsion bar are determined as the design variables. Second, the multi-response robust optimization model, considering random factors (such as the loads), is established by using the stochastic model. Finally, the Monte Carlo sampling method combined with a non-dominated sorting genetic algorithm II (NSGA II) is adopted to solve this robust optimization problem, and then the robust optimization solution is obtained. The research results indicate that the anti-rolling torsion bar weight decreases, and the stiffness and fatigue strength increase. Furthermore, the quality performance of the anti-rolling torsion bar gets better, and the anti-disturbance ability of the anti-rolling torsion bar gets stronger.

https://eprints.bournemouth.ac.uk/34571/

Source: Scopus