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Robust optimization for multiple response using stochastic model.

Dong, S., Yang, X., Tang, Z. and Zhang, J., 2020. Robust optimization for multiple response using stochastic model. In: International Symposium on Uncertainty Quantification and Stochastic Modeling, 29 June- 3 July 2020, Rouen, France, 396 - 405.

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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.

Item Type:Conference or Workshop Item (Paper)
ISSN:2195-4356
Group:Faculty of Media & Communication
ID Code:34571
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:21 Sep 2020 13:48
Last Modified:21 Sep 2020 13:48

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