Skip to main content

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.

Full text available as:

[img] PDF
Robust Optimization for Multiple Response Using Stochastic Model.pdf - Accepted Version
Restricted to Repository staff only
Available under License Creative Commons Attribution Non-commercial.

291kB

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: Symplectic RT2
Deposited On:21 Sep 2020 13:48
Last Modified:14 Mar 2022 14:24

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -