Mixed-Kernel-Based Support Vector Regression Model for Automotive Body Design Optimization under Uncertainty

[+] Author and Article Information
Yudong Fang

State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China

Zhenfei Zhan

State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China

Junqi Yang

174 Shazheng Street, Shapingba District Chongqing, Chongqing 400044 China

Xu Liu

College of Automotive Engineering, Chongqing University, Chongqing 400044, China

1Corresponding author.

ASME doi:10.1115/1.4036990 History: Received February 27, 2017; Revised May 22, 2017


Finite Element (FE) models are commonly used for automotive body design. However, even with increasing speed of computers, the FE-based simulation models are still too time-consuming when the models are complex. To improve the computational efficiency, Support Vector Regression (SVR) model, a potential approximate model, has been widely used as the surrogate of FE model for crashworthiness optimization design. Generally, in the traditional SVR, when dealing with nonlinear data, the single kernel function based projection can’t fully cover data distribution characteristics. In order to eliminate the application limitations of single kernel SVR, a method for reliability-based design optimization based on mixed-kernel-based SVR (MKSVR) is proposed in this research. The mixed kernel is constructed based on the linear combination of radial basis kernel function and polynomial kernel function. Through the particle swarm optimization algorithm, the parameters of the mixed kernel SVR are optimized. The proposed method is demonstrated through a representative analytical RBDO problem and a vehicle lightweight design problem. And the comparison studies for SVR and MKSVR in application indicate that MKSVR surpasses SVR in model accuracy.

Copyright (c) 2017 by ASME
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