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 cannot fully cover data distribution characteristics. In order to eliminate the application limitations of single kernel SVR, a method for reliability-based design optimization (RBDO) 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 (PSO) 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 comparitive studies for SVR and MKSVR in application indicate that MKSVR surpasses SVR in model accuracy.
A Mixed-Kernel-Based Support Vector Regression Model for Automotive Body Design Optimization Under Uncertainty
Manuscript received February 27, 2017; final manuscript received May 22, 2017; published online June 28, 2017. Assoc. Editor: Alba Sofi.
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Fang, Y., Zhan, Z., Yang, J., and Liu, X. (June 28, 2017). "A Mixed-Kernel-Based Support Vector Regression Model for Automotive Body Design Optimization Under Uncertainty." ASME. ASME J. Risk Uncertainty Part B. December 2017; 3(4): 041008. https://doi.org/10.1115/1.4036990
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