A MIXED-KERNEL-BASED SUPPORT VECTOR REGRESSION MODEL FOR AUTOMOTIVE BODY DESIGN OPTIMIZATION

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, SVR, 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 characteristic's. In order to eliminate the limitations of single kernel SVR, a 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. Then the proposed MKSVR is applied to automotive body design optimization. The application of MKSVR is demonstrated by a vehicle design problem for weight reduction while satisfying safety constraints on X direction acceleration and Crush Distance. A comparison study for SVR and MKSVR in application indicates MK SVR surpasses SVR in model accuracy.