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 characteristics. 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 MKSVR surpasses SVR in model accuracy.
A Mixed-Kernel-Based Support Vector Regression Model for Automotive Body Design Optimization
- Views Icon Views
- Share Icon Share
- Search Site
Fang, Y, Zhan, Z, Yang, J, Lu, J, & Chen, C. "A Mixed-Kernel-Based Support Vector Regression Model for Automotive Body Design Optimization." Proceedings of the ASME 2016 International Mechanical Engineering Congress and Exposition. Volume 12: Transportation Systems. Phoenix, Arizona, USA. November 11–17, 2016. V012T16A013. ASME. https://doi.org/10.1115/IMECE2016-67669
Download citation file: