×

Use of support vector regression in structural optimization: application to vehicle crashworthiness design. (English) Zbl 07306560

Summary: Metamodel is widely used to deal with analysis and optimization of complex system. Structural optimization related to crashworthiness is of particular importance to automotive industry nowadays, which involves highly nonlinear characteristics with material and structural parameters. This paper presents two industrial cases using support vector regression (SVR) for vehicle crashworthiness design. The first application aims to improve roof crush resistance force, and the other is lightweight design of vehicle front end structure subject to frontal crash, where SVR is utilized to construct crashworthiness responses. The use of multiple instances of SVR with different kernel types and hyper-parameters simultaneously and select the best accurate one for subsequent optimization is proposed. The case studies present the successful use of SVR for structural crashworthiness design. It is also demonstrated that SVR is a promising alternative for approximating highly nonlinear crash problems, showing a successfully alternative for metamodel-based design optimization in practice.

MSC:

90-XX Operations research, mathematical programming
70-XX Mechanics of particles and systems
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Cherkassky, V.; Ma, Y., Practical selection of SVM parameters and noise estimation for SVM regression, Neural Networks, 17, 113-126 (2004) · Zbl 1075.68632
[2] Clarke, S.; Griebsch, J.; Simpson, T., Analysis of support vector regression for approximation of complex engineering analyses, Journal of Mechanical Design, 127, 1077 (2005)
[3] Dellino, G.; Lino, P.; Meloni, C.; Rizzo, A., Kriging metamodel management in the design optimization of a CNG injection system, Mathematics and Computers in Simulation, 79, 2345-2360 (2009) · Zbl 1166.65343
[4] Duddeck, F., Multidisciplinary optimization of car bodies, Structural and Multidisciplinary Optimization, 35, 375-389 (2008)
[5] Goel, T.; Vaidyanathan, R.; Haftka, R.; Shyy, W.; Queipo, N.; Tucker, K., Response surface approximation of Pareto optimal front in multi-objective optimization, Computer Methods in Applied Mechanics and Engineering, 196, 879-893 (2007) · Zbl 1120.76358
[6] S. Gunn, Support vector machines for classification and regression, ISIS Technical Report 14, 1998.; S. Gunn, Support vector machines for classification and regression, ISIS Technical Report 14, 1998.
[7] Jin, Y., A comprehensive survey of fitness approximation in evolutionary computation, Soft Computing-A Fusion of Foundations, Methodologies and Applications, 9, 3-12 (2005) · Zbl 1059.68089
[8] Kim, J.; Rao, V.; Koomullil, R.; Ross, D.; Soni, B.; Shih, A., Development of an efficient aerodynamic shape optimization framework, Mathematics and Computers in Simulation, 79, 2373-2384 (2009) · Zbl 1168.76046
[9] Lu, Z.; Sun, J.; Butts, K., Linear programming support vector regression with wavelet kernel: a new approach to nonlinear dynamical systems identification, Mathematics and Computers in Simulation, 79, 2051-2063 (2009) · Zbl 1161.65313
[10] Martin, J.; Simpson, T., Use of Kriging models to approximate deterministic computer models, AIAA Journal, 43, 853-863 (2005)
[11] Pan, F.; Zhu, P.; Zhang, Y., Metamodel-based lightweight design of B-pillar with TWB structure via support vector regression, Computers & Structures, 88, 36-44 (2010)
[12] Sanchez, E.; Pintos, S.; Queipo, N., Toward an optimal ensemble of kernel-based approximations with engineering applications, Structural and Multidisciplinary Optimization, 36, 247-261 (2008)
[13] Simpson, T.; Mauery, T.; Korte, J.; Mistree, F., Kriging models for global approximation in simulation-based multidisciplinary design optimization, AIAA Journal, 39, 2233-2241 (2001)
[14] Smola, A.; Scholkopf, B., A tutorial on support vector regression, Statistics and Computing, 14, 199-222 (2004)
[15] Viana, F.; Haftka, R.; Steffen, V., Multiple surrogates: how cross-validation errors can help us to obtain the best predictor, Structural and Multidisciplinary Optimization, 39, 439-457 (2009)
[16] Wang, W.; Xu, Z.; Lu, W.; Zhang, X., Determination of the spread parameter in the Gaussian kernel for classification and regression, Neurocomputing, 55, 643-663 (2003)
[17] Yang, R.; Wang, N.; Tho, C.; Bobineau, J.; Wang, B., Metamodeling development for vehicle frontal impact simulation, Journal of Mechanical Design, 127, 1014 (2005)
[18] Youn, B.; Choi, K.; Yang, R.; Gu, L., Reliability-based design optimization for crashworthiness of vehicle side impact, Structural and Multidisciplinary Optimization, 26, 272-283 (2004)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.