Application of Soft Computing Based Response Surface Techniques in Sizing of A-Pillar Trim with Rib Structures

승용차 A-Pillar Trim의 치수설계를 위한 소프트컴퓨팅기반 반응표면기법의 응용

  • Kim, Seung-Jin (Dept.of Mechanical Engineering, Graduate School of Yonsei University) ;
  • Kim, Hyeong-Gon (Dept.of Mechanical Engineering, Graduate School of Yonsei University) ;
  • Lee, Jong-Su (Dept.of Mechanical Electronics Engineering, Yonsei University) ;
  • Gang, Sin-Il (Dept.of Mechanical Electronics Engineering, Yonsei University)
  • 김승진 (연세대학교 대학원 기계공학과) ;
  • 김형곤 (연세대학교 대학원 기계공학과) ;
  • 이종수 (연세대학교 기계전자공학부) ;
  • 강신일 (연세대학교 기계전자공학부)
  • Published : 2001.03.01


The paper proposes the fuzzy logic global approximate optimization strategies in optimal sizing of automotive A-pillar trim with rib structures for occupant head protection. Two different strategies referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the inherent nonlinearity in analysis model should be accommodated over the entire design space and the training data is not sufficiently provided. The objective of structural design is to determine the dimensions of rib in A-pillar, minimizing the equivalent head injury criterion HIC(d). The paper describes the head-form modeling and head impact simulation using LS-DYNA3D, and the approximation procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and subsequently presents their generalization capabilities in terms of number of fuzzy rules and training data.


  1. Kim, H.-K., and Kang S., 2000, 'Optimum Design of An A-Pillar Trim with Rib Structures for occupant Head Protection,' Proceedings of Symposium on Advanced Vehicle Technologies, ASME, International Mechanical Engineering Congress and Exposition, Orlando, FL, November
  2. Sugeno, M., and Kang, G. T., 1988, 'Structure Identification of Fuzzy Model,' Fuzzy Sets and Systems, Vol. 28, pp. 15-33
  3. Kim, S., and Lee, J., 2000, 'Applications of Fuzzy Inference Systems in Global Approximate Design Optimization,' Proceedings of the 8th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Long Beach, CA
  4. Kim, S., and Lee, J., 2000, 'Development of Global Function Approximations for Design Optimization Using Evolutionary Fuzzy Modeling,' KSME International Journal, November.(in press)
  5. Jang, J.-S. R., 1993, ANFIS: Adaptive Network Based Fuzzy Inference System, IEEE Transactions on Systems, Man and Cybernetics, Vol. 23, pp. 665-685
  6. Zadeh, L.A., 1992, 'Fuzzy Logic, Neural Networks and Soft Computing, Department of Computer Science,' the University of California at Berkeley, Berkeley, CA
  7. Yamakawa, T., 1992, 'A Neo Fuzzy Neuron and Its Applications to System Identification and Prediction of the System Behavior,' Proceedings of the 2nd International Conference on Fuzzy Logic & Neural Networks, pp. 477-483
  8. Berenji, H. R., and Khedkar, P., 1992, 'Learning and Tuning Fuzzy Logic Controllers Through Reinforcements,' IEEE Transactions on Neural Networks, Vol. 3, No. 5, pp. 724-740
  9. Jang, J.-S. R., Sun, C.-T., and Mizutani, E., 1997, Neuro-Fuzzy and Soft Computing, Prentice Hall
  10. Lee, J., and Hajela, P., 1996, 'Parallel Genetic Algorithm Implementation in Multidisciplinary Rotor Blade Design,' Journal of Aircraft, Vol. 33, No. 5, pp. 962-969
  11. Hajela, P., and Lee, J., 1995, 'Genetic Algorithms in Multidisciplinary Robot Blade Design,' Proceedings of the 36th SDM Conference, AIAA Paper No. 95-1144, New Orleans, LA
  12. Roux, W. J., Stander, N., and Haftka, R. T., 1996, 'Response Surface Approximation for Structural Optimization,' AIAA Paper No. 96-4042
  13. Carpenter, W. C., and Barthelemy, J.-F.M., 1993, 'A Comparison of Polynomial Approximations and Artificial Neural Networks as Response Surface,' Structural Optimization, Vol. 5, pp. 166-174
  14. Standrad No 201 1995,: Head Impact Protection, Part 571 - Federal Motor Vehicle Safety Stadard, National Highway Traffic Safety Administration, Department of Transportation, US