Transactions of the Korean Society of Automotive Engineers (한국자동차공학회논문집)

The Korean Society of Automotive Engineers (한국자동차공학회)
권호 표지
DOI QR코드

DOI QR Code

Material Selection Optimization of A-Pillar and Package Tray Using RBFr Metamodel for Minimizing Weight

경량화를 위한 RBFr 메타모델 기반 A-필러와 패키지 트레이의 소재 선정 최적화

  • Jin, Sungwan (Department of Mechanical Engineering, Hanyang University) ;
  • Park, Dohyun (Department of Mechanical Engineering, Hanyang University) ;
  • Lee, Gabseong (Department of Mechanical Engineering, Hanyang University) ;
  • Kim, Chang Won (Hantool Engineering) ;
  • Yang, Heui Won (Research & Development Division, Hyundai Motors Group) ;
  • Kim, Dae Seung (Research & Development Division, Hyundai Motors Group) ;
  • Choi, Dong-Hoon (The Center of Innovative Design Optimization Technology (iDOT), Hanyang University)
  • 진성완 (한양대학교 기계공학과) ;
  • 박도현 (한양대학교 기계공학과) ;
  • 이갑성 (한양대학교 기계공학과) ;
  • 김창원 (한틀엔지니어링) ;
  • 양희원 (현대자동차 고성능차개발팀) ;
  • 김대승 (현대자동차 고성능차개발팀) ;
  • 최동훈 (한양대학교 최적설계신기술연구센터)
  • Received : 2011.09.30
  • Accepted : 2013.02.16
  • Published : 2013.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, we propose the method of optimally selecting material of front pillar (A-pillar) and package tray for minimizing weight while satisfying vehicle requirements on static stiffness and dynamic stiffness. First, we formulate a material selection optimization problem. Next, we establish the CAE procedure of evaluating static stiffness and dynamic stiffness. Then, to enhance the efficiency of design work, we integrate and automate the established CAE procedure using a commercial process integration and design optimization (PIDO) tool, PIAnO. For effective optimization, we adopt the approach of metamodel based approximate optimization. As a sampling method, an orthogonal array (OA) is used for selecting sampling points. The response values are evaluated at the sampling points and then these response values are used to generate a metamodel of each response using the radial basis function regression (RBFr). Using the RBFr models, optimization is carried out an evolutionary algorithm that can handle discrete design variables. Material optimization result reveals that the weight is reduced by 49.8% while satisfying all the design constraints.

Keywords

References

  1. J. S. Koo and H. J. Cho, "Weight-reduction Prediction for the Conceptual Design of Carbody with Shell Type Section Using the Material Substitution Technique," Transactions of KSAE, Vol.15, No.4, pp.17-26, 2007.
  2. Y. K. You, H. J. Yim and K. C. Kim, "Development of an Optimal Design Program for Vehicle Side Body Considering the B.I.W Stiffness and Light Weight," KSAE Spring Conference Proceedings, pp.1729-1735, 2006.
  3. J. Maeng and C. Cho, "Concept Car Development Using Personal Digital Design Process Based on Engineering Technology," Transactions of KSAE, Vol.18, No.5, pp.9-19, 2010.
  4. PIAnO (Process Integration, Automation and Optimization) User's Manual, Version 3.3, PIDOTECH Inc., 2011.
  5. D. C. Montgomery, Design and Analysis of Experiments, John Wiley & Sons, New York, 2005.
  6. Abaqus 6.8-1 Analysis User's Manual, Dassault Systemes Simulia Corp., 2008.
  7. MSC NASTRAN User's Manual, MSC Software Corporation, 2008.
  8. R. H. Myers and D. C. Montgomery, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, John Wiley & Sons, New York, 1995.
  9. M. J. L. Orr, "Introduction to Radial Basis Function Networks," Citeseer, pp.1-67, 1996.
  10. T. Back, Evolutionary Algorithms in Theory and Practice, Oxford University Press, Oxford, 1996.