Journal of the Korean Society of Manufacturing Process Engineers (한국기계가공학회지)

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
권호 표지
DOI QR코드

DOI QR Code

Meta-model Effects on Approximate Multi-objective Design Optimization of Vehicle Suspension Components

차량 현가 부품의 근사 다목적 설계 최적화에 대한 메타모델 영향도

  • Song, Chang Yong (Department of Naval Architecture & Ocean Engineering, Mokpo National University) ;
  • Choi, Ha-Young (Department of Mechanical Engineering, Dongyang Mirae University) ;
  • Byon, Sung-Kwang (Department of Mechanical Engineering, Dongyang Mirae University)
  • 송창용 (목포대학교 조선해양공학과) ;
  • 최하영 (동양미래대학교 기계공학부) ;
  • 변성광 (동양미래대학교 기계공학부)
  • Received : 2019.01.30
  • Accepted : 2019.02.12
  • Published : 2019.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Herein, we performed a comparative study on approximate multi-objective design optimization, to realize a structural design to improve the weight and vibration performances of the knuckle - a car suspension component - considering various load conditions and vibration characteristics. In the approximate multi-objective optimization process, a regression meta-model was generated using the response surfaces method (RSM), while Kriging and back-propagation neural network (BPN) methods were applied for interpolation meta-modeling. The Pareto solutions, multi-objective optimal solutions, were derived using the non-dominated sorting genetic algorithm (NSGA-II). In terms of the knuckle design considered in this study, the characteristics and influence of the meta-model on multi-objective optimization were reviewed through a comparison of the approximate optimization results with the meta-models and the actual optimization.

Keywords

References

  1. Crouse, H. W. and Anglin, L. D., Automotive Mechanics, McGraw-Hill, New York, 1993.
  2. Simpson, T. W., Peplinkski, J., Koch, P. N. and Allen, J. K., "Metamodels for Computer Based Engineering Design: Survey and Recommendations," Engineering with Computers, Vol. 17, No. 2, pp. 129-150, 2001. https://doi.org/10.1007/PL00007198
  3. Lee, K. H. and Kang, D. H., "A robust optimization using the statistics based on kriging metamodel," Journal of mechanical science and technology, Vol. 20, No. 8, pp. 1169-1182, 2006. https://doi.org/10.1007/BF02916016
  4. Haftka, R. T. and Gurdal, Z., Elements of Structural Optimization, Kluwer Academic Publishers, Dordrecht, 1991.
  5. Park, Y. S. and Park, G. J., "A Development of Move Limit Strategy Based on the Accuracy of Approximation for Structural Optimization," Transactions of the Korean Society of Mechanical Engineers A, Vol. 21, No. 8, pp. 1218-1228, 1997. https://doi.org/10.22634/KSME-A.1997.21.8.1218
  6. Jacobs, J. H., Etman, L. F. P., van Keulen, F. and Rooda, J. E., "Framework for Sequential Approximate Optimization," Structural and Multidisciplinary Optimization, Vol. 27, No. 5, pp. 384-400, 2004. https://doi.org/10.1007/s00158-004-0398-8
  7. Kim, S. W., Kang, K., Yoon, K and Choi, D. H., "Design Optimization of an Angular Contact Ball Bearing for the Main Shaft of a Grinder," Mechanism and Machine Theory, Vol. 104, pp. 287-302, 2016. https://doi.org/10.1016/j.mechmachtheory.2016.06.006
  8. Korta, J. A., and Mundo, D., "Multi-objective Micro-geometry Optimization of Gear Tooth Supported by Response Surface Methodology," Mechanism and Machine Theory, Vol. 109, pp. 278-295, 2017. https://doi.org/10.1016/j.mechmachtheory.2016.11.015
  9. Atthaphon, A., Masahiko, S., Yasutada, T. and Masahiro, K., “Hybrid Surrogate model based Multi-fidelity Efficient Global Optimization Applied to Helicopter Blade Design,” Engineering Optimization, Vol. 50, No. 6, pp. 1016-1040, 2018. https://doi.org/10.1080/0305215X.2017.1367391
  10. Park, S. H., Design of Experiments, Minyoung-Sa, Korea, 2009.
  11. McKay, M. D., Beckman, R. J. and Conover, W. J., "Comparison of Three Methods for Selecting Values of Input Variables in The Analysis of Output from a Computer Code," Technometrics, Vol. 21, pp. 239-245, 1979. https://doi.org/10.1080/00401706.1979.10489755
  12. Hong, K. J., Jeon, K. K., Cho. Y. S., Choi, D. H. and Lee, S. J., "A Study on the Construction of Response Surface for Design Optimization," Transactions of the Korean Society of Mechanical Engineers A, Vol. 24, No. 6, pp. 1408-1418, 2000. https://doi.org/10.22634/KSME-A.2000.24.6.1408
  13. Lee, T. H., Seong, J. Y., and Jung, J. J., "Sequential Feasible Domain Sampling of Kriging Metamodel by Using Penalty Function," Transactions of the Korean Society of Mechanical Engineers A, Vol. 30, No. 6, pp. 691-697, 2006. https://doi.org/10.3795/KSME-A.2006.30.6.691
  14. Bishop, C., Neural networks for pattern recognition, Oxford Press, 1995.
  15. Mitchell, T. M., Machine Learning, McGraw-Hill, 1997.
  16. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, pp. 182-197, 2002. https://doi.org/10.1109/4235.996017