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Forging preform shape optimization using surrogate models

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Forging of practical products from simple billet shapes is a complex and nonlinear process due to the multi-disciplinary phenomenon of material flow and processing conditions. General forgings are usually produced in a number of stages in order to avoid defects such as underfill, extra flash, voids, and folds. In spite of advancements in analysis techniques, forging process simulations do not provide function sensitivity information. Hence, the research focuses on exploring efficient non-gradient based preform shape optimization methods. In this research, an attempt is made to develop a preform shape design technique based on interpolative surrogate models, namely Kriging. These surrogate models yield insight into the relationship between output responses and input variables and they facilitate the integration of discipline-dependent analysis codes. Furthermore, error analysis and a comparison between Kriging and other approximation models (response surface and multi-point approximations) are presented. A discussion about what the results mean to a designer is provided. A case study of an automotive component preform shape design is presented for demonstration.
Rocznik
Strony
53--66
Opis fizyczny
Bibliogr. 12 poz., rys., tab., wykr.
Twórcy
autor
  • Wright State University, Dayton, Ohio 45435
Bibliografia
  • [1] A.J. Booker. Case studies in design and analysis of computer experiments. Proceedings of the Section on Physical and Engineering Sciences. American Statistical Association, 1996.
  • [2] E.P. Box, J.S. Hunter. Statistics for Experiments. John Wiley & Sons, Inc., 1978.
  • [3] DEFORAf™ 2D (Design Environment for FORMing) User Manual. Scientific Forming Technologies Corporation, 5038 Reed Road, Columbus, OH 43220, 1999.
  • [4] A. Giunta, L.T. Watson, J. Koehler. A Comparison of Approximation Modeling Techniques: Polynomial Versus Interpolating Models. 7-th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, St. Louis, MI, AIAA, Sept, 2-4, 1998, AIAA-98-4758.
  • [5] S. Kobayashi, S.I. Oh, T. Alt an. Metal Forming and the Finite-Element Method. Oxford University Press, New York, 1989.
  • [6] J.R. Koehler, A.B. Owen. Computer Experiments. In: S. Ghosh, C.R. Rao, eds., Handbook of Statistics, Vol. 13, pp. 261-304. Elsevier, Amsterdam 1996.
  • [7] T. Linda, M.C. Linda. Finding important independent variables through screening designs: a comparison of. 749-757.
  • [8] R.H. Myers, D.C. Montgomery. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. John Wiley & Sons, New York, 1995.
  • [9] J. Sacks, W.J. Welch, T.J. Mitchell, H.R Wynn. Design and Analysis of Computer Experiments. Statistical Science, 4(4): 409-435, 1989.
  • [10] T.W. Simpson, M.T. Mauery, J.J. Korte, F. Mistree. Comparison of response surface and Kriging models for multidisciplinary design optimization. AIAA-98-4755, pp. 381-391, 1998.
  • [11] L. Wang, R.V. Grandhi. Improved two-point function approximations for design optimization. AIAA Journal, 33(9): 1720-1727, 1995.
  • [12] S. Xu, R.V. Grandhi. Multipoint Approximation for Reducing The Response Surface Model Development Cost in Optimization. Proceedings of 1-st ASMO UK/ISSMO Conference, Ilkley, UK, July 8-9, 1999.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPB1-0030-0005
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