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Parameter Identification for Inverse Problems in Metal Forming Simulations

  • J. P. Kleinermann
  • J. P. Ponthot
  • M. Hogge
Conference paper
Part of the Solid Mechanics and its Applications book series (SMIA, volume 109)

Abstract

To improve the quality of numerical simulations results, accurate material behavior models are required. Moreover, more and more complex constitutive laws are being proposed to describe peculiar material properties. Parameter identification is an inverse problem taking place in material model development. It consists in evaluating the material parameters which exist in the chosen model, leading to the most accurate model, minimizing the difference between experimental results and Finite Element Method (FEM) simulations. Hence, the parameter identification problem can be formulated as an optimization problem. We propose, in this paper, to solve this optimization problem with eight optimization methods in order to compare their efficiency and robustness. The eight implemented methods come either from literature, such as conjugate gradient method, BFGS, Levenberg-Marquardt, etc., or from original developments, such as a modified GCMMA method and an optimization method combination technique. At last, an optimization method dedicated to parameter identification problem will be proposed.

Keywords

Inverse Problem Conjugate Gradient Conjugate Gradient Method Descent Direction Finite Element Method Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    C. FLEURY. Sequential convex programming for structural optimization problems. In Rovaznus, editor, Optimization of Large Structural Systems, 1993.Google Scholar
  2. [2]
    C. FLEURY. Optimisation des Structures, Centrale des Cours del’A.E.E.S., Université deLiège, 1994.Google Scholar
  3. [3]
    A. GAVRUS, E. MASSONI, and J.L. CHENOT. A finite element model for the simulation of the torsion and torsion-tension tests. Computer Methodsin Applied Mechanics and Engineering, 103:417–434, 1993.CrossRefGoogle Scholar
  4. [4]
    A. GAVRUS, E. MASSONI, and J.L. CHENOT. An inverse finite element analysis applied to viscoplastic parameter identification. In John Wiley and Sons, editors, Second ECCOMAS Conference on Numerical Methods in Engineering, Paris, September 1996.Google Scholar
  5. [5]
    J.C. GELIN and O. GHOUATI. Une m’éthode d’identification inverse des paramètres matériels pour les comportements non-linéaires. Revue européenne des éléments finis, 4(4):463–485, 1995.MATHGoogle Scholar
  6. [6]
    O. GHOUATI. Identification et Modélisation Numérique Directe et Inversedu comportement Viscoplastique des Alliages d’Aluminium. PhD thesis, U.F.R. des Sciences et Techniques de l’Université de Franche-Comté, July 1994.Google Scholar
  7. [7]
    J.P. KLEINERMANN. Modélisation du cintrage de composants aéronautiques par creep-forming et superplasticité. Master’s thesis, Université de Liège, June 1996.Google Scholar
  8. [8]
    J.P. KLEINERMANN. Optimisation des procédés de mise à forme des matériaux par calcul inverse et calcul de sensibilite. F.R.I.A. activity report, Augustus 1997.Google Scholar
  9. [9]
    J.P. KLEINERMANN. Optimisation des procédés de mise à forme des matériaux par calcul inverse. F.R.I.A. activity report, Augustus 1999.Google Scholar
  10. [10]
    R. MAHNKEN and E. STEIN. A unified approach for parameter identification of inelastic material models in the frame of the finite element method. Computer Methods in Applied Mechanics and Engineering, 136:225–258, 1996.ADSMATHCrossRefGoogle Scholar
  11. [11]
    R. MAHNKEN and E. STEIN. Parameter identification for finite de-formation elasto-plasticity in principal directions. Computer Methods in Applied Mechanics and Engineering, (147):17–39, 1997.ADSMATHCrossRefGoogle Scholar
  12. [12]
    A.J. MORRIS. Foundations of Structural Optimization: A Unified Approach. 1982.Google Scholar
  13. [13]
    D.M. NORRIS, J.R.B. MORRAN, J.K. SCUDDE, and QUINONES D.F. A computer simulation of the tension test. Journal of Mechanical Physicsof Solids, 26:1–19, 1978.ADSCrossRefGoogle Scholar
  14. [14]
    J.P. PONTHOT. Mode d’emploi de la version pilote de METAFOR, module de calcul en grandes deformations. LTAS-Thermomecanique et milieux continus, Université de Liège, http://www.ulg.ac.be/ltas-mct, may 1992.Google Scholar
  15. [15]
    J.P. PONTHOT, D. ROZENWALD, and CHAMBARD A. Modélisation de l’ecrasement d’un joint de portière à l’aide du logiciel metafor. Technical report, LTAS-Thermomécanique et Milieux Continus, Université de Liège, february 1993.Google Scholar
  16. [16]
    D. ROZENWALD. Modélisation Thermomécanique des Grandes Déformations. Application aux Problèmes de Mise à Forme des Métaux, des Elastonères et des Structures Mixtes Métal-Elastomère. PhD thesis, Université de Liège, november 1996.Google Scholar
  17. [17]
    J.C. SIMO. A framework for finite strain elastoplasticity based on maximum plastic dissipation and the multiplicative decomposition. Compute Method in Applied Mechanics and Engineering, 66:199–219, 1988.MathSciNetADSMATHCrossRefGoogle Scholar
  18. [18]
    K. SVANBERG. The method of moving asymptotes, a new method for structural optimization. Internationaljournal for Numerical Methods in Engineering, 24:359–373, 1987.MathSciNetMATHCrossRefGoogle Scholar
  19. [19]
    K. SVANBERG. A globally convergent version of mma without line-search. In N. Olhoff and G.I.N. Rovaznus, editors, Proceedings of the First World Congress of Structural and Multidisciplinary Optimization, ISSMO, pages 9–16, Elsevier Science Ltd., Oxford, may 1995.Google Scholar
  20. [20]
    K. SVANBERG. Non-mixed second derivatives in mma. Technical report, Dept. of Physics, Lund University Sweden, May 1995.Google Scholar
  21. [21]
    W.H. ZHANG and C. FLEURY. Two-point based sequential convex approximations for structural optimization. In E. Dick M. Hogge, editor,3ème Congrès National Belge de Mécanique Théorique et Appliquée, Université de Liège, may 1994.Google Scholar
  22. [22]
    C. ZILLOBER. A globally convergent version of the method of moving asymptotes. Structural Optimization, 6:166–174, 1993.CrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • J. P. Kleinermann
    • 1
  • J. P. Ponthot
    • 1
  • M. Hogge
    • 1
  1. 1.LTAS-Continuum Mechanics and ThermomechanicsUniversity of LiègeLiège-1Belgium

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