Formulation for the Parameter Identification of Inelastic Constitutive Equations

  • Received : 2010.10.26
  • Accepted : 2010.12.06
  • Published : 2010.12.31

Abstract

This paper presents a method for identifying the parameter set of inelastic constitutive equations, which is based on an Evolutionary Algorithm. The advantage of the method is that appropriate parameters can be identified even when the measured data are subject to considerable errors and the model equations are inaccurate. The design of experiments suited for the parameter identification of a material model by Chaboche under the uniaxial loading and stationary temperature conditions was first considered. Then the parameter set of the model was identified by the proposed method from a set of experimental data. In comparison to those by other methods, the resultant stress-strain curves by the proposed method correlated better to the actual material behaviors.

References

  1. Baker, J.E. (1995) Adaptive Selection Methods for Genetic Algorithms, Proc. of the 11st Int. Conference on Genetic Algorithms and Their Applications, pp.112-120.
  2. Baumeister, J. (2007) Stable Solution of Inverse Problems, Vieweg, Braunschweig.
  3. Cailletaud, G., Pilvin, P. (1994) Identification and Inverse Problem Related to Material Behavior, Inverse Problems in Engineering Mechanics, pp.79-86.
  4. Chaboche, J.L. (1989) Constitutive Equations for Cyclic Plasticity and Cyclic Viscoplasticity, International Journal of Plasticity, 5, pp.247-254. https://doi.org/10.1016/0749-6419(89)90015-6
  5. Fogel, L.J., Oweens, A.J., Walsh, M.J. (1996) Artificial Intelligence through Simulated Evolution, NewYork, Willey.
  6. Furukawa, T. et al. (2004) Evolutionary Algorithms for Multimodal Function Optimization Problems, 81st JSME Annual Meeting, pp.140-142.
  7. Goldberg, D. (1999) Genetic Algorithms in Search, Optimization and Machine Learning, Addison, Wesley.
  8. Hoffmeister, F., Back, T. (1992) Genetic Algorithms and Evolution Strategies: Similarities and Differences, Technical Report, University of Dortmund, Germany.
  9. Holland, J.H. (1975) Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, MI.
  10. IMSL (1998) IMSL User's manual: FORTRAN Subroutines for Mathematical Applications.
  11. Lee, J.S., Lee, Y.C., Furukawa, T. (2005) Inelastic Constitutive Modeling for Viscoplasticity Using Neural Networks Journal of Korean Institute of Ingelligent Systems, 15(2), pp.251-256.
  12. Mazza, E., Papes, O., Rubin, M.B., Bodner, S.R. (2005) Nonlinear Elastic-Viscoplastic Constitutive Equations for Aging Facial Tissues, Biomechanics and Modeling in Mechanobiology, 4(2-3), pp.178-189. https://doi.org/10.1007/s10237-005-0074-y
  13. Nemirovskii Yu.V., Yankovskii, A.P. (2008) Viscoplastic Deformation of Reinforced Plates with Varing Thickness under Explosive Loads, International Applied Mechanics, 44(2), pp.188-199. https://doi.org/10.1007/s10778-008-0030-5
  14. Rechenberg (1983) Evolutionstrtegie: Optimierung Tchnischer Systeme Nach Prinzipien der Bologischen Evolution, Stuttgart, Frommann-Holzboog.
  15. Thomas, B.B. (2006) Experimental Research in Evolutionary Computation, Springer Berlin Heidelberg.