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.
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Kleinermann, J.P., Ponthot, J.P., Hogge, M. (2003). Parameter Identification for Inverse Problems in Metal Forming Simulations. In: Ståhle, P., Sundin, K.G. (eds) IUTAM Symposium on Field Analyses for Determination of Material Parameters — Experimental and Numerical Aspects. Solid Mechanics and its Applications, vol 109. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0109-0_9
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DOI: https://doi.org/10.1007/978-94-010-0109-0_9
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