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Inverse identification of hyperelastic parameters by metaheuristic optimization algorithm

Abstract : In the present study, a numerical method based on a metaheuristic parametric algorithm has been developed to identify the constitutive parameters of hyperelastic models, by using FE simulations and full kinematic field measurements. The full kinematic field was measured at the surface of a cruciform specimen submitted to equibiaxial tension. The test was simulated by using the finite element method (FEM). The constitutive parameters used in the numerical model were modified through the optimization process, for the predicted kinematic field to fit with the experimental one. The cost function was formulated as the minimization of the difference between these two kinematic fields. The optimization algorithm is an adaptation of the Particle Swarm Optimization algorithm, based on the PageRank algorithm used by the famous search engine Google.
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Submitted on : Thursday, May 7, 2020 - 6:26:30 PM
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G. Bastos, Adel Tayeb, Jean-Benoit Le Cam, N. Di Cesare. Inverse identification of hyperelastic parameters by metaheuristic optimization algorithm. 11th European Conference on Constitutive Models for Rubber, Jun 2019, Nantes, France. pp.218-222, ⟨10.1201/9780429324710-39⟩. ⟨hal-02499975⟩



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