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 fi eld measurements. The full kinematic fi eld was measured at the surface of a cruciform specimen submitted to equibiaxial tension. The test was simulated by using the fi nite element method (FEM). The constitutive parameters used in the numerical model were modi fi ed through the optimization process, for the predicted kinematic fi eld to fi t with the experimental one. The cost function was formulated as the minimization of the difference between these two kinematic fi elds. The optimization algorithm is an adaptation of the Particle Swarm Optimization algorithm, based on the PageRank algorithm used by the famous search engine Google.