Development of an inverse identification method for identifying constitutive parameters by metaheuristic optimization algorithm: Application to hyperelastic materials

G Bastos (1) , Adel Tayeb (1) , N. Di Cesare (2) , Jean-Benoit Le Cam (1) , Eric Robin (1)

1 IPR - Institut de Physique de Rennes
2 IRTES - M3M - Laboratoire Mécatronique - Méthodes, Modèles et Métiers

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 is measured at the surface of a cruciform specimen submitted to equibiaxial tension. The sample is reconstructed by FE to obtain the numerical kinematic field to be compared with the experimental one. The constitutive parameters used in the numerical model are then modified through the optimization process, for the numerical kinematic field to fit with the experimental one. The cost function is then 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.

Mots clés: Digital Image Correlation; Hyperelasticity; Inverse Identification; Particle Swarm Optimization

Domaines: Mécanique des matériaux [physics.class-ph]; Matériaux et structures en mécanique [physics.class-ph]; Mécanique des structures [physics.class-ph]

G Bastos, Adel Tayeb, N. Di Cesare, Jean-Benoit Le Cam, Eric Robin. Development of an inverse identification method for identifying constitutive parameters by metaheuristic optimization algorithm: Application to hyperelastic materials. SEM Annual conference, Jun 2019, Reno, United States. ⟨10.1007/978-3-030-30098-2_21⟩. ⟨hal-02172551⟩