Sensitivity based virtual fields for identifying hyperelastic constitutive parameters - Université de Rennes Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

Sensitivity based virtual fields for identifying hyperelastic constitutive parameters

Adel Tayeb
Jean-Benoit Le Cam
Eric Robin
M. Grédiac
  • Fonction : Auteur
E. Toussaint
  • Fonction : Auteur

Résumé

In this study, the Virtual Fields Method (VFM) is applied to identify constitutive parameters of hyperelastic models from a heterogeneous test. Digital image correlation (DIC) was used to estimate both displacement and strain fields required by the identification procedure. Two different hyperelastic models were considered the Mooney model and the Ogden model. Applying the VFM to the Mooney model leads to a linear system which involves the hyperelastic parameters due to the linearity of the stress with respect to these parameters. In the case of the Ogden model, the stress is a nonlinear function of the hyperelastic parameters and a suitable procedure shall be used to determine virtual fields leading to the best identification. This complicates the identification procedure and affects its robustness. This is the reason why the sensitivity based virtual field approach recently proposed in case of anisotropic plasticity by Marek et al. (2017) has been successfully implemented to be applied in case of hyperelasticity. Results obtained clearly highlight the benefits of such an inverse identification approach in case of non-linear systems.
Fichier principal
Vignette du fichier
b_Tayeb2019.pdf (243.03 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02499976 , version 1 (07-05-2020)

Licence

Paternité

Identifiants

Citer

Adel Tayeb, Jean-Benoit Le Cam, Eric Robin, F. Canévet, M. Grédiac, et al.. Sensitivity based virtual fields for identifying hyperelastic constitutive parameters. 11th European Conference on Constitutive Models for Rubber, Jun 2019, Nantes, France. pp.163-168, ⟨10.1201/9780429324710-29⟩. ⟨hal-02499976⟩
70 Consultations
158 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More