Identification of Constitutive Parameters Governing the Hyperelastic Response of Rubber by Using Full-field Measurement and the Virtual Fields Method

TL;DR

This paper applies the Virtual Fields Method combined with digital image correlation to identify hyperelastic parameters of rubber, demonstrating improved robustness for nonlinear models like Ogden compared to linear models like Mooney.

Contribution

It introduces a sensitivity-based virtual field approach for hyperelasticity, enhancing parameter identification accuracy for nonlinear models.

Findings

Successful application of VFM with DIC for hyperelastic models

Improved robustness in parameter identification for nonlinear models

Validation of the sensitivity-based virtual field approach in hyperelasticity

Abstract

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 the 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 that involves the hyperelastic parameters thanks 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 should be used to determine virtual fields leading to the best identification. This complicates the identification 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) [1] 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.