• Jacquet, Emmanuelle
• Chambert, Jérôme
• Elouneg, Aflah
• Lejeune, Arnaud
• Bordas, Stéphane
• Sutula, Danas

Abstract

We introduce a finite-element-model-updating-based open-source framework to identify mechanical parameters of heterogeneous hyperelastic materials from in silico generated full-field data which can be downloaded here https://github.com/aflahelouneg/inverse_identification_soft_tissue. The numerical process consists in simulating an extensometer performing in vivo uniaxial tensile experiment on a soft tissue. The reaction forces and displacement fields are respectively captured by force sensor and Digital<br&gtImage Correlation techniques. By means of a forward nonlinear FEM model and an inverse solver, the model parameters are estimated through a constrained optimization function with no quadratic penalty term. As a case study, our Finite Element Model Updating (FEMU) tool has been applied on a model composed of a keloid scar surrounded by healthy skin. The results show that at least 4 parameters can be<br&gtaccurately identified from an uniaxial test only. The originality of this work lies in two major elements. Firstly, we develop a low-cost technique able to characterize the mechanical properties of heterogeneous nonlinear hyperelastic materials. Secondly, we explore the model accuracy via a detailed study of the interplay between discretization error and the error due to measurement uncertainty. Next steps consist<br&gtin identifying the real parameters and so finding the matching preferential directions of keloid scars growth.

Topics

• experiment