Recently, data-driven approaches in the field of material modeling have gained significant attention. A major advantage these is direct integration experimental results into models. Nevertheless, artificial neural networks (ANNs) are especially challenging to interpret from a physical point view, since internal processes ANNs difficult understand. In this work new automatic method for generatio...

Elastomers have wide and ever increasing applications in several industries. For example, elastomeric bushings are used in automotive suspension systems to reduce amplitude of vibrations as well as shocks. In this study, a compressible visco-hyperelastic approach is employed to investigate the behavior of these bushings. Time-discrete form of the material model (Exp–Ln visco-hyperelastic model)...

Soft growing robots have received considerable attention because of their unique locomotion. However, the use a single material for manufacturing causes numerous problems, each part robot requires different characteristics. This study attempts to solve this fundamental problem from perspective, rather than integrating independent solutions. A hyperelastic is proposed building soft robot. Severa...

We discuss and solve an inverse problem in nonlinear elasticity imaging in which we recover spatial distributions of hyperelastic material properties from measured displacement fields. This problem has applications to elasticity imaging of soft tissue because the strain dependence of the apparent stiffness may potentially be used to differentiate between malignant and normal tissues. We account...

Most of hyperelastic models for the constitutive modeling of the typical mechanical behaviour of the arterial wall tissue in literature are based on the test data from different animals and arteries. This paper is concerned with the material parameter identification of several phenomenological hyperelastic models by fitting the data from five extension-inflation tests of the porcine aorta segme...

Within the scope of our recent approach for Efficient Unsupervised Constitutive Law Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning framework discovery parsimonious interpretable constitutive laws with quantifiable uncertainties. As in deterministic EUCLID, do not resort to stress data, but only realistically measurable full-field displacement global reaction...

A comprehensive study on the spherical indentation of hyperelastic soft materials is carried out through combined theoretical, computational, and experimental efforts. Four widely used hyperelastic constitutive models are studied, including neo-Hookean, Mooney-Rivlin, Fung, and Arruda-Boyce models. Through dimensional analysis and finite element simulations, we establish the explicit relations ...