A framework for model base hyper-elastic material simulation

Abstract

In this research, a framework for modelling and simulation of hyper-elastic materials is proposed. The framework explains how to employ strain energy functions as a constitutive model, standard loading test data, and a powerful optimisation method to determine a mathematical function for explaining the mechanical behaviour of a hyper-elastic material using minimum types of loading test data. In the first part, a survey on hyper-elastic constitutive models is presented. Fifty models are collected and classified into six categories. Thereafter, five types of standard loading tests including uniaxial, biaxial, equi-biaxial, pure shear, and simple shear are introduced. It is shown that depending on the loading type, physical parameters, Cauchy, and nominal stress tensors, each constitutive model possesses a particular function. The genetic algorithm as a powerful optimisation method is used to determine the most accurate function for each type of loading test data. It is presented that based on the selected constitutive model and regardless of a number of existing loading types test data, a unique function can be determined for expressing and simulating the mechanical behaviour of the considered hyper-elastic material.

Static Contact Modelling and Analysis for Rail Grinding with Abrasive Belt

“…The relationship between stress and strain of the hyper-elastic rubber does not follow Hooke's law. The relationship between stress and strain is a nonlinear relationship and the constitutive theory of rubber is usually determined by the strain energy function [22][23][24]. ABAQUS software has the significant nonlinear problem handling ability, so the Mooney-Rivlin material model in ABAQUS was selected.…”

Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network

“…At the present stage, the common known approaches for determining hyperelastic material parameters, namely the strain energy density function coefficient, mainly include experiments [ 19 , 20 , 21 ], numerical calculation [ 22 , 23 ] and artificial intelligence methods [ 24 , 25 ]. In particular, artificial intelligence methods can predict the related parameters, which cannot be obtained directly or are difficult to obtain through experiment and simulation, and have received widespread attention in recent years.…”

Constitutive Models for Rubber XII

“…While the scientific and technical litera ture offer a relatively large abundance of models suitable for rubbers (e.g. Bazkiaei et al 2020, Busfield et al 2000, Carleo et al 2018, De Tommasi et al 2019) their adoption has been so far limited in many industrial sectors, including sports surfaces (Carré et al 2006, Cole et al 2018, Kobayashi & Yukawa 2011, Thomson et al 2001 which will be the focus of the present work.…”