Mechanism-based and data-driven approach to developing the constitutive model of viscoelastic elastomers

Abstract

Constitutive modeling of viscoelastic elastomers has been an active field for decades. In this work, we develop a mechanism-based and data-driven method to develop constitutive models of viscoelastic elastomers under large deformation. Based on the theory of finite deformation viscoelasticity, the feature of strain energy density function is utilized when we design the machine learning architecture, which allows for fast generation of qualified artificial data to train artificial neural networks (ANNs). According to the typical microstructures of elastomers, three groups of ANNs are established to determine the strain energy density functions of the hyperelastic and viscous polymer networks, which are further tested by experimental data of our own and those in the literature. The machine learning architecture also allows for flexible expansion of the ANN database to consider newly-developed elastomers. The developed constitutive model of the material automatically satisfies the laws of thermodynamics and can be easily implemented in finite element analysis for more complex structures and loading conditions. The developed numerical and experimental framework provides an efficient paradigm for constitutive modeling of viscoelastic elastomers.