Neural networks meet hyperelasticity: A monotonic approach

Abstract

We apply physics-augmented neural network (PANN) constitutive models to experimental uniaxial tensile data of rubber-like materials whose behavior depends on manufacturing parameters. For this, we conduct experimental investigations on a 3D printed digital material at different mix ratios and consider several datasets from literature, including Ecoflex at different Shore hardness and a photocured 3D printing material at different grayscale values. We introduce a parametrized hyperelastic PANN model which can represent material behavior at different manufacturing parameters. The proposed model fulfills common mechanical conditions of hyperelasticity. In addition, the hyperelastic potential of the proposed model is monotonic in isotropic isochoric strain invariants of the right Cauchy-Green tensor. In incompressible hyperelasticity, this is a relaxed version of the ellipticity (or rank-one convexity) condition. Using this relaxed ellipticity condition, the PANN model has enough flexibility to be applicable to a wide range of materials while having enough structure for a stable extrapolation outside the calibration data. The monotonic PANN yields excellent results for all materials studied and can represent a wide range of largely varying qualitative and quantitative stress behavior. Although calibrated on uniaxial tensile data only, it leads to a stable numerical behavior of 3D finite element simulations. The findings of our work suggest that monotonicity could play a key role in the formulation of very general yet robust and stable constitutive models applicable to materials with highly nonlinear and parametrized behavior.