Precise, efficient and flexible modeling of crystallizing elastomers based on physics-augmented neural networks

TL;DR

This paper introduces a physics-augmented neural network model for accurately and efficiently simulating strain-induced crystallization in rubbery polymers, ensuring physical consistency and broad applicability.

Contribution

The paper develops a novel neural network-based modeling framework that incorporates physics constraints for simulating crystallization in elastomers, with guaranteed physical properties and flexible data usage.

Findings

Model accurately predicts stress and crystallinity evolution.

Framework guarantees thermodynamic consistency and physical properties.

Effective even without crystallinity data for parameterization.

Abstract

We propose a precise and efficient physics-augmented neural network (PANN) to model strain-induced crystallization in rubbery polymers. We demonstrate that the model can be flexibly employed for both unfilled and filled natural rubber (NR). The approach is based on a two potential framework, similar to the concept of generalized standard materials (GSMs). To describe the material behavior, neural network-based free energy and dissipation potentials are employed. The evolution of crystallinity is derived from the two potentials. To ensure boundedness of the crystallinity, a novel constrained GSM-type evolution problem is proposed. To this end, two additional Lagrange multipliers together with the corresponding Karush-Kuhn-Tucker conditions are introduced. As a result, it is guaranteed that crystallinity can be interpreted as a variable of concentration type. The neural network-based potentials ensure all physically desirable properties by construction. Most importantly, objectivity, material symmetry and thermodynamic consistency are automatically fulfilled. In addition, an alternative derivation of the governing model equations in time-discrete form is presented based on an incremental variational framework, which also serves as the basis for a finite element implementation. We demonstrate the predictive capability of the PANN using three different experimental data sets from literature, considering both stress and crystallinity evolution at material point level as well as the corresponding field distributions in a notched specimen. Moreover, we show that model parameterization is also possible when experimental crystallinity data is not available, still enabling suitable stress predictions.