Speaker
Description
The mechanical behavior of elastomers, such as natural rubber, is significantly influenced by intrinsic reinforcement effects occurring at large strains, namely strain-induced crystallization (SIC). The outstanding fracture resistance of the material is essentially attributed to the presence of this effect, which makes it interesting for a wide variety of applications. Common constitutive modelling approaches to SIC usually incorporate micro-mechanical considerations, consequently resulting in computationally expensive formulations. Nevertheless, certain limitations are still not overcome, so that an exact depiction of the complete phenomenon is yet to be accomplished. Recently emerged data-driven approaches using physics-augmented neural networks (PANNs) have been successfully applied to model non-linear inelastic material, cf. [1], achieving high representational worth. In this contribution, a PANN-based approach for the modeling of SIC from a continuum-level point of view is proposed, providing a flexible and efficient, and also precise method for the computational treatment of this effect. In doing so, the suggested invariants-based model, which is constructed to describe purely incompressible deformations, ensures thermodynamic consistency of the dissipative phase transition process through a formal construction as a generalized standard material. Moreover, the fulfillment of further desirable properties of material models, such as convexity considerations and normalized initial states of the descriptive potentials, contributing to a plausible extrapolation behavior of the model, is ensured by definition. The model, calibrated on experimentally obtained data, is subsequently validated for unseen deformation sequences, exhibiting significant advantages over classical constitutive models, both in computational efficiency and accuracy.
References:
[1] M. Rosenkranz, K. A. Kalina, J. Brummund, W. Sun, M. Kästner [2024]: "Viscoelasticity with physics augmented neural networks: model formulation and training methods without prescribed internal variables", Computational Mechanics 74, 1279–1301
