A physics-informed machine-learned model to predict thermo-oxidative aging of rubbers

This paper introduces a novel physics-informed multi-agents constitutive model to propose prediction in quasi-static constitutive behaviour of cross-linked elastomer and the loss of mechanical performance during environmental aging. In this hybrid framework, the effect of thermal-induced, on the behaviour of the material is represented using the proposed model. Those environmental single-mechanism damages change the polymer matrix over time due to massive chain scission, chain formations, and changing the arrangement of molecules in the polymer matrix. We propose a data-driven super-constrained machine-learned engine to represent damage in the polymer matrix and capture the changes in material behaviour, including its inelastic features such as Mullins effect and permanent set in the course of aging. We have simplified the 3D stress-strain tensor mapping problem into a small number of super-constrained 1D mapping problems by means of a sequential order reduction. An assembly of multiple replicated conditional neural-network learning-agents (L-agents) is trained to systematically simplify the high-dimensional mapping problem into multiple 1D problems, each represented by a different type of agent. Our hybrid framework is designed to capture the effect of deformation history, aging time, and aging temperature. The model is validated with respect to a comprehensive set of experiments specifically designed to benchmark model capabilities and also against available data in the literature. Thermodynamic consistency and frame independency have been verified. Besides acceptable predictive abilities, a significant reduction of computational cost to predict behaviour at multiple states of deformation is the most significant feature of this model.