Multiscale Thermodynamics-Informed Neural Networks (MuTINN) for nonlinear structural computations of recycled thermoplastic composites

Abstract

Fiber-reinforced thermoplastic composites are increasingly valued for their light-weight properties, mechanical performance, and recyclability, yet the recycling process introduces microstructural heterogeneities that degrade their mechanical behavior. To address the challenges from a modeling point of view, this study proposes a Multiscale Thermodynamics-Informed Neural Network (MuTINN) approach to predict the nonlinear, anisotropic response of recycled glass fiber-reinforced polyamide 6 composites, with the primary aim of enabling structural simulations in significantly reduced time compared to traditional FE² approaches. The MuTINN framework integrates thermodynamic principles with artificial neural networks (ANNs) to capture the evolution of internal state variables and Helmholtz free energy, eliminating the need for memory-based networks. Finite element simulations of representative volume elements (RVEs) under diverse loading conditions are utilized to provide off-line data for the MuTINN. The latter accurately predicts stress, strain, and energy quantities, accounting for the anisotropic and heterogeneous nature of recycled materials. While trained using numerical simulations at 0◦ and 90◦ orientation specimens, the proposed framework succesfully predicts the response for specimens with 45◦ orientation with error in the maximum stress level up to 1.6%. The model is implemented into commercial finite element analysis (FEA) software via a Meta-UMAT framework, allowing efficient macroscale simulations. Validation against experimental data and finite element-based periodic homogenization confirms the framework’s accuracy for structural computations.