Multiscale Thermodynamics-Informed Neural Networks (MuTINN) for nonlinear structural computations of recycled thermoplastic composites
Saïf Eddine Sekkal, Mohammed El Fallaki Idrissi, Fodil Meraghni, George Chatzigeorgiou, F. Chinesta
Abstract
Fiber-reinforced thermoplastic composites are increasingly valued for their lightweight 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 FE 2 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 successfully 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.