Machine learning-enhanced modelling and experimental analysis of foam-core thermoplastic composites produced via pultrusion

Foam-core thermoplastic composites manufactured by pultrusion offer lightweight, recyclable structural solutions but require precise control of coupled thermal and curing phenomena to ensure uniform properties. While physics-based models can capture these thermochemical interactions, their computational cost limits their use for rapid prediction and process optimisation. This study presents an integrated experimental–numerical–machine learning framework for foam-core thermoplastic pultrusion using Elium® resin. Cure kinetics are characterised by DSC and incorporated into a validated 3D multiphysics model coupling heat transfer and polymerisation. Microscopy confirms limited resin penetration into the foam surface, forming a mechanical interlocking mechanism at the skin–core interface. A large parametric simulation campaign is used to train machine-learning surrogate models (neural networks, random forests, and gradient boosting), achieving R 2 > 0 . 998 and enabling millisecond-level predictions with over 1 0 4 × speed-up compared to finite-element simulations. These surrogates are employed for rapid prediction and process optimisation to identify operating windows that balance throughput, thermal control, energy efficiency, and complete curing. • An integrated experimental–numerical–machine learning framework is developed for foam-core thermoplastic pultrusion. • Cure kinetics of Elium® C599 resin are characterised by DSC and described by the Kamal-Sourour model. • A 3D thermo-chemical simulation captures heat transfer and curing in heterogeneous foam-core profiles, and embedded thermocouple measurements validate the multiphysics model. • Microscopic analysis reveals resin-foam interlocking mechanism responsible for skin-core bonding in foam-core pultruded composites • Machine-learning surrogate models achieve > 1 0 4 × speed-up over FEM with high accuracy. • Surrogate-based optimisation identifies optimal die temperature and pulling speed for throughput, energy efficiency, and thermal safety.