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


Izadi R., Wagner D., Löpitz D., Zopp C., Klaerner M., Michel A., ...More

Composites Part B: Engineering, vol.314, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 314
  • Publication Date: 2026
  • Doi Number: 10.1016/j.compositesb.2026.113476
  • Journal Name: Composites Part B: Engineering
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chimica, Compendex, INSPEC
  • Keywords: COMSOL multiphysics®, DSC, Elium® resin, Foam-core thermoplastic composite, Machine learning, Microscopy analysis, Process optimisation, Pultrusion, Thermochemical modelling, Thermocouple measurements
  • Kocaeli University Affiliated: Yes

Abstract

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 R2>0.998 and enabling millisecond-level predictions with over 104× 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.