ML-based surrogate cure simulation for predicting process time and temperature overshoot in resin transfer moulding

Abstract

The cure stage of thermosetting composites production is critical for the overall process duration and manufacturing costs. While process simulations are commonly used to estimate cure behaviour, real-time predictive capabilities in resin transfer moulding (RTM) remain limited, primarily due to the computational cost of finite element (FE) methods. To address this gap and accurately estimate cure process parameters in RTM, this study proposes a surrogate cure simulation approach based on two state-of-the-art machine learning (ML) voting ensemble models – XGBoost and Light Gradient Boosting Machine – designed to predict cure time and temperature overshoot. To train the model, the cure of an epoxy/carbon fibre flat plate was simulated using the FE solver Marc, providing data over a wide range of conditions. The predictions of temperature overshoot and cure time demonstrate remarkable consistency and high accuracy (R2values up to 98%) with execution times under 30 ms for both variables. Performance was validated against unseen simulation data and further verified through RTM manufacturing trials and differential scanning calorimetry (DSC), confirming cure completion and a final glass transition temperature of 191°C–194°C. Unlike existing studies that remain simulation-focused, this approach bridges process simulation and data-driven modelling, offering a practical tool for real-time optimisation in industrial RTM applications.