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Abstract. The Vacuum-Assisted Resin Infusion Molding (VARIM) process is widely used in wind turbine blade manufacturing due to its cost-effectiveness and reliability. However, challenges such as prolonged curing cycles and defects caused by non-uniform cure remain persistent. To address these issues, multizone heating systems have been developed to enable independent temperature control across blade sections. Yet, optimizing the temperature profile in each zone is computationally intensive, requiring detailed modelling of curing kinetics and heat transfer mechanisms. To overcome these challenges, in this work, a machine learning (ML) based digital twin of the VARIM process was developed using a time-distributed long short-term memory (LSTM) network trained on data generated by a high-fidelity multiphysics solver. The model achieved a predictive accuracy of 96.7 % in replicating the resin curing behavior. Its time-distributed architecture effectively captures the spatial – temporal dependencies across multiple zones, allowing precise prediction of the degree-of-cure evolution. Paired with a gradient-free optimization algorithm, the digital twin reduced curing time by 12.5 % while improving cure uniformity. This AI-driven framework eliminates costly trial-and-error experimentation, and provides a scalable, adaptive solution for improving both quality and productivity in wind turbine blade manufacturing, with strong potential for extension to other composite manufacturing processes. 

Abstract The vacuum-assisted resin infusion mold (VARIM) process is widely used in wind blade manufacturing for its cost-effectiveness and reliability. However, the current method faces challenges such as long curing times and defects due to nonuniform heating across the blade structure. To address this, a multi-zone heated bed setup tailored to blade thickness has been considered. However, determining an optimal temperature for each zone poses a computational challenge, which can be tackled with a novel machine-learning approach. Using a digital twin based on a high-fidelity multiphysics solver, a time-distributed LSTM model was trained to understand complex resin curing dynamics. This eliminates the need for costly lab experiments, as the model learns heating patterns and curing behavior efficiently. Once trained, the ML model acts as a digital twin by predicting the degree of cure for a given temperature setpoint with 96.73% accuracy. This model, when used as a surrogate for a Nelder-mead optimization workflow, improves the curing time by roughly 12.5% and presents a more uniform curing rate throughout the part.