An Artificial Intelligence Approach to Predicting Processing Parameters for Liquid Composite Molded (LCM) Carbon Fibre-Reinforced Plastics (CFRPs)

This study developed a comparative machine learning (ML) framework to predict optimal processing parameters and mechanical properties for automotive-grade carbon fibre-reinforced plastics (CFRPs) produced via Liquid Composite Molding (LCM). Multi-linear- (MLR), support vector- (SVR), random forest- (RFR), and gradient boosting regression (GBR) algorithms were coupled with artificial neural network (ANNs) using multi-layer perceptron (MLP), and single and dual-path functional application programming interface (FAPIs) models to increase the prediction outcomes. The models developed herein can be tailored to the case-specific needs of the application. The results demonstrated that the RFR model, enhanced with hyperparameter tuning, achieved the highest predictive accuracy, explaining 45% of the variability in the data for the linear regression case. The FAPI model using Keras® and Tensorflow® exhibited superior performance for the non-linear case, with test predictions of 78.20% for flexural strength, 73.8% for flexural modulus and 99.97% for binder use. Additionally, thermal and demolding analysis expanded the complexity of the predictions by enhancing the correlation to part quality, ultimately accelerating product development and providing industry with accurate predictive modelling tools.