J. Navarro

Optimizing compression after impact prediction in CFRP using machine learning combined with classical composite laminate theory, SHAP analysis

This research investigates Gradient Boosting and Neural Networks for predicting impact damage characteristics and compression-after-impact strength in CFRPs and confirms the models’ physical interpretability, with feature importance rankings aligning with established composite laminate theory.