After signal feature extraction, these features serve as the input of machine learning/deep learning models to predict the tool wear status in CFRP machining. Among the available methods, BPNN is one of the most widely Although these machine learning models have achieved good prediction accuracy within specific datasets, it often lacks generalizability across varying conditions (e.g. CFRP compositions, fibre orientations, machining parameters). Future efforts should focus on deploying Transfer Learning to adapt pre-trained models to new scenarios, minimizing retraining efforts and enabling rapid deployment in manufacturing environments .