Machine learning and deep learning models, particularly in supervised learning, rely on large volumes of highquality labelled data to effectively learn complex patterns and ensure high prediction accuracy. However, in CFRP machining, the reported data size is often smaller than 100 (as suggested in Table ), which is primarily limited by the costly and time-intensive nature of experimental cutting tests. Data scarcity is evident across all four application settings, including challenges in inspecting machined surface quality, acquiring process monitoring signals, and measuring tool wear. Therefore, data scarcity significantly hinders the implementation of data-driven models for accurate prediction and control in CFRP machining processes.