Although deep learning eliminates the need for manual feature extraction, its computational intensity and susceptibility to overfitting remain significant challenges, particularly in CFRP machining where dataset sizes are often smaller than several hundred samples. To address the data scarcity issue, future work should explore integrating generative models (such as generative adversarial networks (GANs) and variational autoencoder (VAE) ) with deep learning frameworks. GANs and VAE can generate synthetic data to augment small datasets while maintaining data distribution characteristics , thereby improving model training and reducing overfitting risks. Furthermore, advanced deep learning architecture such as Transformers , which employ multi-head attention mechanisms to capture complex relationships and temporal dependencies in data, show significant potential for machining applications. For instance, they could enhance tool wear predictions and adapt to dynamic changes in machining conditions. Future research should focus on utilize and optimize these advanced architectures for machining-specific scenarios, balancing computational efficiency and predictive accuracy to ensure practical applicability in CFRP manufacturing environments. A substantial body of literature on material transition recognition focuses on analysing thrust force, torque and AE signals. Pardo et al. found that the increases in thrust and torque indicated a material transition from CFRP to Al, due to the higher strength of Al than CFRP. Neugebauer et al. utilized AE signals to identify the drill transition point in CFRP/Al stacks drilling, where a change of slope was used to locate the entry point to Al layer. However, the reliability of these approaches is significantly influenced by the process uncertainties, extent of tool wear and dependence on expert knowledge .