In addition to machine learning, several pioneering studies have touched upon the deployment of deep learning neural network. Choi et al. developed a robotic drilling system with multiple-sensors (force, torque, acceleration, voltage, current, and sound) equipped to monitor the CFRP drilling process (see Fig. expensive, requiring significant memory and processing power . This high computational cost can pose challenges for real-time industrial applications in machining of CFRP. Additionally, the "black-box" nature of these models makes it difficult to interpret how specific input factors influence predictions. Future work should prioritize enhancing the interpretability and explainability of deep learning models. One promising direction for future work is physics-informed neural network (PINN), which integrates physical laws with machine learning algorithms to develop models that are both data-driven and physically consistent . For CFRP machining, PINNs could be particularly valuable in modelling complex thermal and mechanical phenomena (e.g. heat generation and dissipation , material deformation and defect formation ), to ensure the predictions adhere to established physical laws. Moreover, current studies on predicting process characteristics and quality/defects primarily rely on static inputs based on predefined machining conditions. These models struggle to adapt to dynamic changes caused by progressive tool wear, variations in machining environments, and process uncertainties, reducing their effectiveness for real-time applications. Future research should explore process monitoring sensor data fusion techniques to effectively capture and account for these dynamic factors.