Machine learning/deep learning models are often regarded as "black-box" approaches, posing a significant limitation due to their inherent lack of transparency. This is particularly problematic in engineering applications, where a clear and physically meaningful analysis is essential. As such, future work should focus on enhancing the interpretability and explainability of these models, as summarized in Fig. (b1-b2). Physics-informed neural network (PINN) represents a promising approach that combines physical laws with machine learning algorithms to create models that are both data-driven and physically consistent. Methods for integrating physics knowledge include: (i) embedding residual terms from partial differential equations (PDEs) directly into the neural network's loss function ; (ii) fusing predictions from both physics-based models and data-driven approaches and (iii) applying physical models as constraints or boundary conditions . Such models are particularly well-suited for modelling of machining tool wear, surface defects and deformation. For instance, Zhao et al. proposed a Physics-informed Latent Variable Model for deformation prediction, offering a flexible framework capable of rapidly predicting deformation during manufacturing for parts with varying geometries. This approach integrates deformation mechanisms with data-driven modelling, ensuring both efficiency and accuracy in predictions, which addresses the computational issues in real-time machining scenarios. However, the application of PINN in the field of CFRP machining is still limited, despite its substantial potential to enhance understanding and interpretability of AI-driven predictions. Additionally, Shapley Additive exPlanations (SHAP) analysis, based on cooperative game theory, provides an effective method for interpreting the contribution of each feature to the model's prediction . Applying SHAP analysis to surface damage prediction can help address this challenge by offering a clearer understanding of how different variables-such as cutting speed, tool geometry, and material properties-affect tool wear and the integrity of the machined CFRP surface.