Several strategies can help mitigate data scarcity in CFRP machining, including the use of digital data from numerical simulations, data augmentation via generative models and transfer learning for domain adaptation, as shown summarized in Fig. (a1-a3). High-fidelity numerical simulations, such as finite element modelling (FEM) and discrete element modelling (DEM) , can effectively replicate complex physical machining processes, provide diverse data on force, temperature and tool wear across various machining conditions . These digitally generated data can serve as a valuable supplement to physical data, enhancing the machine learning/deep learning model training. In addition, recent advancements in generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) , can create synthetic data that mimics real-world scenarios. GANs and VAEs can be trained on limited CFRP machining data to generate new tool wear or surface quality data, helping balance class distributions and enrich the dataset diversity. In addition to generative models, transfer learning is another solution which enables adaption of models trained on specific CFRP machining conditions to new variants without extensive retraining, enabling quicker deployment and reduced resource expenditure. Pre-trained models can "transfer" knowledge to learn key CFRP-specific patterns, significantly reducing the data required. These approaches require further investigation to maximize their potential in data quantity and quality enhancement.