This limitation stems from the inherent challenges of data collection in CFRP machining, where high time and cost requirements constrain the availability of extensive experimental data. The small dataset size adversely impacts the accuracy and robustness of neural network models, as limited training samples reduce the machine learning model's ability to generalize across varying machining conditions and capture the complex, non-linear relationships within the process . To overcome this issue, future efforts should focus on increasing the data quantity through three approaches: generating virtual data using finite element analysis (FEA) numerical simulation models , data augmentation through generative models , and utilizing transfer learning to leverage knowledge from related domains . On the other hand, typical small-sample-prediction machine learning methods (e.g. GPR, Support Vector Regression (SVR)) have shown good performance with small datasets. GPR is particularly suitable for small sample prediction because of its non-parametric nature, allowing it to provide accurate predictions by modelling uncertainty and the underlying distributions of data points .