Fig. Schematic showing the procedure of using data-driven method for the prediction of CFRP machining process characteristics and quality/defects PR is a fundamental machine learning algorithm used in CFRP machining prediction. Ge et al. utilized 2 nd order PR to predict the drilling thrust force, temperature and hole delamination factor under different feed rates and spindle speeds. The model achieved a prediction accuracy of 91.5% -95.7%. Wang et al. deployed the same method in predicting CFRP drilling surface roughness, achieving a prediction accuracy of 95.74%. PR is computationally straightforward; however, as the degree of the polynomial increases, it tends to fit noise or random variations in the data, leading to overfitting . Additionally, PR model often fails to capture the nonlinear and multi-dimensional relationships between machining parameters and outputs, restricting their application to complex CFRP machining scenarios.