Despite its outstanding material properties, CFRP is a hard-to-machine material due to its anisotropy , inhomogeneity , material nonlinearity and thermal-sensitivity . The three main challenges in CFRP machining include: I. The formation of surface defects (including delamination , burr , fibre fracture/deflection , surface cavity and matrix crack ) under excessive cutting force and high temperature II. Rapid tool wear due to the abrasive nature of carbon fibres, leading to deteriorated surface quality, decreased machining efficiency and increased costs III. Lack of efficient and automated defects inspection / recognition and process optimization technologies To address these challenges, physics-based methods (including both analytical models and numerical models ) have been developed to analyse, understand and predict CFRP machining behaviours. However, CFRP machining remains highly complex, involving dynamic material removal behaviour under thermo-mechanical coupling, high strain rates, and process uncertainty . Analytical models, while computationally efficient, often rely on oversimplified assumptions (e.g. uniform material properties and idealized boundary conditions ), which limit their prediction accuracy. Numerical models offer higher accuracy but are hindered by significant computational costs especially when considering thermo-mechanical coupling (e.g., computation time exceeding 20 days) . This significantly compromises the practicality of physics-based approaches . Therefore, there is a need for innovative solutions to achieve accurate and computationally efficient prediction of CFRP machining performance, with improved predictive capabilities and practicality.