For the prediction of process characteristics and machining quality/defects, data-driven methods typically utilize predefined machining conditions as input. These inputs include cutting parameters (e.g. spindle speed, feed rate, depth of cut), cutting tool (geometry, material, coating), CFRP material composition (fibre/matrix, stacking sequence, fibre volume fraction) and coolant conditions. The data-driven model aims to predict process characteristics (force, temperature, energy consumption, vibration etc.) and machining quality/defects indices (delamination, burr, surface roughness) as output, as illustrated in Fig. . In this approach, machine learning / deep learning models can model the complex relationships between these inputs and outputs.