• For the data-driven prediction of tool wear, process monitoring signals from various sensors (force, torque, power, temperature, acoustic emission, etc.) are often collected to capture its dynamic • For data-driven vision-based machining defects recognition, deep learning models (mostly CNN) are trained to automatically extract features from inspection images and identify delamination / burr / crack defects area. Masked images with pre-identified defects are commonly used to train the model, reducing the need for extensive manual calibration and making the system less sensitive to varying lighting conditions. This can achieve automatic recognition of defects from captured images and quantification/classification of delamination/burr factor. A cost-effective, modular closed-loop control systems that integrate multiple subsystems (defect recognition system, process monitoring system, machine control system) is needed to enable real-time decision-making for the adjustment of machining parameters.