Tool wear during CFRP machining is a dynamic process, hence it is challenging to predict solely from static, predefined machining conditions. Therefore, process monitoring signals from various sensors (force, torque, power, temperature, acoustic emission, etc.) are often used as inputs for machine learning / deep learning models for tool wear prediction. As illustrated in Fig. Commonly used sensors can be categorized as internal or external types. Internal sensors are integrated into the machine tool, typically monitoring spindle current and power , which reflect the operational state of the equipment. Current and power data are typically accessible from the machine tool's NC system or can be easily retrofitted, ensuring its practical implementation on the shop floor .