Machine Tool Internal Encoders as Sensors for the Detection of Tool Wear

Tool wear in machining changes the geometry of the cutting edges, which effect the direction and amplitudes of the cutting force components and the dynamics in the machining process. These changes in the forces and dynamics are picked up by the internal encoders and thus can be used for monitoring of changes in process conditions. This paper presents an approach for the monitoring of a multi-tooth milling process. The method is based on the direct measurement of the output from the position encoders available in the machine tool and the application of advanced signal analysis methods. The paper investigates repeatability of the method developed and how to detect wear in an individual tooth in a milling cutter. The results of this work show that various signal features which correlate with tool wear can be extracted from the first few oscillating components, representing the low-frequency components, of the machine axes velocities. The responses from the position encoders exhibit good repeatability, especially short term repeatability while the long-term repeatability is more unreliable. A worn tooth increases the irregularity in the encoder responses and can be identified at an early stage of the cut.