Abstract
The online monitoring of tool wear using signal processing during machining has emerged as a prominent technological means in the advancement towards Industry 4.0. In this investigation, the potential of identifying worn cutting tools is assessed using a microphone, 3-axis accelerometer, and 3-axis dynamometer. A comparison of the signals from a sharp versus worn tool shows a potential to identify the wear state of the tool, provided that the suitable signal processing technique and feature selection are employed. The investigation suggests that the careful selection of the sensor's position has a prominent role in the success of the wear identification.
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