Professor of Mechanical Engineering and Smart Structures, School of Computing Engineering and Mathematics, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—The use of worn cutting tools has a detrimental effect on the surface finish of a workpiece, tool precision and internal machine stress. Worn tools also decrease productivity through unplanned stops, tool changes and increase the production of scrap material. This study investigates an inexpensive and non-intrusive method of inferring tool wear by measuring the audible sounds emitted during a drilling process. A microphone was used to record the machining operation sound of S50C steel, which was drilled using a computer numerical control (CNC) milling machine in wet conditions. The audio signature was examined using a spectrogram, and the extracted sound features of the rotating spindle motor in the frequency domain were used to correlate with tool wear. The results indicated that the frequency of spindle noise was unrelated to tool wear, but although the magnitude of spindle noise significantly increased in accordance with tool wear progression.
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