This paper proposes a tool wear monitoring system using sound signals acquired during milling of aluminium alloys. Tool wear monitoring is important for achieving surface finish and real time control of dimensional accuracy. Experiment was performed in a CNC machining centre with recommended cutting conditions. Tungsten carbide inserts in a face milling cutter was used and the wear conditions were simulated. Statistical features of the signals were fed to random forest tree algorithm. The wavelet features of the signals were also extracted and a decision tree classification model was built. A feature subset selection was performed by feature evaluators with search algorithms. Observations were made on the performance of classifier model using statistical features and with full set of features over subset of wavelet.
S. Ravikumar and Dr. K. I. Ramachandran, “Tool Wear Monitoring of Multipoint Cutting Tool using Sound Signal Features Signals with Machine Learning Techniques”, Materials Today: Proceedings, vol. 5, pp. 25720 - 25729, 2018.