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<p><b>Abstract</b>—An important problem during industrial machining operations is the detection and classification of tool wear. Past research in this area has demonstrated the effectiveness of various feature sets and binary classifiers. Here, the goal is to develop a classifier which makes use of the dynamic characteristics of tool wear in a metal milling application and which replaces the standard binary classification result with two outputs: a prediction of the wear level (quantized) and a gradient measure that is the posterior probability (or confidence) that the tool is worn given the observed feature sequence. The classifier tracks the dynamics of sensor data within a single cutting pass as well as the evolution of wear from sharp to dull. Different alternatives to parameter estimation with sparsely-labeled training data are proposed and evaluated. We achieve high accuracy across changing cutting conditions, even with a limited feature set drawn from a single sensor.</p>
Tool wear, confidence, normalized cross entropy, HMM, sparsely-labeled training, machining, milling.
Randall K. Fish, Gary D. Bernard, Mari Ostendorf, David A. Castanon, "Multilevel Classification of Milling Tool Wear with Confidence Estimation", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 25, no. , pp. 75-85, January 2003, doi:10.1109/TPAMI.2003.1159947
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