Machine Learning for Tool Wear Classification in Milling Based on Force and Current Sensors

Abstract

This paper presents how to classify the wear state of an end-mill based on force and current signals of the linear axes. The data is divided in binary classes based on the maximum flank wear. A support vector machine and a random forest are trained on orthogonal cutting experiments, but the validation is performed on arbitrary tool paths. To achieve this unique level of generality the signals are transformed into the rotation tool coordinate system. The features are extracted over five cutter revolutions. Support vector machines outperform random forests achieving 99,8% and 97% accuracy in the two classes on the test data and 98% and 61% accuracy on the validation data.