Tool Wear State Recognition Based on Machine Learning

Abstract

In order to quickly identify the current state of tool wear in real time, the standard of tool wear state division is analyzed first. The machine learning methods commonly used for pattern recognition classification including SVM, ANN, random forest and so on. In this paper, tool wear data sets are used to build a variety of machine learning models using the constructed feature space. LVQ, random forest and SVM are used to monitor the wear state respectively, and the classification accuracy of the classification model on the test set is calculated respectively.