Tool Wear Prediction in Computer Numerical Control Milling Operations via Machine Learning

Tool life and tool wear contribute significantly to any machining activity and directly affect the quality of the machined part, machining device performance as well as the production rates and costs. This research aims to investigate the performance of six supervised learning algorithms in predicting the cutting tool condition in Computer Numerical Control (CNC) milling operations using a novel form of CNC internal data that eliminate the need for sensory devices installation during the machining process for data acquisition purposes. The employed supervised learning algorithms include Decision Tree, Artificial Neural Network, Support Vector Machine, k-Nearest Neighbor, Logistic Regression and Naive Bayes. The results showed that Decision Tree, Artificial Neural Network, K-Nearest Neighbors and Support Vector Machine achieved overall classification accuracy greater than (85%) while Logistic Regression and Naive Bayes achieved overall classification accuracy of (57.1%) and (60.1%) respectively. Further, naive Bayes was able to correctly predict the cutting tool as worn from the test set despite its lower overall accuracy. In addition, features importance and decision rules were extracted from the Decision Tree algorithm as it achieved the highest overall accuracy score to investigate the most important features that influence the tool condition. The result showed that only three features have the highest influence on the tool condition while decision rules were used to investigate the value of these features to cause the cutting tools to be worn.