A Machine Learning-Based Approach for Predicting Tool Wear in Industrial Milling Processes

Abstract

In industrial machining processes, the wear of a tool has a significant influence on the quality of the produced part. Therefore, predicting wear upfront can result in significant improvements of machining processes. This paper investigates the applicability of machine learning approaches for predicting tool wear in industrial milling processes based on real-world sensor data on exerted cutting forces, acoustic emission and acceleration. We show that both Gradient Boosting Machines and Temporal Convolutional Networks prove particularly useful to this end. The validation was performed using the PHM 2010 tool wear prediction dataset as a benchmark, as well as using a proper dataset gathered from an industrial milling machine. The results show that the approach is able to predict the tool wear within an industrially-relevant error margin.