A framework for tool wear and remaining useful life prediction in machining process using monitoring data and machine learning

Machining is an important type of manufacturing process. Because machining utilizes a tool to cut raw materials, tool fatigue is the main cause of degradation in productivity and efficiency. Hence, tool wear and remaining useful life (RUL), which is related to tool fatigue, should be managed to optimize the machining process. In this study, a framework for tool wear and RUL prediction using monitoring data and machine learning methods is proposed. First, real-world machining data are collected from sensors in the machines through a data acquisition system. Next, feature engineering, including time series feature extraction and correlation coefficient-based feature selection, is employed to construct a concise set of important features from the raw sensor data. Subsequently, tool wear is predicted using machine learning-based regression methods. Finally, RUL is predicted using iterative piecewise linear regression as real-time forecasting, which allows adaptation to changes in tool wear progression patterns and decision thresholds that frequently occur in real-world machining process. The experimental results reveal that (1) the proposed method outperformed the benchmark methods, (2) important features were selected using feature engineering, and (3) the proposed method, used without cutting force data, showed comparable results. Furthermore, we examined the flexibility of the proposed method with an example of a safe RUL prediction by manipulating the decision threshold to reduce the probability of tool failure.