Anomaly detection methods in turning based on motor data analysis

Watanabe, Tsubasa; Kono, Ippei; Onozuka, Hideaki

Abstract A cutting anomaly can be detected by using machine learning and pattern recognition in addition to the conventional method of using cutting knowledge to determine the criteria of detection. However, it is difficult to guarantee that all cutting anomalies can be detected during mass production due to various events that can occur. Moreover, machine learning and pattern recognition are not systemized for use in mass production. In this work, we investigated the detection of turning anomalies during mass production under optimized cutting conditions. We applied a method that utilizes the motor current of each operating axis to monitor the machining state without affecting the machining process and determines the correlation between turning anomalies and motor data. Our target was the unsteady anomalies that appear in mass production, such as chip biting and tool vibration. On the basis of the obtained correlation, we developed a formalized anomaly detection method using traditional statistics and a systemized anomaly detection method using discretization and pattern recognition based on the Mahalanobis Taguchi method with auto parameter tuning to eliminate the need for detailed analysis based on knowledge of cutting phenomena. Both methods achieved a detection accuracy of over 98%.