A machine learning approach for vibration-based multipoint tool insert health prediction on vertical machining centre (VMC)

Abstract

A cutting tool condition is always considered as a key contributor to the machining operation. In-process failure of the tool directly affects the surface roughness of a workpiece, the power consumption of prime mover, and endurance of the process, etc. Thus supervisory system envisioning its health prediction is drawing industry attention. This needs to be adopted by a framework which institutes knowledge built data with the intent to predict defects earlier and prevent it from failure. In this context, the application of ‘Machine Learning’ (ML) methodology would assist the classification of tool condition and its prediction. In an attempt to monitor the multipoint tool insert health, an ML-based approach is presented in this paper. The time-domain vibration response for defect-free and various faulty configurations of four insert tool were collected during face milling performed on a vertical machining center (VMC). Further statistical features were extracted by designing an event-driven algorithm in Visual Basic Environment (VBE). Features exhibited in the Decision Tree (DT) generated by the J48 algorithm serve as ‘most significant’ amongst all extracted features; hence were selected for further classification. Finally, various tool conditions were categorized using six different ‘supervised-tree-based’ algorithms and a comparative study is presented to find the best possible classifier.