Abstract
Interactions of wear debris at the tool-workpiece interface in micro-grinding are quite random which leads to considerable variability in the working life of similar tools. It is not possible to capture the effect of wear debris entrapment on process signals using the available physics-based model, which makes it difficult to identify the tool life stages. The present study highlights the wear pattern and life stages of a polycrystalline diamond tool (PCD) during micro-grinding of BK7 glass. Based on the time and frequency domain cutting force features and tool surface morphology, life of a typical PCD tool could be divided into three stages viz., abrasion stage (0–23% of total tool life), loading stage (23–77% of total tool life), and chipping stage (77–100% of total tool life). A machine learning model utilizing support vector machine (SVM) could predict the life stages of a tool with a prediction accuracy of around 80.5%, and the wear pattern of a new tool coming into service becomes more deterministic on using more datasets for model training. A new modified textured PCD tool, which provided better tool-work interaction and improved debris disposal, shows little variation in cutting force features across many similar design tools which enabled identifying the life stages with higher confidence. Prognosis of tool redressing criterion enabled timely redressing of the tool which led to refined tool surface condition, such as increased number of available chip pockets, greater protrusion height of the abrasives, and lowered roughness of the machined surface.
