In-cycle detection of built-up edge (BUE) from 2-D images of cutting tools using machine vision

Abstract

The built-up edge (BUE) phenomenon that appears under certain machining condition, such as low-to-moderate cutting speed, high depth of cut, dry cutting, cutting of ductile material, etc. is known to have a major effect on the surface quality of the finished workpiece. In the published literature, BUE has been measured using scanning electron microscope and optical microscopes to study its effect on tool life and surface quality. Such measurement methods are only applicable in off-the-machine inspection. Since the BUE extending beyond the tool nose alters the tool geometry and, thus, influences the workpiece roughness profile, detection of BUE outside the nose region is important. This research proposes a new method for detecting BUE from 2-D images of the nose region of the tool using a machine vision approach. Two methods of determining the BUE area are proposed—the subtraction method and polar-radius transformation method. Application of both methods is successfully demonstrated using simulated and real cutting tool images.