Machine vision monitoring of tool wear

Abstract

Automated tool condition monitoring is an enabling technology in the push to develop fully unmanned machining centers. If this goal can be achieved across a broad range of machine tools, then researchers have assisted industry in moving one step closer to attaining truly flexible manufacturing work cells. Recent advances in the field of image processing technology have led to experimentation with machine vision as a potential means of directly evaluating tool condition. In this work, a machine vision system is employed that permits direct milling insert wear measurement to be accomplished in-cycle. The system is characterized by measurement flexibility, good spatial resolution and high accuracy. The flank wear monitoring system consists of an illumination source, CCD camera and high-resolution microscope lens. The extent of flank wear on the milling inserts was measured using the vision system and an image-processing algorithm. Two vision-based parameters were developed and their efficacy in directly quantifying insert flank wear was compared with measurements on a traditional toolmaker's microscope.