On-line tool wear measurement for ball-end milling cutter based on machine vision

Abstract

Cutting tool wear is known to affect tool life, surface quality and production time. In this paper, a new on-line tool wear measuring algorithm is proposed to acquire tool wear using machine vision in order to establish on-line tool wear monitoring model for assessing degree of wear and remaining useful tool life. The algorithm first adopts machine vision to acquire tool wear images from CCD camera on-line for ball-end cutter. Tool tip points are determined and wear detection areas are optimized within captured tool wear images. Tool wear images before machining and in machining process are captured to compare the corresponding image column for judging whether this image column has emerged wear. Then the initial detection of wear edge points with pixel accuracy is given to scan pixel columns within the constructed wear detection areas in vertical direction. The exact detection algorithm of wear edge points with sub-pixel accuracy is proposed to increase the precision of detected wear edge points. The tool wear can be computed based on the detected wear edge points. Experimental work and validation of the established on-line tool wear measurement method are performed in a five-axis milling center by using stainless steel 1Cr18Ni9Ti and ball-end cutter of cemented carbide. The obtained measurement results by using the proposed method are compared with those gotten by measuring directly with microscope. The proposed method is shown to be reliable and effective for on-line tool wear measurement.