Paper Info
Title
Online Detection of Turning Tool Wear Based on Machine Vision
Abstract
To identify the tool wear in real time, this article builds the online detection system and proposes an integrated image processing method to measure the wear width for flank face of turning tool. First, the images of flank face wear are collected by the image acquisition system. Second, the collected images are cropped near the flank face wear, and the wear area and background for the cropped images are separated by k-means clustering algorithm. Then, the wear edge is identified by the grayscale transformation and edge detection algorithm, and the wear width is calculated by Hough transformation. Finally, the cutting experiment is carried out on MAG HTC200 CNC lathe to verify the validity of the proposed method, and the results show that the identified wear width by the proposed method and actual measured wear width for flank face of turning tool are in good agreement and show the proposed method is effective and have micron scale calculation accuracy.
Year
DOI
Venue
2022
10.1115/1.4053919
JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
Keywords
DocType
Volume
wear width, flank face of turning tool, K-means clustering algorithm, Hough transform, online detection, machine vision
Journal
22
Issue
ISSN
Citations 
5
1530-9827
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Xinfeng Dong100.34
Yongsheng Li200.34