Paper Info
Title
A principal component analysis based method to automatically inspect wear of throw-away tips.
Abstract
The automatic inspection of throw-away tips is very important for quality control in precision cutting. We proposed an image processing based method for automatic inspection of the processing wear of throw-away tips. After image denoising, the proposed method utilized image-patch based principal component analysis method to enhance the cutting worn region while suppress the background region. Then the enhanced worn region was automatically segmented by a simple thresholding method followed by post-processing. The area of the segmented worn region was used as a measure of cutting wear degree. We collected three datasets of time-series images that recorded the processing of throw-away tips on a product line. One dataset was used to choose optimal parameters of the proposed method, and the other two datasets were used for evaluate its performances. Experimental results showed that the proposed method was able to inspect the cutting wear with high accuracy. Additionally, it was also showed that the proposed method outperformed the conventional thresholding based method.
Year
DOI
Venue
2016
10.3233/JIFS-169020
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
Principal component analysis,segmentation,worn region,throw-away tips,automatic inspection
Noise reduction,Computer vision,Image processing,Product line,Artificial intelligence,Thresholding,Machine learning,Mathematics,Principal component analysis
Journal
Volume
Issue
ISSN
31
SP2
1064-1246
Citations 
PageRank 
References 
0
0.34
3
Authors
7
Name
Order
Citations
PageRank
Ting Wang1725120.28
Rui Xu2234.30
Xian-Hua Han310928.28
Yen-Wei Chen4720155.73
Yoshitomo Ishizaki500.34
Masaru Miyamoto600.34
Tomohito Hattori700.34