Paper Info
Title
A case-based evolutionary model for defect classification of printed circuit board images
Abstract
In this research, a case-based evolutionary identification model is developed for PCB defect classification problems. Image understanding is the first and foremost step in the inspection of printed circuit boards. This paper presents a two-phase method for the segmentation of printed circuit board (PCB) images. In the first phase, a set of defect images of several existing basic patterns are stored to form a concept space. In the second phase, a new pattern is evolutionally grabbed using some primitive operators generated by calculating the relative position of several similar cases in the concept space. The case-based reasoning system relies on the software agents derived from past experience within the domain database to determine what feature is required to deliver new patterns in satisfying user’s requirements. Experimental results show that the proposed approach is very effective in identifying the defect patterns.
Year
DOI
Venue
2008
10.1007/s10845-008-0074-8
J. Intelligent Manufacturing
Keywords
Field
DocType
Case-based reasoning,Printed circuit board,Defect classification problems,Automatic Optical Inspection (AOI)
Computer vision,Concept space,Segmentation,Printed circuit board,Software agent,Operator (computer programming),Artificial intelligence,Engineering,Case-based reasoning,Reasoning system,Machine learning
Journal
Volume
Issue
ISSN
19
2
0956-5515
Citations 
PageRank 
References 
9
0.63
10
Authors
3
Name
Order
Citations
PageRank
Pei-Chann Chang11752109.32
Li-Yuan Chen290.63
Chin-Yuan Fan347328.27