Document Type
Article
Publication Date
1-1-2012
Abstract
Because decisions made by human inspectors often involve subjective judgment, in addition to being intensive and therefore costly, an automated approach for printed circuit board (PCB) inspection is preferred to eliminate subjective discrimination and thus provide fast, quantitative, and dimensional assessments. In this study, defect classification is essential to the identification of defect sources. Therefore, an algorithm for PCB defect classification is presented that consists of well-known conventional operations, including image difference, image subtraction, image addition, counted image comparator, flood-fill, and labeling for the classification of six different defects, namely, missing hole, pinhole, underetch, short-circuit, open-circuit, and mousebite. The defect classification algorithm is improved by incorporating proper image registration and thresholding techniques to solve the alignment and uneven illumination problem. The improved PCB defect classification algorithm has been applied to real PCB images to successfully classify all of the defects.
Keywords
Defect classication, Defect detection, Printed circuit boards, Automated approach, Defect classification, Human inspectors, Image difference, Image subtraction, Printing defects, Thresholding techniques, Algorithms, Defects.
Divisions
fac_eng
Publication Title
International Journal of Innovative Computing Information and Control
Volume
8
Issue
5A
Publisher
International Journal of Innovative Computing Information and Control
Additional Information
957KT Times Cited:0 Cited References Count:14