Paper Info
Title
A Machine Vision Based Automatic Optical Inspection System for Measuring Drilling Quality of Printed Circuit Boards.
Abstract
In this paper, we develop and put into practice an automatic optical inspection (AOI) system based on machine vision to check the holes on a printed circuit board (PCB). We incorporate the hardware and software. For the hardware part, we combine a PC, the three-axis positioning system, a lighting device, and charge-coupled device cameras. For the software part, we utilize image registration, image segmentation, drill numbering, drill contrast, and defect displays to achieve this system. Results indicated that an accuracy of 5 mu m could be achieved in errors of the PCB holes allowing comparisons to be made. This is significant in inspecting the missing, the multi-hole, and the incorrect location of the holes. However, previous work only focuses on one or other feature of the holes. Our research is able to assess multiple features: missing holes, incorrectly located holes, and excessive holes. Equally, our results could be displayed as a bar chart and target plot. This has not been achieved before. These displays help users to analyze the causes of errors and immediately correct the problems. In addition, this AOI system is valuable for checking a large number of holes and finding out the defective ones on a PCB. Meanwhile, we apply a 0.1-mm image resolution, which is better than others used in industry. We set a detecting standard based on 2-mm diameter of circles to diagnose the quality of the holes within 10 s.
Year
DOI
Venue
2017
10.1109/ACCESS.2016.2631658
IEEE ACCESS
Keywords
Field
DocType
Automatic optical inspection (AOI) system,drill inspection,drilling technique,printed circuits board (PCB),machine vision
Computer vision,Machine vision,Computer science,Printed circuit board,Image segmentation,Software,Artificial intelligence,Drill,Image registration,Automated optical inspection,Positioning system
Journal
Volume
ISSN
Citations 
5
2169-3536
2
PageRank 
References 
Authors
0.44
8
4
Name
Order
Citations
PageRank
Wei-Chen Wang120.44
Shang-Liang Chen272.91
Liang-Bi Chen32618.40
Wan-Jung Chang41312.53