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Cited by 118Industrial Vision Systems and Defect DetectionNon-Destructive Testing TechniquesImage Processing Techniques and Applications

The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.

A Machine Vision Based Automatic Optical Inspection System for Measuring Drilling Quality of Printed Circuit Boards

Open access
Wang, Wei-Chien, Chen, Shang-Liang, Chen, Liang-Bi, Chang, Wan-Jung·2017·IEEE Access
DOI 10.1109/access.2016.2631658
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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 μm 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.

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Crossref · 10.1109/access.2016.2631658

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Research works printed but not linked: 1

  1. jackson, 1979, Determining outer layer registration via a concentricity measuring microscope, Electron Packag Prod, 19, 143

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Metadata

Authors
Wang, W.-C., Chen, S.-L., Chen, L.-B., & Chang, W.-J.
Affiliations
  • Federation University (AU)
  • National Cheng Kung University (TW)
  • Southern Taiwan University of Science and Technology (TW)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2017
Volume
5
Pages
10817-10833
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2016.2631658
OpenAlex
W2549337046
Cited by
118
References
29
Citations by year
2026: 5 · 2025: 14 · 2024: 16 · 2023: 19 · 2022: 19 · 2021: 13 · 2020: 12 · 2019: 11 · 2018: 5 · 2017: 3
FWCI
8.60
Type
journal article
Language
en
Open access
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
Locations
  • IEEE Access (publishedVersion)
  • FedUni ResearchOnline (Federation University Australia) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Ministry of Science and Technology, Taiwan (MOST-106-2632-E-2018-002, MOST-106-2622-8-218-003-TE2)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
  • Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
  • Physical Sciences › Engineering › Media Technology › Image Processing Techniques and Applications
Keywords
automated optical inspection, printed circuit boards, missing holes, image registration, image segmentation, CCD detector

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