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Reseach Article

Quality Control of PCB using Image Processing

by Rasika R. Chavan, Swati A. Chavan, Gautami D. Dokhe, Mayuri B. Wagh, Archana S.Vaidya
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 141 - Number 5
Year of Publication: 2016
Authors: Rasika R. Chavan, Swati A. Chavan, Gautami D. Dokhe, Mayuri B. Wagh, Archana S.Vaidya
10.5120/ijca2016909623

Rasika R. Chavan, Swati A. Chavan, Gautami D. Dokhe, Mayuri B. Wagh, Archana S.Vaidya . Quality Control of PCB using Image Processing. International Journal of Computer Applications. 141, 5 ( May 2016), 28-32. DOI=10.5120/ijca2016909623

@article{ 10.5120/ijca2016909623,
author = { Rasika R. Chavan, Swati A. Chavan, Gautami D. Dokhe, Mayuri B. Wagh, Archana S.Vaidya },
title = { Quality Control of PCB using Image Processing },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 141 },
number = { 5 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 28-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume141/number5/24782-2016909623/ },
doi = { 10.5120/ijca2016909623 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:42:41.424453+05:30
%A Rasika R. Chavan
%A Swati A. Chavan
%A Gautami D. Dokhe
%A Mayuri B. Wagh
%A Archana S.Vaidya
%T Quality Control of PCB using Image Processing
%J International Journal of Computer Applications
%@ 0975-8887
%V 141
%N 5
%P 28-32
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An automated testing system for Printed Circuit Board (PCB) is preferred to get the technological advances in PCBs design and manufacturing, eliminates particular aspects and then provides fast, quantitative, and dimensional impositions. It reduces the testing time and manufacturing cost as human inspectors decisions are ineffective, slow and costly. Thus in this area, digital image processing can be used mainly for the detection of faulty parts or missing components. This system mainly deals with analysis to detect faulty PCB. Digital camera is used in automated visual inspection system that captures image of each sample PCB product. The captured image is then provided to computer for further processing which includes conversion in various forms such as Gray scale image and binarized image. XOR operation is performed on these converted images to obtain the required results. Contour Analysis is performed on these results for classification. Missing components, polarities, circuit breaks, missing tracks these types of faults are detected and classified accordingly. This concept increases the speed and accuracy, eliminates human errors which are frequent in quality testing and also overcomes the weakness in the existing system. Hence the productivity can be increased by replacing manual testing with the proposed concept.

References
  1. Sonal Kaushik, Javed Ashraf, “Automated PCB Defect Detection Using Image Subtraction Method”, International Journal of Computer Science and Network (IJCSN)Volume 1, Issue 5, www.ijcsn.org ISSN 2277-5420, October 2012.
  2. Theingi Aye, Aung Soe Khaing, “Automatic Defect Detection and Classification on Printed Circuit Board”, International Journal of Societal Applications of Computer Science Vol 3 Issue 3 ISSN 2319 – 8443, March 2014.
  3. Xiaojing Tian,Liang Zhao,Huajun Dong, “Application of image processing in the detection of printed circuit board”, IEEE Workshop on Electronic Computer And Application,2014.
  4. N. Yogesh Bagrecha, “Quality Control Using Image Processing”, et al Int. Journal of Engineering Research and Applications ISSN : 2248-9622, Vol. 4, Issue 3( Version 1),pp.15-18 ,March 2014.
  5. Suhasini A ,Sonal D Kalro , Prathiksha B G, Meghashree B S , Phaneendra H D, “PCB Defect Detection Using Image Subtraction Algorithm”, International Journal of Computer Science Trends and Technology (IJCST) – Volume 3 Issue 3, ISSN: 2347-8578 www.ijcstjournal.org, May-June 2015.
  6. “Printed circuit board defect detection using mathematical morphology and MATLAB image processing tools”, Researchgate Article, DOI: 10.1109/ICETC.2010.5530052, January 2010.
  7. Takumi Uemura, Gou Koutaki and Keiichi Uchimura, “Image Segmentation based on Edge Detection using Boundry Code”, International journal of innovative computing, information and computing, Volume 7, Number 10, October 2011.
  8. E. Argyle. “Techniques for edge detection,” Proc. IEEE, vol. 59, pp. 285-286, 2012.
  9. F. Bergholm. “Edge focusing,” in Proc. 8th Int. Conf. Pattern Recognition, Paris, France, pp. 597- 600, 2013.
  10. Mrs. Namrata. S. Mandvikar, “Augmented Reality Using Contour Analysis In e-learning”, International Journal of Innovative Research in Science, Engineering and Technology Vol. 2, Issue 5, May 2013.
  11. Namrata S. Mandvikar1, Sunita Jadhav, “Design and implementation of Augmented Reality learning system using contour analysis”, International Journal on Advanced Computer Theory and Engineering (IJACTE), ISSN (print): 2319-2526,Volume -3, Issue -2, 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Image Processing Printed Circuit Board Defect Detection RGB Gray Scale Binarization Edge Detection Classification system.