CFP last date
20 August 2024
Call for Paper
September Edition
IJCA solicits high quality original research papers for the upcoming September edition of the journal. The last date of research paper submission is 20 August 2024

Submit your paper
Know more
Reseach Article

Automated Bare Board Defect Detection using Pattern Subtraction Technique

by Jithendra P.R. Nayak, Parameshachari B.D.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 36
Year of Publication: 2021
Authors: Jithendra P.R. Nayak, Parameshachari B.D.
10.5120/ijca2021921744

Jithendra P.R. Nayak, Parameshachari B.D. . Automated Bare Board Defect Detection using Pattern Subtraction Technique. International Journal of Computer Applications. 183, 36 ( Nov 2021), 11-15. DOI=10.5120/ijca2021921744

@article{ 10.5120/ijca2021921744,
author = { Jithendra P.R. Nayak, Parameshachari B.D. },
title = { Automated Bare Board Defect Detection using Pattern Subtraction Technique },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2021 },
volume = { 183 },
number = { 36 },
month = { Nov },
year = { 2021 },
issn = { 0975-8887 },
pages = { 11-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number36/32161-2021921744/ },
doi = { 10.5120/ijca2021921744 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:18:48.046976+05:30
%A Jithendra P.R. Nayak
%A Parameshachari B.D.
%T Automated Bare Board Defect Detection using Pattern Subtraction Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 36
%P 11-15
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A Bare board or printed circuit board is the mechanical support that connects the electrical equipment using advanced means, track or signal tracked on copper sheets attached to a continuous substrate. Automated testing of PCBs serves a traditional purpose in computer technology. Aim is to free human inspectors from the inefficient and tedious task of detecting those defects in PCBs that could lead to failure. By comparing a typical PCB image with a faulty PCB image, using a simple extraction algorithm that can highlight major problem areas. Then perform a different image processing function such as blocking the image and filtering the image to remove unwanted edges and sound present in the wrong image and also the effect of noise on the PCB image is eliminated. This method should detect which method is appropriate to obtain the wrong image. Finally, segmentation is used to identify the source of the six various defects such as under etch, mouse bite, pin hole, missing hole, open rotation and short circuit.

References
  1. Bing Hu And Jianhui Wang “Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network” IEEE june 2020
  2. Venkat Anil Adibhatla etal “Defect Detection in Printed Circuit Boards Using You-Only-Look-Once Convolutional Neural Networks” mdpi journal Electronics Aug 2020
  3. Saeed Khalilian etal “PCB Defect Detection Using Denoising Convolutional Auto encoders’’ arXiv:2008.12589 [eess.IV] 2020.
  4. K. Kamalpreet and K. Beant, ”PCB Defect Detection and Classification Using Image Processing,” International Journal of Emerging Researchin Management & Technology, vol. 03, 2014.
  5. W.-Y. Wu, M.-J. J. Wang and C.-M. Liu, ”Automated inspection of printed circuit boards through machine vision,” Computers in Industryvol. 28, no. 2, pp. 103-111, 1996.
  6. P. Wei, C. Liu , M. Liu, Y. Gao and H. Liu, ”CNN-based reference comparison method for classifying bare PCB defects,” The Journal of Engineering, vol. 2018, no. 16, pp. 1528-1533, 2018.
  7. A. K. P, S. N. S and S. . K. V. V, ”A review of PCB defect detection using image processing,” International Journal of Engineering and Innovative Technology (IJEIT), vol. 4, no. 11, pp. 188-192, 2015.
  8. N. Dave, V. Tambade and B. Pandhare, ”PCB Defect Detection Using Image Processing And Embedded System,” International Research Journal of Engineering and Technology (IRJET), vol. 3, no. 5, pp. 1897- 1901, 2016.
  9. S. H. I. Putera and Z. Ibrahim, ”Printed circuit board defect detection using mathematical morphology and MATLAB image processing tools,” in 2010 2nd International Conference on Education Technology and Computer, Shanghai, China, IEEE, 2010.
  10. Z. Ibrahim, S. A. R. Al-Attas, Z. Aspar and M. M. Mokji, ”Performanc evaluation of wavelet-based PCB defect detection and localization algorithm,” in 2002 IEEE International Conference on Industrial Technology 2002. IEEE ICIT ’02., Bankok, Thailand, Thailand, IEEE, 2002.
  11. P. K. Srimani and V. Prathiba, ”Adaptive data mining approach for PCB defect detection and classification,” Indian Journal of Science and Technology, vol. 9, no. 44, pp. 1-9, 2016.
  12. A. Kusiak and C. Kurasek, ”Data mining of printed-circuit board defects,” IEEE Transactions on Robotics and Automation , vol. 17, no. 2, pp. 191-196, 2001.
  13. T. Vafeiadis, N. Dimitriou, D. Ioannidis, T. Wotherspoon, G. Tinkerand D. Tzovaras, ”A framework for inspection of dies attachment on PCB utilizing machine learning techniques,” Journal of Management Analytics, vol. 5, no. 2, pp. 81-94, 2018.
  14. V. A. Adibhatla, J.-S. Shieh, M. F. Abbod, H.-C. Chih, C.-. C. Hsu and J. Cheng, ”Detecting Defects in PCB using Deep Learning via Convolution Neural Networks,” in 2018 13th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT), Taipei, Taiwan, Taiwan, IEEE, 2018, pp. 202-205.
  15. C. Zhang, W. Shi, X. Li, H. Zhang and H. Liu, ”Improved bare PCB defect detection approach based on deep feature learning,” The Journal of Engineering, vol. 2018, no. 16, pp. 1415-1420, 2018.
  16. Y.-S. Deng, A.-C. Luo and M.-J. Dai, ”Building an Automatic Defect Verification System Using Deep Neural Network for PCB Defect Classification,” in 2018 4th International Conference on Frontiers of Signal Processing (ICFSP), Poitiers, France, IEEE, 2018.
  17. L. Zhang, Y. Jin, X. Yang, X. Li, X. Duan, Y. Sun and H. Liu, ”Convolutional neural network-based multi-label classification of PCB defects,” The Journal of Engineering, vol. 2018, no. 16, pp. 1612-1616, 2018.
  18. S. Tang, F. He, X. Huang and J. Yang, ”Online PCB Defect Detector On A New PCB Defect Dataset,” arXiv preprint arXiv:1902.06197, 2019.
  19. A. Mujeeb, W. Dai, M. Erdt and A. Sourin, ”One class based feature learning approach for defect detection using deep autoencoders,” Advanced Engineering Informatics, vol. 42, 2019
Index Terms

Computer Science
Information Sciences

Keywords

Image Processing Printed Circuit Board Defect Classification