Skip to main content

Advertisement

Springer Nature Link
Log in

Using computer theory to detect PCB defects in an IoT environment

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

All kinds of electronic products, such as motherboards, digital cameras, mobile phones and even chip products in manufacturing production processes, require some form of defect detection in an IoT environment. Shortcomings and improvements need to be found before a product is shipped, and various computer applications have been proposed to facilitate this, such as facial recognition, object tracking, digital picture processing and other forms of defect detection automation in an IoT environment, to reduce costs in the industrial production field. Various manufacturers have also invested in computer research aimed at replacing some labour-intensive aspects of key processes. This paper focuses on detection of printed circuit board defects, which plays a key role in the production process of general electronic products. In this study, computer theory was used as the basic architecture. This method can convert spatial domain signals into frequency domain signals, providing users with a specific frequency range. An algorithm is described here, capable of detecting defects in printed circuit boards, such as short circuits, protruding solder, solder dents and open circuits. In this study, the algorithm was used with wavelet transform technology to achieve the purpose of detecting PCB defects in an IoT environment. The results indicated that the technology in question is simple to operate and performs efficiently. It only requires an accurate picture to be inputted (after processing), containing the defect, in order to determine the location of the defect.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5.
Fig. 6
Fig. 7
Fig. 8.
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35

Similar content being viewed by others

References

  1. Trong H (2018) Improvement of methods for detection of characteristic points of biomedical signals based on continuous wavelet transformation in the continuous flow of data. In: Third international conference on human factors in complex technical systems and environments (ERGO)s and environments (ERGO)

  2. Hussain M, Hoang T, Langen C (2018) A design for two-dimensional non-causal deslauriers-dubuc discrete wavelet transformation for real-time video processing on fpga. In: 5th international conference on signal processing and integrated networks (SPIN)

  3. Zhao H, Lian B, Feng J (2011) Adaptive wavelet transformation for speckle reduction in optical coherence tomography pictures. In: IEEE international conference on signal processing, communications and computing (ICSPCC)

  4. Patrick J. Van Fleet (2019) Discrete wavelet transformations: an elementary approach with applications, 2nd edn, ISBN: 978-1-118-97931-0, E-book, Wiley

  5. Zhao X, Huang M, Zhu Q (2012) Analysis of hyperspectral scattering picture using wavelet transformation for assessing internal qualities of apple fruit. In: 24th Chinese Control and Decision Conference (CCDC)

  6. Li Y, Lu W, Gong L (2011) The research of wavelet transformation technology by using surface acoustic wave devices. In: Third international conference on measuring technology and mechatronics automation, Vol. 3

  7. Zaeni A, Kasnalestari T, Khayam U (2018) Application of wavelet transformation symlet type and coiflet type for partial discharge signals denoising. In: 5th international conference on electric vehicular technology (ICEVT)

  8. Zhao X, Nutter B (2016) Content based picture retrieval system using wavelet transformation and multiple input multiple task deep autoencoder. In: IEEE southwest symposium on picture analysis and interpretation (SSIAI)

  9. Zhong J, Yang K (2018) Failure prediction for linear ball bearings based on wavelet transformation and self-organizing map. In: IEEE 4th information technology and mechatronics engineering conference (ITOEC)

  10. Moganti M, Ercal F, Dagli H, Tsunekawa S (1996) Automatic PCB inspection algorithms: a survey. Comput Vis Pict underst 63(2):287–313

    Article  Google Scholar 

  11. Chen CM, Chen L, Gan W, Qiu L, Ding W (2021) Discovering high utility-occupancy patterns from uncertain data. Inf Sci 546:1208–1229

    Article  MathSciNet  Google Scholar 

  12. Chen CM, Huang Y, Wang KH, Kumari S, Wu M (2020) A secure authenticated and key exchange scheme for fog computing. Enterp Inf Syst. https://doi.org/10.1080/17517575.2020.1856422

    Article  Google Scholar 

  13. Chen X, Li A, Zeng X, Guo W, Huang G (2015) Runtime model based approach to IoT application development. Front Comput Sci 9(4):540–553

    Article  Google Scholar 

  14. Chen X, Lin J, Ma Y, Lin B, Wang H, Huang G (2019) Self-adaptive resource allocation for cloud-based software services based on progressive QoS prediction model. Sci China Inf Sci 62(11):219101

    Article  Google Scholar 

  15. Chen X, Wang H, Ma Y, Zheng X, Guo L (2020) Self-adaptive resource allocation for cloud-based software services based on iterative QoS prediction model. Futur Gener Comput Syst 105:287–296

    Article  Google Scholar 

  16. Tatibana M, Lotufo R (1997) Novel automatic pcb inspection technique based on connectivity. In: Computer graphics and picture processing 1997 proceeding, X brazilian symposium, pp. 14–17

  17. Swanson M, Tewfik A (1996) A binary wavelet decomposition of binary pictures. IEEE Trans Pict Process, Vol. 5(12)

  18. Liu R, Shi Y, KoSonocky W, Higgins F (1996) Infrared solder joint inspection on surface mount printed circuit boards. In: Proceeings of 38th Circuits and systems, midwest symposium USA, Vol. 1, 145-148

  19. Huang G, Liu X, Ma Y, Lu X, Zhang Y, Xiong Y (2019) Programming situational mobile web applications with cloud-mobile convergence: an internetware-oriented approach. IEEE Trans Serv Comput 12(1):6–19

    Article  Google Scholar 

  20. Huang G, Ma Y, Liu X, Luo Y, Lu X, Blake M (2015) Model-based automated navigation and composition of complex service Mashups. IEEE Trans Serv Comput 8(3):494–506

    Article  Google Scholar 

  21. Huang G, Xu M, Lin X, Liu Y, Ma Y, Pushp S, Liu X (2017) Shuffle dog: characterizing and adapting user-perceived latency of android apps. IEEE Trans Mob Comput 16(10):2913–2926

    Article  Google Scholar 

  22. Lin B, Huang Y, Zhang J, Hu J, Chen X, Li J (2020) Cost-Driven Offloading for DNN-based Applications over Cloud, Edge and End Devices. IEEE Trans Ind Inf 16(8):5456–5466

    Article  Google Scholar 

  23. Lin C, Kuo H (2010) Evaluation of production yield for process selection. In: 2010 IEEE international conference on industrial engineering and engineering management

  24. Lin Y, Cheng C, Wu T (2018) Fast and accurate yield rate prediction of PCB embedded common-mode filter with artificial neural network. In: 2018 IEEE international symposium on electromagnetic compatibility and 2018 IEEE asia-pacific symposium on electromagnetic compatibility (EMC/APEMC)

  25. Liu X, Huang G, Zhao Q, Mei H, Blake M (2014) iMashup: a mashup-based framework for service composition. Sci China Inf Sci 54(1):1–20

    Article  Google Scholar 

  26. Yaniguchi T, Kacprzak D, Yamada S, Iwahara M (2001) Wavelet-based processing of ECT pictures for inspection of printed circuit board. IEEE Trans Magget 37(4):2790–2793

    Article  Google Scholar 

  27. Ibrahim Z, Al-Attas SAR, Aspar Z, Mokji MM (2002) Performance evaluation of wavelet-based PCB defect detection and localization algorithm, In: Industrial technology, IEEE ICIT 02. Vol. 1, pp. 226 –231

  28. Shifa A, Asghar NM, Ahmed A et al (2020) Fuzzy-logic threat classification for multi-level selective encryption over real-time video streams. J Ambient Intell Human Comput 11:5369–5397. https://doi.org/10.1007/s12652-020-01895-2

    Article  Google Scholar 

  29. Ahmed A, Abdullah S, Bukhsh M, Ahmad I, Mushtaq Z (2022) An energy-efficient data aggregation mechanism for IoT secured by blockchain,". IEEE Access 10:11404–11419. https://doi.org/10.1109/ACCESS.2022.3146295

    Article  Google Scholar 

  30. Ye O, Huang P, Zhang Z, Zheng Y, Fu L, Yang W (2021) Multiview learning with robust double-sided twin SVM. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2021.3088519

    Article  Google Scholar 

  31. Fu L, Li Z, Ye Q et al.(2020) Learning robust discriminant subspace based on joint L2,p- and L2,s-norm distance metrics. In: IEEE Transactions on Neural Networks and Learning Systems. Early Access

  32. Ye Q, Li Z, Fu L et al (2019) Nonpeaked discriminant analysis. IEEE Trans Neural Netw Learn Syst 30(12):3818–3832

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The research was support by Dongguan Polytechnic Project: FPC Surface Defect Intelligent Detection System Based on Machine Vision (ZXF021) and the project of Guangdong 2021 Engineering Technology Research Center (2021GCZX016).

Author information

Authors and Affiliations

  1. School of Electronic Information, Dongguan polytechnic, Dongguan, 523808, China

    Long Gao & Jian Yong Bian

  2. Network Center, Dongguan University of Technology, Dongguan, 523808, China

    Fen Zheng

Authors
  1. Long Gao
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Fen Zheng
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jian Yong Bian
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Fen Zheng.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, L., Zheng, F. & Bian, J.Y. Using computer theory to detect PCB defects in an IoT environment. J Supercomput 78, 18887–18914 (2022). https://doi.org/10.1007/s11227-022-04610-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04610-4

Keywords