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Investigation of visual inspection methodologies for printed circuit board products

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Abstract

Printed circuit board (PCB) inspection is as challenging issue in electronic manufacturing. Visual inspection is broadly used to perform this task and has been instigated by many researchers. This paper focuses on review of 2D vision-based methods in PCB inspection. Traditional and modern advances methods are explored, and their advantages and disadvantages are addressed. Moreover, current research gap through literature review investigation in this domain is addressed and potential solutions are described. Finally, direction for future studies is presented for further improvement of existing methods.

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References

  1. M. Javaid, A. Haleem, R.P. Singh, S. Rab, R. Suman, Exploring impact and features of machine vision for progressive industry 4.0 culture. Sens. Int. 3, 100132 (2022)

    Article  Google Scholar 

  2. M. Nikoufard, M. Hatami, Analysis of ultra-compact TE to TM polarization rotator in InGaAsP and SOI technologies. Opt. Int. J. Light Electron Opt. 153, 9–15 (2018)

    Article  Google Scholar 

  3. Z. Liu, B. Qu, Machine vision based online detection of PCB defect. Microprocess. Microsyst. 82, 103807 (2021)

    Article  Google Scholar 

  4. A.H. Aghamohammadi, A.S. Prabuwono, S. Sahran, M. Mogharrebi, Solar cell panel crack detection using particle swarm optimization algorithm. in 2011 international conference on pattern analysis and intelligence robotics, vol. 1 (2011) pp. 160–164

  5. T.-H. Sun, C.-C. Tseng, M.-S. Chen, Electric contacts inspection using machine vision. Image Vis. Comput. 28(6), 890–901 (2010)

    Article  Google Scholar 

  6. T.A. Waziri, A. Ibrahim, Discrete fix up limit model of a device unit. J. Comput. Cogn. Eng. 2(2), 163–167 (2023)

    Google Scholar 

  7. M.U. Danjuma, B. Yusuf, I. Yusuf, Reliability, availability, maintainability, and dependability analysis of cold standby series-parallel system. J. Comput. Cogn. Eng. 1(4), 193–200 (2022)

    Google Scholar 

  8. Z. Jia, W. Wang, J. Zhang, H. Li, Contact high-temperature strain automatic calibration and precision compensation research. J. Artif. Intell. Technol. 2(2), 69–76 (2022)

    Google Scholar 

  9. H. Liu, P. Yuan, B. Yang, G. Yang, Y. Chen, Head-related transfer function–reserved time-frequency masking for robust binaural sound source localization. CAAI Trans. Intell. Technol. 7(1), 26–33 (2022)

    Article  Google Scholar 

  10. M.C. Ang, A. Aghamohammadi, K.W. Ng, E. Sundararajan, M. Mogharrebi, T.L. Lim, Multi-core frameworks investigation on a real-time object tracking application. J. Theor. Appl. Inf. Technol. 70(1), 163–171 (2014)

    Google Scholar 

  11. M. Nikoufard, M. Hatami, Photonic crystal-based polarization converter for optical communication applications. Int. J. Opt. Photonics 10(2), 111–116 (2016)

    Article  Google Scholar 

  12. M. Ang, E. Sundararajan, K. Ng, A. Aghamohammadi, T. Lim, Investigation of threading building blocks framework on real time visual object tracking algorithm. Appl. Mech. Mater. 666, 240–244 (2014)

    Article  Google Scholar 

  13. A. Aghamohammadi, M.C. Ang, A.S. Prabuwono, M. Mogharrebi, K.W. Ng, Enhancing an automated inspection system on printed circuit boards using affine-sift and triz techniques. in Advances in visual informatics: third international visual informatics conference, IVIC 2013, Proceedings 3, (Selangor, Malaysia, 2013) pp. 128–137, 13–15 Nov 2013

  14. M. Mogharrebi, A.S. Prabuwono, S. Sahran, A. Aghamohammadi, Missing component detection on PCB using neural networks, in Advances in Electrical Engineering and Electrical Machines. ed. by D. Zheng (Springer, Berlin, Heidelberg, 2011), pp.387–394

    Chapter  Google Scholar 

  15. Z. Ibrahim, N.K. Khalid, M.S. Zainal Abidin, An algorithm to group defects on printed circuit board for automated visual inspection. Int. J. Simul. Syst. Sci. Technol. 9(2), 1–10 (2008)

    Google Scholar 

  16. Y. Ou, G. Baoping, H. Tao, G. Xuan, A real-time vision system for defect detection in printed matter and its key technologies. in 2007 2nd IEEE conference on industrial electronics and applications, 2007 pp. 2157–2161

  17. I.-H. Hsiao, C.-Y. Chung, AI-infused semantic model to enrich and expand programming question generation. J. Artif. Intell. Technol. 2(2), 47–54 (2022)

    Google Scholar 

  18. A. Sarkar, A. Biswas, M. Kundu, Development of q-rung orthopair trapezoidal fuzzy Einstein aggregation operators and their application in MCGDM problems. J. Comput. Cogn. Eng. 1(3), 109–121 (2022)

    Google Scholar 

  19. A.H. Aghamohammadi, M.C. Ang, K.W. Ng, M. Mogharrebi, TRIZ-based innovative solution for wheelchair assistive technology. in Visual informatics international seminar, 2014 pp. 152–157

  20. Z. Li, Intelligent media computing technology and application for media convergence. CAAI Trans. Intell. Technol. 7(3), 329–330 (2022)

    Article  MathSciNet  Google Scholar 

  21. A. Aghamohammadi, M.C. Ang, E.A. Sundararajan, N.K. Weng, M. Mogharrebi, S.Y. Banihashem, A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos. PLoS ONE 13(2), e0192246 (2018)

    Article  Google Scholar 

  22. P. Ding, X. Liu, H. Li, Z. Huang, K. Zhang, L. Shao, O. Abedinia, Useful life prediction based on wavelet packet decomposition and two-dimensional convolutional neural network for lithium-ion batteries. Renew. Sustain. Energy Rev. 148, 111287 (2021)

    Article  Google Scholar 

  23. J. Meng, Y. Li, H. Liang, Y. Ma, Single-image dehazing based on two-stream convolutional neural network. J. Artif. Intell. Technol. 2(3), 100–110 (2022)

    Google Scholar 

  24. A. Gasparin, S. Lukovic, C. Alippi, Deep learning for time series forecasting: the electric load case. CAAI Trans. Intell. Technol. 7(1), 1–25 (2022)

    Article  Google Scholar 

  25. E.N. Malamas, E.G.M. Petrakis, M. Zervakis, L. Petit, J.D. Legat, A survey on industrial vision systems, applications and tools. Image Vis. Comput. 21(2), 171–188 (2003)

    Article  Google Scholar 

  26. B. Zitova, J. Flusser, Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003)

    Article  Google Scholar 

  27. B. Kaur, G. Kaur, A. Kaur, Comparative analysis of image registration for printed circuit boards. Int. J. Eng. Technol. 9(3), 2076–2082 (2017)

    Article  Google Scholar 

  28. H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  29. D.G. Lowe, Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)

    Article  Google Scholar 

  30. A.A.I. Hassanin, F.E. Abd El-Samie, G.M. El Banby, A real-time approach for automatic defect detection from PCBs based on SURF features and morphological operations. Multimed. Tools App. 78, 34437–34457 (2019)

    Article  Google Scholar 

  31. S. Zakaria, A. Amir, N. Yaakob, S. Nazemi, Automated detection of printed circuit boards (PCB) defects by using machine learning in electronic manufacturing: current approaches. IOP Conf. Series Mater. Sci. Eng. 767(1), 012064 (2020)

    Article  Google Scholar 

  32. L. Bai, X. Yang, H. Gao, Improved chamfer matching method for surface mount component positioning. IET Image Proc. 11(12), 1265–1272 (2017)

    Article  Google Scholar 

  33. N.K. Khalid, Z. Ibrahim, M.S.Z. Abidin, An algorithm to group defects on printed circuit board for automated visual inspection. Int. J. Simul. Syst. Sci. Technol. 9(2), 1–10 (2008)

    Google Scholar 

  34. C.-H. Wu, D.-Z. Wang, A. Ip, D.-W. Wang, C.-Y. Chan, H.-F. Wang, A particle swarm optimization approach for components placement inspection on printed circuit boards. J. Intell. Manuf. 20, 535–549 (2009)

    Article  Google Scholar 

  35. B. Kaur, G. Kaur, A. Kaur, Detection of defective printed circuit boards using image processing. Int. J. Comput. Vis. Robot. 8(4), 418–434 (2018)

    Article  Google Scholar 

  36. D.-M. Tsai, Y.-C. Hsieh, Automatic visual position and inspection of printed circuit boards using EM algorithm. in 2017 international conference on wireless communications, signal processing and networking (WiSPNET), 2017

  37. L. Dai, Q. Guan, H. Liu, Robust image registration of printed circuit boards using improved SIFT-PSO algorithm. J. Eng. 2018(16), 1793–1797 (2018)

    Article  Google Scholar 

  38. R. Ding, L. Dai, G. Li, H. Liu, TDD-net: a tiny defect detection network for printed circuit boards. CAAI Trans. Intell. Technol. 4(2), 110–116 (2019)

    Article  Google Scholar 

  39. F. Ardhy, F.I. Hariadi, Development of SBC based machine-vision system for PCB board assembly automatic optical inspection. in 2016 international symposium on electronics and smart devices (ISESD),2016 pp. 386–393

  40. R.R. Chavan, S.A. Chavan, G.D. Dokhe, M.B. Wagh, A.S. Vaidya, Quality control of PCB using image processing. Int. J. Comput. App. 141(5), 28–32 (2016)

    Google Scholar 

  41. D.-M. Tsai, Y.-C. Hsieh, Machine vision-based positioning and inspection using expectation–maximization technique. IEEE Trans. Instrum. Meas. 66(11), 2858–2868 (2017)

    Article  ADS  Google Scholar 

  42. J.P. Nayak, B. Parameshachari, K.S. Soyjaudah, R. Banu, A. Nuthan, Identification of PCB faults using image processing. in 2017 international conference on electrical, electronics, communication, computer, and optimization techniques (ICEECCOT), 2017 pp. 1–4

  43. F. Mirzaei, M. Faridafshin, A. Movafeghi, R. Faghihi, Automated defect detection of weldments and castings using Canny, Sobel and Gaussian filter edge detectors: a comparison study. in The proceedings of the 4th Iranian international NDT conference, 2017

  44. K.J. Pithadiya, K.S. Patel, Evaluating the most efficient edge detection technique for inspection of chip resistor. Int. J. Innov. Res. Comput. Commun. Eng 3(9), 8604–8613 (2015)

    Google Scholar 

  45. J.H. Lee, S.I. Yoo, An effective image segmentation technique for the SEM image. in 2008 IEEE international conference on industrial technology, 2008 pp. 1–5

  46. S.R. Newase, High level modeling of PCB defect detection using blob detection Methods, PhD Thesis. California State University, Northridge, 2020, pp. 1–31

  47. E. Șipoș, A. Oneș, L-N. Ivanciu, PCB quality check: optical inspection using color mask and thresholding. in 2022 IEEE international conference on automation, quality and testing, robotics (AQTR), 2022 pp. 1–5 (2022).

  48. K. Fang, Threshold segmentation of PCB defect image grid based on finite difference dispersion for providing accuracy in the IoT based data of smart cities. Int. J. Syst. Assur. Eng. Manag. 13(Suppl 1), 121–131 (2022)

    Article  Google Scholar 

  49. W. Zhao, S.R. Gurudu, S. Taheri, S. Ghosh, M.A. Mallaiyan Sathiaseelan, N. Asadizanjani, PCB component detection using computer vision for hardware assurance. Big Data Cogn. Comput. 6(2), 39 (2022)

    Article  Google Scholar 

  50. M. Azhagan, D. Mehta, H. Lu, S. Agrawal, P. Chawla, M. Tehranipoor, D. Woodard, N. Asadi, A review on automatic bill of material generation and visual inspection on PCBs. in ISTFA 2019: conference proceedings from the 45th international symposium for testing and failure analysis, 2019 pp. 256–265

  51. L.R.D. Abhishek Mukhopadhyay, M.A. Murthy, A. Chakrabarti, I. Mukherjee, P. Biswas, PCB inspection in the context of smart manufacturing, in Research into Design for a Connected World: Proceedings of ICoRD 2019 Volume 1. ed. by A. Chakrabarti (Springer, Singapore, 2019), pp.655–663

    Chapter  Google Scholar 

  52. O. Abedinia, N. Amjady, Net demand prediction for power systems by a new neural network-based forecasting engine. Complexity 21(S2), 296–308 (2016)

    Article  ADS  MathSciNet  Google Scholar 

  53. J. Lian, L. Wang, T. Liu, X. Ding, Z. Yu, Automatic visual inspection for printed circuit board via novel Mask R-CNN in smart city applications. Sustain. Energy Technol. Assess. 44, 101032 (2021)

    Google Scholar 

  54. L.K. Cheong, S.A. Suandi, S. Rahman, Defects and components recognition in printed circuit boards using convolutional neural network. in 10th international conference on robotics, vision, signal processing and power applications: enabling research and innovation towards sustainability, 2019 pp. 75–81

  55. M.B. Akhtar, The use of a convolutional neural network in detecting soldering faults from a printed circuit board assembly. HighTech Innov. J. 3(1), 1–14 (2022)

    Article  MathSciNet  Google Scholar 

  56. C.-H. Lin, S.-H. Wang, C.-J. Lin, Using convolutional neural networks for character verification on integrated circuit components of printed circuit boards. Appl. Intell. 49, 4022–4032 (2019)

    Article  Google Scholar 

  57. D.-U. Lim, Y.-G. Kim, T.-H. Park, SMD classification for automated optical inspection machine using convolution neural network. in 2019 third IEEE international conference on robotic computing (IRC), 2019 pp. 395–398

  58. C.-T. Chu, Z.-X. Lin, Deep learning techniques implement circuit board inspection in smart industrial. in 2021 16th international microsystems, packaging, assembly and circuits technology conference (IMPACT), 2021 pp. 181–184

  59. H. Wang, H. Lu, S.M. Alelaumi, S.W. Yoon, A wavelet-based multi-dimensional temporal recurrent neural network for stencil printing performance prediction. Robot. Comput. Integr. Manuf. 71, 102129 (2021)

    Article  Google Scholar 

  60. Y. Wang, Q. Chen, M. Ding, J. Li, High precision dimensional measurement with convolutional neural network and bi-directional Long Short-Term Memory (LSTM). Sensors 19(23), 5302 (2019)

    Article  ADS  Google Scholar 

  61. J. Kim, J. Ko, H. Choi, H. Kim, Printed circuit board defect detection using deep learning via a skip-connected convolutional auto encoder. Sensors 21(15), 4968 (2021)

    Article  ADS  Google Scholar 

  62. A. Mujeeb, W. Dai, M. Erdt, A. Sourin, One class based feature learning approach for defect detection using deep auto encoders. Adv. Eng. Inform. 42, 100933 (2019)

    Article  Google Scholar 

  63. S. You, PCB defect detection based on generative adversarial network. in 2022 2nd international conference on consumer electronics and computer engineering (ICCECE), 2022 pp. 557–560

  64. X. He, Z. Chang, L. Zhang, H. Xu, H. Chen, Z. Luo, A survey of defect detection applications based on generative adversarial networks. IEEE Access 10, 113493–113512 (2022)

    Article  Google Scholar 

  65. Y. Miao, Z. Liu, X. Wu, J. Gao, Cost-sensitive Siamese network for PCB defect classification. Comput. Intell. Neurosci. 2021, 1–13 (2021)

    Article  Google Scholar 

  66. Z. Ling, A. Zhang, D. Ma, Y. Shi, H. Wen, Deep Siamese semantic segmentation network for PCB welding defect detection. IEEE Trans. Instrum. Meas. 71, 1–11 (2022)

    Article  Google Scholar 

  67. G. Piliposyan, S. Khursheed, Computer vision for hardware trojan detection on a PCB using Siamese neural network. in 2022 IEEE physical assurance and inspection of electronics (PAINE), 2022 pp. 1–7

  68. H. Wu, W. Gao, X. Xu, Solder joint recognition using mask R-CNN method. IEEE Trans. Compon. Packag. Manuf. Technol. 10(3), 525–530 (2019)

    Article  Google Scholar 

  69. X. Ma, Y. Chen, S. Li, W. Du, Z. Tan, Q. Li, Y. Ma, H. Deng, Quality detection method for controller process based on mask R-CNN network model. in 2021 8th international conference on computational science/intelligence and applied informatics (CSII), 2021 pp. 18–22

  70. V.A. Adibhatla, H.-C. Chih, C.-C. Hsu, J. Cheng, M.F. Abbod, J.-S. Shieh, Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once. Math. Biosci. Eng. (2021). https://doi.org/10.3934/mbe.2021223

    Article  Google Scholar 

  71. V.A. Adibhatla, H.-C. Chih, C.-C. Hsu, J. Cheng, M.F. Abbod, J.-S. Shieh, Defect detection in printed circuit boards using you-only-look-once Convolutional Neural Networks. Electronics 9(9), 1547 (2020)

    Article  Google Scholar 

  72. M. Glučina, N. Anđelić, I. Lorencin, Z. Car, Detection and classification of printed circuit boards using YOLO algorithm. Electronics 12(3), 667 (2023)

    Article  Google Scholar 

  73. B. Wang, F. Fan, G. Zhu, J. Wu, Efficient faster R-CNN: used in PCB solder joint defects and components detection. in IEEE 4th international conference on computer and communication engineering technology (CCET), 2021

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  1. College of Intelligent Manufacture, Chongqing Creation Vocational College, Chongqing, 402160, China

    Wenliang Deng

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Deng, W. Investigation of visual inspection methodologies for printed circuit board products. J Opt 53, 1462–1470 (2024). https://doi.org/10.1007/s12596-023-01342-3

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