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Design of automatic vision-based inspection system for solder joint segmentation

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: Computer vision has been widely used in the inspection of electronic components. This paper proposes a computer vision system for the automatic detection, localisation, and segmentation of solder joints on Printed Circuit Boards (PCBs) under different illumination conditions. Design/methodology/approach: An illumination normalization approach is applied to an image, which can effectively and efficiently eliminate the effect of uneven illumination while keeping the properties of the processed image the same as in the corresponding image under normal lighting conditions. Consequently special lighting and instrumental setup can be reduced in order to detect solder joints. These normalised images are insensitive to illumination variations and are used for the subsequent solder joint detection stages. In the segmentation approach, the PCB image is transformed from an RGB color space to a YIQ color space for the effective detection of solder joints from the background. Findings: The segmentation results show that the proposed approach improves the performance significantly for images under varying illumination conditions. Research limitations/implications: This paper proposes a front-end system for the automatic detection, localisation, and segmentation of solder joint defects. Further research is required to complete the full system including the classification of solder joint defects. Practical implications: The methodology presented in this paper can be an effective method to reduce cost and improve quality in production of PCBs in the manufacturing industry. Originality/value: This research proposes the automatic location, identification and segmentation of solder joints under different illumination conditions.
Rocznik
Strony
138--151
Opis fizyczny
Bibliogr. 27 poz., rys., tabl.
Twórcy
autor
autor
  • School of Engineering Systems, Queensland University of Technology, George Street 2, Brisbane QLD 4001, Australia, marn@qut.edu.au
Bibliografia
  • [1] H. H. Lo, M. S. Lu, Printed circuit board inspection using image analysis, IEEE Transactions on Industry Applications 35/2 (1999) 426-432.
  • [2] Z. S. Lee, R. C. Lo, Application of vision image cooperated with multi-light sources to recognition of solder joints for PCB TAAI, Artificial Intelligence and Applications (2002) 425-430.
  • [3] Y. Fang-Chung, K. Chung-Hsien, W. Jein-Jong, Y. Ching- Kun, Reconstructing the 3D solder paste surface model using image processing and artificial neural network, Systems, Man and Cybernetics 3 (2004) 3051-3056.
  • [4] L. Shih-Chieh, C. Chih-Hsien, S. Chia-Hsin, A development of visual inspection system for surface mounted devices on printed circuit board, Proceedings of the 33rd Annual Conference of the IEEE Industrial Electronics Society (IECON) Taipei, Taiwan, 2007, 2440-2445.
  • [5] B. C. Jiang, C. C. Wang, Y. N. Hsu, Machine vision and background remover-based approach for PCB solder joints inspection, International Journal of Production Research 45/2 (2007) 451-464.
  • [6] J. H. Kim, H. S. Cho, S. Kim, Pattern classification of solder joint images using a correlation neural network, Engineering Applications of Artificial Intelligence 9/6 (1996) 655-669.
  • [7] S. L. Bartlett, P. J. Besl, C. L. Cole, R. Jain, D. Mukherjee, K. D. Skifstad, Automatic solder joint inspection, IEEE Transactions on Pattern Analysis and Machine Intelligence 10/1 (1988) 31-43.
  • [8] P. J. Besl, E. J. Delp, R. Jain, Automatic visual solder joint inspection, IEEE Jorunal of Robotics and Automation 1/1 (1985) 42-56.
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  • [11] Y. K. Ryu, H. S. Cho, A neural network approach to Extended Gaussian Image based solder joint inspection, Mechatronics 7/2 (1997) 159-184.
  • [12] T. H. Kim, T. H. Cho, Y. S. Moon, S. H. Park, Visual inspection system for the classification of solder joints, Pattern Recognition 324 (1999) 565-575.
  • [13] J. H. Kim, H. S. Cho, Neural network-based inspection of solder joints using a circular illumination, Image and Vision Computing 13/6 (1995) 479-490.
  • [14] K. W. Ko, H. S. Cho, Solder joints inspection using a neural network and fuzzy rule-based classification method, IEEE Transactions on Electronics Packaging Manufacturing 23/2 (2000) 93-103.
  • [15] T. S. Yun, K. J. Sim, H. J. Kim, Support vector machine-based inspection of solder joints using circular illumination, Electronics Letters 36/11 (2000) 949-951.
  • [16] D. W. Capson, S. K. Eng, A tiered-color illumination approach for machine inspection of solder joints, IEEE Transactions on Pattern Analysis and Machine Intelligence 10/3 (1988) 387-393.
  • [17] R. Fisher, S. Perkins, A. Walker, E. Wolfart, Hypermedia image processing reference, Published by J.Wiley &Sons, Ltd. Published by J.Wiley &Sons, Ltd. 1996.
  • [18] R. C. Gonzalez, R. E. Woods, S. L. Eddins, Digital image processing using Matlab, First ed, Pearson Prentic Hall, 2004.
  • [19] W. Chen, M. J. Er, S. Wu, Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain, IEEE Transactions on Systems, Man, and Cybernetics-Part B 36/2 (2006) 458-466.
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  • [21] A. A. Al-Nu'aimi, R. Qahwaji, Digital colored image watermarking using YIQ color format in discrete wavelet transform domain, http://www.stcex.gotevot.edu.sa /NR/rdonlyres/A00DB3B0-7993-40A5-BE34-C3293D76637D /0/305.pdf.
  • [22] T. W. Ridler, S. Calvard, Picture thresholding using an iterative selection method, IEEE Transactions on Systems, Man and Cybernetics 8/8 (1978) 630-632.
  • [23] R. C. Gonzalez, R. E. Woods, Digital image processing, Second ed, Pearson Prentic Hall, 2002.
  • [24] E. Frew, A. Huster, E. LeMaster, Image thresholding for object detection, 1997.
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  • [26] A. Klimpel, A. Lisiecki, J. Szlek, Welding of girders to insert plates of composite steel-concrete structure of tower in Kuwait, Archives of Materials Science and Engineering 28/7 (2007) 433-436.
  • [27] S. Wiewiórowska, Z. Muskalski, M. Suliga, M. Pełka, The numerical analysis of Hi-temp 095 wire drawing process, Journal of Achievements in Materials and Manufacturing Engineering 27/2 (2007) 175-178.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BOS2-0020-0047
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