Abstract
An automated visual inspection is needed to inspect missing components on bare Printed Circuit Board (PCB). Missing footprints on the PCB will result in lack of electronic components. Therefore, any missing footprint components on the bare PCB lead to reduced performance of electronic boards. In this study, a neural network-based automatic visual inspection system for diagnosis of missing footprints on bare PCB is presented. Five types of footprint components have been classified. The images of the board are acquired and a difference operation is applied on reference image and acquired image to determine the absence of footprints on the PCB. From each footprint component, three types of geometric features are extracted. The neural network training phase is evaluated. Finally, the experimental results are shown to represent the accuracy rate of the algorithm.
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References
Hashim, H.S., Abdullah, S.N.H.S., Prabuwono, A.S.: Automated visual inspection for metal parts based on morphology and fuzzy rules. In: Proc. ICCAIE, pp. 527–531 (2010)
Akbar, H., Prabuwono, A.S.: Webcam based system for press part industrial inspection. IJCSNS 8(10), 170–177 (2008)
Aghamohammadi, A., Prabuwono, A.S., Sahran, S., Mogharrebi, M.: Solar cell panel crack detection using particle swarm optimization algorithm. In: Proc. ICPAIR, pp. 160–164 (2011)
Bin Mansoor, A., Khan, Z., Khan, A.: An application of fuzzy morphology for enhancement of aerial images. In: Proc. 2nd ICAST, pp. 143–148 (2008)
Importance of PCB and footprint, http://www.prototypepcb.com
Acciani, G., Brunetti, G., Chiarantoni, E., Fornarelli, G.: An automatic method to detect missing components in manufactured products. In: Proc. IJCNN, vol. 4, pp. 2324–2329 (2005)
Acciani, G., Brunetti, G., Fornarelli, G.: Application of neural networks in optical inspection and classification of solder joints in surface mount technology. IEEE Transactions on Industrial Informatics 2(3), 200–209 (2006)
Johan, T.M., Prabuwono, A.S.: Recognition of bolt and nut using artificial neural network. In: Proc. ICPAIR, pp. 165–170 (2011)
Ko, K.W., Roh, Y.J., Cho, H.S., Kimn, H.C.: A neural network approach to the inspection of ball grid array solder joints on printed circuit boards. In: Proc. IJCNN, vol. 5, pp. 233–238 (2000)
Lin, S.C., Su, C.H.: A visual inspection system for surface mounted devices on printed circuit board. In: IEEE Conference on Cybernetics and Intelligent Systems, pp. 1–4 (2006)
Matsushima, M., Kawai, N., Fujie, H., Yasuda, K., Fujimoto, K.: Visual inspection of soldering joints by neural network with multi-angle view and principal component analysis. Service Robotics and Mechatronics 16, 329–334 (2010)
Wu, H., Zhang, X., Kuang, Y., Lu, S.: A real-time machine vision system for solder paste inspection. In: Proc. AIM, pp. 205–210 (2008)
Ibrahim, Z., Al-Attas, S.A.R., Aspar, Z.: Coarse resolution defect localization algorithm for an automated visual PCB inspection. Journal Teknologi D (37D), 79–92 (2002)
Khalid, N.K., Ibrahim, Z., Zainal Abidin, M.S.: An algorithm to group defects on printed circuit board for automated visual inspection. IJSSST 9(2), 1–10 (2008)
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© 2011 Springer-Verlag Berlin Heidelberg
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Mogharrebi, M., Prabuwono, A.S., Sahran, S., Aghamohammadi, A. (2011). Missing Component Detection on PCB Using Neural Networks. In: Zheng, D. (eds) Advances in Electrical Engineering and Electrical Machines. Lecture Notes in Electrical Engineering, vol 134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25905-0_51
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DOI: https://doi.org/10.1007/978-3-642-25905-0_51
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