Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Automatic Classification of SMD Packages using Neural Network

신경회로망을 이용한 SMD 패키지의 자동 분류

  • Youn, SeungGeun (Dept. of Control and Robot Engineering, Chungbuk National University) ;
  • Lee, Youn Ae (Dept. of Control and Robot Engineering, Chungbuk National University) ;
  • Park, Tae Hyung (Dept. of Control and Robot Engineering, Chungbuk National University)
  • 연승근 (충북대학교 제어로봇공학과) ;
  • 이윤애 (충북대학교 제어로봇공학과) ;
  • 박태형 (충북대학교 제어로봇공학과)
  • Received : 2014.07.28
  • Accepted : 2015.01.05
  • Published : 2015.03.01
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Abstract

This paper proposes a SMD (surface mounting device) classification method for the PCB assembly inspection machines. The package types of SMD components should be classified to create the job program of the inspection machine. In order to reduce the creation time of job program, we developed the automatic classification algorithm for the SMD packages. We identified the chip-type packages by color and edge distribution of the images. The input images are transformed into the HSI color model, and the binarized histroms are extracted for H and S spaces. Also the edges are extracted from the binarized image, and quantized histograms are obtained for horizontal and vertical direction. The neural network is then applied to classify the package types from the histogram inputs. The experimental results are presented to verify the usefulness of the proposed method.

Keywords

References

  1. J. O. Kim and T. H Park, "Automatic extraction of component inspection regions from printed circuit board by image clustering," The Transactions of The Korean Institute of Electrical Engineers, vol. 61, no. 3, pp. 472-478, Mar. 2012. https://doi.org/10.5370/KIEE.2012.61.3.472
  2. J. O. Kim and T. H. Park, "Automatic extraction of component window for auto-teaching of PCB assembly inspection machines," International Journal of Control Automation and System, vol. 16, no. 11, pp. 1089-1095, Oct. 2010.
  3. B. M. Kim and H. S. Cho, "Neural-network based parts classification for SMT processes," Proc. Society of Photographic Instrumentation Engineers, vol. 4564, pp. 268-279, 2001.
  4. A. J. Crispin and V. Rankov, "Automated inspection of PCB components using a genetic algorithm template-matching approach," International Journal of Advanced Manufacturing Technology, vol. 35, no. 3-4, pp. 293-300, Dec. 2007. https://doi.org/10.1007/s00170-006-0730-0
  5. A. K. Singh, S. Tiwari, and V. P. Shukla, "Wavelet based multi class image classification using neural network," International Journal of Computer Applications, vol. 37, no. 4, Jan. 2012.
  6. I. Khosa and E. Pasero, "Artificial neural network classifier for quality inspection of nuts," Robotics and Emerging Allied Technologies in Engineering, pp. 103-108, Apr. 2014.
  7. J. Shin, J. Huh, T. Choe, C. Kim, and S. Joo, "Adaptive control for lateral motion of an unmanned ground vehicle using neural networks," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 19, no. 11, pp. 998-1003, Nov. 2013. https://doi.org/10.5302/J.ICROS.2013.13.8014
  8. Z. Q. Li, W. S. Baiqing, and G. Hongche, "Obstacles pattern recognition based on BP neural network," International Conference on Mechatronics and Automation, pp. 2574-2578, Aug. 2012.
  9. D. C. Liu, "A study on generalized BP algorithm of neural network and its fault tolerance and generalization capability," Control and Decision, Feb. 1998.
  10. M. Fang, G. X. Yue, and Q. C. Yu, "The study on an application of Otsu method in Canny operator," International Symposium on Information Processing, pp. 109-112, Aug. 2009.
  11. S. B. Lee, T. H. Park, and K. S. Han, "Development of machine vision system based on PLC," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 20, no. 7, pp. 741-749, Jul. 2014. https://doi.org/10.5302/J.ICROS.2014.13.1969

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