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Defect Classification of Components for SMT Inspection Machines
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 Title & Authors
Defect Classification of Components for SMT Inspection Machines
Lee, Jae-Seol; Park, Tae-Hyoung;
 
 Abstract
The inspection machine in SMT (Surface Mount Technology) line detects the assembly defects such as missing, misalignment, loosing, or tombstone. We propose a new method to classify the defect types of chip components by processing the image of PCB. Two original images are obtained from horizontal lighting and vertical lighting. The image of the component is divided into two soldering regions and one packaging region. The features are extracted by appling the PCA (Principle Component Analysis) to each region. The MLP (Multilayer Perceptron) and SVM (Support Vector Machine) are then used to classify the defect types by learning. The experimental results are presented to show the usefulness of the proposed method.
 Keywords
SMT (Surface Mount Technology);AOI (Automated Optical Inspection machine);defect classification;PCA;SVM;
 Language
Korean
 Cited by
 References
1.
J. O. Kim and T. H. Park, "Automatic extraction of comonent window for auto-teaching system of PCB assembly inspection machines," Journal of Institute of Control, Robotics, and Systems (in Korean), vol. 16, no. 11, pp. 1089-1095, 2010. crossref(new window)

2.
J. O. Kim and T. H. Park, "Automatic extraction of comonent inspection regions from printed circuit board by image clustering," Transactions of the Korean Institute of Electrical Engineers, vol. 61, no. 3, pp. 472-478, 2012. crossref(new window)

3.
I. A. EI. Rube, M. Ahmed, and M. Kamel, "Coarse-tofind multiscale affine invariant shape matching and classfication," Proc. of the 17th Int. Conf. on Pattern Recognition, vol. 2, pp. 163-166, 2004.

4.
K. Fredrilsson and E. Ukkonen, "Faster template matching without FFT," 2001 Int. Conf. on Image Processing, vol. 1, pp. 678-681, 2001.

5.
S. D. Wei, S. W. Liu, and S. H. Lai, "Fast template matching by applying Winner-Update on Walsh- Hadamard domain," IEEE Int. Conf. on Acoustics Speech and Signal Processing, vol. 1, pp. 1029-1032, 2007.

6.
H. J. Cho and T. H. Park, "Wavelet transform based image template matching for automatic component inspection," International Journal of Advanced Manufacturing Technology, vol. 50, no. 17, pp. 1033-1039, 2010. crossref(new window)

7.
H. Xie, X. Zhang, Y. Kuang, and G. Ouyang, "Solder joint inspection method for chip component using improved adaboost and decision tree," IEEE Transactions on Components, Packaging and Manufacturing Technology, vol, 1, no. 12, pp. 2018-2027, 2011. crossref(new window)

8.
H. Wu, G. Feng, H. Li, and X. Zeng, "Automated visual insoection of surface mounted chip components," Proc. of the 2010 IEEE Int. Conf. on Mechatronics and Automation, pp. 1789-1794, 2010.

9.
K. W. Ko and H. S. Cho, "Solder joint inspection using a neural network and fuzzy rule-based classification method," IEEE Transactions on Electronics Packaging Manufacturing, vol, 23, no. 2, pp. 93-103, 2000. crossref(new window)

10.
M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of Cognitive Neuroscince, vol, 3, no. 1, pp. 71-86, 1991. crossref(new window)

11.
S. L. Phung and A. B. Douglas, "Skin segmentation using color pixel classification: analysis and comparison," IEEE Transactions on Pattern Analsys and Machine Intelligence, vol. 27, no. 1, pp. 148-157, 2005. crossref(new window)

12.
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, 2012.

13.
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, vol. 20, no. 7, pp. 741-749, 2014. crossref(new window)

14.
S. G. Youn, Y. A. Lee, and T. H. Park, "Automatic classification of SMD packages using neural network," Journal of Institute of Control, Robotics, and Systems, vol. 21, no. 3, pp. 276-282, 2015. crossref(new window)