Machine Vision-Aided Quality Decision System for Solder Joint Defect Evaluation

Chien Chih Wang

doi:10.4028/www.scientific.net/AMM.268-270.1393

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 268-270Machine Vision-Aided Quality Decision System for...

Machine Vision-Aided Quality Decision System for Solder Joint Defect Evaluation

Abstract:

To improve the printed circuit board (PCB) manufacturing process, it is important to have an automatic inspection system that classifies information regarding defects in solder joints. This paper proposes a quality decision system for solder joint defect classification on a PCB. An experiment was conducted to demonstrate the application of the system. The results showed that the inspection accuracy reached 94%, which is superior to the results achieved by other methods. The results of this study provide an effective solution for the inspection of the solder joint quality.

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Periodical:

Applied Mechanics and Materials (Volumes 268-270)

Pages:

1393-1397

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.268-270.1393

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Online since:

December 2012

Authors:

Chien Chih Wang

Keywords:

Automatic Visual Inspection, Defect Classification, Multivariate Analysis

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References

[1] C. C. Wang, B .C. Jiang, PCB Solder Joints Defects Detection and Classification Using Machine Vision, International Journal of Industrial Engineering. Vol. 8, No.4, 2001, pp.359-368.

Google Scholar

[2] B. C. Jiang, C. C Wang, Y. N. Hau, Machine Vision and Background Remover-based Approach for PCB Solder Joints Inspection, International Journal of Production Research, Vol. 45, No.2 , 2007, pp.451-464.

DOI: 10.1080/00207540600607184

Google Scholar

[3] B. C. Jiang, C. C. Wang, H. J. Chen, C. C. Chu, Automatic Bubble Defect Inspection for Microwave Communication Substrates Using Multi-threshold Technique Based Co-Occurrence Matrix, International Journal of Production Research, Vol. 48, No. 8, 2010, pp.2361-2371

DOI: 10.1080/00207540802680534

Google Scholar

[4] B. D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press,1996.

Google Scholar

[5] O. U. Oyeleye, E. A. Lehtihet, Automatic Visual Inspection of Surface Mount Solder Joint Defects, International Journal of Production Research, Vol. 37, 1999, p.1271–1242.

DOI: 10.1080/002075499191229

Google Scholar

[6] K. W. Ko and H. S. Cho, Solder Joints Inspection Using a Neural Network and Fuzzy Rule-Based Classification Method, IEEE Transactions on Electronics Packing Manufacturing, Vol. 23, No, 2, (2000), p.93–103.

DOI: 10.1109/6104.846932

Google Scholar

[7] S. P. Smith, and A. K. Jain, A test to determine the multivariate normality of a dataset. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.10, 1988, p.757–761.

DOI: 10.1109/34.6789

Google Scholar

[8] K. V. Mardai, Measures of multivariate skewness and kurtosis with applications, Biometrika, Vol.36, 1970, p.519–530.

DOI: 10.1093/biomet/57.3.519

Google Scholar