An Efficient Method for Solder Joint Inspection Based on Statistical Learning

Yong Cong Kuang; Gao Fei Ouyang; Hong Wei Xie; Xian Min Zhang

doi:10.4028/www.scientific.net/AMM.121-126.4931

Paper Titles

Variable Step-Size Wavelet Vector Machine Blind Equalization Algorithm
p.4892

Verification of Non-Uniform Heat-Conducting Interphase by Using Finite Element Method
p.4897

Wavelet Analysis of Misalignment Fault of Rotor-Bearing Systems
p.4902

A Quality Model of Lightweight Component for Embedded System
p.4907

Based on Embedded System's Vehicles Immunity PID Controller Design
p.4912

Influence of Low Frequency Electromagnetic Field on the As-Cast Structure of Cu-10%Sn-2%Zn Alloys
p.4917

A Calculation Method of Velocity and Acceleration of Wheel Center Based on Rigid Multi-Body System Dynamics
p.4922

Research on Situation Evaluation Based on Artificial Immune for Network Security
p.4926

An Efficient Method for Solder Joint Inspection Based on Statistical Learning
p.4931

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 121-126An Efficient Method for Solder Joint Inspection...

An Efficient Method for Solder Joint Inspection Based on Statistical Learning

Abstract:

To improve the performance of current solder joint inspection method, an efficient method based on statistical learning is proposed in this paper. In the method, the solder was divided into several sub-regions to determine the defect type. To resolve imbalance problem, an improved over-sampling algorithm was proposed in which the synthetics samples are generated between the boundary samples and their neighbors. AdaBoost was used for feature selection and classification for every sub-region. Experiments results showed that the defects of solder joints can be identified properly using the proposed algorithm.

You might also be interested in these eBooks

Frontiers of Manufacturing and Design Science II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 121-126)

Pages:

4931-4935

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.121-126.4931

Citation:

Cite this paper

Online since:

October 2011

Authors:

Yong Cong Kuang, Gao Fei Ouyang, Hong Wei Xie, Xian Min Zhang

Keywords:

Automatic Optical Inspection, Feature Selection, Over-Sampling, Solder Joint Inspection

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Loh H H，Lu M S． IEEE Transaction on Industry Applications，Vol. 35(2)(1999), pp.426-432.

Google Scholar

[2] Ji X X, Ji X L. Manufacturing information Engineering of China. 35(9)(2006), pp.61-64.

Google Scholar

[3] Koh K.C. Choi H.J., Kim J.S., Cho H.S. Optomechatronic Systems II , Boston, MA, USA 2001: pp.260-267.

Google Scholar

[4] Michael T.G., Joseph S.B.M., Mark W.O. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4)(1999), pp.371-379.

Google Scholar

[5] Nitesh V. Chawla, Nathalie Japkowicz and Aleksander Kolcz. SIGKDD Explorations 6 (1) (2004) 1-6.

Google Scholar

[6] Chawla, N.V., Bowyer K.W., Hall, L. O, Kegelmeyer W.P. Journal of Artificial Intelligence Research 16 (2002), pp.321-357.

Google Scholar

[7] Loh H H, Lu M S. IEEE Transaction on Industry Applications, 35(2)(1999): pp.426-432.

Google Scholar

[8] Wu F P, Zhang X M, Kuang Y C, He, Z Z. Proceedings of the World Congress on Intelligent Control and Automation, Chongqing, China, 2008: pp.240-247.

Google Scholar