Paper Title:
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.

  Info
Periodical
Chapter
Chapter 8: System Modeling and Simulation
Edited by
Dongye Sun, Wen-Pei Sung and Ran Chen
Pages
4931-4935
DOI
10.4028/www.scientific.net/AMM.121-126.4931
Citation
Y. C. Kuang, G. F. Ouyang, H. W. Xie, X. M. Zhang, "An Efficient Method for Solder Joint Inspection Based on Statistical Learning", Applied Mechanics and Materials, Vols. 121-126, pp. 4931-4935, 2012
Online since
October 2011
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Price
$32.00
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