Solder joint inspection using eigensolder features
Soldering & Surface Mount Technology
ISSN: 0954-0911
Article publication date: 11 July 2018
Issue publication date: 15 August 2018
Abstract
Purpose
The authors propose a solder joint recognition method based on eigenspace technology.
Design/methodology/approach
The original solder joint image is transformed into a small set of feature subspace called “eigensolder”, which is the eigenvector of the training set and can represent a solder joint well. Then, the eigensolder feature is extracted by projecting the new solder joint image into the subspace, and the Euclidean distance measure is used to classify the solder joint.
Findings
The experimental results show that the proposed method is superior to the traditional classification method in solder joint recognition, and it can achieve 96.43 per cent recognition rate using only 15 eigenvalue images. It is suitable for the classification with small samples.
Originality/value
Traditional classification method like neural network and statistical method cost long time. Here, Eigensolder method is used to extract feature. Eigensolder method is more efficient, as it uses the principal component analysis method to reduce the feature dimension of input image and only measure the distance to classify.
Keywords
Acknowledgements
Research is supported by Anhui Provincial Natural Science Foundation (Grant No. 1808085QE162).
Citation
Wu, H. and Xu, X. (2018), "Solder joint inspection using eigensolder features", Soldering & Surface Mount Technology, Vol. 30 No. 4, pp. 227-232. https://doi.org/10.1108/SSMT-12-2017-0042
Publisher
:Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited