Solder joint inspection using eigensolder features. Issue 4 (3rd September 2018)

• Record Type: Journal Article
• Authors: Wu, Hao; Xu, Xiangrong
• Is Part Of: Soldering & surface mount technology. Volume 30:Issue 4(2018)
• Page Start: 227
• Page End: 232
• Publication Date: 2018-09-03
• Subjects: Principal component analysis -- Eigensolder feature -- Eigenspace technology -- Solder joint inspection; Brazing -- Periodicals; Solder and soldering -- Periodicals; 671.5605
• DOI: 10.1108/SSMT-12-2017-0042
• Languages: English
• ISSNs: 0954-0911

Abstract

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.