Solder joint defect classification based on ensemble learning

Abstract

Purpose

This paper aims to inspect the defects of solder joints of printed circuit board in real-time production line, simple computing and high accuracy are primary consideration factors for feature extraction and classification algorithm.

Design/methodology/approach

In this study, the author presents an ensemble method for the classification of solder joint defects. The new method is based on extracting the color and geometry features after solder image acquisition and using decision trees to guarantee the algorithm’s running executive efficiency. To improve algorithm accuracy, the author proposes an ensemble method of random forest which combined several trees for the classification of solder joints.

Findings

The proposed method has been tested using 280 samples of solder joints, including good and various defect types, for experiments. The results show that the proposed method has a high accuracy.

Originality/value

The author extracted the color and geometry features and used decision trees to guarantee the algorithm's running executive efficiency. To improve the algorithm accuracy, the author proposes using an ensemble method of random forest which combined several trees for the classification of solder joints. The results show that the proposed method has a high accuracy.

Citation

Wu, H. (2017), "Solder joint defect classification based on ensemble learning", Soldering & Surface Mount Technology, Vol. 29 No. 3, pp. 164-170. https://doi.org/10.1108/SSMT-08-2016-0016