An online quality detection method with ensemble learning on imbalance data for wave soldering

Abstract

Online detection of wave soldering is an important method of inspecting defective products in the workshop. Accurate quality detection can reduce production costs and provide support for quality warning in wave soldering process. However, there are still problems of improving the detection accuracy for defect class. Although class imbalance in data can be addressed by data level methods such as over-sampling and under-sampling, these methods destroy the integrity of the original data set and may cause information loss and overfitting problems. In order to solve the above problems, this article focuses on how to design a new loss function that fuses class weights from focal loss (FS) and sample weights form AdaBoost to improve attention to the minority samples without changing data distribution. In this way, a FS-AdaBoost-RegNet model based on transfer learning is constructed to enhance the detection accuracy in industrial environment. Finally, the images of the wave soldering from an electronic assembly workshop are taken to validate the performance of the proposed method. The experiment on 941 testing samples of the imbalance datasets showed that the FS-AdaBoost-RegNet model with new loss function reached the overall accuracy of 98.39%, the overall recall of 96.19%. The results proved that the proposed method promotes the ability to identify defect class compared with other methods