A reliable solder joint inspection method based on a light-weight point cloud network and modulated loss
Neurocomputing, ISSN: 0925-2312, Vol: 488, Page: 315-327
2022
- 10Citations
- 12Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
High-speed and high-reliability automated quality inspection is widely demanded in the electronics industry. Recently, deep learning theory combined with many non-destructive technologies shows superior performance for inspecting failed soldering. In this paper, a reliable point cloud learning based method is implemented for high-speed solder joint shape defect detection. First, a light-weight neural network named Solder PointNet (SPNet) is proposed. With local group attention mechanisms, SPNet avoids adverse effects of outliers in the scanning point cloud and finds favorable critical point feature adaptively. Then, a modulated loss is designed to ensure reliable low false detection rate. By adjusting the weights of cross-entropy loss, the predictive distribution of defective samples is guided to a smaller range, thereby setting an appropriate threshold to efficiently separate the predictive distribution. Furthermore, the proposed method is further trained and evaluated on the self built solder joint dataset DHU-PAD1000 of point cloud data. Comparison experiments are carried out on the built dataset, and the results show that SPNet achieves higher accuracy with fewer parameters, considerable speed advantage, and more reliable automated detection.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0925231222002466; http://dx.doi.org/10.1016/j.neucom.2022.02.077; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85126304284&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0925231222002466; https://dx.doi.org/10.1016/j.neucom.2022.02.077
Elsevier BV
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