A multimodal fusion method for soldering quality online inspection.
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| Title: | A multimodal fusion method for soldering quality online inspection. |
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| Authors: | Xie, Jian1 (AUTHOR), Guo, Yu1 (AUTHOR) guoyu@nuaa.edu.cn, Liu, Daoyuan1 (AUTHOR), Huang, Shaohua1 (AUTHOR) shaohuah@nuaa.edu.cn, Zheng, Kaiwen1 (AUTHOR), Tao, Yaning1 (AUTHOR) |
| Source: | Journal of Intelligent Manufacturing. Jun2025, Vol. 36 Issue 5, p3271-3284. 14p. |
| Subjects: | Surface mount technology, Solder joints, Industrial electronics, Electronics manufacturing, Electronic industries, Multimodal user interfaces |
| Abstract: | Effective and efficient online quality inspection of poor soldering in Surface Mount Technology (SMT) solder joints, whose defect characteristics are not visually discernible, remains a formidable obstacle in the electronics manufacturing industry, despite the availability of various inspection methods. To overcome this challenge, a novel multimodal fusion method for soldering quality inspection is proposed. First, the method innovatively introduces information from different modalities as input to the inspection model, with the aim to provide more comprehensive information for the decision making of the inspection model. Then, a combination of multimodal gated attention and tensor fusion is used to fuse the features extracted from each modality to form a comprehensive multimodal representation. Finally, this multimodal representation is used to conduct soldering quality inspection. The experimental results demonstrate that the proposed method improves the detection rate of poor soldering significantly, from 93.6 to 99.4%, with a precision level of nearly 100%. The detection rate for visually unapparent soldering defects increases from 49.3 to 95.4%. This meets both manufacturing and customer requirements and to some extent addressing the industry challenge of online SMT soldering quality inspection. This remarkable performance surpasses that of the six mainstream ResNet, ResNext, RegNet, ShuffleNet, EfficientNet and NoisyNet inspection models currently available. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Effective and efficient online quality inspection of poor soldering in Surface Mount Technology (SMT) solder joints, whose defect characteristics are not visually discernible, remains a formidable obstacle in the electronics manufacturing industry, despite the availability of various inspection methods. To overcome this challenge, a novel multimodal fusion method for soldering quality inspection is proposed. First, the method innovatively introduces information from different modalities as input to the inspection model, with the aim to provide more comprehensive information for the decision making of the inspection model. Then, a combination of multimodal gated attention and tensor fusion is used to fuse the features extracted from each modality to form a comprehensive multimodal representation. Finally, this multimodal representation is used to conduct soldering quality inspection. The experimental results demonstrate that the proposed method improves the detection rate of poor soldering significantly, from 93.6 to 99.4%, with a precision level of nearly 100%. The detection rate for visually unapparent soldering defects increases from 49.3 to 95.4%. This meets both manufacturing and customer requirements and to some extent addressing the industry challenge of online SMT soldering quality inspection. This remarkable performance surpasses that of the six mainstream ResNet, ResNext, RegNet, ShuffleNet, EfficientNet and NoisyNet inspection models currently available. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 09565515 |
| DOI: | 10.1007/s10845-024-02413-3 |
