In electronics manufacturing, solder joint defects are a common problem affecting a variety of printed circuit board components. To identify and correct solder joint defects, the solder joints on a circuit board are typically inspected manually by trained human inspectors, which is a very time-consuming and error-prone process. To improve both inspection efficiency and accuracy, in this work we describe an explainable deep learning-based visual quality inspection system tailored for visual inspection of solder joints in electronics manufacturing environments. At the core of this system is an explainable solder joint defect identification system called SolderNet which we design and implement with trust and transparency in mind. While several challenges remain before the full system can be developed and deployed, this study presents important progress towards trustworthy visual inspection of solder joints in electronics manufacturing.
翻译：在电子制造中,焊接联合缺陷是影响各种印刷电路板部件的一个常见问题。为了查明和纠正焊接联合缺陷,电路板上的焊接装置通常由训练有素的人类检查员手工检查,这是一个非常耗时和容易出错的过程。为了提高检查效率和准确性,我们在这项工作中描述了为电子制造环境中的焊接装置进行直观检查而专门设计的、可以解释的深层次的基于学习的视觉质量检查系统。这个系统的核心是一个可以解释的售接联合缺陷识别系统,称为SolderNet,我们以信任和透明的方式设计和实施这个系统。尽管在开发和部署整个系统之前还存在若干挑战,但这一研究提出了在电子制造中对焊接装置进行可信赖的直观检查方面取得的重要进展。
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