论文标题
Soldernet：使用可解释的人工智能对电子制造中的焊接接头进行可信赖的视觉检查
SolderNet: Towards Trustworthy Visual Inspection of Solder Joints in Electronics Manufacturing Using Explainable Artificial Intelligence
论文作者
论文摘要
在电子制造中，焊接关节缺陷是影响各种印刷电路板组件的常见问题。为了识别和纠正焊接关节缺陷，通常通过训练有素的人类检查员手动检查电路板上的焊接接头，这是一个非常耗时且容易出错的过程。为了提高检查效率和准确性，在这项工作中，我们描述了一种可解释的基于深度学习的视觉质量检查系统，该系统量身定制，用于对电子制造环境中焊接接头的目视检查。该系统的核心是一种可解释的焊接关节缺陷识别系统，称为Soldernet，我们以信任和透明度来设计和实施。尽管在可以开发和部署完整系统之前仍存在一些挑战，但本研究为对电子制造中的焊料关节的可信赖视觉检查提供了重要的进展。
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.
