表面贴装技术
印刷电路板
自动光学检测
计算机科学
水准点(测量)
接头(建筑物)
深度学习
焊接
光学(聚焦)
任务(项目管理)
贴片设备
人工智能
工程制图
工程类
材料科学
复合材料
物理
建筑工程
光学
操作系统
系统工程
地理
机器人
大地测量学
作者
Furkan Ülger,Seniha Esen Yüksel,Atila Yılmaz,Dinçer Gökcen
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:72: 1-21
被引量:9
标识
DOI:10.1109/tim.2023.3277935
摘要
Surface Mount Technology (SMT) is a procedure for mounting electronic components to the surface of the printed circuit boards (PCB). Although the SMT procedure is more reliable than the conventional through-hole mounting, many errors may occur in SMT lines. In this paper, we surveyed methods presented for the optical inspection of solder joints on PCBs. The methods are grouped by problem-solving approach: reference-based, machine learning/computer vision, deep learning, and 3D reconstruction. We compare and discuss these approaches according to their advantages and disadvantages, with a focus on the more recent deep learning and 3D reconstruction techniques, and list the public datasets for solder joints inspection. Since defective samples rarely occur in the SMT manufacturing lines, public datasets have few defective samples that are essential for the inspection task. To fill this gap, we publish a public dataset with both normal and different types of SMT errors. Our dataset can be used as a benchmark to compare different algorithms. The dataset is available at https://github.com/furkanulger/solder-joint-dataset.
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