Intelligent detection of solder joints on printed circuit boards

XIE Hong-wei; ZHANG Xian-min; KUANG Yong-cong; OUYANG Gao-fei

doi:10.3788/OPE.20111909.2154

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Intelligent detection of solder joints on printed circuit boards

Article | 更新时间：2020-08-12

- Intelligent detection of solder joints on printed circuit boards
- Optics and Precision Engineering Vol. 19, Issue 9, Pages: 2154-2162(2011)
- 作者机构：
  
  华南理工大学机械与汽车工程学院,广东广州,510640
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20111909.2154
  CLC： TP274.5;TP391.4
- Received：10 January 2011，
  
  Revised：27 February 2011，
  
  Published Online：26 September 2011，
  
  Published：26 September 2011
- 稿件说明：
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谢宏威, 张宪民, 邝泳聪, 欧阳高飞. 印刷电路板焊点的智能检测[J]. 光学精密工程, 2011,19(9): 2154-2162

XIE Hong-wei, ZHANG Xian-min, KUANG Yong-cong, OUYANG Gao-fei. Intelligent detection of solder joints on printed circuit boards[J]. Editorial Office of Optics and Precision Engineering, 2011,19(9): 2154-2162
谢宏威, 张宪民, 邝泳聪, 欧阳高飞. 印刷电路板焊点的智能检测[J]. 光学精密工程, 2011,19(9): 2154-2162 DOI： 10.3788/OPE.20111909.2154.

XIE Hong-wei, ZHANG Xian-min, KUANG Yong-cong, OUYANG Gao-fei. Intelligent detection of solder joints on printed circuit boards[J]. Editorial Office of Optics and Precision Engineering, 2011,19(9): 2154-2162 DOI： 10.3788/OPE.20111909.2154.

摘要

为了提高在线自动光学检测系统(AOI)的自动化程度

提出了一种基于增量聚类的智能焊点检测方法。首先

设计了在线智能AOI的系统框架。然后

根据焊点外观进行归纳分类

将关键子区域的面积特征应用于焊盘特征的量化与提取

将每类样本聚类为若干子类从而实现对多批次焊点的检测。最后

提出一种增量聚类算法

在线检测过程中系统可根据人工维修站反馈信息自动学习新的样本并调整相关检测参数。为了提高增量学习的效率

每次增量学习之前选择少量代表样本用于增量学习。采用提出的AOI系统检测焊点

准确率可达96.5%

平均每个焊点耗费9.3 ms。结果表明

本文提出的检测方法不仅可以对多批次的焊点缺陷进行有效识别

且对生产中工艺条件的变化有自适应能力

智能化程度较高

具有较强的实用价值。

Abstract

To automate present Automatic Optical Inspection (AOI) systems

an intelligent method based on incremental clustering for solder joint inspection is proposed in this paper. Firstly

to meet the demands of practical production

the framework for an intelligent AOI system is designed. Then

all the defects of solder joints are classified into several different types according to their appearances

and the color features in critical regions are extracted. The samples in each class are clustered into several subclasses so that the system is able to inspect solders from different batches. Finally

a new incremental clustering algorithm is proposed. The AOI system can automatically adjust inspection parameters according to the feedback from the repair station. To improve training efficiency

only a few samples are selected. The method proposed is used in an AOI to inspect solder joints

and the inspecting accuracy can reach 96.5% while each solder inspection takes 9.3 ms. The experimental result demonstrates that the proposed method can detect accurately a solder defect from different patches

and can be modified for different manufacturing processes. The intelligence level of the system using the proposed method is high

and it can be used in practical application.

关键词

Keywords

references

KUANG J H, HSU CH M, CHIU W CH, et al.. The variation of shear strength of the lead free Sn/3.0Ag/0.5Cu solder balls. IEEE Proceedings of the ninth Electronics Packaging Technology Conference, Singapore, 2007:910-913.[2] KOMKRIT C, SOTOSHI Y, MASAYOSHI I. Bare PCB inspection system with Sv-GMR sensor eddy-current testing probe[J]. IEEE Sensors Journal, 2007,7(5):890-896.[3] 吴福培,邝泳聪,张宪民,等. 基于模式匹配及其参数自适应的PCB焊点检测[J]. 光学精密工程,2009,17(10):2586-2593. WU F P, KUANG Y C, ZHANG X M,et al.. Pattern matching and based PCB solder parameter adaptive joint inspection[J]. Opt. Precision Eng., 2009,17(10):2586-2593. (in Chinese)[4] CHIU S N, PERN M H. Reflection-area-based feature descriptor for solder joint inspection [J]. Machine Vision and Applications. 2007,8:95-106.[5] LOH H H, LU M S. Printed circuit board inspection using image analysis[J]. IEEE Transactions on Industry Applications, 1999, 5(2):426-432.[6] XIE H W, KUANG Y C, ZHANG X M. A High Speed AOI Algorithm for Chip Component Based on Image Difference. IEEE International Conference of Information & Automation, Zhuhai, China, 2009(4):969-974.[7] IBRAHIM Z, AL-ATTAS S A R,ASPAR Z. Analysis of the wavelet-based image difference algorithm for PCB inspection. Proceedings of the 41st SICE Annual Conference, Osaka, 2002:1525 - 1530.[8] KUK W K, HYUNG S C. Solder Joints Inspection Using a Neural Network and Fuzzy Rule-Based Classification Method [J]. IEEE Transactions on Electronics Packaging Manufacturing, 2000,39(2):93-103.[9] 卢盛林,张宪民,邝泳聪. 基于神经网络的PCB焊点检测方法[J]. 华南理工大学学报(自然科学版), 2008,36(5):135-139. LU SH L, ZHANG X M, KUANG Y C. Neural Network Based Inspecting Method of PCB Solder Joint [J]. Journal of South China University of Technology (Natural Science Edition), 2008, 36(5):135-139.(in Chinese)[10] 黄杰贤,李迪,叶峰,等. 挠性印制电路板焊盘表面缺陷的检测[J]. 光学精密工程,2010,18(11):2443-2453. HUNAG J X, LI D, YE F, et al.. Detection of surface defection of solder on flexible printed circuit[J]. Opt. Precision Eng., 2010,18(11):2443-2453.(in Chinese)[11] 卢盛林,张宪民. 无铅焊点检测光源的分析与优化设计[J]. 光学精密工程,2008,16(8):1377-1383. LU SH L, ZHANG X M. Analysis and optimal design of illuminator for leadfess tin solder joint inspection [J]. Opt. Precision Eng., 2008,16(8):1377-1383. (in Chinese)[12] DUDANI, SAHIBSINGH A. The distance-weighted k-nearest-neighbor rule [J]. IEEE Transactions on Systems, Man and Cybernetics, 1976(6):325-327.[13] WANG D F, YEUNG D S, and TSANG E C. Weighted mahalanobis distance kernels for support vector machines[J]. IEEE Transactions on Neural Networks.2007 18(5):1453-1462.[14] TOU J T, GONZALEZ R C. Pattern Recognition Principle[M]. Boston:Addison Wesley, 1974.

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Related Institution

Department of Mechatronic Engineering, Shantou University

School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China

Institute of Mechanical and Automotive Engineering, Xiamen University of Technology

College of Electronic Information and Automation, Civil Aviation University of China

Key Laboratory of Airborne Optical Imaging and Measurement,Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences

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