Design of vision detection algorithm and system for BGA welding balls

Zhi-wei LUO; Yu-long YANG; Zhi-hong LI

doi:10.3788/OPE.20182609.2190

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Design of vision detection algorithm and system for BGA welding balls

更新时间：2020-08-13

- Design of vision detection algorithm and system for BGA welding balls
- Optics and Precision Engineering Vol. 26, Issue 9, Pages: 2190-2197(2018)
- 作者机构：
  
  厦门理工学院机械与汽车工程学院, 福建厦门 361024
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182609.2190
  CLC： TP23;TP278
- Received：15 May 2018，
  
  Accepted：21 June 2018，
  
  Published：25 September 2018
- 稿件说明：
移动端阅览
Zhi-wei LUO, Yu-long YANG, Zhi-hong LI. Design of vision detection algorithm and system for BGA welding balls[J]. Optics and precision engineering, 2018, 26(9): 2190-2197.
DOI：

Zhi-wei LUO, Yu-long YANG, Zhi-hong LI. Design of vision detection algorithm and system for BGA welding balls[J]. Optics and precision engineering, 2018, 26(9): 2190-2197. DOI： 10.3788/OPE.20182609.2190.

摘要

为了实现对BGA焊球的自动检测，建立了自动视觉检测系统。对系统所采用的焊球特征进行提取及缺陷识别，基于高斯混合模型的分类器对检测算法进行研究。根据焊球的形状和尺寸特征设计了焊球缺陷识别和分类算法，并以锡多、锡少和毛刺缺陷为例，分析典型缺陷的识别算法。以焊球形状的圆度和特征区域的面积等特征参数为评价标准，构建二维特征空间。在二维特征空间线性组合的基础上，构建基于高斯混合模型的分类器。构建了训练样本集，并对该分类器进行训练，根据训练结果并结合应用实际修正了模型，并采用测试集对该分类器进行测试验证。实验结果表明，焊球缺陷检测算法的准确度为97.06%，漏判率为0%，检测可靠度为100%。该视觉检测系统满足了工程运用中对识别准确度、稳定性、可靠性等方面的要求。

Abstract

A vision inspection system was proposed for realizing automated detection of Ball Grating Array (BGA) solder balls. An algorithm for solder ball feature extraction

defect recognition

and classification based on the Gaussian mixture model was developed. The recognition and classification algorithm analyzed weld ball defects according to the shape and size characteristics of the welding ball

and the identification algorithm analyzed typical defects by considering excessive solder

solder deficiency

and burr defects as example cases. The two-dimensional feature space was constructed by considering characteristic parameters such as the standard of solder ball's shape and feature area as evaluation criteria. Based on the linear combination of two-dimensional feature space

a classifier incorporating the Gaussian mixture model was designed. A sample dataset was employed for training the classifier

and the proposed model was thereby modified according to the obtained training results and production practices. The proposed classifier was evaluated by constructing a test dataset. The obtained experimental results show that the accuracy of the solder ball defect detection algorithm is 97.06%

the leak detection rate is zero

and the detection reliability is 100%. Hence

the proposed model can meet the requirements of recognition accuracy

stability

and reliability needed for realizing an automated visual inspection system.

关键词

Keywords

references

伍美珍, 武信, 朱华明.离心雾化法制备焊锡粉工艺参数的优化[J].粉末冶金工业, 2013, 23(1):6-9.

WU M ZH, WU X, ZHU H M. Optimization of technogical parameters on solder powder preparation by centrifugal atomization[J]. Powder Metallurgy Industry, 2013, 23(1):6-9. (in Chinese)

倪广春, 陈旭, 周健.低银焊料超声雾化制粉及焊膏性能研究[J].电子工艺技术, 2013(1):18-21.

NI G CH, CHEN X, ZHOU J. Ultrasonic atomization of low Ag lead-free solder and properties of solder paste[J]. Electronics Process Technology, 2013(1):18-21. (in Chinese)

刘海霞, 雷永平, 夏志东, 等.切丝重熔法制备的BGA焊球及其表面形貌[J].电子元件与材料, 2005, 24(5):21-23.

LIU H X, LEI Y P, XIA ZH D, et al.. Fabrication of solder balls by fine-wire cutting-remelting and its surface appearance[J]. Electronic Components & Materials, 2005, 24(5):21-23. (in Chinese)

李涛, 闫焉服, 高世军.激振喷射工艺参数对BGA焊球直径的影响[J].焊接技术, 2016, 45(5):38-43.

LI T, YAN Y F, GAO S J. Influence of technological parameters of excitation injection on diameter of BGA solder balls[J]. Welding Technology, 2016, 45(5):38-43. (in Chinese)

胥路平, 李军. BGA焊点检测技术研究[J].计量与测试技术, 2013, 40(5):24-25.

XU L P, LI J. Research on detection technology of BGA solder joint[J]. Metrology & Measurement Technique, 2013, 40(5):24-25. (in Chinese)

刘伟, 韩震宇.基于2D投影的BGA焊点X射线检测缺陷图像处理方法[J].无损检测, 2009, 31(6):456-459.

LIU W, HAN ZH Y.BGA solder joint defects image processing method based on 2D imaging by radiographic real time testing[J]. Nondestructive Testing, 2009, 31(6):456-459. (in Chinese)

李乐. 基于精密微焦点X射线的BGA焊点缺陷检测关键技术研究[D]. 广州: 华南理工大学, 2014.

LI L. Key Technologies Research on BGA Solder Joint'S Defect Detection Based on Precision Micro-Focus X-ray [D]. GuangZhou: South China University of Technology, 2014. (in Chinese)

何小嵩, 张占文, 荣伟彬.基于计算机视觉的微球缺陷检测及分类方法[J].强激光与粒子束, 2017, 29(8):75-79.

HE X, ZHANG ZH W, RONG W. Detection and classification of microsphere defects based on computer vision[J]. High Power Laser and Particle Beams, 2017, 29(8):75-79. (in Chinese)

郝勇, 赵翔, 温钦华, 等.基于机器视觉的深沟球轴承滚珠遗漏检测[J].激光与光电子学进展, 2018(2):380-385.

HAO Y, ZHAO X, WEN Q, et al.. Omission detection of deep groove ball bearing ball based on machine vision[J]. Laser & Optoelectronics Progress, 2018(2):380-385.(in Chinese)

胥磊.机器视觉技术的发展现状与展望[J].设备管理与维修, 2016(9):7-9.

XU L.The development and prospect of machine vision technology[J]. Plant Maintenance Engineering, 2016(9):7-9. (in Chinese)

卞正岗.机器视觉技术的发展[J].中国仪器仪表, 2015(6):40-42.

BIAN ZH G.The development of machine vision technology[J]. China Instrumentation, 2015(6):40-42. (in Chinese)

王耀南, 陈铁健, 贺振东, 等.智能制造装备视觉检测控制方法综述[J].控制理论与应用, 2015, 32(3):273-286.

WANG Y N, CHEN T J, HE ZH D, et al.. Review on the machine vision measurement and control technology for intelligent manufacturing equipment[J]. Control Theory & Applications, 2015, 32(3):273-286. (in Chinese)

杨英豪, 柳青, 崔洁.机器视觉在焊点检测中的应用[J].电子工业专用设备, 2014(11):29-32.

YANG Y H, LIU Q, CUI J. Application of inspect ball bonding with machine vision[J]. Equipment for Electronic Products Manufacturing, 2014(11):29-32. (in Chinese)

孙广玲, 唐降龙.基于分层高斯混合模型的半监督学习算法[J].计算机研究与发展, 2004, 41(1):156-161.

SUN G L, TANG X L. A semi-supervised learning algorithm based on a hierarchical GMM[J]. Journal of Computer Research and Development, 2004, 41(1):156-161. (in Chinese)

岳佳, 王士同.高斯混合模型聚类中EM算法及初始化的研究[J].微计算机信息, 2006, 22(11X):244-246.

YUE J, WANG SH T. Algorithm EM and its initialization in gaussian-mixture-model based clustering[J]. Control & Automation, 2006, 22(11X):244-246. (in Chinese)

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