BGA焊球视觉检测算法及系统设计

罗志伟; 杨玉龙; 李志红

doi:10.3788/OPE.20182609.2190

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BGA焊球视觉检测算法及系统设计

更新时间：2020-08-13

- BGA焊球视觉检测算法及系统设计
- Design of vision detection algorithm and system for BGA welding balls
- 光学精密工程 2018年26卷第9期页码：2190-2197
- 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：
  
  [ "罗志伟(1979-), 男, 福建连城人, 博士, 副教授, 硕士生导师, 2002年于郑州轻工业学院获得学士学位, 2005年于福州大学获得硕士学位, 2017年于厦门大学获得博士学位, 主要从事微滴喷射、机器视觉、机电一体化技术等方面的研究。E-mail:ZWLUO@xmut.edu.cn" ]
  [ "杨玉龙(1991-), 男, 福建漳州人, 硕士, 2014年、2017年于厦门理工学院分别获得学士、硕士学位, 主要从事非标自动化设备的机器视觉算法开发和研究。E-mail:yangyulong_large@163.com" ]
- 基金信息：
  
  厦门理工学院高层次人才科技类基金资助项目(YKJ17017R);福建省科技计划基金资助项目(2018H0039);福建省自然科学基金资助项目(2018J01529);国家自然科学基金面上项目(51575464)
- DOI：10.3788/OPE.20182609.2190
  中图分类号： TP23;TP278
- 收稿日期：2018-05-15，
  
  录用日期：2018-6-21，
  
  纸质出版日期：2018-09-25
- 稿件说明：
移动端阅览
罗志伟, 杨玉龙, 李志红. BGA焊球视觉检测算法及系统设计[J]. 光学精密工程, 2018,26(9):2190-2197.

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.
罗志伟, 杨玉龙, 李志红. BGA焊球视觉检测算法及系统设计[J]. 光学精密工程, 2018,26(9):2190-2197. DOI： 10.3788/OPE.20182609.2190.

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

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