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作 者:吴福培[1] 邝泳聪[1] 张宪民[1] 欧阳高飞[1]
机构地区:[1]华南理工大学机械与汽车工程学院,广东广州510640
出 处:《光学精密工程》2009年第10期2586-2593,共8页Optics and Precision Engineering
基 金:国家杰出青年科学基金资助项目(No.50825504);粤港关键领域突破项目(东莞专项No.200816822);广东省广州科技攻关项目(No.2008A010300002)
摘 要:为了提高在线自动光学检测(AOI)系统检测印刷电路板(PCB)焊点的准确率和速度,对PCB焊点进行了研究。通过研究由特定结构光源和3CCD彩色相机获取PCB焊点图像,基于常见的良品、多锡、少锡、假焊等焊点类型,提取焊点图像关键子区域的面积特征。在此基础上,建立了5种焊点类型的特征矩阵模型,并以同类焊点相似程度最大为原则,设计了检测焊点的模式匹配算法。此外,给出了一种参数自适应方法对各检查项所用到的阈值参数进行学习与校正。在实验研究中,对含有1 040个Chip焊点的PCB进行了检测,结果显示提出的算法对焊点检测的准确率可达96.5%,检测所用时间为9 s。研究结果表明,本文算法具有较高检测准确率和检测速度。In order to improve the inspection success rate and velocity of an in-line Automated Optical Inspection (AOI) system of Printed Circuit Boards (PCBs), the solder joints of a PCB are examined. The solder joint images are acquired by a structure illuminator and a 3-CCD color camera. Based on these images, the area features of the conventional types of PCB solder joints such as good, excessive, poor and pseudo are extracted with respect to the key sub region in the solder joint. Five kinds of fea- ture matrix models of solder joints are presented. A pattern matching algorithm for inspecting the solder joint is developed by maximizing the similarity o{ the same type of solder joins. To solve the problem of the threshold determined by experiences, a parameter adaptive learning strategy is presen- ted. Finally, 1 040 chip solder joints on a PCB are inspected. Experimental results show that the suc tess rate is as high as 96.5 % and the inspection time is 9 s by using the proposed algorithm. This indi- cates that the proposed algorithm can achieve both a high success rate and inspection velocity.
关 键 词:自动光学检测系统 焊点检测 特征矩阵 模式匹配 参数自适应
分 类 号:TP274.5[自动化与计算机技术—检测技术与自动化装置] TP391.4[自动化与计算机技术—控制科学与工程]
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