基于模式匹配及其参数自适应的PCB焊点检测

摘要

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 feature matrix models of solder joints are presented.A pattern matching algorithm for inspecting the solder joint is developed by maximizing the similarity of the same type of solder joins.To solve the problem of the threshold determined by experiences,a parameter adaptive learning strategy is presented.Finally,1 040 chip solder joints on a PCB are inspected.Experimental results show that the success rate is as high as 96.5% and the inspection time is 9 s by using the proposed algorithm.This indicates that the proposed algorithm can achieve both a high success rate and inspection velocity.%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.