挠性印制电路板焊盘表面缺陷的检测

黄杰贤; 李迪; 叶峰; 张舞杰

doi:10.3788/OPE.20101811.2443

您当前的位置：

首页 >

文章列表页 >

挠性印制电路板焊盘表面缺陷的检测

微纳技术与精密机械 | 更新时间：2020-08-12

- 挠性印制电路板焊盘表面缺陷的检测
- Detection of surface defection of solder on flexible printed circuit
- 光学精密工程 2010年18卷第11期页码：2443-2453
- 作者机构：
  
  华南理工大学机械与汽车学院,广东广州,510640
- 作者简介：
- 基金信息：
  
  广东省部产学研结合基金资助项目(No.2009B090300200)()
- DOI：10.3788/OPE.20101811.2443
  中图分类号： TN710;TP391.4
- 收稿日期：2010-02-24，
  
  修回日期：2010-03-24，
  
  网络出版日期：2010-11-25，
  
  纸质出版日期：2010-11-25
- 稿件说明：
移动端阅览
黄杰贤, 李迪, 叶峰, 张舞杰. 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

HUANG Jie-xian, LI Di, YE Feng, Zhang Wu-jie. Detection of surface defection of solder on flexible printed circuit[J]. Editorial Office of Optics and Precision Engineering, 2010,18(11): 2443-2453
黄杰贤, 李迪, 叶峰, 张舞杰. 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 DOI： 10.3788/OPE.20101811.2443.

HUANG Jie-xian, LI Di, YE Feng, Zhang Wu-jie. Detection of surface defection of solder on flexible printed circuit[J]. Editorial Office of Optics and Precision Engineering, 2010,18(11): 2443-2453 DOI： 10.3788/OPE.20101811.2443.

摘要

针对挠性印制电路板(FPC)上的焊盘表面缺陷

提出一种基于图像处理技术的智能检测方法。首先

根据缺陷的表现形式对焊盘缺陷进行归纳与分类

采用最大熵值法量化提取焊盘的颜色特征和面积特征;然后

通过评估灰度共生矩阵(GLCM)对纹理颜色变化特征与纹理结构特征量化的有效性

将其应用于焊盘纹理特征的量化与提取。实验分析显示

缺陷焊盘与非缺陷焊盘在某个或多个特征上存在着明显的差异。基于该特点

建立了BP神经网络

以焊盘的颜色、面积、纹理结构、纹理颜色变化特征值作为神经网络的输入

通过学习大量样本

获取最佳权值参数

最终实现对FPC焊盘表面缺陷的检测

检测准确率高达94.6%

50个焊盘的检测时间为300 ms。实验结果表明:本文提出的检测方法不仅能够有效地对焊盘表面缺陷进行识别

而且能够满足在线检测对速度的要求。

Abstract

In order to detect the surface defect on the solder of a Flexible Printed Circuit(FPC)

an inspecting technology based on image processing was presented. Firstly

all the defects on the FPC were classified into several defection sorts according to their defection characters. Then

the maximum entropy was used to locate the solder and extract the square and color characters. After estimating the effectness of the Grey Level Co-occurrence Matrix(GLCM) on the quantification for color and structure characters

it was introduced to quantify and extract colorific and structural textures for solders. An analysis on experiments indicates that the defective solder is obviously different from the non-defective solder in several kinds of quantified charaters. On the basis of the result obove

the BP neural network was established and four kinds of characters were selected as the input of neural network. After all neural network weight parameters were adjusted to the optimization through sample training

the performance of the proposed defect detection algorithm was finally evaluated in an on-line testing. Test shows that 50 inspecting targets cost 300 ms

and the inspecting accuracy can reach 94.6%. The experimental result demonstrates that proposed method can detect accurately the solder defect with low false alarms

and the efficiency can satisfy the requirement of defect inspection in online and real time.

关键词

Keywords

references

<td valign='top'>[1]AYOUB G. Real-time SPC with AOI [J]. Surface Mount Technology Magazine, 2001,15(6):36 - 38.<td valign='top'>[2]WANG Q X, LI D, ZHANG W J. Detecting defects in golden surfaces of flexible printed circuits using optimal gabor filters . Intelligent Information Technology Application,2008, IITA '08. Second International Symposium on Volume 1,Dec.2008:321-325.<td valign='top'>[3]CHEN CH H, WANG C C, LIN CH Y, et al.. Realization of defect automatic inspection system for flexible printed circuit (FPC) . Proceedings of the 35th International MATADOR Conference, 2007:225-228.<td valign='top'>[4]TSAI D M,LIN B T. Defect detection of gold-plated surfaces on PCBs using entropy measures[J].The International Journal of Advanced Mannufacturing Technology</em.2002,20(6):420-428<a href='http://dx.doi.org/10.1007/s001700200172' target='_blank'><img src='../../images/crossref.gif' width='41' height='15' border='0' /><td valign='top'>[5]KAPUR J N,SAHOO P K,WONG A K C.A new method for grey-level picture thresholding using the entropy of the histogram[J]. Computer Vision,Graphics and Image Processing, 1980,3(2):223-237.<td valign='top'>[6]ABUTALEB A S.Automatic thresholding of gray-level picture using two-dimensional entropies[J].Pattern Recognition,1989,47(1):22-32.<td valign='top'>[7]HARALICK R M,SHANMUGAM K,DINSTEIN I. Textural features for image classification[J].IEEE Trans. SMC, 1973,3:610-621.<td valign='top'>[8]WALKER R F,JACKWAY P T,LONGSTAFF D. Genetic algorithm optimization of adaptive multi-scale GLCM features[J].International Journal of Pattern Recognition and Artificial Intelligence, 2003,17(1):17-39.<td valign='top'>[9]DESOKY A H,HALL S A. Entropy measures for texture analysis based on Hadamard transform[J]. Proceedings of the IEEE Southeastcon Conference, 1990(2):467-470.<td valign='top'>[10]DAWSON B R P,PARSONS A J. Texture measures for the identification and monitoring of urban derelict land[J].International Journal of Remote Sensing.1994,15(6):1259-1271<a href='http://dx.doi.org/10.1080/01431169408954159' target='_blank'><img src='../../images/crossref.gif' width='41' height='15' border='0' /><td valign='top'>[11]马立,于瀛洁,程维明,等. BP神经网络补偿并联机器人定位误差[J]. 光学精密工程,2008,16(5):878-883. MA L,YU Y J,CHENG W M,et al..Positioning error compensation for a parallel robot based on BP neural networks[J].Opt.Precision Eng.,2008,16(5):878-883.(in Chinese)<td valign='top'>[12]王自强,李银妹,楼立人,等. BP神经网络用于光镊力的非线性修正[J]. 光学精密工程,2008,16(1):6-10. WANG Z Q,LI Y M,LOU L R,et al..Application of BP neural network to nonlinearity correction of optical tweezer force[J].Opt. Precision Eng.,2008,16(1):6-10.(in Chinese)<td valign='top'>[13]沈铖武,王志乾,刘畅,等. BP神经网络在多位置捷联寻北系统中的应用[J]. 光学精密工程,2009,17(18):1890-1895. SHEN CH W,WANG ZH Q,LIU CH,et al..Application of BP neural network to multi-position strap-down north seeking system[J].Opt.Precision Eng.,2009,17(8):1890-1895.(in Chinese)<td valign='top'>[14]LOH H H, LU M S. Printed circuit board inspection using image analysis [J]. IEEE Transactions on Industry App lications, 1999,35:4262432.

浏览量

578

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于神经网络的全斯托克斯光电探测器

基于圆卷积神经网络的粘连导电粒子检测

面向领域自适应的部分最优传输高光谱图像分类

基于热传导和卷积神经网络的磨床主轴热误差预测

基于刚度预测-神经网络的调姿平台误差补偿