印刷电路板焊点的智能检测

谢宏威; 张宪民; 邝泳聪; 欧阳高飞

doi:10.3788/OPE.20111909.2154

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印刷电路板焊点的智能检测

信息科学 | 更新时间：2020-08-12

- 印刷电路板焊点的智能检测
- Intelligent detection of solder joints on printed circuit boards
- 光学精密工程 2011年19卷第9期页码：2154-2162
- 作者机构：
  
  华南理工大学机械与汽车工程学院,广东广州,510640
- 作者简介：
- 基金信息：
  
  国家杰出青年科学基金资助项目(No.50825504)();广东省科技攻关重点项目(No.2008A010300002)();中央高校基本科研业务费专项资金资助项目(N
- DOI：10.3788/OPE.20111909.2154
  中图分类号： TP274.5;TP391.4
- 收稿日期：2011-01-10，
  
  修回日期：2011-02-27，
  
  网络出版日期：2011-09-26，
  
  纸质出版日期：2011-09-26
- 稿件说明：
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谢宏威, 张宪民, 邝泳聪, 欧阳高飞. 印刷电路板焊点的智能检测[J]. 光学精密工程, 2011,19(9): 2154-2162

XIE Hong-wei, ZHANG Xian-min, KUANG Yong-cong, OUYANG Gao-fei. Intelligent detection of solder joints on printed circuit boards[J]. Editorial Office of Optics and Precision Engineering, 2011,19(9): 2154-2162
谢宏威, 张宪民, 邝泳聪, 欧阳高飞. 印刷电路板焊点的智能检测[J]. 光学精密工程, 2011,19(9): 2154-2162 DOI： 10.3788/OPE.20111909.2154.

XIE Hong-wei, ZHANG Xian-min, KUANG Yong-cong, OUYANG Gao-fei. Intelligent detection of solder joints on printed circuit boards[J]. Editorial Office of Optics and Precision Engineering, 2011,19(9): 2154-2162 DOI： 10.3788/OPE.20111909.2154.

摘要

为了提高在线自动光学检测系统(AOI)的自动化程度

提出了一种基于增量聚类的智能焊点检测方法。首先

设计了在线智能AOI的系统框架。然后

根据焊点外观进行归纳分类

将关键子区域的面积特征应用于焊盘特征的量化与提取

将每类样本聚类为若干子类从而实现对多批次焊点的检测。最后

提出一种增量聚类算法

在线检测过程中系统可根据人工维修站反馈信息自动学习新的样本并调整相关检测参数。为了提高增量学习的效率

每次增量学习之前选择少量代表样本用于增量学习。采用提出的AOI系统检测焊点

准确率可达96.5%

平均每个焊点耗费9.3 ms。结果表明

本文提出的检测方法不仅可以对多批次的焊点缺陷进行有效识别

且对生产中工艺条件的变化有自适应能力

智能化程度较高

具有较强的实用价值。

Abstract

To automate present Automatic Optical Inspection (AOI) systems

an intelligent method based on incremental clustering for solder joint inspection is proposed in this paper. Firstly

to meet the demands of practical production

the framework for an intelligent AOI system is designed. Then

all the defects of solder joints are classified into several different types according to their appearances

and the color features in critical regions are extracted. The samples in each class are clustered into several subclasses so that the system is able to inspect solders from different batches. Finally

a new incremental clustering algorithm is proposed. The AOI system can automatically adjust inspection parameters according to the feedback from the repair station. To improve training efficiency

only a few samples are selected. The method proposed is used in an AOI to inspect solder joints

and the inspecting accuracy can reach 96.5% while each solder inspection takes 9.3 ms. The experimental result demonstrates that the proposed method can detect accurately a solder defect from different patches

and can be modified for different manufacturing processes. The intelligence level of the system using the proposed method is high

and it can be used in practical application.

关键词

Keywords

references

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