一种基于Gerber文件的PCB缺陷检测方法

介绍一种基于Gerber文件的PCB缺陷检测方法。通过读取Gerber文件,对待检测PCB板图像元件进行快速的精准定位,提取定位区域中的图像信息,经过后期算法处理,实现对PCB板缺陷进行检测的目的。     

PCB自动光学检测系统

随着PCB生产复杂度和产量的提高,在PCB的质量检测方法中,相对于传统的人工目检,以图像处理技术为核心的自动光学检测已开始展现出高精度、快速、无损伤,自动化等优势。提出了一种快速、高精度的PCB自动光学检测系统,利用面阵工业CCD以图像分块连续扫描方式为基础,设计了系统硬件平台和软件结构模型,提供了检测算法流程及其关键处理,通过联合调试实验证明,该检测系统速度快、精度高,能够满足有一定实时性要求的生产现场。     

新型高密度电路板的自动光学检测系统设计

为提高自动光学检测系统(AOI)的缺陷检出率,研究了一种采用多色光源照明,利用机器视觉获取被测高密度印刷电路板(HDI型PCB)图像,通过图像处理快速准确地识别出各种缺陷的新型AOI。实验装置由主控计算机、电气控制系统、精密机械运动装置、多色光源照明和图像采集系统等组成。图像处理及识别软件基于OPENCV和VisualStudio2005开发,模块化设计,包括光源控制、图像采集、图像拼接、图像定位、路径规划、缺陷检测和缺陷统计等模块。实验结果表明,新型AOI系统可检出加载HDI型PCB的各种缺陷,缺陷的检出率可达99.9%,误报率只有0.3%。     

PCB自动布线算法介绍

讨论了PCB自动设计中版面图形数据组织和障碍数的建立,比较了链表、二叉树、四维二叉树3种结构的特点.介绍了PCB自动设计中分解算法、图形相交算法及其在图形数据处理中的应用.     

自动光学检测系统约束多窗口定位问题的研究

减少检测路径的总长能够有效地提高光学检测系统的效率。在检测窗口顺序及约束移动范围确定的情况下,提出了一种迭代的最小化检测路径总长的方法。通过分析单检测窗口与前后窗口路径总长最小的问题,提出了分类情况下的最小路径的窗口定位方法,然后给出多窗口的情况下的方法。实验证明该方法能够不但能减少检测路径的长度,同时也能够减少检测窗口的数量。     

国内首台自动光学检测设备研制成功

日前,由中国电子科技集团第45所承担的国际科技合作项目“自动光学检测（AOI）设备技术合作”,通过了国家级验收,技术指标达到了国外同类设备水平,标志着自动光学检测（AOI）设备实现了国产化,填补了国内空白。     

基于二值投影的PCB元件安装缺陷检测算法研究

研究分析了适用于AOI设备的PCB表面安装元件的缺陷检测算法.使用二值投影分析方法对2种元件类型的缺陷检测方法进行了研究,包括针对贴片电阻电容类型的chip元件和集成电路芯片类型的IC元件的缺陷检测方法.使用VC++6.0编写MFC程序实现算法,并制作了各种元件图像进行实验测试.实验结果表明,提出的方法能够快速有效的对两种类型的元件安装缺陷进行检测.     

片状元件的自动光学检测算法

表面贴装自动光学检测算法大多存在计算速度慢或对元件位置差异及图像变异敏感的问题。该文针对片状元件提出一种检测算法,根据元件局部特征区域的灰度变化,通过引入局部主波概率,利用主波特性描述标本图像与被测元件图像之间的相似度,从而判断元件是否存在缺陷并确定缺陷种类。实验表明,该算法可缩小检测范围,降低计算量,提高算法适应性。     

融合RCS-OSA结构的PCB缺陷检测算法

针对当前印制电路板（Printed Circuit Board,PCB）检测参数庞大、人工误检率高等问题,文章研究了一种改进的YOLOv5检测模型。首先使用实时控制系统（Real-Time Control Systems,RCS）模块替换三卷积跨阶段局部瓶颈模块（Cross Stage Partial Bottle Neck Mudule,C3）结构,通过重参数化卷积增强网络的特征提取能力;其次添加One-Shot聚合（One-Shot Aggregation,OSA）结构,一次性聚合多个特征级联;最后堆叠RCS-OSA模块,确保特征复用并加强不同层之间的信息流通。实验结果表明,改进后的算法检测精度达到94.6%,比原模型提高了2.2个百分点。该算法能够高效检测PCB的多种缺陷类型,对PCB质量检测有实际应用价值。     

基于统计分析的PCB组装缺陷特征学习方法*

为了减少自动光学检测系统对用户经验的依赖,提出了一种基于统计分析的PCB组装缺陷特征学习方法。该方法通过对良品和不良品样本图像的统计学习优选出分类能力强的特征,再采用最小风险贝叶斯决策得到特征分类参数。实验结果表明,该算法有效地简化了用户检测程序的编程和调试,提高了AOI的使用效率和准确率。     

A cost-effective and automatic surface defect inspection system for hot-rolled flat steel

Defective steel brings economic and commercial reputation losses to the hot-strip manufacturers, and one of the main difficulties in using machine-vision-based technique for steel surface inspection is time taken to process the massive images suffering from uneven illumination. This paper develops a modular and cost-effective AOI system for hot-rolled flat steel in real time. Firstly, a detailed system topology is constructed according to the design goals covering the vast majority of steel mills, lighting setup and typical defect patterns are presented as well. Secondly, the image enhancement method is designed to overcome the uneven-lighting, over- or under-exposure. Thirdly, the defect detection algorithm is developed based on variance, entropy and average gradient derived from non-overlapping 32×32 pixel blocks of steel surface images. Fourthly, the proposed algorithms are implemented on FPGA in parallel to improve the inspection speed. Finally, 18,071 contiguous images (4096×1024 pixel) acquired from 7 defective steel rolls have been inspected by the realized AOI system to evaluate the performance. The experimental results show that the proposed method is speedy and effective enough for real applications in the hot-rolled steel manufacturing, with 92.11% average accuracy while 5.54% false-negative rate.     

Soldering defect detection in automatic optical inspection

This paper proposes an integrated detection framework of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs). Both localization and classifications tasks were considered. For the localization part, in contrast to the existing methods that are highly specified for particular PCBs, we used a generic deep learning method which can be easily ported to different configurations of PCBs and soldering technologies and also gives real-time speed and high accuracy. For the classification part, an active learning method was proposed to reduce the labeling workload when a large labeled training database is not easily available because it requires domain-specified knowledge. The experiments show that the localization method is fast and accurate. In addition, high accuracy with only minimal user input was achieved in the classification framework on two different datasets. The results also demonstrated that our method outperforms three other active learning benchmarks.     

Design and development of automatic visual inspection system for PCB manufacturing

Inspection of solder joints has been a critical process in the electronic manufacturing industry to reduce manufacturing cost, improve yield, and ensure product quality and reliability. This paper proposes two inspection modules for an automatic solder joint classification system. The “front-end” inspection system includes illumination normalisation, localisation and segmentation. The “back-end” inspection involves the classification of solder joints using the Log-Gabor filter and classifier fusion. Five different levels of solder quality with respect to the amount of solder paste have been defined. The Log-Gabor filter has been demonstrated to achieve high recognition rates and is resistant to misalignment. This proposed system does not need any special illumination system, and the images are acquired by an ordinary digital camera. This system could contribute to the development of automated non-contact, non-destructive and low cost solder joint quality inspection systems.     

基于变邻域蚁群算法的自动光学检测路径规划

在确定取像窗口最少数量及其约束移动范围的前提下,为解决蚁群算法用于自动光学检测路径规划存在的问题,提出一种基于变邻域蚁群算法的自动光学检测路径规划方法。针对蚁群算法收敛速度慢、易陷入局部最优解的问题,提出含有3种邻域结构的变邻域路径搜索方法,改进蚁群算法以快速获得质量优异的可优化路径;针对取像窗口位置可调整的问题,提出变邻域窗口位置调整方法,进一步改善可优化路径,获得最短路径。实验结果表明,该算法比基本的蚁群算法具有更高的求解效率和求解质量,有效提升了自动光学检测系统的在线检测效率。     

PCB检测中图像分割技术研究

光学检测是进行印刷电路板(PCB)装配质量检验的重要手段,应用图像分割技术可以提取PCB中的目标物以进行检测。针对PCB图像分割,提出一种基于改进量子遗传算法的图像分割方法。该方法将基于阈值的图像分割方法转化为阈值优化问题,通过改进量子遗传算法的计算实现最优图像分割阈值的求取。仿真结果验证了该方法的可行性。     

Improving accuracy of automatic optical inspection with machine learning

Electronic devices require the printed circuit board(PCB)to support the whole structure,but the assembly of PCBs suffers from welding problem of the electronic components such as surface mounted devices(SMDs)resistors.The automated optical inspection(AOI)machine,widely used in industrial production,can take the image of PCBs and examine the welding issue.However,the AOI machine could commit false negative errors and dedicated technicians have to be employed to pick out those misjudged PCBs.This paper proposes a machine learning based method to improve the accuracy of AOI.In particular,we propose an adjacent pixel RGB value based method to pre-process the image from the AOI machine and build a customized deep learning model to classify the image.We present a practical scheme including two machine learning procedures to mitigate AOI errors.We conduct experiments with the real dataset from a production line for three months,the experimental results show that our method can reduce the rate of misjudgment from 0.3%–0.5%to 0.02%–0.03%,which is meaningful for thousands of PCBs each containing thousands of electronic components in practice.     

深度报文检测中基于GPU的正则表达式匹配引擎*

提出了一种基于GPU的正则表达式匹配引擎来加速深度报文检测中的模式匹配过程。该引擎基于DFA模型,在匹配时每一个GPU线程处理一个报文,通过大量的并行线程来提高引擎的吞吐量。基于NVIDIA GeForce 9800GT GPU的实验表明,该引擎处理实际网络报文时的吞吐量达到了7.91 Gbps。     

印制电路板图像对准算法研究

使用模板匹配法检测印刷电路板(PCB)质量,对标准图和待检测图之间的图像对准具有很高要求.在定位标志为圆的情况下,应用Hough变换虽然行之有效,但存在占用内存多,计算量大等问题.图像对准分为模糊对准和精确对准两个步骤,利用设置定位圆的先验知识实现模糊对准,并由模糊对准给出定位圆的相关参数,将这些参数运用于精确对准,能使图像的边缘提取和Hough变换变得简单、易行,减少了内存占用,提高了算法的处理速度.