首页 | 本学科首页   官方微博 | 高级检索  
     

集箱管接头内焊缝表面缺陷识别方法研究
引用本文:焦敬品,李思源,常予,吴斌,何存富. 集箱管接头内焊缝表面缺陷识别方法研究[J]. 仪器仪表学报, 2017, 38(12): 3044-3052
作者姓名:焦敬品  李思源  常予  吴斌  何存富
作者单位:北京工业大学机械工程与应用电子技术学院北京100124,北京工业大学机械工程与应用电子技术学院北京100124,北京工业大学机械工程与应用电子技术学院北京100124,北京工业大学机械工程与应用电子技术学院北京100124,北京工业大学机械工程与应用电子技术学院北京100124
基金项目:国家重点研发计划(2016YFF0203002)、国家自然科学基金(11572010,11772013)项目资助
摘    要:针对集箱管接头内焊缝表面缺陷自动检测需要,进行了基于计算机视觉的集箱管接头内焊缝表面缺陷自动识别方法研究。分析了不同焊缝图像的纹理特征,从焊缝图像的灰度共生矩阵中提取出15种可用于焊缝表面缺陷状态表征的特征参数。在此基础上,研究将BP神经网络应用于焊缝表面缺陷自动识别中。分析了灰度共生矩阵的步长、灰度级、神经网络的结构参数及输入特征参数的数量和种类对焊缝图像识别效果的影响,优化出最佳的识别参数。在以上研究基础上,利用优化后的神经网络对内窥镜获得的不同焊接质量的焊缝图像进行了训练和识别。结果表明,提出的基于图像纹理的神经网络识别系统可以很好实现集箱管接头内焊缝焊接状态的自动评价,整体识别率达91%。研究工作为集箱管接头内焊缝焊接质量自动检测做了有益的探索。

关 键 词:焊缝;表面缺陷;纹理特征;灰度共生矩阵;BP神经网络

Defect classification of weld surface in header pipe joint
Jiao Jingpin,Li Siyuan,Chang Yu,Wu Bin and He Cunfu. Defect classification of weld surface in header pipe joint[J]. Chinese Journal of Scientific Instrument, 2017, 38(12): 3044-3052
Authors:Jiao Jingpin  Li Siyuan  Chang Yu  Wu Bin  He Cunfu
Affiliation:College of Mechanical Engineering and Application Electronics Technology, Beijing University of Technology, Beijing 100124, China,College of Mechanical Engineering and Application Electronics Technology, Beijing University of Technology, Beijing 100124, China,College of Mechanical Engineering and Application Electronics Technology, Beijing University of Technology, Beijing 100124, China,College of Mechanical Engineering and Application Electronics Technology, Beijing University of Technology, Beijing 100124, China and College of Mechanical Engineering and Application Electronics Technology, Beijing University of Technology, Beijing 100124, China
Abstract:In order to automatically classify weld surface defect in header pipe joint, Computer Vision based defect classification is studied. The texture features of different weld defects are analyzed, Grey level co occurrence matrix (GLCM) is applied to extract features from digital images, and 15 types of statistical indexes are obtained to characterize the weld surface defects. Back propagation artificial neural network method is used for defect classification. The influence of GLCM parameters, the neural network structure and the number and variety of input parameters on the defect classification performance is analyzed, and optimal neural network structure and input parameters are selected. In further, the optimized network is utilized for training and classifying the images of different weld defects acquired by industrial endoscope. The results show that weld defects detection rate of overall classification can be up to 91%. The proposed method can be used for automatic classification of weld surface defect in header pipe joint.
Keywords:weld   surface defect   texture features   grey level co occurrence matrix   BP neural network
本文献已被 CNKI 等数据库收录!
点击此处可从《仪器仪表学报》浏览原始摘要信息
点击此处可从《仪器仪表学报》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号