期刊文献+

不等厚板搭接焊缝缺陷数字X射线检测 被引量:20

X-ray based defect testing method for a lap joint with unequal thickness steel plates
下载PDF
导出
摘要 在不等厚钢板搭接焊缝X射线检测图像中,由于工件及焊缝厚度变化带来的图像背景灰度差异、焊缝区灰度连续变化等,给基于图像处理的缺陷自动识别带来困难.同时,被检测焊缝装卡的空间位置具有不确定性,垂直布置的焊缝和重力方向存在一定角度,不便于缺陷自动识别及定位.文中给出一种基于不变矩的X射线图像校正方法,解决焊缝图像倾斜问题;在此基础上,给出一种图像噪声抑制、背景去除、图像分割及数学形态学相结合的缺陷识别方法.结果表明,所提方法能有效识别不等厚搭接焊缝中的气孔类缺陷,适用于自动检测. It is difficult to automatically recognize defects using digital image processing method in X-ray image that tested from lap joint with unequal thickness plates. This attributes to the very different gray levels in background image caused by the unequal thickness of the workpiece, and the continuous change gray levels caused by lap joint. Besides, the position of the lap joint is uncertain placed before tested, i.e.there is always an angel between the directions of lap joint and gravity. And this makes it difficult for defect detection and localization. In this paper, a method of X-ray image correction based on invariant moments is presented to solve the problem of image skew. In addition, a defect detection method combined of image noise suppression, background removal,image segmentation and mathematical morphology is presented. The results show that the proposed method can effectively recognize the gas pores in lap joint with unequal thickness, and it is suitable for automatic detection.
作者 迟大钊 马子奇 程怡 王梓明 CHI Dazhao;MA Ziqi;CHENG Yi;WANG Ziming(State Key Laboratory of Advanced Welding and Joining,Harbin Institute of Technology,Habin 150001,China;Shanghai Aerospace Equipments Manufacturer Co.,Ltd.,Shanghai 200245,China)
出处 《焊接学报》 EI CAS CSCD 北大核心 2019年第11期45-48,I0003,共5页 Transactions of The China Welding Institution
基金 国家自然科学基金(51375002) 上海航天科技创新基金(SAST2017-063)
关键词 搭接焊缝 X射线 数字图像处理 缺陷识别 lap joint X-ray digital image processing defect detection
  • 相关文献

参考文献7

二级参考文献41

  • 1杨坪,蒋应田,洪振鹏,张建成,张建筑.数字射线图像缺陷的Canny算子边缘检测[J].无损检测,2008,30(7):422-425. 被引量:3
  • 2侯润石,都东,邵家鑫,王力,常保华.基于局部曲面重构的焊缝X射线图像缺陷分割技术[J].无损检测,2008,30(8):533-535. 被引量:4
  • 3闫成新,桑农,张天序,曾坤.基于局部复杂度的图像过渡区提取与分割[J].红外与毫米波学报,2005,24(4):312-316. 被引量:25
  • 4Lashkia V. Defect detection in X-ray images using fuzzy reasoning [J]. Image and Vision Computing, 2001, 19(5) : 261 -269.
  • 5Guo Linfeng,Opas Chutatape. Influence of discretization in image space on hough transform[ J]. Pattern Recognition, 1999, 32 (4) : 635 -644.
  • 6Warren T, Jia Weini. An automated radiographic NDT system for weld inspection-Flaw detection[ J ]. NDT & E international 1998, 31(3) : 183 -192.
  • 7Daum W, Rose P, Heidt H, et al. Automatic recognition of weld defects in X-ray inspection[ J]. British Journal of NDT, 1987, 29 (3) : 79 -82.
  • 8Kehoe A, Parker G A. Image processing for industrial radiographic inspection: image enhancement [ J ]. British Journal of NDT, 1990, 32(4) : 183 -190.
  • 9Liang D,Zhen W,Zhang G,et al.Proceedings of international symposium on nondestructive testing and stress-strain measurement[C]∥ Automatical Identification of the Defect Level of Welding Seam Based On X-ray Image.Tokyo,1992:267-274.
  • 10Robin N,Strickland,Hee Hahn.Wavelet transform methods for object detection and recovery[J].IEEE Transaction On Image Processing,1997,6(5):724-735.

共引文献67

同被引文献157

引证文献20

二级引证文献64

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部