Automatic recognition of weld defects in x-ray inspection

Daum, Werner; Rose, P. H.; Heidt, H.; Builtjes, J. H.

doi:10.1016/0963-8695(92)90329-f

Cited by 14 publications

(7 citation statements)

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“…Hyatt designed a multiscale method for removing the background structure in the digital radiographs while reserving the defect details [17]. The gray level in defect part usually changed with high spatial frequencies, meanwhile that in normal part varied gradually, namely with low spatial frequencies [18]. Many methods are built on this basis.…”

Section: Defect Segmentationmentioning

confidence: 99%

Review on Computer Aided Weld Defect Detection from Radiography Images

Hou

Zhang

et al. 2020

Applied Sciences

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The weld defects inspection from radiography films is critical for assuring the serviceability and safety of weld joints. The various limitations of human interpretation made the development of innovative computer-aided techniques for automatic detection from radiography images an interest point of recent studies. The studies of automatic defect inspection are synthetically concluded from three aspects: pre-processing, defect segmentation and defect classification. The achievement and limitations of traditional defect classification method based on the feature extraction, selection and classifier are summarized. Then the applications of novel models based on learning(especially deep learning) were introduced. Finally, the achievement of automation methods were discussed and the challenges of current technology are presented for future research for both weld quality management and computer science researchers.

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Section: Defect Segmentationmentioning

confidence: 99%

Review on Computer Aided Weld Defect Detection from Radiography Images

Hou

Zhang

et al. 2020

Applied Sciences

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“…The methods based on smoothing operators (Liang et al, 1992;Shirai, 1969) are incapable of detecting small defects because the smoothing operation blurs the defect image elements, which become less distinguishable from neighboring pixels. Differential image calculation (Danm et al, 1987) is based on the difference between the original image and background image and is not suited for interrogating small-size and low-contrast defects. As the second group of techniques, edge detection (Jain and Bubuisson, 1992;Liang et al, 1992) identifies edges in an image for inferring the boundaries of defects.…”

Section: Digital Detection Of Materials Defectsmentioning

confidence: 99%

Material damage evaluation with measured microdefects and multiresolution numerical analysis

Shen

Mao

Boileau

et al. 2013

International Journal of Damage Mechanics

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This paper presents an alternative method of material damage evaluation based on the X-ray computer tomography-detected microdefects and multiscale computer simulation. This is achieved by developing a method of the digital diagnosis and full-field numerical calculation of material degradation in macroscopic material test specimens. The method comprised three basic components: (a) digital detection and processing of micro/mesoscale material defects of macroscopic material test specimens; (b) multilevel meshing and multilevel finite element analysis for evaluating local/global material degradation; and (c) synchronized experimental and numerical determination of material damage. The unique contributions of the proposed approach include (a) a multilevel finite element meshing and analysis scheme that makes the full-field estimation of material degradation in macroscopic test specimens computationally tractable on regular workstations, (b) full-field exploration of mesoscale material defects (i.e., those with a feature size from several micrometers to a few millimeters), which play a crucial role in failure analysis of engineering components, and (c) the proposed method offers a significantly better accuracy in estimating material degradation in terms of effective modulus than the conventional analytical models in continuum damage mechanics and micromechanics. Test results of aluminum alloys confirm the efficacy of our approach in the digital interrogation of material degradation.

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“…Considering of environmental protecting, radioprotection, practical operating and cost, X-ray is selected to be the testing source [4]. X-ray fluorescence imaging effect is that when X-ray expose some materials such as CaS, ZnS, Pt, Ba(CN) 2 and so on [5], fluorescence sent out from these material can be observed easily in the dark. And the intensity of fluorescence sent out from the same substance is in the direct ratio to the intensity of X-ray after passing through the wood.…”

Section: Wood Defects Image Collection Systemmentioning

confidence: 99%

Image edge detection of wood defects based on multi-fractal analysis

Zhang³

et al. 2008

2008 IEEE International Conference on Automation and Logistics

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A reliable detection method for X-ray image of wood with defects based on the multi-fractal analysis was presented in the paper. Adopting singular Holder index and multi-fractal spectrum theory and computing the singular value and multi-fractal spectrum of each pixel point of image edges of wood defects in order that the images were identified easily. According to the experimental results, the given algorithm can reduce unimportant edge information, while it also keeps some important detail information of the image well by correction of the measure. It is useful and current method for image processing. This study lays a foundation for automatic recognition. It is significant for the modern wood processing and utilization.

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Automatic recognition of weld defects in x-ray inspection

Cited by 14 publications

References 0 publications

Review on Computer Aided Weld Defect Detection from Radiography Images

Review on Computer Aided Weld Defect Detection from Radiography Images

Material damage evaluation with measured microdefects and multiresolution numerical analysis

Image edge detection of wood defects based on multi-fractal analysis

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