where expertise comes together - since 1996 -

The Largest Open Access Portal of Nondestructive Testing (NDT)

Conference Proceedings, Articles, News, Exhibition, Forum, Network and more

where expertise comes together
- since 1996 -
NDT.net Issue - 2019-03 - Articles
NDT.net Issue: 2019-03
Publication: 9th Conference on Industrial Computed Tomography (iCT) 2019, 13-15 Feb, Padova, Italy (iCT 2019)
Session: Poster exhibition
Thu 16:00 Agora
ARTICLE

CAD-based defect inspection with optimal view angle selection based on polychromatic X-ray projection images

Alice Presenti2, Jan Sijbers20, Arnold J. Den Dekker, Jan De Beenhouwer17
imec-VisionLab, Department of Physics; University of Antwerpen36, Antwerpen, Belgium

Abstract: X-ray 3D computed tomography (CT) is a non-destructive method that allows inspection of internal components of an object. In the conventional approach, the comparison between the measured projections and the 3D model is performed by processing a CT reconstruction from projection data. The accuracy of the inspection analysis strictly depends on the reconstruction, which can suffer from numerous artifacts. To overcome this problem, we propose a new method that performs inspection directly in the projection space, simulating realistic projections from the computer aided design (CAD) model. Our method bases its strength on the prior knowledge from the CAD data and knowledge about the inspected object itself. When inspecting for defective or missing components, it is not restrictive to assume that the position of a potential deviation from the nominal geometry is approximately known. Therefore, we define regions of interest (ROIs) for feature extraction by simply projecting a component or a volume around it. Furthermore, based on the nominal geometry, we can identify the projection angles for which a certain component is most visible, to restrict the inspection analysis to projections obtained at these angles. Our procedure for quality control is composed of two main steps: the first one must be performed prior to in-line inspection and consists of i) an accurate alignment to calibrate the system geometry and ii) building of libraries of simulated projection data. These libraries are used in the second, in-line step to perform a fast 3D alignment with respect to the position and orientation of the sample. The knowledge of the sample’s orientation allows one to select only a few projection angles for which a potential defect is most visible and perform classification by extracting features and measures from merely the projections at this limited set of angles, for a predefined ROI. In this paper, we motivate and describe our procedure for limited view in-line inspection of defects and validate our method with experiments on real data.

Keywords: Inspection (93), X-ray (44), 3DCT, angle selection,
*Keywords are freely formed keywords from the authors and thus you may Search also for similar terms.

View:  


Feedback: ()

Share:
More from "Alice Presenti" (1)
2020-02 Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT
More from "Jan Sijbers" (5 of 19)
2020-02 Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT
2020-02 An adaptive probability map for the Discrete Algebraic Reconstruction Technique
2019-03 An Interactive Visual Comparison Tool for 3D Volume Datasets represented by Nonlinearly Scaled 1D Line Plots through Space-filling Curves
2019-03 Fast detection of cracks in ultrasonically welded parts by inline X-ray inspection
2019-03 Tools for the Analysis of Datasets from X-Ray Computed Tomography based on Talbot-Lau Grating Interferometry
... All 19 Details >
More from "Jan De Beenhouwer" (5 of 16)
2020-02 Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT
2020-02 An adaptive probability map for the Discrete Algebraic Reconstruction Technique
2019-03 A low-cost and easy-to-use phantom for cone-beam geometry calibration of a tomographic X-ray system
2019-03 Simulated grating-based x-ray phase contrast images of CFRP-like objects
2019-03 An Interactive Visual Comparison Tool for 3D Volume Datasets represented by Nonlinearly Scaled 1D Line Plots through Space-filling Curves
... All 16 Details >
More from "University of Antwerpen" (5 of 35)
2020-02Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT
2020-02An adaptive probability map for the Discrete Algebraic Reconstruction Technique
2019-05Diffuse versus specular reflection: the influence of hot spots on reflected apparent temperature
2019-05Sublayer composition evaluation of Artwork using active thermography
2019-05Optimised dynamic line scanning thermography for aircraft structures
... All 35 Details >
Share...
We use technical and analytics cookies to ensure that we will give you the best experience of our website - More Info
Accept
top
this is debug window