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

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.

Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT

“…As we already showed in [4], limiting the acquisition to an optimal set of angles, has an advantage both on the overall time consumption and on the performance of the inspection method. A crucial prerequisite for this to work is the quick determination of the orientation of the sample so that the right set of images can be acquired.…”

“…Regardless of the workflow adopted, it is possible to determine the optimal inspection configuration based on the specific task [3]. From our analysis in [4], it is shown that limiting the acquisition to an optimal set of angles, has an advantage both on the overall time consumption and on the performance of the final quality control. Moreover, by exploiting prior-knowledge of the inspected object (CAD model, material properties), it was shown that optimal projection angles as well as a suitable region of interest for inspection can be determined in advance.…”

Design of a Computed Tomography Automation Architecture

“…Presenti et al [4] incorporated CAD data to help perform fast in-line defect inspection. They assumed that there is prior knowledge of both the CAD data of the object and the inspected object itself.…”

“…By directly identifying defects in the projection-space, they eliminated the need to perform 3D reconstruction and reduced the time required for CT analysis. Figure 3 is an example of a partial architecture that has been modified by introducing initial data that change the setup and procedure compared to the generic CT procedure in Figure 2 and represent the method used by Presenti et al [4]. James and Edwards [5] applied robot kinematic modeling for controlling the positioning of parts in beamlines.…”