X-ray computed tomography (XCT) is one of the most powerful imaging techniques in non-destructive testing (NDT) for detecting, analysing and visualising defects such as pores, fibres, cracks etc. in industrial specimens. Detecting defects in X-ray images, however, is still a challenging problem, as it strongly depends on the quality of the XCT images. Numerical XCT simulation proved to be valuable in order to increase both image quality and detection performance. In this work, we thus analyse the differences between traditional segmentation techniques (i.e., k-means, watershed, Otsu thresholding) and deep learning-based methods (i.e., U-Net, V-Net, modified 3D U-Net) in terms of their defect detection capacity using virtual XCT images. For this purpose, we apply the probability of defect detection (POD) approach on simulated X-ray computed tomography data from aluminium cylinder heads. The XCT simulation tool SimCT was used to generate X-ray radiographs and respective reconstructions from a specimen series which features different well-defined defects with varying sizes, shapes and locations. To generate POD curves and to specify detection limits, the segmentation algorithms are used in predefined regions for defect detection via a hit/miss approach. A comparison and visualisation of six different types of defects is illustrated in 2D and 3D images, together with their POD curves and detection limits.
In this work, we apply and adapt established Probability of Detection (POD) methods on inline inspection of aluminium cylinder heads using X-ray computed tomography. The CT simulation tool SimCT [4] is used to acquire virtual images of the specimens including artificial defects, which avoids the manufacturing of calibrated defects of known type (e.g., pore, inclusion, crack etc.), size and location. One of the exemplary defects is discussed as representative result together with the generated POD curves as well as its characteristics (i.e., the minimum detected defect, the maximum missed defect, POD(a90) =0.90 and a90/95).
This work illustrates the use of deep learning methods applied on X-ray computed tomography (XCT) datasets to segment pores and fibres in reinforced composite components from the aeronautic industry by binary semantic segmentation. We first apply data pre-processing, and then employ a modified 3D U-Net, representing a convolutional neural network. Tweaking hyper-parameters, we have reached an optimal model for our datasets. One of the models has reached 99% segmentation accuracy when testing using a Dice function. In our experiments, pores and fibres in XCT datasets of aerospace components, more specifically of glass and carbon fibre reinforced composites, were segmented and analysed. In order to compare this modified 3D U-Net architecture with segmentation methods currently used in the industry, the datasets were also input to conventional Otsu thresholding. Our results shows that modified 3D U-Net performs better than Otsu thresholding, especially on the segmentation of small pores. Modified 3D U-Net also showed reasonable prediction accuracy when testing with an optimised model which was trained with a low number of dataset both for fibre and pore segmentation.
Even though it is a crucial step for achieving suitable results, the preprocessing of data before it is used as input to deep neural networks is often only described as a side note. This work elaborates on the required steps in this preprocessing procedure. Specifically, we provide insights into the selection of appropriate segmentation algorithms to generate reference volumes from X-ray computed tomography (XCT) scans as training data. Furthermore, this work evaluates the criteria for the selection of an appropriate deep learning network architecture, and a quantitative comparison between networks based on U-Net and V-Net.
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