RESUMEN
Laboratory based X-ray micro-CT is a non-destructive testing method that enables three dimensional visualization and analysis of the internal and external morphology of samples. Although a wide variety of commercial scanners exist, most of them are limited in the number of degrees of freedom to position the source and detector with respect to the object to be scanned. Hence, they are less suited for industrial X-ray imaging settings that require advanced scanning modes, such as laminography, conveyor belt scanning, or time-resolved imaging (4DCT). We introduce a new X-ray scanner FleXCT that consists of a total of ten motorized axes, which allow a wide range of non-standard XCT scans such as tiled and off-centre scans, laminography, helical tomography, conveyor belt, dynamic zooming, and X-ray phase contrast imaging. Additionally, a new software tool 'FlexRayTools' was created that enables reconstruction of non-standard XCT projection data of the FleXCT instrument using the ASTRA Toolbox, a highly efficient and open source set of tools for tomographic projection and reconstruction.
RESUMEN
We present visual methods for the analysis and comparison of the results of curved fibre reconstruction algorithms, i.e., of algorithms extracting characteristics of curved fibres from X-ray computed tomography scans. In this work, we extend previous methods for the analysis and comparison of results of different fibre reconstruction algorithms or parametrisations to the analysis of curved fibres. We propose fibre dissimilarity measures for such curved fibres and apply these to compare multiple results to a specified reference. We further propose visualisation methods to analyse differences between multiple results quantitatively and qualitatively. In two case studies, we show that the presented methods provide valuable insights for advancing and parametrising fibre reconstruction algorithms, and support in improving their results in characterising curved fibres.
RESUMEN
We present a new approach to estimate geometry parameters of glass fibers in glass fiber-reinforced polymers from simulated X-ray micro-computed tomography scans. Traditionally, these parameters are estimated using a multi-step procedure including image reconstruction, pre-processing, segmentation and analysis of features of interest. Each step in this chain introduces errors that propagate through the pipeline and impair the accuracy of the estimated parameters. In the approach presented in this paper, we reconstruct volumes from a low number of projection angles using an iterative reconstruction technique and then estimate position, direction and length of the contained fibers incorporating a priori knowledge about their shape, modeled as a geometric representation, which is then optimized. Using simulation experiments, we show that our method can estimate those representations even in presence of noisy data and only very few projection angles available.
