Kamilis, Dimitris
Polydorides, Nick
Lee, Susanne
Desjardins, Joseph
2019-09-11
<p>We present progress in fast, high-resolution imaging, material classification, and fault detection using hyperspectral X-ray measurements. Classical X-ray CT approaches rely on data from many projection angles, resulting in long acquisition and reconstruction times. Additionally, conventional CT cannot distinguish between materials with similar densities. However, in additive manufacturing, the majority of materials used are known a priori. This knowledge allows to vastly reduce the data collected and increase<br>
the accuracy of fault detection. In this context, we propose an imaging method for non-destructive testing of materials based on the combination of spectral X-ray CT and discrete tomography. We explore the use of spectral X-ray attenuation models and measurements to recover the characteristic functions of materials in heterogeneous media with piece-wise uniform composition. We show by means of numerical simulation that using spectral measurements from a small number of angles, our approach can alleviate the typical deterioration of spatial resolution and the appearance of streaking artifacts.</p>
https://doi.org/10.5281/zenodo.3408425
oai:zenodo.org:3408425
eng
Zenodo
https://doi.org/10.5281/zenodo.3408424
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
SFFS, Solid Freeform Foundation Symposium 2019, Austin, US, 12-15 August 2019
Spectral X-ray CT for fast NDT using discrete tomography
info:eu-repo/semantics/conferencePaper