@article{LaquaiSchauppGriescheetal.2022,
  author    = {Laquai, Ren{\´e} and Schaupp, Thomas and Griesche, Axel and M{\"u}ller, Bernd R. and Kupsch, Andreas and Hannemann, Andreas and Kannengiesser, Thomas and Bruno, Giovanni},
  title     = {Quantitative analysis of hydrogen-assisted microcracking in duplex stainless steel through X-ray refraction 3D imaging},
  series = {Advanced engineering materials},
  volume    = {24},
  journal   = {Advanced engineering materials},
  number    = {6},
  publisher = {Wiley-VCH},
  address   = {Weinheim},
  issn      = {1527-2648},
  doi       = {10.1002/adem.202101287},
  pages     = {10},
  year      = {2022},
  abstract  = {While the problem of the identification of mechanisms of hydrogen-assisted damage has and is being thoroughly studied, the quantitative analysis of such damage still lacks suitable tools. In fact, while, for instance, electron microscopy yields excellent characterization, the quantitative analysis of damage requires at the same time large field-of-views and high spatial resolution. Synchrotron X-ray refraction techniques do possess both features. Herein, it is shown how synchrotron X-ray refraction computed tomography (SXRCT) can quantify damage induced by hydrogen embrittlement in a lean duplex steel, yielding results that overperform even those achievable by synchrotron X-ray absorption computed tomography. As already reported in the literature, but this time using a nondestructive technique, it is shown that the hydrogen charge does not penetrate to the center of tensile specimens. By the comparison between virgin and hydrogen-charged specimens, it is deduced that cracks in the specimen bulk are due to the rolling process rather than hydrogen-assisted. It is shown that (micro)cracks propagate from the surface of tensile specimens to the interior with increasing applied strain, and it is deduced that a significant crack propagation can only be observed short before rupture.},
  language  = {en}
}
@article{MagkosKupschBruno2021,
  author    = {Magkos, Sotirios and Kupsch, Andreas and Bruno, Giovanni},
  title     = {Suppression of cone-beam artefacts with Direct Iterative Reconstruction Computed Tomography Trajectories (DIRECTT)},
  series = {Journal of imaging : open access journal},
  volume    = {7},
  journal   = {Journal of imaging : open access journal},
  number    = {8},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2313-433X},
  doi       = {10.3390/jimaging7080147},
  pages     = {9},
  year      = {2021},
  abstract  = {The reconstruction of cone-beam computed tomography data using filtered back-projection algorithms unavoidably results in severe artefacts. We describe how the Direct Iterative Reconstruction of Computed Tomography Trajectories (DIRECTT) algorithm can be combined with a model of the artefacts for the reconstruction of such data. The implementation of DIRECTT results in reconstructed volumes of superior quality compared to the conventional algorithms.},
  language  = {en}
}
@article{EvsevleevPaciornikBruno2020,
  author    = {Evsevleev, Sergei and Paciornik, Sidnei and Bruno, Giovanni},
  title     = {Advanced deep learning-based 3D microstructural characterization of multiphase metal matrix composites},
  series = {Advanced engineering materials},
  volume    = {22},
  journal   = {Advanced engineering materials},
  number    = {4},
  publisher = {Wiley-VCH},
  address   = {Weinheim},
  issn      = {1438-1656},
  doi       = {10.1002/adem.201901197},
  pages     = {6},
  year      = {2020},
  abstract  = {The quantitative analysis of microstructural features is a key to understanding the micromechanical behavior of metal matrix composites (MMCs), which is a premise for their use in practice. Herein, a 3D microstructural characterization of a five-phase MMC is performed by synchrotron X-ray computed tomography (SXCT). A workflow for advanced deep learning-based segmentation of all individual phases in SXCT data is shown using a fully convolutional neural network with U-net architecture. High segmentation accuracy is achieved with a small amount of training data. This enables extracting unprecedently precise microstructural parameters (e.g., volume fractions and particle shapes) to be input, e.g., in micromechanical models.},
  language  = {en}
}