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Wassermann, Günter; Grewen, Johanna

doi:10.1007/978-3-662-13128-2_5

Cited by 1 publication

(1 citation statement)

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“…There is even a maximum physically reasonable tilt angle due to the diffraction setup and incidence angle, resulting in reduced intensity related to the increased attenuation in the elongated irradiation path and the defocussing effect 11 , 12 . This measurement limitation requires accurate pole plot reconstruction algorithms, the calculation of orientation distributions, or measurement adaptations to provide consistent and complete pole plots 13 , 14 .…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning

Meier

Ragunathan

Degener

et al. 2023

Sci Rep

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X-ray diffraction crystallography allows non-destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X-ray diffraction has been highly time-consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep-learning-based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU-based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.

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Section: Introductionmentioning

confidence: 99%