Abstract
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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