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A flexible and standalone forward simulation model for laboratory X-ray diffraction contrast tomography.

Haixing FangDorte Juul JensenYubin Zhang
Published in: Acta crystallographica. Section A, Foundations and advances (2020)
Laboratory X-ray diffraction contrast tomography (LabDCT) has recently been developed as a powerful technique for non-destructive mapping of grain microstructures in bulk materials. As the grain reconstruction relies on segmentation of diffraction spots, it is essential to understand the physics of the diffraction process and resolve all the spot features in detail. To this aim, a flexible and standalone forward simulation model has been developed to compute the diffraction projections from polycrystalline samples with any crystal structure. The accuracy of the forward simulation model is demonstrated by good agreements in grain orientations, boundary positions and shapes between a virtual input structure and that reconstructed based on the forward simulated diffraction projections of the input structure. Further experimental verification is made by comparisons of diffraction spots between simulations and experiments for a partially recrystallized Al sample, where a satisfactory agreement is found for the spot positions, sizes and intensities. Finally, applications of this model to analyze specific spot features are presented.
Keyphrases
  • crystal structure
  • electron microscopy
  • high resolution
  • magnetic resonance
  • deep learning
  • magnetic resonance imaging
  • machine learning
  • convolutional neural network