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Nicola Viganò1,2,3, Alexandre Tanguy4, Simon Hallais4

  • 1MATEIS, INSA Lyon, CNRS (UMR5510), Univ. Lyon, F-69621 Lyon, France.

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This study applies a new mathematical framework to X-ray diffraction data for detailed 3D grain microstructure analysis. The method offers fast, isotropic imaging of evolving materials, improving upon existing X-ray diffraction contrast tomography.

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Area of Science:

  • Materials Science
  • Crystallography
  • Physics

Background:

  • X-ray orientation microscopy (XOM) is a powerful technique for analyzing material microstructures.
  • Existing methods like X-ray diffraction contrast tomography have limitations in resolving complex grain structures.

Purpose of the Study:

  • To apply a novel mathematical framework for full-field X-ray orientation microscopy to experimental near-field diffraction data.
  • To extend the applicability of X-ray diffraction imaging to materials with sub-grains and intra-granular orientation spreads.

Main Methods:

  • Grain-by-grain tomographic reconstructions in six-dimensional position-orientation space using convex optimization.
  • Derivation of 3D orientation maps from 6D reconstruction data and assembly into a common sample volume.
  • Comparison of 3D orientation maps with Electron Backscatter Diffraction (EBSD) surface maps to quantify discrepancies.

Main Results:

  • Successful application of the new framework to polycrystalline sample data, yielding 3D orientation maps.
  • Quantification of local misorientations and grain boundary position discrepancies compared to EBSD.
  • Demonstration of the method's ability to handle sub-grain and intra-granular orientation spreads up to a few degrees.

Conclusions:

  • The new framework enhances X-ray diffraction imaging, enabling detailed characterization of complex material microstructures.
  • The method allows for fast, isotropic 3D characterization and time-lapse observations of microstructural evolution.
  • The approach bypasses traditional orientation indexation, working directly with experimental raw data.