Reconstructing dual-phase nanometer scale grains within a pearlitic steel tip in 3D through 4D-scanning precession electron diffraction tomography and automated crystal orientation mapping - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Ultramicroscopy Année : 2022

Reconstructing dual-phase nanometer scale grains within a pearlitic steel tip in 3D through 4D-scanning precession electron diffraction tomography and automated crystal orientation mapping

Résumé

The properties of polycrystalline materials are related to their microstructures and hence a complete description, including size, shape, and orientation of the grains, is necessary to understand the behavior of materials. Here, we use Scanning Precession Electron Diffraction (SPED) in the Transmission Electron Microscope (TEM) combined with a tilt series to reconstruct individual grains in 3D within a polycrystalline dual-phase cold wire-drawn pearlitic steel sample. Nanoscale ferrite grains and intragranular cementite particles were indexed using an Automated Crystallographic Orientation Mapping (ACOM) tool for each tilt dataset. The grain orientations were tracked through the tilt datasets and projections of the individual grains were reconstructed from the diffraction data using an orientation-specific Virtual Dark Field (VDF) approach for tomographic reconstruction. The algorithms used to process and reconstruct such datasets are presented. These algorithms represent an extension to the ACOM approach that may be straightforwardly applied to other multi-phase polycrystalline materials to enable 3D spatial and orientation reconstructions.
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Dates et versions

hal-03763041 , version 1 (25-10-2022)

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Patrick Harrison, Xuyang Zhou, Saurabh Mohan Das, Pierre Lhuissier, Christian H. Liebscher, et al.. Reconstructing dual-phase nanometer scale grains within a pearlitic steel tip in 3D through 4D-scanning precession electron diffraction tomography and automated crystal orientation mapping. Ultramicroscopy, 2022, 238, pp.113536. ⟨10.1016/j.ultramic.2022.113536⟩. ⟨hal-03763041⟩
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