PolyProc: A Modular Processing Pipeline for X-ray Diffraction Tomography

Direct imaging of three-dimensional microstructure via X-ray diffraction-based techniques gives valuable insight into the crystallographic features that influence materials properties and performance. For instance, X-ray diffraction tomography provides information on grain orientation, position, siz...

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Bibliographic Details
Published in:Integrating materials and manufacturing innovation 2019-09, Vol.8 (3), p.388-399
Main Authors: Kang, Jiwoong, Lu, Ning, Loo, Issac, Senabulya, Nancy, Shahani, Ashwin J.
Format: Article
Language:English
Subjects:
3D Materials Science 2019
Algorithms
Aluminum base alloys
Characterization and Evaluation of Materials
Chemistry and Materials Science
Coarsening
Copper
Crystallography
Grain orientation
Material properties
Materials Science
Metallic Materials
Nanotechnology
Optimization
Structural Materials
Surfaces and Interfaces
Thematic Section: 3D Materials Science
Thin Films
Tomography
Topology
X-ray diffraction
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Summary:Direct imaging of three-dimensional microstructure via X-ray diffraction-based techniques gives valuable insight into the crystallographic features that influence materials properties and performance. For instance, X-ray diffraction tomography provides information on grain orientation, position, size, and shape in a bulk specimen. As such techniques become more accessible to researchers, demands are placed on processing the datasets that are inherently “noisy,” multi-dimensional, and multimodal. To fulfill this need, we have developed a one-of-a-kind function package, PolyProc, that is compatible with a range of data shapes, from planar sections to time-evolving and three-dimensional orientation data. Our package comprises functions to import, filter, analyze, and visualize the reconstructed grain maps. To accelerate the computations in our pipeline, we harness computationally efficient approaches: for instance, data alignment is done via genetic optimization; grain tracking through the Hungarian method; and feature-to-feature correlation through k -nearest neighbors algorithm. As a proof-of-concept, we test our approach in characterizing the grain texture, topology, and evolution in a polycrystalline Al–Cu alloy undergoing coarsening.
ISSN:2193-9764
2193-9772
DOI:10.1007/s40192-019-00147-2