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Ultra-fast quantification of polycrystalline texture via single shot synchrotron X-ray or neutron diffraction

Abstract:
Tracking texture evolution during in situ loading is critical to understand and simulate the dynamic behaviour of microstructure in polycrystalline materials, yet conventional texture quantification methods are sometimes restricted due to various factors, such as acquisition time, sample environment and complex setup. To address this, a novel approach to extract texture information from single shot Time-Of-Flight neutron diffraction pattern has been developed. Another texture analysis approach based on single shot synchrotron X-ray diffraction has also been demonstrated. The effectiveness of two methods is assessed for polycrystalline Nickel-based superalloy polycrystalline samples possessing different textures. Both methods feature a moderate acquisition time of ~10 min and 30 s respectively, as well as a simplified setup which allows adding complex sample environments and the use of additional equipment. Comparison with the referential EBSD texture suggests that both approaches achieve a satisfactory match, though some details of the complex contour profiles in inverse pole figures may be missing. Besides that, a novel metric has been proposed to quantify the matching quality of pole figures. By employing the EPSC modelling approach, it is shown that the texture deviation due to the technique chosen for its evaluation exerts a subtle influence on th macro- and mesoscale simulation results, highlighting the significance of this approach for underpinning robust computational modelling.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.matchar.2022.111827

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Trinity College
Role:
Author
ORCID:
0000-0002-3558-5198


Publisher:
Elsevier
Journal:
Materials Characterization More from this journal
Volume:
186
Article number:
111827
Publication date:
2022-03-04
Acceptance date:
2022-02-28
DOI:
EISSN:
1873-4189
ISSN:
1044-5803


Language:
English
Keywords:
Pubs id:
1243820
Local pid:
pubs:1243820
Deposit date:
2022-06-26
ARK identifier:

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