Journal article
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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- Files:
-
-
(Preview, Accepted manuscript, pdf, 9.1MB, Terms of use)
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- Publisher copy:
- 10.1016/j.matchar.2022.111827
Authors
- 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:
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1873-4189
- ISSN:
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1044-5803
- Language:
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English
- Keywords:
- Pubs id:
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1243820
- Local pid:
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pubs:1243820
- Deposit date:
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2022-06-26
- ARK identifier:
Terms of use
- Copyright holder:
- Elsevier Inc.
- Copyright date:
- 2022
- Rights statement:
- © 2022 Elsevier Inc. All rights reserved.
- Notes:
-
This is the accepted manuscript version of the article. The final version is available from Elsevier at https://doi.org/10.1016/j.matchar.2022.111827
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