A high-throughput technique for determining grain boundary character non-destructively in microstructures with through-thickness grains

Seita, Matteo; Volpi, Marco; Patala, Srikanth; McCue, Ian; Schuh, Christopher A.; Diamanti, Maria Vittoria; Erlebacher, Jonah; Demkowicz, Michael J.

doi:10.1038/npjcompumats.2016.16

Title: A high-throughput technique for determining grain boundary character non-destructively in microstructures with through-thickness grains

Journal Article · 23 June 2016 · npj Computational Materials

DOI: https://doi.org/10.1038/npjcompumats.2016.16 · OSTI ID:1437260

^[1]; Volpi, Marco ^[2]; Patala, Srikanth ^[3]; McCue, Ian ^[4]; Schuh, Christopher A. ^[5]; Diamanti, Maria Vittoria ^[2]; Erlebacher, Jonah ^[4]; Demkowicz, Michael J. ^[6]

Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Materials and Chemical Engineering 'G Natta', Politecnico di Milano, Milan (Italy)
North Carolina State Univ., Raleigh, NC (United States)
Johns Hopkins Univ., Baltimore, MD (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Texas A & M Univ., College Station, TX (United States)

Grain boundaries (GBs) govern many properties of polycrystalline materials. However, because of their structural variability, our knowledge of GB constitutive relations is still very limited. We present a novel method to characterise the complete crystallography of individual GBs non-destructively, with high-throughput, and using commercially available tools. This method combines electron diffraction, optical reflectance and numerical image analysis to determine all five crystallographic parameters of numerous GBs in samples with through-thickness grains. We demonstrate the technique by measuring the crystallographic character of about 1,000 individual GBs in aluminum in a single run. Our method enables cost- and time-effective assembly of crystallography–property databases for thousands of individual GBs. Furthermore, such databases are essential for identifying GB constitutive relations and for predicting GB-related behaviours of polycrystalline solids.

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Research Organization:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0008926

OSTI ID:: 1437260

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 2; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Constrained Bayesian Nonparametric Regression for Grain Boundary Energy Predictions Wang, Haoyu; Patala, Srikanth; Reich, Brian J. arXiv https://doi.org/10.48550/arxiv.2002.00938	preprint	January 2020

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