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Title: Data clustering for the high-resolution alignment of microstructure and strain fields

Abstract

The emergence of small-scale deformation mapping by a combination of scanning electron microscopy and digital image correlation (SEM-DIC) has enabled full-field investigations into the complex roles of microstructure on microscale deformation mechanisms. However, these investigations are hindered by errors in alignment between the microstructure data, generally acquired by electron backscatter diffraction (EBSD), and the microscale strain data obtained by SEM-DIC. Distortions, stitching artifacts, and spatial resolution differences between microstructure and strain fields can lead to misalignments on the order of μms. This alignment uncertainty introduces error into microstructure-strain localization analyses and precludes the examination of deformation mechanisms near grain boundaries. To improve alignment between EBSD-obtained grain boundaries and SEM-DIC strain data, an approach was created wherein a mantle was first established around the EBSD-acquired grain boundaries. Strain data was then clustered within this mantle using a k-means algorithm to identify grain boundary strain localization, and these boundary points were fit to obtain a continuous curve for each individual boundary. Clustered point outliers, such as those due to grain boundary-local dislocation slip, were statistically identified by using an absolute error threshold and removed from the grain boundary fit. The ending identification of grain boundaries in the microscale strain data is significantlymore » improved from EBSD-identified boundaries.« less

Authors:
 [1];  [2]
  1. Univ. of Michigan, Ann Arbor, MI (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  2. Univ. of California, Santa Barbara, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1598953
Alternate Identifier(s):
OSTI ID: 1577221
Report Number(s):
LLNL-JRNL-797192
Journal ID: ISSN 1044-5803; 998699
Grant/Contract Number:  
AC52-07NA27344; SC0013971
Resource Type:
Accepted Manuscript
Journal Name:
Materials Characterization
Additional Journal Information:
Journal Volume: 158; Journal Issue: C; Journal ID: ISSN 1044-5803
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; Grain boundary alignment; Electron backscatter diffraction; Digital image correlation; K-means clustering

Citation Formats

Linne, Marissa A., and Daly, Samantha. Data clustering for the high-resolution alignment of microstructure and strain fields. United States: N. p., 2019. Web. doi:10.1016/j.matchar.2019.109984.
Linne, Marissa A., & Daly, Samantha. Data clustering for the high-resolution alignment of microstructure and strain fields. United States. https://doi.org/10.1016/j.matchar.2019.109984
Linne, Marissa A., and Daly, Samantha. Sun . "Data clustering for the high-resolution alignment of microstructure and strain fields". United States. https://doi.org/10.1016/j.matchar.2019.109984. https://www.osti.gov/servlets/purl/1598953.
@article{osti_1598953,
title = {Data clustering for the high-resolution alignment of microstructure and strain fields},
author = {Linne, Marissa A. and Daly, Samantha},
abstractNote = {The emergence of small-scale deformation mapping by a combination of scanning electron microscopy and digital image correlation (SEM-DIC) has enabled full-field investigations into the complex roles of microstructure on microscale deformation mechanisms. However, these investigations are hindered by errors in alignment between the microstructure data, generally acquired by electron backscatter diffraction (EBSD), and the microscale strain data obtained by SEM-DIC. Distortions, stitching artifacts, and spatial resolution differences between microstructure and strain fields can lead to misalignments on the order of μms. This alignment uncertainty introduces error into microstructure-strain localization analyses and precludes the examination of deformation mechanisms near grain boundaries. To improve alignment between EBSD-obtained grain boundaries and SEM-DIC strain data, an approach was created wherein a mantle was first established around the EBSD-acquired grain boundaries. Strain data was then clustered within this mantle using a k-means algorithm to identify grain boundary strain localization, and these boundary points were fit to obtain a continuous curve for each individual boundary. Clustered point outliers, such as those due to grain boundary-local dislocation slip, were statistically identified by using an absolute error threshold and removed from the grain boundary fit. The ending identification of grain boundaries in the microscale strain data is significantly improved from EBSD-identified boundaries.},
doi = {10.1016/j.matchar.2019.109984},
journal = {Materials Characterization},
number = C,
volume = 158,
place = {United States},
year = {Sun Dec 01 00:00:00 EST 2019},
month = {Sun Dec 01 00:00:00 EST 2019}
}

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Cited by: 6 works
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Works referencing / citing this record:

Grain Boundary Sliding and Slip Transmission in High Purity Aluminum
journal, May 2019