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 »
- Authors:
-
- Univ. of Michigan, Ann Arbor, MI (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- 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}
}
Web of Science
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Works referencing / citing this record:
Grain Boundary Sliding and Slip Transmission in High Purity Aluminum
journal, May 2019
- Linne, M. A.; Venkataraman, A.; Sangid, M. D.
- Experimental Mechanics, Vol. 59, Issue 5