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Title: Machine learning technique to identify grains in polycrystalline materials samples

Abstract

A method of identifying grains in polycrystalline materials, the method including (a) identifying local crystal structure of the polycrystalline material based on neighbor coordination or pattern recognition machine learning, the local crystal structure including grains and grain boundaries, (b) pre-processing the grains and the grain boundaries using image processing techniques, (c) conducting grain identification using unsupervised machine learning; and (d) refining a resolution of the grain boundaries.

Inventors:
; ; ;
Issue Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1771645
Patent Number(s):
10839195
Application Number:
15/672,168
Assignee:
UChicago Argonne, LLC (Chicago, IL)
Patent Classifications (CPCs):
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
G - PHYSICS G16 - INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS G16C - COMPUTATIONAL CHEMISTRY
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Patent
Resource Relation:
Patent File Date: 08/08/2017
Country of Publication:
United States
Language:
English

Citation Formats

Sankaranarayanan, Subramanian, Cherukara, Mathew J., Narayanan, Badri, and Chan, Henry. Machine learning technique to identify grains in polycrystalline materials samples. United States: N. p., 2020. Web.
Sankaranarayanan, Subramanian, Cherukara, Mathew J., Narayanan, Badri, & Chan, Henry. Machine learning technique to identify grains in polycrystalline materials samples. United States.
Sankaranarayanan, Subramanian, Cherukara, Mathew J., Narayanan, Badri, and Chan, Henry. Tue . "Machine learning technique to identify grains in polycrystalline materials samples". United States. https://www.osti.gov/servlets/purl/1771645.
@article{osti_1771645,
title = {Machine learning technique to identify grains in polycrystalline materials samples},
author = {Sankaranarayanan, Subramanian and Cherukara, Mathew J. and Narayanan, Badri and Chan, Henry},
abstractNote = {A method of identifying grains in polycrystalline materials, the method including (a) identifying local crystal structure of the polycrystalline material based on neighbor coordination or pattern recognition machine learning, the local crystal structure including grains and grain boundaries, (b) pre-processing the grains and the grain boundaries using image processing techniques, (c) conducting grain identification using unsupervised machine learning; and (d) refining a resolution of the grain boundaries.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {11}
}

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