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Image-driven discriminative and generative methods for establishing microstructure-processing relationships relevant to nuclear fuel processing pipelines

Published online by Cambridge University Press:  30 July 2021

Elizabeth Kautz ,
Wufei Ma ,
Arun Baskaran ,
Aritra Chowdhury ,
Vineet Joshi ,
Bulent Yener  and
Daniel Lewis
Elizabeth Kautz
Affiliation:
Pacific Northwest National Laboratory - PNNL, Richland, Washington, United States
Wufei Ma
Affiliation:
Rensselaer Polytechnic Institute, Troy, New York, United States
Arun Baskaran
Affiliation:
Rensselaer Polytechnic Institute, Troy, New York, United States
Aritra Chowdhury
Affiliation:
GE Global Research, Niskayuna, New York, United States
Vineet Joshi
Affiliation:
Pacific Northwest National Laboratory - PNNL, Richland, Washington, United States
Bulent Yener
Affiliation:
Rensselaer Polytechnic Institute, Troy, New York, United States
Daniel Lewis
Affiliation:
Rensselaer Polytechnic Institute, Troy, New York, United States

Abstract

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Type
Evaluation of Materials for Nuclear Applications
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 2128 - 2130
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Chowdhury, Aritra, et al. “Image driven machine learning methods for microstructure recognition.” Computational Materials Science 123 (2016): 176-187.CrossRefGoogle Scholar
Ma, Wufei, et al. “Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships.” Journal of Applied Physics 128.13 (2020): 134901.Google Scholar