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Automated Analysis of Grain Growth Under in-situ Irradiation Using Convolutional Neural Network

Published online by Cambridge University Press:  22 July 2022

Xinyuan Xu ,
Zefeng Yu ,
Arthur Motta  and
Xing Wang
Xinyuan Xu
Affiliation:
Ken and Mary Alice Lindquist Department of Nuclear Engineering, The Pennsylvania State University, University Park, PA, USA
Zefeng Yu
Affiliation:
Ken and Mary Alice Lindquist Department of Nuclear Engineering, The Pennsylvania State University, University Park, PA, USA
Arthur Motta
Affiliation:
Ken and Mary Alice Lindquist Department of Nuclear Engineering, The Pennsylvania State University, University Park, PA, USA
Xing Wang*
Affiliation:
Ken and Mary Alice Lindquist Department of Nuclear Engineering, The Pennsylvania State University, University Park, PA, USA
*
*Corresponding author: xvw5285@psu.edu

Abstract

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Type
Correlative Microscopy and High-Throughput Characterization for Accelerated Development of Materials in Extreme Environments
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 2036 - 2037
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Copyright
Copyright © Microscopy Society of America 2022

References

Shen, M et al. , Computational Materials Science 197 (2021). https://doi.org/10.1016/j.commatsci.2021.110560CrossRefGoogle Scholar
Haley, J C. et al. , Acta Materialia 136 (2017). https://doi.org/10.1016/j.actamat.2017.07.011CrossRefGoogle Scholar
Ronneberger, O., Fischer, P. and Brox, T., Lecture Notes in Computer Science 9351 (2015). https://doi.org/10.1007/978-3-319-24574-4_28Google Scholar