Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys-Reference-Cited by-同舟云学术

Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

Published:2023 Issue:23 Volume:25 Page:15970-15987
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Xia Zeyu¹^ORCID,Ma Kan²,Cheng Sibo³^ORCID,Blackburn Thomas²^ORCID,Peng Ziling⁴,Zhu Kewei⁵,Zhang Weihang³,Xiao Dunhui⁶,Knowles Alexander J²,Arcucci Rossella⁷

Affiliation:

1. Queensland University of Technology, Queensland 4006, Australia

2. School of Metallurgy and Materials, University of Birmingham, Birmingham B15 2SQ, UK

3. Data Science Institute, Department of Computing, Imperial College London, London SW7 2AZ, UK

4. Institute of Advanced Science Facilities, Shenzhen 518107, P. R. China

5. Department of Computer Science, University of York, York Y010 5DD, UK

6. School of Mathematical Sciences, Tongji University, Shanghai 200092, P. R. China

7. Department of Earth science & engineering, Imperial College London, London SW7 2BP, UK

Abstract

The performance of advanced materials for extreme environments is underpinned by their microstructure, such as the size and distribution of nano- to micro-sized reinforcing phase(s).

Funder

Horizon 2020

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2023/CP/D3CP00402C

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