Physical Chemistry Chemical Physics, volume 25, issue 23, pages 15970-15987

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

Zeyu Xia ¹

Kan Ma ²

Sibo Cheng ³

Thomas Blackburn ²

Ziling Peng ⁴

Kewei Zhu ⁵

Weihang Zhang ³

Dunhui Xiao ⁶

Alexander J Knowles ²

Rossella Arcucci ⁷

Show full list: 10 authors

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Queensland University of Technology, Queensland 4006, Australia |

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

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 |

Publication type: Journal Article

Publication date: 2023-05-26

Royal Society of Chemistry (RSC)

Journal: Physical Chemistry Chemical Physics

scimago Q2

SJR: 0.721

CiteScore: 5.5

Impact factor: 2.9

ISSN: 14639076, 14639084

DOI: 10.1039/d3cp00402c

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PubMed ID: 37265373

Physical and Theoretical Chemistry

General Physics and Astronomy

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). Chromium-based superalloys are a recently...

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Publisher

Royal Society of Chemistry (RSC)

Journal

Physical Chemistry Chemical Physics

scimago Q2

SJR

0.721

CiteScore

5.5

Impact factor

2.9

ISSN

14639076 (Print)

14639084 (Electronic)

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

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