Austenitic parent grain reconstruction in martensitic steel using deep learning-Reference-Cited by-同舟云学术

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Austenitic parent grain reconstruction in martensitic steel using deep learning

Published:2022-03 Issue: Volume:185 Page:111759
ISSN:1044-5803
Container-title:Materials Characterization
language:en
Short-container-title:Materials Characterization

Author:

Fernandez-Zelaia Patxi,Rossy Andrés Márquez,Campbell Quinn,Nycz Andrzej,Ledford Christopher,Kirka Michael M.

Funder

US Department of Energy

Publisher

Elsevier BV

Subject

Mechanical Engineering,Mechanics of Materials,Condensed Matter Physics,General Materials Science

Reference44 articles.

1. Tensorflow: a system for large-scale machine learning;Abadi,2016

2. An approach to prior austenite reconstruction;Abbasi;Mater. Charact.,2012

3. Ebsd and reconstruction of pre-transformation microstructures, examples and complexities in steels;Abbasi;Mater. Charact.,2014

4. Deep learning for plasticity and thermo-viscoplasticity;Abueidda;Int. J. Plast.,2021

5. Effect of build geometry on the β-grain structure and texture in additive manufacture of ti6al4v by selective electron beam melting;Antonysamy;Mater. Charact.,2013

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1. Texture Analysis of Hot Rolled Martensitic and Bainitic Steels by Electron Backscatter Diffraction to Quantify Parent Austenite States;steel research international;2024-05

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3. Nucleation and growth of metallic crystals in metallurgy and materials processing;Nucleation and Growth in Applied Materials;2024

4. Design Strategy for Art Copper Alloys’ Colors Through Machine Learning and Oxidation Treatment;JOM;2023-03-14

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