Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning-Reference-Cited by-同舟云学术

Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning

Published:2020-01-31 Issue:1 Volume:21 Page:626-634
ISSN:1468-6996
Container-title:Science and Technology of Advanced Materials
language:en
Short-container-title:Science and Technology of Advanced Materials

Author:

Wang Xin¹^ORCID,Xiong Wei¹^ORCID

Affiliation:

1. Physical Metallurgy and Materials Design Laboratory, Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, USA

Funder

National Science Foundation

University of Pittsburgh

Publisher

Informa UK Limited

Subject

General Materials Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/14686996.2020.1808433

Reference101 articles.

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4. Effects of deformation induced phase transformation and twinning on the mechanical properties of austenitic Fe-Mn-Al alloys.

5. Hierarchical microstructure design to tune the mechanical behavior of an interstitial TRIP-TWIP high-entropy alloy

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