Artificial intelligence for the prediction of tensile properties by using microstructural parameters in high strength steels-Reference-Cited by-同舟云学术

Artificial intelligence for the prediction of tensile properties by using microstructural parameters in high strength steels

Published:2020-06 Issue: Volume:11 Page:100699
ISSN:2589-1529
Container-title:Materialia
language:en
Short-container-title:Materialia

Author:

Jung Im Doo,Shin Da Seul,Kim Doohee,Lee Jungsub,Lee Min Sik,Son Hye Jin,Reddy N.S.,Kim Moobum,Moon Seung Ki^ORCID,Kim Kyung Tae,Yu Ji-Hun,Kim Sangshik,Park Seong Jin^ORCID,Sung Hyokyung^ORCID

Funder

National Research Foundation of Korea

Ministry of Science and ICT, South Korea

Ministry of Trade, Industry and Energy

Korea Institute of Materials Science

Publisher

Elsevier BV

Subject

General Materials Science

Reference112 articles.

1. Revisiting Hume-Rothery's rules with artificial neural networks;Zhang;Acta Mater.,2008

2. Uncovering nonlinear structure in real-time stock-market indexes: The S&P 500, the DAX, the Nikkei 225, and the FTSE-100;Abhyankar;J. Bus. Econ. Stat.,1997

3. A tutorial on human activity recognition using body-worn inertial sensors;Bulling;ACM Comput. Surv.,2014

4. Machine-learning phase prediction of high-entropy alloys;Huang;Acta Mater.,2019

5. A hybrid computational-experimental approach for automated crystal structure solution;Meredig;Nat. Mater.,2013

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