A data-driven machine learning approach to predicting stacking faulting energy in austenitic steels
Abstract
Not provided.
- Authors:
- Publication Date:
- Research Org.:
- Texas A & M Univ., College Station, TX (United States). Texas A & M Engineering Experiment Station
- Sponsoring Org.:
- USDOE Office of Fossil Energy (FE)
- OSTI Identifier:
- 1533354
- DOE Contract Number:
- FE0008719
- Resource Type:
- Journal Article
- Journal Name:
- Journal of Materials Science
- Additional Journal Information:
- Journal Volume: 52; Journal Issue: 18; Journal ID: ISSN 0022-2461
- Publisher:
- Springer
- Country of Publication:
- United States
- Language:
- English
- Subject:
- Materials Science
Citation Formats
Chaudhary, N., Abu-Odeh, A., Karaman, I., and Arróyave, R. A data-driven machine learning approach to predicting stacking faulting energy in austenitic steels. United States: N. p., 2017.
Web. doi:10.1007/s10853-017-1252-x.
Chaudhary, N., Abu-Odeh, A., Karaman, I., & Arróyave, R. A data-driven machine learning approach to predicting stacking faulting energy in austenitic steels. United States. https://doi.org/10.1007/s10853-017-1252-x
Chaudhary, N., Abu-Odeh, A., Karaman, I., and Arróyave, R. 2017.
"A data-driven machine learning approach to predicting stacking faulting energy in austenitic steels". United States. https://doi.org/10.1007/s10853-017-1252-x.
@article{osti_1533354,
title = {A data-driven machine learning approach to predicting stacking faulting energy in austenitic steels},
author = {Chaudhary, N. and Abu-Odeh, A. and Karaman, I. and Arróyave, R.},
abstractNote = {Not provided.},
doi = {10.1007/s10853-017-1252-x},
url = {https://www.osti.gov/biblio/1533354},
journal = {Journal of Materials Science},
issn = {0022-2461},
number = 18,
volume = 52,
place = {United States},
year = {Tue Jun 13 00:00:00 EDT 2017},
month = {Tue Jun 13 00:00:00 EDT 2017}
}
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