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Title: Modern data analytics approach to predict creep of high-temperature alloys

Abstract

A breakthrough in alloy design often requires comprehensive understanding in complex multi-component/multi-phase systems to generate novel material hypotheses. We introduce a modern data analytics workflow that leverages high-quality experimental data augmented with advanced features obtained from high-fidelity models. Herein, we use an example of a consistently-measured creep dataset of developmental high-temperature alloy combined with scientific alloy features populated from a high-throughput computational thermodynamic approach. Extensive correlation analyses provide ranking insights for most impactful alloy features for creep resistance, evaluated from a large set of candidate features suggested by domain experts. We also show that we can accurately train machine learning models by integrating high-ranking features obtained from correlation analyses. Consequently, the demonstrated approach can be extended beyond incorporating thermodynamic features, with input from domain experts used to compile lists of features from other alloy physics, such as diffusion kinetics and microstructure evolution.

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [1]
  1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1502571
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Acta Materialia
Additional Journal Information:
Journal Volume: 168; Journal Issue: C; Journal ID: ISSN 1359-6454
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; high-temperature alloys; creep; correlation analysis; machine learning; features; computational thermodynamics

Citation Formats

Shin, Dongwon, Yamamoto, Yukinori, Brady, Michael P., Lee, Sangkeun, and Haynes, James A. Modern data analytics approach to predict creep of high-temperature alloys. United States: N. p., 2019. Web. doi:10.1016/j.actamat.2019.02.017.
Shin, Dongwon, Yamamoto, Yukinori, Brady, Michael P., Lee, Sangkeun, & Haynes, James A. Modern data analytics approach to predict creep of high-temperature alloys. United States. doi:10.1016/j.actamat.2019.02.017.
Shin, Dongwon, Yamamoto, Yukinori, Brady, Michael P., Lee, Sangkeun, and Haynes, James A. Mon . "Modern data analytics approach to predict creep of high-temperature alloys". United States. doi:10.1016/j.actamat.2019.02.017.
@article{osti_1502571,
title = {Modern data analytics approach to predict creep of high-temperature alloys},
author = {Shin, Dongwon and Yamamoto, Yukinori and Brady, Michael P. and Lee, Sangkeun and Haynes, James A.},
abstractNote = {A breakthrough in alloy design often requires comprehensive understanding in complex multi-component/multi-phase systems to generate novel material hypotheses. We introduce a modern data analytics workflow that leverages high-quality experimental data augmented with advanced features obtained from high-fidelity models. Herein, we use an example of a consistently-measured creep dataset of developmental high-temperature alloy combined with scientific alloy features populated from a high-throughput computational thermodynamic approach. Extensive correlation analyses provide ranking insights for most impactful alloy features for creep resistance, evaluated from a large set of candidate features suggested by domain experts. We also show that we can accurately train machine learning models by integrating high-ranking features obtained from correlation analyses. Consequently, the demonstrated approach can be extended beyond incorporating thermodynamic features, with input from domain experts used to compile lists of features from other alloy physics, such as diffusion kinetics and microstructure evolution.},
doi = {10.1016/j.actamat.2019.02.017},
journal = {Acta Materialia},
number = C,
volume = 168,
place = {United States},
year = {2019},
month = {2}
}

Journal Article:
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This content will become publicly available on February 18, 2020
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