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

Journal Article · 18 February 2019 · Acta Materialia

DOI: https://doi.org/10.1016/j.actamat.2019.02.017 · OSTI ID:1502571

Shin, Dongwon  ^[1]; Yamamoto, Yukinori ^[1]; Brady, Michael P. ^[1]; Lee, Sangkeun ^[1]; Haynes, James A. ^[1]

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1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1502571
• Journal Information: Acta Materialia, Journal Name: Acta Materialia Journal Issue: C Vol. 168; ISSN 1359-6454
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English