Modern data analytics approach to predict creep of high-temperature alloys. (15th April 2019)

• Record Type: Journal Article
• Authors: Shin, D.; Yamamoto, Y.; Brady, M.P.; Lee, S.; Haynes, J.A.
• Is Part Of: Acta materialia. Volume 168(2019)
• Page Start: 321
• Page End: 330
• Publication Date: 2019-04-15
• Subjects: High-temperature alloys -- Creep -- Correlation analysis -- Machine learning -- Features -- Computational thermodynamics; Materials -- Periodicals; Materials science -- Periodicals; Materials -- Mechanical properties -- Periodicals; Metallurgy -- Periodicals; Chemistry, Inorganic -- Periodicals; 620.112
• DOI: 10.1016/j.actamat.2019.02.017
• Languages: English
• ISSNs: 1359-6454

Abstract

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. 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. Graphical abstract: Image 1 Highlights: High-quality experimental creep data for training various machine learning models. Correlation analysis to rank features that affect alloy creep represented as Larson-Miller Parameters (LMPs). High-throughput CALPHAD approach to populate scientific alloy features. Alloy hypothsis generation to design advanced high-temperature alloys.