Gaussian-Process-Driven Adaptive Sampling for Reduced-Order Modeling of Texture Effects in Polycrystalline Alpha-Ti

Data-driven tools for finding structure–property (S–P) relations, such as the Materials Knowledge System (MKS) framework, can accelerate materials design, once the costly and technical calibration process has been completed. A three-model method is proposed to reduce the expense of S–P relation mode...

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Bibliographic Details
Published in:JOM (1989) 2019-08, Vol.71 (8), p.2646-2656
Main Authors: Tallman, Aaron E., Stopka, Krzysztof S., Swiler, Laura P., Wang, Yan, Kalidindi, Surya R., McDowell, David L.
Format: Article
Language:English
Subjects:
Adaptive sampling
BASIC BIOLOGICAL SCIENCES
Calibration
Chemistry/Food Science
Data collection
Datasets
Design of experiments
Earth Sciences
Engineering
Environment
Gaussian process
Homogenization
Localization
Microstructure
Multiscale Computational Strategies for Heterogeneous Materials with Defects: Coupling Modeling with Experiments and Uncertainty Quantification
Physics
Principal components analysis
Simulation
Texture
Yield stress
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Summary:Data-driven tools for finding structure–property (S–P) relations, such as the Materials Knowledge System (MKS) framework, can accelerate materials design, once the costly and technical calibration process has been completed. A three-model method is proposed to reduce the expense of S–P relation model calibration: (1) direct simulations are performed as per (2) a Gaussian process-based data collection model, to calibrate (3) an MKS homogenization model in an application to α-Ti. The new methods are compared favorably with expert texture selection on the performance of the so-calibrated MKS models. Benefits for the development of new and improved materials are discussed.
ISSN:1047-4838
1543-1851
DOI:10.1007/s11837-019-03553-1