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Your search keyword '"Yamamoto, Yukinori"' showing total 342 results
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342 results on '"Yamamoto, Yukinori"'

5. Coupling Physics in Machine Learning to Predict Properties of High-temperatures Alloys

Author

Peng, Jian, Yamamoto, Yukinori, Hawk, Jeffrey A., Lara-Curzio, Edgar, and Shin, Dongwon

Subjects
Condensed Matter - Materials Science, Physics - Computational Physics
Abstract

High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperature alloys with an example of the 9-12 wt.% Cr steels yield strength. We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset. Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism. As part of the verification process, the consistency of sub-datasets has been extensively evaluated with respect to temperature and then refined for the boundary conditions of trained ML models. The predicted yield strength of 9Cr steels using the ML models is in excellent agreement with experiments. The current approach introduces physically meaningful constraints in interrogating the trained ML models to predict properties of hypothetical alloys when applied to data-driven materials.

Published
2020
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7. Modern Data Analytics Approach to Predict Creep of High-Temperature Alloys

Author

Shin, Dongwon, Yamamoto, Yukinori, Brady, Michael P., Lee, Sangkeun, and Haynes, J. Allen

Subjects
Condensed Matter - Materials Science
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.

Published
2018
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34. Mechanical Properties and Deformation Behavior of Additively Manufactured 316L Stainless Steel (FY2020)

Author

Byun, T., primary, Gussev, Maxim, additional, Lach, Tim, additional, Mcalister, Michael, additional, Simpson, Joseph, additional, Garrison, Ben, additional, Yamamoto, Yukinori, additional, Joslin, Chase, additional, Carver, J., additional, List III, Fred, additional, Dehoff, Ryan, additional, Terrani, K., additional, Li, Meimei, additional, and Zhang, Xuan, additional

Published
2020
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