About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
|
Symposium
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Artificial Intelligence Applications in Integrated Computational Materials Engineering
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Presentation Title |
Prediction of Material Parameters Using Machine Learning Supported by Large-Scale Phase-Field Simulations of Dendrite Growth |
Author(s) |
Haruki Yano, Souta Fujikawa, Ayano Yamamura, Shinji Sakane, Tomohiro Takaki |
On-Site Speaker (Planned) |
Haruki Yano |
Abstract Scope |
In general, material microstructures are observed two-dimensionally on a cross section. Such microstructural images contain a great deal of information because they are formed as a function of the material's inherent parameters and thermomechanical history during processing. In this study, we attempt to estimate material properties, such as solid-liquid interface energy, its anisotropy, and diffusion coefficient of solute, from cross-sectional images of dendrites formed during alloy solidification. To enable the parameter inference, we use the convolutional neural network (CNN). Learning process of the CNN utilizes the vast number of images obtained from large-scale phase-field simulations of columnar dendrite growth during directional solidification of a binary alloy. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Modeling and Simulation, Solidification |