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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium Artificial Intelligence Applications in Integrated Computational Materials Engineering
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

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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Combined THz-TDS and Raman Spectroscopy for In-Situ Material Identification via a Machine Learning Algorithm
Conditional Diffusion Models for Interlocking Metasurface Design
Data-Driven Modeling of Dislocations for Multi-Scale Simulations
Data and Decision Science-Driven Assessment and Selection of Mg Alloys for Fracturing Applications
Data Assimilation of Multi-Phase-Field Model based on Physically Informed Neural Network
Data Modelling of Through-Life Structural Integrity Assessment of Dissimilar Metal Welds for Nuclear Application
Design of High-Strength Steel Using Machine Learning Techniques
Developing a Foundational Inter-Atomic Potential for Transitional Metal Alloys Using Active Learning
Developing Machine Learning Interatomic Potential for Fe-Cr-Ni Alloys
Developing Reduced Order Models for Phase Field Modeling of Irradiation Damage Using Koopman Operator Theory
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Effect of the Microstructure on Intergranular Fracture in FCC and HCP Polycrystals: A Machine Learning Approach
Enhancing Extrusion Efficiency: Development of a Digital Twin for Glass Reinforced Polymer Processes Using Machine Learning and Real-Time Data Integration
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Generative Adversarial Network (GAN)-Based Microstructure Mapping from Surface Profile For Laser Powder Bed Fusion (LPBF)
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Magnetic RANN Interatomic Potential for Iron
Physical Metallurgy and Machine Learning Guide the Prediction of Continuous Cooling Phase Transformation in Steels
Prediction of Fatigue Indicator Parameter by Graph Neural Network
Prediction of Material Parameters Using Machine Learning Supported by Large-Scale Phase-Field Simulations of Dendrite Growth
Pushing the Limits of Fine Feature Detection in Deep-Learning Assisted 3D X-Ray Microscopy: Characterization of Hierarchical Microstructures in TiC Reinforced Nickel Matrix Composites
Rapid Microstructural Determination from Nano-indentation of High Entropy Alloys Using Machine Learning and Genetic Algorithms
Starrydata Explorers: Visualization Platforms to Overview the Past Reported Experimental Samples
Sustainable Aluminum Alloy Design via Computer Vision
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Tuning Fracture Characteristics for Chiral Aperiodic Monotile Based Composites by Employing Multi-Objective Bayesian Optimization

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