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Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title Simulation-Driven Machine Learning for Predicting Material Properties from Cross-Sectional Dendritic Microstructures
Author(s) Tomohiro Takaki, Haruki Yano, Ayano Yamamura
On-Site Speaker (Planned) Tomohiro Takaki
Abstract Scope Accurate prediction of solidification microstructures is essential for improving the performance of solidified materials. Because dendritic growth evolves under high-temperature and opaque conditions, direct observation is difficult, making numerical simulation an indispensable tool. The phase-field (PF) method can reproduce dendrite growth with high fidelity; however, its practical application is often restricted by the limited availability of material property data, which are challenging to determine experimentally. In this study, we develop a simulation-driven machine learning framework to estimate material properties from cross-sectional dendritic microstructure images obtained through quench experiments. Since collecting extensive experimental datasets with known material properties is not feasible, we generated training images through systematic PF simulations while varying the target properties. A convolutional neural network (CNN) was trained on the simulated dataset, and its predictive capability was assessed using independent test data.
Proceedings Inclusion? Undecided

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