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Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title Unsupervised Microstructure Segmentation of Forged TiAl Alloys Using Machine Learning
Author(s) SangHyun Oh, Hoheok Kim, Junseok Yoon, Jee Hyuk Ahn, Young-Seok Oh, Seong-Hoon Kang
On-Site Speaker (Planned) SangHyun Oh
Abstract Scope Intermetallic titanium aluminide (TiAl) alloys are considered promising lightweight materials for high-temperature applications owing to their superior strength and oxidation resistance. However, systematic optimization of their mechanical properties remains challenging because of complex phase transformations and microstructural evolution during processing. Therefore, accurate classification and quantitative evaluation of microstructures are essential, but existing approaches rely predominantly on manual analysis or predefined segmentation criteria, which are time-consuming and limit objectivity. To address these limitations, this study presents an unsupervised machine learning–based framework for microstructure segmentation that does not require labeled data or prior annotations. Gaussian Mixture Model (GMM) clustering is applied to extract and group microstructural features, enabling effective separation of distinct morphologies, including equiaxed and lamellar regions. The proposed annotation-free approach establishes a reliable basis for quantitative microstructure analysis and supports data-driven strategies for microstructure–property optimization in forged TiAl alloys.
Proceedings Inclusion? Undecided

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