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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Simultaneous Microstructure and Composition-based Machine Learning for Hardness Distribution Prediction & Verification in an Aluminum Alloy

Author(s)

Ming Yi Chen, Mao-Yuan Luo, Tu-Ngoc Lam, Min-Cheng Hsu, Ching-Yu Chiang, Wan-Zhen Hsieh, Wen-Jay Lee, E-Wen Huang*

On-Site Speaker (Planned)

Ming Yi Chen

Abstract Scope

In the present work, we employed various machine learning (ML) approach such as multilayer perceptron (MLP) model to predict localized hardness distribution derived from multiple microstructural features in an A356 aluminum alloy and verified the predictions with nanoindentation hardness map. Various input features consisted of elemental composition from X-ray fluorescence (XRF), Schmid factor from electron backscatter diffraction (EBSD), lattice spacing, residual strain, and full width at half maximum (FWHM) from monochromatic X-ray nano diffraction (XND). Incorporating a combination of all these features enhanced predictive accuracy by 71%, with the explained variance (R²) significantly rising from 0.52 to 0.89, compared to the prediction using only chemical composition. Our findings demonstrate the critical role of feature selection and data filtering in improving both model accuracy and stability, as validated by the combination of experimental observations and machine learning analysis.

Proceedings Inclusion?

Planned:

Keywords

Aluminum, Machine Learning, Other

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