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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Multi-Scale Microstructure–Property Prediction Using Deep Learning
Author(s) Wuguo Chen, Yan Zhao, Mujun Long, Jian Wang, Bin Liang
On-Site Speaker (Planned) Wuguo Chen
Abstract Scope Mechanical properties are strongly governed by microstructural features, yet most image-based prediction methods rely on single-magnification microscopy and target isolated properties. This study proposes a multi-magnification SEM image fusion framework for predicting complete mechanical property curves. SEM images at 1000×, 2000×, and 5000× magnifications, together with corresponding curves, were collected for the same material under 134 processing conditions. After preprocessing and patch segmentation, image patches from the three magnifications under each condition are grouped as a multi-scale input. A multi-branch convolutional neural network extracts features from each magnification and fuses them to learn the relationship between multi-scale microstructures and discretized mechanical property curves. The proposed approach enables curve-level prediction from complementary microstructural information and provides a data-driven route for process–microstructure–property modeling and rapid performance assessment.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Mechanical Properties, Computational Materials Science & Engineering

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