| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
8th World Congress on Integrated Computational Materials Engineering (ICME 2025)
|
| Presentation Title |
Machine Learning Models for Predicting 3D Microstructure--Property Relationships From 2D Images |
| Author(s) |
Guangyu Hu, Sheila Whitman, Marat I. Latypov |
| On-Site Speaker (Planned) |
Marat I. Latypov |
| Abstract Scope |
Engineering properties of structural alloys depend on both alloy composition and 3D microstructure. Modeling the engineering properties requires access to 3D microstructure information, which can be obtained either from direct (yet expensive) 3D experiments or reconstruction from 2D section(s). In this contribution, we present machine learning (ML) approaches to modeling effective properties of heterogeneous materials directly from 2D sections. To this end, we consider statistical learning models based on spatial correlations and microstructure representations obtained with vision transformers as well as deep learning with convolutional neural networks. We train all models on data obtained from micromechanical 3D simulations. Upon training, the presented models only need 2D sections as input, whose experimental acquisition is much more accessible compared to 3D characterization. |
| Proceedings Inclusion? |
Undecided |