About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
Machine Learning Models of Effective Properties with Reduced Requirements on Microstructure |
Author(s) |
Marat I. Latypov |
On-Site Speaker (Planned) |
Marat I. Latypov |
Abstract Scope |
Microstructure--property relationships are key to effective design of structural materials for advanced applications. Advances in computational methods enabled modeling microstructure-sensitive properties using 3D models (e.g., finite elements) based on microstructure representative volumes. The need in high-resolution 3D microstructure data limits a wider adoption of microstructure-sensitive 3D models. In this work, we present machine learning (ML) strategies that are less demanding in terms of 3D microstructure input. We will first discuss ML approaches to modeling effective properties of two-phase materials directly from 2D microstructure sections. We then present ML models for mechanical properties of polycrystalline materials based on graph representations of polycrystals. |
Proceedings Inclusion? |
Planned: |
Keywords |
Modeling and Simulation, Machine Learning, Mechanical Properties |