About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
|
First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Predictive Modeling of Creep Elongation and Reduction in Area in High Temperature Alloys Using Machine Learning |
Author(s) |
Madison Wenzlick, Osman Mamun, Ram Devanathan, Kelly Rose, Jeffrey Hawk |
On-Site Speaker (Planned) |
Madison Wenzlick |
Abstract Scope |
New materials are critical to enabling technological advancements in the energy industry. To address this need, the eXtremeMAT (XMAT) consortium of U.S. Department of Energy national laboratories aims to improve the alloy design process for energy-related materials through the development of novel models and tools to reduce the time from material design to deployment. This work leverages high quality data on high-temperature 9-12% Cr ferritic-martensitic steels and austenitic stainless steels, including alloy composition, processing, heat treatment, microstructure and creep test information. Machine learning regression algorithms were used to model the creep elongation and reduction in area of the alloys to understand the complex relationships between the alloy properties and their mechanical behavior, and to supplement existing creep rupture models. The feature importance and feature interactions were assessed. Domain knowledge was integrated into the model using alloy labeling and expert review to ensure interpretability and alignment with materials science properties. |
Proceedings Inclusion? |
Undecided |