| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
|
| Presentation Title |
Machine Learning Augmented Predictive & Generative Models for Rupture Life in High Temperature Alloys |
| Author(s) |
Madison Wenzlick, Osman Mamun, Ram Devanathan, Kelly Rose, Jeffrey Hawk |
| On-Site Speaker (Planned) |
Madison Wenzlick |
| Abstract Scope |
Exploring the connections between material pedigree and performance is critical to understanding creep behavior. This work leverages the data framework for collection, curation and processing of alloy data established through DOE’s eXtremeMAT project. This work investigates both the semi-empirical and data-driven methods of predicting rupture life. Gradient boosting machine learning algorithms are applied to predict rupture time by first predicting the Larson-Miller Parameter, a commonly applied metric for evaluating creep behavior, as well as directly modeling rupture time. The models were evaluated using high quality 9-12% Cr ferritic-martensitic steel data and the most effective model was applied to austenitic stainless steels. A generative model was applied to generate synthetic data within the alloy space to evaluate the effectiveness of supplementing the dataset with synthetic information. A workflow for incorporating data generation for alloy design is described. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Temperature Materials, Mechanical Properties, Machine Learning |