| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Alloy Development for Energy Technologies: ICME Gap Analysis
|
| Presentation Title |
Data Quality Evaluation and Influence on the Predictability of
Data-Driven Alloy Design
|
| Author(s) |
Sunyong Kwon, Yukinori Yamamoto, Jian Peng, Michael P Brady, Dongwon Shin |
| On-Site Speaker (Planned) |
Sunyong Kwon |
| Abstract Scope |
The lack of high-quality, consistent ground truth data is an often limiting factor in applying machine learning (ML) for alloy design. Although its importance, general practice to evaluate the quality of materials datasets to be used for data analytics has not been established. We propose a quantitative method to evaluate the dataset quality and its influence on the predictability of physics-augmented data-driven surrogate ML models. We use an example creep-rupture experimental data of alumina-forming austenitic (AFA) alloys, of which data has been collected over a decade. The ML model predictability trained by two different qualities of datasets will be discussed. We also present a workflow to assess new alloy compositions worth experimental validation from the ML-predicted millions of hypothetical alloys. The research was sponsored by the Vehicle Technologies Office, the U.S. Department of Energy. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
ICME, High-Temperature Materials, Machine Learning |