About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data informatics: Design of Structural Materials
|
Presentation Title |
Incorporating Historical Data & Past Analyses for Improved Tensile Property Prediction of 9% Cr Steel |
Author(s) |
Madison Wenzlick, Ram Devanathan, Osman Mamun, Kelly Rose, Jeffrey Hawk |
On-Site Speaker (Planned) |
Madison Wenzlick |
Abstract Scope |
Integrating published data-driven results & tools into future analyses strengthens our understanding of materials systems and reduces redundancy. However, the results of machine learning analyses can be difficult to generalize from and interpret. Key methods, assumptions and data limitations are often not included. Additionally, the quality of data published in literature is highly varied. This work explores the challenges in reproducing clustering and regression analyses from literature published using the same dataset, the 9Cr Database generated through DOE’s eXtremeMAT project. This work investigates attributes of data science methods including data manipulation and processing steps, programming tools & functions used, and key assumptions made during analysis that are necessary for reproducing results. These analyses focus on correlation, clustering, and regression methods to drive insights into processing, composition, microstructure impact on tensile strength and yield stress and improve the efficiency of alloy design. |
Proceedings Inclusion? |
Planned: |
Keywords |
Mechanical Properties, Iron and Steel, Other |