About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data informatics: Design of Structural Materials
|
Presentation Title |
Data Science Approaches for Microstructure-property Connections in Structural Materials |
Author(s) |
Elizabeth A. Holm, bo Lei, Katelyn Jones, Ryan Cohn, Nan Gao |
On-Site Speaker (Planned) |
Elizabeth A. Holm |
Abstract Scope |
Optimizing the performance of structural materials often involves engineering the microstructure through composition and process control. Because the microstructural parameter space is high-dimensional and complex, the tools of data science and informatics offer promising approaches for understanding and optimizing the microstructure-properties relationship. Materials properties can be divided into those that depend primarily on the distribution of microstructural features in the microstructure as a whole (i.e. yield strength) and those that are governed by relatively rare, critical features (i.e. fracture initiation). Case studies, drawing from the design of high-temperature alloys, fatigue failure of superalloys, alloy processing, and steel metallurgy, will address the application of data science methods, including computer vision and machine learning, to each property class. In all cases, understanding the characteristics of the data set, designing an information-rich feature representation, and selecting appropriate machine learning schemes are essential to achieving the most useful results. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Mechanical Properties, Modeling and Simulation |