| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Failure, and a Career That is Anything But: An LMD Symposium Honoring J. Wayne Jones
|
| Presentation Title |
Redefining Liquid Metal Embrittlement: Utilizing Machine Learning to Unravel a Liquid Metal Enigma
|
| Author(s) |
Justin E. Norkett, Cameron Frampton, Victoria M Miller |
| On-Site Speaker (Planned) |
Justin E. Norkett |
| Abstract Scope |
Liquid metal embrittlement (LME) has been a persistent curiosity of metallurgists for over 100 years. In that time, and despite numerous efforts, no model has been able to reliably predict embrittlement for any arbitrary combination of liquid and solid metals. This is in large part due to the absence of deep mechanistic understanding of LME. The authors recently proposed a new interpretation of the phenomenology of LME as the result and interplay of no less than three distinct mechanisms. In this talk, the iterative improvement of a machine learning model capable of classifying LME by active mechanism will be described. The experimental work and the model refinements have expanded the understanding of LME phenomena; implications for the future of the field are discussed. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Environmental Effects, Other, |