| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
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| Symposium
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ICME Case Studies: Successes and Challenges for Generation, Distribution, and Use of Public/Pre-Existing Materials Datasets
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| Presentation Title |
Mg Database Project: Mapping Trends and Data Sets of Magnesium and Its Alloys for Improved Mechanical Performance |
| Author(s) |
Suhas Eswarappa Prameela, Suraj Ravindran, Burigede Liu, Padmeya Prashant Indurkar, Babak Ravaji, Caitlyn Schuette, Abigail Park, Fanuel Mammo, Stephanie Hernandez, Timothy Weihs |
| On-Site Speaker (Planned) |
Suhas Eswarappa Prameela |
| Abstract Scope |
Magnesium (Mg) and its alloys continues to draw interest from many researchers and funding agencies across the world. The lightweight metal is poised to bring huge benefits for a wide variety of structural applications. Lessons drawn from the last decade indicate that we need a highly synergetic experimental and computational approach to design these materials for improved mechanical performance. Artificial Intelligence (AI) is now seen as a powerful tool to help engineers design better Mg alloys. However, two critical obstacles remain in implanting successful (machine learning) ML models for Mg alloys. One is the lack of data to train the models. The second is the lack of organization of existing data in the literature. To help mitigate this problem, the Mg database project aims to collect datasets across different parameters critical to the design of Mg alloys, focusing on improving their mechanical performance. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
ICME, Magnesium, Mechanical Properties |