| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing: Materials Design and Alloy Development III -- Super Materials and Extreme Environments
|
| Presentation Title |
Correlating Data from Digital and Virtual Twins of Component Manufacturing via DED |
| Author(s) |
Monica Salgueiro, Carlos Gonzalez, Camilo Prieto, Bernardo Freire, Mihail Babcinschi, Joerg Willem, Mustafa Megahed |
| On-Site Speaker (Planned) |
Mustafa Megahed |
| Abstract Scope |
Additive manufacturing challenges stem from the number of adjustable parameters affecting the component quality. The complexity is amplified by the fact that printed features interact with process parameters affecting the outcome. Physics-based models explain the complexities of phenomena taking place and can be used for initial planning. The virtual twin cannot readily take the influence of printed features and process parameter drift into account. On-line monitors are considered as key tools towards quality assurance and forward feed control. The digital twin however does not necessarily explain the interdependencies of parameters and part quality unless it is combined with the virtual twin. This study utilizes virtual and digital twins of direct energy deposition. The digital twin confirms the virtual twin results. The collated data is used to extract a quick response model that is applied for forward feed control. The improved print quality is presented together with planned system extensions. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, ICME, Machine Learning |