About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Optimization of the Additive Manufacturing Process for Refractory Metals Using Numerical Simulations and Machine Learning |
Author(s) |
Adrian B. Dalagan, Damilola Lawal, Kyle Snyder, Prasanna Balachandran, Richard Martukanitz |
On-Site Speaker (Planned) |
Adrian B. Dalagan |
Abstract Scope |
Refractory metals, including alloys of niobium, tungsten, and tantalum, offer significant opportunity to increase operational temperatures for a wide range of applications, and additive manufacturing is considered a critical enabling technology for wider utilization of these materials. However, the implementation of this process is challenged by inherent properties such as high melting temperatures and oxidation rates, which can form compromising defects such as lack-of-fusion. Additionally, the resultant microstructures influenced by these conditions must also be considered. Thus, this research investigated parameters for adequate energy input and then developed optimal processing maps by factoring these constraints. Numerical simulations predicted melt pool geometry and thermal response over a range of process variables. The thermal data also informed the phase field model for predicting solidification morphology and concentration redistribution. Finally, the compilation of the simulation results and experimental validations allowed for the construction of a machine learning model that defines the optimal parameters. |
Proceedings Inclusion? |
Undecided |