About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithms Development in Materials Science and Engineering
|
Presentation Title |
Leveraging Increasingly Complex Test Artifacts to Accelerate Materials Development: Additively Manufactured Aluminum Metal Matrix Composites |
Author(s) |
Jamila Khanfri, Alex Butler, Aaron Stebner, Animesh Chhotaray |
On-Site Speaker (Planned) |
Jamila Khanfri |
Abstract Scope |
Determining a suitable set of processing parameters to additively manufacture (AM) a new alloy composition is currently a slow process. This bottleneck is due to lack of standardized parameter search workflows, lack of physics-based models of AM systems, and the high time-cost required to print and test samples. We propose dynamic workflow algorithms that leverage a series of increasingly complex representative test artifacts that consider the differences in underlying process models and the real time-cost of each testing level. Aluminum metal matrix composites have already been demonstrated to provide desirable mechanical properties while maintaining the advantageous strength-to-weight ratio of aluminum. More specifically, we have already shown that A1000 powder reinforced with titanium and boron carbide particles provides advantages such as grain refinement and increased strength when printed in powder-bed systems. We use our dynamic methodologies to speed the process parameter optimization of this A1000 MMC on laser-directed-energy-deposition systems. |
Proceedings Inclusion? |
Planned: |
Keywords |
Additive Manufacturing, Aluminum, Machine Learning |