About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
|
First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Physics-Constrained, Inverse Design of High-Temperature, High-Strength, Creep-Resistant Printable Al Alloys Using Machine Learning Methods |
Author(s) |
S. Mohadeseh Taheri-Mousavi, Florian Hengsbach, Mirko Schaper, Greg B. Olson, A. John Hart |
On-Site Speaker (Planned) |
S. Mohadeseh Taheri-Mousavi |
Abstract Scope |
Aluminum alloys that retain strength at high temperatures have various industrial applications including fan blades of jet engines and pistons of combustion engines. However, additive manufacturing (AM) of these alloys is challenging due to the presence of hot cracking. We demonstrate a physics-constrained, inverse design framework with data generated from integrated computational materials engineering (ICME) techniques to explore the compositional space of Al-Zr-Er-Y-Yb-Ni, and identify an optimal alloy composition achieving maximum predicted strength at a temperature of 250ºC. Using only 40 sampling data with our most efficient machine learning algorithm (a neural network), we predicted a composition with coarsening resistance 3.5X greater than a state-of-the-art printable Al-alloy and 1.75X greater than the maximum outcome among 500,000 random compositions. Mechanical testing of coupons 3D-printed from the optimal composition validated our predictions. The numerical framework provides an efficient and robust pathway for alloy design in tandem with the capabilities of AM. |
Proceedings Inclusion? |
Undecided |