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 |
High-Throughput Development of MPEAs by Combined Approach of Additive Manufacturing and Machine Learning Methods |
Author(s) |
Phalgun Nelaturu, Jason Hattrick-Simpers, Michael Moorehead, Santanu Chauduri, Adrien Couet, Dan J Thoma |
On-Site Speaker (Planned) |
Phalgun Nelaturu |
Abstract Scope |
This work details a framework for material discovery by combining high-throughput experimental and computational techniques. Additive manufacturing via directed energy deposition was employed as a high-throughput technique to synthesize alloys in the Cr-Fe-Mn-Mo-Ni system. More than 200 discrete, bulk samples of unique alloy compositions were synthesized, exploring a vast compositional space. Tight compositional control within ±10 at%, and low porosity and unmelted powder fractions of <0.3% were achieved. The rapid synthesis combined with rapid heat treatment, characterization, and nano- and micro-hardness measurements enabled high-throughput evaluation of these materials. The large dataset of experimentally measured properties was used to develop a predictive hardening model using an active machine learning algorithm coupled with thermodynamic modeling. The outcome of this effort was the discovery of a learned parameter, deltaLP, that was representative of the lattice distortion in these alloys. deltaLP was trained using the alloy compositions and was highly predictive of hardness. |
Proceedings Inclusion? |
Undecided |