Additively manufacturable high-strength aluminum alloys with thermally stable microstructures enabled by hybrid machine learning-based design
S. Mohadeseh Taheri-Mousavi, Michael Xu, Florian Hengsbach, Clay Houser, Zhaoxuan Ge, Benjamin Glaser, Shaolou Wei, Mikro Schaper, James M. LeBeau, Greg B. Olson, A. John Hart

TL;DR
This paper presents a hybrid machine learning approach to design high-strength, thermally stable additively manufacturable aluminum alloys with nanoscale precipitates, achieving superior strength and stability compared to existing alloys.
Contribution
It introduces a novel combination of CALPHAD, ICME, and Bayesian optimization for alloy design, enabling the creation of printable aluminum alloys with enhanced properties.
Findings
Designed alloy exhibits strength comparable to wrought Al 7075.
Post-heat treatment, alloy surpasses benchmark printable Al alloys in strength.
Alloy remains crack-free during additive manufacturing.
Abstract
Additively manufactured (AM) aluminum alloys with high strength and thermal stability have broad applications in turbine engines, vacuum pumps, heat exchangers, and many other industrial systems. Employing precipitates with an L1 structure to block dislocation motions is a widespread strategy to strengthen aluminum. However, to achieve high strength, a high volume fraction of small precipitates is required, and these characteristics are generally mutually exclusive. Here, we show that for certain compositions of Al alloys, L1 phases initially precipitate as sub-micron metastable ternary phases under the rapid solidification conditions of powder bed AM, yet the subsequent L1 phases that precipitate during heat treatment of the sample remain at the nanoscale, imparting high strength. For strength to be retained at elevated temperature, these nanoprecipitates must have low…
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Taxonomy
TopicsAdditive Manufacturing and 3D Printing Technologies · Additive Manufacturing Materials and Processes · Manufacturing Process and Optimization
