Creating alloys means combining two or more metals to achieve new and improved properties, such as greater strength or resistance to corrosion. Traditionally, researchers test countless combinations to find the perfect formula, a process that can take months or even years. Simulations can speed things up, but a team of MIT engineers has found an even faster route. In a recent study, they used machine learning to identify a lightweight, 3D-printable aluminum alloy that is five times stronger than conventionally manufactured aluminum. Instead of running over a million simulations, their model narrowed the field to just 40, leading them straight to the ideal mix.
When the team printed the aluminum alloy, it performed as predicted. It was as strong as the most robust aluminum alloys produced today through traditional casting methods. This new material could be used to create strong, lightweight, and heat-resistant components, like fan blades in jet engines. “If we can use lighter, high-strength material, this would save a considerable amount of energy for the transportation industry,” Mohadeseh Taheri-Mousavi, who led the work as a postdoc at MIT and is now an assistant professor at Carnegie Mellon University, said.
The alloy design concept (Credit: Taheri-Mousavi et al.)
The Role of Machine Learning
The research came out of a course that Taheri-Mousavi took at MIT in 2020, where the students had to use computational simulations to design high-performance aluminum alloys. One of the keys to achieving a strong alloy is having its microscopic constituents, its “precipitates,” be as small and as densely packed as possible. So, the class methodically combined aluminum with various types and concentrations of elements to simulate and predict the resulting alloy’s strength. However, the tests failed to deliver a stronger result, leading Taheri-Mousavi to wonder if machine learning could do better.
“At some point, there are a lot of things that contribute nonlinearly to a material’s properties, and you are lost,” Taheri-Mousavi said. “With machine-learning tools, they can point you to where you need to focus, and tell you for example, these two elements are controlling this feature. It lets you explore the design space more efficiently.”
For Taheri-Mousavi’s new study, she used machine-learning techniques designed to comb through data such as the properties of elements, to identify key connections and correlations that should lead to a more desirable outcome. She discovered that with just 40 combinations of aluminum and other elements, the machine learning approach could identify an ideal combination. This combination would have a higher volume fraction of small precipitates, and therefore higher strength, than what the previous studies identified.
Why 3D Printing?
The fact that this new aluminum alloy is 3D printable is no accident. The team specifically designed it for additive manufacturing, since traditional casting methods would have weakened its strength. In metal casting, molten aluminum is poured into a mold and left to cool and harden. The slower this cooling process, the more likely it is that large precipitates will form, reducing the material’s strength. In contrast, 3D printing enables much faster cooling, preserving a fine microstructure. “Sometimes we have to think about how to get a material to be compatible with 3D printing,” says study co-author John Hart. “Here, 3D printing opens a new door because of the unique characteristics of the process — particularly, the fast cooling rate. Very rapid freezing of the alloy after it’s melted by the laser creates this special set of properties.”
For their research, the team sent their alloy formulation to collaborators in Germany, who used laser powder bed fusion (L-PBF) to print test samples. These parts were then sent back to MIT, where tests confirmed the results: the printed alloy had the fine precipitates and high strength that their machine learning model had predicted.
Notably, the new material is:
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Five times stronger than its cast counterpart
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Fifty percent stronger than alloys designed using conventional simulations without machine learning
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Stable at temperatures up to 400°C, an exceptionally high threshold for aluminum alloys.
Transportation Applications and More
This material could be used for various applications, particularly in the transportation sector. Jet engine blades, for instance, are typically cast from titanium, which is more than 50 percent heavier and ten times more expensive than aluminum, or made from advanced composites. Being able to 3D print them in aluminum could make jet engines both lighter and more cost-effective.
Jet engine blades (Credit: Wevolver)
“Because 3D printing can produce complex geometries, save material, and enable unique designs, we see this printable alloy as something that could also be used in advanced vacuum pumps, high-end automobiles, and cooling devices for data centers,” adds John Hart, head of the Department of Mechanical Engineering at MIT. Now, the team is applying similar machine-learning techniques to further optimize other properties of the alloy. To learn more about the aluminum alloy, find their research paper published in Advanced Materials here.
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*Cover Image: Aluminum (in brown) with nanometer scale precipitates (in light blue). The precipitates are arranged in regular, nano-scale patterns (blue and green in circle inset) that impart exceptional strength to the printed alloy. Credit: Felice Frankel