Printable aluminum alloy sets strength record, could help make aircraft parts lighter

MIT engineers have developed a printable aluminum alloy that can withstand high temperatures and is five times stronger than traditionally manufactured aluminum.

The new printable metal is made from a mixture of aluminum and other elements that the team identified using a combination of simulation and machine learning, which significantly reduced the number of possible combinations of materials to search for.

While traditional methods would have required simulating more than a million possible combinations of materials, the team’s new machine learning-based approach only needed to evaluate 40 possible compositions before identifying the ideal mix for a high-strength, printable aluminum alloy.

After printing the alloy and testing the resulting material, the team found that, as predicted, the aluminum alloy was as strong as the strongest aluminum alloys produced using traditional casting methods today.

Researchers envision new printable aluminum could be used to make stronger, lighter, and more heat-resistant products, such as jet engine fan blades. Fan blades are traditionally cast from titanium, a material that is more than 50% heavier and up to 10 times more expensive than aluminum, or made from advanced composite materials.

“Being able to use lighter, stronger materials would save the transportation industry a significant amount of energy,” says Mohadeseh Taheri Mousavi, who led the study as a postdoctoral fellow at MIT and is now an assistant professor at Carnegie Mellon University.

“Because 3D printing produces complex geometries, saves materials, and enables unique designs, we believe this printable alloy could also be used in advanced vacuum pumps, luxury automobiles, and data center cooling equipment,” adds John Hart, MIT Class of 1922 Professor and Chair of the Department of Mechanical Engineering.

Hart and Taheri-Mousavi detail their new printable aluminum design in a paper published in the journal Advanced Materials.

MIT co-authors on the paper include Michael Xu, Clay Houser, Shaolou Wei, James LeBeau, and Greg Olson, as well as Florian Hengsbach and Mirko Schaper of the University of Paderborn in Germany, and Zhaoxuan Ge and Benjamin Glaser of Carnegie Mellon University.

micro sizing

The new work stems from an MIT class Taheri-Moosavi took in 2020, taught by Greg Olson, professor of practice in the Department of Materials Science and Engineering.

As part of the course, students learned how to design high-performance alloys using computer simulations. Alloys are materials made from a mixture of different elements, the combination of which gives the material superior strength and other unique properties.

Olson challenged the class to design an aluminum alloy stronger than the strongest printable aluminum alloy ever designed. As with most materials, the strength of aluminum is highly dependent on its microstructure. The smaller and more closely packed the microscopic elements, or “precipitates,” are, the stronger the alloy will be.

With this in mind, the class used computer simulations to systematically combine aluminum with different types and concentrations of elements to simulate and predict the strength of the resulting alloys. However, this exercise did not yield stronger results. At the end of the class, Taheri-Mousavi reflected on this. Can machine learning do better?

“At some point, there are so many things that contribute nonlinearly to a material’s properties that you get lost,” says Taheri Mousavi.

“Machine learning tools can tell you where you need to focus and tell you, for example, which two factors are controlling this feature. This allows you to explore the design space more efficiently.”

layer by layer

In the new study, Taheri-Moosavi continued where Olson’s class left off, this time seeking to identify stronger recipes for aluminum alloys. This time, she used machine learning techniques designed to efficiently examine data such as element characteristics to identify important connections and correlations that lead to more desirable outcomes and products.

She found that by using just 40 different compositions of aluminum mixed with various elements, their machine learning approach quickly arrived at a recipe for an aluminum alloy with a higher volume fraction of small precipitates and therefore higher strength than those identified in previous research. The strength of this alloy was even higher than what they were able to identify by simulating more than a million possibilities without using machine learning.

The team realized that 3D printing was a better way to physically manufacture this new, stronger, precipitate-free alloy than traditional metal casting, where molten liquid aluminum is poured into a mold and allowed to cool and harden. The longer this cooling time, the more likely individual precipitates will grow.

Researchers have shown that 3D printing, also commonly known as additive manufacturing, is a faster way to cool and solidify aluminum alloys. Specifically, we looked at Laser Bed Powder Fusion (LBPF). This is a technique in which powder is deposited layer by layer in the desired pattern on a surface and then rapidly melted by a laser tracing over the pattern.

The melted pattern is thin enough that it hardens quickly before another layer is deposited and similarly ‘printed’. The research team found that the inherent rapid cooling and solidification of LBPF enables a high-strength aluminum alloy with fewer precipitates, as predicted by machine learning methods.

“Sometimes you have to think about how to make materials compatible with 3D printing,” says study co-author John Hart.

“Here, 3D printing opens new doors due to the unique properties of the process, especially the fast cooling rate. The very rapid freezing of the alloy after melting it with a laser creates this special set of properties.”

The researchers put their idea into practice and ordered a printable powder formulation based on the new aluminum alloy recipe.

They sent a powder mixed with aluminum and five other elements to collaborators in Germany, who printed small samples of the alloy using an in-house LPBF system. The samples were then sent to MIT, where the team performed multiple tests to measure the alloy’s strength and image the sample’s microstructure.

Their results confirmed the predictions made by the initial machine learning search. The printed alloy was five times stronger than the cast alloy and 50% stronger than the alloy designed using traditional simulation without machine learning.

The new alloy’s microstructure consisted of a higher volume fraction of small precipitates and was stable at temperatures up to 400 degrees Celsius, which is extremely high for an aluminum alloy.

Researchers are applying similar machine learning techniques to further optimize other properties of the alloy.

“Our methodology opens new doors for those who want to 3D print alloy designs,” says Taheri-Mousavi. “My dream is that someday passengers looking out their airplane windows will see engine fan blades made from our aluminum alloy.”

This article is republished courtesy of MIT News (web.mit.edu/newsoffice/), a popular site covering news about MIT research, innovation, and education.