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MIT technicians have created a printable aluminum composite capable of enduring elevated temperatures and is five times more robust than aluminum fabricated through conventional methods.
The newly crafted printable metal consists of a blend of aluminum alongside various elements identified by the team through a synthesis of simulations and machine learning, which greatly reduced the number of potential material combinations to consider. While traditional approaches would necessitate simulating over 1 million material options, the team’s innovative machine learning technique required assessing only 40 possible formulations before pinpointing an optimal combination for a high-strength, printable aluminum alloy.
Upon fabricating the alloy and examining the resultant material, the team verified that, as anticipated, the aluminum alloy exhibited strength comparable to the most robust aluminum alloys currently produced using traditional casting techniques.
The researchers foresee that the new printable aluminum could be transformed into more durable, lighter, and temperature-resistant items, such as jet engine fan blades. Typically, fan blades are cast from titanium — a substance over 50 percent heavier and up to 10 times more expensive than aluminum — or constructed from advanced composites.
“Utilizing lighter, high-strength materials could result in significant energy savings for the transportation sector,” states Mohadeseh Taheri-Mousavi, who oversaw the project during her postdoctoral fellowship at MIT and is now an assistant professor at Carnegie Mellon University.
“Given that 3D printing can generate intricate shapes, minimize material waste, and allow for distinctive designs, we regard this printable alloy as valuable for applications in advanced vacuum pumps, luxury vehicles, and cooling systems for data centers,” adds John Hart, the Class of 1922 Professor and chair of the Department of Mechanical Engineering at MIT.
Hart and Taheri-Mousavi elaborate on the new printable aluminum formulation in a study published in the journal Advanced Materials. Co-authors from MIT include Michael Xu, Clay Houser, Shaolou Wei, James LeBeau, and Greg Olson, along with Florian Hengsbach and Mirko Schaper from Paderborn University in Germany, and Zhaoxuan Ge and Benjamin Glaser from Carnegie Mellon University.
Micro-sizing
This new work stemmed from an MIT course Taheri-Mousavi attended in 2020, taught by Greg Olson, a professor in the Department of Materials Science and Engineering. As part of the coursework, students learned to utilize computational simulations for designing high-performance alloys. Alloys are substances formed from a combination of different elements whose mixture imparts exceptional strength and other distinctive characteristics to the overall material.
Olson challenged the class to devise an aluminum alloy that would surpass the strength of the strongest printable aluminum alloy designed thus far. Similar to most materials, the strength of aluminum largely relies on its microstructure: The smaller and more densely arranged its microscopic components, or “precipitates,” the more robust the alloy will be.
Keeping this in mind, the class utilized computer simulations to systematically blend aluminum with various types and concentrations of elements to simulate and forecast the resulting alloy’s strength. However, this exercise did not yield a stronger outcome. By the end of the class, Taheri-Mousavi pondered: Could machine learning produce better results?
“At certain points, numerous factors influence a material’s properties in a nonlinear fashion, leaving you uncertain,” Taheri-Mousavi explains. “Machine-learning tools can direct you to areas of focus and indicate, for instance, which two elements are affecting a specific attribute. This allows for a more efficient exploration of the design landscape.”
Layer by layer
In the fresh investigation, Taheri-Mousavi picked up where Olson’s class concluded, this time aiming to uncover a stronger formulation for the aluminum alloy. She employed machine-learning methods designed to effectively sift through data such as the characteristics of elements, identifying crucial connections and correlations that would lead to a more favorable result or product.
She discovered that, utilizing only 40 combinations of aluminum with distinct elements, their machine-learning strategy swiftly zeroed in on a formulation for an aluminum alloy featuring a higher volume fraction of small precipitates, and thereby greater strength, than what previous studies had indicated. The alloy’s strength even exceeded findings after simulating over 1 million options without employing machine learning.
To physically create this novel strong, small-precipitate alloy, the team recognized that 3D printing would be preferable over traditional metal casting, where molten aluminum is poured into a mold and allowed to cool and solidify. The longer the cooling period, the higher the chance for individual precipitates to expand.
The researchers demonstrated that 3D printing, more broadly known as additive manufacturing, can provide a speedier means of cooling and solidifying the aluminum alloy. They specifically evaluated laser bed powder fusion (LBPF) — a process where a powder is layered on a surface in a specific pattern and then quickly melted by a laser tracing the design. The melted pattern is sufficiently thin to solidify rapidly before an additional layer is applied and similarly “printed.” The team determined that LBPF’s naturally quick cooling and hardening enabled the small-precipitate, high-strength aluminum alloy that their machine learning process had forecasted.
“Sometimes we must consider how to ensure a material is suitable for 3D printing,” remarks study co-author John Hart. “Here, 3D printing opens a new avenue due to the unique characteristics of the procedure — particularly, the swift cooling rate. Very rapid freezing of the alloy post-laser melting generates this distinct set of properties.”
Putting their concept into practice, the researchers requested a formulation of printable powder based on their new aluminum alloy recipe. They sent the powder — a blend of aluminum and five additional elements — to collaborators in Germany, who fabricated small samples of the alloy using their internal LPBF system. The samples were then transported to MIT, where the team conducted multiple tests to assess the alloy’s strength and analyze the samples’ microstructure.
Their findings confirmed the forecasts made by their initial machine learning exploration: The printed alloy was five times more robust than a cast counterpart and 50 percent stronger than alloys designed using conventional simulations without machine learning. The new alloy’s microstructure also included a higher volume fraction of small precipitates and was stable at elevated temperatures of up to 400 degrees Celsius — an exceptionally high temperature for aluminum alloys.
The researchers are now employing similar machine-learning methodologies to further fine-tune other attributes of the alloy.
“Our approach opens up new possibilities for anyone interested in 3D printing alloy design,” states Taheri-Mousavi. “My aspiration is that one day, passengers gazing out their airplane windows will see fan blades of engines constructed from our aluminum alloys.”
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