Printable aluminum alloy sets strength records, may enable lighter aircraft parts
Incorporating machine learning, MIT engineers developed a way to 3D print alloys that are much stronger than conventionally manufactured versions
image:
A new 3-D-printed aluminum alloy is stronger than traditional aluminum, due to a key recipe that, when printed, produces aluminum (illustrated 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.
view moreCredit: Felice Frankel
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 mix of aluminum and other elements that the team identified using a combination of simulations and machine learning, which significantly pruned the number of possible combinations of materials to search through. While traditional methods would require simulating over 1 million possible combinations of materials, the team’s new machine learning-based approach needed only to evaluate 40 possible compositions before identifying an ideal mix for a high-strength, printable aluminum alloy.
When they printed the alloy and tested the resulting material, the team confirmed that, as predicted, the aluminum alloy was as strong as the strongest aluminum alloys that are manufactured today using traditional casting methods.
The researchers envision that the new printable aluminum could be made into stronger, more lightweight and temperature-resistant products, such as fan blades in jet engines. Fan blades are traditionally cast from titanium — a material that is more than 50 percent heavier and up to 10 times costlier than aluminum — or made from advanced composites.
“If we can use lighter, high-strength material, this would save a considerable amount of energy for the transportation industry,” says Mohadeseh Taheri-Mousavi, who led the work as a postdoc at MIT and is now an assistant professor at Carnegie Mellon University.
“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, the Class of 1922 Professor and head of the Department of Mechanical Engineering at MIT.
Hart and Taheri-Mousavi provide details on the new printable aluminum design in a paper published in the journal Advanced Materials. The paper’s MIT co-authors include Michael Xu, Clay Houser, Shaolou Wei, James LeBeau, and Greg Olson, along with Florian Hengsbach and Mirko Schaper of Paderborn University in Germany, and Zhaoxuan Ge and Benjamin Glaser of Carnegie Mellon University.
Micro-sizing
The new work grew out of an MIT class that Taheri-Mousavi took in 2020, which was taught by Greg Olson, professor of the practice in the Department of Materials Science and Engineering. As part of the class, students learned to use computational simulations to design high-performance alloys. Alloys are materials that are made from a mix of different elements, the combination of which imparts exceptional strength and other unique properties to the material as a whole.
Olson challenged the class to design an aluminum alloy that would be stronger than the strongest printable aluminum alloy designed to date. As with most materials, the strength of aluminum depends in large part on its microstructure: The smaller and more densely packed its microscopic constituents, or “precipitates,” the stronger the alloy would be.
With this in mind, the class used computer simulations to methodically combine aluminum with various types and concentrations of elements, to simulate and predict the resulting alloy’s strength. However, the exercise failed to produce a stronger result. At the end of the class, Taheri-Mousavi wondered: Could machine learning 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 says. “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.”
Layer by layer
In the new study, Taheri-Mousavi continued where Olson’s class left off, this time looking to identify a stronger recipe for aluminum alloy. This time, she used machine-learning techniques designed to efficiently comb through data such as the properties of elements, to identify key connections and correlations that should lead to a more desirable outcome or product.
She found that, using just 40 compositions mixing aluminum with different elements, their machine-learning approach quickly homed in on a recipe for an aluminum alloy with higher volume fraction of small precipitates, and therefore higher strength, than what the previous studies identified. The alloy’s strength was even higher than what they could identify after simulating over 1 million possibilities without using machine learning.
To physically produce this new strong, small-precipitate alloy, the team realized 3D printing would be the way to go instead of traditional metal casting, in which molten liquid aluminum is poured into a mold and is left to cool and harden. The longer this cooling time is, the more likely the individual precipitate is to grow.
The researchers showed that 3D printing, broadly also known as additive manufacturing, can be a faster way to cool and solidify the aluminum alloy. Specifically, they considered laser bed powder fusion (LBPF) — a technique by which a powder is deposited, layer by layer, on a surface in a desired pattern and then quickly melted by a laser that traces over the pattern. The melted pattern is thin enough that it solidfies quickly before another layer is deposited and similarly “printed.” The team found that LBPF’s inherently rapid cooling and solidification enabled the small-precipitate, high-strength aluminum alloy that their machine learning method predicted.
“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.”
Putting their idea into practice, the researchers ordered a formulation of printable powder, based on their new aluminum alloy recipe. They sent the powder — a mix of aluminum and five other elements — to collaborators in Germany, who printed small samples of the alloy using their in-house LPBF system. The samples were then sent to MIT where the team ran multiple tests to measure the alloy’s strength and image the samples’ microstructure.
Their results confirmed the predictions made by their initial machine learning search: The printed alloy was five times stronger than a casted counterpart and 50 percent stronger than alloys designed using conventional simulations without machine learning. The new alloy’s microstructure also consisted of a higher volume fraction of small precipitates, and was stable at high temperatures of up to 400 degrees Celsius — a very high temperature for aluminum alloys.
The researchers are applying similar machine-learning techniques to further optimize other properties of the alloy.
“Our methodology opens new doors for anyone who wants to do 3D printing alloy design,” Taheri-Mousavi says. “My dream is that one day, passengers looking out their airplane window will see fan blades of engines made from our aluminum alloys.”
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Written by Jennifer Chu, MIT News
Paper: “Additively Manufacturable High-Strength Aluminum Alloys with Coarsening
https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202509507
Journal
Advanced Materials
Article Title
“Additively Manufacturable High-Strength Aluminum Alloys with Coarsening Resistant Microstructures by Exploiting Rapid Solidification”
Metal, melted, mastered
image:
Prahalada Rao, associate professor of industrial and systems engineering, speaks in the Future Manufacturing Lab in Kelly Hall.
view moreCredit: Photo by Peter Means for Virginia Tech.
Imagine a fleet of submarines sitting idle on a military base in the Pacific because they contain malfunctioning or aging parts.
Each submarine needs a unique replacement, available only from a single machinist more than 5,000 miles away. After months of waiting, the parts finally arrive — only for mechanics to discover a defect, and the process has to start over.
This has been the reality for decades. Researchers at Virginia Tech see a more efficient way forward.
A new study led by Prahalada Rao, associate professor in the College of Engineering, could reshape the way submarines and aircraft are built. He recently published research in the journal Materials and Design that shows how using artificial intelligence (AI) to monitor wire-arc additive manufacturing — essentially welding in 3D — can detect flaws as parts are built, correct them in real time, and ensure they’re ready to use once the print is completed.
The breakthrough comes at a time when the Navy urgently needs faster, more reliable production to maintain its fleet.
“We’ve always relied on conventional machining, but it takes months to produce even a single part,” Rao said. “Additive manufacturing gives us the ability to make those parts much faster and with less waste, which opens up a new way of thinking about how we build.”
Good melt, bad melt
The defense and aerospace supply chains previously relied on mom-and-pop machine shops. These small operations had the expertise to machine, cast, or forge critical components for submarines and aircraft from solid blocks of metal. But their methods were slow, wasteful, resource intensive, and overreliant on a shrinking workforce. Flaws often weren’t discovered until a part was finished, wasting weeks of labor and forcing manufacturers to scrap parts.
After the Cold War, many of these shops closed, and the skills retired with them. Additive manufacturing has helped fill that gap, offering a faster way to produce complex components and dramatically shortening the time it takes to get parts into service.
Not all additive manufacturing works the same way. Some methods are slow and suited for small, intricate parts, like laser powder bed fusion. Rao is currently working on faster approaches known as wire-arc printing and laser-wire printing.
“Wire-arc additive manufacturing is basically welding in 3D,” Rao said. “If laser powder bed fusion additive manufacturing produces a pint of material a day, wire-arc is keg-sized. You can deposit 40 or 50 kilograms of material in just one day. The challenge is making sure that much metal goes down without a single flaw.”
That’s where AI comes in.
Instead of discovering cracks or pores after a part is finished, Rao’s team is training algorithms to spot the warning signs as the metal is being laid down. By studying the “melt pool” of hot metal and comparing what “good” and “bad” prints look like, AI learns to recognize defects in real time and signal adjustments before they spread.
“When the melt pool looked good, the part turned out how we wanted. When it looked bad, we knew what would happen,” Rao said. “So we built a machine learning algorithm that was able to predict with about 90 percent certainty when things were going wrong.”
Mom-and-pop turned Industry 4.0
Rao is part of a larger team of researchers in Virginia Tech Made: Center for Advanced Manufacturing. Virginia Tech Made cultivates cross-campus collaborations, expands partnerships with industry and government, and trains students and manufacturing professionals based on the university's expertise in advanced materials, manufacturing technologies, computational design, data analytics, and digital infrastructure.
As part of the Grado Department of Industrial and Systems Engineering, Rao applies his background in systems thinking to make manufacturing not just faster, but smarter.
“Faster, better, cheaper,” Rao said. “I want to make it better through quality control, faster by not wasting time redoing parts, and cheaper by reducing defects. That’s something we do very well in process control.”
Rao and his team carry out much of their work in his Kelly Hall lab, but they’re also making use of the Learning Factory, Virginia Tech’s hands-on hub for manufacturing education. A smaller laser powder bed fusion machine and a hybrid laser wire printing machine were recently added to the facility — a resource Rao credits to the foresight of department head Eileen Van Aken. He said her push to secure the equipment was key, both for advancing the research and for giving students the chance to train on the same technology used in industry.
“Giving students access to the same machines they’ll see in industry is critical,” Rao said. “That’s why the laser powder bed fusion and laser wire system in the Learning Factory is so valuable. Additive is something you can train quickly, and it prepares our students to step right into the future of manufacturing.”
Original study:DOI: 10.1016/j.matdes.2025.114598
Journal
Materials & Design
Article Title
Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning☆
Article Publication Date
1-Oct-2025
Some like it hot: composite metal foam proves resilient against high stresses at high temperatures
Peer-Reviewed Publicationimage:
Composite metal foam's durability against heat and high strength-to-weight ration make it promising for use in applications ranging from automobile engines to aerospace components to nuclear power technologies.
view moreCredit: NC State University
New research shows that composite metal foam (CMF) is incredibly resilient at high temperatures, able to withstand repeated heavy loads even at temperatures of 400 and 600 degrees Celsius. Coupled with the material’s high strength-to-weight ratio, the finding suggests that CMF could be used in applications ranging from automobile engines to aerospace components to nuclear power technologies.
“CMF has many attractive properties, which make it appealing for a wide range of applications,” says Afsaneh Rabiei, corresponding author of a paper on the work and a professor of mechanical and aerospace engineering at North Carolina State University. “But if you want to use a material in engines, airplane parts or any application involving repeated loading and high temperatures, you need to know how the material will perform.
“This is important for any application, but particularly when equipment failure could affect public health and safety – such as jet engine vanes, ducts, and exhaust flaps; turbine blades; hypersonic vehicle airframes and hot trailing edges of wings; gas and steam turbines; automobile brake system components and internal combustion engine parts; nuclear reactor fuel cladding and many more structures that go in service under extreme conditions of heat and load.”
CMFs are foams that consist of hollow spheres – made of materials such as stainless steel, nickel, or other metals and alloys – embedded in a metallic matrix. The resulting material is both lightweight and remarkably strong at absorbing compressive forces, with potential applications ranging from aircraft wings to vehicle armor and body armor.
In addition, CMF is better at insulating against high heat than conventional metals and alloys, such as steel. The combination of light weight, strength and thermal insulation means that CMF also holds promise for use in storing and transporting nuclear material, hazardous materials, explosives and other heat-sensitive materials.
To see how CMF would perform under repeated stress at high temperatures, the researchers worked with NC State’s Constructed Facilities Laboratory, which is designed to test materials and structures under extreme circumstances.
For this study, the researchers worked with CMFs consisting of steel spheres in a steel matrix. The CMF samples were put through a repeated cycle of loading while exposed to temperatures of 23 degrees C (73 degrees F), 400 degrees C (752 degrees F), and 600 degrees C (1112 degrees F).
At 400 C, the CMF withstood a cycle of loading that alternated between 6 and 60 megapascals (or between 870 and 8702 units of pound-force per square inch) for more than 1.3 million cycles without failure before the researchers halted the test due to time constraints.
At 600 C, the CMF withstood a cycle of loading that alternated between 4.6 and 46 megapascals (or between 667 and 6671 units of pound-force per square inch) for more than 1.2 million cycles without failure before the researchers halted the test due to time constraints.
“Knowing that in a compression-compression fatigue setting, the fatigue life of solid stainless-steel decreases significantly as temperature increases from room temperature to 400 C and 600 C, these results were remarkable,” Rabiei says. “Our findings indicate the fatigue life of the steel-steel CMF is not diminished and that this lightweight material performs tremendously well in the extreme environment of high temperature cyclic loading.
“This discovery is exciting, and we’re open to working with industry partners who would like to explore potential applications for CMF. This work was done with an eye toward developing a material that could be used to improve safety and efficiency related to the shipping of hazardous materials, so that’s one potential application. But these findings are also relevant to any application where equipment may be exposed to high loads and high temperatures.”
The paper, “Performance of Composite Metal Foams Under Cyclic Loading at Elevated Temperatures,” is published open access in the Journal of Materials Science. First author of the paper is Zubin Chacko, a recent Ph.D. graduate from NC State. The paper was co-authored by Gregory Lucier, a research professor at NC State and manager of the Constructed Facilities Laboratory.
This work was done with support from the Department of Transportation’s Pipeline and Hazardous Materials Safety Administration under project number PH95720-0075.
Rabiei is the inventor of composite metal foams. She has assigned related intellectual property to a small business for which she is a shareholder.
Optical images of Steel CMF specimens subjected to compression–compression fatigue testing at various temperatures: a) and b) 23°C, (c) 400°C, and (d) 600°C. All specimens show minimal deformation with uniform axial shortening without shearing, bulging or cracking [Note A:High-Temp. grease seen at top and bottom contact faces of specimen with compression platen squeezed out during test; Note B: The color difference is due to oxide layer thickness - thin chromium oxide at 400 °C gives a golden hue, while thicker high-temperature oxides at ~600 °C produce a bluish-black color.]
Credit
Afsaneh Rabiei, NC State University
Journal
Journal of Materials Science
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Performance of Composite Metal Foams Under Cyclic Loading at Elevated Temperatures
COI Statement
Rabiei is the inventor of composite metal foams. She has assigned related intellectual property to a small business for which she is a shareholder.
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