MIT engineers have developed a printable aluminium alloy that can withstand high temperatures and is five times stronger than traditionally manufactured aluminium.
The new printable metal is made from a mix of aluminium 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 more than a 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 aluminium alloy.
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When they printed the alloy and tested the resulting material, the team confirmed that, as predicted, the aluminium alloy was as strong as the strongest aluminium alloys that are manufactured today using traditional casting methods.
The researchers envision that the new printable aluminium 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 per cent heavier and up to ten times costlier than aluminium – or made from advanced composites.
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‘If we can use lighter, high-strength material, this would save a considerable amount of energy for the transportation industry,’ said Mohadeseh Taheri-Mousavi, 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 centres,’ added Professor John Hart, head of the Department of Mechanical Engineering at MIT.
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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.
Olson challenged the class to design an aluminium alloy that would be stronger than the strongest printable aluminium alloy designed to date. As with most materials, the strength of aluminium 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 aluminium 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?
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‘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.’
Taheri-Mousavi found that using just 40 compositions mixing aluminium with different elements, the machine-learning approach quickly homed in on a recipe for an aluminium 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 more than a million possibilities without using machine learning.
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The team realised 3D printing would be the best way to physically produce this new strong, small-precipitate alloy, rather than traditional metal casting, in which molten liquid aluminium is poured into a mould 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 can be a faster way to cool and solidify the aluminium 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 solidifies 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 aluminium alloy that their machine learning method predicted.
‘Sometimes we have to think about how to get a material to be compatible with 3D printing,’ said 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 aluminium alloy recipe. They sent the powder – a mix of aluminium 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 per cent 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°C – a very high temperature for aluminium alloys.
The researchers are applying similar machine-learning techniques to further optimise other properties of the alloy.
‘Our methodology opens new doors for anyone who wants to do 3D printing alloy design,’ Taheri-Mousavi said. ‘My dream is that one day, passengers looking out their airplane window will see fan blades of engines made from our aluminium alloys.’
The research has been published in Advanced Materials.