Washington State University researchers have used artificial intelligence to identify a faster and less costly way to 3D print a high-performance metal alloy, avoiding the need to manually test more than 100 million possible printing configurations. The advance could eventually make the alloy, which is widely used in aerospace applications and may have uses in other industries, printable on more common commercial equipment. The AI strategy developed by the team could also be useful for other scientific problems involving enormous numbers of possible experiments, including drug discovery.
Researchers from WSU's School of Electrical Engineering and Computer Science and the School of Mechanical and Materials Engineering published the work in the Proceedings of the AAAI Conference on Artificial Intelligence. The project also received the Innovative Deployed Application Award at the organization's annual conference. "Ninety percent of commercial printers cannot print this metal alloy, so given that we were able to find these feasible process parameters, it allows us to use those commercial printers, and we are essentially democratizing the printing of this alloy," said Jana Doppa, Huie-Rogers Endowed Chair Professor of Computer Science and Berry Distinguished Professor in Engineering who led the research.
The material, GRCop-42, is an alloy made from copper, chromium, and niobium. NASA developed it for demanding environments where both heat resistance and efficient heat transfer are essential. Because GRCop-42 has high thermal conductivity while maintaining its strength at extreme temperatures, it is used in aerospace systems, including liquid rocket engine combustion chambers.
Despite its desirable properties and broader potential, however, the alloy is difficult and costly to 3D print because the process typically requires substantial laser power and energy. Previous attempts to print GRCop-42 using the lower wattages available on more common commercial machines had not succeeded. Testing possible printing settings one by one is also impractical.
Each attempt consumes expensive material, requires specialized equipment, and takes considerable human effort. A single print can cost hundreds of dollars, and thoroughly analyzing the finished sample can require several days. "Sometimes they printed a certain configuration, and the product just melted," said Azza Fadhel, first author of the paper and a PhD student in computer science.
"It wasn't really printable, and even with time and money, they wouldn't be able to try all 100 million options. What we were doing in our collaboration is to apply the AI so that we efficiently choose candidates from this very large search space." AI Searches More Than 100 Million Possibilities The researchers started with data from 37 printing configurations that had already failed in earlier experiments conducted in the School of Mechanical and Materials Engineering. Using those results, they developed a method that could estimate how likely an untested combination of settings was to produce a successful print.
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