University of Michigan receives $10.3M DARPA grant to predict metal AM part longevity
April 17, 2025
A team from the University of Michigan, Ann Arbor, Michigan, USA, has received up to $10.3 million from the US Defense Advanced Research Projects Agency (DARPA) for its Predictive Real Time Intelligence for Metal Endurance (PRIME) project. PRIME will work to determine how long additively manufactured parts are likely to last in the field in a four-year project.
Challenges of AM in military deployment
When military equipment fails in remote locations, it can take weeks for a part ordered from the manufacturer to arrive. While Laser Beam Powder Bed Fusion (PBF-LB) Additive Manufacturing may be an expensive way to make parts, downtime is even more costly in hours of lost work. This has led military agencies to consider the commissioning of locally made parts or to bring Additive Manufacturing machines for the in-situ manufacture of parts as required.
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One issue in adopting newer technologies, though, is guaranteeing longevity. Military parts undergo stringent testing, and usually, the manufacturing process is consistent enough that test samples from one machine reliably predict how all parts made by that machine will perform. However, PBF-LB Additive Manufacturing can result in more frequent and random defects in the material structure compared with cold forging.
“Depending on which model of PBF-LB printer you use, you might get different microstructures and different properties. The laser spot size and laser power levels might be different. The scanning strategies might be different. These things change the quality of the part,” said Veera Sundararaghavan, U-M professor of aerospace engineering and principal investigator of the project. “Our aim is to guarantee the quality of the part as you print.”
Sundararaghavan and his collaborators offer a solution: carefully record the manufacturing process and create a digital twin of each part based on the defects that emerge. The team will then validate these models with fatigue tests.
“To understand the lifespan of PBF-LB parts, we must push the current boundaries of the field and detect even the most critical defects that impact component performance. Through the PRIME project, we are doing exactly that: leveraging state-of-the-art monitoring and AI techniques to redefine what’s possible,” said Mohsen Taheri Andani, assistant professor of mechanical engineering at Texas A&M University, who is co-leading the effort to monitor PBF-LB manufacturing.
Integrated monitoring
Three partners – the Additive Manufacturing monitoring company Addiguru, Texas A&M University and the ASTM Additive Manufacturing Center of Excellence – will develop techniques and standards to collect data during PBF-LB manufacturing. They will set up PBF-LB machines with an optical camera and two infrared cameras, capturing near- and far-infrared signals that reveal where heat is building up in the sample.
Addiguru is opting for a multisensor integration including an acoustic sensor – originally designed to pick up birdsong – which will listen for the sounds of porosity defects in the metal. These tools are expected to enable the team to identify defects as small as 0.025 mm, and the sensor suite will be designed such that it can work with most PBF-LB devices.
“Multisensor data, combined with advanced analytics, will provide critical insights to part manufacturers. This project will enable a comprehensive, real-time assessment of part quality, helping manufacturers make informed go/no-go decisions with confidence,” said Shuchi (SK) Khurana, founder and CEO of Addiguru, also co-leading the print monitoring effort.
Digital twins
Meanwhile, part of the U-M contingent will work with the Additive Manufacturing simulation company AlphaSTAR to use that data to develop digital twins of the manufactured parts. They intend to combine advanced physics-based modelling of the PBF-LB Additive Manufacturing process from AlphaSTAR with U-M’s simulations of the part’s structure at the microscale. The modelling and simulation of the microstructure will also help the team identify the residual stresses – stresses built into the part – that may eventually contribute to its demise.
“The microstructures of 3D-printed parts contain crystal grains that produce different properties across different directions, brittle structures known as intermetallic phases, and internal pores that are different from those seen in their conventionally processed counterparts. Microstructure modelling will offer important inputs for fatigue life predictions,” said Lei Chen, associate professor of mechanical engineering at U-M Dearborn, who plays a key role in the microstructure modelling effort.
Finally, U-M researchers will also work with partners at the University of California, San Diego, to run uncertainty quantification models on top of the microstructure models, predicting the resilience of the part over time by digitally testing how the metal responds to the stresses it’s likely to encounter on the job. To validate those predictions, Auburn University will perform fatigue testing on the metal parts, stressing them until they break.
“If PRIME takes off, it’s like giving 3D printing a crystal ball: predicting the lifetime of PBF-LB parts across platforms and turning critical part production into a low-cost, distributed dream,” Sundararaghavan said.
The project is funded through DARPA’s Structures Uniquely Resolved to Guarantee Endurance programme.
