3D-printing metal parts could save weeks of downtime, but DARPA wants a way to certify how long they'll last
To find out how long 3D-printed metal parts are likely to last in the field, the Defense Advanced Research Projects Agency is providing up to $10.3M to a University of Michigan-led team.
When military equipment fails in remote locations, it can take weeks for a part ordered from the manufacturer to arrive. 3D printing, specifically laser powder bed fusion, or LPBF, is an expensive way to make parts, and they likely aren't as sturdy as cold-forged parts. However, downtime is even more expensive in hours of lost work, so military agencies would like the option to commission locally made parts or bring along 3D printers themselves.
The problem is guaranteeing longevity. Military parts undergo stringent testing, and usually the manufacturing process is so uniform that samples of one part made by a particular machine reliably represent how all such parts made by that machine will fare. This is not the case for LPBF, in which defects in the material structure are more common and more random.
In LPBF, a bed of metal powder is hit with lasers so that it solidifies into a cross-section of the desired shape. Then, more powder is added and the lasers fuse the next layer to the first from below. This continues until the part is complete.
"Depending on which model of LPBF 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."
The solution offered by Sundararaghavan and his collaborators is to carefully record the printing process and create a digital twin of each part based on the defects that emerge. Then, the team will computationally model repeated stresses on the part to figure out where cracks form and how long that takes. These fatigue models can incorporate the actual service data to predict when parts will fail. The team will validate these models with fatigue tests. The four-year project is called Predictive Real Time Intelligence for Metal Endurance, or PRIME.
"To understand the lifespan of LPBF 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 LPBF printing.
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 LPBF manufacturing. They will set up LPBF 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 pioneering a multisensor integration including an acoustic sensor. The sensor Addiguru chose was originally designed to pick up birdsong, but here it will listen for the sounds of porosity defects in the metal. These tools will enable the team to identify defects as small as 0.025 millimeters, and the sensor-suite will be designed such that it would work with most LPBF 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.
Meanwhile, part of the U-M contingent will work with the 3D-printing simulation company AlphaSTAR to use that data to develop digital twins of the printed parts. They intend to combine advanced physics-based modeling of the LPBF process from AlphaSTAR with U-M's simulations of the part's structure at the microscale. The modeling and simulation of the microstructure also will help the team identify the residual stresses, or stresses that are 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 modeling 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 modeling 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 discover whether those predictions are correct, 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 LPBF 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 program.
