Last week, Texas A&M University announced that a team of its faculty members will receive a $1.6 million grant to develop a system for accelerating the process that determines the quality and lifespan of 3D printed components used by the military. Essentially, they aim to deliver a game-changing promise: it currently takes a supercomputer an average of 18 months to evaluate one 3D printed part and accurately predict its lifespan, but now, researchers at Texas A&M want to reduce that period to just three days.
The funding for the research comes from the U.S. Defense Advanced Research Projects Agency (DARPA), and it was delegated by the Structures Uniquely Resolved to Guarantee Endurance (SURGE) program. SURGE challenges the current machine-centric approach to part qualification in the additive manufacturing industry, advocating instead for a shift toward evaluating individual parts. The program strives to enable on-demand, globally distributed production, allowing any geometry to be manufactured on any machine, anywhere in the world. Significantly, SURGE also seeks to ensure that these parts meet durability standards under expected service conditions.
From left to right, members of the Texas A&M team Dr. Ibrahim Karaman, Dr. Alaa Elwany, Dr. Mohsen Taheri Andani and Dr. Raymundo Arroyave, next to a metal 3D printer. Photo Credit: Leon Contreras/Texas A&M Engineering
One critical part of achieving that goal is shortening the time it takes to evaluate a part’s lifespan. By evaluating a part in three days with a laptop, compared to 18 months with a supercomputer, parts can be created and deployed faster, encouraging the use of additive manufacturing in critical applications. This could mean more 3D printers at Department of Defense (DoD) bases, and millions of dollars in savings for the DoD. If the Texas A&M team’s approach is utilized, it has the potential to impact the additive industry as a whole.
“This is an exciting moment for the additive manufacturing field, a community that increasingly recognizes the urgent need to accelerate the qualification of 3D-printed parts,” Dr. Taheri Andani, a member of the grant team, said. “By integrating in-situ data with the underlying microstructural features formed during printing, the program will bridge expertise in process monitoring, microstructure characterization, and property evaluation – paving the way for faster, more reliable deployment of additive-manufactured parts.”
How Are the Lifespans of 3D Printed Parts Predicted?
Even when the same parts are printed on the one machine with the same materials, defects can vary in location and size, and these defects are major indications as to when a part may fail. To understand how these variable defects affect part durability, the Texas A&M team will work with Addiguru for the first two-year phase of the grant. Addiguru is a company whose software provides in-situ monitoring and issue detection technology by processing data from optical, thermal and machine sensors.
With Addiguru, the Texas A&M team wants to develop a sensor package that can be installed in a commercial AM platform to monitor the printing process. When the sensor system is perfected, the researchers will create an AI-driven, high-resolution defect-detection system that can read, combine, and process data from various sensor sources. Here, the team is looking to achieve high speed and high accuracy.
Members of the University of Michigan team, Veera Sundararaghavan ( U-M professor of aerospace engineering and principal investigator of the project) and PhD student Michael Philipchuk, working with a 3D printer. Photo Credits: Marcin Szczepanski/Michigan Engineering
A Collaborative Effort
The Texas A&M team is comprised of four people: Dr. Mosen Taheri Andani, assistant professor of mechanical engineering; Dr. Raymundo Arróyave, Chevron Professor (II) of materials science and engineering; Dr. Aala Elwany, professor of industrial and systems engineering; and Dr. Ibrahim Karaman, Chevron Professor and head of the department of materials science and engineering. However, the initiative is part of a larger $10.3 million, four-year grant shared with collaborators at the University of Michigan; Auburn University; the University of California, San Diego; ASTM International and industry partners Addiguru and AlphaStar.
In mid-April, the University of Michigan reported that its team was leading a four-year project called Predictive Real Time Intelligence for Metal Endurance (PRIME). For PRIME, researchers will carefully document the laser powder bed fusion (L-PBF) printing process and use the 3D printing simulation company AlphaStar to create a digital twin of each part, including all defects. Then, along with partners at the University of California, San Diego, they will computationally model repeated stresses on the parts, noting where cracks form. Specifically, they will run uncertainty quantification models on top of the microstructure models to predict the resilience of the part. By seeing how long it takes for these cracks to form, they can predict when parts will fail. Afterwards, these models will be validated with help from Auburn University, whose team will perform fatigue testing on the parts, stressing them until they break.
Veera Sundararaghavan, University of Michigan professor of aerospace engineering and principal investigator of the project, said in an interview with the University of Michigan:
“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.”
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*Cover Photo Credit: Marcin Szczepanski/Michigan Engineering