NSF award to significantly boost Auburn metal AM qualification research

Published: Jan 24, 2024 2:25 PM

By Jeremy Henderson

A successful Major Research Instrumentation (MRI) proposal funded by the National Science Foundation (NSF) will soon significantly boost researchers at Auburn University's National Center for Additive Manufacturing Excellence (NCAME) in model-assisted qualification for additive manufacturing (AM) by establishing microstructure-property relationships in AM metallic materials.

The $704,482 award is to acquire a laboratory-based diffraction contrast tomography (LabDCT) instrument from ZEISS to integrate with an existing X-ray computed tomography system (the Zeiss Xradia 620 Versa system) at Auburn University. The instrument will enable researchers at NCAME, Auburn University and the surrounding region to image the microstructure of crystalline materials non-destructively.

"This addition will give the Xradia 620 the unique ability to map, in 3D, the crystallographic orientations within materials without destroying the test sample," said associate professor of mechanical engineering Shuai Shao, the project's principal investigator (PI).

Shao expects the new capabilities to significantly boost NCAME's efforts toward revealing the complex relationship between AM metallic materials’ volumetric defects and microstructure and their mechanical behavior — a focal point in NCAME's highly regarded AM research portfolio.

"It will also open up new dimensions to our research, such as the direct observation of the fatigue crack initiation with respect to the microstructure in these materials, and the integrated experimentation and simulation with true one-to-one microstructural correspondence," Shao said.

"This instrument will permit unprecedented fidelity in validation of physics-based fatigue models as a true digital twin of an AM specimen can be constructed before testing," said NCAME director and project Co-PI Nima Shamsaei, the Philpott-WestPoint Stevens Distinguished Professor of mechanical engineering. “Those model predictions can be directly compared with experimental observation after testing. Globally, this is at the cutting edge of research for model-assisted AM qualification."

Other Co-PIs on the project are Maria Auad, associate dean for graduate studies and faculty development; Lauren Beckingham, W. Allen and Martha Reed Endowed associate professor in the Department of Civil and Environmental Engineering; and Jingyi (Ginny) Zheng, an assistant professor in the College of Sciences and Mathematics' Department of Mathematics and Statistics.

Media Contact: Jeremy Henderson, jdh0123@auburn.edu, 334-844-3591

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