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Ziabari chosen for Young Professionals in Additive Manufacturing Award

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Deep Learning for Additive Manufacturing

A new deep learning method developed by Oak Ridge National Laboratory improves the verification of 3D printed parts. The method produces clearer images in about a sixth of the time.

Oak Ridge National Laboratory researcher Amir Ziabari has been chosen to receive the Young Professionals in Additive Manufacturing Award by the ASTM International Additive Manufacturing Center for Excellence. The award will be presented during the ASTM International Conference on Advanced Manufacturing in October.  

Ziabari joined ORNL in 2018 as a member of what is now the Multimodal Sensor Analytics Group, where his research applies physics-based and data-driven approaches to enable more accurate, faster scientific image reconstruction for industries ranging from advanced manufacturing to nuclear energy. His recent work on rapid X-ray computed tomography (CT) characterization won a 2025 R&D100 Award, often considered the “Pulitzer of invention.” The technology has been licensed as part of a five-year research collaboration between ORNL and ZEISS, a leading manufacturer of measurement equipment and systems of industrial quality assurance. 

Ziabari, who holds master’s degrees and a doctorate in electrical and computer engineering, is a Senior Member of the Institute of Electrical and Electronics Engineers who also won an ORNL Innovation Award in 2023. He was a member of a team of researchers who won a best paper award at the International Conference on Neuromorphic Systems in 2021 and a team that received a Truth-based CT Reconstruction Challenge Award from the American Association of Physicists in Medicine in 2022.