Showing 9 results for Researcher: Amir K. Ziabari
August 24, 2026
6 MIN READ
ORNL researchers have developed a hybrid manufacturing method that combines 3D printing and electroforming to create leak-free HIP cans for advanced nuclear reactor components. The approach could simplify production of complex metal parts, reduce costs and reliance on overseas manufacturing, and strengthen U.S. supply chains for nuclear energy and defense applications.
September 10, 2025
2 MIN READ
Oak Ridge National Laboratory researcher Amir Ziabari has been chosen to receive the Young Professionals in Additive Manufacturing Award by the Advancing Standards Transforming Markets (ASTM) Additive Manufacturing Center for Excellence. The award will
August 9, 2025
3 MIN READ
Oak Ridge National Laboratory’s Peregrine software, used to monitor and analyze parts created through powder bed additive manufacturing, has released its most advanced dataset to date.
May 7, 2025
6 MIN READ
Research teams at the Department of Energy’s Oak Ridge National Laboratory received computing resource awards to train and test AI foundation models for science. A total of six ORNL projects were awarded allocations from the National Artificial Intelligence Research Resource, or NAIRR, pilot and the Innovative and Novel Computational Impact on Theory and Experiment, or INCITE, program to train their AI models.
January 2, 2025
7 MIN READ
A research partnership between two Department of Energy national laboratories has accelerated inspection of additively manufactured nuclear components, and the effort is now expanding to inspect nuclear fuels.
October 26, 2023
6 MIN READ
Researchers at the Department of Energy’s Oak Ridge National Laboratory have improved flaw detection to increase confidence in metal parts that are 3D-printed using laser powder bed fusion.
August 29, 2023
6 MIN READ
A licensing agreement between the Department of Energy’s Oak Ridge National Laboratory and research partner ZEISS will enable industrial X-ray computed tomography, or CT, to perform rapid evaluations of 3D-printed components using ORNL’s machine
October 14, 2022
5 MIN READ
A new deep-learning framework developed at ORNL is speeding up the process of inspecting additively manufactured metal parts using X-ray computed tomography, or CT, while increasing the accuracy of the results. The reduced costs for time, labor, maintenance and energy are expected to accelerate expansion of additive manufacturing, or 3D printing.
January 5, 2021
1 MIN READ
Algorithms developed at Oak Ridge National Laboratory can greatly enhance X-ray computed tomography images of 3D-printed metal parts, resulting in more accurate, faster scans.