Deep Learning Makes X-Ray CT Inspection of 3D-Printed Parts Faster, More Accurate

 Inspection applications for Deep Learning, 

Deep Learning Makes X-Ray CT Inspection of 3D-Printed Parts Faster, More Accurate

Oak Ridge National Laboratory

S. Heather Duncan, October 14, 2022

Researchers at the U.S. Department of Energy (DOE) Oak Ridge National Laboratory (ORNL) have developed a deep-learning framework that uses X-ray computed tomography (CT) to improve the speed and accuracy of inspecting three-dimensionally (3D) printed metal parts. The framework is being incorporated into software commercial partner ZEISS uses in its equipment at DOE’s Manufacturing Demonstration Facility at ORNL, where companies get help perfecting their 3D printing. ZEISS's Pradeep Bhattad said, "With this, we can inspect every single part coming out of 3D-printing machines. Currently CT is limited to prototyping. But this one tool can propel additive manufacturing (3D printing) toward industrialization."