New National Lab Algorithm Enables Faster, Safer Inspection of Nuclear Materials
Posted: 2024-11-5
Source:
Oak Ridge National Lab
A software algorithm developed by Oak Ridge National Laboratory (ORNL) has reduced the time needed to inspect 3D-printed parts for nuclear applications by 85 percent.
Researchers are now training the algorithm for Idaho National Laboratory (INL) to apply similar methods for irradiated materials and nuclear fuel.
Faster analysis of 3D-printed parts
Additive manufacturing, also called 3D-printing, can enable the domestic fabrication of complex nuclear parts in a short time.
The quality of these products is typically verified through computed tomography or CT scans, which use X-rays to capture images of any weaknesses or errors in the internal structure.
ORNL’s new software algorithm uses machine learning to rapidly reconstruct and analyze the images to significantly cut down on the cost, time, and number of scans needed to perform an inspection.
Researchers at INL applied ORNL’s new algorithm to analyze more than 30 3D-printed sample parts in less than 5 hours of scan time.
It would have taken more than 30 hours to complete each scan without the software—opening the door for potential applications with radioactive materials and fuels.
INL researchers often delay examining materials removed from a nuclear reactor for the safety of lab technicians.
Radiation accrued during long X-ray CT scans can also wear on the detector, limiting its operating life and the accuracy of its images.
Shorter scans would mean less radiation dosage per scan and less waiting, while enabling higher-quality data and faster feedback to performance models.
"If we use this algorithm to reduce the scan time for radioactive materials and fuels, it will increase worker safety and the rate we can evaluate new materials, said Bill Chuirazzi, an instrument scientist and leader of INL’s Diffraction and Imaging group. "Down the road it enables us to expedite the life cycle of new nuclear ideas from conception to implementation in the power grid."
What's Next?
ORNL researcher Amir Ziabari, who first developed the algorithm to produce faster, more accurate scans of 3D-printed metal parts, is now training the software to do the same with radioactive materials and fuels.
The collaboration between ORNL and INL is expected to speed up the development and deployment of new reactor types to decarbonize the power sector.
The software is being supported through DOE’s Advanced Materials and Manufacturing Technologies (AMMT) program to accelerate commercialization of new materials and manufacturing technologies through demonstration and deployment.
Researchers are now training the algorithm for Idaho National Laboratory (INL) to apply similar methods for irradiated materials and nuclear fuel.
Faster analysis of 3D-printed parts
Additive manufacturing, also called 3D-printing, can enable the domestic fabrication of complex nuclear parts in a short time.
The quality of these products is typically verified through computed tomography or CT scans, which use X-rays to capture images of any weaknesses or errors in the internal structure.
ORNL’s new software algorithm uses machine learning to rapidly reconstruct and analyze the images to significantly cut down on the cost, time, and number of scans needed to perform an inspection.
Researchers at INL applied ORNL’s new algorithm to analyze more than 30 3D-printed sample parts in less than 5 hours of scan time.
It would have taken more than 30 hours to complete each scan without the software—opening the door for potential applications with radioactive materials and fuels.
INL researchers often delay examining materials removed from a nuclear reactor for the safety of lab technicians.
Radiation accrued during long X-ray CT scans can also wear on the detector, limiting its operating life and the accuracy of its images.
Shorter scans would mean less radiation dosage per scan and less waiting, while enabling higher-quality data and faster feedback to performance models.
"If we use this algorithm to reduce the scan time for radioactive materials and fuels, it will increase worker safety and the rate we can evaluate new materials, said Bill Chuirazzi, an instrument scientist and leader of INL’s Diffraction and Imaging group. "Down the road it enables us to expedite the life cycle of new nuclear ideas from conception to implementation in the power grid."
What's Next?
ORNL researcher Amir Ziabari, who first developed the algorithm to produce faster, more accurate scans of 3D-printed metal parts, is now training the software to do the same with radioactive materials and fuels.
The collaboration between ORNL and INL is expected to speed up the development and deployment of new reactor types to decarbonize the power sector.
The software is being supported through DOE’s Advanced Materials and Manufacturing Technologies (AMMT) program to accelerate commercialization of new materials and manufacturing technologies through demonstration and deployment.
