National Labs team up to improve efficiency, safety of 3D-printed nuclear components.

To improve the efficiency of emerging nuclear technologies, Oak Ridge National Laboratory (ORNL) has developed a software algorithm that reduces inspection time for 3D-printed parts used in nuclear applications by 85%. This breakthrough, achieved through machine learning, paves the way for faster, safer, and more cost-effective innovation in nuclear energy.

The adoption of 3D printing in the nuclear industry is a promising new development. Nuclear components require an exceptionally high degree of precision, as even the slightest defect could lead to critical safety issues.

Additive manufacturing offers a solution for the highly accurate production of intricate parts with reduced waste and tighter control over quality – an approach that is particularly valuable for nuclear technologies, where reliability is paramount, and the reduction of potential error is key.

One of the most common methods of this technology used for nuclear components is powder bed fusion, which involves using a laser or electron beam to melt and fuse layers of powdered metal. Materials such as stainless steel and specialized nickel alloys are frequently used, as they possess the necessary properties to withstand extreme conditions found in nuclear reactors.

However, inspecting these 3D-printed parts presents its own set of challenges, typically being a time-consuming and resource-intensive process.

The quality of 3D-printed components for nuclear applications is verified through computed tomography (CT) scans, which utilize X-rays to examine the internal structure of each part. Ensuring the integrity of these components is critical, as defects could lead to malfunctions or safety issues.

By leveraging machine learning, ORNL's new algorithm can reconstruct and analyze CT images rapidly, significantly reducing the time required for inspections and thereby reducing costs and radiation exposure while ensuring the highest quality standards are met.

To test this new algorithm, researchers at Idaho National Laboratory (INL) analyzed more than 30 3D-printed parts, reducing what would normally take over 30 hours to just five hours of scan time.

Not only does this reduce costs in time and labor, but it also plays an important role in safety. INL researchers inspect nuclear components both before and after use – initially to ensure their integrity before deployment and later to study how exposure to extreme conditions affects their performance.

However, examining irradiated materials involves significant safety considerations for technicians, as the radioactive nature of these materials requires careful handling to minimize exposure. Long CT scans not only increase technician radiation exposure but also puts wear on detectors, limiting their lifespan and reducing image quality.

Ultimately, shorter scans mean less radiation dosage per scan as well as 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."

By streamlining the evaluation process for new nuclear materials and components, the adoption of this machine-learning algorithm can ultimately lead to quicker deployment of advanced nuclear reactors.

These advanced reactors will play a crucial role in providing reliable, baseload, carbon-free energy, helping to stabilize the power grid as it integrates more renewable energy sources.