ZEISS and ORNL reach licensing agreement enabling inspections of 3D-printed parts using industrial X-ray CT scanning
“ZEISS and ORNL have a long partnership that has led to the development of innovative solutions for automated analysis and qualification.”
ZEISS has reached a licensing agreement with the Department of Energy’s Oak Ridge National Laboratory (ORNL). The agreement will allow ZEISS (ORNL’s research partner) to conduct evaluations of 3D-printed components, which will be performed using industrial X-ray computed tomography (CT), utilising ORNL’s machine learning algorithm, Simurgh.
Key information:
- ZEISS and the ORNL have reached a licensing agreement, allowing ZEISS to utilise ORNL’s Simurgh machine learning algorithm.
- The researchers hope that the algorithm will help CT scanning become more affordable and speed up the scanning process.
- Additionally, the project hopes to improve accuracy and efficiency levels in the detection of defects inside 3D-printed components.
The researchers are hoping that combining machine learning and CT scanning will reduce both the time and cost of inspection processes by more than 10 times, while also enhancing quality levels. The licensing agreement forms part of the five-year research collaboration between ZEISS and the ORNL, which is also supported by DOE’s Advanced Materials and Manufacturing Technologies office, and a Technology Commercialisation Fund award. Additionally, the research project is being conducted at DOE’s Manufacturing Demonstration Facility at the ORNL.
“ZEISS and ORNL have a long partnership that has led to the development of innovative solutions for automated analysis and qualification,” said Paul Brackman, Additive Manufacturing Manager at ZEISS. “We are now looking to further improve process development and qualification for additive manufacturing, to enable large-scale adoption and the shift from prototyping to manufacturing.”
According to the parties involved, the research will focus on the utilisation of CT scanners (as well as other measuring devices) and how the machines see inside 3D-printed components to check for defects (like cracks) during the manufacturing process. One of the main challenges relating to 3D printing is the ability to examine a part to ensure there are no hidden flaws which could negatively impact performance levels. Currently, 3D printing uses advanced characterisation techniques to help users understand the features inside the component. Which is where CT comes in.
“CT is a standard non-destructive technique used in a multitude of different industries to ensure the quality of the component that is being produced,” said Amir Ziabari, a Researcher at ORNL. “But CT is traditionally an expensive and time-consuming process. The challenge is how can we leverage what we know of physics and technology to speed up the CT process to allow it to be more broadly adopted by industry.”
For the project, ZEISS has installed industrial CT systems and scanning electron microscopes which can be used to detect the slightest of defects in 3D-printed parts. Following the scanning process, the data will then be run through complex analytics to determine the location of any flaws. However, this process requires a large amount of computing horsepower which costs both time and money. But the ORNL’s Simurgh framework could speed up the scanning and analysis process, saving time and money whilst also providing more accurate results.
Accurate characterisations are critical for high-value parts, especially when they operate in extreme environments where failure is fatal. For example, ORNL used CT scanning techniques in the certification of nuclear fuel assembly brackets installed at Browns Ferry Nuclear Plant in Alabama in 2021. This was the first time 3D-printed parts had been placed inside a nuclear reactor.
“Understanding what type of defects might be present is incredibly important for understanding material behaviour,” said Ryan Dehoff, MDF Director, who led the nuclear bracket development. “In these types of parts, any defect or tiny pore in the material could result in a catastrophic failure.”
The ORNL is also investigating how CT scanning capabilities can be expanded into additional industries (e.g., microelectronics and batteries) that don’t already utilise these techniques. If possible, this type of characterisation may lead to clean energy breakthroughs.
CT scanning is currently limited due to the size, shape, and material types that it can scan. With these limitations, it makes sense for manufacturers to focus on scanning high-value components or validating a small number of parts that are set for mass production. However, during this project ZEISS and the ORNL hope to reduce the time and cost of CT scanning, allowing it to be as common as visual inspection.
“My ultimate goal, what I would like to achieve, is to make this so fast that we can put this in a production line so every part can be CT scanned rapidly and reliably,” said Ziabari. “If we can get there, that would be a game-changing development that would allow 3D printing to really fulfil its potential.”