Zeiss Licenses ORNL Tech to Inspect 3D Printed Parts

Metal AM Markets

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Long utilized to peer inside metal 3D printed components, CT scanning can reveal trapped powder, cracks, and other deformities. As a non-destructive testing technique, it serves as an invaluable tool for identifying process errors, refining manufacturing methods, and flagging defective parts before shipment. Now, Oak Ridge National Laboratory (ORNL) and German optics leader ZEISS have inked a five-year licensing agreement to collaborate on the use of CT scanning for quality assurance in 3D printed parts.

With the licensing agreement, ZEISS is setting its sights on Simurgh—a tool designed to drastically reduce inspection costs by a factor of ten while increasing accuracy. This is particularly timely, as the industry is poised for a surge in part-specific testing. Though its name might conjure images of a ‘Lord of the Rings’ character, Simurgh is all business. It uses algorithms trained to identify problematic geometries that could signal issues like trapped powder, occlusions, or cracks. This research initiative is backed by the Department of Energy’s Advanced Materials and Manufacturing Technologies Office, located at the Manufacturing Demonstration Facility at ORNL. It has also received support through a Technology Commercialization Fund award.

“CT is a standard nondestructive technique used in a multitude of different industries to ensure the quality of the component that is being produced. 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. 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. If we can get there, that would be a game-changing development that would allow 3D printing to really fulfill its potential,” said ORNL researcher Amir Ziabari.

“ZEISS and ORNL have a long partnership that has led to the development of innovative solutions for automated analysis and qualification. 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,” said Paul Brackman, AM manager at ZEISS.

In this case, Simurgh’s capabilities extend beyond CT scanning; the tool has also been trained on data from scanning electron microscopes. When a part is scanned, Simurgh employs machine learning—specifically deep learning—to automatically analyze and pinpoint areas susceptible to errors. By leveraging machine learning, the system optimizes computing costs while enhancing both speed and accuracy. Notably, Simurgh has scrutinized nuclear fuel assembly brackets destined for the Browns Ferry Nuclear Plant, as well as 3D printed turbine blades.

Zeiss already has a GOM Blade Inspect Pro tool and a host of other turbine blade and blink inspection stuff.

“Understanding what type of defects might be present is incredibly important for understanding material behavior. In these types of parts, any defect or tiny pore in the material could result in a catastrophic failure,” said ORNL´s Manufacturing Demonstration Facility Director Ryan Dehoff.

Amid a surge in critical part production via additive manufacturing (AM) for aerospace and medical fields, the role of CT scanning has also expanded, boosting system sales. This trend hasn’t escaped the attention of metrology giants ZEISS and Nikon. Nikon has purchased SLM Solutions and Morf3D, while ZEISS was an early backer of EOS. More recently, the company invested in Makerverse and Precise Bio and partnered with EOS on a process monitoring solution. It has collaborated with Oak Ridge National Laboratory since 2019, co-developing ZEISS ParAM, a qualification service.

These leading metrology and semiconductor manufacturing firms are far from passively interested in AM. Their engagement is informed by hard data. Having a comprehensive portfolio that includes not only CT but also 3D scanning systems, they are deeply attuned to the additive manufacturing sector. This places them in a unique position to accurately forecast the industry’s trajectory. Their offerings go beyond CT scanners to include 3D scanners for evaluating dimensional accuracy, surface texture, and general precision.

Thanks to Simurgh’s capabilities, it’s now possible to move beyond sampling and examine an entire build or batch, scrutinizing each individual part. Given the inherent process variability in 3D printing, I believe comprehensive testing will become essential for critical components. With the potential for reduced CT scanning costs, we can aim for a future where every part destined for a human body or an aircraft undergoes such rigorous inspection. I, for one, would love to see Ziabari’s vision become reality. We need 3D printing to be safer, and this could be a significant step in that direction.

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