ORNL Advances 3D Printing Quality Control | 3DPrint.com







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When you 3D print something, you expect that the image you see on your computer screen will be perfectly reproduced on the 3D printer. But that’s not always the case, especially when it comes to more complex industrial metal prints. It’s difficult to fully control the material properties that a 3D printed object will have.

“We wrongly assume that what you print will be identical to what was designed,” said Suresh Babu, who holds the Governor’s Chair for Advanced Manufacturing at the University of Tennessee and Oak Ridge National Laboratory (ORNL). “Printing a material involves a very complex temperature profile for the material due to multiple heating, melting, and cooling events that are all interconnected and inherently dependent on one another.”

Suresh Babu

The complex 3D printing process affects things like porosity, defect structure, and nonuniform microstructures, all important factors that affect the final performance of the part. If anything is even a little bit off, the part won’t perform as well as it should. When it comes to critical parts like those used in aerospace applications, performance absolutely has to be perfect, so comprehensive testing and inspection of each part must be conducted. This increases the cost of production and limits the real-world usefulness of additive manufacturing.

Process controls can be implemented to certify 3D printed parts, but the process is long and expensive, and if the geometry of the part changes, the whole process must be started all over again for the new geometry.

“In order for additive manufacturing technologies to be widely implemented in industrial applications, we must revolutionize how we think about the certification process,” said Vincent Paquit, Imaging Scientist and Data Analytics Lead for ORNL’s Manufacturing Demonstration Facility. “We need to get to where we can certify the process, not individual parts.”

“Another major challenge in additive manufacturing is residual stress and distortion,” said Ryan Dehoff, leader of the ORNL’s Deposition Science and Technology Group. “These complicated thermal cycles during processing result in a lot of internal residual stress and can vary widely within a part, which ultimately manifests itself in distortion.”

One effective way of detecting residual stress and defects is through neutron analysis at ORNL’s High Flux Isotope Reactor and Spallation Neutron Source. The process can show warping and porosity within a large component without having to cut that component apart.

“The advantage of neutrons is they can go through metals quite easily. You can see internal structures and features in an additive manufacturing sample,”  said ORNL neutron scientist Hassina Bilheux. “Right now, if you want to do this with X-rays, it’s extremely hard. You need to crank up the energy in your X-rays, and by doing that you lose resolution.”

To fully take advantage of the information neutron analysis provides, researchers need to thoroughly monitor the conditions under which a part is produced. Infrared cameras record the exact temperature when a beam hits the metal powder, while other instruments monitor the pressure within the print chamber, the roughness of the print surface, and variations in the power supply, among other information.

Vincent Paquit

“We start by collecting as much data as possible to establish a link between process intent and final outcome. Every additional sensor adds value to the puzzle,” said Paquit.

ORNL has created a portfolio of open source software tools to help users better understand the nuances of the additive manufacturing process. These tools can be used to certify that a machine is operating as expected and producing good quality parts. An interdisciplinary team is also contextualizing data analytics results by combining expertise in materials science, manufacturing, data analytics, sensing, modeling, high performance computing and neutron science to link the physical phenomena and process outcomes.

“The goal is to examine the compiled data and find the set of events that will induce a certain type of defect, microstructure or property,” Paquit said. “At the end of the day, what we want to be able to do is train a machine-learning technique that will automatically capture this information and output it as a quality metric.”

The process may allow 3D printed parts to be used in critical applications, leading to in-depth analysis of every 3D printed part that can be put directly into service – parts that are “born qualified.”

“I can clearly envision critical parts going on a plane, and the government requiring you to keep a record of everything that happened in the print chamber every single time. The aerospace industry will validate every single object that comes out of a 3D printer,” said Paquit. “They will not fly anything that’s not 100 percent certified for final use. Quality control will definitely not go away.”

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[Source/Images: ORNL]

 





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