Engineer works with 3D printer in a darkened room

Defect-detecting AI could enable 3D printing on industrial scale

Image credit: Dreamstime

An AI-driven method for detecting and predicting defects in 3D printed materials could make industrial use of the technology far more common.

Many industries already rely on the process to rapidly build parts and components. Rocket engine nozzles; pistons for high-performance cars, and custom orthopedic implants are all made using additive manufacturing, a process that involves building parts layer-by-layer using a 3D printer.

However, structural defects that form during the building process is one of the reasons why this approach has not become more widely adopted.

Now, a research team led by Argonne and the University of Virginia (UVA) have developed various imaging and machine-learning techniques to detect and predict the formation of pores in 3D-printed metals in real time with near-perfect accuracy.

The metal samples used in the study were created using a process called laser powder bed fusion, in which metal powder is heated by a laser and then melted into the proper shape. This approach often leads to the formation of pores that can compromise a part’s performance. 

Many additive manufacturing machines have thermal imaging sensors that monitor the build process, but these can miss the formation of pores because they only image the surface of the parts being constructed. The only way to directly detect pores inside dense, metal parts is by using intense X-ray beams.

“Our X-ray beams are so intense that we can image more than a million frames per second,” said Samuel Clark, an assistant physicist at Argonne. These images allowed the researchers to see pore generation in real time.

By correlating X-ray and thermal images, the scientists discovered that pores formed within a sample cause distinct thermal signatures at the surface that thermal cameras can detect.

The researchers then trained a machine-learning model to predict the formation of pores within 3D metals using only thermal images.

They validated the model using data from the X-ray images, which they knew accurately reflected the generation of pores. Then, they tested the model’s ability to detect thermal signals and predict pore generation in unlabelled samples.

Many additive manufacturing machines on the market already have sensors, but they aren’t nearly as accurate as the method the researchers discovered. 

​“Our approach can readily be implemented in commercial systems,” said Kamel Fezzaa, a physicist at Argonne. ​“With only a thermal camera, the machines should be able to detect when and where pores are generated during the printing process and adjust their parameters accordingly.”

For example, if a major defect is detected by a machine early in the manufacturing process, the machine can automatically stop building a part. Even if the build process isn’t halted, the new approach can provide information on where pore defects might be within the part, saving users time during inspection.

“If you have a log file that tells you these four locations could have defects, then you’re just going to check out these four locations instead of looking at the entire part,” said Tao Sun from the University of Virginia.

The ultimate goal is to create a system that not only detects defects, but repairs them during the manufacturing process. Moving forward, the researchers will study sensors that can detect other types of defects that occur during the additive manufacturing process. ​

“In the end, we want to develop a comprehensive system that can tell you not only where you possibly have defects, but also what exactly the defect is and how it might be fixed,” Sun said.

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