The real-time method works with “near-perfect accuracy” according to its developers, a team of researchers at the US Department of Energy’s (DOE) Argonne National Laboratory and the University of Virginia (UVA).
Able to build complex parts quickly, additive manufacturing is used to make parts including rocket engine nozzles, pistons for high performance cars, and custom orthopaedic implants.
Structural defects that can form during the building process have prevented wider adoption however, according to the researchers.
In the new study, metal samples were created using laser powder bed fusion, in which metal powder is heated by a laser and then melted into the required shape. This approach can lead to the formation of pores that 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, the team said, such as those generated by the Advanced Photon Source (APS), a DOE facility at Argonne.
“Our X-ray beams are so intense that we can image more than a million frames per second,” said Samuel Clark, 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 cause distinct thermal signatures at the surface, which thermal cameras can detect.
The researchers then trained a machine learning model to predict the formation of pores within 3D-printed 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.
“The APS offered the 100% accurate ground truth that allowed us to achieve perfect prediction of pore generation with our model,” said Tao Sun, associate professor at UVA.
Many additive manufacturing machines on the market already have sensors, but the researchers claimed “they aren’t nearly as accurate” as the new method.
“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.”
If a major defect is detected by a machine early in the manufacturing process, the machine could automatically stop building a part. Even if the build process is not halted, the new approach could provide information on where pore defects might be within the part, saving 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 Sun.
The team’s ultimate goal is to create a system that not only detects defects, but also repairs them during the manufacturing process. The researchers will also 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,” said Sun.
The method was recently published in Science.
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Content published by Professional Engineering does not necessarily represent the views of the Institution of Mechanical Engineers.