Researchers Use Machine Learning, X-Rays, and a Thermal Camera to Detect 3D Printing Defects Live

Using a machine learning system, the researchers have been able to predict faults from just thermal data captured by a low-cost IR camera.

Researchers at the Argonne National Laboratory and Texas A&M University have discovered a novel approach for reducing defects in 3D-printed metallic objects.

3D printing promises a future where small-volume manufacturing is quick, easy, and affordable — where spare parts can be created on-demand to a stunning accuracy and quality. There's still a rift, however, between 3D printing and traditional manufacturing methods — and it's the quality issue with metal objects 3D printed using selective laser sintering (SLS) which the scientists have looked to address.

The researchers turned to a high-powered X-ray beamline to capture temperature data using a relatively low-cost off-the-shelf thermal camera at the same time as an X-ray image demonstrating whether the sintering process was occurring properly or resulting in otherwise-invisible pockets of porosity beneath the surface.

The type of X-ray system available at Argonne, however, isn't available to commercial 3D printing users — which is where the research comes in. The team took both data sets and used machine learning to form a model which can predict the porosity of the surface based on the recorded thermal history.

"By correlating the results from the APS [X-ray beam] with the less detailed results we can already get in actual printers using infrared technology," explains Ben Gould, co-author of the paper, "we can make claims about the quality of the printing without having to actually see below the surface."

"Right now, there’s a risk associated with 3D printing errors, so that means there’s a cost. That cost is inhibiting the widespread adoption of this technology," says co-author Aaron Greco. "To realize its full potential, we need to lower the risk to lower the cost."

The paper is available under closed-access terms in the journal Additive Manufacturing; more information can be found on the Argonne National Laboratory website.

Gareth Halfacree
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