3D Printing Image
When 3D printing metallic parts, Argonne scientists found a correlation between temperatures at the surface and defects that form below. (Credit: Shutterstock/sspopov)

With its ability to yield parts with complex shapes and minimal waste, additive manufacturing has the potential to revolutionize the production of metallic components. That potential, however, is currently limited by one critical challenge: controlling defects in the process that can compromise the performance of 3D-printed materials.

Researchers have discovered a possible breakthrough solution: Use temperature data at the time of production to predict the formation of subsurface defects so they can be addressed right then and there.

The scientists used the extremely bright, high-powered x-rays at beamline 32-ID-B at Argonne’s Advanced Photon Source (APS), a Department of Energy Office of Science User Facility. They designed an experimental rig that allowed them to capture temperature data from a standard infrared camera viewing the printing process from above while they simultaneously used an x-ray beam taking a side-view to identify whether porosity was forming below the surface.

The scientists used machine learning algorithms to make sense out of the complex data and predict the formation of porosity from the thermal history. The ability to identify and correct defects at the time of printing would have important ramifications for the entire additive manufacturing industry because it would eliminate the need for costly and time-consuming inspections of each mass-produced component.

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