Argonne National Laboratory(ANL) scientists have come up with a new solution for controlling defects that compromise the performance of 3D printed materials.
According to the ANL team, identifying and correcting defects at the time of printing could have important ramifications for the entire additive manufacturing industry; it eliminates the need for costly and time-consuming inspections of each mass-produced component.
The scientists, along with their Texas A&M University counterparts, have used temperature data at the time of production to predict the formation of subsurface defects so they can be addressed right then and there.
“Ultimately you would be able to print something and collect temperature data at the source and you could see if there were some abnormalities, and then fix them or start over,” says study author Aaron Greco. “That’s the big-picture goal.”
For their research, the scientists used the extremely bright, high-powered X-rays at beamline 32-ID-B at Argonne’s Advanced Photon Source (APS).
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 if porosity was forming below the surface.
According to lead author Noah Paulson, this study shows that there is in fact a correlation between surface temperature and porosity formation below.
“Having the top and side views at the same time is really powerful. With the side view, which is what is truly unique here with the APS setup, we could see that under certain processing conditions based on different time and temperature combinations porosity forms as the laser passes over,” notes Paulson.
For example, the paper observed that thermal histories where the peak temperature is low and followed by a steady decline are likely to be correlated with low porosity.
In contrast, thermal histories that start high, dip, and then later increase are more likely to indicate large porosity.
The scientists used machine learning algorithms to make sense out of the complex data and predict the formation of porosity from the thermal history.
While 3D printers typically come equipped with infrared cameras, the cost and complexity make it impossible to equip a commercial machine with the kind of X-ray technology that exists at the APS.
However, by designing a methodology to observe systems that already exist in 3D printers, that wouldn’t be necessary, the scientists contend.
Image and content: sspopov-Shutterstock/ANL