Additive manufacturing's ability to yield parts with complex shapes and minimal waste is currently limited by one critical challenge: controlling defects in the process that can compromise the performance of 3D-printed materials. Researchers have developed a method to use temperature data at the time of production to predict the formation of subsurface defects, so they can be addressed then and there.

The researchers 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. Porosity refers to tiny, often microscopic “voids” that can occur during the laser printing process and that make a component prone to cracking and other failures.

The approach showed that there is a correlation between surface temperature and porosity formation below. They saw that under certain processing conditions based on different time and temperature combinations, porosity forms as the laser passes over. 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 team used machine learning algorithms to make sense out of the complex data and predict the formation of porosity from the thermal history.

By correlating their results with results from actual printers using infrared technology, the researchers can make claims about the quality of the printing without having to actually see below the surface. 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. In traditional manufacturing, the consistency of the process makes it unnecessary to scan every metallic component coming off of the production line.

For more information, contact Aaron C. Greco at This email address is being protected from spambots. You need JavaScript enabled to view it..