TUCSON, AZ—NASA is spending $750,000 on a project undertaken by two University of Arizona (UA) researchers who use machine learning methods to mitigate defects that occur during the 3D printing of jet engine parts. Lockheed Martin Space and the 3D printing companies companies Open Additive  and CompuTherm are working with Mohammed Shafae of systems and industrial engineering and Andrew Wessman of materials science and engineering at the university.

“Andrew and Mohammed are using their combined backgrounds in materials science and systems engineering to look at additive manufacturing from a microscopic level all the way up to the large-scale systems level,” says David W. Hahn, Craig M. Berge dean of the College of Engineering at the University of Arizona. “Advanced manufacturing is one of the college’s research focus areas, and this is a great example of an interdisciplinary effort to advance the field and keep the UA at the forefront.”

The researchers are using a sophisticated sensor system, combined with thermal imaging cameras and high-speed localized cameras to monitor the 3D printing process and identify when and where defects occur. They plan to apply machine learning methods to the data and develop a model that can predict defects when they occur. This will allow scientists to take corrective action to prevent the defects or terminate a process before wasting more time and materials. Research in this area typically uses a single type of sensor to detect specific categories of defects, but this work takes the concept a step further.

“We’re really going to try to learn how these separate categories of defects can be linked to each other, because sometimes the process defects can be the leading cause of the material defects,” says Shafae.