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Researchers predict ‘dangerous’ defects in 3D-printed jet engines and rockets

Professional Engineering

Stock image. The University of Arizona-developed sensor and machine learning system will predict defects in printed metal rockets and jet engines (Credit: Shutterstock)
Stock image. The University of Arizona-developed sensor and machine learning system will predict defects in printed metal rockets and jet engines (Credit: Shutterstock)

A ‘sophisticated’ sensor and machine learning system will predict potentially dangerous defects in 3D-printed jet engines and rockets.

Funded by NASA and involving collaboration with Lockheed Martin Space and other industrial partners, the system is being developed by two engineers at the University of Arizona.  

Precision and quality are key in additive manufacturing, especially when used to create heat-resistant metal parts for jet engines, rockets, or other high temperature environments. 

Sector leader SpaceX has already flown rockets using some 3D-printed parts, and some smaller firms – such as Scottish company Orbex – are using the technique as their principal engine manufacturing process.  

The Arizona team, consisting of Mohammed Shafae and Andrew Wessman, will use $750,000 of NASA funding to focus on two broad categories of defects – process defects (physically visible aberrations such as holes, cracks or two layers not sticking together, which occur when something goes wrong in the printing process), and material defects (variations in chemistry or the arrangement of atoms, which are not visible except with high resolution microscopes).  

The complexity of many additively manufactured parts can make it difficult to find material defects using common inspection methods. They can happen if one layer is still cooling and another hot layer is placed on top, altering the properties of the material beneath. The metal could become brittle, for example, or less able to endure strain.  

“You can think how dangerous that would be if the part were used in a jet engine or a rocket,” said Shafae. “The types of defects we’re focused on are defects that will make the material behave differently than intended.” 

The two engineers said they will use a sophisticated sensor system, combined with thermal imaging cameras and high-speed localised cameras, to monitor the printing process and identify when and where defects occur.  

They will then use machine learning to study the data and develop a model to predict when defects will occur. This will allow manufacturers to take corrective action to prevent 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, the researchers said, but the new 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,” Shafae said. 

Industrial-scale additive manufacturing processes often generate several terabytes of data, which no single researcher could sort through. Taking that step out of human hands could allow for much closer analysis, the researchers said.  

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Content published by Professional Engineering does not necessarily represent the views of the Institution of Mechanical Engineers.


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