ORNL develops AI software to assess 3D printing quality in real-time

The automation of various stages of the 3D printing workflow will accelerate industrial 3D printing. When parts are made at scale, repeatability becomes essential, however 3D printing systems cannot always provide this. Therefore, part of this automation concerns assessing the quality of parts being 3D printed in real time. At the Oak Ridge National Laboratory, researchers have developed an artificial intelligence program to do this. The software collects and analyses data through every step of the manufacturing process, from design, feedstock selection, print build to material testing.

By capturing the information as a part is being 3D printed, the artificial intelligence is able to create a digital clone of each part. This clone provides a trove of data, from the raw material to the operational component. One of the researchers at ORNL, Vincent Paquit, explains: “We then use that data to qualify the part and to inform future builds across multiple part geometries and with multiple materials, achieving new levels of automation and manufacturing quality assurance.” In the future, the aim is to have this data enable the 3D printer to be self-correcting in real-time.

Defect detection | Credits: Luke Scime/ORNL

This concept requires a control method at the end to determine if the parts are ready to be installed in essential applications like cars, airplanes and energy facilities. By developing a computer vision technique that quickly analyzing images captured from cameras installed on the printers, ORNL’s software can control for surface-visible defects. When the software detects any anomalies, it automatically alerts operators so adjustments can be made.