Researchers Explore Machine Learning to Prevent Defects in Metal 3D-Printed Parts in Real Time

For years, Lawrence Livermore National Laboratory engineers and scientists have used an array of sensors and imaging techniques to analyze the physics and processes behind metal 3-D printing in an ongoing effort to build higher quality metal parts the first time, every time. Now, researchers are exploring machine learning to process the data obtained during 3-D builds in real time, detecting within milliseconds whether a build will be of satisfactory quality. In a paper published online Sept. 5 by Advanced Materials Technologies, a team of Lab researchers report developing convolutional neural networks (CNNs), a popular type of algorithm primarily used to process images and videos, to predict whether a part will be good by looking at as little as 10 milliseconds of video. "This is a revolutionary way to look at the data that you can label video by video, or better yet, frame by frame," said principal investigator and LLNL researcher Brian Giera. "The advantage is that you can collect video while you're printing something and ultimately make conclusions as you're printing it. A lot of people can collect this data, but they don't know what to do with it on the fly, and this work is a step in that direction."