Machine learning “fixes” 3D-printed metal parts—before they’re built

Neural networks and advanced algorithms use real-time video to analyze build quality and advise on how to improve it.

3D defects

For years, engineers at Lawrence Livermore National Laboratory  have used sensors and imaging techniques to analyze the physics and processes behind metal 3D printing in order to build high-quality metal parts the first time, every time. Now, they are leveraging machine learning to process data obtained during 3D builds in real time, detecting within milliseconds whether a build will be high quality. More precisely, they are developing convolutional neural networks (CNNs), a type of algorithm commonly used to process images and videos, to predict whether a part will be good by looking at as little as 10 milliseconds of video.

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