We are developing a machine-learning algorithm for vision-based control of additive-manufacturing processes to automatically detect problems and implement rectification strategies, thereby improving quality while reducing time and cost. This technology supports stockpile stewardship and inertial-confinement fusion programs, which require rapid build qualification for the large-scale production of high-quality parts.
Yuan, B., et al. 2018. "Machine-Learning-Based Monitoring of Laser Powder Bed Fusion." Advanced Materials Technologies 3(12): 1800136. doi: 10.1002/admt.201800136. LLNL-JRNL-748383.
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