Rapid Closed-Loop Control of Additive Manufacturing with Machine Learning

Brian Giera | 17-ERD-037

Executive Summary

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.

Publications, Presentations, Etc.

Chu, A., et al. 2019. "Automated Detection and Sorting of Microencapsulation via Machine Learning." Lab on a Chip 19. 1808–1817. doi: 10.1039/c8lc01394b. LLNL-JRNL-748383.

Yuan, B., et al. 2018. "Machine-Learning-Based Monitoring of Laser Powder Bed Fusion." Advanced Materials Technologies 3(12). doi: 10.1002/admt.201800136. LLNL-JRNL-748383. Yuan, B., et al. 2019. "Semi-Supervised Convolutional Neural Networks for In-Situ Video Monitoring of Selective Laser Melting." IEEE Winter Conference on Applications of Computer Vision Proceedings 744-753. doi: 10.1109/WACV.2019.0008. LLNL-PROC-754101.