LLNL researchers have continued to develop their pioneering DIW 3D-printed glass optics technology that allows for the 3D printing of single- and multi-material optical glass compositions in complex shapes. This LLNL invention further proposes incorporating dopants (including, but not limited to TiO2 and Pd) into slurries and inks for 3D printing of glass components that can then be directly…
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Livermore researchers have developed a method for implementing closed-loop control in extrusion printing processes by means of novel sensing, machine learning, and optimal control algorithms for the optimization of printing parameters and controllability. The system includes a suite of sensors, including cameras, voltage and current meters, scales, etc., that provide in-situ process monitoring…
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LLNL researchers have developed a system that relies on machine learning to monitor microfluidic devices. The system includes (at least) a microfluidic device, sensor(s), and a local network computer. The system could also include a camera that takes real-time images of channel(s) within an operating microfluidic device. A subset of these images can be used to train/teach a machine learning…