LLNL researchers have developed additive manufactured fuel targets for IFE. They have been successful in using TPL to fabricate low density (down to 60 mg/cm3) and low atomic number (CHO) polymeric foams for potential targets, and some have been tested at the OMEGA Laser Facility. With TPL, LLNL researchers have also been able to fabricate a full fuel capsule with diameter of ~ 5mm or…
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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…