The platform has three major components:
(1) active-mixing direct-ink-write
(2) in situ characterization substrates or probes
(3) active learning experimental planning system.
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LLNL has developed a method that adds a polyamine based crosslinker and an acid receptor, based on MgO nanoparticles into a polymer bonded PBX, where the polymer binder is a fluoropolymer containing vinylidene difluoride functionality. Crosslinking kinetics can then be controlled by selecting an appropriate amine structure, pressing temperature and optionally the addition of a chemical…
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LLNL researchers uses Additive Manufacturing (AM) to create reinforcing scaffolds that can be integrated with High Explosives (HE) or solid rocket fuel with minimal volume fraction. Its main benefit is to create stability in harsh field conditions. Its secondary benefit is providing another method to finely tune blast performance or fuel burn. Creating complex shapes with structural…
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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…