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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 has developed a system and method that accomplishes volumetric fabrication by applying computed tomography (CT) techniques in reverse, fabricating structures by exposing a photopolymer resin volume from multiple angles, updating the light field at each angle. The necessary light fields are spatially and/or temporally multiplexed, such that their summed energy dose in a target resin volume…
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Livermore researchers have developed a method of fabricating functional polymer-based particles by crosslinking UV-curable polymer drops in mid-air and collecting crosslinked particles in a solid container, a liquid suspension, or an air flow. Particles could contain different phases in the form or layered structures that contain one to multiple cores, or structures that are blended with…
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By combining 3D printing and dealloying., researchers at LLNL have developed a method for fabricating metal foams with engineered hierarchical architectures consisting of pores at least 3 distinct length scales. LLNL’s method uses direct ink writing (DIW), a 3D printing technique for additive manufacturing to fabricate hierarchical nanoporous metal foams with deterministically controlled 3D…
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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…