Keywords
- Show all (58)
- Additive Manufacturing (37)
- 3D Printing (7)
- Manufacturing Improvements (3)
- Synthesis and Processing (2)
- Electric Grid (1)
- Manufacturing Simulation (1)
- Microfabrication (1)
- Precision Engineering (1)
- Sensors (1)
- Volumetric Additive Manufacturing (1)
- (-) Manufacturing Automation (2)
- (-) Material Design (1)
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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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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…