In this issue of Manufacturing Ideas to Watch: Continuous Manufacturing of High Quality Graphene, 3D-Painting, 3D Printing on Imperfect Surfaces, and Plastic Neutron Scintillator Crystals. Let us know what you think by leaving a comment!
Continuous Manufacturing of High Quality Graphene
Researchers at MIT have developed and demonstrated a continuous manufacturing process for producing high-quality graphene. This scalable production method produces graphene membrane filters, with applications including biological separation, desalination, and gas permeable barriers, and more. The process employs a scalable, roll-to-roll approach, depositing the graphene on thin foils using a two-zone chemical vapor deposition system. The graphene is then taken up by a second spool, where it is post-processed to create functional devices. Controlling the temperatures and feed rates of the process enables precise control over the graphene properties, and the creation of seamless, endless sheets.
– John Hart, MIT
3D-Painting: Multi-Material Ceramic Additive Manufacturing
Additive Manufacturing of multi-material ceramics is essential for applications ranging from fuel cells to biomedical devices to defense. Researchers at Northwestern have developed a novel additive manufacturing process that uses 3D-paints to deposit the structure. This direct-write extrusion method uses specially designed paints that contain many of the same components as household paints, but use functional powders and particles, rather than pigments. Much like household paint, shear thinning polymers and a solvent mixture are added to enable deposition, with the solvent evaporating to leave the desired particles. These paints are also composed primarily of the functional particles, rather than the non-functional polymers, enabling final products with desired properties. The 3D-printed ceramic objects can be used as-is, or be thermally processed to burn away the polymer and to densify the ceramic as appropriate for the application.
– Ramille Shah, Northwestern University
3D Printing on Imperfect, Moving Surfaces
Current additive manufacturing systems deposit material along a pre-programed path or pattern as an open-loop control system. Should anything go awry, like running out of material or accidentally bumping the part, open-loop systems have no way of compensating and as a result the build job is typically discarded. Alternatively, if you can detect and measure changes in real time, you enable high quality printing on non-uniform substrates like textiles or a living person in the case of skin. This is also widely applicable for automating the process of remanufacturing heavy machinery. Such a system requires a closed-loop feedback and control system. Researchers at the University of Minnesota have design such a system using computer vision to monitor a surface throughout the printing process and compensate the path of production in real time.
– Michael McAlpine, University of Minnesota
Plastic Neutron Scintillator Crystals to Replace Expensive, Scarce Materials
Neutron detection has broad applicability, ranging from cancer therapy and homeland security to non-destructive testing in manufacturing. But current neutron detection technologies sometimes struggle to distinguish between neutrons and gamma rays. Helium-3-based detectors are insensitive to gamma rays but world supplies of Helium-3 are in steady and imminent decline (since helium slowly sweeps out of the atmosphere and leaves the planet). Crystals of certain organic materials, like stilbene, make adequate detectors but are hard to grow and produce in large quantities. Liquid scintillators, another alternative, tend to be toxic. Polymer-based neutron scintillators have many advantages for large scale production, but their performance has lagged. A team of researchers at Lawrence Livermore National Laboratory has been improving plastic scintillators and recently reported that they are meeting or exceeding the performance of liquid scintillators. Their work may lead to a broader use of neutron detectors, especially in non-critical applications, like manufacturing, where cost is an important factor.
– Natalia P. Zaitseva, Lawrence Livermore National Laboratory
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