Gabriel Lipkowitz
ABSTRACT
Increasing the speed of 3D printing is critical for its widespread adoption as a manufacturing technique. A recently-introduced multi-material 3D printing process called injection CLIP has been shown to accelerate printing by nearly an order of magnitude using computationally-designed microfluidic networks. However, automated design tools for 3D microfluidics have lagged far behind increasing fabrication capabilities and needs. Here, to facilitate the study and application of this novel printing process, we present a fluid dynamics-informed generative design tool for microfluidic networks based on native CAD geometries.
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