The Rheoprinter: online learning of printability conditions

In this project we aim to build a system that "learns" optimum printability conditions on the fly for materials that are primarily utilized in additive manufacturing (AM). The micro-extrusion system measures printability using computer vision and searches for optimum machine parameters using optimization algorithms. Both tasks are happening real-time allowing the system to be both a measurement and a printing device in parallel. With this strategy, we introduce "printability" as an emergent material property that can be efficiently measured and not derived through time-consuming and trial & error parametric studies following classical rheological measurements. Such systems could potentially accelerate the development of novel materials for a wide range of applications.

Click here for on-going progress!

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