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- W4296125747 abstract "3D food printing is an emerging technology to customize food designs and produce personalized foods. Food printing materials are diverse in rheological properties, which makes reliable extrusion-based 3D printing with constant printing parameters a challenge. Food printing often suffers from improper extrusion because of the varying elasticity of the food materials. In this study, a computer vision (CV)-based method is developed to measure the instant extrusion rate and width under constant extrusion pressure/force. The measured extrusion rate and extruded filament width were used to conduct a feedforward control of nozzle motion for a pneumatic 3D food printer. As a result, the CV-based control method improves extrusion line accuracy to 97.6–100% and prevents under-extrusion of white chocolate spread, cookie dough, and processed cheese. The method can also be used to customize filament width with less than 8% of deviation from the target. With a simple measurement setup and a user-friendly software interface, this CV-based method is deployable to most food printing applications to reduce trial-and-error experiments when printing a new food material. • Computer vision measured instant extrusion rate and width during food printing. • Nozzle motion was optimized by extrusion rate and die-swell ratio of food materials. • Optimized nozzle motion improved layer deposition and 3D-stacking of food filaments. • The proposed method is simple and modular to be implemented on existing 3D food printers." @default.
- W4296125747 created "2022-09-17" @default.
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- W4296125747 date "2023-02-01" @default.
- W4296125747 modified "2023-10-16" @default.
- W4296125747 title "Improving 3D food printing performance using computer vision and feedforward nozzle motion control" @default.
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- W4296125747 doi "https://doi.org/10.1016/j.jfoodeng.2022.111277" @default.
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