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.
Original language | English |
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Article number | 111277 |
Journal | Journal of Food Engineering |
Volume | 339 |
DOIs | |
Publication status | Published - Feb 2023 |
Keywords
- 3D food printing
- Computer vision
- Die swell
- Food rheology
- Optical flow