Improving 3D food printing performance using computer vision and feedforward nozzle motion control
In this study, researchers from Wageningen University & Research developed a computer vision (CV)-based method 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.
Read the full publication Improving 3D food printing performance using computer vision and feedforward nozzle motion control in the Journal of Food Engineering.