Improving 3D food printing performance using computer vision and feedforward nozzle motion control

Yizhou Ma, Jelle Potappel, Aneesh Chauhan, Maarten A.I. Schutyser, Remko M. Boom, Lu Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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 languageEnglish
Article number111277
JournalJournal of Food Engineering
Volume339
DOIs
Publication statusPublished - Feb 2023

Keywords

  • 3D food printing
  • Computer vision
  • Die swell
  • Food rheology
  • Optical flow

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