Quantitative analysis of 3D food printing layer extrusion accuracy: Contextualizing automated image analysis with human evaluations

Abstract

3D food printing can customize food appearance, textures, and flavors to tailor to specific consumer needs. Current 3D food printing depends on trial-and-error optimization and experienced printer operators, which limits the adoption of the technology by general consumers. Digital image analysis can be applied to monitor the 3D printing process, quantify printing errors, and guide optimization of the printing process. We here propose an automated printing accuracy assessment tool based on layer-wise image analysis. Printing inaccuracies are quantified based on over- and under-extrusion with reference to the digital design. The measured defects are compared to human evaluations via an online survey to contextualize the errors and identify the most useful measurements to improve printing efficiency. The survey participants marked oozing and over-extrusion as inaccurate printing which matched the results obtained from automated image analysis. Although under-extrusion was also quantified by the more sensitive digital tool, the survey participants did not perceive consistent under-extrusion as inaccurate printing. The contextualized digital assessment tool provides useful estimations of printing accuracy and corrective actions to avoid printing defects. The digital monitoring approach may accelerate the consumer adoption of 3D food printing by improving the perceived accuracy and efficiency of customized food printing.

Keywords

• Direct ink writing
• Consumer insights
• Extrusion-based 3D food printing
• Process monitoring