Abstract
Material extrudability is a prerequisite for printing 3D structures with extrusion-based food printing. Food materials with different shear-thinning behaviors were printed to develop predictive models for extrudability. A dataset of 131 unique combinations of materials and printing parameters was collected. Image analysis was employed to rapidly quantify extrudability as determined by mode width, line height and width consistency of line filaments. The relation between the printing pressure and the volumetric flow rate followed a power-law relation, which characterizes the extent of shear-thinning of the food materials. Both regression and classification models were trained and tested using the random forest algorithm. The model performance indicated that extrudability can be predicted with moderate to high accuracy by using rheological measurements and printing parameters as inputs. The predictive workflow developed in this study provides a framework to quantitatively assess and predict extrudability for 3D printing of complex food materials. Industrial relevanceExtrusion-based 3D printing has been applied to customize food designs and can potentially enable personalized nutrition. To achieve this, we need to be able to effectively print complex food materials with high accuracy. Variations in composition and rheological properties of complex food materials make achieving proper extrudability not trivial at this moment, and this limits their applicability to extrusion-based food printing. Here, we developed an image analysis tool to measure line filament extrusion of complex food materials that vary in shear-thinning behaviors. We then built predictive models to estimate material extrudability based on material's shear-thinning properties and printing parameters. Data-driven prediction of material's extrudability can help avoid excessive trial-and error experiments in future.
Highlights
Material extrudability is a prerequisite for printing 3D structures with extrusion-based food printing
Shear thinning has been suggested as a desirable property for smooth extrusion in 3D food printing as it reduces the pressure needed for printing while stabilizing the geometry after extrusion (Liu et al, 2019)
Prior to building multivariate predictive models, we separately examined the univariate relation of the printing pressure and nozzle speed with the extrudability
Summary
Material extrudability is a prerequisite for printing 3D structures with extrusion-based food printing. Food materials with different shear-thinning behaviors were printed to develop predictive models for extrudability. We built predictive models to estimate material extrud ability based on material’s shear-thinning properties and printing parameters. This study collected the variables that were known to represent tablet rheology in pharmaceutical manufacturing and modelled the complex relationship between materials and printing pa rameters to predict the printability of unknown tableting materials. This hybrid modelling approach is known as “gray-box modelling”, which combines mechanistic insight (white box models) and data-driven models (black box models) to maximize the predictability in manufacturing (Roupas, 2008).
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Innovative Food Science & Emerging Technologies
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
