Abstract
This study aims to obtain a desirable 3D printing product based on the knowledge of the material and suitable printing parameters. This study used high-methoxy pectin (HMP) as the ingredient of pectin jelly candy to understand the effect of different pectin concentrations and printing parameters (nozzle height, extrusion rate, printing layer height, nozzle movement speed, and nozzle diameter). Machine learning was used to learn and analyze the data of different 3D printing parameters to find out a suitable parameter. Rheological analysis revealed that a 16% pectin (w/v) concentration had the height of G′ and G″, and all pectin jelly candy showed the characteristic of shearing thinning. A parameter analysis decision tree revealed that the pectin concentration of 12–14% (w/v), printing layer height below 1.5 mm, extrusion rate below 0.305 mm3/s, nozzle height above 0.5 mm, and printing rate of 5–10 mm were able to allow pectin jelly candy to be printed with an error below 5%. Machine learning helps researchers find appropriate parameters and reach the design of molding height quickly, and it helps them discuss how molecule interaction causes different 3D printing results.
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