AI-Based 3D Food Printing Using Standard Composite Materials

Abstract

3D printing is one of the ways to advance the technology of the 4th industrial revolution. Instead of making a casting tool for the desired product, it directly produces the product through 3D printing. 3D printing can produce customized products for each individual, so it is possible to construct a small smart factory. In particular, AI (Artificial Intelligence) technology learns and judges legal judgments, cancer diagnosis, appropriateness judgments and standards for food ingredients, etc. that humans used to derive results. In the era of COVID-19, 3D food printing becomes an important turning point for non-face-to-face business and personalized business. 3D food printing is a technology that enables direct production of small quantities using 3D digital design and personalized nutrition data. However, the current development stage of 3D food printing technology is only at the level of making a product with a simple form or only one material, and separate material processing is required to reach an appropriate level of print quality due to the printing characteristics of various food groups [1]. In addition, there are not enough structured data available for learning, and no prior development and indicators have been developed for standard composite materials that can be applied to various foods to reach printability. In this paper, we use AI machine learning to obtain adequate print quality in 3D food printing. We study supervised learning, unsupervised learning, and reinforcement learning of AI machine learning, and design algorithms. In AI machine learning unsupervised learning, conformity and non-conformity are determined, and the result of the derived standard composite material value is applied to papers to evaluate printing adequacy. Through AI machine learning reinforcement learning, print aptitude is evaluated through rheological analysis, and big data values of various food groups applied with standard composite materials are secured.