This project aims to develop predictive models to reduce trial-and-error experiments and to investigate robust adaptive control to 3D-print a variety of food materials for product prototyping and personalized nutrition.
Current extrusion-based 3D food printing focus on printing single-component or binary food systems at room temperature. The printing of complex food systems has however not been consistently achieved. Successful printing of complex food systems contributes to the future development of rapid food prototyping and personalized nutrition. The low adaptability of the current printing control system requires trial-and-error experiments prior to printing new food materials. Improving the adaptability of the printing system will lead to precise dosing and smooth extrusion of complex food materials, which will ultimately benefit the stability and quality of printed foods.
We aim to develop an adaptive printing control system for printing complex food materials with high accuracy and quality. We are integrating sensors (e.g. vision camera) into the extrusion system to monitor physico-chemical properties of food materials and printing quality. Data-driven models will then predict printing quality based on inputs such as specific printing conditions and properties of food materials. We will eventually develop a printing parameter recommendation system to best inform 3D food printing users for formulation development and printing optimizations of various food materials.