Research on Optimizing Forming Accuracy in Food 3D Printing Based on Temperature-Pressure Dual Closed-Loop Control.

Area of Science:

• Food Science and Technology
• Mechanical Engineering
• Control Systems

Background:

• 3D printing of food, particularly starch-based materials, faces challenges in achieving high accuracy due to material property variations and external interferences.
• Existing systems often struggle with precise control of nozzle temperature and extrusion pressure, leading to defects like over-extrusion and under-extrusion.

Purpose of the Study:

• To develop and validate a new 3D printing system for starch-based food products.
• To enhance the printing accuracy and stability of high-viscosity food materials using a novel control strategy.

Main Methods:

• Implementation of a temperature-pressure double closed-loop collaborative control system.
• Utilizing a hybrid Bang-Bang and PID control for rapid and accurate nozzle temperature adjustment.
• Employing an adaptive fuzzy PID algorithm for real-time optimization of extrusion pressure.

Main Results:

• Achieved a printing accuracy of up to 98% at 40 °C.
• Reduced pressure fluctuation by 80% compared to conventional methods.
• Improved molding accuracy of complex structures to 97%, significantly mitigating over-extrusion and under-extrusion issues.

Conclusions:

• The proposed 3D printing system effectively addresses the accuracy limitations in starch food printing.
• The collaborative control strategy provides a stable and high-precision solution for printing high-viscosity food materials.
• This advancement enables the creation of intricate food structures with enhanced fidelity.