METHODS AND TECHNIQUES FOR ADAPTIVE CONTROL OF POLYMER EXTRUSION TEMPERATURE IN FFF/FDM TECHNOLOGY USING APID AND MPC ALGORITHMS

Abstract

Advances in additive manufacturing are rapidly being adopted in industry, medicine, education, and other fields. One of the most widespread and relatively inexpensive additive manufacturing technologies is Fused Filament Fabrication (FFF) or Fused Deposition Modeling (FDM) printing, which operates on the principle of layer-by-layer extrusion of molten polymer material – the filament. At the same time, the quality of manufactured products depends on numerous parameters, such as: the design of the 3D printer, the material, the software, and the environmental conditions during production. A separate factor affecting the quality of 3D printing is the stability of the filament extruder’s temperature, since even minor deviations in the extruder temperature within the range of 3 °C to 5 °C can lead to defects in the formation of model layers, low adhesion between layers, deformations, and a reduction in the mechanical strength of the products. Given this problem, the task of improving the accuracy and stability of temperature control during the filament extrusion process while printing is particularly relevant, especially under dynamic thermal loads that arise during model fabrication. Traditional control methods, which are based on the use of PID controllers, do not always ensure the required temperature response time due to the inertia of the extruder heating head, the influence of external factors, the extruder travel speed, the material feed rate, and the intensity of model cooling. This paper analyzes the physical and thermal processes occurring during filament extrusion in FFF/FDM 3D printing technology and investigates the effect of temperature conditions on dimensional deviations and the occurrence of surface defects in printed parts. Modern methods of temperature control and approaches to its regulation are considered, including classical and intelligent control algorithms. The advantages and disadvantages of modern approaches to implementing Adaptive PID (APID) adaptive automated extrusion temperature control and Model Predictive Control (MPC)-based predictive control are analyzed. A key feature of these algorithms is the ability to make dynamic changes to control parameters depending on the current thermal state of the system, which allows for accounting for variable printing conditions and reducing the impact of external disturbances during product printing. Experimental studies were conducted on a modified 3D printer using Klipper software, which enabled flexible configuration of control parameters and high-frequency monitoring of printing temperature regimes. During the experiments, the effectiveness of various control algorithms was evaluated based on overshoot, stabilization time, temperature fluctuation amplitude, and the quality of the printed products. The results obtained indicate that the use of adaptive control methods reduces temperature fluctuations, increases the stability of the extrusion process, and improves the quality of printed products. The practical significance of this work lies in the potential to use the developed approach to modernize existing 3D printer control systems and enhance the efficiency of additive manufacturing.