Pressure control in material extrusion additive manufacturing

Abstract

This paper discusses the effect called undesirable extrusion dynamics that occur during material extrusion printing or fused deposition modeling. This effect is revealed during high acceleration and deceleration of printing head and results in over-extrusion on corners of parts and printed layer unevenness. To minimize this effect, modern control systems use Advance algorithms for dynamics control. However, such disadvantages as inertia, reduced printing speed, need for manual calibration, as well as uncertainty of the influence of material, nozzle geometry, and printing process parameters on the algorithm performance do not allow this group of solutions to be applied on an industrial scale. The paper presents a study of the influence of printing characteristics, such as material type, extruder type, printing temperature, layer and nozzle geometry, on extrusion dynamics through a series of experiments. The experiments were carried out on a Creality Ender 3 printer. The obtained experimental data will allow us to deepen the understanding of extrusion dynamics influence in FDM printing; they are also used in the present study to modify existing dynamics control algorithms. The paper proposes a modification of Advance algorithm based on machine learning. It is proposed to implement an algorithm containing two trained neural network models. One model predicts changes in printing head motion to minimize residual defects and increase average print speed. The second model predicts the compensation parameter for specific printing conditions without the need for manual calibration. A neural network model was trained to determine the compensation parameter depending on the type of material, layer thickness, nozzle geometry and printing temperature. The model was trained based on experimental data. The developed algorithm was introduced into Linear Advance algorithm of the Marlin firmware and tested on Creality Ender 3. The experiments showed that the developed model can successfully predict the required compensation and can be applied during the printing process. The proposed algorithm helps to facilitate and automate the process of extrusion dynamics compensation which will expand the possibilities of FDM printers’ application in industrial conditions. The obtained algorithm can improve the accuracy and printing speed which will subsequently help to increase the economic independence and competitiveness of small design organizations and enterprises in Russia that use 3D printers. This research expands the possibilities of rapid prototyping and may help to ensure the rapid creation of pilot batches.