Modeling and Feedforward Deposition Control in Fused Filament Fabrication

Abstract

Fused filament fabrication (FFF) is the predominant of the available additive manufacturing (AM) method, requiring precise material deposition through an extrusion and motion system. There are two types of extruders used in commercial FFF printers: direct drive (DD) and Bowden. Regardless of the type of extruder, accurate material deposition requires precise synchronization between positioning (motion) control and extrusion control. However, the synchronization is challenging due to nonlinearities of the flow inside the nozzle (i.e., extrusion). Synchronization defects include continuous defects during acceleration/deceleration and discontinuous defects at extrusion start/stop. Additionally, to help mitigate the discontinuous defects, retraction and advancement processes must be applied in practice.

To mitigate the synchronization defects in FFF, extrusion control is achieved via feedforward (FF) control techniques due to difficulties in sensing extrusion rate in real-time for feedback control. However, existing FF extrusion control techniques only rely on simple linear models to control the highly nonlinear extrusion process, leading to significant extrusion inaccuracies. The major contributions of this dissertation are in developing: (1) accurate control-oriented models; and (2) model-based FF controllers, both aimed at mitigating continuous and discontinuous defects in FFF using DD and Bowden extruders. To accurately capture the extrusion dynamics, this dissertation proposes an empirical nonlinear model for continuous extrusion and a phenomenological model (P-model) for discontinuous processes, along with two FF control strategies. The first control approach uses the standard linear model to adjust motion only and avoid extrusion nonlinearity; the second adjusts extrusion based on the proposed nonlinear model for high synchronization accuracy. Experimental results are presented in this dissertation to demonstrate the effectiveness of the proposed models and model-based FF control strategies.

The proposed models and FF controllers focus on DD systems. Applying them to Bowden systems is challenging due to the sluggish extrusion response, especially with elastic filaments like thermoplastic polyurethane (TPU). To address this, a hybrid extruder design that has been introduced in 3D printing communities is adopted by this dissertation. The design combines a Bowden and a DD extruder to leverage the benefits of both systems. The motivation of the design is to utilize a bulky, frame-mounted Bowden extruder to supply the main extrusion force, while a low-mass DD extruder adjusts the feeding velocity to counteract the sluggish extrusion response. However, a critical part missing from the hybrid extruder framework is control. To address this, the dissertation proposes a dynamic model for the hybrid extruder and uses the model to design an optimal FF extrusion controller aimed at indirectly minimizing the moving mass of the hybrid extruder, while achieving the same response as a standard DD extruder. The hybrid extruder and FF controller show transient responses as fast as a standard DD extruder with up to 65% less force required. Motor data collected demonstrates a 69% motor mass reduction, indicating the potential to achieve the same extrusion accuracy as standard DD extruders while significantly reducing the resulting vibration of the printer, as demonstrated using a case study.

The broader impact of this dissertation is to achieve high deposition accuracy with reduced trial-and-error through control. Additionally, the proposed setups in this dissertation can provide valuable insights for the design of FFF extruders in both academic research and industry applications.