System Identification and Modeling of Temperature and Flow Rate Dynamics in Fused Filament Fabrication Process

Additive manufacturing processes like fused filament fabrication (FFF) have gained widespread adoption due to their versatility and cost-effectiveness in creating complex geometries. However, to optimize and control the FFF process effectively, it is crucial from a control perspective to develop accurate process models that capture temperature and flow rate dynamics. This study explores the modeling of FFF process dynamics through system identification techniques, utilizing the process sensing capabilities for a robotic FFF testbed. Various model structures are investigated including first-order and second-order transfer function models, process models, and state space models. The analysis shows that a first-order process model with a first-order disturbance component offers the best fit for the experimental data, demonstrating strong generalized performance (NRMSE fit > 90%). Moreover, an analytical process model for polymer flow rate dynamics developed from first-principles modeling is validated against its empirical counterpart derived through system identification, showing close agreement across operating conditions (NRMSE fit 70 to 90%). These findings advance the fundamental understanding of FFF dynamics and provide a basis for future model-based control strategies.