Physics-Based Predictive Modeling of Gravity-Induced Sagging in Support-Free Pellet Additive Manufacturing

Area of Science:

• Additive Manufacturing
• Materials Science
• Computational Fluid Dynamics

Background:

• Pellet additive manufacturing (PAM) faces challenges with gravity-induced sagging in support-free structures.
• Existing models lack predictive capabilities for sagging phenomena.

Purpose of the Study:

• To develop and validate a physics-based numerical model for thermofluid dynamics of sagging in PAM.
• To correlate process parameters with filament deflection for optimized support-free fabrication.

Main Methods:

• Developed a predictive finite element (FE) model incorporating temperature-dependent non-Newtonian properties and heat transfer.
• Validated the model through systematic experiments on a desktop-scale PAM 3D printer using polylactic acid (PLA).
• Investigated effects of nozzle temperature, printhead speed, screw speed, and fan cooling.

Main Results:

• Optimized process parameters reduced bridge deflection by 64.91%.
• Active fan cooling was the most effective parameter for reducing sagging due to accelerated solidification.
• Increased printhead speed decreased sagging, while higher screw speeds and extrusion temperatures increased it.
• The FE model accurately predicted experimental outcomes and identified the critical cooling temperature for sagging cessation (150-160 °C for PLA).

Conclusions:

• The validated numerical model provides a robust foundation for understanding and mitigating sagging in PAM.
• Effective tuning of process parameters, particularly fan cooling, can unlock advanced support-free 3D printing capabilities in PAM.