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Optimizing Nozzle Structure and Parameters for Continuous Fiber Prepreg Filament 3D Printing.

Sheng Qu¹, Qi Zhang¹, Beiying Liu¹

¹School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China.

|April 26, 2025

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This study optimized 3D printing parameters for continuous fiber-reinforced composite filament (CFRCF) using a simulation model. Optimal settings enhance molten state stability for improved CFRCF/PLA printing.

Area of Science:

Materials Science
Additive Manufacturing
Polymer Engineering

Background:

Continuous fiber-reinforced composite filament (CFRCF) 3D printing requires precise control over process parameters.
The molten state behavior of the filament is crucial for successful printing and part integrity.
Optimizing nozzle design and printing conditions is essential for stable CFRCF extrusion.

Purpose of the Study:

To investigate the impact of printing temperature, speed, and nozzle length on the molten state of CFRCF during 3D printing.
To optimize these parameters using a simulation model and Box-Behnken response surface methodology.
To achieve enhanced stability in continuous fiber prepreg filament printing.

Main Methods:

Utilized a simulation model to analyze the molten state of CFRCF/PLA filament.

Keywords:

3D printing continuous fiber prepreg filament nozzle process parameters

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Employed Box-Behnken response surface methodology for parameter optimization.

Designed a 1 mm diameter nozzle with an 8 mm heating zone for continuous fiber composite filament polylactic acid (CFRCF/PLA).

Main Results:

Determined optimal printing parameters: 220 °C printing temperature, 300 mm/min printing speed, and 0.2 mm printing layer height.
The simulation model identified key relationships between process parameters and filament molten state.
Experimental validation confirmed enhanced printing stability with the optimized nozzle and parameters.

Conclusions:

Optimized printing parameters significantly improve the stability of continuous fiber prepreg filament extrusion.
Simulation-driven optimization is effective for fine-tuning CFRCF 3D printing processes.
The findings contribute to advancing the reliability and quality of composite additive manufacturing.