Precision control and parameter optimization in screw extrusion 3D printing of polypropylene materials

Area of Science:

• Materials Science and Engineering
• Additive Manufacturing Technologies

Background:

• Polypropylene (PP) is crucial for automotive parts due to its mechanical properties.
• Fused Deposition Modeling (FDM) is a key additive manufacturing (AM) technique in the automotive industry.
• Pure PP parts often suffer from poor dimensional accuracy, limiting their application.

Purpose of the Study:

• To investigate and enhance the dimensional accuracy of 3D-printed pure PP components.
• To identify optimal processing parameters for FDM of PP materials.
• To provide a reference for improving dimensional accuracy in AM parts.

Main Methods:

• Investigated key FDM parameters: infill percentage, pattern, layer thickness, and extrusion temperature.
• Utilized fluid simulation and mathematical modeling to correlate parameters with accuracy.
• Employed Taguchi experimental design with Signal-to-Noise ratio and ANOVA analyses.

Main Results:

• Lower extrusion temperature, infill percentage, and layer thickness positively impact dimensional accuracy.
• Optimal parameters identified: 210°C extrusion temperature, 40% infill, 0.3mm layer thickness, and concentric circle infill pattern.
• Parameter optimization significantly improves dimensional accuracy in X, Y, and Z directions.

Conclusions:

• Processing parameter optimization is critical for achieving high dimensional accuracy in FDM-printed PP parts.
• The determined optimal parameters offer a practical guideline for manufacturers.
• This research contributes to the advancement of AM for automotive applications.