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Fused Filament Fabrication FFF of Metal-Ceramic Components
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Mathematical Modeling and Optimization of Fused Filament Fabrication (FFF) Process Parameters for Shape Deviation

Zohreh Shakeri1, Khaled Benfriha1, Mohammadali Shirinbayan2

  • 1Arts et Metiers Institute of Technology, CNAM, PIMM, HESAM University, F-75013 Paris, France.

Polymers
|November 13, 2021
PubMed
Summary

Optimizing fused filament fabrication (FFF) process parameters like infill pattern and layer height significantly improves the circularity and cylindricity of 3D printed parts. This study identifies key parameters for enhanced component quality in additive manufacturing.

Keywords:
ANOVAFFFTaguchi designcylindricityprocess optimizationresponse surface

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Area of Science:

  • Additive Manufacturing
  • Materials Science
  • Mechanical Engineering

Background:

  • Fused filament fabrication (FFF) is a key additive manufacturing (AM) technique for rapid prototyping.
  • Optimizing FFF process parameters is crucial for enhancing the quality and dimensional accuracy of 3D printed parts.
  • Traditional manufacturing methods are being supplemented by rapid prototyping to reduce product development cycles.

Purpose of the Study:

  • To optimize FFF process parameters for improved shape deviation, specifically cylindricity and circularity.
  • To identify the optimal settings for infill pattern, thickness, number of walls, and layer height.
  • To establish a linear regression model correlating control variables with shape accuracy.

Main Methods:

  • Taguchi optimization method was employed to systematically analyze parameter effects.
  • Analysis of Variance (ANOVA) and Signal-to-Noise (S/N) ratio were used for evaluation and optimization.
  • Experiments were conducted using MarkForged® with Nylon White (PA6) material.

Main Results:

  • The optimal parameters for enhanced circularity and cylindricity were identified as: hexagonal infill pattern, 5 mm thickness, 2 wall layers, and 1.125 mm layer height.
  • A linear regression model was developed to predict shape deviation based on the selected control variables.
  • Confirmation tests validated the effectiveness of the optimized parameters.

Conclusions:

  • The study successfully optimized FFF process parameters to minimize shape deviation in 3D printed components.
  • The identified optimal settings provide a practical guideline for achieving higher quality parts in FFF.
  • The developed regression model aids in predicting and controlling the cylindricity and circularity of FFF parts.