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Multi-Parametric Optimization of 3D-Printed Components.

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Optimizing Fused Deposition Modeling (FDM) 3D printing parameters, specifically raster angle and internal raster values, significantly enhances part quality and dimensional accuracy. This research identifies optimal settings for precise and consistent component production.

Keywords:
3D printing accuracy3D printing process parametersFDM printing

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Area of Science:

  • Additive Manufacturing
  • Materials Science
  • Mechanical Engineering

Background:

  • Advancements in 3D printing, particularly Fused Deposition Modeling (FDM), are crucial for cost-effective production of high-quality components.
  • Optimizing process parameters is essential to overcome limitations in form and dimensional tolerances of 3D-printed parts.
  • Current strategies require refinement to ensure precision and consistency in FDM outputs.

Purpose of the Study:

  • To experimentally and theoretically optimize FDM process parameters for improved 3D-printed part quality.
  • To identify specific printing strategies that enhance dimensional accuracy and surface finish.
  • To establish optimal parameter settings for minimizing geometric deviations in FDM components.

Main Methods:

  • Utilized Design Expert software (version 9.0.6.2) for experimental design and analysis.
  • Conducted an Analysis of Variance (ANOVA) to validate the accuracy of developed models.
  • Assessed form and dimensional tolerances using a 3D Coordinate Measuring Machine.

Main Results:

  • A raster angle of 45° combined with internal raster values between 0.5048 and 0.726 was found to minimize flatness, cylindricity, and dimensional deviations.
  • Internal raster values below 0.308 led to insufficient support and increased deviations.
  • Higher internal raster values improved stability and interlayer adhesion, enhancing overall part integrity.

Conclusions:

  • The identified optimal parameter settings (45° raster angle, 0.5048–0.726 internal raster values) are validated for producing precise and consistent 3D-printed parts.
  • Optimizing deposition patterns and thermal dynamics through these parameters significantly improves FDM part quality.
  • This study provides a data-driven approach to enhance the reliability and accuracy of FDM manufacturing.