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<p>Additive manufacturing (AM) of composite materials using Fused Deposition Modeling (FDM) presents significant challenges due to the complex interactions between process parameters and material behavior. This study applies a Definitive Screening Design (DSD) framework to optimize five critical FDM parameters—nozzle temperature, bed temperature, print speed, infill density, and layer thickness—for pure polylactic acid (PLA) and two composite variants (PLA-Wood and PLA-Copper). The DSD approach efficiently identifies key factors and their quadratic and interaction effects using only 13 experimental runs, enabling a comprehensive analysis of flexural strength and Shore D hardness. Results demonstrate that infill density and layer thickness have the most significant influence, with copper-filled PLA achieving up to 30.36 MPa in flexural strength and wood-filled PLA reaching 62.7 in hardness. The DSD models achieved high predictive accuracy (<i>R</i><sup>2</sup> &gt; 91%) while reducing experimental effort by over 80% compared to traditional full factorial designs. These findings highlight the utility of DSD for modeling complex parameter-material interactions in FDM, providing a valuable framework for optimizing multi-material 3D printing with limited resources.</p>

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Optimization of FDM 3D printing parameters of PLA and composite materials using definitive screening design

  • 期刊论文

  • 发表日期：

    2025年7月25日

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  • Maroua Jabeur,
  • Slim Souissi,
  • Abdessalem Jerbi,
  • Ahmed Elloumi

摘要

Additive manufacturing (AM) of composite materials using Fused Deposition Modeling (FDM) presents significant challenges due to the complex interactions between process parameters and material behavior. This study applies a Definitive Screening Design (DSD) framework to optimize five critical FDM parameters—nozzle temperature, bed temperature, print speed, infill density, and layer thickness—for pure polylactic acid (PLA) and two composite variants (PLA-Wood and PLA-Copper). The DSD approach efficiently identifies key factors and their quadratic and interaction effects using only 13 experimental runs, enabling a comprehensive analysis of flexural strength and Shore D hardness. Results demonstrate that infill density and layer thickness have the most significant influence, with copper-filled PLA achieving up to 30.36 MPa in flexural strength and wood-filled PLA reaching 62.7 in hardness. The DSD models achieved high predictive accuracy (R2 > 91%) while reducing experimental effort by over 80% compared to traditional full factorial designs. These findings highlight the utility of DSD for modeling complex parameter-material interactions in FDM, providing a valuable framework for optimizing multi-material 3D printing with limited resources.