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<p>The optimization of Fused Filament Fabrication (FFF) process parameters is crucial for improving the mechanical properties of PLA/wood composites, yet traditional statistical methods often fail to capture complex, nonlinear parameter interactions effectively. This study applies the Artificial Bee Colony (ABC) algorithm to optimize layer height, infill density, infill pattern, and raster orientation, comparing its performance with Analysis of Variance (ANOVA). PLA/wood composites were fabricated using an 80:20 ratio, and mechanical testing was conducted to evaluate compressive strength, hardness, and tensile strength. The ABC algorithm demonstrated higher prediction accuracy, with R<sup>2</sup> values of 0.96 for compressive strength, 0.93 for hardness, and 0.95 for tensile strength, significantly reducing prediction errors compared to ANOVA. Experimental validation confirmed an experimental compressive strength of 82.4&#xa0;MPa, theoretical value of 83.78&#xa0;MPa (error 1.69%), hardness of 83.54 Shore D, theoretical value of 83.60 Shore D (error 0.11%), and tensile strength of 59.7&#xa0;MPa, theoretical value of 59.95&#xa0;MPa (error 0.41%). The results demonstrate that ABC-based optimization significantly enhances process efficiency and mechanical performance, making it a promising tool for advanced additive manufacturing applications, including multi-material 3D printing and sustainable bio-composite fabrication.</p>

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Enhancing mechanical properties of 3D-printed PLA/wood composites: a metaheuristic and statistical perspective

  • 期刊论文

  • 发表日期：

    2025年4月19日

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  • Nikhil Bharat,
  • Vijay Kumar,
  • D. Veeman,
  • M. Vellaisamy

摘要

The optimization of Fused Filament Fabrication (FFF) process parameters is crucial for improving the mechanical properties of PLA/wood composites, yet traditional statistical methods often fail to capture complex, nonlinear parameter interactions effectively. This study applies the Artificial Bee Colony (ABC) algorithm to optimize layer height, infill density, infill pattern, and raster orientation, comparing its performance with Analysis of Variance (ANOVA). PLA/wood composites were fabricated using an 80:20 ratio, and mechanical testing was conducted to evaluate compressive strength, hardness, and tensile strength. The ABC algorithm demonstrated higher prediction accuracy, with R2 values of 0.96 for compressive strength, 0.93 for hardness, and 0.95 for tensile strength, significantly reducing prediction errors compared to ANOVA. Experimental validation confirmed an experimental compressive strength of 82.4 MPa, theoretical value of 83.78 MPa (error 1.69%), hardness of 83.54 Shore D, theoretical value of 83.60 Shore D (error 0.11%), and tensile strength of 59.7 MPa, theoretical value of 59.95 MPa (error 0.41%). The results demonstrate that ABC-based optimization significantly enhances process efficiency and mechanical performance, making it a promising tool for advanced additive manufacturing applications, including multi-material 3D printing and sustainable bio-composite fabrication.