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This study investigates the influence of key Fused Deposition Modeling (FDM) parameters on the hardness of polylactic acid-wood (PLA-Wood) composites using the Taguchi method. A total of nine specimens were produced with an L9 orthogonal array to systematically examine the effects of infill density (25%, 50%, 75%), infill pattern (Grid, Cubic, Gyroid), and nozzle temperature (200 °C, 215 °C, 230 °C) on hardness properties. A Bambu Lab P1S 3D printer was utilized with 30% wood fiber filled PLA-Wood (Porima, Türkiye) filament to fabricate the samples, and Shore D hardness was measured in accordance with ASTM D224. The highest hardness value, 77.2 Shore D, was recorded at 50% infill density, grid infill pattern, and 215 °C nozzle temperature, while the lowest value, 72.9 Shore D, was observed at 25% infill density, gyroid infill pattern, and 230 °C. Analysis of variance (ANOVA) confirmed the dominant influence of infill density and nozzle temperature, while regression modeling (R2 = 0.996, adjusted R2 = 0.982) successfully captured the non-linear effects of these parameters and provided predictive capability.

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Experimental Analysis and Regression-Based Evaluation of FDM Process Parameters on the Hardness of PLA-Wood Biocomposites

  • 会议论文

  • 发表日期：

    2026年

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  • Mücahit Osman Türkan,
  • Mustafa Öncül,
  • Melih Savran

摘要

This study investigates the influence of key Fused Deposition Modeling (FDM) parameters on the hardness of polylactic acid-wood (PLA-Wood) composites using the Taguchi method. A total of nine specimens were produced with an L9 orthogonal array to systematically examine the effects of infill density (25%, 50%, 75%), infill pattern (Grid, Cubic, Gyroid), and nozzle temperature (200 °C, 215 °C, 230 °C) on hardness properties. A Bambu Lab P1S 3D printer was utilized with 30% wood fiber filled PLA-Wood (Porima, Türkiye) filament to fabricate the samples, and Shore D hardness was measured in accordance with ASTM D224. The highest hardness value, 77.2 Shore D, was recorded at 50% infill density, grid infill pattern, and 215 °C nozzle temperature, while the lowest value, 72.9 Shore D, was observed at 25% infill density, gyroid infill pattern, and 230 °C. Analysis of variance (ANOVA) confirmed the dominant influence of infill density and nozzle temperature, while regression modeling (R2 = 0.996, adjusted R2 = 0.982) successfully captured the non-linear effects of these parameters and provided predictive capability.