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<p>Fused Deposition Modeling (FDM), a technique within the material extrusion category of additive manufacturing, fabricates parts by depositing molten material along a predefined path in successive layers. The final quality of FDM-printed components is significantly influenced by multiple process parameters, making their systematic evaluation and optimization critical. Although extensive research has focused on optimizing either dimensional or geometric accuracy, few investigations have integrated both aspects within a single framework. This omission limits a comprehensive understanding of how dimensional deviations (e.g., size errors along X, Y, and Z axes) and geometric deviations (e.g., form, orientation, and location tolerances) interact, thereby constraining effective quality control of printed components. The present study addresses this gap through a comprehensive multi-objective optimization of five critical FDM parameters, layer thickness (E), infill density (I), infill pattern (P), wall thickness (W), and number of top layers (ST), to simultaneously improve both dimensional and geometric accuracy. A Taguchi L16 orthogonal array combined with signal-to-noise (S/N) analysis was first employed to identify the most influential factors. This was followed by a global optimization using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), enabling the capture of interdependencies between the two accuracy types. Measurements were conducted along all three principal axes, encompassing a wide spectrum of tolerance types to ensure a holistic quality assessment. The novelty of this work lies in its integrated optimization approach, which unites two research areas typically investigated in isolation. Unlike prior Taguchi–NSGA-II applications limited to a single accuracy metric, this methodology quantitatively demonstrates that concurrent optimization delivers superior outcomes compared to single-objective strategies. The optimal parameter configuration, hexagonal infill pattern, 0.09&#xa0;mm layer thickness, and 100% infill density achieved a minimum combined deviation of 1.66&#xa0;mm, with dimensional and geometric deviations of 3.68&#xa0;mm and 1.68&#xa0;mm, respectively. These results yield practical, data-driven guidelines for achieving balanced accuracy in industrial FDM applications.</p>

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Multi-objective optimization of fused deposition modeling processes parameters for enhanced dimensional and geometric accuracy using taguchi method and NSGA-II

  • 期刊论文

  • 发表日期：

    2025年9月9日

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  • Nadjat Hamdaoui,
  • Sif Eddine Sadaoui,
  • Nasreddine Zeroudi

摘要

Fused Deposition Modeling (FDM), a technique within the material extrusion category of additive manufacturing, fabricates parts by depositing molten material along a predefined path in successive layers. The final quality of FDM-printed components is significantly influenced by multiple process parameters, making their systematic evaluation and optimization critical. Although extensive research has focused on optimizing either dimensional or geometric accuracy, few investigations have integrated both aspects within a single framework. This omission limits a comprehensive understanding of how dimensional deviations (e.g., size errors along X, Y, and Z axes) and geometric deviations (e.g., form, orientation, and location tolerances) interact, thereby constraining effective quality control of printed components. The present study addresses this gap through a comprehensive multi-objective optimization of five critical FDM parameters, layer thickness (E), infill density (I), infill pattern (P), wall thickness (W), and number of top layers (ST), to simultaneously improve both dimensional and geometric accuracy. A Taguchi L16 orthogonal array combined with signal-to-noise (S/N) analysis was first employed to identify the most influential factors. This was followed by a global optimization using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), enabling the capture of interdependencies between the two accuracy types. Measurements were conducted along all three principal axes, encompassing a wide spectrum of tolerance types to ensure a holistic quality assessment. The novelty of this work lies in its integrated optimization approach, which unites two research areas typically investigated in isolation. Unlike prior Taguchi–NSGA-II applications limited to a single accuracy metric, this methodology quantitatively demonstrates that concurrent optimization delivers superior outcomes compared to single-objective strategies. The optimal parameter configuration, hexagonal infill pattern, 0.09 mm layer thickness, and 100% infill density achieved a minimum combined deviation of 1.66 mm, with dimensional and geometric deviations of 3.68 mm and 1.68 mm, respectively. These results yield practical, data-driven guidelines for achieving balanced accuracy in industrial FDM applications.