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<p>In this study, the tensile strength of polylactic acid (PLA) components manufactured via the fused filament fabrication (FFF) process was systematically investigated by analyzing the influence of three critical process parameters: infill density (40%, 60%, and 80%), layer thickness (0.20, 0.25, and 0.30&#xa0;mm), and printing speed (40, 50, and 60&#xa0;mm/s). The experimental framework was developed using the Taguchi method to minimize the number of required tests while maximizing the robustness of parameter evaluation. Tensile specimens were fabricated in accordance with ASTM D638 Type I standards to ensure uniformity and comparability in mechanical testing. A fuzzy logic-based predictive model was constructed to estimate the tensile strength of the printed samples based on the defined input parameters. The model achieved a tensile strength prediction range of 42.40–52.30&#xa0;MPa, demonstrating high predictive accuracy with an average absolute error rate of 1.53%, closely aligning with the experimentally obtained results. This strong correlation underscores the effectiveness of fuzzy logic in capturing nonlinear and complex interdependencies within FFF process parameters. This study addresses a significant gap in fuzzy logic applications for PLA tensile strength prediction by focusing exclusively on infill density, layer thickness, and printing speed. The outcomes indicate that the integration of a fuzzy logic-based approach can serve as a reliable and interpretable tool for predicting mechanical performance in FFF-produced PLA parts, potentially contributing to improved process optimization in additive manufacturing applications.</p>

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Fuzzy Logic-Based Prediction of Tensile Strength in Fused Filament Fabrication: A Case Study on Polylactic Acid

  • 期刊论文

  • 发表日期：

    2025年8月11日

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  • İnayet Burcu Toprak

摘要

In this study, the tensile strength of polylactic acid (PLA) components manufactured via the fused filament fabrication (FFF) process was systematically investigated by analyzing the influence of three critical process parameters: infill density (40%, 60%, and 80%), layer thickness (0.20, 0.25, and 0.30 mm), and printing speed (40, 50, and 60 mm/s). The experimental framework was developed using the Taguchi method to minimize the number of required tests while maximizing the robustness of parameter evaluation. Tensile specimens were fabricated in accordance with ASTM D638 Type I standards to ensure uniformity and comparability in mechanical testing. A fuzzy logic-based predictive model was constructed to estimate the tensile strength of the printed samples based on the defined input parameters. The model achieved a tensile strength prediction range of 42.40–52.30 MPa, demonstrating high predictive accuracy with an average absolute error rate of 1.53%, closely aligning with the experimentally obtained results. This strong correlation underscores the effectiveness of fuzzy logic in capturing nonlinear and complex interdependencies within FFF process parameters. This study addresses a significant gap in fuzzy logic applications for PLA tensile strength prediction by focusing exclusively on infill density, layer thickness, and printing speed. The outcomes indicate that the integration of a fuzzy logic-based approach can serve as a reliable and interpretable tool for predicting mechanical performance in FFF-produced PLA parts, potentially contributing to improved process optimization in additive manufacturing applications.