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<p>The aim of this research is to optimize fused deposition modeling parameters to enhance mechanical characteristics of carbon fiber-reinforced nylon (ePA-CF) composites. The study examines the influence of nozzle temperature, infill %, and printing speed on the mechanical characteristics of 3D printed samples using the Taguchi technique through a L9 orthogonal array. The specimens are printed using a Flashforge Guider 2&#xa0;s printer, while tensile and flexural testing are carried out based on ASTM standards. The yield and flexural strengths values are converted into signal-to-noise ratios for the analysis. The findings showed that the infill % had the greatest impact on both attributes which is further confirmed by analysis of variance (ANOVA). In order to predict the mechanical characteristics based on the input parameters, two models are developed: a regression model and an artificial neural network (ANN), with the ANN exhibiting greater accuracy. The best parameter configurations for enhanced mechanical performance are found by multi-objective optimization employing the CRITIC-ARAS techniques. Based on the results, the combination of medium nozzle temperature, slower printing speed, and higher infill % is recommended for the enhanced mechanical characteristics of ePA-CF composites.</p>

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Characterization of ePA/Carbon Fiber Composite Developed through Fused Deposition Modeling

  • 期刊论文

  • 发表日期：

    2025年5月7日

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  • P. M. Gopal,
  • V. Kavimani,
  • R. Arulmurugan,
  • Nadir Ayrilmis

摘要

The aim of this research is to optimize fused deposition modeling parameters to enhance mechanical characteristics of carbon fiber-reinforced nylon (ePA-CF) composites. The study examines the influence of nozzle temperature, infill %, and printing speed on the mechanical characteristics of 3D printed samples using the Taguchi technique through a L9 orthogonal array. The specimens are printed using a Flashforge Guider 2 s printer, while tensile and flexural testing are carried out based on ASTM standards. The yield and flexural strengths values are converted into signal-to-noise ratios for the analysis. The findings showed that the infill % had the greatest impact on both attributes which is further confirmed by analysis of variance (ANOVA). In order to predict the mechanical characteristics based on the input parameters, two models are developed: a regression model and an artificial neural network (ANN), with the ANN exhibiting greater accuracy. The best parameter configurations for enhanced mechanical performance are found by multi-objective optimization employing the CRITIC-ARAS techniques. Based on the results, the combination of medium nozzle temperature, slower printing speed, and higher infill % is recommended for the enhanced mechanical characteristics of ePA-CF composites.