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  • Küçük Resim
    Öğe
    Experimental Study and ANN Development for Modeling Tensile and Surface Quality of Fiber-Reinforced Nylon Composites
    (MDPI AG, 2025-05-30) Osman Ulkir; Fatma Kuncan; Fatma Didem Alay
    Additive manufacturing (AM) is gaining widespread adoption in the manufacturing industry due to its capability to fabricate intricate and high-performance components. In parallel, the increasing emphasis on functional materials in AM has highlighted the critical need for accurate prediction of the mechanical behavior of composite systems. This study experimentally investigates the tensile strength and surface quality of carbon fiber-reinforced nylon composites (PA12-CF) fabricated via fused deposition modeling (FDM) and models their behavior using artificial neural networks (ANNs). A Taguchi L27 orthogonal array was employed to design experiments involving five critical printing parameters: layer thickness (100, 200, and 300 µm), infill pattern (gyroid, honeycomb, and triangles), nozzle temperature (250, 270, and 290 °C), printing speed (50, 100, and 150 mm/s), and infill density (30, 60, and 90%). An analysis of variance (ANOVA) revealed that the infill density had the most significant influence on the resulting tensile strength, contributing 53.47% of the variation, with the strength increasing substantially at higher densities. In contrast, the layer thickness was the dominant factor in determining surface roughness, accounting for 53.84% of the variation, with thinner layers yielding smoother surfaces. Mechanistically, a higher infill density enhances the internal structural integrity of the parts, leading to an improved load-bearing capacity, while thinner layers improve the interlayer adhesion and surface finish. The highest tensile strength achieved was 69.65 MPa, and the lowest surface roughness recorded was 9.18 µm. An ANN model was developed to predict both the tensile strength and surface roughness based on the input parameters. Its performance was compared with that of three other machine learning (ML) algorithms: support vector regression (SVR), random forest regression (RFR), and XGBoost. The ANN model exhibited superior predictive accuracy, with a coefficient of determination (R2 > 0.9912) and a mean validation error below 0.41% for both outputs. These findings demonstrate the effectiveness of ANNs in modeling the complex relationships between FDM parameters and composite properties and highlight the significant potential of integrating ML and statistical analysis to optimize the design and manufacturing of high-performance AM fiber-reinforced composites.
  • Küçük Resim
    Öğe
    Predicting Mechanical Properties of FDM‐Produced Parts Using Machine Learning Approaches
    (Wiley, 2025-02-23) Mahmut Özkül; Fatma Kuncan; Osman Ulkir
    Additive manufacturing (AM), especially fused deposition modeling (FDM), has been widely used in industrial production processes in recent years. The mechanical properties of parts produced by FDM can be predicted through the correct selection of printing parameters. In this study, 25 machine learning (ML) algorithms were used to predict the mechanical properties (hardness, tensile strength, flexural strength, and surface roughness) of acrylonitrile butadiene styrene (ABS) samples fabricated by FDM. Experiments were conducted using three different layer thicknesses (100, 150, 200 μm), infill densities (50%, 75%, 100%), and nozzle temperatures (220°C, 230°C, 240°C). The effects of printing parameters on mechanical properties were investigated through analysis of variance (ANOVA). This analysis results indicated that infill density had the most significant effect on hardness (55.56%), tensile strength (80.02%), and flexural strength (77.13%). In addition, the layer thickness was identified as the most influential parameter on the surface roughness, with an effect of 70.89%. The prediction performance of the ML algorithms was evaluated based on the mean absolute error (MAE), root mean squared error, mean squared error, and R-squared (R2) values. The KSTAR algorithm best predicted both hardness and surface roughness, with MAE values of 0.006 and 0.009, respectively, and an R2 value of up to 0.99. For the prediction of tensile and flexural strength, the MLP algorithm was determined to be the most successful method, achieving high accuracy (R2 > 0.99) for both properties. In addition, comparison graphs between the predicted and actual results showed high overall accuracy, with a particularly strong agreement for hardness, tensile strength, and surface roughness. The study identified the algorithms with the best prediction performance and provided recommendations for predicting the 3D printing process based on these findings.