Yazar "Gunes, Seyhmus" seçeneğine göre listele

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    Öğe
    Application of artificial neural network to evaluation of dimensional accuracy of 3D-printed polylactic acid parts
    (Wiley, 2024) Gunes, Seyhmus; Ulkir, Osman; Kuncan, Melih
    Additive manufacturing (AM) has begun to replace traditional fabrication because of its advantages, such as easy manufacturing of parts with complex geometry, and mass production. The most important limitation of AM is that dimensional accuracy cannot be achieved in all parts. Dimensional accuracy is essential for high reliability, high performance, and useful final products. This study investigates the impact of printing parameters on the dimensional accuracy of samples fabricated through fused deposition modeling (FDM), an additive manufacturing (AM) method utilizing polylactic acid (PLA) material. The experimental design process was performed using Taguchi methodology. ANOVA was used to determine the most important parameter affecting accuracy. Based on experimental studies, the optimal printing parameters for parts are determined as follows: concentric infill pattern, 3 mm wall thickness, 70% infill density, and a layer thickness of 200 mu m. Artificial neural network (ANN) was used in the evaluation and prediction of the results. The R-square (R2) performance evaluation criterion was above 95% from the ANN results. This value shows that the results are significant. The data acquired from this study may assist in identifying optimal parameters that contribute to the fabrication of samples with high dimensional accuracy using the FDM method. image
  • [ X ]
    Öğe
    Modelling and fabrication of flexible strain sensor using the 3D printing technology
    (Sage Publications Ltd, 2024) Gunes, Seyhmus; Ulkir, Osman; Kuncan, Melih
    The use of additive manufacturing (AM) or 3D printing in sensor technology is increasing daily because it can fabricate complex structures quickly and accurately. This study presents the modeling, fabrication, and characterization processes for the development of a resistance type flexible strain sensor. The finite element model of the sensor was developed using COMSOL software and was verified experimentally. The experimental results agreed well with the simulation results. The fabrication process was performed using the molding technique. The flexible substrate of the strain sensor was fabricated by fused deposition modeling (FDM), an AM method, with dimensions of 20 mm x 60 mm and a thickness of 2 mm. In this process, a flexible and durable elastomer material called thermoplastic polyurethane (TPU) was used. The liquid conductive silver was then injected into the mold channels. The characterization process was performed by establishing experimental and numerical setups. Studies were conducted to maximize sensitivity by changing the geometric properties of the sensor. At the 30% strain level, sensitivity increased by 9% when the sensor thickness decreased from 2 to 1.2 mm. As a result of the gradually applied force, the strain sensor showed a maximum displacement of 34.95 mm. Tensile tests were also conducted to examine the effects of stress accumulation on the flexible base. The results of this study show that the strain sensor exhibits high linearity-sensitivity and low hysteresis performance.