Yazar "Ulkir, Osman" seçeneğine göre listele

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    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
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    Design and Analysis of MEMS-Based Capacitive Power Inverter Using Electrostatic Transduction
    (2024) Turan, Salih Rahmi; Ulkir, Osman; Kuncan, Melih
    In this study, a capacitive microelectromechanical system (MEMS) based DC/AC power inverter design for renewable energy applications is proposed, modeled, and analyzed. In the proposed approach, electrostatic actuation is preferred to develop a DC/AC power inverter with varying phase overlap lengths for solar energy systems. The operating voltage required during the analysis is applied to the active part as the tensile stress. Thus, the maximum displacement is achieved with less instability. The developed inverter is based on MEMS to achieve miniaturized performances, producing smooth sine wave output, efficiently obtaining the signal frequency, and low power consumption. The proposed inverter has a thickness of 325 ?m, an active settlement area of 45x45x0.585 mm3, and an initial capacitance value of 2.9 pF. In addition, a 50 Hz mechanical resonance frequency was used to be compatible with the frequency of the city network. It can convert voltage values between 0.5V and 24V DC with a MEMS power inverter. Since the inverter is based on a capacitive structure, it provides near-zero power consumption. The frequency and waveform of the converted DC/AC signal match the AC signal of a power grid with an efficiency of 5%.
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    Energy Consumption Prediction of Additive Manufactured Tensile Strength Parts Using Artificial Intelligence
    (Mary Ann Liebert, Inc, 2024) Ulkir, Osman; Bayraklilar, Mehmet Said; Kuncan, Melih
    The manufacturing sector's interest in additive manufacturing (AM) methods is increasing daily. The increase in energy consumption requires optimization of energy consumption in rapid prototyping technology. This study aims to minimize energy consumption with determined production parameters. Four machine learning algorithms are preferred to model the energy consumption of the fused deposition modeling-based 3D printer. The real-time measured test sample data were trained, and the prediction model between the parameters of 3D fabrication and the energy consumption was created. The predicted model was evaluated using five performance criteria. These are mean square error (MSE), mean absolute error (MAE), root mean squared error (RMSE), R-squared (R2), and explained variance score (EVS). It has been seen that the Gaussian Process Regression model predicts energy consumption in the AM with high accuracy: R2 = 0.99, EVS = 0.99, MAE = 0.016, RMSE = 0.022, and MSE = 0.00049.
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    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.
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    Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm
    (Mdpi, 2024) Ulkir, Osman; Bayraklilar, Mehmet Said; Kuncan, Melih
    As additive manufacturing (AM) processes become integrated with artificial intelligence systems, the time and cost of the fabrication process decrease. In this study, the raster angle, an important parameter in the manufacturing process, was examined using fused deposition modeling (FDM), an AM method. The optimal value of this parameter varies depending on the designed product geometry. By changing the raster angle, the distribution of stresses and strains within the printed object can be modified, potentially influencing the mechanical behavior of the object. Thus, the correct estimation of the raster angle is essential for obtaining parts with high mechanical properties. The focus of this study is to reduce the fabrication time and cost of products by intertwining machine learning (ML) systems with mechanical systems. Its novelty is that ML has never been applied for FDM raster angle estimation. The estimation and modeling of the raster angle were performed using five different ML algorithms. These algorithms include a support vector machine (SVM), Gaussian process regression (GPR), an artificial neural network (ANN), decision tree regression (DTR), and random forest regression (RFR). Data for training were generated using various shapes and geometries, then trained in the MATLAB software, and a prediction model between the input parameters and the raster angle was created. The predicted model was evaluated using five performance criteria. The RFR model predicts the raster angle in the FDM test data with R-squared (R2) = 0.92, an explained variance score (EVS) = 0.92, a mean absolute error (MAE) = 0.012, a root mean square error (RMSE) = 0.056, and a mean squared error (MSE) = 0.0032. These values are R2 = 0.93, EVS = 0.93, MAE = 0.010, RMSE = 0.051, and MSE0.0025 for the training data. RFR is significantly superior to the other prediction algorithms. The proposed model predicts the optimum raster angle for any geometry.