Yazar "Bayraklilar, Mehmet Said" seçeneğine göre listele

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    Dimensional Accuracy of Acrylonitrile Butadiene Styrene Material Produced by Additive Manufacturing Method
    (Springer, 2024) Bayraklilar, Mehmet Said
    Mass customization is designing and manufacturing customized products with mass production efficiency and speed. Additive manufacturing (AM), one of the mass customization methods, is still expensive compared to mass production but continues to develop and become widespread daily. In addition, additive manufacturing has numerous limitations, such as slow print speed, less accuracy and repeatability, and limited material selection for a particular application. Therefore, this article determined the optimum parameters to improve dimensional accuracy in the AM method. The most common materials used in the additive manufacturing method are acrylonitrile butadiene styrene (ABS) and polylactic acid. Dimensional accuracy is one of the most critical parameters to meet quality standards in additive manufacturing, as in all production methods. Dimensional accuracy is the most critical parameter for smooth joining, especially for interlocking parts. The production parameters of an AM affect dimensional accuracy and the product's mechanical properties and surface quality. Optimal parameters vary to ensure dimensional accuracy in different ways. This study determined optimum parameters for dimensional accuracy, minimum filament consumption, and the production time of some basic shapes produced using ABS material by the FDM method. Cubic infill pattern, two shells, 50% infill pattern, and 0.2 mm wall thickness can be considered optimal for all shapes, although the optimal parameters for different shapes are different. Artificial neural networks (ANN) were used for the estimation of the experimental results. The estimations (R-2) made by ANN in this study are over 90%.
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    Öğe
    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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    Öğe
    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.