Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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    Machine Learning-Based Weather Prediction With Radiosonde Observations
    (JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, 2024-07-16) Gogen, Eralp; Guney, Selda
    From the past to the present, weather forecasting holds significant importance for humanity. The precise execution of weather forecasting enables the implementation of precautions against natural disasters such as floods, tsunamis, etc., thereby minimizing the adverse effects that may arise. In this study, weather prediction is conducted using Radiosonde data. Within this prediction, estimations for both the highest and lowest temperatures are made employing machine learning algorithms. Unlike previous temperature prediction studies in the literature, a three-year dataset of Radiosonde observations is utilized. This dataset, measured at intervals of 1mbar up to an altitude of 40 km from the ground, allows for a more accurate modeling of the atmosphere compared to other studies in the literature. In this model, predictions for the highest and lowest temperatures for the next day are made. In this stage, the effects of normalization, feature extraction, or selection on the results are analyzed, and the most suitable model for prediction is determined. The software, implemented in the MATLAB environment, compares different regression methods. As a result of these analyses, utilizing the Gaussian Process Regression (GPR) method, the highest temperature prediction for the next day is achieved with the highest accuracy, with a mean square root deviation of 1.2. Using the same method, the lowest temperature prediction is made with a mean square root deviation ratio of 2.4. The results indicate more successful temperature predictions compared to studies in the literature.
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    Optimization Of Drilling Process Parameters For Additive Manufacturing Parts Produced Using The Fdm Method
    (JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, 2024-09-15) Zorer, Ezgi Selen; Ayhan, Emre; Yurdakul, Mustafa; Ic, Yusuf Tansel
    The melt deposition modeling method (FDM) is one of the increasingly widespread additive manufacturing methods, known as 3D printing, based on the layered assembly of material filaments. However, it is seen that the parts produced with FDM in the aviation industry do not have the desired dimensional and geometric tolerance values. For this reason, different manufacturing methods are used to bring the parts produced by the FDM method to the desired tolerance values. In this study, experiments were carried out to improve the tolerance values of the holes on the plates made of polycarbonate material, which is widely used in prototyping and production tools (welding, drilling, fixing) with FDM, and the optimum processing parameters were determined using the integrated design of experiment and TOPSIS methods. According to the obtained results, the optimum drilling parameters for the plate without pre-drilling case could be obtained by selecting HSS as the drill material, using cutting fluid, and setting the feed rate to 390.9091 mm/min and the spindle speed to 1000 rpm. For the pre-drilled plate, the optimum drilling parameters were again obtained by selecting the drill material HSS, using cutting fluid and applying the feed rate to 369.6970 mm/min and the spindle speed to 781.8182 rpm.