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    Determination of the active molecule as a potential drug against covid-19 virus using molecular docking and hybrid AHP-GRA method
    (Yildiz Technical Univ, 2023) Kaya, Yunus; Yildiz, Aytac
    In this study, it is aimed to determine the most effective molecule to be used as an active ingredient against the covid-19 virus among the 15 molecules proposed by adding some elec-tronegative groups to some molecules used in the ebola virus. In the first stage of the study, the proposed molecules are optimized in DFT / B3LYP method and 6-311G ++ (d, p) basis set, dipole moment, entropy, energy of HOMO and LUMO orbitals and band gap energies are calculated. In addition, the interactions of these molecules with the Covid-19 main protease enzyme (PDB no = 6LU7) are examined with the Autodock vina program. Correlation anal-ysis is performed using the IBM SPSS Statistics 23 program with the values obtained from molecular docking and DFT calculations, and it is determined that there is no statistically significant relationship between the band gap factor and free docking energy. In the second stage of the study, the importance weights of the parameters belonging to the molecules are determined by the Analytical Hierarchy Process (AHP) method. Then, the mol-ecules are ranked by preference using the Gray Relational Analysis (GRA) method. According to the results of the sensitivity analysis performed at the end of the study, it is determined that the 1D6-CN molecule is the most effective molecule to be used as an active ingredient against the covid-19 virus.
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    Optimization of the Cutting Parameters Affecting the Turning of AISI 52100 Bearing Steel Using the Box-Behnken Experimental Design Method
    (Mdpi, 2023) Yildiz, Aytac; Ugur, Levent; Parlak, Ismail Enes
    In this study, we aimed to optimize the cutting parameters that affect the minimum temperature and power consumption in the turning of AISI 52100 bearing steel. For this, the Box-Behnken experimental design method, which was used for the lowest number of experiments in the experimental systems created using the response surface method (RSM), was used. The cutting parameters affecting the turning of the AISI 52100 bearing steel were determined as the cutting speed, depth of cut, and feed rate based on a literature research. The temperature and power consumption values were obtained via analyses according to the experimental design method determined by the finite element analysis (FEM) method. The results obtained were analyzed in Design Expert 13 software. According to the analysis results, the parameter values were determined for the minimum temperature and power consumption. The temperature and power consumption variables were affected by all three parameters, namely the cutting speed, depth of cut, and feed rate. For the minimum temperature and power consumption, a cutting speed of 162.427 m/min, depth of cut of 1.395 mm, and feed rate of 0.247 mm/rev, as well as the feed rate parameters, affected both the temperature and power consumption the most. In addition, it was determined that the cutting speed parameter had the least effect on both the temperature and power consumption variables. In addition, validation experiments were carried out in a real experimental environment with optimum values for the cutting parameters. The results showed that the output values obtained within the limits of the study with the obtained equation were quite close (3.3% error for temperature, 6.6% error for power consumption) to the real experimental outputs.
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    Real-time detection of plastic part surface defects using deep learning-based object detection model
    (Elsevier Sci Ltd, 2024) Celik, Mirac Tuba; Arslankaya, Seher; Yildiz, Aytac
    In this study, it was aimed to detect defects in plastic parts produced in a company operating in the automotive sub -industry using the YOLOv8 object detection model. The defect types seen in plastic parts were evaluated with the help of Pareto analysis, and scratches, stains and shine were selected as the most common defect types, and data on the three defect types were collected. YOLOv8 models were trained using faulty part images. As a result of the training, the highest mean average precision value of 0.990 was obtained in the YOLOv8s model, and the shortest training time was obtained in the YOLOv8n model. In the YOLOv8s model, which gave the highest mAP value, hyperparameter adjustment was made according to the batch size and learning rate values. The testing phase was carried out with the hyperparameter values that gave the best results and the mAP value was obtained as 0.902.