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    Determination of flow distance of the fluid metal due to fluidity in ductile iron casting by artificial neural networks approach
    (De Gruyter Poland Sp Z O O, 2022) Teke, Cagatay
    Ductile irons (DIs) have properties such as high strength, ductility, and toughness, as well as a low degree of melting, good fluidity, and good machining. Having these characteristics make them the most preferred among cast irons. The combination of excellent properties, especially in DI castings with a thin section, make it an alternative for steel casting and forging. But in the manufacture of thin-section parts, fluidity characteristics need to be improved and the liquid metal must fill the mold completely. The fluidity of liquid metal is influenced by many factors depending on the casting processes such as material and mold properties, casting temperature, inoculation, globalization, and grain refinement. In this study, an artificial neural network (ANN) model has been developed that allows for determining the flow distance of the liquid metal in the sand mold casting method under changing casting conditions of DI. Thus, the flow distance was estimated depending on the cross-sectional thickness during the sand casting under changing casting conditions. The experimental parameters were determined as casting temperature, liquid metal metallurgy quality, cross-sectional thickness, and filling time. Filling modeling was performed with FlowCast software. When the results were examined, it was seen that the developed ANN model has high success in predicting the flow distances of the liquid metal under different casting conditions. The calculated coefficient of determination (R (2)) value of 0.986 confirms the high prediction performance of the model.
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    Modeling design parameters with Taguchi experimental method for obtaining operating conditions for Cu(II) removal through adsorption process
    (Desalination Publ, 2019) Serencam, Huseyin; Ozdemir, Akin; Teke, Cagatay; Ucurum, Metin
    One of the environmental concerns deals with the removal process of pollutants in water and wastewater. In this paper, a removal process was used to eliminate pollutants in water and wastewater. The aim of this paper was to obtain an optimum adsorption condition for the highest metal ions adsorbed (MIA) mg/g. For this particular purpose, a Taguchi L-18 design was conducted. In addition, the signal-to-noise ratio was analyzed for optimum adsorption levels. The analysis of variance was also performed to evaluate the effect of each adsorption condition on MIA (mg/g) values. Moreover, an optimization model was also proposed to find the best optimal setting of adsorption levels. Then, the confirmation tests were performed using optimum coded levels of the adsorption parameters for the verification purpose. The results of the experimental study showed a good performance that Cu(II) removal capacity was found to be 43.66 mg/g. To the best of our knowledge, this research is the first to study natural stone as an efficient, inexpensive and cheap adsorbent for the removal process.
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    A novel approach to finding optimum operating conditions of design factors for the grinding experiment
    (Taylor & Francis Inc, 2021) Ucurum, Metin; Ozdemir, Akin; Teke, Cagatay; Tekin, Ilker
    The efficiency of grinding experiments is an important issue for many industries. In this paper, a central composite design-based methodology was proposed to investigate the four design factors that affect the particle sizes. The four design factors were specified as mill speed (% of N-c ), ball filling ratio (f(c) ), powder filling ratio (j(b) ) and grinding time (min). Another important issue was how to obtain an optimum operating condition for four design factors. For this particular purpose, a novel dual response optimization model was proposed using the particle sizes (d (10), d (50), and d (90)) and the span value concept. This proposed approach was compared to the desirability function-based optimization concept. The verification study of the experiment was also carried out. The results of the grinding experiment runs showed that the optimum operating conditions were mill speed 73.495% of N-c , ball filling ratio 0.354, powder filling ratio 0.157, and grinding time 70 min. In addition, d (10), d (50), and d (90) were found 3.31 mu m, 12 mu m, and 45.6 mu m, respectively. The span value was also found at 3.52.
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    A novel weighted mean-squared error optimization model to obtain optimal conditions of adsorption factors for a lead removal process
    (Desalination Publ, 2021) Ozdemir, Akin; Teke, Cagatay; Serencam, Huseyin; Ucurum, Metin; Gundogdu, Ali
    Lead (Pb) removal process from wastewater is an important issue to prevent health problems for people. For this particular purpose, a low-cost adsorbent may be beneficial for improving the adsorption capacity for the Pb removal process. The aims of this paper are four-fold. First of all, a D-optimal experimental design was selected to reduce experimental runs and its cost. Second, the effect of four adsorption design factors, stirring speed (rpm), adsorbent dosage (g), pH level, and initial metal concentration (ppm), was examined. Also, the yellow natural stone, which is from Bayburt, Turkey, was used as a cheap adsorbent for the Pb removal process from the solution. Third, a novel weighted mean-squared error optimization model was developed to obtain optimal adsorption levels for adsorption factors. Besides, a verification study was conducted to verify the results of the adsorption experiment. Finally, the lead (Pb( II)) removal capacity of the yellow Bayburt stone was obtained to be 46.031 mg/g, and the results of the experiment from the proposed methodology showed a good performance for the removal study.
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    Prediction of gamma ray spectrum for 22Na source by feed forward back propagation ANN model
    (Pergamon-Elsevier Science Ltd, 2023) Teke, Cagatay; Akkurt, Iskender; Arslankaya, Seher; Ekmekci, Ismail; Gunoglu, Kadir
    The radiation has been used in a variety of different fields since its discovery and thus its measurement becomes vital in these industries. Different type detector may be used to measure gamma rays depends on the purposes of measurements. Gamma ray energy spectrum is an important to determine either elemental analysing of a sample or radiation shielding purposes. On the other hand, Artificial Neural Network (ANN) may be used to predict and analysing of gamma-ray spectrum. In this study, gamma ray spectrum from 22Na source detected in NaI (Tl) detector was estimated by ANN. There have been installed ten different ANN models to find the network structure that produces the best predictive value for the gamma ray spectrum NaI (Tl) Detector. Estimation study has been continued with the ANN model with be possessed of lowest error value. ANN model was created by using energy, distance and gamma-rays energy spectrum (called Io) values. In the ANN model developed using the feed forward back propagation algorithm, were used artificial neurons two in the input layer, ten in the hidden layer and one in the output layer. For the case of present work, the experimental data was used 70% for education, 20% for validation and 10% for testing. The estimated values obtained with the ANN model were compared with the experimental results and a good correlation has been found between them (R2 = 0.99).