Yazar "Sagiroglu, S." seçeneğine göre listele

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    EFFECT OF HEAT TREATMENT ON THE WEAR AND CORROSION BEHAVIORS OF A GRAY CAST IRON COATED WITH A COLMONOY 88 ALLOY DEPOSITED BY HIGH VELOCITY OXYGEN FUEL (HVOF) THERMAL SPRAY
    (Croatian Metallurgical Soc, 2013) Oz, A.; Samur, R.; Mindivan, H.; Demir, A.; Sagiroglu, S.; Yakut, A. K.
    The present work has been conducted in order to determine the influence of heat treatment on the wear and corrosion behaviours of a gray cast iron substrate coated with a Ni base coating deposited by HVOF thermal spray. The wear resistance of the coatings was obtained using a reciprocating wear tester by rubbing a 10 mm diameter steel ball on the coatings at normal atmospheric conditions. Corrosion tests were performed using potentiodynamic polarization measurements in a 3,5 % NaCl solution. It was observed that the corrosion and wear resistance of the coatings increased along with the reduction of porosity and roughness by the heat treatment.
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    Effect of heat treatment on the wear and corrosion behaviors of a gray cast iron coated with a colmonoy 88 alloy deposited by high velocity oxygen fuel (HVOF) thermal spray
    (2013) Öz, A.; Samur, R.; Mindivan, H.; Demir, A.; Sagiroglu, S.; Yakut, A.K.
    The present work has been conducted in order to determine the influence of heat treatment on the wear and corrosion behaviours of a gray cast iron substrate coated with a Ni base coating deposited by HVOF thermal spray. The wear resistance of the coatings was obtained using a reciprocating wear tester by rubbing a 10 mm diameter steel ball on the coatings at normal atmospheric conditions. Corrosion tests were performed using potentiodynamic polarization measurements in a 3,5 % NaCl solution. It was observed that the corrosion and wear resistance of the coatings increased along with the reduction of porosity and roughness by the heat treatment.
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    Prediction of engine performance for an alternative fuel using artificial neural network
    (2012) Çay, Y.; Çiçek, A.; Kara, F.; Sagiroglu, S.
    This study deals with artificial neural network (ANN) modeling to predict the brake specific fuel consumption, effective power and average effective pressure and exhaust gas temperature of the methanol engine. To obtain training and testing data, a number of experiments were performed with a four-cylinder, four-stroke test engine operated at different engine speeds and torques. Using some of the experimental data for training, an ANN model based on standard back propagation algorithm was developed. Then, the performance of the ANN predictions was measured by comparing the predictions with the experimental results. Engine speed, engine torque, fuel flow, intake manifold mean temperature and cooling water entrance temperature have been used as the input layer, while brake specific fuel consumption, effective power, average effective pressure and exhaust gas temperature have also been used separately as the output layer. After training, it was found that the R 2 values are close to 1 for both training and testing data. RMS values are smaller than 0.015 and mean errors are smaller than 3.8% for the testing data. This shows that the developed ANN model is a powerful one for predicting the brake specific fuel consumption, effective power and average effective pressure and exhaust gas temperature of internal combustion engines. © 2011 Elsevier Ltd. All rights reserved.