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    Effect of Deep Cryogenic Treatment on Microstructure, Mechanical Properties, and Residual Stress of AISI 52100 Bearing Steel
    (Engineered Science Publisher, 2023) Kara, F.; Çiçek, A.; Demir, H.
    This study covers tensile, fatigue, and material characterization tests on AISI 52100 material. In this study, the effects of deep cryogenic treatment (DCT) applied to the material at different holding hours on the mechanical properties (macro-hardness, micro-hardness, yield and tensile strength), microstructure, change in residual austenite volume ratio and residual stress values were examined. Five different holding times (12, 24, 36, 48 and 60h) were employed to the bearing steel to compare the effect of holding time in the deep cryogenic temperature. The metallographic findings showed that the deep cryogenic treatment (DCT) decreased the retained austenite and hence improved the micro-hardness, due to more homogenized carbide distribution and the elimination of the retained austenite, compared with the conventional heat treatment (CHT). The improvements in the maximum tensile strength of DCT specimens were 5.2%, 3.6%, 3.35%, 2.9% and 1.7% for DCT-36, DCT-48, DCT-60, DCT-24 and DCT-12, respectively. The highest value in macro and micro-hardness was obtained with the DCT-36 sample. It was observed that the best fatigue performance was obtained with the DCT-12 sample. In addition, the fatigue life of DCT-12, DCT-36, DCT-24, DCT-48 and DCT-60 samples increased by 122%, 108%, 100%, 40% and 12%, respectively, compared to the CHT sample. The lowest stress values for both axial and circumferential tensile residual stresses were obtained in the DCT-12 sample. © Engineered Science Publisher LLC 2023.
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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.