Yazar "Samtas, Gurcan" seçeneğine göre listele

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    Mathematically Modeling Parameters Influencing Surface Roughness in CNC Milling
    (Pamukkale Univ, 2012) Nas, Engin; Samtas, Gurcan; Demir, Halil
    In this study, steel AISI 1050 is subjected to process of face milling in CNC milling machine and such parameters as cutting speed. feed rate. cutting tip, depth of cut influencing the surface roughness are investigated experimentally. Four different experiments are conducted by creating different combinations for parameters. In conducted experiments. cutting tools, which are coated by PVD method used in forcing steel and spheroidal graphite cast iron are used. Surface roughness values. which are obtained by using specified parameters with cutting tools, are measured and correlation between measured surface roughness values and parameters is modeled mathematically by using curve fitting algorithm. Mathematical models are evaluated according to coefficients of determination (R-2) and the most ideal one is suggested for theoretical works. Mathematical models. which are proposed for each experiment, are estipulated.
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    Modelling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis
    (Assoc Mechanical Engineers Technicians Slovenia, 2012) Cicek, Adem; Kivak, Turgay; Samtas, Gurcan; Cay, Yusuf
    In this study, the effects of cutting parameters (i.e., cutting speed, feed rate) and deep cryogenic treatment on thrust force (Ff) have been investigated in the drilling of AISI 316 stainless steel. To observe the effects of deep cryogenic treatment on thrust forces, M35 HSS twist drills were cryogenically treated at -196 degrees C for 24 h and tempered at 200 degrees C for 2 h after conventional heat treatment. The experimental results showed that the lowest thrust forces were measured with the cryogenically treated and tempered drills. In addition, artificial neural networks (ANNs) and multiple regression analysis were used to model the thrust force. The scaled conjugate gradient (SCG) learning algorithm with the logistic sigmoid transfer function was used to train and test the ANNs. The ANN results showed that the SCG learning algorithm with five neurons in the hidden layer produced the coefficient of determinations (R-2) of 0.999907 and 0.999871 for the training and testing data, respectively. In addition, the root mean square error (RMSE) was 0.00769 and 0.009066, and the mean error percentage (MEP) was 0.725947 and 0.930127 for the training and testing data, respectively.