Mechanical Engineering / Makina Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/4129
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Article Citation - WoS: 6Citation - Scopus: 6Intelligence Modeling of the Transient Asperity Temperatures in Meshing Spur Gears(Elsevier Ltd., 2005) Atan, Ebubekir; Özdemir, SerhanTemperature rise in the contact zone of meshing gears is a serious problem in gear design. The temperature rise on lubricated surfaces may result in the significant decrease on the material strength and lubricant viscosity which reduces the film thickness, causing solid to solid contact. The equations and the evaluations of the rise in temperature were given in [Proc. VDI Berichte 2 (1665) (2002) 615-626] and reiterated in this paper briefly. The data from [Proc. VDI Berichte 2 (1665) (2002) 615-626] are used to establish an artificial intelligence model where a multi layer feedforward neural network has been employed. The model accepts surface roughness, gear ratio, horsepower and the number of teeth as input variables, and outputs calculated pinion surface asperity temperatures. The aim of the present work is to provide a straightforward and simple way to compute the asperity temperature rise for a given set of variables, R-square value for the computed temperature values proves the method satisfactory.Article Citation - WoS: 135Citation - Scopus: 157The Use of Ga-Anns in the Modelling of Compressive Strength of Cement Mortar(Elsevier Ltd., 2003) Akkurt, Sedat; Özdemir, Serhan; Tayfur, Gökmen; Akyol, BurakIn this paper, results of a project aimed at modelling the compressive strength of cement mortar under standard curing conditions are reported. Plant data were collected for 6 months for the chemical and physical properties of the cement that were used in model construction and testing. The training and testing data were separated from the complete original data set by the use of genetic algorithms (GAs). A GA-artificial neural network (ANN) model based on the training data of the cement strength was created. Testing of the model was also done within low average error levels (2.24%). The model was subjected to sensitivity analysis to predict the response of the system to different values of the factors affecting the strength. The plots obtained after sensitivity analysis indicated that increasing the amount of C3S, SO3 and surface area led to increased strength within the limits of the model. C2S decreased the strength whereas C3A decreased or increased the strength depending on the SO3 level. Because of the limited data range used for training, the prediction results were good only within the same range. The utility of the model is in the potential ability to control processing parameters to yield the desired strength levels and in providing information regarding the most favourable experimental conditions to obtain maximum compressive strength.Conference Object Citation - WoS: 1Citation - Scopus: 6Genetic Algorithm-Artificial Neural Network Model for the Prediction of Germanium Recovery From Zinc Plant Residues(Taylor and Francis Ltd., 2002) Akkurt, Sedat; Özdemir, Serhan; Tayfur, GökmenA multi-layer, feed-forward, back-propagation learning algorithm was used as an artificial neural network (ANN) tool to predict the extraction of germanium from zinc plant residues by sulphuric acid leaching. A genetic algorithm (GA) was used for the selection of training and testing data and a GA-ANN model of the germanium leaching system was created on the basis of the training data. Testing of the model yielded good error levels (r2 = 0.95). The model was employed to predict the response of the system to different values of the factors that affect the recovery of germanium and the results facilitate selection of the experimental conditions in which the optimum recovery will be achieved.