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Analysis of Effects of Sizes of Orifice and Pockets on the Rigidity of Hydrostatic Bearing Using Neural Network Predictor System  

Canbulut, Fazil (Erciyes University, Faculty of Eng., Mechanical Engineering Department, Kayseri)
Sinanoglu, Cem (Erciyes University, Faculty of Eng., Mechanical Engineering Department, Kayseri)
Yildirim, Sahin (Erciyes University, Faculty of Eng., Mechanical Engineering Department, Kayseri)
Publication Information
Journal of Mechanical Science and Technology / v.18, no.3, 2004 , pp. 432-442 More about this Journal
Abstract
This paper presents a neural network predictor for analysing rigidity variations of hydrostatic bearing system. The designed neural network has feedforward structure with three layers. The layers are input layer, hidden layer and output layer. Two main parameter could be considered for hydrostatic bearing system. These parameters are the size of bearing pocket and the orifice dimension. Due to importancy of these parameters, it is necessary to analyse with a suitable optimisation method such as neural network. As depicted from the results, the proposed neural predictor exactly follows experimental desired results.
Keywords
Hydrostatic Bearing; Orifice Dimension; Neural Predictor;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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