Journal of Mechanical Science and Technology

  • 제18권3호
  • /
  • Pages.432-442
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  • 2004
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  • 1738-494X(pISSN)
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  • 1976-3824(eISSN)
대한기계학회 (The Korean Society of Mechanical Engineers)
권호 표지

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)
  • 발행 : 2004.03.01
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초록

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.

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참고문헌

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