Analysis of effects of sizes of orifice and pockets on the rigidity of hydrostatic bearing using neural network predictor system

Canbulut F., SINANOGLU C., YILDIRIM Ş.

KSME INTERNATIONAL JOURNAL, cilt.18, sa.3, ss.432-442, 2004 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 3
  • Basım Tarihi: 2004
  • Doi Numarası: 10.1007/bf02996108
  • Dergi Adı: KSME INTERNATIONAL JOURNAL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.432-442
  • Erciyes Üniversitesi Adresli: Evet

Özet

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.