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Design of an Artificial Neural Network for Analysis of Frictional Power Loss of Hydrostatic Slipper Bearings

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Abstract

In this study, the frictional power loss of the slippers affecting the performance of axial piston pumps and motors was investigated experimentally and theoretically. The working parameters and the slipper geometry causing minimum frictional power loss were determined. The system was also modeled by an artificial neural network. As can be seen in both approaches, the proposed neural network predictor can be employed in experimental applications of such systems.

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Authors and Affiliations

  1. Tribology Research Laboratory, Mechanical Engineering Department, Engineering Faculty, Erciyes University, Kayseri, 38039, Turkey

    F. Canbulut, Ş. Yildirim & C. Sinanoğlu

Authors
  1. F. Canbulut
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  2. Ş. Yildirim
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  3. C. Sinanoğlu
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Canbulut, F., Yildirim, Ş. & Sinanoğlu, C. Design of an Artificial Neural Network for Analysis of Frictional Power Loss of Hydrostatic Slipper Bearings. Tribology Letters 17, 887–899 (2004). https://doi.org/10.1007/s11249-004-8097-6

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  • DOI: https://doi.org/10.1007/s11249-004-8097-6

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