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Design of artificial neural networks for slipper analysis of axial piston pumps

Fazil Canbulut (Mechanical Engineering Department, Faculty of Engineering, Erciyes University, Kayseri, Turkey)
Erdem Koç (Textile Engineering Department, Faculty of Engineering and Architecture, Çukurova University, Adana, Turkey)
Cem Sinanoğlu (Mechanical Engineering Department, Faculty of Engineering, Erciyes University, Kayseri, Turkey)

Industrial Lubrication and Tribology

ISSN: 0036-8792

Article publication date: 6 March 2009

Abstract

Purpose

The purpose of this paper is to experimentally and theoretically investigate slippers, which have an important role on power dissipation in the swash plate axial piston pumps.

Design/methodology/approach

The slipper geometry and working conditions affected on the slipper performance have been analyzed experimentally. The model of the slipper system has been established by original neural network (NN) method.

Findings

First, the effects of the slipper geometry with smooth and conical sliding surfaces on the slipper performance were experimentally analyzed. Smooth sliding surface slippers showed a better performance then the conical surface ones. According to the results, the neural predictor would be used as a predictor for possible experimental applications on modeling this type of system.

Originality/value

This paper discusses a new modeling scheme known as artificial NNs an experimental and a NN approach have been employed for analyzing axial piston pumps. The simulation results suggest that the neural predictor would be used as a predictor for possible experimental applications on modeling bearing system.

Keywords

Citation

Canbulut, F., Koç, E. and Sinanoğlu, C. (2009), "Design of artificial neural networks for slipper analysis of axial piston pumps", Industrial Lubrication and Tribology, Vol. 61 No. 2, pp. 67-77. https://doi.org/10.1108/00368790910940383

Publisher

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Emerald Group Publishing Limited

Copyright © 2009, Emerald Group Publishing Limited