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.
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.
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.
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.
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
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