This paper presents an investigation for analyzing the efficiency of axial piston pumps in a variety conditions using a proposed neural network. Since slippers affect the performance of the system considerably, the effects of surface roughness on lubrication have been studied in slippers with varying hydrostatic bearing areas and surface roughness. The neural network structure is very suitable for this kind of system. The network is capable of predicting the leakage oil quantity of the experimental system. The network has parallel structure and fast learning capacity. It is also easy to see from the experimental results that the leakage oil quantity was caused by surface roughness, orifice diameter and the size of hydrostatic bearing area, loading pressure and the number of rotations. It can be outlined from the results for both approaches, neural network could be modeled slipper bearing systems in real time applications.

Purpose -The purpose of this paper is to investigate experimentally slippers, which have an important role on power dissipation in the swash plate axial piston pumps. Since slippers affect the performance of the system considerably, the effects of surface roughness on lubrication have been studied in slippers with varying hydrostatic bearing areas and surface roughness. Design/methodology/approach -An experimental set-up was designed to determine the performance of slippers, which are capable of increasing the efficiency of axial piston pumps, in different conditions. Findings -The findings suggest that the frictional power loss has been caused by surface roughness, capillary tube diameter, and the size of the hydrostatic bearing area, supply pressure and the relative velocity. In the case of the 0.7 and 9.5 mm surface roughness more power is needed to overcome the friction force between slippers and slipper plates, but less power loss occurs with the slippers with surface roughness of 1.5 mm. The slippers with surface roughness of 1.5 mm are considered, because of the optimum power loss. Moreover, the power loss decreases with increasing capillary tube diameter and supply pressure. Originality/value -In order to investigate slipper behaviour under different operating conditions, with different capillary tube size and supply pressure an experimental work was carried out for finding exact design parameters of the real time system.

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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PurposeThe 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/approachThe 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.FindingsFirst, 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/valueThis 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.