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.
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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