Design of artificial neural networks for slipper analysis of axial piston pumps
| Autor: | Cem Sinanoğlu, Fazıl Canbulut, Erdem Koç |
|---|---|
| Přispěvatelé: | Çukurova Üniversitesi |
| Rok vydání: | 2009 |
| Předmět: |
Piston pump
Engineering Artificial neural network business.industry Mechanical Engineering Hydrostatics Axial piston pump Mechanical engineering Conical surface Structural engineering Dissipation Pumps Surfaces Coatings and Films law.invention Piston General Energy law Surface metrology Surface roughness business Surface-roughness measurement Neural networks |
| Zdroj: | Industrial Lubrication and Tribology. 61:67-77 |
| ISSN: | 0036-8792 |
| DOI: | 10.1108/00368790910940383 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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