Prediction of Slipper Pressure Distribution and Leakage Behaviour in Axial Piston Pumps Using ANN and MGGP
Created by W.Langdon from
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- @Article{Ozmen:2019:MPE,
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author = "Ozkan Ozmen and Cem Sinanoglu and Turgay Batbat and
Aysegul Guven",
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title = "Prediction of Slipper Pressure Distribution and
Leakage Behaviour in Axial Piston Pumps Using ANN and
MGGP",
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journal = "Mathematical Problems in Engineering",
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year = "2019",
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pages = "Article ID 7317520",
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keywords = "genetic algorithms, genetic programming",
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identifier = "RePEc:hin:jnlmpe:7317520",
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oai = "oai:RePEc:hin:jnlmpe:7317520",
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URL = "https://downloads.hindawi.com/journals/mpe/2019/7317520.pdf",
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DOI = "doi:10.1155/2019/7317520",
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size = "14 pages",
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abstract = "The pressure distribution (PD) and leakage between the
slipper and swash plate in an axial piston pump (APP)
have a considerable impact on the pump efficiency,
affecting aspects such as the load bearing and wear
performance of the slipper. Herein, multigene genetic
programming (MGGP) and artificial neural network (ANN)
machine learning methods (MLMs) are incorporated into a
novel approach towards predictive modeling of the PD
and leakage on the slipper, which can function
hydrostatically/hydrodynamically. Experimentally
measured data are used as input for the MGGP and ANN
models. The validity of the MGGP and ANN models is
verified using test data excluded from the analyses. In
addition, the model results are compared with analytic
equations (AEs). Both MLMs are found to exhibit strong
agreement with the measured data. In particular, the
ANN model exhibits superior prediction performance to
the MGGP model and AEs.",
- }
Genetic Programming entries for
Ozkan Ozmen
Cem Sinanoglu
Turgay Batbat
Aysegul Guven
Citations