Analysis of effects of sizes of orifice and pockets on the rigidity of hydrostatic bearing using neural network predictor system

Canbulut, Fazıl; Sınanoğlu, Cem; Yıldırım, Şahin

doi:10.1007/bf02996108

Cited by 19 publications

(18 citation statements)

References 11 publications

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“…Artificial neural networks (ANNs) have a number of highly interconnected processing units called as neurons [3]. The overall behaviour of ANNs is analogous to the human brain [3].…”

Section: *Author For Correspondencementioning

confidence: 99%

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Prediction of output parameters in wire electrical discharge machining of EN-31 steel by artificial neural networks

Singh¹,

Singh²

2016

IJATEE

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“…Artificial neural networks (ANNs) have a number of highly interconnected processing units called as neurons [3]. The overall behaviour of ANNs is analogous to the human brain [3].…”

Section: *Author For Correspondencementioning

confidence: 99%

“…The overall behaviour of ANNs is analogous to the human brain [3]. ANNs can be trained using data sets.…”

Section: *Author For Correspondencementioning

confidence: 99%

Prediction of output parameters in wire electrical discharge machining of EN-31 steel by artificial neural networks

Singh¹,

Singh²

2016

IJATEE

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“…In the literature, the artificial neural network (ANN) is one of the most commonly used modelling systems for slippers. Canbulut et al [14] considered the orifice sizes and slipper pocket and developed an ANN model to analyse the system rigidity in a bearing. The results obtained for test data submitted to the trained network exhibited close agreement with the experiment results.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Slipper Pressure Distribution and Leakage Behaviour in Axial Piston Pumps Using ANN and MGGP

Özmen

Sınanoğlu

Batbat

et al. 2019

Mathematical Problems in Engineering

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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 modelling 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.

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“…A neural network predictor for analyzing rigidity variations of hydrostatic bearing system has been developed in [6]. Two main parameters could be considered for the hydrostatic bearing system.…”

Section: Introductionmentioning

confidence: 99%

Design of an Artificial Neural Network for Analysis of Frictional Power Loss of Hydrostatic Slipper Bearings

2004

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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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Analysis of effects of sizes of orifice and pockets on the rigidity of hydrostatic bearing using neural network predictor system

Cited by 19 publications

References 11 publications

Prediction of output parameters in wire electrical discharge machining of EN-31 steel by artificial neural networks

Prediction of output parameters in wire electrical discharge machining of EN-31 steel by artificial neural networks

Prediction of Slipper Pressure Distribution and Leakage Behaviour in Axial Piston Pumps Using ANN and MGGP

Design of an Artificial Neural Network for Analysis of Frictional Power Loss of Hydrostatic Slipper Bearings

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