Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model

In this paper, an alternative method to predict the noise of a submersible Axial Piston Pump (APP) for different valve seat materials is presented. The proposed method is composed of an Artificial Neural Network (ANN) model trained using experimental data and integrated with a hybrid algorithm consi...

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Main Authors: Hassan A. Babikir, Mohamed Abd Elaziz, Ammar H. Elsheikh, Ezzat A. Showaib, M. Elhadary, Defa Wu, Yinshui Liu
Format: Article
Language:English
Published: Elsevier 2019-09-01
Series:Alexandria Engineering Journal
Online Access:http://www.sciencedirect.com/science/article/pii/S1110016819300961
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spelling doaj-56a41159bad24eb7afe6d368171ce3322021-06-02T15:04:44ZengElsevierAlexandria Engineering Journal1110-01682019-09-0158310771087Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network modelHassan A. Babikir0Mohamed Abd Elaziz1Ammar H. Elsheikh2Ezzat A. Showaib3M. Elhadary4Defa Wu5Yinshui Liu6State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; Faculty of Engineering, University of Sinnar, Sinnar, SudanDepartment of Mathematics, Faculty of Science, Zagazig University, Zagazig, EgyptState Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; Department of Production Engineering and Mechanical Design, Tanta University, Tanta 31527, EgyptDepartment of Production Engineering and Mechanical Design, Tanta University, Tanta 31527, EgyptDepartment of Mechanical Engineering, Faculty of Engineering, Alexandria University, Alexandria, EgyptState Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; Corresponding authors.In this paper, an alternative method to predict the noise of a submersible Axial Piston Pump (APP) for different valve seat materials is presented. The proposed method is composed of an Artificial Neural Network (ANN) model trained using experimental data and integrated with a hybrid algorithm consists of Cat Swarm Optimization (CSO) and Firefly Algorithm (FA) algorithms. The hybrid CSFA algorithm is used as a subroutine in the ANN model to estimate the ANN weights. The FA is used as local operator to improve the exploitation ability of CSO. The obtained results prove the excellence of the proposed method in predicting the noise of APP considering four different valve seat materials (Polytetrafluoroethylene (PTFE), Polyetheretherketone (PEEK), Aliphatic polyamides (NYLON), and stainless steel (316 L)), five speed levels, and six system pressures. Moreover, the effects of different mechanical properties of the valve seat materials as well as operating conditions (speed and system pressure) have been investigated. Keywords: Axial piston pump, Valve seat materials, Noise prediction, Artificial neural networkhttp://www.sciencedirect.com/science/article/pii/S1110016819300961
collection DOAJ
language English
format Article
sources DOAJ
author Hassan A. Babikir
Mohamed Abd Elaziz
Ammar H. Elsheikh
Ezzat A. Showaib
M. Elhadary
Defa Wu
Yinshui Liu
spellingShingle Hassan A. Babikir
Mohamed Abd Elaziz
Ammar H. Elsheikh
Ezzat A. Showaib
M. Elhadary
Defa Wu
Yinshui Liu
Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model
Alexandria Engineering Journal
author_facet Hassan A. Babikir
Mohamed Abd Elaziz
Ammar H. Elsheikh
Ezzat A. Showaib
M. Elhadary
Defa Wu
Yinshui Liu
author_sort Hassan A. Babikir
title Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model
title_short Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model
title_full Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model
title_fullStr Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model
title_full_unstemmed Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model
title_sort noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model
publisher Elsevier
series Alexandria Engineering Journal
issn 1110-0168
publishDate 2019-09-01
description In this paper, an alternative method to predict the noise of a submersible Axial Piston Pump (APP) for different valve seat materials is presented. The proposed method is composed of an Artificial Neural Network (ANN) model trained using experimental data and integrated with a hybrid algorithm consists of Cat Swarm Optimization (CSO) and Firefly Algorithm (FA) algorithms. The hybrid CSFA algorithm is used as a subroutine in the ANN model to estimate the ANN weights. The FA is used as local operator to improve the exploitation ability of CSO. The obtained results prove the excellence of the proposed method in predicting the noise of APP considering four different valve seat materials (Polytetrafluoroethylene (PTFE), Polyetheretherketone (PEEK), Aliphatic polyamides (NYLON), and stainless steel (316 L)), five speed levels, and six system pressures. Moreover, the effects of different mechanical properties of the valve seat materials as well as operating conditions (speed and system pressure) have been investigated. Keywords: Axial piston pump, Valve seat materials, Noise prediction, Artificial neural network
url http://www.sciencedirect.com/science/article/pii/S1110016819300961
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