A new artificial neural network model integrated with a cat swarm optimization algorithm for predicting the emitted noise during axial piston pump operation

Document Type : Original Article


1 Production Engineering and Mechanical Design Department, Tanta University, Tanta, Egypt.

2 Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt.

3 School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.



This study presents a new artificial intelligence based methodology to predict the emitted noise of an Axial Piston Pump (APP). The suggested method depends on augmentation of conventional Artificial Neural Network (ANN) via integration with Cat Swarm Optimization (CSO). CSO is used to obtain the optimal structure of ANN. The training and testing of the approach were accomplished using experimental data sets considering six system operating pressures (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 MPa) and five speed levels (600, 900, 1200, 1500, and 1800 rpm). Two valve seat materials were investigated: polyetheretherket one (PEEK) and 316L stainless steel. A reasonable agreement was observed between the predicted
results obtained by the developed method and the experimental data.