Prediction of maximum pressure of journal bearing using ANN with multiple input parameters

Abstract

ABSTRACT The purpose of this paper is to predict the maximum pressure of journal bearing using ANN. The maximum pressure values are analysed using FEM and predicted using ANN. The FEM analysis is performed for micropolar lubricated hybrid journal bearing, and the results are used for training and testing the ANN model. Feedforward backpropagation algorithm is used for ANN model development. The externally applied load and rotational speed of the journal are considered as input parameters for performance predictions. ANN predictions are made within and outside the prescribed range of input parameters. This approach shows better predictions for journal bearing performance. Results obtained from FEM and ANN are in close agreement with each other. The percentage error less than 0.5 is observed for training and testing of the ANN model. The prediction error is in the range of −0.6% to 0.48%. In this paper, a mathematical model is established using FEM. The performance predictions obtained using ANN are very useful because much time can be saved which would be otherwise consumed in the experimental or theoretical bearing analysis.