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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.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Notes on contributors
Sunil Kumar
Mr. Sunil Kumar is an Associate Professor (Mechanical Engineering) at Chitkara University, Punjab, India. He received his M. Tech in Mechanical Engineering from BBSBEC, Punjab Technical University, India. His research interests include Tribology and Artificial Intelligence.
Vijay Kumar
Dr. Vijay Kumar is a Professor and Dean (Mechatronics Engineering) at Chitkara University, Punjab, India. He received his M. Tech and PhD in Mechanical Engineering from IIT Roorkee, India. His research interests include Machine Design, Tribology and Robotics.
Anoop Kumar Singh
Dr. Anoop Kumar Singh is a Professor and Dean (Mechanical Engineering) at Chitkara University, Punjab, India. He received his M. Tech in Rotodynamic Machines from Punjab University, Chandigarh, India and PhD in Mechanical Engineering from PEC University, Chandigarh, India. His research interests include Manufacturing, Non-Destructive Testing and Surface Characteristics.