Artificial Neural Network Model for Estimation of Wear and Temperature in Pin-disc Contact

Abstract

This work aims to investigate experimentally the parameters affecting on the wear debris and the temperature rise due to friction as well as developing the artificial neural network model (ANN) using MATLAB program for predicting the wear, and temperature of disc and pad. Two types of disc made from aluminum and steel are slipping against pad and carried out under dry conditions at different time, rotational speed, and load to examine the wear. The results show that the wear and temperature are increased with increasing the sliding speed, and load or contact time. In addition, the wear of pad is higher when it's contact with aluminum disc, while the temperature of pad is higher when its contact with steel. The ANN model was successfully shows that there is a high ability to predict the wear and temperature as well as the results of model corresponding with the experimental results.

Comparative Analysis of Multiple Linear Regression and Artificial Neural Network for Predicting Friction and Wear of Automotive Brake Pads Produced from Palm Kernel Shell

“…It was observed that the selected neural network prediction of the wear rate and coefficient of friction were in agreement with the measured values. This was confirmed by the values of MAE and RMSE reasonably low; R and NSE reasonably high as reported by [14,6].…”

“…Artificial Neural Networks (ANNs) are revolutionary computing paradigms that try to mimic the biological brain. These ANNs are modeling techniques that are especially useful to address problems where solutions are not clearly formulated or where the relationships between inputs and outputs are not sufficiently known [6,1].…”

“…The performance evaluation of multiple regression and artificial neural network in predicting parameters in engineering have been carried out by many researchers. Abdulkareem and Mohammed [6] investigated the use of artificial neural network for the estimation of wear and temperature in disc and pad. Two types of disc made from aluminum and steel were made to slide against the pad under dry conditions at different time, rotational speed, initial temperature of the disc and load in order to examine the wear.…”

Predictions of Friction Coefficient in Hydrodynamic Journal Bearing Using Artificial Neural Networks

“…The significance of this technology is that ANN models can be trained based on experimental data to recognize solutions. The ANN prediction method has been used in several applications [5][6][7][8][9][10][11][12][13][14][15], such as wear [5][6][7][8][9][10][11][12] and friction [13][14][15].…”

“…An experimental plan was performed on a pin-on-disc machine for obtained experimental results under unlubricated conditions. Hassan et al in [10] gave investigated experimentally the parameters affecting on the wear debris and the temperature rise due to friction as well as developing the model ANN using MATLAB program for predicting the wear, and temperature of disc and pad. Since the hybrid materials had not been enough researched, Sathyabalan et al in [11] have presented application of ANN on the MMC (hybrid LM6 aluminium) which is reinforced by SiC and flyash.…”