Prediction of friction coefficient of treated betelnut fibre reinforced polyester (T-BFRP) composite using artificial neural networks

Abstract

The current work is an attempt of using artificial neural network configuration to predict frictional performance of treated betelnut fibre reinforced polyester (T-BFRP) composite. Experimental dataset at different applied loads (5–30 N) and sliding distances (0–6.72 km) was used to train the ANN configuration with a large volume of experimental data (492 sets) where three different fibre mat orientations were considered (anti parallel, parallel and normal orientations). Results obtained from the developed ANN model were compared with experimental results. It is found that the experimental and numerical results showed good accuracy when the developed ANN model was trained with Levenberg–Marqurdt training function.