Prediction of specific wear rate for LM25/ZrO2 composites using Levenberg–Marquardt backpropagation algorithm

In this study, the wear estimation capability of RSM and artificial neural network (ANN) modelling techniques are examined and compared in this study. Though both RSM and ANN model performed well, ANN-based approach is found to be better in fitting to measure output response in comparison with the RSM model. The comparison of the productive capacity of RSM and LMBP (Levenberg–Marquardt backpropagation) neural network architecture for modelling the output, as well as output, predicted for the wear samples in terms of various statistical parameters such as coefficient of determination (R2), etc., has been done. The coefficient of determination (R2) is higher for which the evaluated value shows that the ANN models have a higher modelling ability than the RSM model. The comparison between the experimental value and predicted value obtained by the ANN and RSM models reveals the coefficient of model determination (R2) for the ANN and RSM model is close to unity. The results obtained from the comparison of specific wear rate values using ANN and RSM were proved to be close to the reading recorded experimentally with a 99% confidence level. Keywords: Artificial neural networks, Coefficient of determination, Levenberg–Marquardt backpropagation, Wear, Specific wear rate, Mean Squared Error