Comparative Analysis of Abrasive Wear Using Response Surface Method and Artificial Neural Network

Abstract

This research work deals with the application of response surface methodology and artificial neural network-based mathematical modelling of abrasive wear volume for a dry sliding wear of PTFE pin. The experiments were designed based on central composite design. The disc speed, load and sliding distance have been selected as parameters of the process, while the abrasive wear volume has been selected as an output. The ANNOVA test revealed that the disc speed has maximum influence and contributes 28.21% of abrasive wear volume followed by load, which contributes 12.83% of abrasive wear volume. The two models were compared using root mean square error and absolute standard deviation. The artificial neural network-predicted values of abrasive wear volume were found in close agreement with the actual experimental results as compared to response surface methodology predicted results and hence recommended for the similar studies