Title: Surface roughness analysis on machining of nimonic C-263 alloy using ANN and RSM techniques

Authors: C. Ezilarasan; V.S. Senthil Kumar; A. Velayudham; K. Palanikumar

Addresses: Department of Mechanical Engineering, College of Engineering, Anna University, Guindy, Chennai-25, India. ' Department of Mechanical Engineering, College of Engineering, Anna University, Guindy, Chennai-25, India. ' Combat Vehicles Research and Development Establishment, Chennai-54, Avadi, India. ' Sri Sairam Institute of Technology, Chennai-44, India

Abstract: Surface roughness is one of the most important parameters used to evaluate the surface integrity of the machined components. Therefore, in this work an attempt has been made to investigate the effect of the cutting parameters (cutting speed, feed rate and depth of cut) on the surface roughness in machining the nimonic C-263 alloy. An empirical model has been developed for predicting the surface roughness, using the response surface methodology (RSM) and artificial neural networks (ANN). The experimental results revealed that among the parameters considered, the feed rate is the most significant machining parameter, which influences the surface roughness. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the surface roughness in the machining of the nimonic C-263 alloy. A comparison of the neural network models with the regression model was carried out. The influence of the different parameters and their interactions are studied and presented in this study.

Keywords: nimonic C-263 alloys; surface roughness; response surface methodology; RSM; artificial neural networks; ANNs; surface quality; cutting speed; feed rate; depth of cut.

DOI: 10.1504/IJPTECH.2011.044902

International Journal of Precision Technology, 2011 Vol.2 No.4, pp.340 - 354

Published online: 15 Jan 2012 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article