Surface roughness analysis on machining of nimonic C-263 alloy using ANN and RSM techniques
by C. Ezilarasan; V.S. Senthil Kumar; A. Velayudham; K. Palanikumar
International Journal of Precision Technology (IJPTECH), Vol. 2, No. 4, 2011

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.

Online publication date: Sun, 15-Jan-2012

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Precision Technology (IJPTECH):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com