FNN based online monitoring of flank wear during turning of EN-31 steel
by Alakesh Manna, Sandeep M. Salodkar
International Journal of Machining and Machinability of Materials (IJMMM), Vol. 8, No. 1/2, 2010

Abstract: This paper describes predictive machining approach with fuzzy neural network (FNN) modelling of the cutting tool flank wear in order to estimate the performance of CNMG 12 04 08 E-M 6630 insert during turning of EN-31 alloy steel. In the present work, a new approach for cutting tool wear detection with cutting conditions estimated wear through acoustic emission (AE) signal is presented. The measured tool wear and estimated tool wear by conditions monitoring and detected signals are compared and graphically analysed. Investigated results prove that the new method of FNN is reliable and appropriate to control and monitor the cutting tool wear.

Online publication date: Thu, 05-Aug-2010

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 Machining and Machinability of Materials (IJMMM):
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