Modelling cutting power and tool wear in turning of aluminium matrix composites using Artificial Neural Networks
by Liujie Xu, J. Paulo Davim
International Journal of Materials and Product Technology (IJMPT), Vol. 32, No. 2/3, 2008

Abstract: Aluminium matrix composites have been investigated since 1970s because of the high performance of these materials for aerospace, aircraft and automotive industries. This paper builds Artificial Neural Network (ANN) machining models of aluminium matrix composites according to cutting parameters. Feedforward ANN is created and trained using comprehensive data sets tested by the authors, and good performances of networks are achieved. The prediction results show the tool wear and the machining power are highly influenced by the cutting velocity. The increase in the feed leads to moderate decrease in the tool wear and moderate increase in the machining power.

Online publication date: Fri, 27-Jun-2008

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 Materials and Product Technology (IJMPT):
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