Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Sign In to gain access to subscriptions and/or personal tools.
Journal of Thermoplastic Composite Materials
This Article
Right arrow Full Text (OnlineFirst PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Gaitonde, V. N.
Right arrow Articles by Davim, J. P.
Social Bookmarking
 Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Article

Modeling and Analysis of Machinability Characteristics in PA6 and PA66 GF30 Polyamides through Artificial Neural Network

V. N. Gaitonde1*, S. R. Karnik2, Francisco Mata3, and J. Paulo Davim4

1 Department of Industrial and Production Engineering, B. V. B. College of Engineering and Technology, Karnataka, India
2 Department of Electrical and Electronics Engineering, B. V. B. College of Engineering and Technology, Karnataka, India
3 Polytechnic School of Almaden, University of Castilla-La Mancha, Almaden, Spain
4 Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

* To whom correspondence should be addressed. E-mail: gaitondevn{at}yahoo.co.in.


   Abstract

The traditional metallic materials are replaced by some applications for turning process in PA6 and PA66 GF30 polyamides due to excellent properties such as high specific strength and stiffness, wear resistance, dimensional stability, low weight and directional properties. The addition of short fibers to the polyamides improves the properties over the unreinforced polyamides. As a result of these improved properties and potential applications in several fields of engineering, there is a need to understand the machining of unreinforced and reinforced polyamides. Selection of cutting tool and process parameters is important in machining of these composites. This article presents the application of artificial neural network (ANN) modeling to assess the machinability characteristics of unreinforced polyamide (PA6) and reinforced polyamide with 30% of glass fibers (PA66 GF30). The effects of process parameters such as work material, tool material, cutting speed, and feed rate on three aspects of machinability, namely, machining force, power, and specific cutting force have been analyzed through a multilayer feed forward ANN. The input–output patterns required for training are obtained through turning experiments planned as per full factorial design. The model analysis revealed that the minimum machining force results at low feed rate and independent of cutting speed, whereas the power is minimal when both the cutting speed and feed rate are at low levels for PA6 and PA66 GF30 polyamides machining irrespective of the cutting tool. On the other hand, the specific cutting force is minimal at low cutting speed and high feed rate in case of PA6 material, whereas high values of cutting speed and feed rate are essential for minimizing the specific cutting force for PA66 GF30 polyamide machining.

First published on September 29, 2009
Journal of Thermoplastic Composite Materials 2009, doi:10.1177/0892705709349319


Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?