Cover Image

Tool wear prediction models during end milling of glass fibre-reinforced polymer composites

Link to publisher's homepage at http://link.springer.com/

Saved in:
Bibliographic Details
Main Authors: Azwan Iskandar, Azmi, Lin, Richard J.T., Bhattacharyya, Debes
Other Authors: azwaniskandar@unimap.edu.my
Format: Article
Language:English
Published: Springer-Verlag London. 2013
Subjects:
Tool wear prediction
End milling
GFRP composites
Regression analysis
Neuro-fuzzy modelling
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/27119
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.unimap-27119
record_format dspace
spelling my.unimap-271192013-07-25T07:59:01Z Tool wear prediction models during end milling of glass fibre-reinforced polymer composites Azwan Iskandar, Azmi Lin, Richard J.T. Bhattacharyya, Debes azwaniskandar@unimap.edu.my Tool wear prediction End milling GFRP composites Regression analysis Neuro-fuzzy modelling Link to publisher's homepage at http://link.springer.com/ Composite products are often subjected to secondary machining processes as integral part of component manufacture. However, rapid tool wear becomes the limiting factor in maintaining consistent machining quality of the composite materials. Hence, this study demonstrates the development of an indirect approach in predicting and monitoring the wear on carbide tool during end milling using multiple regression analysis (MRA) and neuro-fuzzy modelling. Although the results have indicated that acceptable predictive capability can be well achieved using MRA, the application of neuro-fuzzy yields a significant improvement in the prediction accuracy. It is apparent that the accuracies are pronounced as a result of nonlinear membership function and hybrid learning algorithms. Using the developed models, a timely decision for tool re-conditioning or tool replacement can be achieved effectively. 2013-07-25T07:59:01Z 2013-07-25T07:59:01Z 2013-07 Article The International Journal of Advanced Manufacturing Technology, 2013, vol. 67(1-4), pages 701-718 0268-3768 http://link.springer.com/article/10.1007/s00170-012-4516-2 http://hdl.handle.net/123456789/27119 en Springer-Verlag London.
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Tool wear prediction
End milling
GFRP composites
Regression analysis
Neuro-fuzzy modelling
spellingShingle Tool wear prediction
End milling
GFRP composites
Regression analysis
Neuro-fuzzy modelling
Azwan Iskandar, Azmi
Lin, Richard J.T.
Bhattacharyya, Debes
Tool wear prediction models during end milling of glass fibre-reinforced polymer composites
description Link to publisher's homepage at http://link.springer.com/
author2 azwaniskandar@unimap.edu.my
author_facet azwaniskandar@unimap.edu.my
Azwan Iskandar, Azmi
Lin, Richard J.T.
Bhattacharyya, Debes
format Article
author Azwan Iskandar, Azmi
Lin, Richard J.T.
Bhattacharyya, Debes
author_sort Azwan Iskandar, Azmi
title Tool wear prediction models during end milling of glass fibre-reinforced polymer composites
title_short Tool wear prediction models during end milling of glass fibre-reinforced polymer composites
title_full Tool wear prediction models during end milling of glass fibre-reinforced polymer composites
title_fullStr Tool wear prediction models during end milling of glass fibre-reinforced polymer composites
title_full_unstemmed Tool wear prediction models during end milling of glass fibre-reinforced polymer composites
title_sort tool wear prediction models during end milling of glass fibre-reinforced polymer composites
publisher Springer-Verlag London.
publishDate 2013
url http://dspace.unimap.edu.my/xmlui/handle/123456789/27119
_version_ 1643795163946942464
score 13.222552

Similar Items