Tool wear prediction models during end milling of glass fibre-reinforced polymer composites
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2013
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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. |
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Tool wear prediction End milling GFRP composites Regression analysis Neuro-fuzzy modelling |
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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 |
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Link to publisher's homepage at http://link.springer.com/ |
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azwaniskandar@unimap.edu.my |
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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 |
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Tool wear prediction models during end milling of glass fibre-reinforced polymer composites |
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tool wear prediction models during end milling of glass fibre-reinforced polymer composites |
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Springer-Verlag London. |
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2013 |
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http://dspace.unimap.edu.my/xmlui/handle/123456789/27119 |
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1643795163946942464 |