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dc.contributor.authorAzwan Iskandar, Azmi
dc.contributor.authorLin, Richard J.T.
dc.contributor.authorBhattacharyya, Debes
dc.date.accessioned2012-10-10T05:11:33Z
dc.date.available2012-10-10T05:11:33Z
dc.date.issued2011-03
dc.identifier.citationAdvanced Materials Research, vol. 214, 2011, pages 329-333en_US
dc.identifier.issn1022-6680
dc.identifier.urihttp://www.scientific.net/AMR.214.329
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/21278
dc.descriptionLink to publisher's homepage at http://www.ttp.net/en_US
dc.description.abstractThis paper presents development of tool wear prediction models in end milling of glass fibre reinforced polymer (GFRP) composites. Adaptive network based fuzzy inference system (ANFIS) was employed to accurately predict the amount of tool wear as a function of spindle speed, feed rate and measured machining forces. End milling experiments were performed with K20 tungsten carbide end mill cutter under dry condition in order to gather all experimental data. Results show that ANFIS is capable of estimating tool wear with excellent accuracy in the highly nonlinear region of tool wear and the machining forces relationships. Statistical analyses of the two tool wear-machining force ANFIS models reveal that the tool wear-feed force relationship has better predictive capability compared to that of the tool wear-cutting force relationshipen_US
dc.language.isoenen_US
dc.publisherTrans Tech Publications.en_US
dc.subjectEnd millingen_US
dc.subjectFuzzy logicen_US
dc.subjectGlass fibre reinforced polymeren_US
dc.subjectMachinabilityen_US
dc.subjectTool wearen_US
dc.titleFuzzy logic predictive model of tool wear in end milling glass fibre reinforced polymer compositesen_US
dc.typeArticleen_US
dc.contributor.urlazwaniskandar@unimap.edu.myen_US


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