Paper Title:

Fuzzy Logic Predictive Model of Tool Wear in End Milling Glass Fibre Reinforced Polymer Composites

Periodical Advanced Materials Research (Volume 214)
Main Theme Advances in Key Engineering Materials
Edited by Zeng Zhu
Pages 329-333
DOI 10.4028/www.scientific.net/AMR.214.329
Citation A.I. Azmi et al., 2011, Advanced Materials Research, 214, 329
Online since February 2011
Authors A.I. Azmi, Richard J.T. Lin, Debes Bhattacharyya
Keywords End Milling, Fuzzy Logic, Glass Fiber-Reinforced Polymer, Machinability, Tool Wear
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This 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 relationship.