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

摘要：

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.

关键词： Tool wear machinability end milling glass fibre reinforced polymer fuzzy logic

作者: Azmi A.I. Lin R.J.T. Bhattacharyya D.

作者单位: Centre for Advanced Composite Materials,Department of Mechanical Engineering,The University of Auckland,Private Bag 92019,Auckland,New Zealand

会议类型: 国际会议

会议名称: the 2011 International Conference on Key Engineering Materials(ICKEM 2011)(2011关键工程材料国际会议)

会议地点: 三亚

会议语种:英文

页码: 329-333

在线出版日期: 2011-03-25（万方平台首次上网日期，不代表论文的发表时间）