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首页> 外文会议>Advances in key engineering materials >Fuzzy Logic Predictive Model of Tool Wear in End Milling Glass Fibre Reinforced Polymer Composites
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Fuzzy Logic Predictive Model of Tool Wear in End Milling Glass Fibre Reinforced Polymer Composites

机译：端铣玻璃纤维增​​强聚合物复合材料刀具磨损的模糊逻辑预测模型

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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.
机译：本文介绍了玻璃纤维增​​强聚合物（GFRP）复合材料端铣削中刀具磨损预测模型的发展。基于自适应网络的模糊推理系统（ANFIS）被用来精确地预测刀具磨损量与主轴转速，进给速度和测得的加工力的关系。为了收集所有实验数据，使用K20碳化钨立铣刀在干燥条件下进行了立铣实验。结果表明，ANFIS能够在高度非线性的刀具磨损和加工力关系中以极高的精度估算刀具磨损。对两个刀具磨损力ANFIS模型的统计分析表明，与刀具磨损-切削力关系相比，刀具磨损-进给力关系具有更好的预测能力。

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