机械科学与技术2017，Vol.36Issue(4)：586-591,6.DOI:10.13433/j.cnki.1003-8728.2017.0415
采用BP神经网络预测碳纤维增强树脂基复合材料的钻削力
Prediction of Drilling Force in Drilling Process of CFRP via Back Propagation Neural Network
摘要
Abstract
The double cone drill was used to drill carbon fiber reinforced plastics (CFRP).A model for describing the drilling axial force and the spindle speed and feed rate was established by uasing the artificial neural networks with Back-propagation algorithm.Under the processing parameters,the comparative analysis of the change law of drilling axial force among three double cone drill which have variant ratio of the second cutting edge to the principal cutting edge.The results show that comparing with the multivariable linear regression model,the relative error prediction value via BP neural network model is lower than theexperimental,which the prediction errors via BP neural network model were below 3％.The maximum error via multiple linear regression model was 12.46％.BP neural network could be used to establish more accurately axial force prediction model.From the point of view reduce drilling axial force,the double cone drill with the ratio of the second cutting edge to the principal cutting edge has to be equal to 1 should be adopted.关键词
碳纤维复合材料/BP神经网络/双锋角钻头/钻削轴向力Key words
CFRP/BP neural network/the double cone drill/drilling axial force分类
机械制造引用本文复制引用
刘洋,李鹏南,陈明,唐思文,邱新义..采用BP神经网络预测碳纤维增强树脂基复合材料的钻削力[J].机械科学与技术,2017,36(4):586-591,6.基金项目
国家自然科学基金项目(51275168)与国家科技重大专项项目(2012ZX04003031)资助 （51275168）
