高速钻削复合材料加工参数对粗糙度影响研究  

Effect of process parameters on the roughness in high-speed drilling of composite materials

作　　者：林伟[1] 涂俊翔[1] 

机构地区：[1]福州大学机械工程及自动化学院,福建福州350116

出　　处：《合肥工业大学学报（自然科学版）》2015年第1期11-14,78,共5页Journal of Hefei University of Technology：Natural Science

基　　金：国家自然科学基金资助项目(51075074);福建省自然科学基金资助项目(2013J01262)

摘　　要：文章研究了碳纤维复合材料高速钻削过程中主轴转速、进给量和刀具刀尖角对孔壁表面粗糙度的影响,建立了孔壁表面粗糙度BP神经网络预测模型。结果表明:粗糙度随主轴转速的增加先增大后逐渐减小,在主轴转速为8 500r/min时,孔壁表面粗糙度最大;在进给量小于0.1mm/r,粗糙度随进给量增大而增大,关系曲线呈一阶线性关系,在进给量增大到0.1mm/r后趋于平稳;孔壁粗糙度随着钻头刀尖角的增大而逐渐减小。构建BP神经网络模型对孔壁表面粗糙度进行预测,得到的结果与实验结果基本一致,表明其可以有效地预测孔壁表面粗糙度的变化。In this paper,the effect of spindle speed,feed rate and tool apex angle on hole＇s surface roughness in high-speed drilling of carbon fiber-reinforced plastics（CFRP）is investigated and a BP neural network model for predicting the hole＇s surface roughness is established.The experimental results show that the roughness increases with the increase of spindle speed then decreases gradually,and the maximum surface roughness value appears at the spindle speed of 8 500r/min;the roughness increases linearly with the increase of feed rate when the feed rate is less than 0.1mm/r;the roughness appears to remain constant when the feed rate is greater than 0.1mm/r;the roughness decreases gradually with the increase of tool apex angle.The practical application of the BP neural network model proves that its prediction is consistent with the experimental results and it is able to effectively predict hole＇s surface roughness.

关 键 词：碳纤维复合材料 高速钻削 孔壁粗糙度 BP神经网络 

分 类 号：TH140.7[一般工业技术—材料科学与工程]