航空合金钢30CrMnSiA深孔钻削试验研究  

Experimental Study on Deep Hole Drilling of 30CrMnSiA Alloy Steel

作　　者：严金凤[1] 申小平[1] 荆琴[1] Yan Jinfeng;Shen Xiaoping;Jing Qin

机构地区：[1]南京理工大学

出　　处：《工具技术》2018年第10期39-42,共4页Tool Engineering

摘　　要：在航空合金钢30CrMnSiA深孔钻削中,孔径偏差是影响钻削质量的重要因素,本文通过正交实验设计法,进行了30CrMnSiA航空合金钢Ф2.7mm深孔钻削试验,研究了主轴转速、进给速度和孔深三个钻削参数对孔径偏差的影响规律。通过极差分析发现,孔深是影响孔径偏差的主要因素,同时,分别建立了航空合金钢30CrMnSiAФ2.7mm深孔钻削孔径偏差BP神经网络预测模型和指数预测模型。结果表明:基于BP神经网络的孔径偏差预测模型平均相对误差仅为2. 31%,相对于指数预测模型具有较高的预测精度。Experimental study on deep hole drilling of 30CrMnSiA alloy steel is conducted by orthogonal design method. Range analysis is applied to evaluate the influence of various drilling parameters on hole diameter deviation and it can be seen that the hole depth is the main influence factor of hole diameter deviation. Meanwhile, BP neural network and the index model are applied to construct the prediction model for hole diameter deviation. By comparing between predictive value and experimental value, the BP neural network prediction model has higher precision compared with the index prediction model.

关 键 词：30CrMnSiA合金钢 深孔钻削 孔径偏差 极差分析 BP神经网络 指数模型 

分 类 号：TG501[金属学及工艺—金属切削加工及机床] TH161.1[机械工程—机械制造及自动化]