• 清洁切削加工工艺 •

### TC17钛合金低温铣削表面粗糙度预测

1. 南京航空航天大学机电学院，南京，210016
• 出版日期:2022-03-10 发布日期:2022-03-24
• 通讯作者: 赵威（通信作者），男，1977年生，教授、博士研究生导师。研究方向为高性能加工技术与装备、可持续制造技术。E-mail:nuaazw@nuaa.edu.cn。
• 作者简介:雷勇，男，1998年生，硕士研究生。研究方向为先进切削技术。E-mail:3152326794@qq.com。
• 基金资助:
国家重点研发计划（2018YFB2002202）

### Prediction of Surface Roughness for Cryogenic Milling TC17 Titanium Alloys

LEI Yong;ZHAO Wei;HE Ning;LI Liang

1. College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing,210016
• Online:2022-03-10 Published:2022-03-24

Abstract: The cryogenic milling tests of TC17 titanium alloy were carried out to evaluate the machined surface roughness under varying cutting conditions. Firstly, the regression analysis method was used to establish an empirical model of the surface roughness, the effects of jet temperature, feed per tooth, milling speed, and radial cutting width on the surface roughness were investigated. And then a prediction model based on BP neural network of surface roughness was established and compared with the empirical model. The results show that the empirical model depicts a strong correlation(R2=0.92) between surface roughness and parameters. The most influential factor on surface roughness is feed per tooth, followed by the jet temperature, the radial cutting width and the milling speed, respectively. The mean square error between the predicted and the experimental values is as 1.73×10-4 μm2, and the maximum relative error is as 8.81%, and the error variation changes more significantly. For the neural network model, the mean square error between the predicted and the test value is as 3.53×10-5 μm2, the maximum relative error is as 3.64%, and the error changes uniformly. Compared with the empirical model, the neural network model has higher prediction accuracy and generalization ability, and may predict the effects of various parameters on surface roughness better.