关键词: TC17钛合金, 低温铣削, 表面粗糙度, 经验模型, BP神经网络模型

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.

Key words:  , TC17 titanium alloy, cryogenic milling, surface roughness, empirical model, BP neural network model