目的 通过车削加工TB9钛合金试验，定量研究不同位置的振动特性对表面粗糙度的影响规律，并建立基于振动参数的表面粗糙度预测模型。方法 选用涂层硬质合金刀具对TB9钛合金线材进行车削加工。通过8704B25和3333A2加速度传感器对试验过程中不同位置的切削振动进行检测。运用Matlab对振动加速度信号进行处理和分析。采用TR2000高精度表面粗糙度仪测量工件表面粗糙度。结果 车削系统不同位置的振动特性均与表面粗糙度存在线性关系。车削系统中刀具振动加速度均方根值、主轴振动加速度均方根值以及后导向振动加速度均方根值与表面粗糙度的Pearson相关系数分别为0.379 93、0.331 90、0.181 95。表面粗糙度预测模型的预测平均百分比误差小于3%。结论 车削加工时刀具、主轴以及后导向的车削振动均对表面粗糙度有一定影响。车削系统不同位置的振动特性对表面粗糙度的影响次序为刀具>主轴>后导向，可见距离切削位置越近的振动对车削加工表面粗糙度的影响越大。基于振动参数的表面粗糙度预测模型的准确度较高，可作为表面粗糙度的预测模型。

In this paper, the influence law of vibration characteristics of different positions on surface roughness is studied. And a surface roughness prediction model based on vibration parameters is established. The experiment was carried on in a TB9 titanium alloy system with cemented carbide tools. The cutting vibration at different positions was detected by 8704B25 and 3333A2 acceleration sensors. The vibration acceleration signals were processed and analyzed by Matlab. TR2000 high-precision surface roughness meter was used to measure the surface roughness of the machined parts. The experiment results show that there is a significant linear relationship between the vibration characteristics of the turning system at different positions and the surface roughness; Pearson correlation coefficients between root mean square value of tool vibration acceleration, root mean square value of spindle vibration acceleration and root mean square value of rear guide vibration acceleration and surface roughness are 0.379 93, 0.331 90 and 0.181 95, respectively; and the average percentage error of surface roughness prediction model is less than 3%. It is concludes that the turning vibration of the tool, the spindle and the rear guide all have a certain effect on the surface roughness in turning titanium alloy wire; the order of the influence of the vibration characteristics at different positions of the turning system on the surface roughness is cutter > spindle > rear guide. It can be seen that the closer the vibration to the cutting position, the greater the impact on the surface roughness of the turning process; the surface roughness prediction model based on vibration parameters has a higher accuracy and can be used as a surface roughness prediction model.