螺杆铣床主轴振动对螺杆转子表面质量影响分析  

Influence of spindle vibration of screw milling machine on surface quality of a screw rotor

作　　者：林泽利 孙兴伟[1,2] 杨赫然 张维锋[1,2] 董祉序 赵泓荀 LIN Zeli;SUN Xingwei;YANG Heran;ZHANG Weifeng;DONG Zhixu;ZHAO Hongxun(School of Mechanical Engineering,Shenyang University of Technology,Shenyang 110870,China;Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province,Shenyang 110870,China)

机构地区：[1]沈阳工业大学机械工程学院,沈阳110870 [2]辽宁省复杂曲面数控制造技术重点实验室,沈阳110870

出　　处：《振动与冲击》2024年第16期185-191,246,共8页Journal of Vibration and Shock

基　　金：辽宁省应用基础研究计划项目(2022JH2/101300214);2022年度辽宁省教育厅高等学校基本科研项目面上项目(LJKMZ20220459)。

摘　　要：为探究螺杆铣床的主轴振动对工件表面质量影响规律。通过螺杆转子外包络铣削加工试验,建立主轴振动特性预测的神经网络模型,使用预测模型预测分析不同加工参数下主轴振动特性的变化规律,分析主轴振动与工件表面粗糙度值之间的影响规律。采用压电集成电路型加速度传感器对铣削过程中主轴振动特征值进行测量,采用TR200便携式表面粗糙度仪测量工件表面粗糙度值。针对主轴振动特征值与表面粗糙度值进行灰色关联度分析,结果表明信号峰值与表面粗糙度值关系显著,表面粗糙度值随信号峰值减小而降低。使用粒子群算法在构建的主轴振动预测模型进行工艺参数寻优,利用试验进行模型精度验证,误差在6%以内。加工试验结果表明采用最佳参数组合加工工件可使主轴振动信号峰值减小,从而获得较高的表面质量。该方法对螺杆转子生产实践工艺选择具有一定的启发和指导意义,也可为金属切削加工的表面质量提升提供参考。To investigate the influence of spindle vibration on the surface quality of workpieces in screw milling machines.A neural network model for predicting the vibration characteristics of the main shaft is established through experiments on the outer envelope milling of screw rotors.The prediction model is used to predict and analyze the changes in the vibration characteristics of the main shaft under different processing parameters,and to analyze the influence of the main shaft vibration on the surface roughness value of the workpiece.The IEPE(Piezoelectric Integrated Circuit)type acceleration sensor is used to measure the characteristic values of spindle vibration during the milling process,and the TR200 portable surface roughness meter is used to measure the surface roughness value of the workpiece.Grey correlation analysis is conducted on the characteristic values of spindle vibration and surface roughness values,and the results show a significant relationship between signal peak and surface roughness values.The surface roughness value decreased with the decrease of signal peak.Particle swarm optimization algorithm is used to optimize process parameters in the constructed spindle vibration prediction model.The accuracy of the model is verified by actual processing,and the results show that the error of the model is within 6%.The actual processing test results show that the peak value of the spindle vibration signal can be reduced and higher surface quality could be obtained by using the optimal parameter.The method proposed in this article has certain inspiration and guiding significance for the selection of practical production processes for screw rotors,and can also provide reference for improving the surface quality of metal cutting.

关 键 词：外包络铣削 主轴振动 表面粗糙度 神经网络预测 

分 类 号：TH161.1[机械工程—机械制造及自动化] TB535[理学—物理]