机械动力学

基于空间统计学的机床动力学特性

  • 李天箭 ,
  • 丁晓红 ,
  • 程凯
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  • 1. 上海理工大学机械工程学院  上海  200090;
    2. 哈尔滨工业大学机电学院  哈尔滨  150001
作者简介:李天箭,女,1975年出生,博士,讲师。主要研究方向为机床优化设计。 E-mail:litianjian99@163.com

收稿日期: 2014-11-12

  修回日期: 2015-06-24

  网络出版日期: 2015-11-05

基金资助

国家自然科学基金资助项目(51405300, 50875174, 51175347)

 

Machine Tool Dynamics Based on Spatial Statistics
 

  • LI Tianjian ,
  • DING Xiaohong ,
  • CHENG Kai
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  • 1. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200090;
    2. School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001

Received date: 2014-11-12

  Revised date: 2015-06-24

  Online published: 2015-11-05

摘要

机床刚度、固有频率等动力学特性随着机床部件位置、姿态在工作空间中的变化而变化。对机床动力学特性的研究不仅需要考虑到机床质量、刚度、阻尼值的大小,还应重视机床加工点的空间位置变化。采用空间统计学方法,以超精密机床固有频率这一关键动力学性能为例,分析机床动力学性能与机床位置姿态之间的数学关系,选取机床动态特性变异函数,建立动力学性能变化预测的Kriging方法模型,研究动力学特性在工作空间中的变化规律以及动力学特性空间信息的表述方法。将所建立的模型与正交多项式方法、径向基神经网络方法、二阶响应面方法等方法建立动力学性能预测分析模型比较,空间统计学Kriging方法所建立的模型R2检验大于0.96,在四种模型建构方式中为精确度最优,能够在完整工作空间中准确地描述机床动力学特性。基于空间统计学的机床动力学特性研究为机床的动力学设计提供了新的设计分析方法及相应的技术支持。

本文引用格式

李天箭 , 丁晓红 , 程凯 . 基于空间统计学的机床动力学特性[J]. 机械工程学报, 2015 , 51(21) : 87 -94 . DOI: 10.3901/JME.2015.21.087

Abstract

The dynamic characteristics of machine tools, such as stiffness and natural frequency vary with the changing of position and posture of the machine components in working space. Not only the mass, stiffness, damping ratios should be considered during the research of the dynamic characteristics of machine tools, the spatial position change of machining point should also be paid more attention. Spatial statistical method is adopted, and the machine tool’s natural frequency is taken as the critical dynamic characteristic, thus the mathematical relation between the machine tool’s dynamic characteristics and its position and posture is analyzed. The machine tool’s dynamic performance variation function is selected, and the Kriging method model to predict dynamic characters is established, then the prediction of the changing rules of machine tool’s dynamic characteristics is realized. The established model is compared with the dynamic characteristics predication models established by using orthogonal polynomial method, the RBF neural network method and the second order response surface method, and result shows that the R-Squared value of the model using spatial statics Kriging method is 0.96, which is the optimum in the four models, thus it can accurately describe the machine tool’s dynamic characteristics in complete working space. The research of machine tools dynamics based on spatial statistics provides a new design and analyze method and technical support for the dynamic design of the ultra-precision machine tools.

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