基于空间统计学的机床动力学特性

发布时间：2018-03-17 15:33

本文选题：机床动力学　切入点：加工空间　出处：《机械工程学报》2015年21期 　论文类型：期刊论文

【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。
[Abstract]:The dynamic characteristics of machine tools, such as stiffness and natural frequency, change with the position and attitude of machine parts in the workspace. It is also necessary to pay attention to the spatial position change of machining points of machine tools. Taking the key dynamic performance of ultra-precision machine tool natural frequency as an example, the mathematical relationship between machine tool dynamic performance and position and attitude of machine tool is analyzed by using the method of spatial statistics. The variation function of machine tool dynamic characteristics is selected to establish the Kriging method model for predicting the dynamic performance change. The variation law of dynamic characteristics in workspace and the representation method of spatial information of dynamic characteristics are studied. The established model is combined with the orthogonal polynomial method, the radial basis function neural network method, the method of radial basis function neural network, and the method of radial basis function neural network. The second order response surface method (RSM) was used to establish the model of prediction and analysis of dynamic performance. The R2 test of the model established by the Kriging method of spatial statistics was greater than 0.96, and the accuracy of the model was the best among the four models. The dynamic characteristics of machine tools can be accurately described in the complete workspace. The research on the dynamic characteristics of machine tools based on spatial statistics provides a new design analysis method and corresponding technical support for the dynamic design of machine tools.
【作者单位】：上海理工大学机械工程学院;哈尔滨工业大学机电学院;
【基金】：国家自然科学基金资助项目(51405300,50875174,51175347)
【分类号】：TG502.1

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