• CN:11-2187/TH
  • ISSN:0577-6686

›› 2007, Vol. 43 ›› Issue (3): 128-134.

• 论文 • 上一篇    下一篇

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结构参数对砂轮主轴系统动态性能的影响

罗筱英;唐进元   

  1. 中南大学机电工程学院
  • 发布日期:2007-03-15

EFFECT OF STRUCTURE PARAMETERS ON DYNAMIC PROPERTIES OF SPINDLE SYSTEM

LUO Xiaoying;TANG Jinyuan   

  1. School of Mechanical and Electrical Engineering, Central South University
  • Published:2007-03-15

摘要: 以某数控螺旋锥齿轮磨齿机为研究对象,建立主轴系统的有限元模型,并进行模态分析和谐响应分析,得出特定工况及不同结构参数下主轴系统的较低阶固有频率、振型以及位移的幅频响应曲线和动柔度的Nyquist曲线,进一步分析支承跨距、支承刚度等结构参数对主轴系统固有特性的影响以及对其抗振性能的影响。分析过程中,运用APDL语言将磨齿机主轴系统的关键尺寸参数化,修改预置参数中的跨距后可重新生成新模型,从而使建模和求解的效率大大提高。研究表明,支承刚度对模态影响较大,刚度增加可使第一阶固有频率较大幅度地提升,支承跨距对模态的影响相对较小。要提高该数控螺旋锥齿轮磨齿机的加工精度及加工能力,应使支承刚度达到3 450 N/μm,同时将主轴系统支承跨距由原来的260 mm增大到300 mm。

关键词: 动态性能, 固有频率, 谐响应, 有限元分析, 振型, 主轴系统, 多偏差流, 关键工序, 偏差传递, 误差溯源, 自适应加权网络

Abstract: The relations between a CNC spiral bevel gear grinder spindle and it’s structure parameters are investigated. The FEM model of the spindle system, the modal analysis and the harmony response analysis of the spindle system are developed. The lower–order mode’s shape, natural frequency, the response curve of the amplitude frequency and the Nyquist curve are obtained in a certain conditions. The effect of the supporting span and the bearing stiffness on the spindle system intrinsic property and the vibration resistance are analyzed. By APDL modeling method an analysis model parameterized the key structure dimension of the spindle system is formed. Analysis results show that the first natural frequency is increased in large scale as the bearing stiffness increases, and that the supporting span has relatively less influence on the first natural frequency, and in order to improve the manufacturing accuracy of the CNC spiral bevel gear grinder it is exercisable to increase the bearing stiffness to 3 450 N/mm and increase the span to 300 mm.

Key words: Dynamic properties, Finite element analysis (FEA), Harmony response, Mode shape, Natural frequency, Spindle system, Adaptive weighted network, Error source diagnosis, Key process, Variation propagation, Multiple variance flow

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