数控机床进给单元滚动结合部轴向动态特性参数识别

发布时间：2018-10-10 12:49

【摘要】：针对现有结合部参数识别方法不能精确识别装配状态下数控机床进给单元滚动结合部动态特性参数问题,提出进给单元装配状态下滚动结合部轴向动态特性参数识别方法。将丝杠简化为弹性杆件以建立简谐力作用下丝杠及工作台的轴向振动方程,基于该方程建立结合部轴向刚度与阻尼参数识别模型;通过实验测量丝杠支撑点间距离、简谐激振力频率及幅值、丝杠轴端截面轴向振动速度幅值等参数建立识别方程组;基于遗传算法优化求解该方程组,识别出滚动结合部轴向动态特性参数。以自行研制的滚珠丝杠进给实验台为研究对象,基于该方法对其左、右端轴承组及滚珠丝杠副结合部轴向刚度、阻尼参数进行识别,并对识别结果的正确性进行实验验证。结果表明所提方法正确、有效,丝杠轴端截面轴向振动速度幅值的理论计算值与实验测试值绝对误差在7.7μm/s以内,参数识别精度较高。
[Abstract]:In order to solve the problem of dynamic characteristic parameters of feed unit rolling joint in NC machine tool under assembly state, a method of identifying axial dynamic characteristic parameters of rolling joint part in feed unit assembly state is presented in view of the problem that the existing identification methods of joint parameters can not accurately identify the dynamic characteristics of the rolling joint part in the assembly state of NC machine tool. This paper simplifies the lead screw as an elastic member to establish the axial vibration equation of the lead screw and the worktable under the simple harmonic force. Based on the equation, the identification model of the axial stiffness and damping parameters of the joint is established, and the distance between the supporting points of the lead screw is measured by experiments. The identification equations of the frequency and amplitude of the harmonic excitation force and the axial vibration velocity of the axial section of the lead screw are established, and the parameters of the axial dynamic characteristics of the rolling joint are identified by solving the equations optimally based on the genetic algorithm. Based on this method, the axial stiffness and damping parameters of the left and right end bearing groups and the ball screw pairs are identified, and the correctness of the identification results is verified by experiments. The results show that the proposed method is correct and effective. The absolute error between the theoretical and experimental values of axial vibration velocity at the axial end of the lead screw is within 7.7 渭 m / s, and the accuracy of parameter identification is high.
【作者单位】：上海理工大学机械工程学院;
【基金】：国家自然科学基金资助项目(50975179) 上海市教委科研创新资助项目(11ZZ136) 上海市科委科研计划资助项目(13160502500) 沪江基金(D14005)
【分类号】：TG659

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