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• A discrete dynamic modeling of a ball screw drive system for investigating the relationship between preload level and axial natural frequency of the ball screw system is proposed. • Preload level can be estimated from the axial natural frequency of the ball screw system and the current working table displacement. • Signal processing method for measuring the real time preload level of ball screw drive system in operational mode is proposed. • Three ranges of preload level (high, normal, low preload) are effectively and intuitively monitored during operational mode. • (Revision) An impact test results was added to verify predicted natural frequency of axial mode of table. In this paper, we propose a method to monitor the preload of an operational ball screw drive system mostly used in CNC machine tools by evaluating the natural frequency of the screw nut in the axial direction and the corresponding working table displacement. First, we derived a discrete dynamic model and used it to study the variation in the ball screw system preload. Second, we proposed a method for monitoring the ball screw preload in operational mode via the motor current signal and the screw nut axial vibration signal. We apply this method by acquiring and analyzing the signals from the system as it undergoes a transition in its motion, then compose the frequency response function (FRF) of the ball screw system. This enables us to detect the axial natural frequency of the ball screw drive system. The ball screw preload is then calculated and monitored as the system is operational. We provide an efficient and intuitive method to monitor the variation in preload level of an operational ball screw system. This method can be used as an indicator of the health status of the drive system in particular, as well as for the entire machine tool in general. [ABSTRACT FROM AUTHOR]

To improve the tracking performance of ball screw drive systems, the linear quadratic regulator(LQR) control with generalized extended state observer(GESO) was adopted. The state equation and the transfer function of two degrees of freedom mass model were established based on ball screw drive system with axial vibration characteristics. A LQR controller was designed according to the state equation of the model. To eliminate the matched and mismatched external disturbances of system and to suppress the noise interference of feedback, the generalized extended state observer was designed. The performance of the designed controller was verified by simulations and comparison experiments. The simulation and comparative experiment results show that the designed LQR controller with generalized extended state observer designed has high tracking accuracy and can suppress external interference and feedback noise to guarantee the robustness of feed system. [ABSTRACT FROM AUTHOR]

In view of the time-varying complexity of the heat source for the ball screw feed system, this paper proposes an adaptive inverse problem-solving method to estimate the time-varying heat source and temperature field of the feed system under working conditions. The feed system includes multiple heat sources, and the rapid change of the moving heat source increases the difficulty of its identification. This paper attempts to develop a numerical calculation method for identifying the heat source by combining the experiment with the optimization algorithm. Firstly, based on the theory of heat transfer, a new dynamic thermal network model was proposed. The temperature data signal and the position signal of the moving nut captured by the sensors are used as input to optimize the solution of the time-varying heat source. Then, based on the data obtained from the experiment, finite element software parametric programming was used to optimize the estimate of the heat source, and the results of the two heat source prediction methods are compared and verified. The other measured temperature points obtained by the experiment were used to compare and verify the inverse method of this numerical calculation, which illustrates the reliability and advantages of the dynamic thermal network combined with the genetic algorithm for the inverse method. The method based on the on-line monitoring of temperature sensors proposed in this paper has a strong application value for heat source and temperature field estimation of complex mechanical structures.

In view of the time-varying complexity of the heat source for the ball screw feed system, this paper proposes an adaptive inverse problem-solving method to estimate the time-varying heat source and temperature field of the feed system under working conditions. The feed system includes multiple heat sources, and the rapid change of the moving heat source increases the difficulty of its identification. This paper attempts to develop a numerical calculation method for identifying the heat source by combining the experiment with the optimization algorithm. Firstly, based on the theory of heat transfer, a new dynamic thermal network model was proposed. The temperature data signal and the position signal of the moving nut captured by the sensors are used as input to optimize the solution of the time-varying heat source. Then, based on the data obtained from the experiment, finite element software parametric programming was used to optimize the estimate of the heat source, and the results of the two heat source prediction methods are compared and verified. The other measured temperature points obtained by the experiment were used to compare and verify the inverse method of this numerical calculation, which illustrates the reliability and advantages of the dynamic thermal network combined with the genetic algorithm for the inverse method. The method based on the on-line monitoring of temperature sensors proposed in this paper has a strong application value for heat source and temperature field estimation of complex mechanical structures. [ABSTRACT FROM AUTHOR]