Method for identifying feed-drive system dynamic properties using a motor current

Liu, Xing; Mao, Xinyong; Liu, Hongqi; Li, Bin; Guan, Chuangfu; Zhang, Zisheng; Luo, Baoming; Peng, Fangyu

doi:10.1016/j.ijmachtools.2016.08.007

Cited by 13 publications

(6 citation statements)

References 17 publications

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“…The current spectrum shows that the current fluctuates and has the same frequency as the axial vibration. Therefore, the axial vibrations in the FDS are indicated by the motor current [47].…”

Section: ( )mentioning

confidence: 99%

Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems

Liu,

Lu,

Yonezawa

et al. 2023

Mathematics

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Dual-ball-screw feed drive systems (DBSFDSs) are designed for most high-end manufacturing equipment. However, the mismatch between the dynamic characteristic parameters (e.g., stiffness and inertia) and the P-PI cascade control method reduces the accuracy of the DBSFDSs owing to the structural characteristic changes in the motion. Moreover, the parameters of the P-PI cascade controller of the DBSFDSs are always the same even though the two axes have different dynamic characteristics, and it is difficult to tune two-axis parameters simultaneously. A new application of the combination of the grey wolf optimization (GWO) algorithm and the P-PI cascade controller is presented to solve these problems and enhance the motion performance of DBSFDSs. The novelty is that the flexible coupling model and dynamic stiffness obtained from the motor current can better represent the two-axis coupling dynamic characteristics, and the GWO algorithm is used to adjust the P-PI controller parameters to address variations in the positions of the moving parts and reflect characteristic differences between the two axes. Comparison of simulation and experimental results validated the superiority of the proposed controller over existing ones in practical applications, showing a decrease in the tracking error of the tool center and non-synchronization error of over 34% and 39%, respectively.

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Section: ( )mentioning

confidence: 99%

Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems

Liu,

Lu,

Yonezawa

et al. 2023

Mathematics

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“…Aiming at the machine tool load inertia identification algorithm, Li et al [16] proposed a machine tool working modal identification method to predict the dynamic characteristics of the machine tool at different feed speeds, and concluded that with the increase of the feed speed. Zhang et al [17] established the relationship between the stiffness and frequency of the feed system and the acceleration by establishing a variable coefficient dynamic model with acceleration; Zou et al [18] aimed at For the vertical ball screw feed system, a variable coefficient dynamic model was established considering the gravity change of the spindle system, the magnitude and direction of the inertial force and the variable dynamic characteristics of the vertical ball screw feed system under different accelerations were explored; Liu et al [19] studied the corresponding relationship between the current and the vibration of the feed system by identifying the natural frequency of the feed system by using the inertial force sequence generated by the random dry running of the workbench.…”

Section: Introductionmentioning

confidence: 99%

An optimization method of acceleration and deceleration time of feed system based on load inertia

Zhou,

Yang,

Guo

et al. 2024

Meas. Sci. Technol.

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The acceleration and deceleration time is usually a constant value in the process of computer numerical control (CNC) machine tool processing, which cannot adapt to the change of external load and greatly affects processing efficiency. This paper proposes an optimization method for the acceleration and deceleration time of the feed system based on load inertia, which provides the basis for the adaptive adjustment of the acceleration and deceleration time of the feed system. Firstly, by establishing the dynamic model of the servo system, the acceleration and deceleration method is used to identify the external load inertia under different working conditions. The prediction model of the current variance based on load inertia and acceleration and deceleration time parameters is established by using the response surface method, and then the multi-objective particle swarm optimization algorithm is used to build the acceleration and deceleration time optimization model based on the load inertia. At the same time, the inertia identification part is compared with the model reference adaptive system method and the empirical formula estimation method based on current and velocity, the simulation and cutting experiment results show that the inertia identification method based on acceleration and deceleration optimizes the other method. Finally, the machining experiments are carried out on three-axis and five-axis machine tools with the same machine tool type, and by adding different counterweight blocks to change the external load. The test proves that the acceleration and deceleration adaptive adjustment strategy based on load inertia can effectively improve processing efficiency and reduce the fluctuation of the processing load.

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“…In 2013-2015, scholars [9][10][11] excited the machine tool with the inertial force produced by the random feed movement of worktable and the spindle. In 2016, Liu X et al [12] used this method as a reference to verify the validity of the proposed current-based modal parameter identification method.…”

Section: Introductionmentioning

confidence: 99%

Identification Method of Modal Parameters of Machine Tools Under Periodic Cutting Excitation

Yin

Li²,

Qin

et al. 2020

IEEE Access

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The dynamics of a machine tool have an important influence on the quality and efficiency of the machining process. In this paper, the modal parameters identification method of machine tools under normal cutting excitation is proposed based on the fact that the random components in the cutting force can provide an effective excitation. However, the generated cutting force during machining contains strong periodic components and does not satisfy the white noise assumption. Directly applying the operational modal analysis (OMA) method will face serious harmonic interference. Therefore, it is difficult to ensure the accuracy of the identified parameters. The cepstrum editing method is proposed to eliminate the periodic component. And the modal parameters are extracted from the remaining signal by using the OMA method. The experimental results show that the proposed method works well under normal cutting conditions.

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Method for identifying feed-drive system dynamic properties using a motor current

Cited by 13 publications

References 17 publications

Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems

Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems

An optimization method of acceleration and deceleration time of feed system based on load inertia

Identification Method of Modal Parameters of Machine Tools Under Periodic Cutting Excitation

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