Intelligent spindle micro-vibration control method and system based on digital twin model drive

Abstract

The application discloses a kind of based on digital twin model driven intelligent spindle micro-amplitude vibration control method and system, method specifically is: based on intelligent spindle physical entity characteristic parameter and operating condition, establishes intelligent spindle digital twin model;Based on spindle digital twin model, the mapping relationship between rotor unbalance centrifugal force, gyroscopic moment and bearing gap and spindle / shell vibration response is established;Based on intelligent spindle micro-amplitude vibration response, consider the intelligent spindle-milling-actuating closed-loop system of coupling action of spindle milling force and control force;Intelligent spindle vibration signal is input to controller, and control voltage is exported according to objective function and constraint condition, and control voltage signal is amplified after being input to piezoelectric actuator, and drive it to exert active control force to intelligent spindle system, to suppress the micro-amplitude vibration of spindle;The present application considers the comprehensive influence of spindle milling force and actuator control force on spindle vibration response, and realizes spindle micro-amplitude vibration suppression.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent and digital mechanical diagnosis, and specifically relates to an intelligent spindle micro-vibration control method and system driven by a digital twin model. Background Art

[0002] The intelligent spindle is a core functional component of the new generation of intelligent machine tools and represents the future direction of spindle development. The growing demand for high-speed and efficient machining of complex precision parts in fields such as aviation and aerospace is driving the development of spindle performance indicators such as speed, accuracy, and reliability to new heights. The key difference between intelligent spindles and conventional spindles lies in their sensing, decision-making, and execution capabilities. By real-time monitoring and control of operating signals such as vibration, temperature, speed, and torque, intelligent spindles can achieve higher speed, accuracy, and reliability than conventional spindles, thereby achieving higher machining efficiency. In 2010, three members of the International Academy of Production Research (CIRP): Professor Brecher of RWTH Aachen University in Germany, Professor Abele of Technical University of Darmstadt, and Professor Altintas of the University of British Columbia in Canada, jointly published a paper in the CIRP Annals, reviewing the research progress of machine tool spindle units in recent decades. They pointed out that the future development trend of spindles is to improve their reliability and processing efficiency by integrating sensors and actuators on the spindle (Abele E, Altintas Y, Brecher C. Machine tools spindle units [J]. CIRP Annals - Manufacturing Technology, 2010, 59 (2): 781-802.). Therefore, developing an intelligent spindle-milling-actuation closed-loop system to achieve spindle micro-vibration suppression is of great significance to improving spindle processing accuracy and efficiency.

[0003] Closed-loop feedback control of intelligent spindles primarily involves measuring the vibration response of the spindle / workpiece system using displacement / acceleration sensors and feeding the measured response signal back to a designed controller. The controller then outputs a corresponding control signal to drive an actuator to apply a control force to the spindle / workpiece system, thereby suppressing the spindle's vibration response. Ma Haifeng et al. from Shanghai Jiao Tong University constructed a fast tool servo structure based on a piezoelectric actuator and simulated and experimentally verified the effectiveness of spindle milling vibration control using a sliding film control method (Ma H, Wu J, Yang L, et al. Active chatter suppression with displacement-only measurement in turning process [J]. Journal of Sound and Vibration, 2017, 401.). Monnin et al. from the Swiss Federal Institute of Technology in Zurich proposed two vibration control strategies based on the H2 algorithm: interference mechanism and stabilization mechanism ([1]Monnin J, Kuster F, Wegener K.Optimal control for chatter mitigation in milling—Part 1:Modeling and control design[J].Control Engineering Practice, 2014, 24(1):167-175.[2]Monnin J, Kuster F, Wegener K.Optimal control for chatter mitigation in milling—Part 2:Experimental validation[J].Control Engineering Practice, 2014, 24:167-175.). The former does not consider cutting force in modeling and improves the overall milling stability by increasing the system damping; the latter considers regenerative milling force in modeling and significantly improves the stability within the specified speed range by generating additional resonance peaks in the system.

[0004] A literature review reveals that most current model-based active control research ignores the cutting force model and instead establishes only a dynamic model of the spindle system. Active control strategies are then developed based on this model, aiming to improve the overall milling stability of the system by increasing system damping, among other measures. This often results in a "perfect" phenomenon: while the minimum stable cutting depth across the entire speed range is improved, stability within the target speed range is not maximized. Control studies that consider cutting forces within the controlled system model often fail to implement online corrections to the system model or controller to account for the uncertainties inherent in the spindle-milling system during the cutting process. This results in insufficient controller robustness and limited control effectiveness. Summary of the Invention

[0005] In order to solve the problems existing in the prior art, the present invention discloses an intelligent spindle micro-vibration control method driven by a digital twin model, and proposes an intelligent spindle micro-vibration active control method based on model predictive control. The spindle milling force is integrated from the input item into the controlled system model, which reduces the uncertainty of the system model, continuously corrects the model prediction error through feedback correction, improves the fidelity of the system model relative to the intelligent spindle-milling physical entity, and enhances the robustness of the controller and the effectiveness of vibration control.

[0006] In order to achieve the above objectives, the present invention provides, on one hand, an intelligent spindle micro-vibration control method based on digital twin model driving, which is characterized by comprising the following steps:

[0007] S1, obtaining the entity characteristic parameters and working condition parameters of the intelligent spindle;

[0008] S2, based on the entity characteristic parameters and working condition parameters of the S1 intelligent spindle, establish a digital twin model of the intelligent spindle;

[0009] S3, based on the S2 intelligent spindle digital twin model, studies the changing patterns of the intelligent spindle's micro-vibration response to the unbalanced centrifugal force, gyroscopic torque, and bearing-rotor clearance caused by the rotational speed, and establishes a mapping relationship between the intelligent spindle rotor's unbalanced centrifugal force, gyroscopic torque, and bearing clearance and the spindle / housing vibration response;

[0010] S4, based on the mapping relationship between the intelligent spindle rotor unbalanced centrifugal force, gyroscopic torque and bearing clearance and the spindle / housing vibration response obtained in S3, establish an intelligent spindle-milling-actuation closed-loop system;

[0011] S5, based on the intelligent spindle-milling-actuation closed-loop system obtained in S4, takes suppressing the micro-vibration of the spindle as the control goal, inputs the intelligent spindle vibration signal into the controller, and the controller outputs the corresponding control voltage according to the objective function and constraint conditions;

[0012] S6, based on the control voltage output by the controller in S5, amplifies the control voltage signal and inputs it to the piezoelectric actuator, driving it to apply active control force to the intelligent spindle system to suppress the micro-vibration of the spindle.

[0013] Furthermore, the entity characteristic parameters of the intelligent spindle described in S1 include the geometric structure parameters and material properties of the spindle rotor, angular contact ball bearing and housing; the geometric structure parameters are obtained from the design drawing file of the intelligent spindle, and the operating condition parameters refer to the operating speed and cutting parameters of the intelligent spindle.

[0014] Further, the intelligent spindle digital twin model in S2 is established by coupling a spindle rotor finite element model with the load capacity and load moment of the angular contact ball bearing, the spindle rotor finite element model is calculated based on a Timoshenko beam element, and the load capacity and load moment of the angular contact ball bearing are calculated based on a Gupta ball bearing model.

[0015] Further, the intelligent spindle-milling-actuator closed-loop system in S4 is established by feeding back the vibration response at the current time of the system to the controller for analysis, driving the actuator to apply a positive control force to the spindle system, calculating the vibration response of the spindle system at the next time, updating and iterating the positive control force applied to the actuator based on the newly calculated vibration response of the spindle system, and then performing the next calculation to obtain the intelligent spindle-milling-actuator closed-loop system.

[0016] Further, the objective function (J) of the controller in S5 is defined as:

[0017]

[0018] wherein Z is the predicted output of the controller, Z ref is the reference output of the controller, ΔF a is the change of the actuator control force, Q and R are two constant matrices representing the output error weight matrix and the control change weight matrix, respectively;

[0019] The constraint condition of the controller is represented as:

[0020] F amin ≤F a (n+j|n)≤F amax

[0021] ΔF amin ≤ΔF a (n+j|n)≤ΔF amax

[0022] wherein F amin and F amax represent the minimum and maximum values of the actuator output force, respectively; ΔF amin and ΔF amax represent the minimum and maximum limits of the actuator output force change, respectively.

[0023] Further, the actuator in S6 applies a positive control force to the intelligent spindle system, which means that the optimal control change sequence ΔF ap (n) is obtained by solving the quadratic programming problem of the controller based on the established objective function and constraint condition, and the control force (Fa (n) = ΔF a (n) + F a (n-1)) and applies it to the spindle system via a piezoelectric actuator.

[0024] In another aspect, an intelligent spindle system is provided, comprising a displacement sensor, a signal conditioner, a power amplifier and a piezoelectric actuator connected in sequence, the displacement sensor is arranged along the radial direction of the intelligent spindle, the piezoelectric actuator is arranged outside the bearing sleeve, the controller is further connected with a memory and a display device through an I / O interface, and the controller controls the micro-vibration of the intelligent spindle based on the intelligent spindle micro-vibration control method based on the digital twin model.

[0025] In addition, an intelligent spindle micro-vibration control system is provided, comprising an intelligent spindle parameter acquisition module, an intelligent spindle digital twin model establishment module, an intelligent spindle vibration response analysis module, a controller design module and an actuator design module.

[0026] The intelligent spindle parameter acquisition module is used to acquire the physical characteristic parameters and working condition parameters of the spindle.

[0027] The intelligent spindle digital twin model establishment module is used to first establish a finite element model of the spindle rotor according to the physical characteristic parameters and working condition parameters of the spindle, and then apply the bearing capacity and bearing moment of the angular contact ball bearing to the corresponding spindle structure to obtain the digital twin model of the intelligent spindle.

[0028] The intelligent spindle vibration response analysis module is used to analyze the variation law of the micro-vibration response of the intelligent spindle caused by the unbalanced centrifugal force, gyroscopic moment and bearing-rotor gap, and establish the mapping relationship between the unbalanced centrifugal force, gyroscopic moment and bearing gap of the intelligent spindle rotor and the vibration response of the spindle / housing.

[0029] The controller design module is used to input the intelligent spindle vibration signal into the controller, and the controller outputs the corresponding control voltage according to the objective function and the constraint condition.

[0030] The actuator design module is used to input the control voltage signal amplified by the power amplifier into the piezoelectric actuator, drive it to apply active control force to the intelligent spindle system, and further suppress the micro-vibration of the spindle.

[0031] The present application also provides a computer device comprising a processor and a memory, the memory is used to store a computer executable program, the processor reads the computer executable program from the memory and executes, and the processor can realize the intelligent spindle micro-vibration control method based on the digital twin model driven by the processor when executing the computer executable program.

[0032] At the same time, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, the intelligent spindle micro-vibration control method based on digital twin model driving according to the present invention can be implemented.

[0033] Compared with the prior art, the present invention has at least the following beneficial effects:

[0034] The present invention proposes an intelligent spindle micro-vibration control method based on digital twin model drive, which can not only greatly improve the intelligent spindle rotation accuracy, but also significantly improve the intelligent spindle milling processing stability; the present invention proposes an intelligent spindle milling vibration active control method based on model predictive control, which integrates the spindle milling force from the input item into the controlled system model, reduces the uncertainty of the system model, improves the fidelity of the system model relative to the intelligent spindle-milling physical entity, and enhances the robustness of the controller and the effectiveness of vibration control; the present invention establishes an intelligent spindle-milling-actuation closed-loop system, considers the comprehensive influence of the spindle milling force and the actuator control force on the spindle vibration response, and realizes the suppression of spindle micro-vibration, which is of great significance to improving the spindle processing accuracy and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of an intelligent spindle-milling-actuation closed-loop system;

[0036] Figure 2 It is a schematic diagram of intelligent active spindle vibration control; DETAILED DESCRIPTION

[0037] The present invention will be described in detail below with reference to the accompanying drawings.

[0038] Existing research on intelligent spindle vibration control methods faces the following challenges: Most current model-based active control studies fail to consider the cutting force model and instead establish only a dynamic model of the spindle system. Active control strategies are then developed based on this model, aiming to improve the overall milling stability of the system by increasing system damping, among other measures. This often results in a "perfect" phenomenon: while the minimum stable cutting depth across the entire speed range is improved, stability within the target speed range is not maximized. Control studies that consider cutting forces within the controlled system model often fail to implement online corrections to the system model or controller to account for the uncertainties inherent in the spindle-milling system during the cutting process, resulting in insufficient controller robustness and limited control effectiveness. To address these issues, the present invention proposes an intelligent spindle micro-vibration active control method based on model predictive control. The spindle milling force is integrated into the controlled system model as an input term, reducing the uncertainty of the system model. Feedback correction continuously corrects the model prediction error, improving the fidelity of the system model relative to the physical entity of the intelligent spindle-milling system, enhancing the robustness of the controller and the effectiveness of vibration control, and thus achieving active micro-vibration control of the intelligent spindle.

[0039] like Figure 1 and Figure 2 As shown, the present invention proposes an intelligent spindle micro-vibration control method based on digital twin model driving, which includes the following steps:

[0040] S1, obtaining the entity characteristic parameters and operating parameters of the intelligent spindle; the characteristic parameters include the geometric structure parameters and material properties of the spindle rotor, angular contact ball bearing, and housing; the geometric structure parameters are obtained from the design drawing file of the intelligent spindle, and the operating condition parameters refer to the operating speed and cutting parameters of the intelligent spindle.

[0041] S2, based on the physical characteristic parameters and operating condition parameters of the S1 intelligent spindle, establish a digital twin model of the intelligent spindle; the establishment of the digital twin model of the intelligent spindle is obtained by coupling the finite element model of the spindle rotor with the load-bearing capacity and load-bearing torque of the angular contact ball bearing, the finite element model of the spindle rotor is calculated based on the Timoshenko beam unit, and the load-bearing capacity and load-bearing torque of the angular contact ball bearing are calculated based on the Gupta ball bearing model.

[0042] S3, based on the S2 intelligent spindle digital twin model, simulates the changing pattern of the intelligent spindle's micro-vibration response to the unbalanced centrifugal force, gyroscopic torque, and the fitting clearance between the bearing and the rotor caused by the speed; the simulation of the changing pattern of the intelligent spindle's micro-vibration response to the unbalanced centrifugal force, gyroscopic torque, and the fitting clearance between the bearing and the rotor caused by the speed refers to the intelligent spindle establishing a mapping relationship between the rotor's unbalanced centrifugal force, gyroscopic torque, and bearing clearance and the spindle / housing vibration response.

[0043] S4, based on the mapping relationship between the rotor unbalanced centrifugal force, gyroscopic torque and bearing clearance and the spindle / housing vibration response obtained in S3, an intelligent spindle-milling-actuation closed-loop system is established; the establishment of the intelligent spindle-milling-actuation closed-loop system means that during the milling process, the intelligent spindle feeds back the vibration response of the system at the current moment to the controller for analysis, drives the actuator to apply active control force to the spindle system, calculates the vibration response of the spindle system at the next moment, and updates and iterates the active control force applied by the actuator based on the newly calculated vibration response of the spindle system, and then performs the next calculation, thus forming an intelligent spindle-milling-actuation closed-loop system.

[0044] S5, based on the S4 intelligent spindle-milling-actuation closed-loop system, takes suppressing the spindle's micro-vibration as the control goal, inputs the intelligent spindle vibration signal into the controller, and the controller outputs the corresponding control voltage according to the objective function and constraints. The objective function (J) of the controller is defined as:

[0045]

[0046] Where Z is the predicted output of the controller, Z ref is the reference output of the controller, ΔF a is the change in the actuator control force, Q and R are two constant matrices, representing the output error weight matrix and the control change weight matrix respectively.

[0047] The constraints of the controller are expressed as:

[0048] F amin ≤F a (n+j|n)≤F amax

[0049] ΔF amin ≤ΔF a (n+j|n)≤ΔF amax

[0050] Where, F amin and F amax Respectively represent the minimum and maximum output force of the actuator; ΔF amin and ΔF amax They represent the minimum and maximum limits of the actuator output force variation respectively.

[0051] S6, based on the control voltage output by the controller in S5, amplifies the control voltage signal by the power amplifier and inputs it to the piezoelectric actuator, driving the piezoelectric actuator to apply active control force to the intelligent spindle system, thereby suppressing the micro-vibration of the spindle. Driving the piezoelectric actuator to apply active control force to the intelligent spindle system means obtaining the optimal control variation sequence ΔF by solving the quadratic programming problem of the controller according to the established objective function and constraints. ap (n), calculate the control force (F a (n) = ΔF a (n)+F a (n-1)) and applies it to the spindle system through a piezoelectric actuator.

[0052] The present invention can provide an intelligent spindle micro-vibration control system, comprising a displacement sensor, a signal conditioner, a power amplifier and a piezoelectric actuator connected in sequence, the displacement sensor being arranged along the radial direction of the intelligent spindle, the piezoelectric actuator being arranged on the outside of the bearing sleeve, and the controller being connected to a memory and a display device through an I / O interface. The controller controls the micro-vibration of the intelligent spindle based on the digital twin model-driven intelligent spindle micro-vibration control method of the present invention.

[0053] The present invention also provides an intelligent spindle micro-vibration control system driven by a digital twin model, an intelligent spindle parameter acquisition module, an intelligent spindle digital twin model establishment module, an intelligent spindle vibration response analysis module, a controller design module, and an actuator design module;

[0054] The intelligent spindle parameter acquisition module is used to obtain the physical characteristic parameters and working condition parameters of the spindle;

[0055] The intelligent spindle digital twin model building module is used to first establish a finite element model of the spindle rotor based on the spindle's physical characteristic parameters and operating parameters. The load capacity and load torque of the angular contact ball bearing are then applied to the corresponding spindle structure to obtain the intelligent spindle digital twin model.

[0056] The intelligent spindle vibration response analysis module is used to analyze the changing patterns of the intelligent spindle's micro-vibration response due to the unbalanced centrifugal force, gyroscopic torque, and the clearance between the bearing and the rotor caused by the rotational speed.

[0057] The controller design module is used to input the intelligent spindle vibration signal into the controller, and the controller outputs the corresponding control voltage according to the objective function and constraint conditions;

[0058] The actuator design module is used to amplify the control voltage signal through the power amplifier and input it into the piezoelectric actuator, driving it to apply active control force to the intelligent spindle system, thereby suppressing the micro-vibration of the spindle.

[0059] In addition, the present invention can also provide a computer device, including a processor and a memory, the memory is used to store computer executable programs, the processor reads part or all of the computer executable programs from the memory and executes them, and when the processor executes part or all of the computer executable programs, it can realize the intelligent spindle micro-vibration control method based on digital twin model drive described in the present invention.

[0060] On the other hand, the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the intelligent spindle micro-vibration control method based on digital twin model drive described in the present invention.

[0061] The computer device may be a vehicle-mounted computer, a notebook computer, a desktop computer or a workstation.

[0062] The processor can be a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or an off-the-shelf field programmable gate array (FPGA).

[0063] The memory of the present invention may be an internal storage unit of a laptop computer, desktop computer or workstation, such as a memory or a hard disk; or an external storage unit, such as a mobile hard disk or a flash memory card.

[0064] Computer-readable storage media may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules or other data. Computer-readable storage media may include: read-only memory (ROM), random access memory (RAM), solid-state drives (SSD) or optical disks, etc. Among them, random access memory may include resistance random access memory (ReRAM) and dynamic random access memory (DRAM).

[0065] Finally, it should be noted that the purpose of disclosing the embodiments is to facilitate a further understanding of the present invention. However, those skilled in the art will appreciate that various substitutions and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the present invention should not be limited to the contents disclosed in the embodiments, and the scope of protection claimed by the present invention shall be determined by the scope defined in the claims.

Claims

1. An intelligent spindle micro-vibration control method based on digital twin model driving, characterized in that: The following steps are involved: S1, obtaining the entity characteristic parameters and working condition parameters of the intelligent spindle; S2, based on the entity characteristic parameters and working condition parameters of the S1 intelligent spindle, establish a digital twin model of the intelligent spindle; S3, based on the S2 intelligent spindle digital twin model, studies the changing patterns of the intelligent spindle's micro-vibration response to the unbalanced centrifugal force, gyroscopic torque, and bearing-rotor clearance caused by the rotational speed, and establishes a mapping relationship between the intelligent spindle rotor's unbalanced centrifugal force, gyroscopic torque, and bearing clearance and the spindle / housing vibration response; The intelligent spindle digital twin model is established by coupling the spindle rotor finite element model with the load-bearing capacity and load-bearing torque of the angular contact ball bearing, wherein the spindle rotor finite element model is calculated based on the Timoshenko beam element, and the load-bearing capacity and load-bearing torque of the angular contact ball bearing are calculated based on the Gupta ball bearing model; S4, based on the mapping relationship between the unbalanced centrifugal force, gyroscopic torque and bearing clearance of the intelligent spindle rotor and the vibration response of the spindle / housing obtained in S3, an intelligent spindle-milling-actuation closed-loop system is established; said establishing of the intelligent spindle-milling-actuation closed-loop system means that during the milling process, the intelligent spindle feeds back the vibration response of the system at the current moment to the controller for analysis, drives the actuator to apply an active control force to the spindle system, calculates the vibration response of the spindle system at the next moment, and iterates the active control force applied by the actuator based on the newly calculated vibration response of the spindle system, and then performs the next calculation to obtain the intelligent spindle-milling-actuation closed-loop system; S5, based on the intelligent spindle-milling-actuation closed-loop system obtained in S4, takes suppressing the micro-vibration of the spindle as the control goal, inputs the intelligent spindle vibration signal into the controller, and the controller outputs the corresponding control voltage according to the objective function and constraint conditions; S6, based on the control voltage output by the controller in S5, amplifies the control voltage signal and inputs it to the piezoelectric actuator, driving it to apply active control force to the intelligent spindle system to suppress the micro-vibration of the spindle.

2. The intelligent spindle micro-vibration control method based on digital twin model driving according to claim 1 is characterized in that: The entity characteristic parameters of the intelligent spindle described in S1 include the geometric structure parameters and material properties of the spindle rotor, angular contact ball bearing and housing; the geometric structure parameters are obtained from the design drawing file of the intelligent spindle, and the operating condition parameters refer to the operating speed and cutting parameters of the intelligent spindle.

3. The intelligent spindle micro-vibration control method based on digital twin model driving according to claim 1 is characterized in that: The objective function of the controller described in S5 ( J ) is defined as: Where, is the predicted output of the controller, is the reference output of the controller, is the change in the actuator control force, and are two constant matrices, representing the output error weight matrix and the control change weight matrix respectively; The constraints of the controller are expressed as: Where, and Respectively represent the minimum and maximum output force of the actuator; and They respectively represent the minimum and maximum limits of the actuator output force change.

4. The method for controlling micro-vibration of an intelligent spindle based on digital twin model driving according to claim 1 is characterized in that: The driving actuator in S6 applies active control force to the intelligent spindle system, which means that the optimal control variation sequence is obtained by solving the quadratic programming problem of the controller according to the established objective function and constraints. , calculate the control force that needs to be applied at the current moment ( ), and applies it to the spindle system through a piezoelectric actuator.

5. An intelligent spindle micro-vibration control system, characterized in that: Intelligent spindle parameter acquisition module, intelligent spindle digital twin model establishment module, intelligent spindle vibration response analysis module, controller design module, actuator design module; The intelligent spindle parameter acquisition module is used to obtain the physical characteristic parameters and working condition parameters of the spindle; The intelligent spindle digital twin model building module is used to first establish a finite element model of the spindle rotor based on the spindle's physical characteristic parameters and operating parameters. The load capacity and load torque of the angular contact ball bearing are then applied to the corresponding spindle structure to obtain the intelligent spindle digital twin model. The intelligent spindle vibration response analysis module is used to analyze the changing patterns of the intelligent spindle's micro-vibration response to the unbalanced centrifugal force, gyroscopic torque, and bearing-rotor clearance caused by the rotational speed, and to establish a mapping relationship between the intelligent spindle rotor's unbalanced centrifugal force, gyroscopic torque, and bearing clearance and the spindle / housing vibration response. The controller design module is used to input the intelligent spindle vibration signal into the controller, and the controller outputs the corresponding control voltage according to the objective function and constraint conditions; The actuator design module is used to amplify the control voltage signal through the power amplifier and input it into the piezoelectric actuator, driving it to apply active control force to the intelligent spindle system, thereby suppressing the micro-vibration of the spindle.

6. A computer device, characterized in that: It includes a processor and a memory, the memory is used to store a computer executable program, the processor reads the computer executable program from the memory and executes it, and when the processor executes the computer executable program, it can implement the intelligent spindle micro-vibration control method based on digital twin model driving as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that A computer program is stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the intelligent spindle micro-vibration control method based on digital twin model driving as described in any one of claims 1 to 4.

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