Self-adaptive active vibration reduction control execution system and method for intelligent vibration reduction boring cutter

Abstract

The invention provides a self-adaptive active vibration reduction control execution system and method for an intelligent vibration reduction boring cutter. The system comprises a multi-source signal sensing unit, an active execution unit, a signal processing and feature extraction unit, an intelligent decision-making unit and a bidirectional communication unit. The signal processing and feature extraction unit and the intelligent decision-making unit are integrated in the same electronic cabin. And the multi-source signal sensing unit, the active execution unit and the electronic cabin are arranged in the boring cutter bar and are encapsulated by heat-conducting silica gel. The two-way communication unit is arranged on the outer side of the tail end of the cutter bar. According to the system, machining chatter in a wide frequency domain range is effectively restrained, and the stability, machining precision and surface quality of deep hole boring of difficult-to-machine materials are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of precision machining technology, and in particular to an adaptive active vibration reduction control execution system and method for intelligent vibration-damping boring tools. Background Technology

[0002] Deep hole boring is a core process in the manufacturing of key components for high-end equipment such as aero-engines and heavy-duty gas turbines. Due to the large length-to-diameter ratio and low system stiffness of the tool holder during machining, chatter is easily induced. Chatter not only leads to surface defects and dimensional inconsistencies, but also causes rapid tool wear and even breakage, severely restricting machining efficiency and product quality. It is one of the main technical bottlenecks in achieving high-precision and high-efficiency machining. Effective chatter control is crucial to ensuring the quality of deep hole boring.

[0003] However, existing technologies suffer from slow response and insufficient practicality, making it difficult to construct stable and reliable vibration suppression closed loops under harsh working conditions. Furthermore, most existing strategies rely on reactive avoidance or limited attenuation, leading to reduced machining efficiency. They also lack rapid and precise means to suppress sudden chatter, hindering the further development of deep hole boring towards higher efficiency and precision.

[0004] Therefore, there is an urgent need to develop an adaptive active vibration reduction control execution system and method for intelligent vibration reduction boring tools. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive active vibration reduction control execution system and method for intelligent vibration reduction boring tools, so as to solve the problems existing in the prior art.

[0006] The technical solution adopted to achieve the purpose of this invention is as follows: an adaptive active vibration reduction control execution system for intelligent vibration reduction boring tools, comprising a multi-source signal sensing unit, an active execution unit, a signal processing and feature extraction unit, an intelligent decision-making unit, and a two-way communication unit.

[0007] The signal processing and feature extraction unit and the intelligent decision-making unit are integrated within the same electronic cabin. The multi-source signal sensing unit, the active execution unit, and the electronic cabin are arranged inside the boring bar and encapsulated with thermally conductive silicone. The bidirectional communication unit is located on the outer side of the tail end of the boring bar. The signal processing and feature extraction unit is signal-connected to the multi-source signal sensing unit and the intelligent decision-making unit. The intelligent decision-making unit is electrically connected to the active execution unit via a drive circuit. The intelligent decision-making unit is signal-connected to the bidirectional communication unit.

[0008] The multi-source signal sensing unit is embedded near the cutting head. This unit is used to collect raw state signals of vibration, cutting force, and temperature during the machining process.

[0009] The signal processing and feature extraction unit is used to preprocess and perform frequency domain analysis on the original state signal to extract feature signals. The intelligent decision-making unit embeds an adaptive control algorithm. The intelligent decision-making unit calculates the optimal control command in real time based on the feature signals.

[0010] The active actuator is a high-frequency response actuator. It is sandwiched between the multi-source signal sensing unit and the electronics compartment. The active actuator receives optimal control commands and acts on the tool holder structure to actively counteract chatter.

[0011] The bidirectional communication unit is connected to the CNC system. Before machining, the bidirectional communication unit receives process parameters from the CNC system. During machining, the bidirectional communication unit uploads the vibration status and health diagnostic information inside the tool to the CNC system in real time.

[0012] Furthermore, the multi-source signal sensing unit includes a high-frequency response vibration acceleration sensor, a dynamic cutting force sensor, and a temperature sensor. The high-frequency response vibration acceleration sensor has a frequency response range of not less than 5kHz and a measurement range of not less than ±500g. The dynamic cutting force sensor adopts a thin-film or micro-strain type structure and is embedded near the cutting head.

[0013] Furthermore, the signal processing and feature extraction unit includes a low-noise amplifier circuit, an anti-aliasing filter, and an analog-to-digital converter. The signal processing and feature extraction unit performs noise reduction and filtering on the original state signal, and extracts the dominant frequency and amplitude features in real time through fast Fourier transform and order analysis.

[0014] Furthermore, the intelligent decision-making unit adopts a DSP+FPGA collaborative computing architecture and embeds an adaptive control algorithm.

[0015] Furthermore, the FPGA handles high-speed FFT calculations, while the DSP runs the adaptive control algorithm. This adaptive control algorithm is a hierarchical fusion control algorithm, consisting of an upper-level model predictive controller and a lower-level adaptive fuzzy PID controller. The upper-level model predictive controller, based on the discrete state-space model of the boring bar vibration reduction system obtained from system identification experiments, uses a look-ahead rolling approach to solve a finite-time optimization problem. Its core optimization objective function J is:

[0016] Where y(k+i|k) is the predicted value of the system output at time k+i from time k. r(k+i) is the desired reference trajectory, which is usually set to zero in the vibration suppression scenario. Δu(k+i|k) is the future control increment to be optimized. H_p is the prediction time domain, and H_c is the control time domain. Q and R are weight matrices, which penalize the output error and the change in control quantity, respectively. The input of the lower-level adaptive fuzzy PID controller is the deviation e_fuzz(k) between the reference control quantity u_mpc(k) optimized by MPC and the actual control output u(k) and its rate of change Δe_fuzz(k). The output is the real-time adjustment of the PID controller parameters ΔK_p(k), ΔK_i(k), and ΔK_d(k). Its fuzzy inference rule adopts the following form:

[0017] ,

[0018] .

[0019] Among them, NB, NS, PS, PM, and PB are labels for fuzzy sets.

[0020] The final control quantity u(k) of the fusion algorithm is given by the following formula:

[0021] in,

[0022]

[0023] K_p0, K_i0, K_d0 are the initial parameters of the PID controller, and ΔK_p(k), ΔK_i(k), ΔK_d(k) are the parameter increments adjusted in real time by the fuzzy inference system.

[0024] Furthermore, the active actuation unit is a magnetorheological fluid damper. The response time of the magnetorheological fluid damper is less than 5ms, and the continuously adjustable damping force range is not less than 200N.

[0025] Furthermore, the active execution unit is a piezoelectric stack actuator. The piezoelectric stack actuator has a response time of less than 1ms, an output force of not less than 800N, and a displacement of not less than 30μm.

[0026] Furthermore, it also includes a self-learning module. The self-learning module stores historical processing data and corresponding optimal control parameters, and establishes a mapping model of processing parameters-vibration characteristics-control commands based on machine learning algorithms, which is used to optimize the response speed and accuracy of subsequent control processes.

[0027] The present invention also discloses an adaptive active vibration reduction control method for an intelligent vibration-damping boring tool based on the above-described execution system, comprising the following steps:

[0028] S1: Vibration, cutting force and temperature signals are collected in real time through a multi-source signal sensing unit.

[0029] S2: The signal processing and feature extraction unit performs filtering, noise reduction, and FFT analysis on the signal to extract the current dominant frequency fn and its amplitude An.

[0030] S3: The intelligent decision-making unit compares fn and An with the preset flutter threshold. If An exceeds the threshold, the control command is calculated in real time according to the built-in algorithm model.

[0031] S4: Send the control command to the active execution unit to drive the generation of the corresponding vibration damping force.

[0032] S5: Packs and sends system status information to the CNC system via a two-way communication unit, and receives machine tool operating parameters fed back by the CNC system.

[0033] S6: Repeat steps S1 to S5 to form a closed-loop control until the processing is completed.

[0034] Furthermore, the adaptive control algorithm also receives spindle speed S and feed rate F information from the CNC system, uses order tracking analysis to distinguish between forced vibration and self-excited vibration, and precisely suppresses the self-excited chatter frequency.

[0035] The technical effects of this invention are beyond doubt:

[0036] A. It achieves in-situ, active, and real-time suppression of chatter, eliminating the need to avoid chatter by reducing the speed or feed rate, thereby maximizing the machining efficiency potential of the machine tool while ensuring the quality and precision of the machined surface.

[0037] B. The integrated intelligent tool design overcomes the shortcomings of traditional external systems, such as bulkiness and poor reliability. It can withstand harsh cutting environments with high temperature, high pressure, and high impact, and meets the stringent requirements of industrial continuous production.

[0038] C. Through deep two-way information interaction with the machine tool CNC system via standard industrial bus, it can not only execute instructions but also provide feedback on process data, providing key support for the adaptive optimization of process parameters and even the construction of digital production lines. Attached Figure Description

[0039] Figure 1 Here is the overall flowchart of the adaptive active vibration reduction control system;

[0040] Figure 2 This is a schematic diagram of the high-speed signal processing and parallel computing architecture inside an FPGA.

[0041] Figure 3 This is a flowchart of the decision-making process for the DSP's internal adaptive control algorithm.

[0042] Figure 4 Flowchart of a composite control method;

[0043] Figure 5 This is a schematic diagram of the internal hardware integration of an intelligent vibration-damping boring tool.

[0044] In the diagram: 1. Multi-source signal sensing unit; 2. Active execution unit; 3. Signal processing and feature extraction unit; 4. Intelligent decision-making unit; 5. Two-way communication unit; 6. Electronic cabin. Detailed Implementation

[0045] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0046] Example 1:

[0047] See Figure 1 , Figure 2 , Figure 3 and Figure 5 This embodiment provides an adaptive active vibration reduction control execution system for intelligent vibration reduction boring tools, including a multi-source signal sensing unit 1, an active execution unit 2, a signal processing and feature extraction unit 3, an intelligent decision-making unit 4, and a two-way communication unit 5.

[0048] The signal processing and feature extraction unit 3 and the intelligent decision-making unit 4 are integrated within the same electronic compartment 6. The multi-source signal sensing unit 1, the active execution unit 2, and the electronic compartment 6 are arranged inside the boring bar and encapsulated with thermally conductive silicone. The bidirectional communication unit 5 is located on the outer side of the tail end of the boring bar. The signal processing and feature extraction unit 3 is signal-connected to the multi-source signal sensing unit 1 and the intelligent decision-making unit 4. The intelligent decision-making unit 4 is electrically connected to the active execution unit 2 via a drive circuit. The intelligent decision-making unit 4 is signal-connected to the bidirectional communication unit 5.

[0049] The multi-source signal sensing unit 1 is embedded near the cutting head. The multi-source signal sensing unit 1 is used to collect the original state signals of vibration, cutting force and temperature during the machining process.

[0050] The signal processing and feature extraction unit 3 is used to preprocess and perform frequency domain analysis on the original state signal to extract feature signals. The intelligent decision-making unit 4 embeds an adaptive control algorithm. The intelligent decision-making unit 4 calculates the optimal control command in real time based on the feature signals.

[0051] The active actuator 2 is a high-frequency response actuator. The active actuator 2 is sandwiched between the multi-source signal sensing unit 1 and the electronic compartment 5. The active actuator 2 receives optimal control commands and acts on the tool holder structure to actively counteract chatter.

[0052] The bidirectional communication unit 5 is connected to the CNC system. Before machining, the bidirectional communication unit 5 receives process parameters from the CNC system. During machining, the bidirectional communication unit 5 uploads the vibration status and health diagnosis information inside the tool to the CNC system in real time.

[0053] This embodiment provides a highly integrated, responsive, and intelligent adaptive active vibration reduction control system and method, which can interact with sensor signals in real time and calculate the optimal vibration reduction parameters during the cutting process. By dynamically adjusting the tool damping characteristics through an active execution unit, chatter can be effectively suppressed.

[0054] Example 2:

[0055] The main content of this embodiment is the same as that of Embodiment 1. The multi-source signal sensing unit 1 includes a high-frequency response vibration acceleration sensor, a dynamic cutting force sensor, and a temperature sensor. The high-frequency response vibration acceleration sensor has a frequency response range of not less than 5kHz and a measurement range of not less than ±500g. It uses a triaxial vibration sensor developed by Chongqing University and is installed near the tool head using a threaded fixing method with an open hole. The dynamic cutting force sensor adopts a thin-film or micro-strain type structure and is bonded and installed near the tool head.

[0056] Example 3:

[0057] The main content of this embodiment is the same as that of Embodiment 1 or 2, wherein the signal processing and feature extraction unit 3 includes a low-noise amplifier circuit, an anti-aliasing filter, and an analog-to-digital converter. The signal processing and feature extraction unit 3 performs noise reduction and filtering on the original state signal, and extracts the main oscillation frequency and amplitude features in real time through fast Fourier transform and order analysis.

[0058] Example 4:

[0059] This embodiment is similar in content to any one of embodiments 1 to 3, wherein the intelligent decision-making unit 4 adopts a DSP+FPGA collaborative computing architecture and embeds an adaptive control algorithm. The FPGA is responsible for high-speed FFT operations, and the DSP runs the adaptive control algorithm. The adaptive control algorithm is a hierarchical fusion control algorithm, which consists of an upper-level model predictive controller and a lower-level adaptive fuzzy PID controller. The upper-level model predictive controller is based on the discrete state-space model of the boring bar vibration reduction system obtained from the system identification experiment, and solves a finite-time domain optimization problem in a look-ahead rolling manner. Its core optimization objective function J is:

[0060] Where y(k+i|k) is the predicted value of the system output at time k+i from time k. r(k+i) is the desired reference trajectory, which is usually set to zero in the vibration suppression scenario. Δu(k+i|k) is the future control increment to be optimized. H_p is the prediction time domain, and H_c is the control time domain. Q and R are weight matrices, which penalize the output error and the change in control quantity, respectively. The input of the lower-level adaptive fuzzy PID controller is the deviation e_fuzz(k) between the reference control quantity u_mpc(k) optimized by MPC and the actual control output u(k) and its rate of change Δe_fuzz(k). The output is the real-time adjustment of the PID controller parameters ΔK_p(k), ΔK_i(k), and ΔK_d(k). Its fuzzy inference rule adopts the following form:

[0061] ,

[0062] .

[0063] Among them, NB, NS, PS, PM, and PB are labels for fuzzy sets.

[0064] The final control quantity u(k) of the fusion algorithm is given by the following formula:

[0065] in,

[0066]

[0067] K_p0, K_i0, K_d0 are the initial parameters of the PID controller, and ΔK_p(k), ΔK_i(k), ΔK_d(k) are the parameter increments adjusted in real time by the fuzzy inference system.

[0068] Example 5:

[0069] This embodiment is essentially the same as any one of embodiments 1-4, except that the active execution unit is a magnetorheological fluid damper. The response time of the magnetorheological fluid damper is less than 5ms, and the continuously adjustable damping force range is not less than 200N. The intelligent decision-making unit calculates and generates damping control commands in real time based on the characteristics. The magnetorheological damper converts harmful flutter energy into heat energy, thereby suppressing vibration.

[0070] Example 6:

[0071] This embodiment is essentially the same as any one of embodiments 1-4, except that the active execution unit is a piezoelectric stack actuator. The piezoelectric stack actuator has a response time of less than 1 ms, an output force of not less than 800 N, and a displacement of not less than 30 μm. The intelligent decision-making unit calculates and generates stiffness control commands in real time based on the characteristics. The voltage signal drives the piezoelectric stack to produce high-frequency micro-deformation, thereby applying an active force to the tool holder. This active force directly counteracts and neutralizes the force causing chatter, achieving active vibration suppression.

[0072] Example 7:

[0073] This embodiment is essentially the same as any one of embodiments 1 to 6, but it also includes a self-learning module. The self-learning module stores historical processing data and corresponding optimal control parameters, and establishes a mapping model of processing parameters, vibration characteristics, and control commands based on machine learning algorithms to optimize the response speed and accuracy of subsequent control processes.

[0074] Example 8:

[0075] This embodiment provides an adaptive active vibration reduction control method for an intelligent vibration-damping boring tool using the execution system described in any one of embodiments 1 to 7, comprising the following steps:

[0076] S1: Vibration, cutting force and temperature signals are collected in real time through the multi-source signal sensing unit 1.

[0077] S2: Signal processing and feature extraction unit 3 performs filtering, noise reduction and FFT analysis on the signal to extract the current dominant frequency fn and its amplitude An.

[0078] S3: The intelligent decision-making unit 4 compares fn and An with the preset chatter threshold. If An exceeds the threshold, it calculates the control command in real time according to the built-in algorithm model. The adaptive control algorithm also receives spindle speed S and feed rate F information from the CNC system, uses order tracking analysis to distinguish between forced vibration and self-excited vibration, and precisely suppresses the self-excited chatter frequency.

[0079] S4: Send the control command to the active execution unit 2 to drive the generation of the corresponding vibration damping force.

[0080] S5: The system status information is packaged and sent to the CNC system through the bidirectional communication unit 5, and the machine tool operating parameters fed back by the CNC system are received.

[0081] S6: Repeat steps S1 to S5 to form a closed-loop control until the processing is completed.

[0082] Example 9:

[0083] The main content of this embodiment is the same as any one of embodiments 1 to 7. In this embodiment, the system is used for adaptive vibration suppression in deep hole boring under varying working conditions.

[0084] In the boring of cylinder bores of large marine diesel engines, due to the uneven material or fluctuation of allowance in the workpiece and the continuous change of machining depth, the dynamic characteristics of the cutting process are time-varying, and a vibration reduction system with fixed parameters is difficult to be effective throughout the entire process.

[0085] The system described in this invention is integrated into a deep hole boring tool with an aspect ratio greater than 8. Upon start of machining, the system is powered on and initialized. A multi-source signal sensing unit continuously acquires vibration signals from the tool holder. A signal processing unit acquires data at a sampling rate of 50kHz and performs real-time FFT analysis, continuously monitoring the changing trends of the dominant oscillation frequency f_n and its amplitude A_n. The model predictive control (MPC) algorithm embedded in the intelligent decision-making unit not only focuses on the current vibration amplitude but also predicts the system state in the next few steps based on the spindle speed and feed rate. When a trend of continuous increase in A_n approaching the chatter threshold is detected (i.e., chatter precursor), the algorithm immediately takes action. The MPC algorithm calculates the optimal damping force command and sends it to the active execution unit (magnetorheological damper). The damper adjusts the damping coefficient to the target value within 5ms, generating a damping force opposite to the vibration phase.

[0086] Throughout the deep hole boring process, even when encountering material hardness boundaries or sudden increases in allowance, the system can proactively suppress chatter before it fully erupts. The vibration amplitude will be stably controlled below a safe threshold, thus avoiding processing efficiency losses due to mid-process stoppages for parameter adjustments and ensuring consistent surface quality across the entire hole wall.

[0087] Example 10:

[0088] The main content of this embodiment is the same as any one of embodiments 1 to 7. In this embodiment, the system is used for chatter suppression in high-speed finishing of difficult-to-machine materials.

[0089] In the aerospace field, high-speed precision boring of titanium alloy (such as Ti-6Al-4V) components is used to achieve high surface integrity. This type of material is prone to high-frequency chatter, requiring extremely high machining accuracy and surface roughness. The system is applied to the precision boring tool of a high-speed machining center. The system pays particular attention to high-frequency vibration components above 2kHz. The signal processing unit uses a high-order anti-aliasing filter to ensure the accuracy of high-frequency signal sampling. Addressing the rapid and nonlinear characteristics of high-frequency chatter, the intelligent decision-making unit employs an adaptive fuzzy PID algorithm. This algorithm can nonlinearly adjust the control parameters (proportional, integral, and derivative coefficients) according to real-time changes in vibration frequency and amplitude, achieving a finer and faster control response. The active execution unit uses a piezoelectric stacked actuator, utilizing its extremely fast response speed of less than 1ms to track and cancel high-frequency vibration signals.

[0090] During high-speed finishing, the system effectively suppresses high-frequency chatter caused by titanium alloys. The actual vibration intensity on the workpiece surface can be reduced by more than an order of magnitude, which can potentially increase the surface roughness Ra value from over 1.6 μm to below 0.4 μm, meeting the stringent surface quality requirements of aerospace components, while significantly reducing tool wear.

[0091] Example 11:

[0092] The main content of this embodiment is the same as any one of embodiments 1 to 7. In this embodiment, the execution system is used in a digital workshop or intelligent production line.

[0093] The system is deeply integrated with the machine tool CNC system and Manufacturing Execution System (MES) through a two-way communication unit (such as an EtherCAT interface). Before machining, the system receives process information such as workpiece material and target dimensions from the MES. During machining, it provides real-time feedback to the CNC system on the tool's health status (such as vibration trends and temperature) and the current vibration reduction effect. When the intelligent decision-making unit determines that adjusting its own damping alone is insufficient to completely suppress chatter (e.g., vibration amplitude continuously exceeds the limit), it sends a request to the CNC system via the two-way communication module to suggest fine-tuning the spindle speed. Based on this request, the CNC system can make small-scale speed adjustments within a preset reasonable range to change the excitation frequency and escape the chatter zone.

[0094] The system's built-in self-learning module records the optimal control parameters under different materials, tool overhangs, and cutting parameters. When encountering similar working conditions again, the system can quickly recall historical optimal parameters, shortening the control convergence time and becoming increasingly intelligent with use.

[0095] This embodiment transforms the boring tool from a simple execution tool into an intelligent node in the intelligent production line with sensing, decision-making, and interaction capabilities. This not only improves the stability and quality of individual machining operations but also provides a valuable data foundation and execution capabilities for achieving full lifecycle management of tools, predictive maintenance, and continuous optimization of machining processes.

Claims

1. An adaptive active vibration damping control execution system for intelligent vibration damping boring tools, characterized in that: It includes a multi-source signal sensing unit (1), an active execution unit (2), a signal processing and feature extraction unit (3), an intelligent decision-making unit (4), and a two-way communication unit (5). The signal processing and feature extraction unit (3) and the intelligent decision-making unit (4) are integrated in the same electronic cabin (6); the multi-source signal sensing unit (1), the active execution unit (2) and the electronic cabin (6) are arranged inside the boring bar and encapsulated with thermally conductive silicone; the bidirectional communication unit (5) is arranged on the outside of the tail end of the boring bar; the signal processing and feature extraction unit (3) is signal-connected to the multi-source signal sensing unit (1) and the intelligent decision-making unit (4); the intelligent decision-making unit (4) is electrically connected to the active execution unit (2) through a drive circuit; the intelligent decision-making unit (4) is signal-connected to the bidirectional communication unit (5); The multi-source signal sensing unit (1) is embedded near the cutting head; the multi-source signal sensing unit (1) is used to collect the original state signals of vibration, cutting force and temperature during the machining process; The signal processing and feature extraction unit (3) is used to preprocess and perform frequency domain analysis on the original state signal to extract feature signals; the intelligent decision-making unit (4) is embedded with an adaptive control algorithm; the intelligent decision-making unit (4) calculates the optimal control command in real time based on the feature signals; The active execution unit (2) is a high-frequency response actuator; the active execution unit (2) is sandwiched between the multi-source signal sensing unit (1) and the electronic cabin (5); the active execution unit (2) receives the optimal control command and acts on the tool holder structure to actively counteract chatter; The bidirectional communication unit (5) is connected to the CNC system. Before machining, the bidirectional communication unit (5) receives process parameters from the CNC system. During machining, the bidirectional communication unit (5) uploads the vibration state and health diagnosis information inside the tool to the CNC system in real time.

2. The adaptive active vibration reduction control execution system for intelligent vibration-damping boring tools according to claim 1, characterized in that: The multi-source signal sensing unit (1) includes a high-frequency response vibration acceleration sensor, a dynamic cutting force sensor and a temperature sensor; the frequency response range of the high-frequency response vibration acceleration sensor is not less than 5kHz and the range is not less than ±500g; the dynamic cutting force sensor adopts a thin film or micro strain type structure and is embedded near the cutting head.

3. The adaptive active vibration reduction control execution system for intelligent vibration-damping boring tools according to claim 1, characterized in that: The signal processing and feature extraction unit (3) includes a low-noise amplifier circuit, an anti-aliasing filter, and an analog-to-digital converter; the signal processing and feature extraction unit (3) performs noise reduction and filtering on the original state signal, and extracts the main oscillation frequency and amplitude features in real time through fast Fourier transform and order analysis.

4. The adaptive active vibration reduction control execution system for intelligent vibration-damping boring tools according to claim 1, characterized in that: The intelligent decision-making unit (4) adopts a DSP+FPGA collaborative computing architecture and embeds an adaptive control algorithm.

5. The adaptive active vibration reduction control execution system for intelligent vibration-damping boring tools according to claim 4, characterized in that: The FPGA handles high-speed FFT calculations, while the DSP runs an adaptive control algorithm. This adaptive control algorithm is a hierarchical fusion control algorithm, consisting of an upper-level model predictive controller and a lower-level adaptive fuzzy PID controller. The upper-level model predictive controller, based on the discrete state-space model of the boring tool vibration reduction system obtained from system identification experiments, uses a look-ahead rolling approach to solve a finite-time optimization problem. Its core optimization objective function J is: ; Where y(k+i|k) is the predicted value of the system output at time k+i in the future; r(k+i) is the desired reference trajectory, which is usually set to zero in the vibration suppression scenario; Δu(k+i|k) is the future control increment to be optimized; H_p is the prediction time domain, and H_c is the control time domain; Q and R are weight matrices, which penalize the output error and the change in control quantity, respectively; the input of the lower-level adaptive fuzzy PID controller is the deviation e_fuzz(k) between the reference control quantity u_mpc(k) optimized by MPC and the actual control output u(k) and its rate of change Δe_fuzz(k), and the output is the real-time adjustment of the PID controller parameters ΔK_p(k), ΔK_i(k), and ΔK_d(k); its fuzzy inference rule adopts the following form: ， ； Among them, NB, NS, PS, PM, and PB are labels for fuzzy sets; The final control quantity u(k) of the fusion algorithm is given by the following formula: ; in, ， K_p0, K_i0, K_d0 are the initial parameters of the PID controller, and ΔK_p(k), ΔK_i(k), ΔK_d(k) are the parameter increments adjusted in real time by the fuzzy inference system.

6. The adaptive active vibration reduction control execution system for intelligent vibration-damping boring tools according to claim 1, characterized in that: The active actuation unit is a magnetorheological fluid damper; the response time of the magnetorheological fluid damper is less than 5ms, and the damping force is continuously adjustable within a range of not less than 200N.

7. The adaptive active vibration reduction control execution system for intelligent vibration-damping boring tools according to claim 1, characterized in that: The active execution unit is a piezoelectric stack actuator; the piezoelectric stack actuator has a response time of less than 1ms, an output force of not less than 800N, and a displacement of not less than 30μm.

8. The adaptive active vibration reduction control execution system for intelligent vibration-damping boring tools according to claim 1, characterized in that: It also includes a self-learning module; the self-learning module stores historical processing data and corresponding optimal control parameters, and establishes a mapping model of processing parameters-vibration characteristics-control commands based on machine learning algorithms, which is used to optimize the response speed and accuracy of subsequent control processes.

9. An adaptive active vibration reduction control method for an intelligent vibration-damping boring tool using an execution system according to any one of claims 1 to 8, characterized in that, Includes the following steps: S1: Vibration, cutting force and temperature signals are collected in real time through the multi-source signal sensing unit (1); S2: Signal processing and feature extraction unit (3) performs filtering, noise reduction and FFT analysis on the signal to extract the current main oscillation frequency fn and its amplitude An; S3: The intelligent decision-making unit (4) compares fn and An with the preset flutter threshold. If An exceeds the threshold, it calculates the control command in real time according to the built-in algorithm model. S4: Send the control command to the active execution unit (2) to drive the generation of the corresponding vibration damping force; S5: The system status information is packaged and sent to the CNC system through the bidirectional communication unit (5), and the machine tool operation parameters fed back by the CNC system are received; S6: Repeat steps S1 to S5 to form a closed-loop control until the processing is completed.

10. The adaptive active vibration reduction control execution system for an intelligent vibration-damping boring tool according to claim 9, characterized in that: The adaptive control algorithm also receives spindle speed S and feed rate F information from the CNC system, uses order tracking analysis to distinguish between forced vibration and self-excited vibration, and precisely suppresses the self-excited chatter frequency.

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