Deep hole machining intelligent active vibration suppression tool system and method

Abstract

This invention relates to the field of mechanical manufacturing technology, and more particularly to an intelligent active vibration damping tool system and method for deep hole machining. It includes a tool holder body for a boring bar, the tool holder body having a standardized mounting section with a mating structure, and further includes a vibration damping ring module and an active vibration damping controller. The vibration damping ring module includes a ring-shaped body, the inner wall of which has a positioning structure. A vibration sensor and a smart material actuator are integrated within the ring-shaped body. The vibration damping ring module is detachably mounted on the standardized mounting section. The active vibration damping controller is electrically connected to the vibration damping ring module. The system is configured to execute an intelligent active vibration damping method. This invention uses a detachable vibration damping ring module, which facilitates installation and maintenance, reduces operating costs, and employs a multi-mode intelligent active vibration damping strategy, integrating multi-modal information to improve the accuracy and precision of vibration state identification. The system responds rapidly, has adaptive optimization capabilities, and always maintains the best vibration damping effect.

Description

Technical Field

[0001] This invention relates to the field of mechanical manufacturing technology, and in particular to an intelligent active vibration damping tool system and method for deep hole machining. Background Technology

[0002] During deep hole machining, the tool holder's large length-to-diameter ratio and poor rigidity make it prone to vibration under cutting forces, severely affecting machining accuracy and surface quality. Existing technologies mainly suffer from the following problems: The passive vibration damping effect is limited: For example, the resin damping strip used in Chinese patent CN116967788A can only suppress vibrations within a specific frequency range, and its vibration damping performance decreases with wear. Active vibration suppression is complex to implement: Traditional active vibration suppression solutions require complex structural modifications to the tool holder, which is difficult to implement and costly, making it difficult to promote and apply in industrial production. Lack of intelligent adaptability: Existing solutions cannot adjust vibration suppression strategies in real time according to changes in processing conditions; Narrow vibration suppression bandwidth: It is not effective in suppressing broadband vibrations, especially high-frequency flutter.

[0003] To address the aforementioned problems, this invention provides an intelligent active vibration suppression tool system and method for deep hole machining, which achieves efficient active vibration suppression by minimizing the modification of existing boring tool holders. Summary of the Invention

[0004] The purpose of this invention is to solve the problems existing in the prior art by proposing an intelligent active vibration damping tool system and method for deep hole machining.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a deep hole machining intelligent active vibration damping tool system, comprising a boring bar shank body, wherein the boring bar body is provided with a standardized mounting section with a mating structure, and further comprising: The vibration damping ring module includes a ring-shaped body, the inner wall of which is provided with a positioning structure, and a vibration sensor and a smart material actuator are integrated inside the ring-shaped body. The vibration damping ring module is detachably installed on the standardized mounting section. An active vibration damping controller is electrically connected to the vibration damping ring module.

[0006] Furthermore, the annular body adopts a split structure, including an upper ring body and a lower ring body, which are connected by a connecting structure, including a positioning pin and a bolt; The standardized installation section is located at the antinode of the tool holder vibration wave, as determined by vibration modal analysis. The positioning structure includes an anti-rotation structure one; The mating structure includes an anti-rotation structure two that mates with the anti-rotation structure one.

[0007] Furthermore, the vibration sensor includes an accelerometer and an acoustic emission sensor; The smart material actuators are multiple and are evenly distributed circumferentially on the inner wall of the vibration damping ring module. The smart material actuators include any one of piezoelectric ceramic actuators, magnetostrictive actuators, or electrostrictive actuators. The vibration damping ring module is provided with a wiring cavity, and an aviation plug is provided inside the wiring cavity.

[0008] A method for intelligent active vibration suppression in deep hole machining, applied to an intelligent active vibration suppression tool system for deep hole machining as described above, includes the following steps: S1. Vibration signals during the processing are collected in real time by a vibration sensor, and the vibration signals include acceleration signals and acoustic emission signals; S2. Preprocess and extract features from the vibration signal, wherein feature extraction is performed on feature parameters extracted from the time domain, frequency domain, and time-frequency domain of the acceleration signal; S3. Based on a preset multi-level threshold system, comprehensively evaluate the current vibration state according to the extracted feature parameters; S4. Based on the evaluation results of the vibration state, automatically select the corresponding vibration suppression strategy; S5. Drive the smart material actuator to generate vibration damping force, and optimize the control parameters in real time according to the vibration damping effect.

[0009] Furthermore, the vibration states include slight vibration, early flutter, significant vibration, and severe vibration; The vibration suppression strategy includes a monitoring mode, a predictive vibration suppression mode, a standard vibration suppression mode, and a powerful vibration suppression mode, which can be switched between each other.

[0010] Furthermore, the predictive vibration suppression mode, standard vibration suppression mode, and powerful vibration suppression mode each employ different closed-loop control algorithms; among them, the powerful vibration suppression mode employs a fuzzy control algorithm that can adjust control parameters online.

[0011] Furthermore, the assessment of the vibration state is based on a multi-level threshold system, including an acceleration threshold and an acoustic emission threshold, wherein the acceleration threshold includes a first acceleration threshold and a second acceleration threshold.

[0012] Furthermore, the time-domain features include the effective acceleration value, peak acceleration value, and kurtosis of the acceleration signal extracted from the acceleration signal; the frequency-domain features include the 1 / 3 octave spectrum and centroid frequency extracted from the acceleration signal; and the time-frequency-domain features include the wavelet packet energy spectrum based on the acceleration signal.

[0013] Furthermore, the real-time optimization of the control parameters aims to minimize vibration energy, and the control parameters include vibration damping force coefficient, phase compensation angle, convergence step size, and filter length.

[0014] Furthermore, the switching conditions for the vibration suppression strategy include: The conditions for switching to predictive vibration suppression mode include acoustic emission signal ≥ acoustic emission threshold; The conditions for switching to standard vibration damping mode include that the effective value of vibration acceleration is greater than or equal to the first acceleration threshold. The conditions for switching to the strong vibration suppression mode include that the effective value of the vibration acceleration is greater than or equal to the second acceleration threshold.

[0015] Compared with existing technologies, the advantages of this invention are: 1. Modular Design Innovation: Compared with the traditional integrated vibration damping tool holder, the present invention adopts a detachable vibration damping ring module, which can upgrade the vibration damping function without replacing the entire tool holder system. The modular structure facilitates maintenance and replacement, greatly reducing the cost of use. The same vibration damping ring module can be used for tools of different specifications. It is only necessary to process a standardized installation section on the tool holder to match the vibration damping ring module. 2. Intelligent vibration suppression strategy: Compared with the traditional single vibration suppression mode, the present invention adopts a multi-mode intelligent vibration suppression strategy, which automatically selects the optimal vibration suppression strategy for different vibration states; compared with passive vibration suppression technology, the active vibration suppression method of the present invention can effectively suppress broadband vibration, especially significantly improving the suppression effect on high frequency flutter. 3. Multimodal information fusion: Compared with a single sensor system, this invention uses an accelerometer and an acoustic emission sensor to work together to achieve all-round vibration monitoring from micro to macro. Through multi-source information fusion, the accuracy and precision of vibration state identification are improved. 4. Rapid response capability: The system has a rapid response time and can effectively intervene in the early stages of vibration to avoid deterioration of processing quality caused by increased vibration; 5. Adaptive optimization capability: Compared with fixed parameter control systems, this invention has online self-learning and parameter self-optimization functions, which can adapt to different processing conditions and tool states, and always maintain the best vibration suppression effect; 6. Improved machining economy: Increased tool life and significantly reduced tooling costs; reduced machining scrap rate and increased product qualification rate; reduced overall production costs; simple and quick installation, maintenance only requires repairing or replacing corresponding modules, such as tool holders and vibration damping ring modules, greatly reducing maintenance difficulty and equipment downtime. Attached Figure Description

[0016] Figure 1 This is a structural framework diagram of the device of the present invention; Figure 2This is a flowchart of the process of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0019] Example 1, such as Figure 1 As shown, a deep hole machining intelligent active vibration damping tool system includes a boring bar shank body, the shank body having a standardized mounting section with a mating structure, and further includes: The vibration damping ring module includes a ring-shaped body, the inner wall of which is provided with a positioning structure, and a vibration sensor and a smart material actuator are integrated inside the ring-shaped body. The vibration damping ring module is detachably installed on the standardized mounting section. An active vibration damping controller is electrically connected to the vibration damping ring module.

[0020] The annular body adopts a split structure, including an upper ring body and a lower ring body. The upper ring body and the lower ring body are connected by a connecting structure, which includes a positioning pin and a bolt. The standardized installation section is located at the antinode of the tool bar vibration wave determined by vibration modal analysis. In this embodiment, it is determined by finite element analysis and is usually located at a distance of 1.5 to 2 times the diameter of the tool bar from the tool head. The positioning structure includes an anti-rotation structure one, which can be an annular boss, a polygonal cross-section, or other anti-rotation structures. The mating structure includes a second anti-rotation structure that mates with the first anti-rotation structure. The second anti-rotation structure can be a groove, a plane, or other anti-rotation structures.

[0021] The positioning and mating structures, besides providing positioning, also serve the important purpose of preventing rotation. The annular boss, polygonal cross-section, etc., can achieve circumferential limiting through fitting with the corresponding mating structures, effectively preventing relative rotation between the vibration damping ring module and the tool holder. During deep hole machining, the tool holder is subjected to complex torque and vibration loads. Traditional planar connections or single bolt connections are prone to relative rotation, leading to positioning deviations of the vibration sensor and incorrect directions of the vibration damping force. The mating design of the positioning and mating structures ensures that: 1. Precise positioning to prevent deviation of the vibration damping force direction; 2. Distribute the torque load evenly to prevent the connecting bolts from bearing the full shear force; 3. Maintain the stable orientation of the vibration sensor to ensure the accuracy of vibration signal measurement; 4. Improve connection stiffness and enhance vibration damping force transmission efficiency.

[0022] The vibration sensor includes an accelerometer and an acoustic emission sensor; The smart material actuators are multiple, and in this embodiment there are eight, which are evenly distributed circumferentially on the inner wall of the vibration damping ring module. The smart material actuators include any one of piezoelectric ceramic actuators, magnetostrictive actuators or electrostrictive actuators. The vibration damping ring module is provided with a wiring cavity, the wiring cavity has a protection level of at least IP67, and an aviation plug is provided inside the wiring cavity.

[0023] The wiring cavity must have a protection rating of at least IP67, which is of critical importance: Dust prevention: Completely prevents dust from entering and avoids short circuits caused by conductive dust; In terms of waterproofing: It can be briefly immersed in water to a depth of no more than 1 meter to effectively prevent coolant from seeping in; Sealing performance: Silicone rubber sealing rings with a compression of 25% are used to ensure long-term sealing reliability; Environmental adaptability: Adaptable to complex environments such as oil and coolant at deep hole machining sites; Safety guarantee: Prevent electrical short circuits and ensure safe and stable system operation.

[0024] During system operation, vibration sensors monitor vibration signals in real time. The active vibration suppression controller processes and extracts features from the signals, and evaluates the vibration state based on a multi-level threshold system. When the vibration exceeds the set threshold, the active vibration suppression controller calculates the optimal vibration suppression signal based on the vibration characteristics (frequency and amplitude), drives the piezoelectric ceramic actuator to generate anti-phase vibration, and transmits the vibration suppression force to the tool holder through the vibration suppression ring module to achieve vibration suppression.

[0025] Example 2, based on Example 1, takes an intelligent active vibration damping tool system applied to a deep hole drilling and boring machine as an example. For a tool holder with a diameter of 50mm and a length of 1500mm, used for machining deep holes in 45# steel workpieces, the following modifications are made to the tool holder body: A standardized mounting section is installed on the tool holder body 75mm from the tool head. This section is precision machined, with a length of 40mm, a diameter of 50mm, and a surface roughness Ra≤0.8μm. An annular positioning groove is machined in the middle of the standardized mounting section. The groove is 3mm wide and 0.5mm deep, and the bottom of the groove is rounded with R0.3 to ensure uniform stress distribution.

[0026] Setting a precision-machined standardized mounting section on the tool holder body not only enables rapid and accurate positioning of the vibration damping ring module, but also has the following advantages: 1. It avoids the complex modification of the entire tool holder, requiring only precision machining of local areas, which greatly reduces the modification cost; 2. Standardized design allows the same vibration damping ring module to be used for tool holders of different specifications, improving the versatility of the equipment; 3. The precise coordination of the standardized installation sections ensures the efficiency of vibration damping force transmission and guarantees the vibration damping effect.

[0027] In this embodiment, the vibration damping ring module adopts the following detailed structure: The annular body is made of 7075-T6 aviation aluminum alloy. Its split design makes installation and disassembly simple and reliable, avoiding the cumbersome process of disassembling the entire tool holder system required by traditional integral structures. This reduces equipment downtime and improves production efficiency. The upper and lower ring bodies are precisely positioned using two locating pins, secured with high-strength bolts. The locating pins ensure consistent installation accuracy, guaranteeing a consistent installation position each time, while the high-strength bolts provide sufficient connection rigidity, ensuring uniform and reliable connection rigidity even under strong vibration. The inner wall of the annular body is machined with an annular boss, 0.5mm high, which forms a clearance fit with the annular groove of the tool holder.

[0028] Eight piezoelectric ceramic actuators are arranged circumferentially on the inner wall of the annular body, generating uniform radial damping force to effectively suppress vibrations in different directions. The coordinated operation of multiple piezoelectric ceramic actuators produces sufficient damping force to counteract strong vibrations, improving system stability. Each piezoelectric ceramic actuator has a mounting surface roughness Ra≤0.4μm, is bonded with epoxy resin, and cured at a pressure of 0.2MPa. Each piezoelectric ceramic actuator has an output force range of 1~1000N and a displacement range of 0~30μm.

[0029] The vibration sensor comprises a miniature triaxial accelerometer and an acoustic emission sensor. The accelerometer is screw-mounted, with a range of ±50g and a frequency response of 0.5~10kHz, ensuring the capture of full-frequency vibration signals from low-frequency oscillation to high-frequency flutter. The acoustic emission sensor has a frequency range of 100kHz~1MHz, a sensitivity of ≥80dB, and is fixed via an interference fit through mounting holes.

[0030] The accelerometer senses the macroscopic mechanical motion of the tool holder body; the acoustic emission sensor senses the energy release from microscopic damage within the workpiece material, providing early warning capabilities. The accelerometer and acoustic emission sensor complement each other, enabling comprehensive vibration monitoring from microscopic to macroscopic levels, thus improving the accuracy and reliability of vibration signal identification.

[0031] Electrical connection system: The wiring cavity has an IP67 protection rating, effectively preventing coolant and chips from entering and ensuring electrical safety. The wiring cavity incorporates an XS12 series 6-pin aviation connector; the aviation connector design facilitates quick connection and reduces installation time. The signal cable uses twisted-pair shielded cable, and the power cable has a cross-sectional area of ​​0.75mm². 2 All cables are fitted with polytetrafluoroethylene (PTFE) insulating sleeves.

[0032] Example 3, as Figure 2 As shown, based on the above embodiments, the installation and commissioning process includes intelligent active vibration suppression and parameter optimization and adaptation. The installation and commissioning steps include: Step 1: Surface cleaning treatment: Clean the standardized installation section of the tool holder with acetone to ensure that there is no oil stains. Inspect the inner surface of the vibration damping ring module to ensure that there are no burrs or oil stains. Step 2, Mechanical Positioning and Installation: Align the annular boss of the vibration damping ring module with the annular groove of the tool holder. First, insert the positioning pin of the annular body to initially determine the position. Then, tighten the high-strength bolts in three stages: first, pre-tighten with a torque of 10 N·m to allow the components to initially fit together; then increase to 20 N·m to eliminate gaps at the mating surfaces; finally, reach a final tightening torque of 30 N·m to ensure uniform and reliable connection rigidity. The staged tightening process ensures uniform stress distribution and extends the service life of the high-strength bolts. Check the coaxiality of the vibration damping ring module and the tool holder; the error should be <0.02 mm. Step 3, Electrical System Connection: First, connect the output terminals of the accelerometer and acoustic emission sensor in the vibration damping ring module to the signal acquisition port of the active vibration damping controller via shielded cables to ensure stable and interference-resistant signal transmission. These shielded cables are responsible for transmitting the vibration signals measured by the accelerometer and acoustic emission sensor to the active vibration damping controller. Next, connect the power output terminal of the active vibration damping controller to the eight piezoelectric ceramic actuators in sequence via high-voltage cables. This provides the piezoelectric ceramic actuators with the energy and control commands required to perform vibration damping actions, and applies preload to ensure mechanical coupling. Subsequently, connect the active vibration damping controller to a 220V main power supply, and provide the required 24V DC power to the vibration sensor and the required 0~150V drive voltage to the piezoelectric ceramic actuators through its built-in different power modules. Finally, establish a communication link between the active vibration damping controller and the host computer using an industrial Ethernet cable, and use an IP67-rated aviation connector to complete the sealed connection of all external interfaces, realizing a fully closed-loop electrical control from signal perception and intelligent decision-making to power execution. Step 4: System Debugging and Calibration: Perform zero-point calibration of the vibration sensor to improve measurement accuracy, ensuring the offset is <0.5%; perform displacement calibration of the piezoelectric ceramic actuator to ensure control accuracy, ensuring linearity >99%, providing accurate reference data for intelligent control. The active vibration damping controller automatically detects vibration sensor signals and presets vibration thresholds and damping parameters.

[0033] Example 4, as Figure 1-2 As shown, an intelligent active vibration suppression method, applied to an intelligent active vibration suppression tool system for deep hole machining as described in any of the above embodiments, includes the following steps: S1. Vibration signals during the processing are collected in real time by a vibration sensor, and the vibration signals include acceleration signals and acoustic emission signals; S2. Preprocess and extract features from the vibration signal, wherein feature extraction is performed on feature parameters extracted from the time domain, frequency domain, and time-frequency domain of the acceleration signal; S3. Based on a preset multi-level threshold system, comprehensively evaluate the current vibration state according to the extracted feature parameters; S4. Based on the evaluation results of the vibration state, automatically select the corresponding vibration suppression strategy; S5. Drive the smart material actuator to generate vibration damping force, and optimize the control parameters in real time according to the vibration damping effect.

[0034] The vibration states include slight vibration, early flutter, obvious vibration, and severe vibration; The vibration suppression strategy includes a monitoring mode, a predictive vibration suppression mode, a standard vibration suppression mode, and a powerful vibration suppression mode, which can be switched between each other.

[0035] The predictive vibration suppression mode, standard vibration suppression mode, and powerful vibration suppression mode each employ different closed-loop control algorithms; specifically, the powerful vibration suppression mode uses a fuzzy control algorithm capable of online adjustment of control parameters. In this embodiment, the predictive vibration suppression mode uses a PID control algorithm, the standard vibration suppression mode uses an LMS adaptive filtering algorithm, and the powerful vibration suppression mode uses a fuzzy PID control algorithm.

[0036] The vibration state assessment is based on a multi-level threshold system, including an acceleration threshold and an acoustic emission threshold. The acceleration threshold includes a first acceleration threshold and a second acceleration threshold. A commonly used value range is: the first acceleration threshold is 3~6 m / s². 2 The second acceleration threshold is 8~12 m / s². 2 The acoustic emission threshold is 140~160dB.

[0037] The time-domain features include the effective acceleration value, peak acceleration value, and kurtosis of the acceleration signal extracted from the acceleration signal; the frequency-domain features include the 1 / 3 octave spectrum and centroid frequency extracted from the acceleration signal; and the time-frequency-domain features include the wavelet packet energy spectrum based on the acceleration signal.

[0038] The real-time optimization of the control parameters aims to minimize vibration energy. The control parameters include vibration damping force coefficient, phase compensation angle, convergence step size, and filter length.

[0039] The switching conditions for the vibration suppression strategy include: The conditions for switching to predictive vibration suppression mode include acoustic emission signal ≥ acoustic emission threshold; The conditions for switching to standard vibration damping mode include that the effective value of vibration acceleration is greater than or equal to the first acceleration threshold. The conditions for switching to the strong vibration suppression mode include that the effective value of the vibration acceleration is greater than or equal to the second acceleration threshold.

[0040] Example 5: Based on the above examples, this example and Example 4 relate to intelligent active vibration suppression during the processing of the present invention. The specific operation steps and methods are as follows: Step 1: Basic Parameter Settings Monitoring parameters are set according to the material properties of the workpiece. In this embodiment, for a No. 45 steel workpiece, the first acceleration threshold is set to 5 m / s². 2 (Significant vibration), second acceleration threshold 10 m / s² 2 (Severe vibration), acoustic emission threshold is 150dB (early flutter).

[0041] Step 2: Vibration signal (multimodal signal) acquisition and preprocessing: This step simultaneously acquires two physical signals: the acceleration signal output from the accelerometer and the acoustic emission signal output from the acoustic emission sensor. Details are as follows: To ensure complete capture of the vibration signal, the accelerometer begins acquiring the vibration acceleration signal at a sampling frequency of 100 kHz, while the acoustic emission sensor monitors high-frequency stress waves at a sampling frequency of 2 MHz. The active vibration damping controller acquires voltage signals from the accelerometer and acoustic emission sensor. These voltage signals are analog signals and contain the following complex information: Amplitude: The intensity of vibration; Frequency components: The frequency of the simple harmonic waves that make up the vibration is the key to determining the type of vibration (such as imbalance, misalignment, flutter); Phase: The relative time relationship between different frequency components; Timing information: the pattern of signal change over time, whether it is continuous, transient or sudden.

[0042] After the data acquisition is completed, the active vibration damping controller performs the following preprocessing: Filtering: Analog signal from accelerometer: Use a 100Hz~20kHz bandpass filter to remove low-frequency jitter and high-frequency electronic noise; Analog signal from acoustic emission sensor: Use a 100kHz~1MHz bandpass filter to remove mechanical noise and electrical interference; Amplification: A programmable gain amplifier is used to amplify the analog signal, the amplitude of the analog signal is standardized, the dynamic range of the analog-to-digital converter is fully utilized, and the measurement accuracy is improved. Conversion: High-speed, high-resolution analog-to-digital converters (ADCs) are used for sampling to convert each analog signal into a digital signal that can be processed by the active vibration damping controller.

[0043] Step 3: Multi-dimensional feature extraction: Feature extraction is the basis for intelligent decision-making. The active vibration damping controller extracts features from the following three dimensions: 1. Temporal characteristics: Statistical calculations are performed directly on the preprocessed digital signal sequence, which is fast, provides intuitive physical meaning, and can reflect the overall energy and impact of vibrations. Extracted features include: RMS acceleration: reflects the average energy of vibration and is the most direct basis for judging obvious and severe vibration; Peak acceleration: Captures the maximum instantaneous impact force, used to detect instantaneous, sudden, and violent impacts; Acceleration kurtosis: It is particularly sensitive to impact signals and is used to detect changes in vibration characteristics early. The kurtosis value of normal vibration is close to 3. If it is greater than 4, it can indicate the presence of strong impact vibration (such as early chipping). This is a key indicator for predicting faults in the time domain. 2. Frequency domain characteristics: Performing a Fast Fourier Transform (FFT) on a digital signal converts it from the time axis to the frequency axis, clearly revealing the frequency components that make up the vibration. This is a crucial tool for diagnosing vibration sources such as tool eccentricity, spindle imbalance, and chatter. Extracted features include: 1 / 3 octave band spectrum: Divide the spectrum into multiple frequency bands and analyze the vibration energy in each band to help locate the vibration source (such as spindle imbalance, tool chatter, etc.). Center of gravity frequency: Represents the main concentrated frequency band of vibration energy. Shifts in the center of gravity frequency can indicate changes in the type of vibration. After obtaining the 1 / 3 octave band spectrum, the system weights the spectrum according to the current workpiece material type by calling a preset frequency weight vector (e.g., [0.2, 0.5, 0.3] for processing stainless steel). Specifically, the weighted frequency band spectrum [i] = original frequency band spectrum [i] × frequency weight [i], where i represents the frequency band index. This weighting process highlights the most sensitive frequency band vibrations during material processing, suppresses the influence of frequency band vibrations that are unimportant to the current material, and improves the accuracy and specificity of vibration state judgment.

[0044] Frequency weights are pre-determined experimentally and stored in the system based on the processing vibration characteristics of different materials. For example, by conducting multiple cutting tests on specimens made of different materials, collecting vibration signals, and analyzing the weight of the influence of energy in each frequency band on processing quality, frequency weights for different materials such as 45 steel and stainless steel are determined.

[0045] 3. Time-frequency domain characteristics: The wavelet packet transform is employed, which can simultaneously provide local information in both the time and frequency domains. It is highly suitable for analyzing non-stationary, transient signals (such as the incubation and eruption processes of flutter), overcoming the limitation of FFT, which can only handle stationary signals. Extracted features include: Wavelet packet energy spectrum: It can display the energy distribution of acceleration signal in different frequency bands and time periods in the form of a spectrum, which can accurately capture when and at what frequency the vibration begins to increase. It is extremely suitable for capturing the early signs of non-stationary processes such as flutter.

[0046] Step 4: Feature Fusion and Vibration State Assessment The function and judgment criteria of each feature: ① Core vibration intensity characteristics: RMS acceleration: reflects the average energy of vibration. Judgment criterion: Effective value of acceleration ≥ 5 m / s² 2 And the effective value of acceleration is <10 m / s² 2 (Significant vibration), effective acceleration value ≥10m / s² 2 (Severe vibration); Peak acceleration: Detects instantaneous impacts and sets a high peak acceleration threshold, such as 15 m / s². 2 , Judgment criteria: Peak acceleration ≥ 15 m / s² 2 (There was a violent impact); ② Vibration property identification characteristics: Acceleration kurtosis: Identifying impact vibrations Judgment criteria: Acceleration kurtosis ≈ 3 (normal vibration), acceleration kurtosis > 4 (impact present), acceleration kurtosis > 5 (severe impact); Wavelet packet energy spectrum: By performing time-frequency analysis on acceleration signals, it can be used to detect non-stationary processes such as early flutter; Judgment criteria: Focus on the 100~300kHz frequency band where the energy is ≥150dB (abnormal acoustic emission signal); ③ Vibration frequency analysis characteristics 1 / 3 octave band spectrum: Analysis of vibration frequency distribution Judgment criteria: The energy in a specific frequency band exceeds the dynamic reference value established based on normal processing conditions; Center of gravity frequency: Monitors changes in the dominant vibration frequency. Judgment criterion: Calculate the deviation of the current center of gravity frequency from the expected normal center of gravity frequency value under the current processing state. Moving to lower frequencies may indicate structural loosening or wear. Moving to a higher frequency may cause chatter or a change in tool condition.

[0047] Step 5: Vibration Suppression Strategy Decision: The active vibration damping controller compares the extracted features with various thresholds to assess the vibration state. Based on the assessed vibration state, it uses an intelligent algorithm to determine which vibration damping strategy to adopt, according to Table 1 below: Table 1 Comparison of Vibration State and Vibration Suppression Strategy slight vibration All eigenvalues ​​are below each threshold, representing background vibrations during processing. <![CDATA[Acoustic emission signal < 150 dB and effective acceleration value < 5 m / s 2 > Monitoring mode none Early flutter The acoustic emission signal first exceeds the acoustic emission threshold, indicating the onset of microscopic damage; however, macroscopic vibrations have not yet intensified. <![CDATA[Acoustic emission signal ≥ 150 dB and effective acceleration value < 5 m / s 2 > Predictive vibration suppression mode PID control algorithm Obvious vibration Macroscopic vibrations have occurred, with the effective value of acceleration exceeding the first acceleration threshold, resulting in a measurable impact on processing quality. <![CDATA[The effective value of acceleration ≥ 5 m / s 2 and < 10 m / s 2 > Standard vibration damping mode LMS Adaptive Filtering Algorithm violent vibration The vibration was extremely strong, with the effective acceleration exceeding the second acceleration threshold, possibly originating from hard point impact or deep chatter, jeopardizing machining safety. <![CDATA[The effective value of acceleration ≥ 10m / s 2 > Powerful vibration damping mode Fuzzy PID control algorithm Based on the assessed vibration state, different intelligent algorithms are applied, exhibiting the following characteristics: 1. Under slight vibration suppression conditions, the vibration energy is very small and has no impact on processing quality. The system only monitors without applying suppression force, which can save energy, reduce wear on piezoelectric ceramic actuators, and extend their lifespan. 2. Early flutter refers to the stage detected by acoustic emission sensors where vibration begins to develop at the microscopic level but has not yet manifested in macroscopic acceleration. If no intervention is taken at this stage, it will quickly develop into severe macroscopic vibration. In this case, the system employs a predictive vibration suppression mode, applying a gentle damping force. The PID control algorithm is simple in structure, has a fast response, and requires minimal computation. 3. The LMS algorithm has self-learning capabilities. When the vibration frequency is complex and time-varying, it can update the filter weights in real time, accurately track and cancel the changing vibration signal, and has extremely strong robustness. It is an efficient and adaptive algorithm for dealing with mainstream vibrations. 4. The fuzzy PID control algorithm combines the intelligent reasoning capability of fuzzy logic with the precise control of PID. Under extreme conditions such as system nonlinearity, severe vibration, and inaccurate models, it can intelligently adjust PID parameters based on expert experience (fuzzy rules) to ensure the system's strong robustness and stability, and prevent runaway.

[0048] Step Six: Multi-mode Intelligent Active Vibration Suppression After determining the vibration suppression strategy, the system controls the piezoelectric ceramic actuator to actively suppress vibration through the following steps: 1. Control Signal Generation: The system calculates the control voltage in real time based on the selected control algorithm. The control voltage range is 0~150V to ensure the piezoelectric ceramic actuator operates within a safe range. The calculation formula is U(t)=G×A×sin(2πft+φ), where U(t) is the control voltage output at time t, which is the voltage signal directly applied to the piezoelectric ceramic actuator. The voltage level determines the magnitude of the output displacement and force of the piezoelectric ceramic actuator. G is the vibration damping gain coefficient (range 0.3~1.0, depending on different modes), A is the vibration amplitude, f is the dominant vibration frequency, and φ is the phase compensation angle (usually set to 180°±15° to ensure that the damping force and vibration force are out of phase). The vibration amplitude A and the dominant vibration frequency f are obtained from real-time analysis of the current acceleration signal.

[0049] 2. Vibration Suppression Mode Execution: Based on the vibration suppression strategy determined by the decision, the system enters the following different modes: ① Monitoring Mode: The system automatically enters monitoring mode after power-on, continuously acquiring vibration signals and extracting characteristic values. When the acoustic emission signal is <150dB and the effective acceleration value is <5m / s², the monitoring mode is activated. 2 During this time, the monitoring status is maintained and the piezoelectric ceramic actuator is in standby mode; ② Predictive vibration suppression mode: When the acoustic emission signal is ≥150dB and the effective value of acceleration is <5m / s² 2 The system automatically switches to predictive vibration suppression mode. It reads real-time acoustic emission sensor readings, calculates the error e(t) between the readings and the set acoustic emission threshold, and calculates the control output at time t using a PID algorithm: Control Output = K p ×e(t)+K i ×∫e(t)dt+K d ×de(t) / dt, K p K is a proportionality coefficient that determines the system response speed. i K is the integral coefficient, used to eliminate steady-state error. dThe differential coefficient suppresses overshoot oscillation. The damping force gain coefficient G=0.3~0.5 provides gentle damping and avoids over-control. The control quantity is converted into a driving voltage of 0~60V to drive the piezoelectric ceramic actuator to generate a small damping force. ③ Standard vibration damping mode: When the effective value of acceleration is ≥5m / s² 2 and <10m / s 2 The system switches to standard vibration damping mode.

[0050] Accelerometer signals are collected as reference input X(n), where X(n) is a reference input signal vector containing vibration data over a past period. The filter output y(n) = W(n)ᵀ×X(n) is calculated, where W(n) is the weight coefficient vector of the adaptive filter, and y(n) is the predicted output calculated by the filter based on the current weights and the input. The error signal e(n) = d(n) - y(n) is obtained by comparing it with the desired signal d(n). In the system, the goal is to make the vibration zero, so usually d(n) = 0.

[0051] The updated filter weights are W(n+1) = W(n) + μ × e(n) × X(n), where μ is the convergence step size. In this embodiment, the standard vibration suppression mode adopts LMS adaptive filtering with a convergence step size μ=0.02, a filter length of 128 points, and an output drive voltage of 20~100V, automatically tracking vibration changes; ④ Strong vibration suppression mode: When the effective value of acceleration is ≥10m / s² 2 Immediately activate the powerful vibration suppression mode. Employing fuzzy PID control with a suppression gain of 0.8~1.0, it quickly suppresses severe vibrations. Vibration signals are collected, and e and ec are calculated. e is the error, and ec is the rate of change of the error, which is the difference between the current error and the error at the previous moment, reflecting the speed and direction of the error change. The parameter adjustment amount [ΔK] is calculated using fuzzy inference. p ,ΔK i ,ΔK d ], ΔK p ,ΔK i ,ΔK d The adjustment amount to the original PID parameters is calculated by the fuzzy inference engine. Traditional PID parameters are fixed, while fuzzy PID dynamically and intelligently fine-tunes the PID parameters based on the current error (e) and the rate of change of error (ec), enabling the system to maintain excellent performance even under complex nonlinear conditions. Update PID controller parameters: Actual parameters = Basic parameters + [ΔK] p ,ΔK i ,ΔK d ], Basic parameters (basic proportional coefficient K) pb The basic integral coefficient K ib The fundamental differential coefficients K db ) is the initial setting value of the active vibration damping controller, which is a baseline parameter set based on experience during the system installation and commissioning phase, for example: K pb =1.0,K ib =0.5,K db =0.1, these parameters provide a stable control basis. Incremental parameter (adjustment amount of basic proportional coefficient ΔK) p Basic integral coefficient adjustment ΔK i Adjustment amount ΔK of basic differential coefficients d ) is the parameter adjustment amount calculated in real time by the fuzzy PID control algorithm based on the current vibration state. For example, when the vibration is severe, ΔK p It could be +0.3, ΔK i =-0.1, ΔK d It is +0.05. Actual parameters (actual base ratio K) pa Actual integral coefficient adjustment K ia Actual differential coefficient adjustment K da These are the PID parameters that are ultimately applied to the current control cycle; The specific calculation method is as follows: K pa =K pb +ΔK p , K ia =K ib +ΔK i , K da =K db +ΔK d ; It outputs a drive voltage of 80~150V, and the maximum vibration damping force of the piezoelectric ceramic actuator can reach 1000N. The fuzzy rules in the fuzzy PID control algorithm are formulated based on expert experience, and some specific rules are as follows: If the error e is large and the rate of change of error ec is large, then the output ΔK is large. p For large, ΔK i For small, ΔK d For the middle; If the error e is moderate and the error change rate ec is moderate, then the output ΔK is moderate. p For the middle, ΔK i For the middle, ΔK d For the middle; If the error e is small and the rate of change of error ec is small, then the output ΔK is small. p For small, ΔK i For large, ΔK d Small; The above are just some examples. The fuzzy rule base contains a series of rules based on expert experience to cover various possible combinations of error (e) and error change rate (ec). 3. Safety protection mechanism: The system monitors the status of the piezoelectric ceramic actuator in real time. When the temperature exceeds 80℃, it will automatically reduce the load and stop driving when the temperature exceeds 85℃ to ensure equipment safety.

[0052] Example 6: Based on the above examples, the parameter optimization and adaptation steps of the present invention are as follows: Step 1: Real-time Effect Evaluation: The system performs a comprehensive evaluation of the vibration suppression effect every 100ms. Evaluation indicators include three key metrics: vibration energy attenuation rate, surface quality prediction index, and system energy consumption coefficient. The vibration suppression effect is quantified by the vibration energy attenuation rate = (vibration energy before suppression - vibration energy after suppression) ÷ vibration energy before suppression × 100%. A comprehensive score (0-100 points) is generated by combining the surface quality prediction index and the system energy consumption evaluation. The purpose of this step is to accurately grasp the vibration suppression effect and provide a data foundation for parameter optimization.

[0053] Step 2, Multi-objective parameter optimization: Based on the evaluation results, gradient descent is used to optimize the parameters of the active vibration damping controller to improve its performance. The objective function of the gradient descent method is L = α × (1 - vibration attenuation rate) + β × energy consumption coefficient + γ × stability index, where α, β, and γ are weighting coefficients. This objective function is used to evaluate the balance between vibration damping effect and system energy consumption. Constraints include: piezoelectric ceramic actuator output force ≤ 80% of rated value, control drive voltage ≤ 150V, and system temperature ≤ 80℃. The parameter update formula is θ. next =θ current -η×∇L(θ), where: θ: Represents the control parameter vector of the active vibration damping controller to be optimized, containing all the variables that need to be optimized, specifically θ=[K p ,K i ,K d ,μ,G]; K p ,K i ,K d The proportional coefficient, integral coefficient, and derivative coefficient of a PID controller; μ: Convergence step size of the LMS adaptive filtering algorithm; G: Vibration damping force gain coefficient; θ current: Represents the control parameter vector for the current control cycle, i.e., the control parameter values ​​before optimization; θ next : Represents the control parameter vector for the next control cycle, i.e., the optimized control parameter values ​​calculated using the gradient descent method; η: Learning rate (e.g., 0.01), which controls the step size of parameter updates; ∇L(θ): The gradient of the objective function L with respect to the control parameter vector θ, indicating the direction and magnitude of adjustment of each control parameter to maximize the decrease of the objective function L.

[0054] Step 3: Adaptive Strategy Adjustment: The control strategy is adjusted in real time according to changes in the machining process. When the workpiece material changes or the tool wears, the system automatically adjusts the threshold parameters and control algorithm parameters. The state recognition model is continuously optimized through deep learning algorithms to improve the accuracy of vibration state recognition.

[0055] The system automatically adjusts control parameters based on tool wear and workpiece material properties. As tool usage time increases, the system automatically relaxes various thresholds according to the formulas: "Updated acceleration threshold = Acceleration threshold × (1 + Wear coefficient), Updated acoustic emission threshold = Acoustic emission threshold × (1 + Wear coefficient)". The wear coefficient is calculated as min(0.3, tool usage time / expected total tool life). Different vibration damping gain coefficients and frequency weights are used for different materials; for example, the vibration damping gain coefficient is set to 1.2 when machining stainless steel and 1.0 for 45# steel. This step allows the system to adapt to different machining conditions.

[0056] Step 4: Historical Data Learning and Model Update: The system records complete processing data for each workpiece, including extracted features, control parameters, and vibration suppression effects. When the accumulated data exceeds 100 records, the system retrains the state recognition model offline. Once the accuracy of the new state recognition model is verified, the online state recognition model is updated. System performance is continuously improved through continuous learning. The state recognition model can, for example, employ a deep learning network structure combining 1D-CNN and LSTM. 1D-CNN is used to extract spatial features from the vibration signal, and LSTM is used to capture the temporal dependencies of the vibration signal. Of course, other suitable machine learning models can also be used.

[0057] Through the cyclical execution of these four steps in this embodiment, the system achieves precise evaluation of vibration suppression effect, real-time optimization of control parameters, adaptive adjustment of machining conditions, and continuous performance improvement. This closed-loop optimization mechanism ensures that the system maintains excellent vibration suppression performance during long-term use, reduces vibration amplitude, improves the surface roughness Ra value of the machined surface, and extends tool life, providing a reliable technical guarantee for high-precision deep hole machining.

[0058] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent active vibration suppression in deep hole machining, applied to an intelligent active vibration suppression tool system for deep hole machining, the system comprising a tool holder body for a boring bar, the tool holder body having a standardized mounting section with a mating structure, the standardized mounting section being positioned at the antinode of the tool holder vibration wave determined by vibration modal analysis; further comprising: A vibration damping ring module includes a ring-shaped body with a split structure, comprising an upper ring and a lower ring, which are connected by a connecting structure including a positioning pin and bolts. The inner wall of the ring-shaped body has a positioning structure, including an anti-rotation structure one. A mating structure includes an anti-rotation structure two that mates with the anti-rotation structure one. The ring-shaped body integrates a vibration sensor and a smart material actuator. The vibration sensor includes an accelerometer and an acoustic emission sensor. Multiple smart material actuators are circumferentially distributed on the inner wall of the vibration damping ring module. These smart material actuators include any one of piezoelectric ceramic actuators, magnetostrictive actuators, or electrostrictive actuators. The vibration damping ring module has a wiring cavity containing an aviation connector. The vibration damping ring module is detachably mounted on a standardized mounting section. An active vibration damping controller is electrically connected to the vibration damping ring module; Its characteristics include the following steps: S1. Vibration signals during the processing are collected in real time by a vibration sensor, and the vibration signals include acceleration signals and acoustic emission signals; S2. Preprocess and extract features from the vibration signal, wherein the feature extraction is to extract feature parameters from the time domain, frequency domain, and time-frequency domain of the acceleration signal; S3. Based on a preset multi-level threshold system, the current vibration state is comprehensively evaluated according to the extracted feature parameters; the vibration state includes slight vibration, early flutter, obvious vibration, and severe vibration; the multi-level threshold system includes an acceleration threshold and an acoustic emission threshold, and the acceleration threshold includes a first acceleration threshold and a second acceleration threshold. S4. Based on the evaluation results of the vibration state, automatically select the corresponding vibration suppression strategy; the vibration suppression strategy includes monitoring mode, predictive vibration suppression mode, standard vibration suppression mode, and powerful vibration suppression mode, and the monitoring mode, predictive vibration suppression mode, standard vibration suppression mode, and powerful vibration suppression mode can be switched between each other; the switching conditions of the vibration suppression strategy include: The conditions for switching to predictive vibration suppression mode include acoustic emission signal ≥ acoustic emission threshold; The conditions for switching to standard vibration damping mode include that the effective value of vibration acceleration is greater than or equal to the first acceleration threshold. The conditions for switching to the strong vibration suppression mode include that the effective value of the vibration acceleration is greater than or equal to the second acceleration threshold. S5. Drive the smart material actuator to generate vibration damping force, and optimize the control parameters in real time according to the vibration damping effect.

2. The intelligent active vibration suppression method for deep hole machining according to claim 1, characterized in that: The predictive vibration suppression mode, standard vibration suppression mode, and powerful vibration suppression mode each employ different closed-loop control algorithms; among them, the powerful vibration suppression mode employs a fuzzy control algorithm that can adjust control parameters online.

3. The intelligent active vibration suppression method for deep hole machining according to claim 1, characterized in that: The time-domain features include the effective value of acceleration, peak acceleration, and kurtosis of acceleration extracted from the acceleration signal; the frequency-domain features include the 1 / 3 octave spectrum and centroid frequency extracted from the acceleration signal; and the time-frequency-domain features include the wavelet packet energy spectrum based on the acceleration signal.

4. The intelligent active vibration suppression method for deep hole machining according to claim 1, characterized in that: The real-time optimization of the control parameters aims to minimize vibration energy. The control parameters include vibration damping force coefficient, phase compensation angle, convergence step size, and filter length.

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