Intelligent machining control method and system for automobile part milling
Patent Information
- Application Number
- CN202511975417.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-25
- Publication Date
- 2026-05-26
Smart Images
Figure CN122085869A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of CNC machining and intelligent manufacturing technology, and in particular to an intelligent machining control method and system for milling automotive parts. Background Technology
[0002] In actual production, especially when milling thin-walled parts or complex curved surfaces, industrial control software is usually used to drive CNC equipment to execute precise cutting commands. However, the cutting process has significant dynamic time-varying characteristics. Vibration and stress will continuously change with the tool position, material removal amount, and temperature. Once machining chatter occurs, it can easily lead to workpiece scrap, tool chipping, or even machine tool damage.
[0003] In existing technologies, methods for suppressing chatter mostly employ a passive adjustment strategy of "post-intervention." In these methods, the system collects processing data through sensors and only triggers a control mechanism to adjust parameters after chatter has clearly occurred or its amplitude exceeds a set threshold. This approach typically relies on fixed parameter settings or slow adjustment logic, attempting to restore system stability by reducing rotational speed or feed rate after instability occurs. However, this lag in control commands makes it difficult to capture parameter drift over time. Often, the issue is only detected when the system is nearing instability or has already caused irreversible quality defects, completely missing the opportunity for early intervention and leading to exponential amplification of vibration.
[0004] In summary, existing technologies suffer from the inability to track the dynamic decreasing trend of the modal damping ratio in real time and the lack of an early intervention mechanism. Summary of the Invention
[0005] This invention provides an intelligent machining control method and system for milling automotive parts to solve the problems of being unable to track the dynamic decreasing trend of the modal damping ratio in real time and lacking an early intervention mechanism.
[0006] Firstly, in order to solve the above-mentioned technical problems, the present invention provides an intelligent machining control method for milling automotive parts, comprising:
[0007] The original vibration signals and force data during the cutting process are acquired, and the signals are preprocessed to obtain the original vibration time-domain sequence and force time-domain sequence.
[0008] Based on the original vibration time-domain sequence and the force time-domain sequence, adaptive signal purification and dynamic feature calculation are performed to obtain the real-time time-varying characteristic sequence of the modal damping ratio.
[0009] Based on the real-time time-varying characteristic sequence, system stiffness fluctuation analysis and threshold critical state determination are performed. When the modal damping ratio is determined to be close to the preset dynamic descent threshold, the damping ratio trend is extrapolated and the descent start point is locked to obtain the early intervention timing point.
[0010] Based on the early intervention timing and the real-time time-varying characteristic sequence, the deviation trend between the modal damping ratio and the dynamic descent threshold is calculated. Based on the deviation trend, the suppression intensity is graded and matched, and a variable parameter control command sequence containing the corresponding adjustment amplitude is generated.
[0011] Based on the variable parameter control command sequence, and using the preset production efficiency evaluation model and tool life evaluation model, multi-dimensional performance evaluation and parameter optimization screening operations are performed to obtain the optimal combination of control parameters.
[0012] Based on the preferred combination of control parameters, a basic suppression signal is generated, and combined with vibration feedback data collected in real time by sensors for signal fusion and dynamic vector correction, and the final flutter suppression control signal is output.
[0013] Secondly, the present invention provides an intelligent milling control system for automotive parts, comprising:
[0014] The data acquisition module is used to acquire the original vibration signals and force data during the cutting process, and to perform signal preprocessing to obtain the original vibration time-domain sequence and force time-domain sequence.
[0015] The feature calculation module is used to perform adaptive signal purification and dynamic feature calculation based on the original vibration time-domain sequence and the force time-domain sequence to obtain the real-time time-varying characteristic sequence of the modal damping ratio;
[0016] The prediction and judgment module is used to perform system stiffness fluctuation analysis and threshold critical state judgment based on the real-time time-varying characteristic sequence, and when the modal damping ratio is determined to be close to the preset dynamic descent threshold, the module performs damping ratio trend extrapolation and descent start point locking to obtain the early intervention timing point.
[0017] The instruction generation module is used to calculate the deviation trend between the modal damping ratio and the dynamic descent threshold based on the early intervention timing and the real-time time-varying characteristic sequence, perform suppression intensity grading matching based on the deviation trend, and generate a variable parameter control instruction sequence containing the corresponding adjustment amplitude.
[0018] The parameter optimization module is used to perform multi-dimensional performance evaluation and parameter optimization screening operations based on the variable parameter control command sequence, a preset production efficiency evaluation model and tool life evaluation model, to obtain the optimal combination of control parameters.
[0019] The closed-loop control module is used to generate a basic suppression signal based on the preferred combination of control parameters, and to perform signal fusion and dynamic vector correction by combining the vibration feedback data collected in real time by the sensor, and output the final flutter suppression control signal.
[0020] Compared with the prior art, the present invention has the following beneficial effects:
[0021] (1) This invention acquires the modal damping ratio by collecting vibration and force data through sensors, determines its time-varying trend using adaptive filtering, and predicts the future downward trend by employing a Kalman filter algorithm when the damping ratio approaches the dynamic decline threshold, thereby locking in the timing of early intervention. This method can capture this dynamic chain process, overcoming the control lag problem caused by parameter drift and the fact that traditional methods can only be adjusted after the fact. Thus, it can provide effective early warning and active intervention before chatter actually occurs, avoiding workpiece scrap and tool damage, and realizing real-time tracking and stable control of the dynamic time-varying machining process.
[0022] (2) This invention generates a variable parameter control command sequence for early intervention timing, and selects a control gain combination that balances production efficiency and tool life from the command sequence based on a production efficiency mapping table and a tool life mapping table. This method utilizes multidimensional interpolation calculation and accumulated wear integral to balance the contradiction between machining speed and tool load while suppressing chatter, solving the problem that traditional fixed parameter control cannot simultaneously meet the requirements of high quality and high efficiency, thereby significantly improving the surface quality of automotive parts and extending tool life.
[0023] (3) This invention uses an adaptive filtering algorithm to purify the original signal and uses wavelet transform to separate the high-frequency disturbance components caused by abrupt changes in surface geometry for complex curved surfaces. Through this signal processing method, it can effectively eliminate false interference caused by environmental noise and geometric features, accurately extract the system stiffness sequence that reflects the actual changes in structural stiffness, and combine the multi-channel mutual verification mechanism of force and vibration data to ensure the accuracy and robustness of modal damping ratio estimation, thereby improving the reliability of the control system in complex processing environments, ensuring the accuracy of damping ratio judgment in thin-walled and complex curved surface processing, and effectively preventing system instability caused by misjudgment or noise interference. Attached Figure Description
[0024] Figure 1 This is a schematic diagram of the intelligent machining control method for milling automotive parts provided in the first embodiment of the present invention;
[0025] Figure 2 This is a schematic diagram of the intelligent machining control system for milling automotive parts provided in the second embodiment of the present invention. Detailed Implementation
[0026] 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.
[0027] Reference Figure 1 The first embodiment of the present invention provides an intelligent machining control method for milling automotive parts, comprising the following steps:
[0028] S11: Obtain the original vibration signal and force data during the cutting process, and perform signal preprocessing to obtain the original vibration time-domain sequence and force time-domain sequence;
[0029] S12, based on the original vibration time-domain sequence and the force time-domain sequence, perform adaptive signal purification and dynamic feature calculation to obtain the real-time time-varying characteristic sequence of the modal damping ratio;
[0030] S13, based on the real-time time-varying characteristic sequence, perform system stiffness fluctuation analysis and threshold critical state determination, and when the modal damping ratio is determined to be close to the preset dynamic descent threshold, perform damping ratio trend extrapolation and descent start point locking to obtain the early intervention timing point.
[0031] S14. Based on the early intervention timing and the real-time time-varying characteristic sequence, calculate the deviation trend between the modal damping ratio and the dynamic descent threshold, perform suppression intensity grading matching based on the deviation trend, and generate a variable parameter control command sequence containing the corresponding adjustment amplitude.
[0032] S15, based on the variable parameter control command sequence, and based on the preset production efficiency evaluation model and tool life evaluation model, perform multi-dimensional performance evaluation and parameter optimization screening operations to obtain the optimal control parameter combination;
[0033] S16. Based on the preferred combination of control parameters, a basic suppression signal is generated, and combined with the vibration feedback data collected in real time by the sensor for signal fusion and dynamic vector correction, and the final flutter suppression control signal is output.
[0034] In step S11, the original vibration signals and force data during the cutting process are acquired, and signal preprocessing is performed to obtain the original vibration time-domain sequence and force time-domain sequence, including:
[0035] By using an accelerometer and a force gauge installed in the spindle area of the machine tool, the simulated mechanical vibration and three-dimensional cutting force during the cutting process are collected simultaneously.
[0036] Based on the simulated mechanical vibration and the simulated triaxial cutting force, analog-to-digital conversion and time alignment are performed to obtain the original vibration time-domain sequence and the force time-domain sequence.
[0037] First, hardware deployment and signal acquisition are performed. An industrial-grade triaxial piezoelectric accelerometer is fixed to a measuring point near the tool on the spindle housing of the CNC machine tool using rigid screws. This is used to capture the simulated mechanical vibration quantities along the three orthogonal directions X, Y, and Z in the cutting area. At the same time, a triaxial cutting force measuring instrument is installed below the workpiece fixture base to sense the corresponding simulated triaxial cutting force quantities during the cutting process in real time. The above sensors are connected using a multi-channel synchronous data acquisition card. According to the sampling theorem, the sampling frequency is set to more than 2.56 times the estimated chatter frequency (to fully cover the chatter frequency band, which is usually in the range of 200Hz to 5000Hz during high-speed milling of automotive parts, it can be set to 25600Hz to ensure that the signal is not distorted). The acquired analog voltage signal is converted into a digital signal in 24 bits by an analog-to-digital converter.
[0038] To address the slight time deviations that may arise from differences in response speed between different sensors, this step uses the sampling time sequence of the vibration signal as a reference time axis and employs linear interpolation to resample and align the force signal. Specifically, for any target time on the reference time axis... Find the sampling moment that is immediately preceding the target moment in the original sampling sequence of the force signal. and the next sampling time (satisfy Subsequently, interpolation weighting coefficients are calculated based on the relative position of the target time within the preceding and following sampling intervals. The calculation formula is:
[0039]
[0040] This weighting coefficient This characterizes the normalized distance from the target time to the previous sampling time; the greater the distance, the more normalized the distance. The larger, the corresponding The smaller the weight (i.e., the weight of the previous moment), the better. Next, the weighted average formula is used to calculate the aligned force amplitude corresponding to the target moment. The calculation formula is:
[0041]
[0042] in and These represent the measured amplitudes of the original force signal at the preceding and following moments.
[0043] Finally, the aligned data are integrated in chronological order to generate two identical two-dimensional data matrices as the final output, where the original vibration time-domain sequence is in the form of... The force-time sequence is in the form of For example, the first row of data in the generated original vibration time-domain sequence might be: , representing the triaxial acceleration state at the initial sampling moment, and so on to form a complete time series containing tens of thousands of sampling points.
[0044] In step S12, adaptive signal purification and dynamic feature calculation are performed based on the original vibration time-domain sequence and the force time-domain sequence to obtain the real-time time-varying characteristic sequence of the modal damping ratio, including:
[0045] Based on the original vibration time-domain sequence and the force time-domain sequence, noise suppression processing is performed using the LMS adaptive filtering algorithm to obtain a purified signal sequence.
[0046] Based on the purification signal sequence, a short-time Fourier transform is performed to obtain the amplitude and phase data corresponding to the dominant frequency;
[0047] Based on the amplitude and phase data, a Hilbert transform is performed to obtain the instantaneous envelope curve and the instantaneous phase curve;
[0048] Based on the instantaneous envelope curve, a natural logarithmic linear fit is performed to calculate the instantaneous attenuation rate. Combined with the instantaneous phase curve, the real-time time-varying characteristic sequence of the modal damping ratio is obtained.
[0049] First, to extract narrowband features related to the cutting mode from the raw signal containing mixed broadband environmental noise, an LMS adaptive filtering algorithm based on the adaptive line spectrum enhancement (ALE) principle is used to clean the original vibration time-domain sequence. Specifically, a transverse FIR filter of order L is constructed to filter the original vibration signal... As the main input channel, After delay step size ( The physical essence of this is the decorrelation delay, which is set based on the autocorrelation characteristics of the background noise. A common setting method is to acquire a segment of ambient background noise data before cutting begins, calculate its autocorrelation function, and then select an autocorrelation function amplitude that decays to either the first zero point or its initial value. The number of lag points corresponding to (approximately 36.8%) is used as The baseline value; for example, if the autocorrelation function of the background noise decays to 0 after a lag of 5 sampling points, then set... To ensure that the noise components in the reference input are uncorrelated with the noise components in the main input, thus avoiding the filter modeling noise, the signal after this step is used as the reference input vector. At each iteration time k, calculate the output estimate of the filter. ,in Given the current weight vector; calculate the prediction error. This error mainly consists of broadband random noise components. Subsequently, the weight vector is updated using gradient descent, with the update formula as follows:
[0050]
[0051] Repeat this process until the error converges, at which point the filter output... This is a cleaned signal sequence with broadband noise removed.
[0052] Next, a short-time Fourier transform is performed on the purified signal sequence to map the time-domain signal to the time-frequency domain. In the time-frequency spectrum, the frequency point with the largest energy amplitude is searched as the dominant frequency. And extract the amplitude envelope of that frequency point as a function of time. With phase sequence Subsequently, based on Bedrosian theorem, an analytic signal was constructed using the Hilbert transform. The Hilbert transform result of the analytic signal, where the imaginary part is the real part; calculate the instantaneous envelope of the analytic signal. and instantaneous phase .
[0053] Finally, for the instantaneous envelope The decay curve is obtained by taking the natural logarithm. Select a sliding fitting window that contains at least one complete vibration cycle, and within the window... Perform a univariate linear regression fitting, and the fitting equation is:
[0054]
[0055] The absolute value of the slope This is the instantaneous attenuation coefficient. Simultaneously, for the instantaneous phase... The instantaneous angular frequency is obtained by taking the first derivative. Based on the damping definition formula for a single-degree-of-freedom system, the modal damping ratio at each moment is calculated, thereby generating a real-time time-varying characteristic sequence of the modal damping ratio:
[0056]
[0057] It should be noted that the filter order L is set based on the signal coherence length, and is usually set according to the recommendations for signal processing in ISO 13373-1 (Standard for Monitoring and Diagnosing Machine Vibration Conditions) to a value that can cover the main impulse response length of the system (e.g., L=64 or 128), and the convergence step size factor. These are iterative parameters that control the stability of the algorithm, and their settings are based on the power characteristics of the input signal. A general setting method follows... The constraints, among which Let L be the average power estimate of the reference input signal (which can be calculated from the mean square value), and L be the filter order. In practical engineering, to prevent divergence caused by sudden changes in signal energy, a safety margin factor is usually introduced. (Value range 0.01-0.1), using the normalized LMS algorithm setting formula. ( To prevent the use of tiny positive numbers with a denominator of zero (for example, when the average signal power is 0.5 watts, the filter order is 64, and the safety factor is 0.05), the following settings are made: .
[0058] The selection of the window function should conform to the IEC 61260 (Electroacoustics - Octave and fractional octave filters) standard, using a Hanning window with high sidelobe attenuation to reduce spectral leakage; window length... The settings are based on the required frequency resolution. , must meet For example, sampling rate If a frequency resolution better than 15Hz is required to distinguish dense modes, the window length should be at least 2048 points. Simultaneously, the window length for logarithmic decay fitting should be set based on the free decay response characteristics in structural dynamics, and should include at least 3-5 vibration cycles to eliminate the influence of random disturbances on slope estimation and ensure that the damping ratio identification results meet the statistical significance requirements for transient feature extraction in ISO 10816 (Mechanical Vibration Evaluation Standard).
[0059] In step S13, based on the real-time time-varying characteristic sequence, system stiffness fluctuation analysis and threshold critical state determination are performed. When the modal damping ratio is determined to be close to the preset dynamic descent threshold, damping ratio trend extrapolation and descent start point locking are performed to obtain the early intervention timing point, including:
[0060] Based on the real-time time-varying characteristic sequence and the pre-stored reference excitation signal, the cross-correlation function is calculated to obtain the system stiffness sequence;
[0061] Based on the system stiffness sequence, multi-level decomposition is performed using db4 wavelet transform to obtain the stiffness fluctuation amplitude sequence.
[0062] Based on the stiffness fluctuation amplitude sequence, the real-time time-varying characteristic sequence is validated to obtain the validated damping ratio value. When the validated damping ratio value is determined to be lower than the sum of the preset dynamic descent threshold and the preset offset, a trigger signal is obtained.
[0063] Based on the trigger signal and the real-time time-varying characteristic sequence, the state estimation recursion is performed using the Kalman filter algorithm to obtain a damping ratio prediction sequence containing the damping ratio estimate.
[0064] Based on the damping ratio prediction sequence, the continuous subsequence with the largest descent slope is extracted, and it is determined whether the estimated damping ratio at the end of the continuous subsequence is lower than the dynamic descent threshold. If it is lower, time-domain backtracking positioning calculation is performed to obtain the early intervention timing point.
[0065] First, in order to accurately assess the structural stiffness characteristics of the machining system, it is necessary to obtain the pre-stored reference excitation signal. The signal is acquired by performing a single-point excitation impact test (i.e., offline modal impact test) on the end of the spindle equipped with a standard tool using a modal hammer with a force sensor while the machine tool is stopped. The system pulse response signal excited by this is collected simultaneously, and the time-domain segment containing the main modal decay characteristics is extracted as the reference excitation signal. This signal characterizes the ideal dynamic properties of the system under static conditions. Next, based on the real-time time-varying characteristic sequence... With the reference excitation signal The calculation is performed using the cross-correlation function formula:
[0066]
[0067] in Let T be the time delay variable, and T be the length of the integration time window (e.g., 0.5 seconds). This is achieved by extracting... Peak latency Using physical mapping relationships (That is, the greater the response delay, the weaker the system stiffness). A system stiffness sequence K(t) reflecting the change in structural stiffness of the processing area is generated. In order to quantify the cross-correlation peak delay into a system stiffness value with physical meaning, an offline calibration needs to be performed after the initial installation or replacement of major components. By performing the cross-correlation calculation under known static high stiffness conditions (such as machine tool no-load, spindle locked), the reference delay is obtained. Combined with the machine tool structural parameters or the approximate static stiffness value (in N / μm) under this state measured by static loading experiments, the proportionality coefficient is determined to be the product of the approximate static stiffness value and the value of the reference delay. After that, the system stiffness sequence calculated in real time during the processing can be converted by multiplying the cross-correlation peak delay by the proportionality coefficient. The stiffness fluctuation amplitude sequence is the high-frequency fluctuation component of the system stiffness sequence after wavelet decomposition.
[0068] Subsequently, in order to eliminate non-flutter interference caused by abrupt changes in geometric curvature during surface machining, the Daubechies 4 (db4) wavelet basis was used to perform a 4-level decomposition of K(t), and the first-level detail coefficients were extracted to reconstruct the stiffness fluctuation amplitude sequence. Based on this fluctuation sequence, the damping ratio was corrected by referring to a pre-defined "stiffness fluctuation-damping confidence level" mapping table. This mapping table was established based on the ISO 10816 mechanical vibration evaluation standard and historical processing data statistics, i.e., collecting a large number of historical processing cases and statistically analyzing different stiffness fluctuation amplitude ranges (e.g., Within this range, the variance of the calculated damping ratio compared to the standard value is normalized using the reciprocal of the variance to generate a confidence coefficient. (Range 0~1), thus the damping ratio value after verification is calculated. .
[0069] Based on this, to determine the critical state, a dynamic descent threshold needs to be set. With preset offset ;in, The setting is based on the cutting stability lobe diagram theory. The general setting method is to find the corresponding critical cutting stability boundary on the SLD diagram based on the current spindle speed and cutting depth, and then read the minimum critical damping ratio required for that boundary as... (For example, the theoretical critical value at 12000 rpm is 0.015). The preset offset is set based on the statistical noise level of the damping ratio when the system is in a stable cutting state. The general setting method is to collect damping ratio data during the stable cutting stage and calculate its standard deviation. ,Pick (Three standard deviations) as the preset offset (e.g.) ), to prevent noise from triggering falsely; when At that time, a trigger signal is generated.
[0070] Based on the trigger signal, the Kalman filter algorithm is used to... State estimation is performed, and the state equation is: The observation equation is ,in For the system's true damping ratio, Let A=1 be the state transition matrix (assuming short-term linear change), H=1 be the observation matrix, and w and v be the process noise and observation noise, respectively. The damping ratio prediction sequence for the next N steps is obtained through recursion. Subsequently, the continuous subsequence with the largest absolute value of the descent slope is extracted from this sequence, and it is determined whether the duration of this subsequence exceeds the preset flutter incubation period. The incubation period is set based on the regenerative chatter mechanism. A common setting method is to take the time required for the spindle to rotate 10-20 revolutions (e.g., rotational speed S rpm). (seconds); if the condition is met and the end value of the subsequence is lower than Then the starting point of descent is locked. And perform time-domain backtracking positioning calculation:
[0071]
[0072] in This refers to the servo system response latency (usually determined by the reciprocal of the response bandwidth given in the servo driver manual, such as 20ms). The data processing time (determined by the system's processing cycle, such as 5ms) is the final result. This refers to the early intervention point where control commands can be effectively issued before flutter occurs.
[0073] In step S14, based on the early intervention timing and the real-time time-varying characteristic sequence, the deviation trend between the modal damping ratio and the dynamic descent threshold is calculated. Based on the deviation trend, suppression intensity is graded and matched, and a variable parameter control command sequence containing corresponding adjustment amplitudes is generated, including:
[0074] Based on the aforementioned early intervention timing, data segments corresponding to the time windows in the real-time time-varying characteristic sequence are extracted, and the difference between the values in the data segments and the dynamic descent threshold is calculated to obtain the damping ratio deviation sequence.
[0075] Based on the damping ratio deviation sequence, the number of sampling points that continuously exceed the preset deviation upper limit is counted, and a hierarchical mapping is performed based on the number of sampling points to obtain the suppression intensity level identifier;
[0076] Based on the suppression intensity level identifier, an initial gain set is extracted from a preset control parameter library, and the initial gain set is subjected to online parameter identification using the recursive least squares method to obtain a real-time adjusted gain sequence.
[0077] Based on the real-time adjustment gain sequence, the actuator saturation limit value is compared and scaled to obtain a variable parameter control command sequence.
[0078] First, in order to quantify the system's tendency to become unstable under critical conditions, the early intervention timing determined in step S13 is used. At the starting time, a truncation of length is extracted from the real-time time-varying characteristic sequence. (For example, a data segment is set to 50ms, the length of which is determined based on the spindle speed and the reciprocal of the chatter frequency to ensure coverage of at least 3 complete vibration cycles); for each sampling point i within this data segment, its relationship with the dynamic descent threshold is calculated using the deviation calculation formula. The distance is used to obtain the damping ratio deviation sequence E, where the positive value of E(i) directly reflects the degree of insufficient damping of the system:
[0079]
[0080] Next, in order to eliminate the minute numerical fluctuations caused by the inherent noise of the sensor, a preset upper limit for deviation is set. The upper limit is set based on the statistical characteristics of sensor measurement noise. The specific method is to collect damping ratio monitoring data for a period of time in the "air cutting" state where the machine tool spindle reaches the rated speed but does not contact the workpiece, and calculate its standard deviation. According to statistics The guidelines will Set as (For example, take) =0.002), ensuring that the deviations involved in the judgment have statistical significance of over 99.7%; statistically analyzing the damping ratio deviation sequence, continuously satisfying... Number of sampling points Based on this quantity, the suppressed demand is divided into different levels. For example, if This corresponds to "Level 1 fine-tuning" (L1). This corresponds to "secondary inhibition" (L2). This corresponds to "Level 3 Strong Intervention" (L3).
[0081] Subsequently, based on the determined suppression intensity level identifier, the corresponding initial gain set is extracted from the preset control parameter library. The preset control parameter library is a lookup table built based on a large number of historical cutting experiments and finite element simulation analysis (FEM). It stores the mapping relationship between different instability levels and the initial values of optimal PID parameters, and is periodically updated offline using newly generated machining data. To cope with the time-varying nonlinearity of the cutting process, the initial gain set is fine-tuned online using the recursive least squares (RLS) method to construct a cost function:
[0082]
[0083] Where y(j) is the desired system damping response. A regression vector containing input and output data. The real-time gain vector to be identified. Let be the forgetting factor (taken as 0.98). The gain parameter is recursively updated at each sampling time using a recursive formula:
[0084]
[0085] in The gain matrix is the basis for this iterative process within the current control time window. It will automatically stop when it ends, and the final output will be... This refers to a real-time adjusted gain sequence that minimizes control error.
[0086] Finally, to prevent the calculated control voltage from exceeding the actuator's physical tolerance limit, the rated drive voltage of the piezoelectric ceramic actuator is obtained. (e.g., 100V) converts the real-time gain adjustment sequence into voltage commands. Then a comparison is performed, if Then, the formula is used for proportional scaling:
[0087]
[0088] This calculation ensures that the amplitude of the output command is strictly limited within the linear operating range of the hardware, thereby obtaining a variable parameter control command sequence that can effectively suppress chatter without damaging the hardware.
[0089] It should be noted that the setting of the actuator saturation limit value must strictly refer to the rated voltage and maximum safe voltage range specified in IEC 60034 (Rated ratings and performance of rotating electrical machines) or relevant piezoelectric ceramic actuator industry standards (such as IEEE Std 176), reserving a safety margin of 10%-15% to prevent overvoltage breakdown. Regarding online parameter identification, it specifically refers to the process of dynamically estimating system model parameters or control parameters during system operation using real-time acquired input and output data through recursive algorithms such as RLS or Kalman filtering. This description conforms to the standard definition in ISO 13373-2 (Machine vibration condition monitoring and diagnosis - data processing). Furthermore, the control parameter library in this technical solution covers parameter sets based on various control strategies such as PID, fuzzy control, or model predictive control (MPC). Those skilled in the art can select the appropriate parameter structure according to the type of actual controller, and its protection scope is not limited to the PID parameter form described in this embodiment.
[0090] In step S15, based on the variable parameter control command sequence and a preset production efficiency evaluation model and tool life evaluation model, a multi-dimensional performance evaluation and parameter optimization screening operation is performed to obtain an optimal combination of control parameters, including:
[0091] Based on the variable parameter control command sequence, a point-by-point extraction operation is performed to obtain the control gain combination corresponding to the spindle speed range and feed rate range;
[0092] Based on the control gain combination, multidimensional interpolation calculation is performed using a preset production efficiency mapping table, and cumulative wear integral calculation is performed to obtain efficiency score sequence and lifespan score sequence respectively.
[0093] Based on the efficiency score sequence and the lifespan score sequence, a weighted summation is performed to obtain a comprehensive score sequence containing scores;
[0094] Based on the comprehensive scoring sequence, candidate combinations with scores greater than the preset comprehensive optimization threshold are selected, and the surface roughness prediction formula is used to calculate the candidate combinations to obtain the preferred control parameter combination.
[0095] First, in order to establish the mapping relationship between control parameters and physical processing state, the variable parameter control command sequence output in step S14 (which contains the real-time gain vector on the time series) is processed. Perform point-by-point extraction and state alignment operations. Specifically, using the timestamp of the instruction sequence as an index, synchronously read the real-time operating parameters of the CNC system at the corresponding moment, and extract the PID gain parameters at each moment. Spindle speed at the current moment (Unit: r / min) and feed rate (Unit: mm / min) Combined, a control gain combination set containing N sets of data is constructed. ;
[0096] Next, a pre-set production efficiency mapping table is used to score the performance of each data set. The production efficiency mapping table is established based on the spindle speed range specified in the machine tool technical specifications. With feed rate range The mesh is discretized according to a preset step size (e.g., rotational speed step size 100 r / min, feed step size 50 mm / min) to construct a two-dimensional mesh matrix; for each mesh node in the matrix... The theoretical removal rate is calculated using the Material Removal Rate (MRR) formula:
[0097]
[0098] in The axial cutting depth set for the current process. The radial cutting width is calculated, and all node values are divided by the maximum allowable removal rate of the machine tool. Max-min normalization is performed to generate a production efficiency mapping table with values ranging from [0,1]. Based on this mapping table, for any set of real-time operating conditions... The relative efficiency coefficient is calculated using bilinear interpolation. Specifically, the bounding point of the current operating condition is located in the mapping table. The four nearest neighbor grid points Calculate the horizontal normalized distance and the vertical normalized distance:
[0099] ;
[0100] ;
[0101] The accurate efficiency score sequence was calculated using the bilinear interpolation formula. :
[0102]
[0103] Meanwhile, based on the extended form of Taylor's tool life formula, the instantaneous tool wear intensity under the current working condition is calculated, and the expected tool life calculation formula is as follows:
[0104]
[0105] in The life constant is related to the tool material. For cutting linear velocity ( (This refers to the diameter of the cutting tool, in mm). Z represents the feed per tooth (Z is the number of cutting teeth). Let p, q, and r be the axial depth of cut, and p, q, and r be the indices of the influence of cutting speed, feed rate, and depth of cut on the tool life, respectively (for example, for milling steel with carbide tools, p, q, and r are usually taken as...). ); to obtain the expected lifespan (Unit: min) Then calculate the unit sampling interval. Internal damage increment:
[0106]
[0107] The cumulative damage is then converted into a normalized lifetime score sequence using a formula (set to 0 if the result is less than 0, indicating lifetime depletion):
[0108]
[0109] Subsequently, in order to achieve a balance among multiple objectives, the efficiency score sequence and the lifetime score sequence are weighted and summed, and the calculation formula is as follows:
[0110]
[0111] in Weighted by production efficiency preference, As the tool protection preference weight, and satisfying The weighting coefficients are set based on the company's real-time production scheduling priorities. A common method for setting these coefficients is to establish a cost function. ( For machine time cost, To account for tool change costs, regression analysis was performed on historical order data. When downtime costs exceeded 60% (e.g., for rush orders), a threshold was set. Conversely, set .
[0112] Finally, in order to identify the "elite" parameters from the massive solution space, a preset comprehensive optimization threshold is set. The threshold is set based on the Pareto optimality principle in statistical process control. The method involves collecting comprehensive score data from the most recent 100 processing batches to construct a probability density function (PDF), calculating the value at which the cumulative distribution function (CDF) reaches 85% as the threshold (i.e., selecting the top 15% of excellent solutions), ensuring that the selected parameters are statistically significantly better than the average level; selecting those that meet the criteria... Candidate combinations were identified, and the predicted roughness value for each candidate combination was calculated using the theoretical surface roughness prediction formula:
[0113]
[0114] in Feed per revolution (in units of) ), Let be the radius of the blade tip arc, and select one of them. The set of parameters with the smallest values is selected as the optimal combination of control parameters that balances efficiency, lifespan, and quality.
[0115] It should be noted that the exponential parameters p, q, r and constants in the tool life model... The results must be obtained strictly according to ISO 3685:1993 (Test Method for Life of Single-Flute Turning Tools) or ISO 8688-2 (Test Method for Life of Milling Cutters - Face Milling) standards, through standard cutting experiments. Regarding bilinear interpolation, it is a standard algorithm in numerical analysis for smoothing two-dimensional discrete data, widely used in image processing and finite element post-processing. This technical solution utilizes it to ensure scoring continuity under non-mesh node conditions. The selection of the 85th percentile for the comprehensive optimization threshold is based on the definition of "excellent process capability" in Six Sigma management, aiming to eliminate mediocre parameter solutions and focus on approximately optimal solutions near the Pareto front.
[0116] It is worth noting that the surface roughness prediction formula provides a theoretical lower limit reference value for surface roughness estimated based on geometric motion relationships under ideal and stable cutting conditions. In this invention, this value is mainly used to assist in sorting and distinguishing multiple candidate control parameter combinations with similar comprehensive scores, reflecting the theoretical surface texture differences under different feed rates. The surface roughness obtained in actual machining is affected by multiple factors such as cutting vibration, tool wear, and material properties. Those skilled in the art will understand that the prediction accuracy can be improved by combining the theoretical prediction value with an empirical correction coefficient based on actual process data, or by using a more complex roughness prediction model trained by machine learning. This does not depart from the core idea of this invention.
[0117] In step S16, a basic suppression signal is generated according to the preferred combination of control parameters, and signal fusion and dynamic vector correction are performed by combining the vibration feedback data collected in real time by the sensor to output the final flutter suppression control signal, including:
[0118] Based on the preferred combination of control parameters and the pre-established historical flutter case library, inference and multidimensional interpolation calculations are performed to obtain a preliminary flutter suppression gain sequence.
[0119] Based on the preliminary flutter suppression gain sequence, peak smoothing adjustment is performed to obtain the basic suppression signal sequence;
[0120] Based on the basic suppression signal sequence and the vibration feedback data collected in real time by the sensor, a reverse vector superposition operation is performed to obtain the final flutter suppression control signal.
[0121] It should be noted that the vibration feedback data collected in real time by the sensors and the original vibration signal collected in step S11 for calculating the modal damping ratio both originate from the same set of accelerometers installed in the main shaft area. Through multi-threading or real-time interruption mechanisms, while continuously executing steps S11 to S15, the original voltage signals of the sensors are read in parallel at a higher sampling rate, and the vibration velocity vector for closed-loop feedback is quickly generated by an independent real-time signal processing thread (including integration, filtering, etc.). The data stream of this feedback channel is aligned with the data stream of the front-end monitoring and prediction channels in time through a unified system clock to ensure that the feedback signal used for fusion corresponds strictly in time with the control command issued at the current moment.
[0122] First, in order to improve the initial response speed of the control system by utilizing historical machining experience, the optimal combination of control parameters (including spindle speed n and feed rate) output in step S15 is used. Feature matching is performed in a pre-established historical chatter case library. This library stores a large number of historical operating conditions and their corresponding optimal suppression gain vectors. In the initial stage of use or for new tool / material combinations, a series of cutting experiments are conducted within a safe and controllable test operating range. In each experiment, the spindle speed, feed rate, and chatter suppression gain vector optimized using the method of this invention or verified by manual tuning are recorded, forming a case record. Multiple case records covering typical machining ranges are accumulated to form an initial case library. In subsequent actual machining processes, the optimal control parameter combination that finally stabilizes after each successful chatter suppression and its corresponding operating condition are added to the case library as new cases after filtering and deduplication, achieving self-evolution and updating of the case library. The case library is stored in a database format, supporting fast retrieval and interpolation calculation based on operating condition parameters. The k-nearest neighbor algorithm (k-NN) is used to search for the k (e.g., k=5) reference cases in the case library that are closest to the current operating condition in Euclidean distance, and an inverse distance weighted interpolation method is used to calculate the preliminary chatter suppression gain sequence. Specifically, first calculate the current operating point. With the i-th reference case point Euclidean distance between them:
[0123] .
[0124] Next, to assign greater weight to cases that are closer in distance (i.e., have more similar operating conditions), the weighting coefficients are calculated:
[0125]
[0126] Where p is the power exponent, usually p=2 to amplify the influence of nearest neighbors. Finally, the preliminary gain vector after interpolation is calculated using the weighted average formula:
[0127]
[0128] in, The optimal gain vector is stored for the i-th reference case. To prevent abrupt gain changes, ... An exponentially weighted moving average smoothing process is applied to obtain a stable basic suppression signal sequence. .
[0129] Simultaneously, vibration feedback data is collected in real time by an accelerometer installed on the spindle side. Specifically, a data acquisition card (DAQ) is used to collect vibration data at a preset high-frequency sampling rate. Simultaneously read the analog voltage values of the X, Y, and Z channels. The high-frequency sampling rate... The settings are based on the Shannon-Nyquist sampling theorem and the phase margin requirements of closed-loop control. The general setting method is to first determine or estimate the highest chatter frequency of the machine tool spindle system. (Typically between 2000Hz and 5000Hz), to ensure minimal phase delay of the signal in the digital control loop (typically required to be less than 10 degrees), the sampling rate is set to... 10 to 20 times (for example, if the highest flutter frequency is 2kHz, then set) The collected raw data, after analog-to-digital conversion and DC-DC removal, is used to construct a real-time vibration acceleration vector. .
[0130] Subsequently, in order to suppress flutter using the principle of active damping, the vibration acceleration signal needs to be converted into a vibration velocity signal. This step utilizes the trapezoidal integral method or a leakage integrator to convert the vibration acceleration vector... Real-time numerical integration is performed to obtain the vibration velocity vector. :
[0131]
[0132] in The sampling interval is... The vibration velocity vector calculated at the current moment To prevent integral drift, a high-pass filter with a cutoff frequency of 5Hz is connected in series after the integrator to filter out low-frequency trend terms.
[0133] Next, signal fusion and dynamic vector correction are performed using the Hadamard product principle. This process aims to achieve independent gain modulation of vibrations along each axis. Specifically, a fundamental suppressed signal sequence containing three axial gain components is used. With the current vibration acceleration vector The formula for performing element-wise multiplication is:
[0134]
[0135] This operation enables differentiated damping force matching for varying stiffness characteristics in the X, Y, and Z directions. Finally, based on the principle of active damping, the modulated signal is converted into a reverse control command, calculated using the following formula:
[0136]
[0137] in This is the voltage-to-force conversion coefficient matrix for the power amplifier and actuator (unit: V / (m / s²)), with the negative sign " "This represents the generation of a damping force in the opposite direction to the vibration velocity ( This consumes the system's vibrational energy, ultimately resulting in an output of This is the final flutter suppression control signal that drives the triaxial actuator to generate a reverse suppressive force.
[0138] It should be noted that the inverse distance weighted interpolation method is a deterministic spatial interpolation method widely used in numerical analysis and geographic information systems, and its core logic conforms to the general algorithm definition in the GIS field. The "Hadamard product" is a standard definition of matrix operations in linear algebra, specifically referring to the product of corresponding elements of two matrices or vectors of the same dimension. This technical solution utilizes this mathematical tool to achieve decoupling gain adjustment in a multi-axis control system. Regarding the aforementioned vibration signal processing and control strategy, the integral conversion of vibration velocity conforms to the recommended specifications in ISO 10816-1 (Mechanical vibration—Measurement and evaluation of machine vibration on non-rotating parts) standard regarding the use of vibration velocity as an indicator of intensity, because velocity most directly reflects the magnitude of vibration energy; simultaneously, the voltage-force conversion coefficient... The settings should refer to the IEC 60034-14 standard and the "force-voltage hysteresis curve" provided by the piezoelectric actuator manufacturer. The slope of the linear segment should be selected as the calibration value, usually between 0.2V / N and 1.0V / N, to ensure the linearity of the control force output.
[0139] In summary, this invention achieves accurate prediction and active suppression of chatter in the milling of automotive parts by real-time tracking of the time-varying characteristics of the modal damping ratio, using the Kalman filter algorithm to predict the damping ratio decrease trend to lock in the timing of early intervention, and combining a variable parameter optimization strategy that takes into account both production efficiency and tool life.
[0140] Reference Figure 2 The second embodiment of the present invention provides an intelligent machining control system for milling automotive parts, comprising:
[0141] The data acquisition module is used to acquire the original vibration signals and force data during the cutting process, and to perform signal preprocessing to obtain the original vibration time-domain sequence and force time-domain sequence.
[0142] The feature calculation module is used to perform adaptive signal purification and dynamic feature calculation based on the original vibration time-domain sequence and the force time-domain sequence to obtain the real-time time-varying characteristic sequence of the modal damping ratio;
[0143] The prediction and judgment module is used to perform system stiffness fluctuation analysis and threshold critical state judgment based on the real-time time-varying characteristic sequence, and when the modal damping ratio is determined to be close to the preset dynamic descent threshold, the module performs damping ratio trend extrapolation and descent start point locking to obtain the early intervention timing point.
[0144] The instruction generation module is used to calculate the deviation trend between the modal damping ratio and the dynamic descent threshold based on the early intervention timing and the real-time time-varying characteristic sequence, perform suppression intensity grading matching based on the deviation trend, and generate a variable parameter control instruction sequence containing the corresponding adjustment amplitude.
[0145] The parameter optimization module is used to perform multi-dimensional performance evaluation and parameter optimization screening operations based on the variable parameter control command sequence, a preset production efficiency evaluation model and tool life evaluation model, to obtain the optimal combination of control parameters.
[0146] The closed-loop control module is used to generate a basic suppression signal based on the preferred combination of control parameters, and to perform signal fusion and dynamic vector correction by combining the vibration feedback data collected in real time by the sensor, and output the final flutter suppression control signal.
[0147] It should be noted that the intelligent machining control system for milling automotive parts provided in this embodiment of the invention is used to execute all the process steps of the intelligent machining control method for milling automotive parts described in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.
[0148] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a cold-plate intelligent processing control program. When the processor executes the computer program, it implements the steps described in the various embodiments of the cold-plate intelligent processing control method, for example... Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above system embodiments, such as the feature calculation module.
[0149] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.
[0150] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.
[0151] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.
[0152] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.
[0153] If the modules / units integrated into the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or system capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.
[0154] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.
[0155] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.
Claims
1. A method for intelligent milling control of automotive parts, characterized in that, include: The original vibration signals and force data during the cutting process are acquired, and the signals are preprocessed to obtain the original vibration time-domain sequence and force time-domain sequence. Based on the original vibration time-domain sequence and the force time-domain sequence, adaptive signal purification and dynamic feature calculation are performed to obtain the real-time time-varying characteristic sequence of the modal damping ratio. Based on the real-time time-varying characteristic sequence, system stiffness fluctuation analysis and threshold critical state determination are performed. When the modal damping ratio is determined to be close to the preset dynamic descent threshold, the damping ratio trend is extrapolated and the descent start point is locked to obtain the early intervention timing point. Based on the early intervention timing and the real-time time-varying characteristic sequence, the deviation trend between the modal damping ratio and the dynamic descent threshold is calculated. Based on the deviation trend, the suppression intensity is graded and matched, and a variable parameter control command sequence containing the corresponding adjustment amplitude is generated. Based on the variable parameter control command sequence, and using the preset production efficiency evaluation model and tool life evaluation model, multi-dimensional performance evaluation and parameter optimization screening operations are performed to obtain the optimal combination of control parameters. Based on the preferred combination of control parameters, a basic suppression signal is generated, and combined with vibration feedback data collected in real time by sensors for signal fusion and dynamic vector correction, and the final flutter suppression control signal is output.
2. The intelligent machining control method for milling automotive parts according to claim 1, characterized in that, The process of acquiring the original vibration signals and force data during the cutting process, and performing signal preprocessing to obtain the original vibration time-domain sequence and force time-domain sequence includes: By using an accelerometer and a force gauge installed in the spindle area of the machine tool, the simulated mechanical vibration and three-dimensional cutting force during the cutting process are collected simultaneously. Based on the simulated mechanical vibration and the simulated triaxial cutting force, analog-to-digital conversion and time alignment are performed to obtain the original vibration time-domain sequence and the force time-domain sequence.
3. The intelligent machining control method for milling automotive parts according to claim 1, characterized in that, The step of performing adaptive signal purification and dynamic feature calculation based on the original vibration time-domain sequence and the force time-domain sequence to obtain the real-time time-varying characteristic sequence of the modal damping ratio includes: Based on the original vibration time-domain sequence and the force time-domain sequence, noise suppression processing is performed using the LMS adaptive filtering algorithm to obtain a purified signal sequence. Based on the purification signal sequence, a short-time Fourier transform is performed to obtain the amplitude and phase data corresponding to the dominant frequency; Based on the amplitude and phase data, a Hilbert transform is performed to obtain the instantaneous envelope curve and the instantaneous phase curve; Based on the instantaneous envelope curve, a natural logarithmic linear fit is performed to calculate the instantaneous attenuation rate. Combined with the instantaneous phase curve, the real-time time-varying characteristic sequence of the modal damping ratio is obtained.
4. The intelligent machining control method for milling automotive parts according to claim 1, characterized in that, The process involves performing system stiffness fluctuation analysis and threshold critical state determination based on the real-time time-varying characteristic sequence, and when the modal damping ratio is determined to be close to a preset dynamic descent threshold, performing damping ratio trend extrapolation and locking the descent start point to obtain an early intervention timing point, including: Based on the real-time time-varying characteristic sequence and the pre-stored reference excitation signal, the cross-correlation function is calculated to obtain the system stiffness sequence; Based on the system stiffness sequence, multi-level decomposition is performed using db4 wavelet transform to obtain the stiffness fluctuation amplitude sequence. Based on the stiffness fluctuation amplitude sequence, the real-time time-varying characteristic sequence is validated to obtain the validated damping ratio value. When the validated damping ratio value is determined to be lower than the sum of the preset dynamic descent threshold and the preset offset, a trigger signal is obtained. Based on the trigger signal and the real-time time-varying characteristic sequence, the state estimation recursion is performed using the Kalman filter algorithm to obtain a damping ratio prediction sequence containing the damping ratio estimate. Based on the damping ratio prediction sequence, the continuous subsequence with the largest descent slope is extracted, and it is determined whether the estimated damping ratio at the end of the continuous subsequence is lower than the dynamic descent threshold. If it is lower, time-domain backtracking positioning calculation is performed to obtain the early intervention timing point.
5. The intelligent machining control method for milling automotive parts according to claim 1, characterized in that, The step involves calculating the deviation trend between the modal damping ratio and the dynamic descent threshold based on the early intervention timing and the real-time time-varying characteristic sequence, performing suppression intensity grading matching based on the deviation trend, and generating a variable parameter control command sequence containing corresponding adjustment amplitudes, including: Based on the aforementioned early intervention timing, data segments corresponding to the time windows in the real-time time-varying characteristic sequence are extracted, and the difference between the values in the data segments and the dynamic descent threshold is calculated to obtain the damping ratio deviation sequence. Based on the damping ratio deviation sequence, the number of sampling points that continuously exceed the preset deviation upper limit is counted, and a hierarchical mapping is performed based on the number of sampling points to obtain the suppression intensity level identifier; Based on the suppression intensity level identifier, an initial gain set is extracted from a preset control parameter library, and the initial gain set is subjected to online parameter identification using the recursive least squares method to obtain a real-time adjusted gain sequence. Based on the real-time adjustment gain sequence, the actuator saturation limit value is compared and scaled to obtain a variable parameter control command sequence.
6. The intelligent machining control method for milling automotive parts according to claim 1, characterized in that, The process involves performing multi-dimensional performance evaluation and parameter optimization based on the variable parameter control command sequence, using a preset production efficiency evaluation model and tool life evaluation model, to obtain an optimal combination of control parameters, including: Based on the variable parameter control command sequence, a point-by-point extraction operation is performed to obtain the control gain combination corresponding to the spindle speed range and feed rate range; Based on the control gain combination, multidimensional interpolation calculation is performed using a preset production efficiency mapping table, and cumulative wear integral calculation is performed to obtain efficiency score sequence and lifespan score sequence respectively. Based on the efficiency score sequence and the lifespan score sequence, a weighted summation is performed to obtain a comprehensive score sequence containing scores; Based on the comprehensive scoring sequence, candidate combinations with scores greater than the preset comprehensive optimization threshold are selected, and the surface roughness prediction formula is used to calculate the candidate combinations to obtain the preferred control parameter combination.
7. The intelligent machining control method for milling automotive parts according to claim 1, characterized in that, The process involves generating a basic suppression signal based on the preferred combination of control parameters, and then combining this signal with vibration feedback data acquired in real time by sensors for signal fusion and dynamic vector correction to output the final flutter suppression control signal, including: Based on the preferred combination of control parameters and the pre-established historical flutter case library, inference and multidimensional interpolation calculations are performed to obtain a preliminary flutter suppression gain sequence. Based on the preliminary flutter suppression gain sequence, peak smoothing adjustment is performed to obtain the basic suppression signal sequence; Based on the basic suppression signal sequence and the vibration feedback data collected in real time by the sensor, a reverse vector superposition operation is performed to obtain the final flutter suppression control signal.
8. A smart milling control system for automotive parts, characterized in that, include: The data acquisition module is used to acquire the original vibration signals and force data during the cutting process, and to perform signal preprocessing to obtain the original vibration time-domain sequence and force time-domain sequence. The feature calculation module is used to perform adaptive signal purification and dynamic feature calculation based on the original vibration time-domain sequence and the force time-domain sequence to obtain the real-time time-varying characteristic sequence of the modal damping ratio; The prediction and judgment module is used to perform system stiffness fluctuation analysis and threshold critical state judgment based on the real-time time-varying characteristic sequence, and when the modal damping ratio is determined to be close to the preset dynamic descent threshold, the module performs damping ratio trend extrapolation and descent start point locking to obtain the early intervention timing point. The instruction generation module is used to calculate the deviation trend between the modal damping ratio and the dynamic descent threshold based on the early intervention timing and the real-time time-varying characteristic sequence, perform suppression intensity grading matching based on the deviation trend, and generate a variable parameter control instruction sequence containing the corresponding adjustment amplitude. The parameter optimization module is used to perform multi-dimensional performance evaluation and parameter optimization screening operations based on the variable parameter control command sequence, a preset production efficiency evaluation model and tool life evaluation model, to obtain the optimal combination of control parameters. The closed-loop control module is used to generate a basic suppression signal based on the preferred combination of control parameters, and to perform signal fusion and dynamic vector correction by combining the vibration feedback data collected in real time by the sensor, and output the final flutter suppression control signal.