Multi-axis linkage numerical control lathe machining data processing method and system

Abstract

The application relates to the field of numerical control machining data processing, in particular to a multi-axis linkage numerical control lathe machining data processing method and system. The method comprises the following steps: acquiring vibration information, performing time-frequency analysis, and extracting energy change and instantaneous peak value characteristics in a preset high frequency band; comparing the characteristics with preset judgment basis, identifying early signs of chatter, and generating machining parameter adjustment instructions for spindle speed, feed speed and / or cutting depth when the chatter is identified; acquiring the adjusted vibration information to perform time-frequency analysis, evaluating the chatter suppression effect, and further adjusting the machining parameters and / or issuing a warning or shutdown alarm, so that a closed-loop active suppression control of micro-cutting chatter is formed. The method can solve the problem that the multi-axis linkage numerical control lathe cannot capture key and instantaneous high-frequency vibration mode information in the data acquisition and analysis framework, leading to inaccurate tool wear prediction, reduced machining quality, increased workpiece scrap rate and reduced production efficiency.

Description

Technical Field

[0001] This application relates to the field of CNC machining data processing, and more specifically, to a method and system for processing machining data on a multi-axis linkage CNC lathe. Background Technology

[0002] In the modern industrial internet environment, multi-axis linkage CNC lathes generate a large amount of heterogeneous data during the machining process, including machining parameters, equipment status, and quality inspection results.

[0003] Existing data acquisition and analysis frameworks, configured for conventional stable operating conditions, struggle to adapt to the high-frequency, irregular vibration signals encountered when machining new high-hardness alloys and complex geometric workpieces. Insufficient sampling rates lead to undersampling and aliasing, causing critical microscopic chatter and early tool chipping characteristics to be lost or distorted during the acquisition phase. This results in distorted input data, rendering tool health assessment and life prediction models based on this data ineffective. Frequent erroneous decisions to replace tools prematurely or too late lead to decreased surface quality, substandard dimensional accuracy, increased scrap rates, and higher costs. To compensate for model failures, production lines are forced to rely on manual inspections and temporary interventions, resulting in frequent downtime and difficulties in implementing plans. Simultaneously, the production management system, lacking accurate real-time status data, cannot effectively schedule and optimize operations, gradually plunging production into a vicious cycle of inefficiency and high costs.

[0004] To address this problem, existing technologies urgently need improvement. Summary of the Invention

[0005] This application discloses a data processing method and system for multi-axis linkage CNC lathes, aiming to solve the problem that existing data acquisition and analysis frameworks cannot capture key, instantaneous high-frequency vibration mode information when multi-axis linkage CNC lathes are machining new high-hardness alloy materials and workpieces with complex geometric features. This leads to inaccurate tool wear prediction, decreased machining quality, increased workpiece scrap rate, and reduced production efficiency.

[0006] The technical solution of this application is as follows:

[0007] In a first aspect, this application discloses a method for processing machining data on a multi-axis linkage CNC lathe, including:

[0008] Vibration information of a multi-axis linkage CNC lathe during machining is acquired, and the vibration information is preliminarily processed to obtain preliminarily processed vibration information. The preliminary processing includes at least digital filtering of the vibration information to remove background noise and interference components unrelated to micro-cutting chatter.

[0009] Time-frequency analysis was performed on the initially processed vibration information to obtain the time spectrum. Energy changes and instantaneous peak characteristics located in a preset specific high-frequency band were extracted from the time spectrum. The specific high-frequency band was used to characterize the early vibration modes of micro-cutting chatter.

[0010] Based on the energy changes and instantaneous peak characteristics, the data are compared with the preset criteria for judging micro-cutting chatter to identify whether there are early signs of micro-cutting chatter.

[0011] Upon identifying early signs of micro-cutting chatter, machining parameter adjustment instructions are generated to suppress the micro-cutting chatter. These instructions are used to adjust the machining parameters of the multi-axis CNC lathe, which include at least one of spindle speed, feed rate, and / or depth of cut.

[0012] The vibration information after the processing parameter adjustment command is executed is obtained and time-frequency analysis is performed to obtain the energy change and instantaneous peak characteristics after adjustment. The chatter suppression effect of the processing parameter adjustment command is evaluated based on the energy change and instantaneous peak characteristics after adjustment.

[0013] Based on the evaluation results of chatter suppression effect, the machining parameters are readjusted and / or chatter warnings or shutdown alarms are issued to form a closed-loop active suppression control for micro-cutting chatter.

[0014] Furthermore, time-frequency analysis is performed on the initially processed vibration information to obtain the time spectrum. From the time spectrum, energy changes and instantaneous peak characteristics located in a preset specific high-frequency band are extracted, including:

[0015] The initially processed vibration information is split into a first signal stream and a second signal stream;

[0016] Envelope demodulation analysis is performed on the first signal stream to extract impact pulse features for characterizing spindle bearing damage, and bearing condition indication information is generated based on the impact pulse features.

[0017] Time-frequency analysis is performed on the second signal stream to obtain the time spectrum, and the energy change and instantaneous peak characteristics related to micro-cutting chatter located in a preset specific high-frequency band are extracted from the time spectrum;

[0018] Based on energy changes and instantaneous peak characteristics, the data is compared with preset criteria for judging micro-cutting chatter to identify early signs of micro-cutting chatter, including:

[0019] Based on energy changes and instantaneous peak characteristics, bearing status indication information is used as the basis for correction. Combined with the preset micro-cutting chatter judgment criteria, abnormal signals with overlapping spectra but different sources within a preset specific high-frequency band are distinguished to identify whether there are early signs of micro-cutting chatter.

[0020] Furthermore, upon identifying early signs of micro-cutting chatter, machining parameter adjustment instructions are generated to suppress micro-cutting chatter, including:

[0021] The vibration information after preliminary processing is analyzed in a preset specific high-frequency band to obtain at least one spectral peak.

[0022] Calculate the spectrum broadening factor for the peak frequency and / or statistically analyze the fluctuation of the peak frequency in adjacent time-frequency analysis windows. When the spectrum broadening factor exceeds a preset broadening threshold and / or the fluctuation of the peak frequency exceeds a preset fluctuation threshold, it is determined that there are signs of nonlinear flexibility.

[0023] Based on nonlinear flexibility indicators and historical chatter suppression effects, a cutting system flexibility evaluation factor is generated, which is used to characterize the current flexibility state of the cutting system.

[0024] The energy change and instantaneous peak characteristics are correlated with the cutting system flexibility assessment factor to form a chatter situation label. The chatter situation label includes at least one or more of the following information: chatter frequency, chatter intensity, and cutting system flexibility assessment factor.

[0025] In a pre-set flutter suppression strategy effectiveness rating table, the effectiveness ratings of multiple pre-set flutter suppression strategies are stored using flutter scenario labels as indexes.

[0026] When early signs of micro-cutting chatter are identified and machining parameter adjustment instructions need to be generated, the preset chatter suppression strategy with the highest effect score is selected from the chatter suppression strategy effect score table according to the current chatter situation label, and machining parameter adjustment instructions are generated according to the selected preset chatter suppression strategy.

[0027] Based on the chatter suppression effect after executing the machining parameter adjustment command, the effect score of the preset chatter suppression strategy corresponding to the chatter suppression strategy effect score table is updated, and the cutting system flexibility evaluation factor is updated synchronously based on the updated chatter suppression effect.

[0028] Furthermore, after generating the cutting system flexibility evaluation factor based on nonlinear flexibility indicators and historical chatter suppression effects, it also includes:

[0029] The initially processed vibration information is distributed to at least the first and second analysis channels;

[0030] In the first analysis channel, high-frequency chatter characteristics are analyzed on the vibration information after diversion to identify nonlinear flexibility signs introduced by tool microslippage and generate first flexibility indication information.

[0031] In the second analysis channel, the spindle system flexibility characteristics are analyzed on the diverted vibration information to identify the flexibility characteristics of the machine tool spindle and / or feed drive system components, and generate second flexibility indication information.

[0032] Based on the first and second flexibility indication information, the flexibility evaluation factor of the cutting system is corrected, and the corrected flexibility evaluation factor of the cutting system is used for subsequent processing.

[0033] Furthermore, based on the chatter suppression effect after executing the processing parameter adjustment command, the effect score of the preset chatter suppression strategy corresponding to the chatter suppression strategy effect score table is updated, including:

[0034] After executing the selected preset flutter suppression strategy, the flutter suppression effect index is calculated based on the energy change and / or instantaneous peak characteristics in a preset specific high-frequency band before and after execution.

[0035] A set inertia interval for score updates is defined. When the flutter suppression effect index obtained from multiple consecutive executions of the preset flutter suppression strategy continuously deviates from the inertia interval, the effect score of the preset flutter suppression strategy selected under the current flutter scenario label is adjusted, where:

[0036] When the flutter suppression effect index, which represents the degree of reduction in flutter amplitude, is greater than the first preset threshold, the effect score of the selected preset flutter suppression strategy under the current flutter scenario label is improved.

[0037] When the flutter suppression effect index, which indicates a reduction in flutter amplitude, is lower than the second preset threshold and / or indicates an increase in flutter amplitude, the effect score of the selected preset flutter suppression strategy under the current flutter scenario label is reduced.

[0038] A time decay factor is introduced to weight the flutter suppression effect index obtained at different time points and update the effect score;

[0039] After a preset time interval and / or after processing a preset number of workpieces, the effect scores of all preset chatter suppression strategies in the chatter suppression strategy effect score table are reduced by a uniform attenuation coefficient based on the time attenuation factor.

[0040] Furthermore, a time decay factor is introduced to weight the flutter suppression effect indicators obtained at different time points, including:

[0041] Acquire real-time vibration information and perform time-frequency analysis on the real-time vibration information to extract specific frequency variation features and / or damping variation features to characterize the inherent characteristics of the cutting system;

[0042] Obtain information on the current workpiece material type, processing stage, and machine tool operating status;

[0043] Based on specific frequency variation characteristics and / or damping variation characteristics, combined with the current workpiece material type, processing stage and machine tool operating status, evaluate the drift speed of the inherent characteristics of the cutting system;

[0044] The time decay factor is dynamically adjusted based on the drift velocity.

[0045] When updating the effect score of the preset flutter suppression strategy in the flutter suppression strategy effect score table, the weights corresponding to the dynamically adjusted time decay factor are used to weight the flutter suppression effect index obtained at different times.

[0046] Furthermore, the inertia interval for score updates is defined, including:

[0047] Acquire real-time vibration information, perform time-frequency analysis on the real-time vibration information, and extract specific frequency variation features and / or damping variation features to characterize the inherent characteristics of the cutting system;

[0048] Obtain information on the current workpiece material type, processing stage, and machine tool operating status;

[0049] Based on specific frequency variation characteristics and / or damping variation characteristics, combined with the current workpiece material type, processing stage and machine tool operating status, evaluate the drift speed and fluctuation amplitude of the inherent characteristics of the cutting system;

[0050] The size of the inertial interval for score updates is dynamically adjusted based on drift speed and fluctuation amplitude.

[0051] Furthermore, after a preset time interval and / or after processing a preset number of workpieces, the effectiveness scores of all preset chatter suppression strategies in the chatter suppression strategy effectiveness scoring table are attenuated overall using a uniform attenuation coefficient based on the time attenuation factor, including:

[0052] Acquire real-time vibration information and perform time-frequency analysis on the real-time vibration information to extract specific frequency variation features and / or damping variation features to characterize the inherent characteristics of the cutting system;

[0053] Obtain information on the current workpiece material type, processing stage, and machine tool operating status;

[0054] Based on specific frequency variation characteristics and / or damping variation characteristics, combined with the current workpiece material type, processing stage and machine tool operating status, evaluate the drift speed of the inherent characteristics of the cutting system;

[0055] The overall attenuation rate and / or uniform attenuation coefficient are dynamically determined based on the drift velocity, and the dynamically determined overall attenuation rate and / or uniform attenuation coefficient are used when performing overall attenuation.

[0056] Furthermore, the overall attenuation rate and / or uniform attenuation coefficient are dynamically determined based on the drift velocity, including:

[0057] The preset flutter suppression strategies in the flutter suppression strategy effectiveness rating table are divided into at least two strategy types according to the controlled object and / or adjustment parameter type;

[0058] Based on the drift velocity and the preset drift sensitivity parameters corresponding to each strategy type, the overall decay rate and / or uniform decay coefficient are categorized and corrected to obtain the decay rate corresponding to different strategy types.

[0059] When periodically decaying the overall effectiveness score of all strategies, the effectiveness score is decayed by applying the corresponding decay rate to different strategy types.

[0060] Secondly, this application also discloses a multi-axis linkage CNC lathe machining data processing system, including:

[0061] The information acquisition module is used to acquire vibration information of a multi-axis linkage CNC lathe during the machining process, perform preliminary processing on the vibration information to obtain preliminary processed vibration information. The preliminary processing includes at least digital filtering of the vibration information to remove background noise and interference components unrelated to micro-cutting chatter.

[0062] The time-frequency analysis module is used to perform time-frequency analysis on the initially processed vibration information to obtain the time spectrum. From the time spectrum, the energy change and instantaneous peak characteristics located in a preset specific high-frequency band are extracted. The specific high-frequency band is used to characterize the early vibration mode of micro-cutting chatter.

[0063] The chatter identification module is used to compare energy changes and instantaneous peak characteristics with preset micro-cutting chatter judgment criteria to identify whether there are early signs of micro-cutting chatter.

[0064] The instruction generation module is used to generate machining parameter adjustment instructions to suppress micro-cutting chatter when early signs of micro-cutting chatter are identified. The machining parameter adjustment instructions are used to adjust the machining parameters of the multi-axis linkage CNC lathe. The machining parameters include at least one of spindle speed, feed rate and / or depth of cut.

[0065] The effect evaluation module is used to acquire vibration information after the processing parameter adjustment command is executed and perform time-frequency analysis to obtain the energy change and instantaneous peak characteristics after adjustment, and evaluate the chatter suppression effect of the processing parameter adjustment command based on the energy change and instantaneous peak characteristics after adjustment.

[0066] The parameter adjustment and alarm module is used to readjust the machining parameters and / or issue chatter warnings or shutdown alarms based on the evaluation results of the chatter suppression effect, forming a closed-loop active suppression control for micro-cutting chatter.

[0067] Beneficial effects: The multi-axis linkage CNC lathe machining data processing method disclosed in this application obtains the vibration information of the multi-axis linkage CNC lathe during the machining process and performs preliminary processing on the vibration information, including digital filtering to remove background noise and irrelevant interference components. This effectively solves the problem that high-frequency and irregular vibration signals cannot be captured sufficiently in detail in the prior art, and avoids the loss or distortion of early vibration mode information caused by "undersampling" or "aliasing". Attached Figure Description

[0068] Figure 1 This is a flowchart illustrating a multi-axis linkage CNC lathe machining data processing method provided in this application.

[0069] Figure 2 A flowchart of a multi-axis linkage CNC lathe machining data processing system provided in this application.

[0070] In the diagram: 1. Information acquisition module; 2. Time-frequency analysis module; 3. Flutter recognition module; 4. Command generation module; 5. Effect evaluation module; 6. Parameter adjustment and alarm module. Detailed Implementation

[0071] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0072] In modern industrial production, multi-axis CNC lathes play a crucial role in machining high-precision, high-efficiency parts. However, traditional data processing methods often struggle to effectively integrate and analyze massive amounts of multi-source machine tool operation data when faced with machining tasks involving new materials or complex geometries, particularly in capturing high-frequency, instantaneous physical phenomena. This leads to biases in judging machine tool operating conditions and machining stability, consequently affecting production efficiency and product quality. Specifically, when machining new high-hardness alloy materials and workpieces with complex geometries, the high-frequency, irregular vibration signals generated during cutting often suffer from undersampling or aliasing due to the limitations of the sensor sampling rate in existing data acquisition modules. This results in the loss or distortion of high-frequency vibration mode information indicating micro-chipping of the tool or the initial stage of cutting chatter. This lack of critical information renders tool wear prediction models ineffective, leading to decreased machining quality, increased workpiece scrap rates, increased production costs, and frequent production line downtime for manual intervention, severely restricting production efficiency and the optimization capabilities of management systems.

[0073] Reference Figure 1 In response, this application proposes a method for processing machining data on a multi-axis linkage CNC lathe, including:

[0074] S1000: Acquire vibration information of a multi-axis linkage CNC lathe during machining, perform preliminary processing on the vibration information to obtain preliminary processed vibration information. The preliminary processing includes at least digital filtering of the vibration information to remove background noise and interference components unrelated to micro-cutting chatter.

[0075] S2000: Performs time-frequency analysis on the initially processed vibration information to obtain the time spectrum. Extracts the energy change and instantaneous peak characteristics located in a preset specific high-frequency band from the time spectrum. The specific high-frequency band is used to characterize the early vibration mode of micro-cutting chatter.

[0076] S3000: Based on energy changes and instantaneous peak characteristics, it compares them with preset micro-cutting chatter judgment criteria to identify whether there are early signs of micro-cutting chatter.

[0077] S4000: Upon identifying early signs of micro-cutting chatter, generate machining parameter adjustment instructions to suppress micro-cutting chatter. The machining parameter adjustment instructions are used to adjust the machining parameters of the multi-axis CNC lathe, which include at least one of spindle speed, feed rate and / or depth of cut.

[0078] S5000: Acquires vibration information after the machining parameter adjustment command is executed and performs time-frequency analysis to obtain the adjusted energy change and instantaneous peak characteristics, and evaluates the chatter suppression effect of the machining parameter adjustment command based on the adjusted energy change and instantaneous peak characteristics;

[0079] S6000: Based on the evaluation results of chatter suppression effect, the machining parameters are readjusted and / or chatter warnings or shutdown alarms are issued to form a closed-loop active suppression control for micro-cutting chatter.

[0080] Specifically, "vibration information" refers to the mechanical vibration signals collected by sensors (such as accelerometers and force sensors) installed on the machine tool structure, spindle, tool, or workpiece during the machining process of a multi-axis CNC lathe. This data is time-series data reflecting the dynamic response during the cutting process. "Microscopic chatter" is a high-frequency, small-amplitude self-excited vibration caused by the combined effects of cutting force, machine tool structural flexibility, and tool wear. Although the amplitude is small, it significantly affects the surface finish and tool life. "Specific high-frequency bands" are frequency ranges determined based on experience, theoretical analysis, or experimental data to characterize the early vibration modes of microscopic chatter. These ranges are typically higher than the vibration frequencies of conventional machine tool structures and are related to the high-frequency dynamic response of the cutting process. "Machining parameter adjustment commands" are automatically generated or recommended adjustment commands based on identified early signs of microscopic chatter. These commands are used to change machining parameters such as spindle speed, feed rate, and / or depth of cut, thereby altering cutting dynamics to suppress chatter. "Closed-loop active suppression control" refers to forming a feedback loop through real-time monitoring, analysis, judgment, and adjustment, enabling the system to dynamically adjust its behavior according to the actual operating state to suppress chatter.

[0081] For vibration information acquisition, high-sensitivity accelerometers can be placed on the machine tool spindle box, tool holder, or workpiece fixture to collect vibration signals in real time, which are then transmitted to the data processing unit via a data acquisition card. Alternatively, non-contact sensors such as laser vibrometers can be used to measure the vibration of the tool or workpiece surface to reduce interference with the machining process. After acquiring the raw vibration information, the vibration information is preliminarily processed, including at least digital filtering. For example, low-pass filters can be used to remove high-frequency noise, and band-pass filters can be used to suppress interference components within a specific frequency range, such as power supply frequency interference or machine tool coolant pump vibration. Detrending processing can also be performed to eliminate DC components or slow changing trends, ensuring the accuracy of subsequent analysis.

[0082] In time-frequency analysis, methods such as Short-Time Fourier Transform (STFT), Wavelet Transform (WT), or Hilbert-Huang Transform (HHT) can be employed. For example, using STFT, time-domain vibration information is converted into a time-frequency spectrum by setting appropriate window functions and window lengths, thereby characterizing the changes in frequency components over time. Subsequently, energy changes and instantaneous peak features located within a preset specific high-frequency band are extracted from the time-frequency spectrum. This specific high-frequency band is used to characterize the early vibration modes of micro-cutting chatter. For example, a high-frequency band from 5 kHz to 20 kHz can be set, the sum of energy in each time window within this band can be calculated to characterize energy changes, and the point with the highest spectral amplitude within this band can be identified as the instantaneous peak feature.

[0083] The extracted energy changes and instantaneous peak characteristics are compared with preset criteria for judging micro-cutting chatter to identify the presence of early signs of micro-cutting chatter. For example, an energy threshold and / or an instantaneous peak value threshold can be preset. When the energy change or instantaneous peak value exceeds the threshold within a specific high-frequency band, it is determined that there may be early signs of chatter. A comprehensive judgment can also be made by combining indicators such as energy change rate and peak frequency stability to improve the reliability of the identification.

[0084] Upon identifying early signs of micro-cutting chatter, the system generates machining parameter adjustment commands to suppress the chatter. These commands adjust the machining parameters of the multi-axis CNC lathe, including at least one of spindle speed, feed rate, and / or depth of cut. For example, when chatter signs are detected, the system automatically generates commands to reduce spindle speed, feed rate, or depth of cut based on preset rules or models, and sends these commands directly to the CNC system for parameter adjustment.

[0085] After executing the machining parameter adjustment command, the system continues to acquire the adjusted vibration information and perform time-frequency analysis to obtain the adjusted energy changes and instantaneous peak characteristics. The chatter suppression effect is evaluated based on the changes in characteristics before and after adjustment. For example, the percentage reduction in high-frequency energy or the degree of decrease in instantaneous peak amplitude can be calculated to quantify the chatter suppression effect. If the evaluation results show limited suppression, the system can further fine-tune the machining parameters according to a preset optimization algorithm, such as further reducing the spindle speed or feed rate. If the chatter continues to worsen or reaches a dangerous level, a chatter warning is issued to prompt manual intervention, or a shutdown alarm is directly triggered to prevent equipment damage or workpiece scrap.

[0086] By constructing a closed-loop active suppression control process that encompasses vibration information acquisition, preliminary processing, time-frequency analysis, chatter identification, generation of machining parameter adjustment commands, effect evaluation, and readjustment or alarm, this application achieves early, accurate identification and active suppression of micro-cutting chatter at the high-frequency vibration mode level. Compared with existing technologies, this method has significant advantages in fine-grained capture of high-frequency vibration modes, real-time feedback adjustment, and closed-loop control. This enables multi-axis CNC lathes to better adapt to the machining needs of new materials and complex workpieces, effectively reducing workpiece scrap rates and production costs, minimizing production line downtime caused by chatter, and improving machining stability, product quality, and overall production management.

[0087] In another embodiment of this application, S2000 is further proposed to include:

[0088] S2100: The initially processed vibration information is split into a first signal stream and a second signal stream;

[0089] S2200: Perform envelope demodulation analysis on the first signal stream to extract impact pulse features for characterizing spindle bearing damage, and generate bearing condition indication information based on the impact pulse features;

[0090] S2300: Perform time-frequency analysis on the second signal stream to obtain the time spectrum, and extract the energy change and instantaneous peak characteristics related to micro-cutting chatter located in a preset specific high-frequency band from the time spectrum;

[0091] S2400: Based on energy changes and instantaneous peak characteristics, bearing status indication information is used as the correction basis. Combined with the preset micro-cutting chatter judgment criteria, it distinguishes abnormal signals with overlapping spectra but different sources within a preset specific high-frequency band and identifies whether there are early signs of micro-cutting chatter.

[0092] Specifically, the initially processed vibration information is split into a first signal stream and a second signal stream to logically separate the data according to their potential physical source and analytical purpose: the first signal stream is dedicated to detecting spindle bearing damage characteristics, while the second signal stream focuses on identifying micro-cutting chatter. Envelope demodulation analysis is performed on the first signal stream to extract periodic impact components from complex vibration signals, identifying typical manifestations of internal defects in the spindle bearing (such as damage to balls, inner / outer rings, or cages). The frequency and amplitude of the impact pulses characterize the degree and type of bearing damage, generating bearing condition indication information. This bearing condition indication information can be a binary signal indicating "damage present / damage absent" or a quantitative indicator of the degree of damage.

[0093] Simultaneously, time-frequency analysis is performed on the second signal stream, using methods such as Short-Time Fourier Transform (STFT), Wavelet Transform, or Hilbert-Huang Transform (HHT) to obtain its time spectrum. Energy changes and instantaneous peak characteristics within a preset specific high-frequency band are extracted and used as effective indicators of early signs of micro-cutting chatter. When identifying the presence of early signs of micro-cutting chatter, bearing condition indication information is introduced as a correction basis: when determining whether an abnormal signal within a specific high-frequency band originates from micro-cutting chatter, the health status of the spindle bearing is simultaneously referenced. For example, when the bearing condition indication information indicates severe bearing damage that may produce spectral characteristics similar to chatter within a specific high-frequency band, the system will correct or appropriately exclude signals in that frequency band, avoiding misjudging bearing failure as chatter. This distinguishes abnormal signals with overlapping spectra but different origins within the preset specific high-frequency band, improving the accuracy of identifying early signs of micro-cutting chatter.

[0094] This application solves the problem of traditional methods being easily interfered with by mechanical component failures such as spindle bearings when identifying micro-cutting chatter by splitting the initially processed vibration information and introducing bearing status indication information as a correction basis. The envelope demodulation analysis of the first signal stream independently identifies spindle bearing damage characteristics and generates clear bearing status indication information, while the second signal stream focuses on extracting the time-frequency characteristics of micro-cutting chatter. When an abnormal signal is detected in a preset specific high-frequency band, the system combines the bearing status indication information for judgment: if bearing damage may produce a signal with a spectrum overlapping with chatter characteristics, the chatter judgment is corrected using the bearing status indication information to avoid misjudging bearing fault signals as chatter. This enables more accurate location and identification of true early signs of micro-cutting chatter, providing a more reliable basis for subsequent machining parameter adjustments.

[0095] In another embodiment of this application, step S4000 is further proposed to include:

[0096] S4100: Perform time-frequency analysis on the pre-processed vibration information within a preset specific high-frequency band to obtain at least one spectral peak.

[0097] S4200: Calculate the spectrum broadening factor for the peak spectrum and / or statistically analyze the fluctuation of the peak frequency in adjacent time-frequency analysis windows. When the spectrum broadening factor exceeds a preset broadening threshold and / or the fluctuation of the peak frequency exceeds a preset fluctuation threshold, it is determined that there are signs of nonlinear flexibility.

[0098] S4300: Based on nonlinear flexibility indicators and historical chatter suppression effects, a cutting system flexibility assessment factor is generated. The cutting system flexibility assessment factor is used to characterize the current flexibility state of the cutting system.

[0099] S4400: Correlate energy changes, instantaneous peak characteristics, and cutting system flexibility assessment factors to form chatter scenario labels. The chatter scenario labels include at least one or more of the following information: chatter frequency, chatter intensity, and cutting system flexibility assessment factors.

[0100] S4500: In the pre-set flutter suppression strategy effect rating table, the effect ratings of multiple pre-set flutter suppression strategies are stored using the flutter scenario label as an index.

[0101] S4600: When early signs of micro-cutting chatter are identified and machining parameter adjustment instructions need to be generated, the preset chatter suppression strategy with the highest effect score is selected from the chatter suppression strategy effect score table according to the current chatter situation label, and machining parameter adjustment instructions are generated according to the selected preset chatter suppression strategy.

[0102] S4700: Based on the chatter suppression effect after executing the machining parameter adjustment command, update the effect score of the preset chatter suppression strategy corresponding to the chatter suppression strategy effect score table, and update the cutting system flexibility evaluation factor synchronously based on the updated chatter suppression effect.

[0103] Specifically, the pre-processed vibration information is analyzed using time-frequency analysis within a preset high-frequency band to accurately capture the frequency components of micro-cutting chatter. The obtained spectral peaks typically correspond to the dominant chatter frequency, and their location and intensity are important indicators of chatter characteristics. The spectral broadening factor characterizes the extent to which the spectral peaks broaden along the frequency axis; an increase in this factor may indicate increased nonlinearity in the cutting system or the occurrence of multimodal chatter. The fluctuation of the peak frequency reflects the stability of the chatter frequency; larger fluctuations are usually associated with nonlinear flexibility or rapid changes in system parameters. Preset broadening and fluctuation thresholds serve as criteria for judging signs of nonlinear flexibility. When the spectral broadening factor and / or the fluctuation of the peak frequency exceed the threshold, the cutting system is considered to exhibit signs of nonlinear flexibility. Nonlinear flexibility refers to the nonlinear behavior of the cutting system's stiffness or damping characteristics under external excitation, such as micro-slippage between the tool and the workpiece, and changes in contact stiffness. These are important driving factors for the occurrence and development of micro-cutting chatter.

[0104] The cutting system flexibility assessment factor is used to quantify the current flexibility state of the cutting system. Its generation takes into account both the nonlinear flexibility indicators identified in real time and the historical chatter suppression effect data, so as to more comprehensively and accurately reflect the dynamic characteristics of the system.

[0105] The generation of the cutting system flexibility assessment factor is a multi-stage and comprehensive process. It combines real-time identification of nonlinear flexibility indicators under the current machining state with empirical learning of historical chatter suppression effects, and is corrected through multi-channel analysis, aiming to comprehensively and accurately characterize the current flexibility state of the cutting system.

[0106] 1. Identification of nonlinear flexibility indicators

[0107] First, the system needs to identify nonlinear flexibility indicators from the initially processed vibration data. This is mainly done through the following two aspects:

[0108] Spectrum broadening factor calculation: Time-frequency analysis of the initially processed vibration information within a preset high-frequency band can yield at least one spectral peak. The spectrum broadening factor is an indicator that measures the width of the distribution of these spectral peaks along the frequency axis. In an ideal linear system, vibration modes typically exhibit sharp spectral peaks. However, when the cutting system exhibits nonlinear flexibility (e.g., the contact stiffness between the tool and workpiece varies with the cutting force, or there is micro-slippage), these nonlinear factors cause energy to be distributed over a wider frequency range, resulting in a broadening of the spectral peaks. The system calculates the broadening factor of this spectral peak.

[0109] Peak frequency fluctuation statistics: In addition to spectral broadening, nonlinear flexibility can also lead to instability in the chatter frequency. The system will statistically analyze the fluctuation of the peak frequency in adjacent time-frequency analysis windows. If the cutting system exhibits nonlinear flexibility, the chatter frequency may not remain at a fixed value, but rather fluctuate or drift within a certain range.

[0110] The system determines that there are signs of nonlinear flexibility when the calculated spectral broadening factor exceeds a preset broadening threshold, and / or the fluctuation of the peak frequency exceeds a preset fluctuation threshold. These thresholds are preset based on experience, experimental data, or system modeling, and are used to distinguish between normal fluctuations and significant changes caused by nonlinear flexibility.

[0111] 2. Considerations on the effectiveness of historical flutter suppression

[0112] While identifying signs of nonlinear flexibility, the system also incorporates historical chatter suppression effects to generate a cutting system flexibility evaluation factor. This part demonstrates the system's learning and adaptive capabilities.

[0113] Historical data accumulation: The system continuously records the effectiveness of various flutter suppression strategies under different processing scenarios. This historical data includes the scenario labels when flutter occurs (such as flutter frequency, intensity, and flexibility assessment factor at the time), as well as the suppression strategies adopted and their final suppression effects (e.g., percentage reduction in flutter amplitude, degree of improvement in processing quality).

[0114] Experience-based learning and correlation: When a current nonlinear flexibility indicator is identified, the system queries the historical database to find historical records similar to the current situation (including the type and severity of the nonlinear flexibility indicator). By analyzing the correlation between different flexibility states and chatter suppression effectiveness under these similar situations, the system can preliminarily determine the potential impact of the current nonlinear flexibility on chatter suppression. For example, if historical data shows that certain suppression strategies are generally ineffective when a specific nonlinear flexibility indicator appears, this may suggest that the current cutting system's flexibility state is poorly responsive to these strategies.

[0115] 3. Preliminary generation and correction of the cutting system flexibility evaluation factor

[0116] Based on the identified nonlinear flexibility indicators and consideration of historical chatter suppression effects, the system will initially generate a cutting system flexibility assessment factor. This factor is a quantitative indicator used to characterize the overall flexibility state of the current cutting system.

[0117] To make this evaluation factor more comprehensive and accurate, the system will make further corrections:

[0118] Vibration information splitting analysis: The initially processed vibration information will be split into at least two independent analysis channels:

[0119] The first analysis channel (tool microslip nonlinear flexibility analysis): In this channel, the system performs high-frequency chatter characteristic analysis on the split vibration information, specifically identifying nonlinear flexibility indicators introduced by tool microslip. Tool microslip refers to the tiny relative sliding that occurs at the tool-workpiece contact interface during cutting, which generates specific high-frequency nonlinear vibration components. By analyzing these high-frequency characteristics (e.g., enhancement of specific high-frequency energy, appearance of nonlinear harmonics), the system generates first flexibility indication information to characterize the local nonlinear flexibility of the tool-workpiece contact interface.

[0120] The second analysis channel (spindle system flexibility analysis): In this channel, the system performs spindle system flexibility characteristic analysis on the diverted vibration information to identify the flexibility characteristics of the machine tool spindle and / or feed drive system components. This may include analyzing the vibration characteristics of components such as spindle bearings, transmission chains, and guideways, for example, identifying changes in natural frequencies and damping through modal analysis, or identifying impacts caused by bearing failures through envelope demodulation analysis. The system generates second flexibility indication information to characterize the overall flexibility of the machine tool's structural components.

[0121] Flexibility Assessment Factor Correction: Finally, the system corrects the initially generated cutting system flexibility assessment factor based on the first and second flexibility indication information. The correction process can employ weighted averaging, fuzzy logic reasoning, or machine learning-based fusion algorithms to comprehensively consider the effects of local nonlinear flexibility (tool micro-slippage) and global structural flexibility (spindle system), thereby obtaining a more comprehensive and accurate corrected cutting system flexibility assessment factor that reflects the overall flexibility state of the current cutting system. This corrected factor will be used for the subsequent generation of chatter scenario labels.

[0122] For example:

[0123] Suppose that when machining a new type of titanium alloy workpiece on a multi-axis CNC lathe, the system acquires real-time data through vibration sensors.

[0124] 1. Nonlinear flexible sign recognition:

[0125] Time-frequency analysis was performed on the initially processed vibration information. Within the preset high-frequency band of 5kHz-15kHz, a spectral peak was detected at 8kHz.

[0126] The spectral broadening factor of the 8kHz peak is calculated to be 0.8, and the frequency fluctuation of the peak in adjacent time-frequency analysis windows is calculated to be 50Hz.

[0127] The preset broadening threshold is 0.6, and the sway threshold is 30Hz. Since 0.8 > 0.6 and 50Hz > 30Hz, the system is determined to have significant nonlinear flexibility.

[0128] 2. Considerations for historical flutter suppression effectiveness:

[0129] The system queried the historical database and found that in the past, when processing similar titanium alloys and exhibiting similar nonlinear flexibility, certain strategies of "reducing the feed rate" generally scored higher, while strategies of "increasing the spindle speed" scored lower. This suggests that the current system may be more sensitive to adjustments in feed rate.

[0130] 3. Preliminary generation of cutting system flexibility evaluation factors:

[0131] Based on the aforementioned nonlinear flexibility indicators (high broadening, high runout) and historical experience (sensitivity to feed rate adjustments), the system initially generates a cutting system flexibility evaluation factor, for example, an initial value of 0.75, indicating that the current system has high flexibility.

[0132] 4. Adjustment of flexible assessment factors:

[0133] First analysis channel: Vibration information is diverted. In the first analysis channel, through high-frequency chatter feature analysis, the system detects a significant nonlinear harmonic component at the harmonics of the tool engagement frequency. This is identified as nonlinear flexibility introduced by micro-slippage between the tool edge and the workpiece, generating first flexibility indication information (e.g., a local nonlinear flexibility index of 0.8).

[0134] Second analysis channel: In the second analysis channel, by analyzing the vibration of the spindle system, the system found that the specific characteristic frequency energy of the spindle bearing was slightly increased and the radial runout of the spindle was slightly increased. This was identified as a slight change in the structural flexibility of the spindle system, generating second flexibility indication information (e.g., structural flexibility index of 0.6).

[0135] Correction: The system fuses and corrects the initial flexibility assessment factor of 0.75 with the first flexibility indicator information of 0.8 and the second flexibility indicator information of 0.6. For example, through a weighted average model (assuming the initial flexibility assessment factor has a weight of 0.5, the local flexibility weight is 0.3, and the structural flexibility weight is 0.2), the corrected cutting system flexibility assessment factor may become: 0.75*0.5 (initial weight) + 0.8*0.3 (local flexibility weight) + 0.6*0.2 (structural flexibility weight) = 0.375 + 0.24 + 0.12 = 0.735. This corrected factor of 0.735 will more accurately characterize the current cutting system's combined state of local micro-slippage and slight structural flexibility.

[0136] This revised cutting system flexibility assessment factor (0.735) will then be used to generate chatter scenario labels, guiding the system to select the most appropriate machining parameter adjustment instructions from the chatter suppression strategy effectiveness rating table.

[0137] Flutter context label is a multi-dimensional information set that integrates key information such as the current flutter frequency, flutter intensity, and cutting system flexibility assessment factor to form a unique description of the current flutter state and provide accurate context for subsequent strategy selection.

[0138] The chatter suppression strategy effectiveness rating table stores the historical performance of different preset chatter suppression strategies under various chatter scenarios. Using chatter scenario tags as an index, the system can quickly retrieve and compare the potential effects of each strategy. When early signs of micro-cutting chatter are identified and a machining parameter adjustment command needs to be generated, the system selects the preset chatter suppression strategy with the highest effectiveness rating from the chatter suppression strategy effectiveness rating table based on the current chatter scenario tag, and generates the machining parameter adjustment command. Furthermore, based on the chatter suppression effect after executing the machining parameter adjustment command, the effectiveness rating of the corresponding preset chatter suppression strategy in the rating table is updated, and the cutting system flexibility evaluation factor is updated simultaneously. This enables the system to have self-learning and adaptive capabilities, allowing it to continuously optimize the suppression strategy based on actual feedback.

[0139] This application proposes a solution that introduces nonlinear flexibility indication recognition and cutting system flexibility assessment factors, thereby elevating the system from "only looking at the amplitude" to a deeper understanding of the intrinsic mechanism of flutter.

[0140] In some preferred embodiments, the following specific example illustrates the situation: Suppose that during machining on a multi-axis CNC lathe, the system initially detects early signs of microscopic cutting chatter. At this point, time-frequency analysis is performed on the initially processed vibration information within a preset specific high-frequency band (e.g., 1000Hz-3000Hz), identifying the dominant spectral peak at 1850Hz. The spectral broadening factor of this peak is further calculated to be 0.75, and the frequency fluctuation between adjacent time-frequency analysis windows is statistically determined to be 30Hz. If the preset broadening threshold is 0.6 and the preset fluctuation threshold is 20Hz, then it is determined that there are signs of nonlinear flexibility. Based on the aforementioned signs of nonlinear flexibility and the chatter suppression effect in similar scenarios in historical machining data, the system generates a cutting system flexibility assessment factor, for example, 0.65, indicating that the current cutting system is in a moderately flexible state. Subsequently, the detected chatter frequency (1850Hz), chatter intensity (e.g., medium), and cutting system flexibility assessment factor (0.65) are correlated to form a specific chatter scenario label. It should be noted that this specific chatter scenario label does not require exhaustive enumeration of all possible continuous values ​​for chatter frequency, chatter intensity, and cutting system flexibility assessment factor. In specific implementations, chatter frequency can be quantized by frequency range, chatter intensity can be divided into discrete levels (e.g., low, medium, high), and cutting system flexibility assessment factor can be graded by numerical range, thus forming a multi-dimensional label combination for indexing. The pre-set chatter suppression strategy effectiveness rating table can be initialized and stored only for combinations of frequency ranges, intensity levels, and flexibility levels. During system operation, when a new chatter scenario is detected and the rating table does not have a corresponding label, a new record is added as needed, and its rating is updated based on the subsequent suppression effect. This method achieves effective coverage and indexing of different chatter scenarios without exhaustively enumerating all continuous values. The system uses the chatter scenario label as an index to query a pre-set chatter suppression strategy effectiveness rating table. Assuming strategy A (e.g., reducing spindle speed by 500 rpm and increasing feed rate by 0.05 mm / rev) scores 85 points and strategy B (e.g., reducing depth of cut by 0.1 mm) scores 70 points, the system selects strategy A, which has the higher score, and generates corresponding machining parameter adjustment instructions. After executing strategy A, the system again acquires vibration information to evaluate the chatter suppression effect. If the chatter amplitude is significantly reduced, the system increases the effectiveness rating of strategy A under that chatter scenario label (e.g., from 85 to 90 points), and synchronously updates the cutting system flexibility evaluation factor based on the new suppression effect, thereby achieving continuous optimization and adaptive adjustment of the chatter suppression strategy.

[0141] In another embodiment of this application, it is further proposed that, after S4300, the following is also included:

[0142] S4310: The pre-processed vibration information is diverted to at least the first analysis channel and the second analysis channel;

[0143] S4311: In the first analysis channel, high-frequency chatter characteristic analysis is performed on the vibration information after diversion to identify nonlinear flexibility signs introduced by tool micro-slippage and generate first flexibility indication information.

[0144] S4312: In the second analysis channel, the spindle system flexibility characteristics are analyzed on the vibration information after diversion to identify the flexibility characteristics of the machine tool spindle and / or feed drive system components, and generate second flexibility indication information.

[0145] S4313: Based on the first flexibility indication information and the second flexibility indication information, the cutting system flexibility evaluation factor is corrected, and the corrected cutting system flexibility evaluation factor is used for subsequent processing.

[0146] Specifically, diverting the initially processed vibration information to at least a first and a second analysis channel means using software algorithms to copy or guide the same vibration data to two or more independent analysis paths, allowing each analysis channel to process the same batch of digitized vibration data in parallel. The purpose of this diversion is to perform multi-dimensional and targeted analysis of the vibration information, extracting features related to the flexibility of the cutting system from different perspectives.

[0147] In the first analysis channel, high-frequency chatter feature analysis is performed on the split vibration information to identify nonlinear flexibility indicators introduced by tool microslip and generate first flexibility indication information. High-frequency chatter feature analysis focuses on higher-frequency components in the vibration signal, which are closely related to the microscopic dynamic behavior of the tool-workpiece contact interface during cutting. Tool microslip refers to the small relative sliding between the tool and workpiece under cutting force, introducing nonlinear dynamic characteristics and manifesting as a specific high-frequency vibration mode. The first flexibility indication information is used to quantify the degree of this local nonlinear flexibility, for example, by using high-frequency energy amplitude, spectral broadening within a specific frequency range, or the intensity of nonlinear harmonic components.

[0148] In the second analysis channel, the split vibration information is analyzed for spindle system flexibility characteristics to identify the flexibility properties of the machine tool spindle and / or feed drive system components, and to generate second flexibility indication information. The spindle system flexibility characteristic analysis focuses on evaluating the dynamic stiffness or compliance of structural components such as the spindle, bearings, feed screws, and guideways. These flexibility characteristics alter the natural frequency and damping of the cutting system, having a global impact on the generation and evolution of chatter. The second flexibility indication information is used to quantify the structural flexibility state, for example, by using indicators such as spindle radial runout, bearing vibration characteristic frequency energy, or the response amplitude of the feed drive system at a specific frequency.

[0149] Furthermore, based on the first and second flexibility indication information, the cutting system flexibility assessment factor is corrected, and the corrected factor is used for subsequent processing. That is, based on the existing preliminary cutting system flexibility assessment factor, the first and second flexibility indication information, representing local nonlinear flexibility and global structural flexibility respectively, are introduced to correct or refine the assessment factor. The correction process can employ methods such as weighted averaging, fuzzy logic reasoning, or machine learning models to fuse the two types of flexibility indication information, resulting in a more comprehensive and accurate cutting system flexibility assessment factor that reflects the overall flexibility state of the current cutting system. The corrected cutting system flexibility assessment factor can be used for subsequent chatter scenario labeling and chatter suppression strategy selection.

[0150] The proposed solution involves diverting the initially processed vibration information to at least a first analysis channel and a second analysis channel, and performing high-frequency chatter characteristic analysis and spindle system flexibility characteristic analysis respectively, thereby identifying the source and characteristics of the cutting system's flexibility from different dimensions.

[0151] In some preferred embodiments, for example, during machining on a multi-axis CNC lathe, the system has generated a preliminary cutting system flexibility assessment factor based on nonlinear flexibility indicators and historical data. To further improve the accuracy of this assessment factor, the initially processed vibration information is split. In the first analysis channel, high-frequency envelope demodulation analysis is performed on the vibration information, detecting significant nonlinear harmonic components and spectral broadening near the tool meshing frequency. This is identified as micro-slippage caused by micro-wear on the tool edge, and a first flexibility indication is generated, indicating higher local tool flexibility. In the second analysis channel, modal analysis is performed on the vibration information, revealing a decrease in the damping ratio of the spindle system at a specific frequency. This indicates slight loosening of the spindle bearings or connectors, which is identified as increased structural flexibility of the spindle system, and a second flexibility indication is generated, indicating higher spindle system flexibility. Subsequently, the system merges and corrects the local flexibility index (0.7), the structural flexibility index (0.5), and the initial cutting system flexibility assessment factor (0.6). Based on the weights of the two flexibility sources' influence on the current chatter situation, a corrected cutting system flexibility assessment factor of 0.65 is obtained, which more accurately reflects the combined state of simultaneous local and structural flexibility. This corrected assessment factor is used to construct more precise chatter situation labels, such as labeling chatter frequency and intensity, and indicating "high local flexibility accompanied by moderate structural flexibility." This guides the system to select a type of machining parameter adjustment instruction that simultaneously optimizes tool wear and spindle system stiffness, such as simultaneously reducing the feed rate to reduce tool load and fine-tuning the spindle speed to avoid the spindle system's resonant frequency.

[0152] In another embodiment of this application, it is further proposed that, based on the chatter suppression effect after executing the processing parameter adjustment command, the effect score of the preset chatter suppression strategy corresponding to the chatter suppression strategy effect score table is updated, including:

[0153] S4710: After executing the selected preset flutter suppression strategy, calculate the flutter suppression effect index based on the energy change and / or instantaneous peak characteristics within a preset specific high-frequency band before and after execution;

[0154] S4720: Set the inertia range for score updates. When the flutter suppression effect index obtained from multiple consecutive executions of the preset flutter suppression strategy continuously deviates from the inertia range, the effect score of the preset flutter suppression strategy selected under the current flutter scenario label is adjusted, where:

[0155] S4730: When the flutter suppression effect index, which represents the degree of reduction in flutter amplitude, is greater than the first preset threshold, the effect score of the selected preset flutter suppression strategy under the current flutter scenario label is improved.

[0156] S4740: When the flutter suppression effect index, which indicates a reduction in flutter amplitude, is lower than the second preset threshold and / or indicates an increase in flutter amplitude, reduce the effect score of the selected preset flutter suppression strategy under the current flutter scenario label.

[0157] S4750: Introduces a time decay factor to weight the flutter suppression effect index obtained at different time points and update the effect score;

[0158] S4760: After a preset time interval and / or after a preset number of workpieces have been processed, the effect scores of all preset chatter suppression strategies in the chatter suppression strategy effect score table are reduced by a uniform attenuation coefficient based on the time attenuation factor.

[0159] Specifically, the chatter suppression effect index refers to a numerical value used to quantify the degree to which machining parameter adjustment commands suppress chatter in micro-cutting. This index is calculated based on the energy change and / or instantaneous peak characteristics of vibration information within a preset specific high-frequency band before and after executing the machining parameter adjustment command. For example, it calculates the percentage decrease in energy after adjustment relative to energy before adjustment, or the relative reduction in the instantaneous peak amplitude, thereby providing an objective and quantifiable basis for evaluating the actual effect of the selected chatter suppression strategy.

[0160] The inertia range for score updates can be understood as a preset numerical range used to determine whether changes in the flutter suppression effect index are significant enough to trigger score adjustments. For example, it can be set as an interval around zero change [-5%, +5%]. Only when the flutter suppression effect index exceeds this inertia range multiple times consecutively (e.g., 3 or 5 times) is the strategy effect considered to have changed substantially, requiring adjustment of the effect score. This is to avoid frequent changes in the strategy score due to occasional measurement errors or short-term fluctuations, thereby improving the robustness and stability of score updates.

[0161] In practical applications, the first and second preset thresholds are used to define the critical levels of the flutter suppression effect index. When the reduction in flutter amplitude represented by the flutter suppression effect index is greater than the first preset threshold, the strategy is considered to have achieved a significant suppression effect, and its effect score should be increased. For example, the first preset threshold can be set to a 20% reduction in flutter amplitude. When the reduction in flutter amplitude represented by the flutter suppression effect index is less than the second preset threshold (e.g., a 5% reduction) and / or represents an increase in flutter amplitude, the strategy is considered to be ineffective or worsening flutter, and its effect score should be decreased to incentivize effective strategies and penalize ineffective or harmful strategies.

[0162] Furthermore, a time decay factor is introduced to weight the chatter suppression effect indicators obtained at different time points. That is, the more recent the chatter suppression effect indicator, the greater its weight and the more significant its impact on the current performance score; conversely, the older the indicator, the smaller its weight. For example, an exponential decay function can be used to calculate the time decay factor, ensuring that the performance score reflects the current state of the cutting system and the latest performance of the strategy in a timely manner, avoiding score distortion caused by outdated historical data.

[0163] Furthermore, after a preset time interval (e.g., every 24 hours) and / or after completing a preset number of workpieces (e.g., every 100 workpieces), based on a time decay factor, the effectiveness scores of all preset chatter suppression strategies in the chatter suppression strategy effectiveness scoring table are uniformly decayed by a decay coefficient, for example, by a uniform multiplication by a decay coefficient of 0.9 or 0.95. This simulates the long-term drift of the inherent characteristics of the cutting system or environmental changes, allowing the scores of strategies that have not been verified for a long time or may no longer be applicable to gradually decrease, making room for new strategies or strategies more adapted to the current environment, and maintaining the dynamic adaptability of the scoring system.

[0164] The proposed solution effectively filters the impact of occasional noise and short-term fluctuations on strategy scoring by introducing an inertial range and preset threshold for scoring updates, avoiding unnecessary frequent adjustments and ensuring stable and accurate scoring updates. Through a time decay factor, the recent chatter suppression effect index is given greater weight in scoring updates, addressing the problem of insufficient timeliness of historical data. By performing overall decay at preset time intervals or after processing a preset number of workpieces, the long-term drift of the inherent characteristics of the cutting system is simulated, enabling the scoring system to reflect the current processing status in a timely manner.

[0165] In some preferred embodiments, assuming that under a specific flutter scenario, the system identifies early signs of micro-cutting flutter and selects a preset flutter suppression strategy A from the flutter suppression strategy effectiveness rating table based on the current flutter scenario label. After executing strategy A, by monitoring vibration information within a preset specific high-frequency band, the flutter suppression effectiveness index is calculated as a 25% reduction in flutter amplitude. The system first checks whether this index continuously deviates from the inertial range (e.g., [-5%, +5%]). If three consecutive executions of strategy A achieve a similar significant reduction (e.g., more than 20%), and the reduction is greater than a first preset threshold (e.g., 15%), then the effectiveness score of strategy A under the current flutter scenario label is improved.

[0166] Furthermore, to reflect timeliness, it is assumed that strategy A obtained multiple sets of inhibition effect indicators at different time points in the past, such as a 25% reduction in the most recent instance, a 20% reduction in the instance before that, and an 18% reduction in an earlier instance. The system assigns weights corresponding to time decay factors to these indicators, such as 0.5, 0.3, and 0.2, and calculates a weighted average based on these weights to update the effect score of strategy A.

[0167] Furthermore, to address the long-term drift of the inherent characteristics of the cutting system, it is assumed that the system is configured to perform an overall attenuation after every 100 workpieces are machined. When the 100th workpiece is machined, the system will multiply the scores of all strategies in the chatter suppression strategy effectiveness rating table by an attenuation coefficient of 0.95, causing a slight decrease in the scores of all strategies. This encourages the system to explore and verify new or recently better performing strategies in the future to adapt to potentially changed machining environments.

[0168] In another embodiment of this application, a time decay factor is further proposed to weight the flutter suppression effect index obtained at different time points, including:

[0169] S4751: Acquire real-time vibration information and perform time-frequency analysis on the real-time vibration information to extract specific frequency variation features and / or damping variation features used to characterize the inherent characteristics of the cutting system;

[0170] S4752: Obtain information on the current workpiece material type, processing stage, and machine tool operating status;

[0171] S4753: Evaluate the drift rate of the inherent characteristics of the cutting system based on specific frequency variation characteristics and / or damping variation characteristics, combined with the current workpiece material type, machining stage and machine tool operating status;

[0172] S4754: Dynamically adjusts the time decay factor based on drift velocity;

[0173] S4755: When updating the effect score of the preset flutter suppression strategy in the flutter suppression strategy effect score table, the weight corresponding to the dynamically adjusted time decay factor is used to weight the flutter suppression effect index obtained at different times.

[0174] Specifically, real-time vibration information refers to vibration signals continuously collected by sensors (such as accelerometers) during machining on a multi-axis CNC lathe. Time-frequency analysis of this real-time vibration information, using methods such as Short-Time Fourier Transform (STFT), wavelet transform, or Hilbert-Huang transform, can yield the time and frequency distribution of the vibration signal, allowing the extraction of specific frequency variation characteristics and / or damping variation characteristics of the cutting system's inherent properties. The specific frequency variation characteristics can be the shift or broadening of the cutting system's natural frequencies in a specific mode, while the damping variation characteristics can be the change in the vibration decay rate, used to characterize the current stiffness and damping state of the cutting system.

[0175] Simultaneously, information on the current workpiece material type, machining stage, and machine tool operating status is acquired and used as the machining context to assist in determining external driving factors affecting changes in the inherent characteristics of the cutting system. For example, the hardness of the workpiece material, the machining stage (roughing, finishing), and operating parameters such as the machine tool spindle speed and feed rate all affect the dynamic response of the cutting system.

[0176] Based on the extracted specific frequency variation characteristics and / or damping variation characteristics, and in conjunction with the current workpiece material type, machining stage, and machine tool operating status, the drift rate of the inherent characteristics of the cutting system is evaluated. Drift rate refers to the rate at which the inherent characteristics of the cutting system change over time. For example, a significant decrease in natural frequency or a significant reduction in damping within a short period of time usually indicates a substantial change in cutting conditions or system state.

[0177] The time decay factor is dynamically adjusted based on the assessed drift velocity. When the drift velocity is high, indicating drastic changes in the cutting system state, the time decay factor should be increased to reduce the weight of earlier chatter suppression performance indicators more quickly, emphasizing recent data. When the drift velocity is low, the time decay factor is decreased to maintain a higher weight for historical data in the scoring update, fully utilizing existing experience. Finally, when updating the effectiveness score of the preset chatter suppression strategy in the chatter suppression strategy effectiveness scoring table, the weights corresponding to the dynamically adjusted time decay factor are used to weight the chatter suppression performance indicators obtained at different times, ensuring the accuracy and real-time nature of the effectiveness score update.

[0178] This application's solution dynamically assesses the drift rate of the inherent characteristics of the cutting system by real-time monitoring of changes in these characteristics, combined with machining context information such as workpiece material type, machining stage, and machine tool operating status. Based on this, it adaptively adjusts the time decay factor. When the inherent characteristics of the cutting system drift rapidly, the reference value of historical data for the current state decreases rapidly. By increasing the time decay factor, the weight decay of old data is accelerated, allowing the chatter suppression strategy effectiveness score to quickly reflect the latest system state and avoiding misjudgments due to reliance on outdated data. When the inherent characteristics of the cutting system are relatively stable, historical data still has high reference value. By decreasing the time decay factor, historical data continues to play a role in score updates, improving the stability and robustness of the score. Therefore, it ensures that the chatter suppression strategy effectiveness score always closely reflects the actual state of the current cutting system and the true effect of the strategy.

[0179] In another embodiment of this application, it is further proposed that setting the inertial interval for score updates includes:

[0180] S4721: Acquire real-time vibration information, perform time-frequency analysis on the real-time vibration information, and extract specific frequency variation features and / or damping variation features to characterize the inherent characteristics of the cutting system;

[0181] S4722: Obtain information on the current workpiece material type, processing stage, and machine tool operating status;

[0182] S4723: Based on specific frequency variation characteristics and / or damping variation characteristics, combined with the current workpiece material type, processing stage and machine tool operating status, evaluate the drift speed and fluctuation amplitude of the inherent characteristics of the cutting system;

[0183] S4724: Dynamically adjust the size of the inertial interval for score updates based on drift speed and fluctuation amplitude.

[0184] Specifically, real-time vibration information refers to the vibration signals acquired in real time by sensors (such as accelerometers) mounted on the machine tool or cutting tool during the machining process of a multi-axis CNC lathe. Time-frequency analysis of the real-time vibration information, using methods such as Short-Time Fourier Transform (STFT), Wavelet Transform (WT), or Hilbert-Huang Transform (HHT), can obtain the distribution characteristics of the vibration signal in time and frequency, and extract specific frequency variation features and / or damping variation features to characterize the inherent properties of the cutting system. The specific frequency variation features can be the natural frequency shift or broadening of the cutting system in a specific mode, and the damping variation features can be the change in the vibration decay rate of the system, reflecting the changes in the stiffness and damping of the cutting system.

[0185] Simultaneously, information on the current workpiece material type, machining stage, and machine tool operating status is acquired. This information serves as crucial contextual parameters to aid in determining the potential causes and trends of changes in the inherent characteristics of the cutting system. For instance, different workpiece materials possess varying cutting properties and stiffness; machining stages (such as roughing and finishing) significantly impact cutting forces; and machine tool operating status (such as spindle speed, feed rate, and temperature) directly affects the machine tool's dynamic response.

[0186] Furthermore, based on the extracted specific frequency variation characteristics and / or damping variation characteristics, combined with the current workpiece material type, machining stage, and machine tool operating status, the drift rate and fluctuation amplitude of the inherent characteristics of the cutting system can be evaluated. Drift rate refers to the slow, continuous change trend of the inherent characteristics of the cutting system over time or machining progress, such as the gradual decrease in system stiffness due to tool wear; fluctuation amplitude refers to the random or periodic disturbances of the inherent characteristics of the cutting system in the short term, such as vibration changes caused by material inhomogeneity or transient impacts. The above evaluation can be achieved through empirical models, machine learning models, or physics-based models.

[0187] Therefore, the size of the inertia interval for score updates can be dynamically adjusted based on the drift speed and fluctuation amplitude. When the drift speed is fast or the fluctuation amplitude is large, it indicates that the system state is changing drastically. The inertia interval can be appropriately reduced to improve the sensitivity of score updates and enable the system to respond more quickly to the real changes in flutter suppression effect. When the drift speed is slow and the fluctuation amplitude is small, it indicates that the system is relatively stable. The inertia interval can be appropriately increased to avoid frequent adjustments to the score due to small fluctuations and to improve the robustness of the evaluation.

[0188] The solution proposed in this application addresses the problem of insufficient adaptability of traditional fixed inertia ranges in dynamic machining environments by introducing an evaluation of the inherent characteristics of the cutting system, namely drift speed and fluctuation amplitude.

[0189] In some preferred embodiments, such as during batch machining on a multi-axis CNC lathe, as machining time increases, the cutting tool gradually wears down, and the overall stiffness of the cutting system slowly decreases. This manifests in real-time vibration information as a slight downward drift of the natural frequency of a specific mode, with the assessed drift velocity gradually increasing. Simultaneously, due to subtle differences in the materials of different batches of workpieces, the cutting force experiences transient fluctuations, and the amplitude of the damping variation characteristics increases. The method of this application continuously acquires real-time vibration information and performs time-frequency analysis to extract the natural frequency drift trend and damping fluctuation. It also acquires the current workpiece material type (e.g., stainless steel 304), machining stage (e.g., finishing), and machine tool operating status (e.g., spindle speed 10000 rpm, feed rate 500 mm / min), thereby assessing the drift velocity and fluctuation amplitude. In the initial stages of machining, when the tool is sharp and the system is stable, the drift speed and fluctuation amplitude are small, allowing for a larger inertia range to ensure stable score updates. As machining progresses into the mid-to-late stages, tool wear intensifies, and the natural frequency drift speed increases. The system automatically narrows the inertia range for score updates, making changes in the chatter suppression effect index more easily identified as exceeding the inertia range, thus allowing for more timely adjustments to the chatter suppression strategy's effectiveness score. If material hard spots appear during machining, causing a sudden increase in vibration fluctuation amplitude, the system will further adjust the inertia range based on the increased fluctuation amplitude to adapt to this transient change. Through these dynamic adjustments, the system can more accurately and promptly assess the actual effect of the chatter suppression strategy, avoiding evaluation lag or misjudgment caused by a fixed inertia range, and ensuring the effectiveness of closed-loop active suppression control.

[0190] In another embodiment of this application, step S4760 is further proposed to include:

[0191] S4761: Acquire real-time vibration information and perform time-frequency analysis on the real-time vibration information to extract specific frequency variation features and / or damping variation features used to characterize the inherent characteristics of the cutting system;

[0192] S4762: Obtain information on the current workpiece material type, processing stage, and machine tool operating status;

[0193] S4763: Evaluate the drift rate of the inherent characteristics of the cutting system based on specific frequency variation characteristics and / or damping variation characteristics, combined with the current workpiece material type, machining stage and machine tool operating status;

[0194] S4764: Dynamically determine the overall attenuation rate and / or uniform attenuation coefficient based on the drift velocity, and use the dynamically determined overall attenuation rate and / or uniform attenuation coefficient when performing overall attenuation.

[0195] Specifically, real-time vibration information refers to vibration signals collected in real time by sensors mounted on the machine tool or cutting tool during the machining process of a multi-axis CNC lathe. These signals contain the dynamic response information of the cutting system under the current operating conditions. Time-frequency analysis of the real-time vibration information, such as using short-time Fourier transform, wavelet transform, or Hilbert-Huang transform, can yield the distribution of the vibration signal in time and frequency, thereby extracting specific frequency variation characteristics and / or damping variation characteristics. Specific frequency variation characteristics can refer to the natural frequency shift of the cutting system in a specific mode, and damping variation characteristics can refer to the change in the system's damping ratio; both are used to characterize the changes in the inherent properties of the cutting system.

[0196] Furthermore, the current workpiece material type, machining stage, and machine tool operating status are important factors affecting the inherent characteristics of the cutting system. For example, different materials have different cutting mechanics properties, the cutting parameters and stress states differ between roughing and finishing stages, and machine tool operating status such as spindle speed and feed rate also significantly affect the system's dynamic response. This information can be obtained through the machine tool's CNC system, sensors, or operator input.

[0197] The drift rate of an inherent characteristic of a cutting system refers to its rate of change over time. By comprehensively analyzing extracted specific frequency variation characteristics and / or damping variation characteristics in conjunction with the current workpiece material type, machining stage, and machine tool operating state, the rate of change of inherent characteristics can be assessed. For example, when tool wear intensifies or the hardness of the machined material changes, the inherent frequency and damping of the cutting system may shift significantly within a short period, resulting in a higher drift rate. Based on the assessed drift rate, the overall decay rate and / or uniform decay coefficient (the overall decay rate can be quantified using the uniform decay coefficient) can be dynamically determined: when the drift rate is high, a faster overall decay rate or a larger uniform decay coefficient is used to reduce the weight of the old strategy score more quickly, adapting to the new system state; when the drift rate is low, a slower overall decay rate or a smaller uniform decay coefficient is used to maintain the smoothness and stability of the scoring.

[0198] This application's solution addresses the problem of traditional fixed attenuation coefficients being ill-suited to dynamically changing cutting systems by introducing an assessment of the inherent drift velocity of the cutting system. By acquiring real-time vibration information and performing time-frequency analysis, specific frequency variation characteristics and / or damping variation characteristics can be accurately captured. Combined with contextual information such as the current workpiece material type, machining stage, and machine tool operating status, the drift velocity of the inherent characteristics of the cutting system is comprehensively evaluated. Since the drift velocity directly reflects the rate of change in system state, the overall attenuation rate and / or unified attenuation coefficient can be dynamically determined accordingly: when the system changes rapidly, increasing the overall attenuation rate promptly eliminates outdated strategy scores, ensuring the timeliness of the chatter suppression strategy effectiveness rating table; when the system changes slowly, decreasing the overall attenuation rate avoids excessive attenuation, maintaining the continuity and reliability of the rating system. Therefore, the update mechanism of the chatter suppression strategy effectiveness rating table is more intelligent and adaptive, more accurately reflecting the current cutting system state.

[0199] In some preferred embodiments, for example, a multi-axis CNC lathe is machining a batch of aerospace aluminum alloy workpieces with slight differences in hardness. In the initial stages of machining, the inherent characteristics of the cutting system are relatively stable, and the assessed drift speed is low. The system can use a small uniform attenuation coefficient to attenuate the overall chatter suppression strategy effectiveness score to maintain score stability. As machining progresses, the tool gradually wears and encounters workpiece areas with slightly higher hardness, causing the inherent characteristics of the cutting system (e.g., the natural frequency and damping of the spindle-tool system) to drift more rapidly. At this point, the system acquires vibration information in real time and performs time-frequency analysis, extracting specific frequency change characteristics and damping change characteristics. Combined with the current workpiece material type, machining stage, and machine tool operating status, the system assesses a significant increase in drift speed. Based on this drift speed, the system dynamically increases the overall attenuation rate and / or the uniform attenuation coefficient, for example, adjusting the uniform attenuation coefficient from 0.95 to 0.85, causing the old chatter suppression strategy effectiveness score to attenuate more quickly, thus enabling the scoring table to adapt more rapidly to the new dynamic characteristics brought about by tool wear and workpiece hardness changes. With this dynamic adjustment, the system can update the effectiveness evaluation of different chatter suppression strategies in a timely manner. When early signs of micro-cutting chatter are subsequently identified, the machining parameter adjustment command that best suits the current working conditions is selected first, so as to achieve more accurate and effective chatter suppression.

[0200] In another embodiment of this application, the step of dynamically determining the overall attenuation rate and / or uniform attenuation coefficient based on the drift velocity includes:

[0201] S4764-1: The preset flutter suppression strategies in the flutter suppression strategy effectiveness rating table shall be divided into at least two strategy types according to the controlled object and / or the type of adjustment parameter;

[0202] S4764-2: Based on the drift velocity and the preset drift sensitivity parameters corresponding to each strategy type, the overall decay rate and / or uniform decay coefficient are typified and corrected to obtain the decay rate corresponding to different strategy types.

[0203] S4764-3: When periodically decaying the overall effectiveness score of all strategies, the effectiveness score shall be decayed by applying the corresponding decay rate to different strategy types.

[0204] Specifically, the preset chatter suppression strategies in the chatter suppression strategy effectiveness evaluation table are divided into at least two strategy types based on the controlled object and / or the type of adjustment parameters. This means classifying the strategies according to the machine tool components (e.g., spindle, feed system, cutting tool, etc.) and / or the machining parameters they adjust (e.g., spindle speed, feed rate, depth of cut, toolpath, etc.). For example, they can be classified as "spindle speed adjustment strategies," "feed rate adjustment strategies," and "depth of cut adjustment strategies." The controlled object is used to limit the machine tool components or systems directly affected by the chatter suppression strategy, and the adjustment parameter type is used to describe which machining parameters the strategy changes to achieve chatter suppression, thus clarifying the scope and execution method of the strategy.

[0205] Furthermore, based on the drift speed and the preset drift sensitivity parameters corresponding to each strategy type, the overall attenuation rate and / or uniform attenuation coefficient are typified to obtain the attenuation rate corresponding to different strategy types. The drift sensitivity parameter is a numerical value used to characterize the sensitivity of a certain type of chatter suppression strategy to the drift of the inherent characteristics of the cutting system. For example, for "spindle speed adjustment strategies," if it is highly sensitive to the drift of spindle system stiffness or damping, its drift sensitivity parameter can be set higher; for "feed rate adjustment strategies," corresponding drift sensitivity parameters can be set according to tool wear or changes in workpiece material. Through typification, different strategy types have an attenuation rate that matches their drift sensitivity during overall attenuation, thus more accurately reflecting the changes in the applicability of various strategies under system drift.

[0206] Therefore, when periodically decaying the overall effectiveness scores of all strategies, the effectiveness scores of different strategy types are decayed at their respective decay rates. When the inherent characteristics of the cutting system drift, the effectiveness scores of strategy types that are more sensitive to drift decay at a faster rate to reflect the rapid decline in their applicability; the effectiveness scores of strategy types that are relatively less sensitive to drift decay at a slower rate to retain their reference value under the current operating conditions.

[0207] The solution proposed in this application classifies chatter suppression strategies and introduces drift sensitivity parameters for each strategy type. It then differentiates and dynamically adjusts the overall attenuation rate and / or uniform attenuation coefficient, enabling the chatter suppression strategy effectiveness rating table to more accurately reflect the actual effectiveness of different strategy types under the current cutting system state, thus avoiding the scoring distortion problem caused by the uniform attenuation coefficient.

[0208] Reference Figure 2 The specific embodiments of this application also disclose a multi-axis linkage CNC lathe machining data processing system, including:

[0209] Information acquisition module 1 is used to acquire vibration information of multi-axis linkage CNC lathe during machining, perform preliminary processing on the vibration information to obtain preliminary processed vibration information. The preliminary processing includes at least digital filtering of the vibration information to remove background noise and interference components unrelated to micro-cutting chatter.

[0210] The time-frequency analysis module 2 is used to perform time-frequency analysis on the initially processed vibration information to obtain the time spectrum. From the time spectrum, the energy change and instantaneous peak characteristics located in a preset specific high-frequency band are extracted. The specific high-frequency band is used to characterize the early vibration mode of micro-cutting chatter.

[0211] The chatter identification module 3 is used to compare the energy changes and instantaneous peak characteristics with the preset micro-cutting chatter judgment criteria to identify whether there are early signs of micro-cutting chatter.

[0212] The instruction generation module 4 is used to generate machining parameter adjustment instructions to suppress micro-cutting chatter when early signs of micro-cutting chatter are identified. The machining parameter adjustment instructions are used to adjust the machining parameters of the multi-axis linkage CNC lathe. The machining parameters include at least one of spindle speed, feed rate and / or depth of cut.

[0213] The effect evaluation module 5 is used to acquire vibration information after the processing parameter adjustment command is executed and perform time-frequency analysis to obtain the energy change and instantaneous peak characteristics after adjustment, and evaluate the chatter suppression effect of the processing parameter adjustment command based on the energy change and instantaneous peak characteristics after adjustment.

[0214] The parameter adjustment and alarm module 6 is used to readjust the machining parameters and / or issue chatter warnings or shutdown alarms based on the evaluation results of the chatter suppression effect, forming a closed-loop active suppression control for micro-cutting chatter.

[0215] The multi-axis linkage CNC lathe machining data processing system proposed in this application can perform real-time, refined processing and analysis of vibration information during machining through the coordinated work of its various functional modules, thereby effectively identifying early signs of micro-cutting chatter.

[0216] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for processing machining data on a multi-axis linkage CNC lathe, characterized in that, include: Vibration information of a multi-axis linkage CNC lathe during machining is acquired, and the vibration information is preliminarily processed to obtain preliminarily processed vibration information. The preliminary processing includes at least digital filtering of the vibration information to remove background noise and interference components unrelated to micro-cutting chatter. Time-frequency analysis is performed on the initially processed vibration information to obtain the time spectrum. Energy changes and instantaneous peak characteristics located in a preset specific high-frequency band are extracted from the time spectrum. The preset specific high-frequency band is used to characterize the early vibration modes of micro-cutting chatter. Based on the energy change and the instantaneous peak characteristics, compare them with the preset micro-cutting chatter judgment criteria to identify whether there are early signs of micro-cutting chatter. Upon identifying early signs of micro-cutting chatter, machining parameter adjustment instructions are generated to suppress the micro-cutting chatter. These instructions are used to adjust the machining parameters of a multi-axis CNC lathe, which include at least one of spindle speed, feed rate, and / or depth of cut. The vibration information after the processing parameter adjustment command is executed is obtained and time-frequency analysis is performed to obtain the adjusted energy change and instantaneous peak characteristics. The chatter suppression effect of the processing parameter adjustment command is evaluated based on the adjusted energy change and instantaneous peak characteristics. Based on the evaluation results of the chatter suppression effect, the machining parameters are readjusted and / or chatter warnings or shutdown alarms are issued to form a closed-loop active suppression control for micro-cutting chatter. Upon identifying early signs of micro-cutting chatter, machining parameter adjustment instructions are generated to suppress micro-cutting chatter, including: The vibration information after preliminary processing is subjected to time-frequency analysis within the preset specific high-frequency band to obtain at least one spectral peak. The spectrum broadening factor is calculated for the peak frequency and / or the fluctuation of the peak frequency in adjacent time-frequency analysis windows is statistically analyzed. When the spectrum broadening factor exceeds a preset broadening threshold and / or the fluctuation of the peak frequency exceeds a preset fluctuation threshold, it is determined that there are signs of nonlinear flexibility. Based on the nonlinear flexibility indicators and historical chatter suppression effects, a cutting system flexibility evaluation factor is generated, which is used to characterize the current flexibility state of the cutting system. The energy change, the instantaneous peak characteristics, and the cutting system flexibility evaluation factor are correlated to form a chatter situation label. The chatter situation label includes at least one or more of the following: chatter frequency, chatter intensity, and the cutting system flexibility evaluation factor. In a pre-set flutter suppression strategy effectiveness rating table, the flutter scenario label is used as an index to store the effectiveness ratings of multiple pre-set flutter suppression strategies; When early signs of micro-cutting chatter are identified and the machining parameter adjustment command needs to be generated, the preset chatter suppression strategy with the highest effect score is selected from the chatter suppression strategy effect score table according to the current chatter situation label, and the machining parameter adjustment command is generated according to the selected preset chatter suppression strategy. Based on the chatter suppression effect after executing the machining parameter adjustment command, the effect score of the preset chatter suppression strategy corresponding to the chatter suppression strategy effect score table is updated, and the cutting system flexibility evaluation factor is updated synchronously based on the updated chatter suppression effect.

2. The data processing method for multi-axis linkage CNC lathe machining according to claim 1, characterized in that, The initially processed vibration information is subjected to time-frequency analysis to obtain a time spectrum. Energy changes and instantaneous peak characteristics located within a preset specific high-frequency band are extracted from the time spectrum, including: The pre-processed vibration information is split into a first signal stream and a second signal stream; Envelope demodulation analysis is performed on the first signal stream to extract impact pulse features for characterizing spindle bearing damage, and bearing condition indication information is generated based on the impact pulse features. Time-frequency analysis is performed on the second signal stream to obtain the time spectrum, and energy changes and instantaneous peak characteristics related to micro-cutting chatter located in a preset specific high-frequency band are extracted from the time spectrum; Based on the energy change and the instantaneous peak characteristics, a comparison is made with preset micro-cutting chatter judgment criteria to identify whether there are early signs of micro-cutting chatter, including: Based on the energy changes and instantaneous peak characteristics, and using the bearing status indication information as a correction basis, combined with the preset micro-cutting chatter judgment criteria, abnormal signals with overlapping spectra but different sources within the preset specific high-frequency band are distinguished to identify whether there are early signs of micro-cutting chatter.

3. The data processing method for multi-axis linkage CNC lathe machining according to claim 1, characterized in that, Based on the aforementioned nonlinear flexibility indicators and historical chatter suppression effects, after generating the cutting system flexibility evaluation factor, the following further steps are included: The pre-processed vibration information is then distributed to at least the first analysis channel and the second analysis channel. In the first analysis channel, high-frequency chatter feature analysis is performed on the vibration information after diversion to identify nonlinear flexibility signs introduced by tool micro-slippage and generate first flexibility indication information. In the second analysis channel, the spindle system flexibility characteristics are analyzed on the diverted vibration information to identify the flexibility characteristics of the machine tool spindle and / or feed drive system components, and generate second flexibility indication information. Based on the first flexibility indication information and the second flexibility indication information, the flexibility evaluation factor of the cutting system is corrected, and the corrected flexibility evaluation factor of the cutting system is used for subsequent processing.

4. The data processing method for multi-axis linkage CNC lathe machining according to claim 1, characterized in that, Based on the chatter suppression effect after executing the processing parameter adjustment command, the effect score of the preset chatter suppression strategy corresponding to the chatter suppression strategy effect score table is updated, including: After executing the selected preset flutter suppression strategy, the flutter suppression effect index is calculated based on the energy change and / or instantaneous peak characteristics in a preset specific high-frequency band before and after execution. A set inertia interval for score updates is established. When the flutter suppression effect index obtained by repeatedly executing the preset flutter suppression strategy deviates continuously from the inertia interval, the effect score of the preset flutter suppression strategy selected under the current flutter scenario label is adjusted, wherein: When the flutter suppression effect index, which represents the degree of reduction in flutter amplitude, is greater than a first preset threshold, the effect score of the selected preset flutter suppression strategy under the current flutter scenario label is improved. When the flutter suppression effect index, which indicates a reduction in flutter amplitude, is lower than a second preset threshold and / or indicates an increase in flutter amplitude, the effect score of the selected preset flutter suppression strategy under the current flutter scenario label is reduced. A time decay factor is introduced to weight the flutter suppression effect index obtained at different time points, and the effect score is updated accordingly; After a preset time interval and / or after processing a preset number of workpieces, the effect scores of all preset chatter suppression strategies in the chatter suppression strategy effect score table are reduced as a whole by a uniform attenuation coefficient based on the time attenuation factor.

5. The data processing method for multi-axis linkage CNC lathe machining according to claim 4, characterized in that, A time decay factor is introduced to weight the flutter suppression effect indices obtained at different time points, including: Acquire real-time vibration information and perform time-frequency analysis on the real-time vibration information to extract specific frequency variation features and / or damping variation features to characterize the inherent characteristics of the cutting system; Obtain information on the current workpiece material type, processing stage, and machine tool operating status; Based on the specific frequency variation characteristics and / or damping variation characteristics, combined with the current workpiece material type, processing stage and machine tool operating status, evaluate the drift speed of the inherent characteristics of the cutting system; The time decay factor is dynamically adjusted based on the drift velocity. When updating the effect score of the preset flutter suppression strategy in the flutter suppression strategy effect score table, the flutter suppression effect index obtained at different times is weighted by the weight corresponding to the dynamically adjusted time decay factor.

6. The data processing method for multi-axis linkage CNC lathe machining according to claim 4, characterized in that, Define the inertia interval for score updates, including: Acquire real-time vibration information, perform time-frequency analysis on the real-time vibration information, and extract specific frequency variation features and / or damping variation features to characterize the inherent characteristics of the cutting system; Obtain information on the current workpiece material type, processing stage, and machine tool operating status; Based on the specific frequency variation characteristics and / or damping variation characteristics, combined with the current workpiece material type, processing stage and machine tool operating status, evaluate the drift speed and fluctuation amplitude of the inherent characteristics of the cutting system; The size of the inertial interval for score updates is dynamically adjusted based on the drift speed and the fluctuation amplitude.

7. The data processing method for multi-axis linkage CNC lathe machining according to claim 4, characterized in that, After a preset time interval and / or after processing a preset number of workpieces, the effect scores of all preset chatter suppression strategies in the chatter suppression strategy effect scoring table are decayed overall based on the time decay factor using a uniform decay coefficient, including: Acquire real-time vibration information and perform time-frequency analysis on the real-time vibration information to extract specific frequency variation features and / or damping variation features to characterize the inherent characteristics of the cutting system; Obtain information on the current workpiece material type, processing stage, and machine tool operating status; Based on the specific frequency variation characteristics and / or damping variation characteristics, combined with the current workpiece material type, processing stage and machine tool operating status, evaluate the drift speed of the inherent characteristics of the cutting system; The overall attenuation rate and / or uniform attenuation coefficient are dynamically determined based on the drift velocity, and the dynamically determined overall attenuation rate and / or uniform attenuation coefficient are used when performing the overall attenuation.

8. The data processing method for multi-axis linkage CNC lathe machining according to claim 7, characterized in that, The overall attenuation rate and / or uniform attenuation coefficient are dynamically determined based on the drift velocity, including: The preset flutter suppression strategies in the flutter suppression strategy effectiveness rating table are divided into at least two strategy types according to the controlled object and / or the type of adjustment parameter; Based on the drift velocity and the preset drift sensitivity parameters corresponding to each strategy type, the overall decay rate and / or uniform decay coefficient are typified and corrected to obtain the decay rate corresponding to different strategy types. When periodically decaying the overall performance score of all strategies, the performance score is decayed using the corresponding decay rate for each strategy type.

9. A data processing system for multi-axis linkage CNC lathe machining, characterized in that, The method for processing machining data on a multi-axis linkage CNC lathe as described in claim 1 includes: The information acquisition module is used to acquire vibration information of a multi-axis linkage CNC lathe during the machining process, perform preliminary processing on the vibration information to obtain preliminary processed vibration information, and the preliminary processing includes at least digital filtering of the vibration information to remove background noise and interference components unrelated to micro-cutting chatter. The time-frequency analysis module is used to perform time-frequency analysis on the initially processed vibration information to obtain the time spectrum. From the time spectrum, the energy change and instantaneous peak characteristics located in a preset specific high-frequency band are extracted. The specific high-frequency band is used to characterize the early vibration mode of micro-cutting chatter. The chatter identification module is used to compare the energy change and the instantaneous peak characteristics with preset micro-cutting chatter judgment criteria to identify whether there are early signs of micro-cutting chatter. The instruction generation module is used to generate machining parameter adjustment instructions for suppressing micro-cutting chatter when early signs of micro-cutting chatter are identified. The machining parameter adjustment instructions are used to adjust the machining parameters of a multi-axis linkage CNC lathe, and the machining parameters include at least one of spindle speed, feed rate and / or depth of cut. The effect evaluation module is used to acquire vibration information after the processing parameter adjustment command is executed and perform time-frequency analysis to obtain the adjusted energy change and instantaneous peak characteristics, and evaluate the chatter suppression effect of the processing parameter adjustment command based on the adjusted energy change and instantaneous peak characteristics. The parameter adjustment and alarm module is used to readjust the machining parameters and / or issue chatter warnings or shutdown alarms based on the evaluation results of the chatter suppression effect, forming a closed-loop active suppression control for micro-cutting chatter.

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