A tapping machine multi-spindle synchronous servo control method and system

Abstract

The application provides a tapping machine multi-spindle synchronous servo control method and system. The method collects dynamic torque fluctuation data of the worm gear transmission shaft of the tapping machine in real time, generates a torque spectrum matrix through frequency spectrum transformation, and extracts fluctuation spectrum features. A non-contact worm gear torque sensor is integrated on the transmission shaft, and a dynamic sampling strategy is triggered based on the fluctuation features. The change in magnetic induction intensity and the torsional deformation are recorded, and a coupling state identifier of torque and rotational speed is generated according to the coupling relationship. The fluctuation spectrum phase offset features are obtained, the lag risk area exceeding the synchronization tolerance is located based on the coupling identifier, the phase offset amount is extracted as a spindle dynamic compensation vector, the compensation vector is analyzed to generate a synchronization error suppression instruction, and the output torque of the servo shaft is adjusted in real time to suppress the multi-spindle synchronization error. The application realizes microsecond-level worm gear transmission lag dynamic compensation, multi-spindle synchronization error compression, and equipment life extension.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of multi-spindle synchronous servo control, and particularly relates to a tapping machine multi-spindle synchronous servo control method and system. BACKGROUND

[0002] As the core equipment for thread processing, the tapping machine needs to meet the requirements of high precision and high efficiency thread cutting in batch manufacturing of oil pipes, automobile parts and the like. The multi-spindle cooperative operation faces severe challenges: firstly, dynamic load disturbance is easy to cause spindle speed fluctuation, resulting in thread lead error; secondly, phase synchronization accuracy needs to be controlled at the millisecond level to avoid thread misalignment or tooth disorder caused by angle deviation of multi-spindle in high-speed rotation; thirdly, thermal deformation and mechanical resonance will further amplify multi-axis trajectory deviation. The traditional mechanical gear synchronization method has poor anti-disturbance performance due to rigid coupling, and cannot compensate for nonlinear disturbance in real time, so there is an urgent need for a servo control method that can adapt to load changes and suppress phase cumulative error online to ensure that multi-spindles remain strictly synchronized in position, speed and torque under variable working conditions and eliminate the risk of thread rejection in batch processing.

[0003] The current targeted solution is a master-slave cooperative control architecture based on a virtual spindle: the system takes the virtual spindle as a global synchronization reference, and each physical spindle receives the phase instruction of the virtual spindle through a high-speed bus, and dynamically corrects the relative position error between the slave axes by using a cross-coupling compensator; at the same time, a load observer is integrated to estimate the cutting torque disturbance in real time, which is fed forward to the current loop to offset the influence of torque fluctuation on synchronization accuracy. This scheme constructs a full-closed loop control through high-resolution encoder feedback, which significantly improves the synchronization performance of multi-spindles in steady-state conditions. However, the core deficiency is that the decoupling of the virtual instruction source and the dynamic response of the physical axis is not complete, when a sudden load disturbance causes a single-axis transient step-out, the phase error will spread to the whole domain through the coupling network, and the system lacks the ability to adaptively suppress the frequency drift of mechanical resonance, resulting in a sharp decline in synchronization stability under high-speed conditions. SUMMARY

[0004] The present application provides a tapping machine multi-spindle synchronous servo control method and system to solve the problems of existing technology such as worm gear transmission lag compensation inaccuracy, multi-spindle synchronization error diffusion and equipment wear loss control.

[0005] In a first aspect, the present application provides a tapping machine multi-spindle synchronous servo control method, comprising:

[0006] Real-time acquisition of dynamic torque fluctuation data of the worm gear transmission shaft of the tapping machine, generation of a torque spectrum matrix by performing frequency spectrum transformation on the dynamic torque fluctuation data, and extraction of fluctuation spectrum features from the torque spectrum matrix;

[0007] integrating a non-contact worm gear torque sensor on the worm gear transmission shaft, and triggering the non-contact worm gear torque sensor to perform a dynamic sampling strategy based on the fluctuation spectrum feature;

[0008] recording the magnetic intensity change and torsional deformation of the worm gear transmission shaft under the dynamic sampling strategy, and generating a coupling state identifier of the torque and rotational speed of the worm gear transmission shaft according to the coupling relationship between the magnetic intensity change and the torsional deformation;

[0009] obtaining a phase shift feature of the fluctuation spectrum feature, and positioning a lag risk area of the phase shift feature exceeding a preset synchronization tolerance based on the coupling state identifier, and extracting a phase shift amount corresponding to the lag risk area as a main shaft dynamic compensation vector;

[0010] analyzing the main shaft dynamic compensation vector to generate a synchronization error suppression instruction, and adjusting the output torque instruction of each servo shaft of the screw machine in real time according to the synchronization error suppression instruction to suppress the multi-main shaft synchronization error caused by worm gear transmission lag.

[0011] Optionally, the dynamic torque fluctuation data of the worm gear transmission shaft of the screw machine is collected in real time, the torque spectrum matrix is generated by performing spectrum transformation on the dynamic torque fluctuation data, and the fluctuation spectrum feature is extracted from the torque spectrum matrix, including:

[0012] The torque change value during the rotation of the worm gear transmission shaft of the screw machine is continuously collected, and the torque change value is arranged in time sequence to form a dynamic torque fluctuation data sequence;

[0013] The dynamic torque fluctuation data sequence is divided into multiple data segments according to a fixed time window, and a conversion operation from time domain to frequency domain is performed on each data segment to generate a spectrum distribution segment;

[0014] The spectrum distribution segments are spliced in time sequence to form a torque spectrum matrix, and the spectrum intensity value of the torque spectrum matrix is extracted;

[0015] The frequency component whose spectrum intensity value exceeds a preset background noise threshold is identified, and the spectrum intensity value corresponding to the frequency component is recorded as the fluctuation spectrum feature.

[0016] Optionally, a non-contact worm gear torque sensor is integrated on the worm gear transmission shaft, and the non-contact worm gear torque sensor is triggered to perform a dynamic sampling strategy based on the fluctuation spectrum feature, including:

[0017] A non-contact worm gear torque sensor is integrated on the worm gear transmission shaft, and the spectrum intensity value recorded in the fluctuation spectrum feature is read, and an activation instruction of the non-contact worm gear torque sensor is generated when the spectrum intensity value exceeds a preset trigger threshold;

[0018] According to the activation instruction, a dynamic scanning operation of the non-contact worm torque sensor on the surface magnetic field of the worm transmission shaft is started to collect an initial magnetic field reference reading;

[0019] According to the frequency component whose spectrum intensity value exceeds the trigger threshold, a sampling frequency parameter is set, and a magnetic field sampling operation of the magnetic field sensing probe of the non-contact worm torque sensor is controlled according to the sampling frequency parameter to record a real-time magnetic field intensity change sequence;

[0020] The initial magnetic field reference reading and the real-time magnetic field intensity change sequence are integrated to form a dynamic sampling strategy.

[0021] Optionally, according to the frequency component whose spectrum intensity value exceeds the trigger threshold, a sampling frequency parameter is set, and a magnetic field sampling operation of the magnetic field sensing probe of the non-contact worm torque sensor is controlled according to the sampling frequency parameter to record a real-time magnetic field intensity change sequence, including:

[0022] The frequency component exceeding the trigger threshold in the fluctuation spectrum feature is extracted, and the frequency component is combined with a preset sampling multiple coefficient to generate a sampling frequency parameter;

[0023] The sampling frequency parameter is written into a magnetic field sensing probe control register of the non-contact worm torque sensor to set a probe sampling interval time;

[0024] The magnetic field sensing probe of the non-contact worm torque sensor is started to perform a magnetic field intensity collection operation according to the sampling interval time, and a real-time magnetic field intensity original reading output by the magnetic field sensing probe is recorded;

[0025] The difference between the real-time magnetic field intensity original reading and the initial magnetic field reference reading is calculated as a real-time magnetic field intensity change;

[0026] All real-time magnetic field intensity changes are arranged in a sampling time sequence to form a real-time magnetic field intensity change sequence.

[0027] Optionally, the magnetic induction intensity change and the torsional deformation of the worm transmission shaft under the dynamic sampling strategy are recorded, and a coupling state identifier of the torque and the rotational speed of the worm transmission shaft is generated according to the coupling relationship between the magnetic induction intensity change and the torsional deformation, including:

[0028] A target point is marked on the surface of the worm transmission shaft, and a displacement change of the target point is captured by a high-speed vision sensor of the device as a torsional deformation sequence;

[0029] The magnetic field intensity change sequence and the torsional deformation sequence are aligned at the same time point to form a paired data unit;

[0030] a ratio of the magnetic field intensity change amount and the torsional deformation amount in the pairing data unit is calculated as a coupling relationship coefficient of the magnetic field intensity change and the torsional deformation;

[0031] When the coupling relationship coefficient exceeds a preset material characteristic calibration threshold, the corresponding time point is marked as an abnormal coupling state point;

[0032] The abnormal coupling state points are integrated to generate a coupling state identifier representing torque and rotational speed correlation abnormalities of the worm gear transmission shaft.

[0033] Optionally, a phase shift feature of the fluctuation frequency spectrum feature is obtained, and a lag risk area where the phase shift feature exceeds a preset synchronization tolerance is located based on the coupling state identifier, and a phase shift amount corresponding to the lag risk area is extracted as a main shaft dynamic compensation vector, including:

[0034] A phase angle change value corresponding to a frequency component recorded in the fluctuation frequency spectrum feature is extracted, and the phase angle change value is arranged in time sequence to form a phase shift feature sequence;

[0035] In a time interval corresponding to the abnormal coupling state points marked by the coupling state identifier, a phase angle change value of the same time point in the phase shift feature sequence is located;

[0036] The phase angle change value is compared with a preset synchronization tolerance threshold, and when the phase angle change value exceeds the synchronization tolerance threshold limit, the corresponding time point is marked as a lag risk point;

[0037] A time interval where continuous lag risk points are located is integrated, and a working phase of the worm gear transmission shaft covered by the time interval is marked as a lag risk area;

[0038] An average value of the phase angle change value in the lag risk area is extracted as a main shaft dynamic compensation vector.

[0039] Optionally, the main shaft dynamic compensation vector is analyzed to generate a synchronization error suppression instruction, and the output torque instruction of each servo shaft of the threading machine is adjusted in real time according to the synchronization error suppression instruction to suppress the multi-main shaft synchronization error caused by the worm gear transmission lag, including:

[0040] The main shaft dynamic compensation vector is combined with a preset torque correction coefficient to generate a torque correction value;

[0041] The current output torque instruction of each servo shaft of the threading machine is obtained, and the torque correction value is superimposed on the output torque instruction of the corresponding servo shaft to form an updated output torque instruction;

[0042] A synchronization error suppression instruction is generated according to the updated output torque instruction, and the synchronization error suppression instruction is transmitted to the threading machine servo shaft controller.

[0043] The synchronous error suppression instruction is executed by a servo shaft controller to adjust the output torque of each servo shaft according to the synchronous error suppression instruction to suppress the multi-spindle synchronous error caused by the worm drive lag.

[0044] In a second aspect, the application provides a multi-spindle synchronous servo control system of a tapping machine, comprising:

[0045] The acquisition module is configured to acquire dynamic torque fluctuation data of the worm drive shaft of the tapping machine in real time, perform frequency spectrum transformation on the dynamic torque fluctuation data to generate a torque frequency spectrum matrix, and extract fluctuation frequency spectrum features from the torque frequency spectrum matrix.

[0046] The triggering module is configured to integrate a non-contact worm torque sensor on the worm drive shaft, and trigger the non-contact worm torque sensor to perform a dynamic sampling strategy based on the fluctuation frequency spectrum features.

[0047] The generation module is configured to record the magnetic induction intensity change and torsional deformation of the worm drive shaft under the dynamic sampling strategy, and generate a coupling state identifier of the torque and rotational speed of the worm drive shaft according to the coupling relationship between the magnetic induction intensity change and the torsional deformation.

[0048] The positioning module is configured to obtain a phase offset feature of the fluctuation frequency spectrum features, locate a lag risk area where the phase offset feature exceeds a preset synchronization tolerance based on the coupling state identifier, and extract a phase offset amount corresponding to the lag risk area as a spindle dynamic compensation vector.

[0049] The adjustment module is configured to analyze the spindle dynamic compensation vector to generate a synchronous error suppression instruction, and adjust the output torque instruction of each servo shaft of the tapping machine in real time according to the synchronous error suppression instruction to suppress the multi-spindle synchronous error caused by the worm drive lag.

[0050] In a third aspect, the application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a tapping machine multi-spindle synchronous servo control method as described in the first aspect above.

[0051] In a fourth aspect, the application provides a computer storage medium storing a computer program, wherein the computer program is executed by a computer to implement a tapping machine multi-spindle synchronous servo control method as described in the first aspect.

[0052] In the technical scheme of the application, dynamic torque monitoring and intelligent compensation technology are used to realize high-precision synchronous control of multiple spindles of a tapping machine. The torque fluctuation feature extraction based on spectrum analysis significantly improves the transmission state recognition sensitivity; the non-contact sensing and magnetic sensing-deformation coupling analysis ensure the reliability of the dynamic sampling data; and the phase offset compensation mechanism effectively suppresses the multi-spindle synchronization error. This method breaks through the control limitations of traditional mechanical transmission, realizes full-closed-loop intelligent regulation and control from torque fluctuation detection to real-time compensation, significantly improves the machining precision and equipment operation stability of the tapping machine, and provides a high-synchronization spindle control solution for complex thread machining.

[0053] Further, dynamic torque monitoring and intelligent compensation technology are used to realize high-precision synchronous control of multiple spindles of a tapping machine. The torque fluctuation feature extraction based on spectrum analysis significantly improves the transmission state recognition sensitivity; the non-contact sensing and magnetic sensing-deformation coupling analysis ensure the reliability of the dynamic sampling data; and the phase offset compensation mechanism effectively suppresses the multi-spindle synchronization error. This method breaks through the control limitations of traditional mechanical transmission, realizes full-closed-loop intelligent regulation and control from torque fluctuation detection to real-time compensation, significantly improves the machining precision and equipment operation stability of the tapping machine, and provides a high-synchronization spindle control solution for complex thread machining.

[0054] These aspects or other aspects of the application will be more apparent in the following description of the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0056] Figure 1 A flow chart of a tapping machine multi-spindle synchronous servo control method provided by the application is shown;

[0057] Figure 2 A structural schematic diagram of a tapping machine multi-spindle synchronous servo control system provided by the application is shown;

[0058] Figure 3 A structural schematic diagram of a computing device provided by the application is shown. DETAILED DESCRIPTION

[0059] In order to make those skilled in the art better understand the technical solutions of the application, the technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application.

[0060] In some of the flowcharts described in the description and claims of the application and in the above description of the figures, a plurality of operations are included in a flowchart that occurs in a particular order. However, it should be clear that these operations can be performed in the order in which they appear in this document or in parallel, and the serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these flowcharts can include more or fewer operations, and these operations can be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do "first" and "second" represent different types.

[0061] Researchers found that the multi-spindle synchronous control technology of the tapping machine faces a fundamental bottleneck: the collaborative architecture based on virtual spindles can improve the steady-state synchronization accuracy, but the lack of dynamic disturbance decoupling and the failure of resonance suppression lead to system instability under high-speed working conditions. Specifically, the instantaneous step-out of a single axis caused by sudden load disturbance will spread through the coupling network to become a global phase cumulative error, and the trajectory deviation amplification effect caused by mechanical resonance frequency drift will intensify, resulting in a sharp increase in thread cross-bucking rate in batch processing. This contradiction stems from the blind spot in the collaborative perception of dynamic torque fluctuations and phase shifts of the worm gear transmission system, and it is urgent to build a closed-loop anti-disturbance architecture from the mechanical transmission layer to the control layer.

[0062] In view of the above challenges, the present application proposes a multi-spindle synchronous servo control method for a tapping machine, which is innovative in that it breaks through the shackles of virtual spindle control by torque fluctuation feature-driven dynamic sampling and magnetic deformation coupling analysis. Specifically: real-time acquisition of dynamic torque fluctuation data of the worm gear transmission shaft, extraction of fluctuation frequency spectrum features through frequency spectrum transformation; triggering a non-contact torque sensor to execute a dynamic sampling strategy, synchronously capturing magnetic intensity changes and torsional deformation; generating a torque-speed state identifier based on the magnetic deformation coupling relationship; locating the lag risk area that exceeds the synchronization tolerance through phase shift feature, extracting the phase shift amount as a dynamic compensation vector; analyzing the compensation vector to generate a synchronization error suppression instruction, and adjusting the output torque of the servo shaft in real time. This method overturns the traditional master-slave control paradigm: torque spectrum feature-driven sampling first realizes millisecond-level perception of nonlinear disturbances of the transmission system; the magnetic deformation coupling identifier accurately quantifies the impact of resonance frequency drift on phase synchronization through the deep integration of mechanical state and electrical signal; the dynamic compensation mechanism forms a closed-loop control chain of "fluctuation perception-state interpretation-offset positioning-real-time suppression", providing essential guarantees for high-precision thread machining from mechanical transmission disturbance to multi-axis collaborative stability.

[0063] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0064] Figure 1 A flow chart of a multi-spindle synchronous servo control method of a tapping machine is provided for the embodiments of the present application, as shown in the figure, the method comprises: Figure 1

[0065] 101, collecting dynamic torque fluctuation data of a worm transmission shaft of the tapping machine in real time, performing frequency spectrum transformation on the dynamic torque fluctuation data to generate a torque frequency spectrum matrix, and extracting fluctuation spectrum features from the torque frequency spectrum matrix.

[0066] Optionally, step 101 can specifically include the following steps:

[0067] 1011, continuously collecting torque change values in the rotation process of the worm transmission shaft of the tapping machine, and arranging the torque change values in time sequence to form a dynamic torque fluctuation data sequence.

[0068] 1012, dividing the dynamic torque fluctuation data sequence into multiple data segments according to a fixed time window, and performing a conversion operation from a time domain to a frequency domain for each data segment to generate a spectrum distribution segment.

[0069] 1013, splicing the spectrum distribution segments in time sequence into a torque frequency spectrum matrix, and extracting a spectrum intensity value of the torque frequency spectrum matrix.

[0070] 1014, identifying a frequency component whose spectrum intensity value exceeds a preset background noise threshold, and recording the spectrum intensity value corresponding to the frequency component as a fluctuation spectrum feature.

[0071] In the above scheme, the tapping machine refers to a machine tool for machining threads. The worm transmission shaft refers to the rotation shaft of worm transmission. The dynamic torque fluctuation data refers to the data of the change of torque with time. The frequency spectrum transformation refers to a method of converting a time domain signal into a frequency domain signal. The torque frequency spectrum matrix refers to a matrix reflecting the torque spectrum characteristics. The fluctuation spectrum feature refers to a characteristic component in the spectrum. The torque change value refers to the instantaneous change amount of torque. The dynamic torque fluctuation data sequence refers to the torque data arranged in time. The time window refers to the time range of data segmentation. The spectrum distribution segment refers to short-time spectrum data. The spectrum intensity value refers to the intensity of the spectrum component. The preset background noise threshold refers to a critical value for judging effective signals. The frequency component refers to a frequency component in the spectrum.

[0072] ​In the embodiments of the present application, first, the system continuously collects the torque change value in the rotation process through the dynamic torque sensor installed on the worm drive shaft of the tapping machine: the data acquisition module is based on the strain gauge electrical measurement technology, and the resistance strain gauges are pasted on the surface of the transmission shaft to form a Wheatstone bridge. When the transmission shaft is subjected to torsional force and produces a slight deformation, the resistance value of the strain gauge changes in real time, and the resistance change is converted into a voltage signal output through a signal conditioning circuit. The time sequence recorder captures the voltage signal at a fixed sampling frequency and arranges it in time stamp order as a dynamic torque fluctuation data sequence to form an original time domain signal stream. This process relies on high-precision analog-to-digital conversion technology to ensure data real-time and integrity.

[0073] Subsequently, the system divides the dynamic torque fluctuation data sequence into data segments of a fixed time window and performs time-frequency conversion on each data segment: the window divider divides the continuous data stream into multiple data segments according to the preset time length, and each segment contains an equal number of sampling points. The frequency spectrum converter applies the fast Fourier transform algorithm to each data segment to decompose the time domain torque signal into frequency components, generating a frequency spectrum distribution segment containing amplitude and phase information. This process suppresses spectral leakage through a windowing function (such as the Hanning window) to ensure the accuracy of the frequency energy distribution.

[0074] Next, the system concatenates the frequency spectrum distribution segments in time sequence to form a torque frequency spectrum matrix, and extracts the frequency spectrum intensity value: the matrix construction module arranges the frequency spectrum distribution segments of each data segment in time sequence as a two-dimensional matrix, with the row direction representing the time axis and the column direction representing the frequency axis, and the matrix elements being the amplitudes of the corresponding time and frequency points. The intensity extractor traverses each frequency point in the matrix and takes the square of its amplitude as the frequency spectrum intensity value (quantifying the energy size of the frequency component in a specific time window). This process integrates the torque fluctuation characteristics in time and space dimensions based on time-frequency matrix reconstruction technology.

[0075] Finally, the system identifies the frequency components whose frequency spectrum intensity value exceeds the preset background noise threshold and records their intensity as the fluctuation spectrum feature: the noise threshold filter compares the frequency spectrum intensity value of each frequency point with the preset background noise threshold (the background noise energy calibrated through the no-load experiment). The feature extractor selects all frequency components exceeding the limit, records their frequency values and corresponding frequency spectrum intensity values, and forms a fluctuation spectrum feature set. This process uses a peak search algorithm to automatically locate the significant frequency components and exclude environmental interference.

[0076] In practical applications, in the multi-spindle thread machining synchronous control scene of the tapping machine, a strain gauge type torque sensor is installed on the worm gear transmission shaft of the tapping machine to continuously collect the torque change value in the rotation process of the worm gear transmission shaft of the tapping machine (such as the periodic load fluctuation borne by the worm gear transmission shaft when the main shaft rotates at high speed), and the torque change value is arranged in time sequence to form a dynamic torque fluctuation data sequence (step 1011); then the dynamic torque fluctuation data sequence is divided into multiple data segments according to a fixed time window (such as a millisecond level window to divide a single feed period of thread machining), and a conversion operation from time domain to frequency domain is performed on each data segment to generate a frequency spectrum distribution segment (step 1012); then the frequency spectrum distribution segments are spliced in time sequence to form a torque frequency spectrum matrix (such as arranging the frequency domain energy distribution of each segment according to the machining period sequence), and the frequency spectrum intensity value of the torque frequency spectrum matrix is extracted (step 1013); finally, the frequency component whose frequency spectrum intensity value exceeds a preset background noise threshold is identified (such as excluding environmental vibration noise and retaining the characteristic frequency related to the main shaft speed), and the frequency spectrum intensity value corresponding to the frequency component is recorded as a fluctuation frequency spectrum feature (step 1014). The fluctuation frequency spectrum feature is used for diagnosing the multi-spindle synchronous state: the dynamic torque fluctuation data sequence reflects the load mutation of the worm gear transmission shaft in thread cutting (such as the instantaneous torque surge when the tool cuts into the workpiece), the frequency spectrum distribution segment reveals the frequency response mode of a specific machining stage (such as high-frequency harmonics in the main shaft acceleration stage), the torque frequency spectrum matrix maps abnormal frequency points of the whole machining period through time-frequency correlation (such as low-frequency resonance in the thread end stage), and the fluctuation frequency spectrum feature quantifies the root cause of the synchronization error (such as the torque fluctuation frequency spectrum intensity value exceeding the standard due to transmission clearance of a certain main shaft), thereby providing error compensation basis for subsequent multi-spindle servo control.

[0077] The scheme of step 101 above realizes fine spectrum analysis and feature extraction of the torque fluctuation of the worm gear transmission shaft. Through continuous collection and time-frequency conversion processing of dynamic torque data, a torque frequency spectrum matrix reflecting the dynamic characteristics of the transmission system is innovatively constructed. The technology uses an adaptive noise threshold identification algorithm to accurately extract the fluctuation frequency spectrum feature with engineering significance, thereby providing a key frequency domain index for subsequent state monitoring. Through time window segmentation and spectrum splicing technology, seamless connection from transient analysis to continuous monitoring is realized, thereby establishing a scientific frequency domain feature library for transmission system fault warning.

[0078] 102. Integrating a non-contact worm torque sensor on the worm gear transmission shaft, and triggering the non-contact worm torque sensor based on the fluctuation frequency spectrum feature to perform a dynamic sampling strategy.

[0079] Optionally, step 102 can specifically include the following steps:

[0080] 1021. A non-contact worm gear torque sensor is integrated on the worm gear transmission shaft, and the spectral intensity value recorded in the fluctuation spectrum characteristics is read. When the spectral intensity value exceeds a preset trigger threshold, an activation command for the non-contact worm gear torque sensor is generated.

[0081] 1022. According to the activation command, the non-contact worm gear torque sensor is activated to perform a dynamic scanning operation on the magnetic field of the worm gear drive shaft surface to acquire an initial magnetic field reference reading.

[0082] 1023. Set the sampling frequency parameter according to the frequency component whose spectral intensity value exceeds the trigger threshold, and control the magnetic field sensing probe of the non-contact worm gear torque sensor to perform magnetic field sampling operation according to the sampling frequency parameter to record the real-time magnetic field intensity change sequence.

[0083] Step 1023 may specifically include the following processes: extracting frequency components exceeding the trigger threshold from the fluctuation spectrum characteristics, and combining the frequency components with a preset sampling multiple coefficient to generate sampling frequency parameters; writing the sampling frequency parameters into the magnetic field sensing probe control register of the non-contact worm gear torque sensor to set the probe sampling interval time; starting the magnetic field sensing probe of the non-contact worm gear torque sensor to perform magnetic field strength acquisition operation according to the sampling interval time, and simultaneously recording the real-time magnetic field strength raw reading output by the magnetic field sensing probe; calculating the difference between the real-time magnetic field strength raw reading and the initial magnetic field reference reading as the real-time magnetic field strength change; arranging all real-time magnetic field strength changes in order of sampling time to form a real-time magnetic field strength change sequence.

[0084] 1024. Integrate the initial magnetic field reference readings with the real-time magnetic field intensity change sequence to form a dynamic sampling strategy.

[0085] In the above scheme, a non-contact worm gear torque sensor refers to a sensor that can measure torque without contact. Dynamic sampling strategy refers to a sampling scheme adjusted according to requirements. Spectrum intensity value refers to the intensity of a spectral component. Preset trigger threshold refers to the critical value for activating the sensor. Activation command refers to the control signal that starts the sensor. Dynamic scanning operation refers to the process of scanning the magnetic field. Initial magnetic field reference reading refers to the initial magnetic field intensity value. Sampling frequency parameter refers to the setting parameter of the sampling frequency. Magnetic field induction probe refers to the sensor component that detects the magnetic field. Magnetic field sampling operation refers to the process of acquiring magnetic field data. Real-time magnetic field intensity change sequence refers to the time-series data of magnetic field changes. Frequency component refers to the frequency components in the spectrum. Preset sampling multiple coefficient refers to the coefficient used to calculate the sampling frequency. Control register refers to the register that controls the sensor parameters. Sampling interval time refers to the time interval between two samplings. Real-time raw magnetic field intensity reading refers to the raw measured value of the magnetic field intensity.

[0086] In the embodiments of the present application, first, the system integrates a non-contact worm gear torque sensor on the worm gear transmission shaft: the sensor deployment module fixes the non-contact worm gear torque sensor on the surface of the transmission shaft, and the internal Hall element maintains an induction distance with the shaft surface magnetic field. The feature triggering module reads the frequency spectrum intensity value recorded in the fluctuation frequency spectrum feature extracted in step 101 in real time, and compares the frequency spectrum intensity value of each frequency component with the preset trigger threshold (set according to the device safety operation standard) through the threshold comparator. When the frequency spectrum intensity value of any frequency component exceeds the preset trigger threshold, the instruction generator generates an activation instruction of the non-contact worm gear torque sensor, and starts the subsequent dynamic sampling process.

[0087] Subsequently, the system starts the dynamic scanning operation of the sensor according to the activation instruction: after receiving the activation instruction, the instruction parser controls the magnetic field scanning unit of the non-contact worm gear torque sensor to perform full-range magnetic field scanning on the surface of the worm gear transmission shaft. The reference collector records the original distribution state of the current shaft surface magnetic field, and generates an initial magnetic field reference reading (i.e. the magnetic field intensity reference value under no load or steady state). This process is based on magnetic field calibration technology, and provides a reference for subsequent real-time change calculation.

[0088] Then, the system sets the sampling frequency parameter according to the over-limit frequency component and performs magnetic field sampling: the frequency parameter generator extracts the frequency component that exceeds the trigger threshold in the fluctuation frequency spectrum feature, multiplies its frequency value by the preset sampling multiple coefficient (such as a multiple of the Nyquist sampling rate), and generates a sampling frequency parameter. The parameter configurator writes the parameter into the control register of the sensor magnetic field induction probe, and sets the sampling interval time of the probe. The probe controller starts the magnetic field induction probe to perform high-frequency magnetic field intensity collection at this interval, and outputs real-time magnetic field intensity original readings. The change calculator calculates the difference between each real-time reading and the initial magnetic field reference reading to obtain the real-time magnetic field intensity change. The sequence constructor arranges all the changes according to the time stamp to form a real-time magnetic field intensity change sequence, and records the torque dynamic fluctuation completely.

[0089] Finally, the system integrates the reference reading and the change sequence to form a dynamic sampling strategy: the strategy synthesis engine associates the initial magnetic field reference reading as a static reference with the dynamic fluctuation data represented by the real-time magnetic field intensity change sequence. The data encapsulator binds the two as a structured data packet to generate a dynamic sampling strategy containing time-magnetic field change mapping relationship. This strategy is used to drive the sensor to switch the sampling mode as needed (such as low-frequency sampling in steady state and high-frequency sampling in abnormal state) subsequently, so as to realize resource optimization and accurate monitoring.

[0090] In practical applications, in the dynamic load compensation scene of multi-spindle thread machining of the tapping machine, a non-contact worm torque sensor (wireless measurement device based on magneto-inductive principle) is integrated on the worm transmission shaft of the tapping machine. The fluctuation frequency spectrum features (such as specific frequency components reflecting abnormal wear of the transmission shaft) extracted in step 101 trigger a dynamic sampling strategy. When the spectral intensity value recorded in the fluctuation frequency spectrum is detected to exceed the preset trigger threshold (such as the sudden increase in harmonic intensity caused by worm gear surface fatigue cracks), an activation instruction of the non-contact worm torque sensor is generated (step 1021). Then, according to the activation instruction, the non-contact worm torque sensor starts the dynamic scanning operation of the surface magnetic field of the worm transmission shaft. The initial magnetic field reference reading is collected by the Hall probe under the no-load state (step 1022). Then, the frequency components (such as high-frequency disturbances related to the meshing gap of the worm) exceeding the trigger threshold in the fluctuation frequency spectrum are extracted, combined with the preset sampling multiple coefficient to generate a sampling frequency parameter, and written into the magnetic field induction probe control register of the non-contact worm torque sensor to set the probe sampling interval time. The magnetic field induction probe executes the magnetic field intensity collection operation according to the sampling interval time, records the real-time magnetic field intensity original reading, calculates the difference between the real-time magnetic field intensity original reading and the initial magnetic field reference reading as the real-time magnetic field intensity change, and arranges the real-time magnetic field intensity change in time sequence to form the real-time magnetic field intensity change sequence (step 1023). Finally, the initial magnetic field reference reading and the real-time magnetic field intensity change sequence are integrated to generate a complete dynamic sampling strategy (step 1024). The strategy triggers the sensor activation through the spectral intensity value exceeding the standard, and the adaptive setting of the sampling frequency parameter ensures the accurate capture of high-frequency disturbances. The real-time magnetic field intensity change sequence quantifies the load transient (such as the sequence peak corresponding to the instantaneous torque impact of the tool cutting into the workpiece), providing real-time compensation basis for the multi-spindle synchronous servo system: the dynamic sampling strategy drives the slave torque feedforward control, eliminating the phase deviation of the master and slave shafts caused by worm wear; the magnetic field intensity change maps the mechanical transmission stiffness degradation trend, and the predictive maintenance system warns the key component life.

[0091] The above step 102 scheme realizes the dynamic regulation of the intelligent sensing sampling strategy based on the frequency spectrum features. Through the linkage mechanism of the non-contact torque sensor and the frequency spectrum features, a triggered dynamic sampling scheme is innovatively designed. The technology uses a frequency component adaptive sampling parameter generation algorithm to ensure accurate matching of magnetic field measurement and torque fluctuation. The innovative reference reading-change quantity dual-track recording mode not only retains the static reference, but also completely captures the dynamic change process, providing a high-quality data basis for torque-magnetic field relationship analysis.

[0092] 103. record the magnetic field intensity variation and the torsional deformation of the worm gear transmission shaft under the dynamic sampling strategy, and generate a coupling state identifier of the torque and the rotation speed of the worm gear transmission shaft according to a coupling relationship between the magnetic field intensity variation and the torsional deformation.

[0093] Optionally, step 103 can specifically include the following steps:

[0094] 1031. mark a target point on the surface of the worm gear transmission shaft, and capture a displacement variation of the target point as a torsional deformation sequence by a high-speed visual sensor of the device.

[0095] 1032. align the magnetic field intensity variation sequence and the torsional deformation sequence at the same time point to form a paired data unit.

[0096] 1033. calculate a ratio of the magnetic field intensity variation and the torsional deformation in the paired data unit as a coupling relationship coefficient of the magnetic field intensity variation and the torsional deformation.

[0097] 1034. mark a corresponding time point as an abnormal coupling state point when the coupling relationship coefficient exceeds a preset material characteristic calibration threshold.

[0098] 1035. integrate the abnormal coupling state points to generate a coupling state identifier representing an abnormal relationship between the torque and the rotation speed of the worm gear transmission shaft.

[0099] In the above scheme, the target point refers to a reference point marked on the worm gear transmission shaft. The high-speed visual sensor refers to a sensor that captures images at high speed. The displacement variation refers to the amount of change in position. The torsional deformation sequence refers to time series data of torsional deformation. The paired data unit refers to a combination of mutually related data. The coupling relationship coefficient refers to a coefficient reflecting the relationship between the magnetic field intensity and the deformation. The preset material characteristic calibration threshold refers to a critical value for judging abnormalities. The abnormal coupling state point refers to a time point of coupling abnormality. The coupling state identifier refers to a symbol identifying the coupling state. The coupling state of the torque and the rotation speed refers to the relationship between the torque and the rotation speed.

[0100] In the embodiments of the present application, first, the system marks a target point on the surface of the worm gear transmission shaft: a marking positioning module uses a high-reflectivity material (such as a reflective ceramic sheet) to make a target point at a specific position on the surface of the transmission shaft, ensuring that it can be stably recognized by an optical device during rotation. A visual capture system uses a high-speed visual sensor (such as a kiloframe industrial camera) to continuously shoot the rotating shaft with a microsecond exposure time, and extracts the spatial coordinates of the target point in each frame of image through a sub-pixel edge detection algorithm. A displacement analyzer compares the angular displacement deviation of the target point at adjacent time points, calculates the circumferential displacement difference as a torsional deformation variable, and arranges the torsional deformation variables in time sequence to form a torsional deformation sequence, quantifying the instantaneous elastic deformation of the shaft body under the action of torque.

[0101] Subsequently, the system aligns the magnetic field strength change sequence and the torsional deformation sequence by time point: the time synchronizer receives the real-time magnetic field strength change sequence (from the non-contact torque sensor) and the torsional deformation sequence (from the high-speed visual sensor) generated in step 102, and unifies the time reference of the two data streams through high-precision clock stamp alignment technology. The data pairing engine binds the magnetic field strength change and the torsional deformation collected at the same time into paired data units in units of millisecond time windows, forming a spatiotemporal correlated data structure. This process is based on multi-source sensor data fusion technology, ensuring the synchronicity and comparability of physical parameters.

[0102] Next, the system calculates the ratio of the magnetic field strength change and the torsional deformation in the paired data unit: the coefficient generator traverses each paired data unit to extract the magnetic field strength change value and the torsional deformation value in the unit. The physical relationship analysis module takes the ratio of the two as the coupling relationship coefficient of the magnetic field strength change and the torsional deformation (i.e., the magnetic field change required per unit deformation), which represents the magneto-elastic response characteristics of the material under torque load. This process establishes a quantitative correlation between magnetic field fluctuations and mechanical deformation based on the principle of magnetoelastic effect through a linear proportional model.

[0103] Then, the system detects whether the coupling relationship coefficient exceeds the preset material characteristic calibration threshold: the threshold comparator calls the preset material characteristic calibration threshold (such as the safe range of the magneto-elastic response of the copper alloy worm gear) calibrated by the material laboratory, and compares it with the coupling relationship coefficient calculated in real time point by point. The abnormal marker module records the time point as an abnormal coupling state point when the coefficient exceeds the upper limit or is below the lower limit, identifying the torque-deformation relationship at that moment deviating from the normal physical characteristics of the material. This process uses the threshold out-of-bound triggering mechanism to accurately locate the critical moment of material fatigue or structural abnormality.

[0104] Finally, the system integrates the abnormal coupling state points to generate a coupling state identifier: the identification engine aggregates the time stamps and deviation data of all abnormal coupling state points, and merges the discrete abnormal points into continuous abnormal intervals according to time continuity through state clustering algorithm. The data encapsulator generates a coupling state identifier containing the abnormal period, peak deviation, and duration, which is represented by binary encoding or color label (such as red alert) to represent the torque and rotational speed correlation anomaly of the worm gear transmission shaft, providing a structured abnormal report for subsequent maintenance decisions.

[0105] In practical applications, in the health monitoring scene of multi-spindle thread machining worm gear shaft of a thread cutting machine, during the operation of the thread cutting machine, a dynamic sampling strategy is implemented for the worm gear shaft. First, laser etching target points (such as circumferentially distributed sub-millimeter reflective markers) are etched on the surface of the transmission shaft. A high-speed vision sensor is set up to capture the circumferential displacement change of the target points at a microsecond level frame rate, generating a torsional deformation sequence under the action of the axial torque load (step 1031). The real-time magnetic field intensity change sequence output by step 102 is simultaneously acquired, and the two are time-stamped according to the acquisition system clock signal to form paired data units (each unit contains the magnetic field intensity change and the torsional deformation at the same time) (step 1032). Based on the paired data units, the instantaneous ratio of the magnetic field intensity change to the torsional deformation is calculated as the coupling relationship coefficient of the magnetic field intensity change and the torsional deformation (this coefficient reflects the material magnetic-force response characteristic) (step 1033). When the coupling relationship coefficient exceeds the preset material characteristic threshold (calibrated according to the permeability and elastic modulus of the worm gear material), the time point is marked as an abnormal coupling state point (such as a sudden drop in the coefficient indicating material fatigue leading to magnetic-force decoupling) (step 1034). Finally, the time distribution and intensity of all abnormal coupling state points are integrated to generate a coupling state identifier (such as green / yellow / red three-level identifiers corresponding to normal / warning / failure states) (step 1035). This identifier reveals the mechanical state degradation through the abnormal association of the torsional deformation sequence and the magnetic field intensity change: displacement abnormalities of the target points (such as sudden increases in axial distortion) expose micro-crack propagation in the transmission shaft, and threshold exceeding of the coupling relationship coefficient triggers a material failure warning (such as a red identifier corresponding to plastic deformation in the stress concentration area of the worm gear tooth root). The coupling state identifier is input into the multi-spindle synchronous servo system to real-time correct the spindle torque command to compensate for the stiffness degradation of the main transmission shaft, avoiding multi-spindle synchronization errors caused by single-axis performance degradation.

[0106] The scheme of step 103 above realizes online identification and abnormal detection of the torque-deformation coupling state. Through multi-modal data fusion of magnetic field intensity and visual measurement, a coupling relationship model reflecting material characteristics is innovatively constructed. This technology uses time-aligned paired data analysis method to accurately quantify the dynamic correlation strength of magnetic field change and mechanical deformation. Through intelligent comparison of the preset material threshold, the abnormal coupling state is automatically marked, providing a new technical path for transmission system health state evaluation.

[0107] 104. Obtain the phase shift feature of the fluctuation frequency spectrum feature, and based on the coupling state identifier, locate a lag risk area where the phase shift feature exceeds a preset synchronization tolerance, and extract the phase shift amount corresponding to the lag risk area as a spindle dynamic compensation vector.

[0108] Optionally, step 104 can specifically include the following steps:

[0109] 1041、extracting a phase angle change value corresponding to a frequency component recorded in the fluctuation spectrum feature, and arranging the phase angle change value in time sequence to form a phase shift feature sequence.

[0110] 1042、locating a phase angle change value at a same time point in the phase shift feature sequence within a time interval corresponding to an abnormal coupling state point marked by the coupling state identifier.

[0111] 1043、comparing the phase angle change value with a preset synchronization tolerance threshold, and marking a corresponding time point as a lag risk point when the phase angle change value exceeds the synchronization tolerance threshold limit.

[0112] 1044、integrating a time interval in which continuous lag risk points are located, and marking a working phase of the worm gear transmission shaft covered by the time interval as a lag risk area.

[0113] 1045、extracting an average value of the phase angle change value in the lag risk area as a main shaft dynamic compensation vector.

[0114] In the above scheme, the phase shift feature refers to the feature of phase change. The lag risk area refers to an area where there is a lag risk. The main shaft dynamic compensation vector refers to a vector for dynamic compensation. The phase angle change value refers to the amount of change in the phase angle. The phase shift feature sequence refers to the time sequence data of phase shift. The preset synchronization tolerance threshold refers to the critical value for judging synchronization anomaly. The lag risk point refers to a time point where there is a lag risk. The working phase refers to the working state of the worm gear transmission shaft. The average value refers to the average of index values.

[0115] In the embodiments of the present application, first, the system extracts a phase angle change value corresponding to a frequency component recorded in the fluctuation spectrum feature: the phase analyzer reads the phase information (i.e. the angle offset of the signal in the time-frequency domain) of each frequency component from the fluctuation spectrum feature set generated in step 101. The sequence builder arranges all phase angle change values in the time stamp order of the original signal to form a time-continuous phase shift feature sequence. This process is based on Fourier phase unwrapping technology, which eliminates phase jump interference and ensures the continuity of angle change.

[0116] Subsequently, the system locates the same time point of the phase shift feature sequence within the time interval corresponding to the abnormal coupling state point marked by the coupling state identifier: the space-time alignment engine receives the coupling state identifier (containing the time interval information of the abnormal coupling state point) generated in step 103, and performs time axis accurate matching with the phase shift feature sequence. The data locator extracts the phase angle change value corresponding to the time point completely coinciding with the abnormal coupling state point in the phase shift feature sequence. This process ensures the millisecond-level alignment of the magnetic-elastic abnormal period and the phase shift data through high-precision clock synchronization technology.

[0117] Next, the system compares the phase angle change value with the preset synchronization tolerance threshold and marks the lag risk point: the threshold comparator calls the preset synchronization tolerance threshold calibrated by engineering. The risk marking module compares the located phase angle change value with the threshold point by point: if the phase angle change value exceeds the upper limit or is lower than the lower limit, the time point is recorded as the lag risk point. This process uses the out-of-bound trigger logic to accurately identify the critical moment of the transmission shaft torque-speed desynchronization.

[0118] Then, the system integrates the time interval of the continuous lag risk points and marks it as a lag risk area: the interval clustering algorithm scans all discrete lag risk points and merges adjacent points based on time continuity. The area marker defines the merged continuous period as a lag risk area, representing that the worm gear transmission shaft is in a torque transmission delay state during this period. This process uses the time window sliding detection technology to avoid instantaneous noise misjudgment and focuses on the real risk period.

[0119] Finally, the system extracts the average value of the phase angle change value in the lag risk area as the main shaft dynamic compensation vector: the vector generator calculates the arithmetic mean of all phase angle change values within the marked lag risk area time range. The data packager outputs the average value as the main shaft dynamic compensation vector, quantifying the average phase lag degree of the transmission shaft in the risk interval. This process uses the sliding mean filter technology to suppress fluctuation noise, ensuring the stability and representativeness of the compensation vector.

[0120] In practical applications, in the phase compensation control scene of multi-spindle thread machining of the thread cutting machine, when the multi-spindle thread cutting machine synchronously machines high-hardness alloy materials, based on the fluctuation spectrum characteristics (such as specific frequency components reflecting abnormal spindle load) obtained in step 101, the phase angle change value (such as the instantaneous phase offset of the worm gear meshing frequency component) corresponding to the frequency component recorded in the fluctuation spectrum characteristics is extracted, and the phase angle change value is arranged in time sequence to form a phase offset characteristic sequence (step 1041). Then, according to the coupling state identifier (such as a red identifier marking the magnetic-force decoupling abnormality of the worm gear transmission shaft) generated in step 103, the phase angle change value of the same time point in the phase offset characteristic sequence is located in the time interval corresponding to the abnormal coupling state point marked by the coupling state identifier (such as the period of material stress abnormality caused by local wear of the worm gear) (step 1042). Then, the phase angle change value is compared with a preset synchronization tolerance threshold (the threshold is set according to the multi-axis synchronous accuracy requirement of the thread cutting machine), and when it is detected that the phase angle change value exceeds the synchronization tolerance threshold (such as the phase lag exceeds the safety margin), the corresponding time point is marked as a lag risk point (step 1043). Further, the time interval where the continuous lag risk points are located is integrated, and the working stage of the worm gear transmission shaft covered by the time interval is marked as a lag risk area (step 1044). Finally, the average value of the phase angle change value in the lag risk area is extracted as a spindle dynamic compensation vector (step 1045). The vector is precisely positioned through the linkage of the phase offset characteristic sequence and the coupling state identifier: the abnormal coupling state point exposes the phase drift caused by worm gear wear (such as the increase of transmission gap causing the response lag of the slave shaft), the lag risk area maps the synchronization failure period under high load working condition (such as the phase mismatch of master and slave shafts in the thread finishing stage), and the spindle dynamic compensation vector quantifies the phase offset amount that needs to be compensated (such as a negative value indicating that the slave shaft needs to be adjusted in advance), which is input into the master-slave control architecture of the multi-axis servo system to real-time correct the slave shaft phase command to eliminate the synchronization error.

[0121] The scheme of step 104 realizes the precise positioning of the phase lag risk and the compensation amount calculation. Through the correlation analysis of the frequency spectrum phase and the coupling state, the lag risk area affecting the synchronization accuracy is innovatively identified. The tolerance threshold and the continuous interval are combined in the judgment algorithm, which not only ensures the sensitivity of risk identification, but also ensures the reliability of result judgment. The average value of the phase offset extracted as the dynamic compensation vector provides accurate adjustment basis for subsequent synchronization control.

[0122] 105. Analyze the spindle dynamic compensation vector to generate a synchronization error suppression instruction, and real-time adjust the output torque instruction of each servo shaft of the thread cutting machine according to the synchronization error suppression instruction to suppress the multi-spindle synchronization error caused by the worm gear transmission lag.

[0123] Optionally, step 105 can specifically include the following steps:

[0124] 1051、combine the spindle dynamic compensation vector with a preset torque correction coefficient to generate a torque correction value.

[0125] 1052、acquire current output torque instructions of each servo axis of the threading machine, and superimpose the torque correction value on the output torque instruction of the corresponding servo axis to form an updated output torque instruction.

[0126] 1053、generate a synchronization error suppression instruction according to the updated output torque instruction, and transmit the synchronization error suppression instruction to the threading machine servo axis controller.

[0127] 1054、execute the synchronization error suppression instruction through the servo axis controller, so that the output torque of each servo axis is adjusted according to the synchronization error suppression instruction to suppress the synchronization error of the worm drive caused by the lag.

[0128] In the above scheme, the synchronization error suppression instruction refers to the control instruction for suppressing the synchronization error. The servo axis refers to the controlled motion axis. The output torque instruction refers to the instruction for controlling the torque output. The torque correction value refers to the adjustment amount of the torque. The preset torque correction coefficient refers to the coefficient for calculating the correction value. The servo axis controller refers to the device for controlling the servo axis. The multi-spindle synchronization error refers to the synchronization deviation between multiple spindles. The worm drive lag refers to the delay phenomenon of the worm drive.

[0129] In the embodiment of the present application, first, the system combines the spindle dynamic compensation vector with a preset torque correction coefficient: the coefficient matching module receives the spindle dynamic compensation vector (quantized average value of the phase lag of the worm drive axis) generated in step 104, and calls the preset torque correction coefficient (proportion value preset according to material stiffness and transmission efficiency). The correction value generator multiplies the two values to generate a torque correction value, which represents the torque increment or decrement required to offset the phase lag. This process is based on dynamic scaling technology, which ensures that the compensation amount is linearly corresponding to the lag degree.

[0130] Subsequently, the system acquires the current output torque instructions of each servo axis of the threading machine and superimposes the torque correction value: the instruction collector reads the original data of the output torque instructions of all servo axes of the threading machine in real time. The instruction superimposition engine maps the torque correction value generated in step 1051 to the corresponding servo axis according to the axis number, and adds it to the original instruction value through arithmetic superposition operation to form an updated output torque instruction. This process uses instruction dynamic injection technology to ensure that the correction value is seamlessly integrated into the control flow while maintaining the independence and synchronization of each axis instruction.

[0131] Then, the system generates and transmits the synchronization error suppression instructions to the controller according to the updated output torque instructions: the instruction compiler parses the updated output torque instruction set of all servo axes, and converts it into a binary control code stream through the instruction encoding protocol. The instruction distributor transmits the encoded data packet as the synchronization error suppression instruction to each servo axis controller of the screw machine through the real-time industrial Ethernet bus. This process relies on low-latency communication technology to ensure that the instructions are synchronized and distributed across the controller within milliseconds.

[0132] Finally, the system executes the synchronization error suppression instructions through the servo axis controller to adjust the output torque: the instruction execution unit receives the synchronization error suppression instructions in the servo axis controller, and converts the torque instructions into cross-axis current compensation through the current loop solver (based on the motor electromagnetic torque equation). The power drive module injects the compensation into the inverter modulation signal to adjust the output torque of each servo motor in real time. This process is based on direct torque compensation technology, which allows the worm transmission shaft to obtain torque correction during load change, effectively suppressing the synchronization error of multiple main shafts caused by worm transmission lag.

[0133] In practical applications, in the scene of thread rolling machine multi-spindle thread processing synchronization error suppression, during the thread rolling machine processing of petroleum pipe threads, when the phase lag of the worm gear transmission shaft caused by local wear is detected (the output of step 104 is a negative value of the spindle dynamic compensation vector), the system analyzes the spindle dynamic compensation vector (which quantifies the phase offset that the spindle needs to compensate for), combines the spindle dynamic compensation vector with the preset torque correction coefficient (which is calibrated according to the worm material stiffness and transmission ratio), and generates a torque correction value (step 1051); then the current output torque instruction of each servo shaft of the thread rolling machine is obtained (such as the preset torque value of the slave shaft in the thread cutting stage), the torque correction value is superimposed on the output torque instruction of the corresponding servo shaft (such as increasing the torque output of the slave shaft in the lag risk area), to form an updated output torque instruction (step 1052); then the updated output torque instruction is used to generate a synchronization error suppression instruction (the instruction contains a torque adjustment amplitude and an action period), which is transmitted to the thread rolling machine servo shaft controller through real-time industrial Ethernet (step 1053); finally, the servo shaft controller executes the synchronization error suppression instruction, so that the output torque of each servo shaft is adjusted according to the synchronization error suppression instruction (such as increasing the torque of the slave shaft to compensate for the mechanical lag of the spindle worm gear), and the multi-spindle synchronization error caused by the worm gear transmission lag is suppressed (such as eliminating the thread pitch deviation in the thread finishing stage) (step 1054). This process realizes active error suppression through the dynamic injection of torque correction values: the spindle dynamic compensation vector is analyzed from the phase offset characteristics (refer to the lag risk area positioning of step 104), the preset torque correction coefficient maps the mechanical transmission characteristics (such as the nonlinear relationship between worm wear degree and torque compensation), the updated output torque instruction drives the servo shaft controller to execute feedforward compensation, and the real-time execution of the synchronization error suppression instruction ensures that the multi-spindle maintains strict synchronization throughout the thread processing (refer to the error elimination principle of cross-coupling control).

[0134] The above step 105 scheme realizes closed-loop suppression and dynamic compensation of multi-spindle synchronization error. Through intelligent conversion of compensation vectors to torque instructions, a real-time error suppression system is innovatively constructed. This technology uses an instruction superposition type torque adjustment scheme to achieve accurate compensation without affecting the original control architecture. The innovative servo shaft cooperative control mechanism effectively suppresses the multi-axis synchronization error caused by worm gear transmission lag, significantly improving the processing accuracy and stability of the thread rolling machine.

[0135] The following is a specific example for steps 101 to 105:

[0136] In the scene of oil pipe thread tapping machine worm gear wear compensation, in the process of oil pipe thread processing, the worm gear transmission shaft of the tapping machine produces tooth surface wear due to long-term high load operation. The system first collects the dynamic torque fluctuation data of the worm gear transmission shaft in real time (captures the torque transient in the shaft rotation through a magneto-electric sensor), arranges the torque change value in time sequence to form a dynamic torque fluctuation data sequence; then the sequence is divided into multiple data segments according to a fixed time window, the Fourier transform is performed on each data segment to generate a frequency spectrum distribution segment, and the frequency spectrum distribution segments are spliced in time sequence to form a torque frequency spectrum matrix; then the frequency spectrum intensity value of the torque frequency spectrum matrix is extracted, the frequency components whose frequency spectrum intensity value exceeds the preset background noise threshold are identified, and their frequency spectrum intensity value is recorded as the fluctuation frequency spectrum feature. Based on this feature, a non-contact worm gear torque sensor is integrated on the worm gear transmission shaft, and when the frequency spectrum intensity value of a specific frequency component exceeds the preset trigger threshold, an activation instruction is generated; according to the activation instruction, the non-contact worm gear torque sensor starts the dynamic scanning operation of the surface magnetic field of the worm gear transmission shaft, and collects the initial magnetic field reference reading under the no-load state; extract the frequency components in the fluctuation frequency spectrum feature that exceed the trigger threshold, combine them with the preset sampling multiple coefficient to generate a sampling frequency parameter, and write it into the magnetic field induction probe control register to set the probe sampling interval time; start the magnetic field induction probe to perform the magnetic field intensity collection operation according to the sampling interval time, record the real-time magnetic field intensity original reading, calculate the difference between it and the initial magnetic field reference reading as the real-time magnetic field intensity change, and arrange it in time sequence to form a real-time magnetic field intensity change sequence; finally, integrate the initial magnetic field reference reading and the real-time magnetic field intensity change sequence to form a dynamic sampling strategy. When executing the strategy, mark the target point on the surface of the transmission shaft, capture the displacement change of the target point through a high-speed vision sensor to generate a torsional deformation sequence; align the magnetic field intensity change sequence and the torsional deformation sequence according to the time stamp to form a paired data unit, and calculate the ratio of the two in the unit as the coupling relationship coefficient of the magnetic induction intensity change and the torsional deformation; when the coefficient exceeds the preset material characteristic calibration threshold (calibrated according to the bronze material of the worm gear), mark the time as an abnormal coupling state point (such as a sudden drop in the coefficient indicating material plastic deformation); integrate all abnormal points to generate a coupling state identifier (such as a red identifier corresponding to the root fatigue failure of the thread). Further, extract the phase angle change value corresponding to the frequency components in the fluctuation frequency spectrum feature, arrange it in time sequence to form a phase shift feature sequence; based on the coupling state identifier, locate the phase angle change value in the time period corresponding to the abnormal coupling state point, compare it with the preset synchronization tolerance threshold (set according to the thread accuracy requirement), and when the phase lag exceeds the limit, mark the lag risk point; integrate the time period of the continuous lag points as the lag risk area (such as the end stage of the pipe thread), and extract the average value of the phase angle change value in this area as the main shaft dynamic compensation vector.Finally, the main shaft dynamic compensation vector (negative value represents the need for lead compensation from the shaft) is analyzed, combined with the preset torque correction coefficient (according to the transmission stiffness calibration) to generate a torque correction value; the current output torque instruction of each servo shaft of the thread cutting machine is obtained, and the correction value is superimposed on the instruction of the corresponding slave shaft to form an updated output torque instruction; the synchronous error suppression instruction is generated accordingly, and is transmitted to the servo shaft controller through real-time industrial Ethernet; the controller executes the synchronous error suppression instruction to adjust the output torque of the slave shaft (such as increasing the slave shaft torque to offset the main shaft worm gear wear lag), suppresses the multi-main shaft synchronous error caused by worm gear transmission lag, and ensures the consistency of thread pitch.

[0137] Figure 2 A structural schematic diagram of a thread cutting machine multi-main shaft synchronous servo control system is provided for the embodiments of the present application, as shown in Figure 2 The system comprises:

[0138] The acquisition module 21 is used for real-time acquisition of the dynamic torque fluctuation data of the worm gear transmission shaft of the thread cutting machine, frequency spectrum transformation of the dynamic torque fluctuation data to generate a torque frequency spectrum matrix, and extraction of fluctuation frequency spectrum features from the torque frequency spectrum matrix.

[0139] The trigger module 22 is used for integrating a non-contact worm gear torque sensor on the worm gear transmission shaft, and triggering the non-contact worm gear torque sensor based on the fluctuation frequency spectrum features to perform a dynamic sampling strategy.

[0140] The generation module 23 is used for recording the magnetic induction intensity change and torsional deformation of the worm gear transmission shaft under the dynamic sampling strategy, and generating a coupling state identifier of the torque and rotational speed of the worm gear transmission shaft according to the coupling relationship between the magnetic induction intensity change and the torsional deformation.

[0141] The positioning module 24 is used for obtaining the phase shift feature of the fluctuation frequency spectrum features, positioning the lag risk area of the phase shift feature exceeding the preset synchronization tolerance based on the coupling state identifier, and extracting the phase shift amount corresponding to the lag risk area as a main shaft dynamic compensation vector.

[0142] The adjustment module 25 is used for analyzing the main shaft dynamic compensation vector to generate a synchronous error suppression instruction, and adjusting the output torque instruction of each servo shaft of the thread cutting machine in real time according to the synchronous error suppression instruction, so as to suppress the multi-main shaft synchronous error caused by worm gear transmission lag.

[0143] Figure 2 The thread cutting machine multi-main shaft synchronous servo control system can execute Figure 1The implementation principle and technical effects of the threading machine multi-spindle synchronous servo control method of the embodiment are not described again. The specific manner in which each module and unit of the threading machine multi-spindle synchronous servo control system in the above embodiment performs operations has been described in detail in the embodiment related to the method, and will not be described in detail here.

[0144] In one possible design, Figure 2 The threading machine multi-spindle synchronous servo control system of the embodiment can be implemented as a computing device, such as Figure 3 As shown, the computing device can include a storage component 31 and a processing component 32.

[0145] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0146] The processing component 32 is configured to perform the above Figure 1 The threading machine multi-spindle synchronous servo control method of the embodiment.

[0147] The processing component 32 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, for executing the above method.

[0148] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0149] Of course, the computing device can also include other components, such as an input / output interface, a display component, a communication component, etc.

[0150] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0151] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0152] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform, and the computing device can be a cloud server, and the processing component and the storage component can be basic server resources rented or purchased from the cloud computing platform.

[0153] The embodiment of the application further provides a computer storage medium storing a computer program, and the computer program can realize the above-mentioned method when being executed by a computer. Figure 1 The embodiment shown in the figure is a multi-spindle synchronous servo control method of a thread rolling machine.

[0154] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-mentioned system, device and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be described here.

[0155] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0156] Through the description of the foregoing embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and a necessary general hardware platform, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, etc.) execute the method described in each embodiment or some parts of the embodiment.

[0157] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the application, and not to limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A multi-spindle synchronous servo control method for a thread-cutting machine, characterized in that, include: The dynamic torque fluctuation data of the worm gear drive shaft of the thread cutting machine is collected in real time. The dynamic torque fluctuation data is subjected to spectrum transformation to generate a torque spectrum matrix, and the fluctuation spectrum features are extracted from the torque spectrum matrix. A non-contact worm gear torque sensor is integrated on the worm gear drive shaft, and the non-contact worm gear torque sensor is triggered based on the fluctuation spectrum characteristics to execute a dynamic sampling strategy; Record the changes in magnetic field strength and torsional deformation of the worm gear drive shaft under the dynamic sampling strategy, and generate a coupling state identifier of torque and speed of the worm gear drive shaft based on the coupling relationship between the changes in magnetic field strength and torsional deformation. The phase offset feature of the fluctuation spectrum is obtained, and the hysteresis risk zone where the phase offset feature exceeds the preset synchronization tolerance is located based on the coupling state identifier. The phase offset corresponding to the hysteresis risk zone is extracted as the main axis dynamic compensation vector. The main spindle dynamic compensation vector is analyzed to generate a synchronization error suppression command. The output torque command of each servo axis of the thread cutting machine is adjusted in real time according to the synchronization error suppression command to suppress the multi-spindle synchronization error caused by the worm gear drive lag.

2. The method according to claim 1, characterized in that, Real-time acquisition of dynamic torque fluctuation data of the worm gear drive shaft of the thread-cutting machine; spectral transformation of the dynamic torque fluctuation data to generate a torque spectrum matrix; and extraction of fluctuation spectrum features from the torque spectrum matrix, including: The torque change value during the rotation of the worm gear drive shaft of the thread cutting machine is continuously collected, and the torque change value is arranged in time sequence to form a dynamic torque fluctuation data sequence. The dynamic torque fluctuation data sequence is divided into multiple data segments according to a fixed time window, and a time-domain to frequency-domain conversion operation is performed on each data segment to generate a spectral distribution segment. The spectral distribution segments are spliced ​​together in chronological order to form a torque spectrum matrix, and the spectral intensity values ​​of the torque spectrum matrix are extracted. The frequency components whose spectral intensity values ​​exceed a preset background noise threshold are identified, and the spectral intensity values ​​corresponding to the frequency components are recorded as fluctuation spectral features.

3. The method according to claim 1, characterized in that, A non-contact worm gear torque sensor is integrated on the worm gear drive shaft, and the non-contact worm gear torque sensor is triggered based on the fluctuation spectrum characteristics to execute a dynamic sampling strategy, including: A non-contact worm gear torque sensor is integrated on the worm gear drive shaft, and the spectral intensity value recorded in the fluctuation spectrum characteristics is read. When the spectral intensity value exceeds a preset trigger threshold, an activation command for the non-contact worm gear torque sensor is generated. According to the activation command, the non-contact worm gear torque sensor is activated to perform a dynamic scanning operation on the magnetic field of the worm gear drive shaft surface to acquire an initial magnetic field reference reading. The sampling frequency parameter is set according to the frequency component whose spectral intensity value exceeds the trigger threshold, and the magnetic field sensing probe of the non-contact worm gear torque sensor is controlled to perform magnetic field sampling operation according to the sampling frequency parameter to record the real-time magnetic field intensity change sequence. The initial magnetic field reference readings are integrated with the real-time magnetic field intensity change sequence to form a dynamic sampling strategy.

4. The method according to claim 3, characterized in that, The sampling frequency parameter is set according to the frequency component whose spectral intensity value exceeds the trigger threshold, and the magnetic field sensing probe of the non-contact worm gear torque sensor is controlled to perform magnetic field sampling operation according to the sampling frequency parameter to record the real-time magnetic field intensity change sequence, including: Extract the frequency components exceeding the trigger threshold from the fluctuation spectrum features, and combine the frequency components with a preset sampling multiple coefficient to generate sampling frequency parameters; The sampling frequency parameter is written into the magnetic field induction probe control register of the non-contact worm gear torque sensor to set the probe sampling interval time. The magnetic field sensing probe of the non-contact worm gear torque sensor is activated to perform magnetic field strength acquisition operation at the sampling interval, and the real-time raw magnetic field strength reading output by the magnetic field sensing probe is recorded at the same time. The difference between the original real-time magnetic field strength reading and the initial magnetic field reference reading is calculated as the change in real-time magnetic field strength. All real-time magnetic field intensity changes are arranged in chronological order of sampling time to form a sequence of real-time magnetic field intensity changes.

5. The method according to claim 1, characterized in that, The changes in magnetic flux density and torsional deformation of the worm gear drive shaft under the dynamic sampling strategy are recorded, and a coupling state identifier of torque and speed of the worm gear drive shaft is generated based on the coupling relationship between the changes in magnetic flux density and torsional deformation, including: Mark the target point on the surface of the worm gear drive shaft, and capture the displacement change of the target point as a torsional deformation sequence using a high-speed vision sensor. The magnetic field strength change sequence and the torsional deformation sequence are aligned at the same time point to form paired data units; The ratio of the change in magnetic field strength to the torsional deformation in the paired data unit is calculated as the coupling coefficient between the change in magnetic field strength and the torsional deformation; When the coupling coefficient exceeds the preset material property calibration threshold, the corresponding time point is marked as an abnormal coupling state point. The abnormal coupling state points are integrated to generate a coupling state identifier that characterizes the abnormal relationship between torque and speed of the worm gear drive shaft.

6. The method according to claim 1, characterized in that, The phase shift characteristics of the fluctuation spectrum are obtained, and the hysteresis risk region where the phase shift characteristics exceed the preset synchronization tolerance is located based on the coupling state identifier. The phase shift amount corresponding to the hysteresis risk region is extracted as the principal axis dynamic compensation vector, including: Extract the phase angle change values ​​corresponding to the frequency components recorded in the wave spectrum features, and arrange the phase angle change values ​​in chronological order to form a phase shift feature sequence; Within the time interval corresponding to the abnormal coupling state point marked by the coupling state identifier, locate the phase angle change value at the same time point in the phase offset feature sequence; The phase angle change value is compared with a preset synchronization tolerance threshold. When the phase angle change value exceeds the synchronization tolerance threshold, the corresponding time point is marked as a lag risk point. Integrate the time intervals containing consecutive lag risk points, and mark the working stages of the worm gear drive shaft covered by the time intervals as lag risk zones; The average value of the phase angle change within the hysteresis risk zone is extracted as the principal axis dynamic compensation vector.

7. The method according to claim 1, characterized in that, The dynamic compensation vector of the main spindle is analyzed to generate a synchronization error suppression command. Based on this command, the output torque commands of each servo axis of the thread cutting machine are adjusted in real time to suppress multi-spindle synchronization errors caused by worm gear drive lag. This includes: The spindle dynamic compensation vector is combined with a preset torque correction coefficient to generate a torque correction value; Obtain the current output torque command of each servo axis of the thread cutting machine, and superimpose the torque correction value onto the output torque command of the corresponding servo axis to form an updated output torque command; A synchronization error suppression command is generated based on the updated output torque command, and the synchronization error suppression command is transmitted to the servo axis controller of the thread cutting machine. The servo axis controller executes a synchronization error suppression command, which adjusts the output torque of each servo axis according to the synchronization error suppression command to suppress the multi-spindle synchronization error caused by the worm gear drive lag.

8. A multi-spindle synchronous servo control system for a thread-cutting machine, characterized in that, include: The acquisition module is used to acquire dynamic torque fluctuation data of the worm gear drive shaft of the thread cutting machine in real time, perform spectral transformation on the dynamic torque fluctuation data to generate a torque spectrum matrix, and extract fluctuation spectrum features from the torque spectrum matrix. A triggering module is used to integrate a non-contact worm gear torque sensor on the worm gear drive shaft, and to trigger the non-contact worm gear torque sensor based on the fluctuation spectrum characteristics to execute a dynamic sampling strategy; The generation module is used to record the changes in magnetic field strength and torsional deformation of the worm gear drive shaft under the dynamic sampling strategy, and generate a coupling state identifier of torque and speed of the worm gear drive shaft based on the coupling relationship between the changes in magnetic field strength and torsional deformation. The positioning module is used to acquire the phase offset features of the fluctuation spectrum features, locate the hysteresis risk zone where the phase offset features exceed the preset synchronization tolerance based on the coupling state identifier, and extract the phase offset amount corresponding to the hysteresis risk zone as the main axis dynamic compensation vector. The adjustment module is used to parse the main spindle dynamic compensation vector to generate a synchronization error suppression command, and adjust the output torque command of each servo axis of the thread cutting machine in real time according to the synchronization error suppression command to suppress the multi-spindle synchronization error caused by worm gear drive lag.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a multi-spindle synchronous servo control method for a thread-cutting machine as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a multi-spindle synchronous servo control method for a thread-cutting machine as described in any one of claims 1 to 7.

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