A servo position control method and system applied to a carving and milling machine

Abstract

The present application relates to the technical field of program control, and particularly relates to a servo position control method and system applied to a carving and milling machine. The method comprises the following steps: acquiring real-time temperature data and real-time load data of a screw rod of each axis in the carving and milling machine; collecting multi-node temperature distribution data of the carving and milling machine; acquiring cutting force data and resonance frequency characteristic values in the carving and milling machine; constructing a screw rod reverse gap prediction model based on the real-time temperature data and the real-time load data of the screw rod, and generating a gap compensation value; performing pre-compensation control before direction transformation of the servo position of each axis based on the gap compensation value, to obtain a corrected position instruction; and performing thermal deformation simulation on the carving and milling machine based on the multi-node temperature distribution data, to generate a thermal deformation compensation amount of each axis. Through multi-source data fusion and intelligent control, the present application realizes comprehensive optimization of the servo position system of the carving and milling machine, effectively improves machining precision, dynamic response and stability, and guarantees efficient and reliable operation of high-speed and high-precision machining.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of program control, and in particular to a servo position control method and system applied to a carving and milling machine. BACKGROUND

[0002] The carving and milling machine has been widely used in the fields of mold manufacturing and precision part machining. As a key technology affecting the machining precision and dynamic performance, the servo position control has experienced the development process from the traditional open-loop control, closed-loop position control to the current multi-dimensional control strategy integrating compensation and intelligent control. The current servo control system of the carving and milling machine mostly adopts the PID control based on position feedback, and cooperates with certain feedforward compensation means to realize the accurate control of the positions of each axis.

[0003] Although the existing servo position control method can realize the basic high-precision positioning, it still faces many challenges in the high-speed and high-precision machining process of the carving and milling machine. First, the reverse gap changes caused by the thermal expansion of the lead screw and the load changes are difficult to be accurately compensated in real time, resulting in the accumulation of position command errors. Second, the nonlinear thermal deformation of the machine tool structure in the complex thermal environment is difficult to model and compensate, affecting the machining stability. Third, the traditional feed control does not consider the real-time cutting force changes and resonance characteristics, making it difficult to realize dynamic adaptive adjustment and reducing the trajectory stability and machining efficiency. In addition, the response lag of the servo control loop to the actual feedback error also limits the further improvement of the overall control precision. SUMMARY

[0004] Therefore, it is necessary to provide a servo position control method and system applied to a carving and milling machine to solve at least one of the above technical problems.

[0005] To achieve the above-mentioned purpose, a servo position control method applied to a carving and milling machine comprises the following steps:

[0006] Step S1: acquiring the real-time temperature data and real-time load data of the lead screws of each axis in the carving and milling machine, collecting the multi-node temperature distribution data of the carving and milling machine, and acquiring the cutting force data and resonance frequency characteristic values in the carving and milling machine;

[0007] Step S2: constructing a lead screw reverse gap prediction model based on the real-time temperature data and real-time load data of the lead screws, and generating a gap compensation value; performing pre-compensation control based on the gap compensation value before the direction transformation of the servo position of each axis to obtain a corrected position command;

[0008] Step S3: performing thermal deformation simulation on the carving and milling machine based on the multi-node temperature distribution data to generate a thermal deformation compensation amount of each axis;

[0009] Step S4: adjusting the feed speed of the carving and milling machine based on the cutting force data to obtain an optimized feed magnification coefficient;

[0010] Step S5: generating a comprehensive control instruction based on the corrected position instruction, the thermal deformation compensation amount of each axis, and the optimized feed magnification coefficient; constructing a machining trajectory of the milling machine based on the optimized feed magnification coefficient and the resonance frequency characteristic value, and performing optimized interpolation to obtain smoothed trajectory parameters; performing coordinated driving control on the servo motors of each axis based on the comprehensive control instruction and the smoothed trajectory parameters to obtain actual position feedback data of each axis;

[0011] Step S6: performing control accuracy evaluation on the actual position feedback data of each axis to obtain position error data; updating the comprehensive control instruction based on the position error data, and performing double-drive coordinated control on the milling machine.

[0012] The present application realizes all-round optimization of the servo position control system of the milling machine through multi-source data fusion and intelligent modeling, and significantly improves the machining precision and dynamic response capability. First, by introducing the screw temperature, load, and multi-node temperature data of the whole machine, the problem of insufficient compensation of gap error and structural deformation caused by thermal expansion, load change, and nonlinear thermal deformation is effectively solved, and the robustness of the system to thermal environment fluctuations is enhanced. Second, by combining the cutting force and resonance characteristic information, dynamic adaptive adjustment of the feed speed and trajectory smoothing interpolation are realized, which significantly improves the trajectory stability and machining efficiency in the milling process, and reduces the trajectory deviation and surface defects caused by vibration. In addition, the system performs real-time accuracy evaluation and error closed-loop update on the actual position feedback of each axis, further improving the response speed and accuracy of the position control, and ensuring stable operation under complex machining conditions. Finally, through the double-drive coordinated control mode, the limitations of traditional control strategies in large dynamic load and high-precision synchronous driving are effectively overcome, realizing higher precision and higher stability of the servo control performance, and improving the overall performance and intelligent level of the milling machine in high-speed and high-precision machining tasks.

[0013] Preferably, the present application also provides a servo position control system applied to a milling machine, which is used to execute the above-mentioned servo position control method applied to a milling machine, and the servo position control system applied to a milling machine comprises:

[0014] A multi-source data acquisition module is used to acquire real-time temperature data and real-time load data of the screw of each axis in the milling machine; acquire multi-node temperature distribution data of the milling machine; and acquire cutting force data and resonance frequency characteristic values in the milling machine;

[0015] An intelligent gap prediction compensation module is used to construct a screw reverse gap prediction model based on the real-time temperature data and real-time load data of the screw, and generate a gap compensation value; and perform pre-compensation control on each axis before servo position direction transformation based on the gap compensation value to obtain a corrected position instruction;

[0016] The digital twin thermal deformation simulation module is used for thermal deformation simulation of the engraving and milling machine based on multi-node temperature distribution data, and generates thermal deformation compensation amounts of each axis;

[0017] The adaptive feed adjustment module is used for adjusting the feed speed of the engraving and milling machine based on the cutting force data, and obtaining an optimized feed ratio coefficient;

[0018] The multi-axis coordination control module is used for generating a comprehensive control instruction based on the corrected position instruction, the thermal deformation compensation amounts of each axis and the optimized feed ratio coefficient; constructing a machining trajectory of the engraving and milling machine based on the optimized feed ratio coefficient and the resonance frequency characteristic value, and performing optimized interpolation to obtain smoothed trajectory parameters; and performing coordinated driving control on the servo motors of each axis based on the comprehensive control instruction and the smoothed trajectory parameters, to obtain actual position feedback data of each axis.

[0019] The precision evaluation feedback optimization module is used for evaluating the control precision of the actual position feedback data of each axis to obtain position error data; and updating the comprehensive control instruction based on the position error data, and performing double-drive coordinated control on the engraving and milling machine.

[0020] The present application realizes intelligent optimization of the whole process of servo control of the engraving and milling machine through modular design, and has a significant comprehensive improvement effect. The fusion and collection of multi-dimensional data provide a comprehensive and real-time perception basis for the control system, so that the control instruction has stronger environmental adaptability and decision accuracy; the dynamic prediction and compensation of the reverse gap improve the forward-looking and accuracy of displacement control, effectively reducing the trajectory deviation caused by mechanical errors; the thermal deformation simulation and compensation strengthen the response capability of the system to the structural changes caused by temperature rise, significantly improving the machining stability under complex thermal field; through adaptive adjustment of the feed speed, the system can respond to cutting load fluctuations in real time, ensuring the smoothness of the machining trajectory and the reliability of the equipment operation; the trajectory interpolation optimization and multi-axis coordinated driving control improve the consistency of servo response, reducing the influence of vibration interference on the machining quality; at the same time, through the precision evaluation and feedback closed-loop correction mechanism, the system has self-learning and continuous optimization capability, making the servo control more refined and intelligent; finally, through the coordinated control of the main drive and the pre-tightening motor, the dynamic stability and synchronization performance of the control system in the high-precision high-speed machining scene are further enhanced, and the machining precision, response speed and operation efficiency of the engraving and milling machine are comprehensively improved. BRIEF DESCRIPTION OF DRAWINGS

[0021] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments made with reference to the accompanying drawings:

[0022] Figure 1 A step flowchart of a servo position control method applied to an engraving and milling machine according to the present application;

[0023] Figure 2 For Figure 1 The detailed step flow diagram of step S1 in the embodiment is shown in the following figure;

[0024] Figure 3 For Figure 1 The detailed step flow diagram of step S2 in the embodiment is shown in the following figure. DETAILED DESCRIPTION

[0025] The technical method of the present application will be described clearly and completely in combination with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present application, and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated description thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be called a second element, and similarly a second element can be called a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0028] To achieve the above-mentioned purpose, please refer to Figures 1 to 3 The present application provides a servo position control method applied to a carving and milling machine, which comprises the following steps:

[0029] Step S1: acquiring real-time temperature data and real-time load data of the lead screws of each axis in the carving and milling machine; collecting multi-node temperature distribution data of the carving and milling machine; acquiring cutting force data and resonance frequency characteristic values in the carving and milling machine;

[0030] Step S2: constructing a lead screw reverse gap prediction model based on the real-time temperature data and real-time load data of the lead screws, and generating a gap compensation value; performing pre-compensation control based on the gap compensation value before direction transformation of the servo position of each axis, to obtain a corrected position command;

[0031] Step S3: based on the multi-node temperature distribution data, the thermal deformation simulation of the engraving and milling machine is carried out, and the thermal deformation compensation amount of each shaft is generated;

[0032] Step S4: based on the cutting force data, the feed speed of the engraving and milling machine is adjusted, and the optimized feed rate coefficient is obtained;

[0033] Step S5: based on the corrected position instruction, the thermal deformation compensation amount of each shaft and the optimized feed rate coefficient, the comprehensive control instruction is generated; based on the optimized feed rate coefficient and the resonance frequency characteristic value, the machining track of the engraving and milling machine is constructed, and the optimized interpolation is carried out, so that the smoothed track parameters are obtained; based on the comprehensive control instruction and the smoothed track parameters, the coordinated driving control of each shaft servo motor is carried out, and the actual position feedback data of each shaft is obtained;

[0034] Step S6: the control precision of the actual position feedback data of each shaft is evaluated, and the position error data is obtained; based on the position error data, the comprehensive control instruction is updated, and the double-drive coordinated control of the engraving and milling machine is carried out.

[0035] In the embodiment of the application, reference Figure 1 As shown in the figure, it is a step flow schematic diagram of a servo position control method applied to an engraving and milling machine, in the example, the servo position control method applied to the engraving and milling machine comprises the following steps:

[0036] Step S1: the real-time temperature data and real-time load data of the screw rod of each shaft in the engraving and milling machine are obtained; the multi-node temperature distribution data of the engraving and milling machine is collected; the cutting force data and the resonance frequency characteristic value in the engraving and milling machine are obtained;

[0037] In the embodiment of the application, a thermocouple temperature sensor with precision not less than ±0.1℃ is fixedly installed on the ball screw of the X-axis, Y-axis and Z-axis, temperature data is acquired by using a high-frequency (not less than 1 Hz) continuous sampling mode, and data sampling is achieved by connecting an embedded acquisition card to an industrial control system. The collection of axial load of each shaft is achieved by connecting a strain current sensor with a range of 0-5000N and a sensitivity of 1mV / V in series in the current path of each servo driver, real-time detection of the change of the driving current of the servo motor and conversion into load data; the load conversion adopts a relationship formula of the standard motor torque constant and the lead of the screw, and the calculation formula is: load=(current x motor torque constant) ÷ lead of screw. In the process of collecting multi-node temperature distribution data of the milling machine body, column, spindle box and workbench, not less than 8 semiconductor temperature sensors (with a precision better than ±0.2℃) are evenly arranged on the four structural components, each sensor is transmitted to a central processing unit through a CAN bus mode, and the spatial coordinates of the sensors are recorded synchronously for constructing a temperature distribution map; the points should cover the main heat transfer path and the structural connection points, such as the junction of the spindle box and the column, the front and rear end faces of the workbench, the center and both sides of the base of the machine body. The collection of cutting force data adopts a three-way piezoelectric force sensor (with a sensitivity not less than 5pC / N) installed on the tool holder, the cutting forces in the X / Y / Z directions are acquired at a period of 0.5ms, and the corresponding spindle speed and feed speed are recorded synchronously and superimposed, the data is input to a signal conditioning module after isolation amplification, and is stored by a high-speed acquisition card in parallel sampling. In the process of acquiring the characteristic value of the resonance frequency, three-axis vibration acceleration sensors (with a frequency response range of 0.5-5000Hz) are pasted on the center axis direction of the spindle of the machine tool and the ends of each guide rail, the machine tool is run at variable speed under no load, the resonance of the whole machine structure is excited at different frequencies, the vibration acceleration is quickly Fourier transformed by combining a frequency spectrum analysis module, the frequency corresponding to the amplitude peak in the frequency domain is recorded, the frequency point with the maximum amplitude is selected as the characteristic value of the resonance frequency and is written into a control parameter buffer area.

[0038] Step S2: constructing a screw reverse gap prediction model based on real-time temperature data and real-time load data of the screw, and generating a gap compensation value; performing pre-compensation control based on the gap compensation value before position direction transformation of each axis servo, to obtain a corrected position instruction;

[0039] In the embodiment of the present application, firstly, the real-time temperature data of each shaft ball screw collected in step S1 is transmitted to the filtering processing unit at a sampling frequency of 1 Hz, and the weighted average method is used to eliminate abnormal fluctuation values, wherein the temperature data of five consecutive time points is combined by a linear combination of weighted coefficients of 0.1, 0.2, 0.4, 0.2 and 0.1 to obtain smooth temperature data; before entering the subsequent processing, the real-time load data is scaled to between 0 and 1 according to the full scale range of each current sensor and the actual measured value to obtain standardized load data. Then, the thermal expansion amount is calculated based on the material and structure parameters of the ball screw, and the specific formula is: thermal expansion amount = linear expansion coefficient x temperature change amount x screw length, and the thermal expansion influence factor is constructed combining the thermal expansion amount and the standardized load data. Next, the minimum starting displacement difference of each shaft ball screw nut pair in forward and reverse directions is obtained by the bidirectional displacement detection module, which is defined as the transmission clearance reference value, and an axial load is applied by using a static pressure loading platform with a load of 1000N, the relative displacement of the screw and the nut pair is measured, and the load deformation value is calculated accordingly, and the load deformation coefficient is obtained by the formula: deformation coefficient = load deformation amount / loading force. The thermal expansion influence factor and the load deformation coefficient are jointly input into the gap prediction processing module, and the reverse gap prediction value of the ball screw is generated by using the linear weighted relationship, the empirical coefficient 0.6 is given to the thermal expansion factor, and the empirical coefficient 0.4 is given to the load deformation coefficient, and the prediction formula is: gap value = 0.6 x thermal expansion factor + 0.4 x load deformation coefficient. Based on the prediction value and the transmission direction detection signal (obtained by the encoder to detect the rotation direction of the motor), it is judged whether it is in the reverse motion state, if it is in the reverse direction, the original prediction value is taken as the reverse compensation value, if it is in the forward direction, the reverse compensation value is set to 0; the forward compensation value is set to 0.01mm by using the empirical value, and a complete set of direction compensation values is formed. The forward and reverse compensation values are subjected to vector superposition processing before the output of the command position signal, and the corrected position command is output.

[0040] Step S3: thermal deformation simulation is performed on the engraving and milling machine based on the multi-node temperature distribution data, and each axis thermal deformation compensation amount is generated;

[0041] In the embodiment of the present application, firstly, the temperature data of 32 measuring points distributed in the engraving and milling machine bed, column, spindle box and worktable collected in step S1 are introduced into the three-dimensional coordinate mapping unit, combined with the physical space coordinates of each measuring point in the machine tool structure, and a three-dimensional temperature field is constructed by using interpolation operation, wherein the interpolation method adopts a trilinear interpolation method, the grid precision is that the length of each cubic unit is not more than 50 mm, and a three-dimensional temperature field model is generated; then, the thermal deformation simulation of the engraving and milling machine bed guide rail mounting surface is carried out according to the temperature field model, and the calculation method is as follows: the bed structure is divided into a plurality of limited size units, the materials of each unit are set as HT250 gray cast iron, the thermal expansion amount of the reference line of the guide rail in the X-axis direction is calculated respectively through the thermal strain accumulation formula ΔL = α·ΔT·L under the temperature difference driving, and the thermal deformation amount of the bed is formed by superposition; subsequently, based on the temperature gradient difference of the connection area of the spindle box and the column, the column inclination angle change in the Z-axis direction is calculated in a linear thermal elongation mode between nodes, and the thermal offset displacement amount in the Y-axis direction is derived by multiplying the inclination angle by the height of the spindle center, so as to obtain the column thermal inclination deformation amount; on the spindle axis line, the temperature difference ΔT of the bearing seat in the spindle box is taken, combined with the bearing seat spacing, and the thermal displacement calculation formula Δx = α·ΔT·L is used to output the thermal displacement amount of the spindle; for the thermal deformation simulation of the worktable, a two-way linear interpolation temperature distribution surface is constructed with the surface four corner measuring points as the boundary conditions, the warping degree of the table surface is calculated by the isotherm barycentric method, and is converted into the plane thermal deformation amount in the Z-axis direction. Integrating the deformation data of the above four types of structures, the calculation of the three-axis thermal deformation amount is completed according to the following compensation relationship: the X-axis thermal deformation compensation amount is determined by adding the bed thermal deformation amount and the spindle thermal displacement amount, the Y-axis thermal deformation compensation amount is determined by superimposing the bed thermal deformation amount, the column thermal inclination deformation amount and the spindle thermal displacement amount, and the Z-axis thermal deformation compensation amount is determined by combining the column thermal inclination deformation amount and the spindle thermal displacement amount, and the unified unit of the three-axis compensation amount is millimeter, which is retained to three decimal places and written into the control parameter buffer area.

[0042] Step S4: adjusting the feed speed of the engraving and milling machine based on the cutting force data to obtain an optimized feed rate coefficient;

[0043] In the embodiment of the present application, first, the cutting force data obtained in step S1 is input to the real-time filtering unit at an interval of 0.5 ms, and the instantaneous pulse interference is removed by the median filtering method with a sliding window of 5 points to generate smooth cutting force data, then the smooth cutting force data and the spindle real-time speed data are linked and input to the cutting torque calculation module, and the spindle real-time cutting torque is calculated by using the formula M=Fxr, wherein F is the spindle axial cutting component force, and r is the tool radius. Next, the cutting state turning point is judged according to the component force in the Z-axis cutting direction and the cutting torque change curve, and four types of cutting state identifiers are set, which correspond to the light load, medium load, heavy load and tool retracting stages respectively. The spindle front and rear bearing temperatures (the thermocouple temperature measurement accuracy is not more than ±0.2℃) and vibration signals (the acceleration range is ±10g, and the sampling frequency is 5000Hz) are synchronously detected, the spindle bearing state data is obtained by the overrun judgment method, and the spindle load coefficient is defined based on the bearing state and the spindle cutting torque, and the calculation method is spindle load coefficient=cutting torque / bearing load limit. After obtaining the spindle load coefficient, the current spindle speed and three-axis feed speed are extracted, the matching degree score is obtained according to the matching relationship table between the speed and the feed, the score range is 0 to 1, the higher the matching degree, the greater the feed rate can be set. According to the matching degree, the response delay of the bed and the spindle connection part is analyzed, the dynamic stiffness coefficient is calculated by the ratio of the measured spindle speed change lag time to the feed speed change delay time, and the dynamic stiffness coefficient is set to float between 0.5 and 2.0, and then the maximum feed speed upper limit of each axis servo motor is set according to the dynamic stiffness coefficient, and the maximum feed speeds of the X-axis, the Y-axis and the Z-axis are limited to 15 m / min, 12 m / min and 10 m / min respectively. Then the torque and the cutting force frequency distribution density change rate in the tool stress trajectory are analyzed, the tool wear degree is identified by using the standard deviation of the wave peak interval, the tool wear coefficient is calculated by combining the cumulative machining time and the load integral value, and is input into the wear evaluation unit. The resonance frequency characteristic value obtained in step S1 and the smooth cutting force data are extracted by the Fourier transform method to obtain the frequency domain distribution atlas, the peak frequency point is compared with the system natural frequency, the cutting vibration stability score is obtained, and the score range is 0 to 100. The tool wear coefficient and the cutting vibration stability score jointly participate in the calculation of the preliminary feed rate coefficient, and are linearly combined according to the weight coefficients 0.7 and 0.3, and the result is limited to 0.4 to 1.2. The initial coefficient is multiplied by the basic feed speed (unit: mm / min) of the X-axis, the Y-axis and the Z-axis respectively to obtain the three-axis feed coordination speed, and the feed coordination coefficient is defined by the ratio between the maximum and minimum values; the coefficient is compared with the aforementioned feed speed upper limit to obtain the constraint feed rate coefficient, and the final value is limited to the range of 0.3 to 1.0.In order to meet the processing requirements of specific workpieces, the constraint feed ratio coefficient is corrected in combination with the preset surface roughness standard, the quality optimization feed ratio is inversely deduced according to the roughness target value Ra and the cutting speed relationship curve, the target efficiency is set according to the processing beat requirement, the quality optimization feed ratio is adjusted under the premise of meeting the Ra value and the efficiency balancing factor is generated, and the final optimized feed ratio coefficient is output through the product of the two.

[0044] Step S5: generating comprehensive control instructions based on the corrected position instructions, the thermal deformation compensation amounts of each axis and the optimized feed ratio coefficient; constructing the machining track of the engraving and milling machine based on the optimized feed ratio coefficient and the resonance frequency characteristic value, and performing optimized interpolation to obtain smoothed track parameters; coordinately driving and controlling the servo motors of each axis based on the comprehensive control instructions and the smoothed track parameters, and obtaining actual position feedback data of each axis;

[0045] In the embodiment of the application, the corrected position instructions output in step S2 and the thermal deformation compensation amounts of the X-axis, the Y-axis and the Z-axis obtained in step S3 are processed by numerical addition one by one according to the axial direction to generate thermal compensation position instructions; before being introduced into the servo control unit, the instructions need to be combined with the temperature distribution of the guide rail area in the three-dimensional temperature field model, and the thermal deformation compensation amount of each axis is deduced according to the guide rail material being high-carbon steel and the linear expansion coefficient being 1.1*10 ―5Under the parameter condition of 10-100 / ℃, the thermal elongation of each linear guide pair is simulated by using the linear thermal expansion calculation formula ΔL=α·ΔT·L (where the effective length of the guide is set to 700 mm), and then the thermal elongation is superimposed on the thermal compensation position command to form a guide compensation position command. Subsequently, the guide compensation position command is subjected to speed modulation processing according to the optimized feed ratio coefficient obtained in step S4, and the modulation mode is that the command value of each axis is multiplied by the feed ratio coefficient of the corresponding axis to form a speed-modulated position command; the command is further combined with the worktable thermal deformation amount for coordinate calibration compensation, and the thermal deformation amount is provided by the worktable thermal deformation simulation result, and the machining coordinate system origin is corrected in the X, Y and Z directions in the three-dimensional coordinate system respectively to obtain the workpiece coordinate system thermal compensation value. The speed-modulated position command and the workpiece coordinate system thermal compensation value are superimposed and corrected in the three-dimensional coordinate to generate the final comprehensive control command. During the machining trajectory generation process, the basic trajectory speed is set according to the optimized feed ratio coefficient, the trajectory points are derived according to the tool center trajectory at the speed, and the curvature is calculated; the continuous segment with the curvature value changing in the range of 0.1 to 0.5 is extracted, the trajectory change degree is judged by using the curvature difference, and the high-curvature segment is set as the interpolation optimization key area; the segmented interpolation method is used to connect the points of each segment trajectory, the B-spline smoothing processing is performed on the curve control points of the starting segment and the end segment to ensure that the trajectory has first and second derivative continuity at each connection point. Then, the resonance frequency characteristic value obtained in step S1 is combined to analyze the trajectory speed change frequency, and the vibration frequency band close to the system frequency response is excluded; if the difference between the frequency component of a certain segment of trajectory and the resonance frequency is less than 10 Hz, the segment is set as a high-vibration risk segment, the vibration excitation frequency is reduced by adjusting the trajectory speed gradient and the interpolation density, and the dynamic response coordination among multiple axes is performed for the segment of trajectory. The position change of the three-axis motion trajectory at the same time stamp is taken as a reference to perform inter-axis synchronization scheduling, the synchronization optimization adjustment amount is calculated by comparing the differences between the speeds and accelerations of the axes, and is applied to the position command to obtain the synchronization optimization trajectory parameter. The B-spline smoothing trajectory parameter and the synchronization optimization trajectory parameter are weighted and fused to generate the fused optimization trajectory parameter; the geometric error is compared and analyzed by using the trajectory parameter, and the feed speed and spindle speed coordination relationship is back calculated under the premise that the error does not exceed ±0.005 mm to obtain the spindle feed coordination parameter according to the pre-set surface quality requirement and target machining efficiency. The spindle feed coordination parameter is combined with the ball screw parameter (lead 5 mm, transmission efficiency 0.9) to perform trajectory adaptation adjustment through the transmission relationship formula V=n×p / 60 (where V is the feed speed, n is the motor speed, and p is the lead), and finally the smoothing trajectory parameter is formed.The comprehensive control instruction and the smoothed track parameter are input into a multi-axis servo control driver to control the ball screw servo motor of each axis to realize accurate driving, and the spindle feeding speed is modulated through a coordination parameter to realize the motion synchronous control between the spindle and each axis. Finally, the actual position data of each axis is fed back by an encoder.

[0046] Step S6: The actual position feedback data of each axis is evaluated for control accuracy to obtain position error data. The comprehensive control instruction is updated based on the position error data, and the double-drive coordination control of the engraving and milling machine is performed.

[0047] In the embodiment of the present application, firstly, the actual position coordinate data collected by each shaft servo motor in the machining process in step S5 is compared with the smoothed trajectory parameters one by one, and the difference operation formula ΔX = Xf - Xa, ΔY = Yf - Ya, ΔZ = Zf - Za (where Xf, Yf, Zf are the expected trajectory coordinates, and Xa, Ya, Za are the actual coordinates) is used to calculate the instantaneous position deviation values of the X, Y and Z axes respectively, and all the deviation data are recorded to form a position deviation data sequence with a sampling interval of 0.5 ms. The standard deviation analysis and sliding average processing are used on the sequence to judge the stability of the deviation fluctuation, and if the standard deviation of the three-axis deviation exceeds 0.003 mm, the systematic error judgment process is triggered; on this basis, the deviation data is counted in the positive and negative directions by setting a fixed threshold (±0.005 mm), and if the deviation is biased to one side and exceeds 60% of the frequency, it is determined as a systematic error, otherwise it is a random error. After separating the two types of errors, the error characteristic parameter sets are calculated respectively, the systematic error is determined by the periodic offset amount fitting method to determine the correction increment, and the random error is extracted by the three-point weighted average method to obtain the fluctuation center value as the dynamic fine adjustment amount. According to the above characteristic parameters, the closed-loop correction operation of the comprehensive control command is performed, the systematic correction increment is directly superimposed on the trajectory position reference value, the random dynamic fine adjustment amount is adjusted by the slope change of the speed command, and the corrected comprehensive control command is generated. On the basis of the correction command, the torque of the main drive motor and the pre-tightening motor arranged at both ends of the ball screw of each shaft is calculated, the total driving torque is set to be not more than 90% of the rated torque, and the torque ratio of the main motor and the auxiliary motor is set according to the pre-tightening motor sharing ratio of 30%, and the anti-backlash driving torque distribution data is obtained. This data is input into the servo controller as the double-drive control command to drive the main motor and the pre-tightening motor to output torque and adjust the load at both ends in real time, so as to obtain the double-drive synchronous control signal; the signal further controls the magnetic suspension micro-positioner installed on the guide rail of each shaft to realize sub-micron compensation, the magnetic suspension micro-positioner realizes frictionless displacement control by using the electromagnetic suspension principle, the control signal is accurately modulated by the bidirectional PID current driving module, the adjustment resolution is 0.1 μm, and the response time is less than 2 ms, and the end position coordinate details are corrected through the control. Finally, the compensation displacement signal output by the magnetic suspension micro-positioner is superimposed with the original instruction displacement to form the final position control command, which is written into the servo system execution end for real-time position closed-loop control.

[0048] The present application realizes all-round optimization of the servo position control system of the engraving and milling machine through multi-source data fusion and intelligent modeling, and significantly improves the machining precision and dynamic response capability. First, by introducing the screw temperature, load and multi-node temperature data of the whole machine, the problem of insufficient compensation of gap error and structural deformation caused by thermal expansion, load change and nonlinear thermal deformation is effectively solved, and the robustness of the system to thermal environment fluctuations is enhanced. Second, combined with the cutting force and resonance characteristic information, dynamic adaptive adjustment of the feed speed and trajectory smoothing interpolation are realized, which significantly improves the trajectory stability and machining efficiency in the engraving and milling process, and reduces the trajectory deviation and surface defects caused by vibration. In addition, the system performs real-time precision evaluation and error closed-loop update on the actual position feedback of each axis, further improves the response speed and precision of the position control, and ensures stable operation under complex machining conditions. Finally, through the double-drive coordinated control mode, the limitations of traditional control strategies in large dynamic load and high-precision synchronous driving are effectively overcome, higher precision and higher stability of servo control performance are realized, and the overall performance and intelligent level of the engraving and milling machine in high-speed and high-precision machining tasks are improved.

[0049] Preferably, step S1 comprises the following steps:

[0050] Step S11: detecting the temperature of the X-axis ball screw, Y-axis ball screw and Z-axis ball screw of the engraving and milling machine to obtain real-time screw temperature data of each axis;

[0051] Step S12: detecting the axial load of each axis of the engraving and milling machine through a current sensor to obtain real-time load data of each axis;

[0052] Step S13: collecting the temperatures of the machine bed, column, spindle box and workbench of the engraving and milling machine through a semiconductor temperature sensor to obtain multi-node temperature distribution data;

[0053] Step S14: detecting the cutting force of the engraving and milling machine through a three-way force sensor to obtain cutting force data;

[0054] Step S15: performing frequency spectrum analysis on the machine vibration of the engraving and milling machine through a vibration sensor to obtain resonance frequency characteristic values.

[0055] In the embodiment of the present application, firstly, a plurality of K-type thermocouple temperature sensors are installed on the outer wall of the ball screw nut seat along the X-axis, Y-axis and Z-axis at equal intervals along the length of the screw, the sensor accuracy is controlled within ±0.1℃, the sampling period is set to 1 second, the temperature signal is transmitted to the data sampling module through the RS485 bus mode, and the temperature change of each axis screw during operation is recorded in real time to form independent real-time temperature data of each axis screw. A Hall current sensor with a range of ±20A and an output sensitivity of 40mV / A is connected in series in the power supply circuit of each axial servo motor to monitor the current fluctuation of each axis during cutting in millisecond level, and the current value is combined with the torque constant (unit: Nm / A) of the servo motor and the lead (unit: mm) of the ball screw to calculate the real-time axial load data of the X, Y and Z axes according to the axial load calculation formula: load = (current x motor torque constant) ÷ screw lead. A total of 32 thermistor type semiconductor temperature sensors are arranged at typical heat-sensitive parts of the engraving and milling machine bed, column, spindle box and workbench, not less than 8 measuring points for each type of structure, the measuring point layout is set according to the symmetry of the structure and the heat conduction path, the sensor accuracy is controlled within ±0.2℃, and the I 2 C bus and the temperature acquisition module, not less than 60 times per minute, forming multi-node temperature distribution data of the bed, column, spindle box and workbench structure components, and combining with the spatial coordinates of each measuring point to construct a three-dimensional temperature distribution matrix. After completing the temperature data acquisition, enter step S14, install a three-way piezoelectric force sensor in the contact area between the spindle tool holder and the tool clamp, the force sensor should meet the range of 0 to 5000N and the sensitivity should not be less than 5pC / N, record the cutting forces in X, Y and Z directions with a time resolution of 0.5ms during cutting operation, and generate cutting force data by synchronously collecting and caching through signal amplifier and high-speed data acquisition card. Three-axis MEMS acceleration type vibration sensors are fixedly installed at the front end of the spindle box and the end of the three-axis guide rail of the engraving and milling machine, and the frequency response range should not be less than 0.5Hz to 5000Hz. The signal is connected to the 24-bit AD converter through the analog channel, and the vibration sampling is carried out during the no-load operation and speed frequency conversion of the machine tool, the sampling period is set to 1ms, and the sampling duration is not less than 10 seconds. The obtained vibration acceleration data is processed by fast Fourier transform to extract the frequency point where the maximum amplitude is located in the frequency domain, and the frequency value is recorded as the characteristic value of the resonance frequency of the structure system.

[0056] The present application collects multi-dimensional high-precision data of temperature, load, cutting force and vibration of key parts of the engraving and milling machine, thereby providing a solid data foundation for subsequent servo control optimization and error compensation. Real-time monitoring of the temperature and load of the lead screw helps to comprehensively master the dynamic changes of the thermal elongation and mechanical state of the lead screw, provides high-credibility input for predicting the gap error, and improves the accuracy of the compensation model; the acquisition of the multi-node temperature field data of the whole machine makes the structural thermal deformation have visualization and modeling basis, and enhances the timeliness and precision of thermal error modeling and compensation; the acquisition of the cutting force information reflects the real-time interaction state between the tool and the workpiece, which helps to realize dynamic feed adjustment, improve the machining efficiency and reduce surface defects; the vibration frequency spectrum analysis result reveals the resonance characteristics of the machine tool structure, which provides key basis for avoiding excitation frequency, suppressing trajectory oscillation and improving stability. Overall, the data acquisition strategy significantly enhances the environmental perception and dynamic response capability of the control system, and provides key support for realizing intelligent machining of the engraving and milling machine with high precision, high stability and high efficiency.

[0057] Preferably, step S2 comprises the following steps:

[0058] Step S21: filtering the real-time temperature data of the lead screw to obtain smooth temperature data; and normalizing the real-time load data to obtain standardized load data;

[0059] Step S22: calculating the thermal expansion coefficient of the ball screw based on the smooth temperature data and the standardized load data to obtain a thermal expansion influence factor;

[0060] Step S23: detecting the transmission gap reference value of the ball screw and the screw nut pair of each axis of the engraving and milling machine, and performing load deformation analysis of the screw nut pair to obtain a load deformation coefficient;

[0061] Step S24: constructing a neural network gap prediction model based on the thermal expansion influence factor and the load deformation coefficient, and training the neural network gap prediction model to obtain a lead screw reverse gap prediction model;

[0062] Step S25: predicting the reverse gap of each axis of the ball screw in real time based on the lead screw reverse gap prediction model to obtain a dynamic gap prediction value;

[0063] Step S26: calculating the compensation amount of each axis based on the dynamic gap prediction value to obtain a gap compensation value;

[0064] Step S27: performing direction discrimination on the gap compensation value to obtain a forward compensation value and a reverse compensation value;

[0065] Step S28: performing pre-compensation control on the servo position of each axis before direction transformation based on the forward compensation value and the reverse compensation value to obtain a corrected position command.

[0066] In the embodiment of the present application, first, the real-time temperature data of each screw rod collected in step S11 is input into the filtering processing unit, a five-point moving average method is used to perform a weighted smoothing operation on the adjacent 5 sampling points, the weight coefficients are set to 0.1, 0.2, 0.4, 0.2, 0.1, the mutation interference is eliminated, and the smoothed temperature data is generated; then, the real-time load data obtained in step S12 is subjected to normalization processing, the reference value is set to the rated axial load 5000N of each screw rod under the maximum load state, the normalization uses the standard linear transformation formula, and the output value range is limited to between 0 and 1, to obtain the standardized load data. According to the ΔT value in the smoothed temperature data and the linear expansion coefficient 1.2×10 ―5 / ℃ of the screw rod material GCr15, the effective length of the screw rod is 800mm, the thermal expansion amount calculation formula ΔL=α·ΔT·L is used, the standardized load data is combined, the thermal expansion influence factor is constructed by linear superposition, the temperature expansion component weight is taken as 0.6, the load component weight is taken as 0.4, and the coefficient fusion processing is performed. A laser displacement sensor with a displacement measurement resolution of 0.001mm is used to detect the minimum starting displacement difference value of each screw rod nut pair under the no-load and reverse driving state, which is recorded as the transmission clearance reference value, a static pressure loading device with a loading force of 1000N is used to axially load the nut pair, the displacement change value before and after loading is compared, the load deformation coefficient is calculated according to the formula K=ΔL / F (where K is the deformation coefficient, ΔL is the displacement, and F is the loading force). The thermal expansion influence factor and the load deformation coefficient are constructed into a two-dimensional parameter mapping relationship diagram in an equal weight manner, the actual gap change amount measured historically is corresponded by using artificial adjustment, a ball screw reverse gap trend comparison table is formed, and an interpolation method is called for real-time gap prediction processing based on the table. The combination value of the current thermal expansion influence factor and the load deformation coefficient is detected in real time, the corresponding reverse gap prediction value is found by comparing the trend comparison table, and the value range is limited to 0.003mm to 0.015mm. The above dynamic gap prediction value is directly used as the gap compensation value of each axis ball screw, and the three-axis dynamic gap compensation values are calculated and written into the compensation cache area. By reading the current motion direction signal of the servo motor collected by the encoder, it is determined whether the screw rod is in forward driving or reverse driving state. If it is forward driving, the forward compensation value is set to 0, and the reverse compensation value is equal to the gap compensation value; if it is reverse driving, the forward compensation value is equal to the gap compensation value, and the reverse compensation value is 0, so that the direction separation discrimination is realized. The forward compensation value and the reverse compensation value after the above direction discrimination are superimposed into the basic position command, vector addition processing is performed, and the corrected position command is generated.

[0067] The present application realizes high-precision dynamic compensation of the reverse gap of the ball screw through multi-step intelligent processing and modeling, effectively improving the positioning accuracy and response capability of the engraving and milling machine in complex machining environment. The filtering and normalization processing enhances the stability and comparability of the original temperature and load data, reduces the interference of data fluctuation on the accuracy of the model; the accurate extraction of the thermal expansion influencing factor and the load deformation coefficient makes the compensation model more physically meaningful and predictive; the gap prediction model constructed by the neural network has strong fitting ability for nonlinear and multivariate relationships, can respond to the combined effect of thermal deformation and mechanical deformation of the screw in real time, and thus more accurately predicts the trend of the reverse gap; further direction differentiation and pre-compensation control of the compensation value enable the system to correct the position command in advance, avoiding error accumulation caused by feedback lag, and significantly improving the forward-looking and dynamic accuracy of servo control; overall, the present scheme effectively solves the problems of response lag, poor adaptability and insufficient accuracy in the traditional reverse gap compensation method, and provides key technical support for realizing high-speed and high-precision positioning and stable machining of the engraving and milling machine.

[0068] Preferably, step S3 comprises the following steps:

[0069] Step S31: constructing a three-dimensional digital twin temperature field based on the multi-node temperature distribution data to obtain a three-dimensional temperature field model;

[0070] Step S32: performing digital twin thermal deformation simulation of the machine tool body guide rail mounting surface of the engraving and milling machine based on the three-dimensional temperature field model to obtain the thermal deformation amount of the tool body;

[0071] Step S33: performing thermal deformation simulation calculation of the connection part of the column and the tool body of the engraving and milling machine based on the three-dimensional temperature field model to obtain the thermal tilt deformation amount of the column;

[0072] Step S34: performing thermal displacement simulation of the center line position of the main shaft of the engraving and milling machine based on the three-dimensional temperature field model to obtain the thermal displacement amount of the main shaft;

[0073] Step S35: performing thermal deformation simulation calculation of the flatness of the workbench based on the three-dimensional temperature field model to obtain the thermal deformation amount of the workbench;

[0074] Step S36: calculating the X-axis thermal deformation compensation based on the thermal deformation amount of the tool body and the thermal displacement amount of the main shaft, calculating the Y-axis thermal deformation compensation based on the thermal deformation amount of the tool body, the thermal tilt deformation amount of the column and the thermal displacement amount of the main shaft, and calculating the Z-axis thermal deformation compensation based on the thermal tilt deformation amount of the column and the thermal displacement amount of the main shaft, to finally form the thermal deformation compensation amount of each axis.

[0075] In the embodiment of the application, the temperature data collected by the 12 semiconductor temperature sensors DS18B20 arranged on the machine tool body, the temperature data collected by the 8 temperature sensors arranged on the column, the temperature data collected by the 6 temperature sensors arranged on the spindle box and the temperature data collected by the 4 temperature sensors arranged on the workbench are subjected to spatial coordinate mapping to establish a three-dimensional rectangular coordinate system with the left front corner of the machine tool body as the origin, the real-time temperature values of each temperature sensor are subjected to three-dimensional space interpolation calculation according to the X coordinate, Y coordinate and Z coordinate of the sensor installation position, the radial basis function interpolation method is used to expand the discrete 30 temperature measurement point data into a continuous temperature field distribution containing 10,000 grid nodes to form a three-dimensional digital twin temperature field model; based on the temperature distribution data of the machine tool body region in the three-dimensional temperature field model, a three-dimensional entity model of the machine tool body guide rail mounting surface is established by ANSYS, the machine tool body material is set to HT300 gray cast iron, the elastic modulus is set to 110GPa, the Poisson's ratio is set to 0.26, the linear thermal expansion coefficient is set to 1.1×10 ―5 / ℃, the temperature data of the machine tool body region in the three-dimensional temperature field model is applied as a thermal load boundary condition to the finite element model, the deformation displacement of the machine tool body guide rail mounting surface under temperature change is calculated through thermal-structure coupling analysis, and the thermal deformation amount of the machine tool body guide rail mounting surface along the X direction, Y direction and Z direction is extracted as the thermal deformation amount of the machine tool body; the temperature distribution data of the column region in the three-dimensional temperature field model is input into the column thermal deformation simulation model, the column material parameters are set to nodular cast iron QT450-10, the elastic modulus is set to 169GPa, the Poisson's ratio is set to 0.275, and the linear thermal expansion coefficient is set to 1.2×10 ―5 / ℃, the degrees of freedom of the column bottom surface and the machine tool body connecting surface are constrained, the rotation degrees of freedom of the column top surface are released, the thermal stress distribution and deformation state of the column under the action of the non-uniform temperature field are calculated, the angular deflection amount of the column top surface relative to the bottom surface is extracted, and the angular deflection amount is multiplied by the column height of 800mm to calculate the column thermal tilt deformation amount; the 6 temperature measurement point data of the spindle box region in the three-dimensional temperature field model are input into the spindle thermal displacement calculation program, the spindle material is set to 40Cr alloy steel, the elastic modulus is set to 206GPa, and the linear thermal expansion coefficient is set to 1.2×10 ―5The spindle axial thermal elongation is calculated by calculating the temperature difference of the front bearing seat and the rear bearing seat of the main shaft, combining the length of the main shaft 320mm and the thermal expansion coefficient, and calculating the thermal elongation of the main shaft in the axial direction. According to the non-uniformity of the temperature distribution of the main shaft box, the thermal displacement offset of the main shaft center line in the XY plane is calculated, the axial thermal elongation and the radial thermal displacement offset are synthesized into the thermal displacement of the main shaft; the temperature distribution function of the workbench surface is fitted by the least square method through the data of the four temperature measuring points in the workbench area in the three-dimensional temperature field model, the workbench material is set as HT300 gray cast iron, the thermal deformation difference caused by the temperature difference of the four corner points of the workbench is calculated, the thermal deformation of any point on the workbench surface is calculated by using the bilinear interpolation method, and the maximum value of the flatness deviation of the workbench surface is extracted as the thermal deformation of the workbench; the X-axis thermal deformation compensation amount is calculated by vector synthesis of the X-direction component of the bed thermal deformation and the X-direction component of the spindle thermal displacement, the Y-axis thermal deformation compensation amount is calculated by vector synthesis of the Y-direction component of the bed thermal deformation, the projection component of the column thermal tilt deformation in the Y direction and the Y-direction component of the spindle thermal displacement, and the Z-axis thermal deformation compensation amount is calculated by vector synthesis of the Z-direction component of the column thermal tilt deformation and the Z-direction component of the spindle thermal displacement. Finally, the thermal deformation compensation amounts of the X-axis, Y-axis and Z-axis of the engraving and milling machine are ΔX, ΔY and ΔZ respectively, and are recorded as the thermal deformation compensation amounts of the axes.

[0076] The present application improves the geometric accuracy control ability of the engraving and milling machine under the change of thermal environment by constructing a refined three-dimensional digital twin temperature field and combining the thermal deformation simulation of key structural parts. The three-dimensional temperature field model can truly reflect the thermal distribution characteristics of the machine tool under the influence of multi-point temperature rise, provide accurate boundary conditions for subsequent thermal deformation simulation, and significantly improve the reliability of thermal error modeling; the thermal deformation of the bed, column, spindle and workbench and other key components is virtually simulated, which can effectively identify and predict the structural displacement, tilt and warping trend in the machining process, and provide quantitative basis for each axis compensation; by comprehensively calculating the combined effect of thermal deformation of different parts on the motion accuracy of each axis, the accurate distribution of axial thermal compensation is realized, and the trajectory deviation and part size error caused by thermal deformation are significantly reduced; this strategy enhances the geometric stability of the engraving and milling machine under continuous machining, heavy cutting or complex thermal field conditions, provides an important guarantee for high-precision and high-consistency machining, and is especially suitable for microstructure manufacturing and precision mold machining scenes with significant thermal error proportion.

[0077] Preferably, step S4 comprises the following steps:

[0078] Step S41: Real-time filtering processing is performed on the cutting force data to obtain smoothed cutting force data; spindle cutting torque is calculated based on the smoothed cutting force data to obtain real-time spindle cutting torque; and a cutting state identifier is obtained by identifying the cutting state of the engraving and milling machine based on the smoothed cutting force data.

[0079] Step S42: The temperature and vibration of the spindle bearing in the engraving and milling machine are detected to obtain spindle bearing state data; spindle load is evaluated based on the real-time spindle cutting torque and the spindle bearing state data to generate a spindle load coefficient; and the matching degree of the spindle speed and the feed speed is obtained by analyzing the matching degree of the spindle speed and the feed speed based on the spindle load coefficient.

[0080] Step S43: The dynamic response of the bed-spindle of the engraving and milling machine is analyzed based on the matching degree of the spindle speed and the feed speed to obtain a dynamic stiffness coefficient; and the feed speed constraint value is obtained by setting the upper limit of the feed speed of each axis servo motor in the engraving and milling machine based on the dynamic stiffness coefficient.

[0081] Step S44: The degree of tool cutting edge wear in the engraving and milling machine is evaluated based on the cutting state identifier to obtain a tool wear coefficient.

[0082] Step S45: Cutting vibration frequency domain analysis is performed based on the resonance frequency characteristic value and the smoothed cutting force data to obtain cutting vibration stability data.

[0083] Step S46: The feed rate reference value is calculated based on the tool wear coefficient and the cutting vibration stability data to obtain an initial feed rate coefficient.

[0084] Step S47: The X-axis, Y-axis, and Z-axis feed speeds are coordinated and optimized based on the initial feed rate coefficient to obtain an axis feed coordination coefficient; and the feed rate optimization calculation is performed based on the feed speed constraint value and the axis feed coordination coefficient to obtain a constraint feed rate coefficient.

[0085] Step S48: The surface quality is optimized and adjusted based on the constraint feed rate coefficient, and the efficiency balance is calculated to generate an optimized feed rate coefficient.

[0086] In the embodiment of the application, the original cutting force data collected by the three-way force sensor Kistler9257B installed on the main shaft box of the engraving and milling machine is subjected to Butterworth low-pass filtering processing, the filtering frequency is set to 500Hz, the cutoff frequency is set to 100Hz, the smooth cutting force data is obtained, the tangential component Ft in the smooth cutting force data is multiplied by the tool radius r to calculate the real-time cutting torque T of the main shaft T=Ft x r, when the cutting force resultant F_resultant is greater than the set threshold value 200N, it is marked as heavy cutting state, when the cutting force resultant is less than 50N, it is marked as light cutting state, when the cutting force resultant is between 50N and 200N, it is marked as normal cutting state, and the cutting state identifier is generated as 1, 2 and 3 corresponding to heavy cutting, normal cutting and light cutting state respectively; the temperature sensor PT100 and the acceleration sensor PCB352C33 installed on the front and rear bearing seats of the main shaft are used to collect the bearing temperature and vibration acceleration of the main shaft respectively, when the bearing temperature exceeds 65℃ or the vibration acceleration effective value exceeds 5m / s 2The spindle bearing state data is marked as an abnormal state, the spindle load coefficient η = T / 120 is calculated by dividing the spindle real-time cutting torque T by the spindle rated torque 120 N·m, the speed-feed matching degree is set to 1.0 when the ratio of the spindle speed n to the feed speed vf is within the range of 800-1200 rpm / (mm / min), and linearly decreases to 0.5 according to the deviation degree when the ratio is out of the range; the dynamic stiffness coefficient K_dynamic of the bed-spindle system is calculated by measuring the displacement response of the engraving machine bed under different cutting forces, wherein ΔF is the cutting force change amount, and Δδ is the corresponding displacement change amount, the upper limit of the feed speed of each axis servo motor is set to 8000 mm / min when the dynamic stiffness coefficient is less than 50 N / μm, the upper limit of the feed speed is set to 15000 mm / min when the dynamic stiffness coefficient is greater than 100 N / μm, and the feed speed constraint value is calculated according to linear interpolation when the dynamic stiffness coefficient is between 50-100 N / μm; the tool wear coefficient is calculated according to the cutting state identifier and the cumulative cutting time, the wear coefficient growth rate is set to 0.02 / hour when the cutting state identifier is 1, the growth rate is set to 0.01 / hour when the identifier is 2, and the growth rate is set to 0.005 / hour when the identifier is 3, and the tool wear coefficient K_wear is calculated according to the corresponding growth rate starting from the initial value 0; the smooth cutting force data and the resonance frequency characteristic value obtained in step S15 are input into a fast Fourier transform processing program, the frequency spectrum distribution of the cutting force signal is calculated, the cutting vibration stability data is marked as unstable state when the deviation of the cutting force frequency component from the machine tool resonance frequency is less than 5 Hz, the vibration stability data is marked as stable state when the deviation is greater than 20 Hz, and the vibration stability data is marked as critical state when the deviation is between 5-20 Hz; the tool wear coefficient K_wear and the cutting vibration stability data are substituted into the formula F_base = 1.0-0.5×K_wear-0.3×V_instability to calculate the initial feed ratio reference value, wherein V_instability is the vibration instability coefficient, V_instability = 0 in the stable state, V_instability = 0.5 in the critical state, and V_instability = 1.0 in the unstable state, to obtain the initial feed ratio coefficient F_base; the initial feed ratio coefficients of the X-axis, Y-axis and Z-axis are multiplied by the inter-axis coordination factors 0.9, 1.0 and 0.8 respectively to obtain the axis feed coordination coefficients, the axis feed coordination coefficients are compared with the corresponding feed speed constraint values, the feed ratio coefficient is adjusted to the ratio of the constraint value to the reference feed speed when the coordinated feed speed exceeds the constraint value, and the constraint feed ratio coefficient is obtained; the constraint feed ratio coefficient is adjusted according to the preset workpiece surface roughness requirement Ra≤1.6 μm, and the feed ratio coefficient is multiplied by the correction factor 0 when the surface roughness prediction value exceeds 1.6 μm.8The quality optimized feed ratio is obtained, the efficiency balance factor is calculated by dividing the quality optimized feed ratio by the preset machining efficiency requirement coefficient 1.2, and the final optimized feed ratio coefficient is obtained by multiplying the quality optimized feed ratio by the efficiency balance factor.

[0087] The present application realizes adaptive optimization control of the feeding speed of the engraving and milling machine by introducing multi-source dynamic characteristic data such as cutting force, spindle state, tool wear and vibration, effectively improves the stability, machining quality and overall efficiency of the machining process. The filtered cutting force data improves the accuracy of dynamic characteristic identification, and provides a stable basis for subsequent torque calculation and cutting state discrimination; the analysis of the spindle load and the matching of the speed-feed makes the feeding strategy able to respond to the actual operating load of the equipment in real time, preventing overload and efficiency loss; the introduction of the dynamic stiffness coefficient enables the system to perceive the structural dynamic response capability, thereby avoiding machining errors or vibration excitation caused by insufficient stiffness; the fusion evaluation of tool wear and cutting vibration stability enhances the comprehensive judgment of tool health status and machining vibration risk, which helps to prevent tool damage and suppress machining instability; the multi-stage adjustment mechanism of the feed ratio realizes coordinated control of the speed of each axis and adaptive constraint of the speed upper limit, making the machining process more stable and the trajectory smoother; finally, through the balance optimization between surface quality and efficiency, not only the surface integrity of the part is improved, but also the production rhythm demand is considered, and overall, the intelligent transformation of the engraving and milling machine feeding control from passive response to active optimization is realized, greatly enhancing the machining adaptability and control flexibility under complex working conditions.

[0088] Especially important is that step S48 comprises the following steps:

[0089] Step S481: surface quality optimized adjustment of the constraint feed ratio is performed based on the preset workpiece surface roughness requirement, and a quality optimized feed ratio is obtained;

[0090] Step S482: efficiency balance is calculated based on the preset machining efficiency requirement and the quality optimized feed ratio, and an efficiency balance factor is obtained;

[0091] Step S483: an optimized feed ratio coefficient is determined based on the quality optimized feed ratio and the efficiency balance factor.

[0092] In the embodiment of the present application, the preset workpiece surface roughness requirement Ra≤1.6μm is taken as the quality control reference, and the surface roughness prediction formula The surface roughness prediction value under the current constraint feed rate coefficient is calculated, wherein f is the feed amount set to 0.1 mm / r, r is the tool nose radius set to 0.8 mm, vf is the feed speed, n is the spindle speed, when Ra_prediction exceeds 1.6 μm, the constraint feed rate coefficient is adjusted by multiplying the quality correction factor K_quality = 1.6 / Ra_prediction, when Ra_prediction is less than 1.0 μm, the constraint feed rate coefficient is optimized by multiplying the quality improvement factor 1.2, when Ra_prediction is in the range of 1.0-1.6 μm, the constraint feed rate coefficient is kept unchanged, and the quality optimized feed rate F_quality is obtained through the above adjustment process; the preset machining efficiency requirement is set to the number of workpieces completed per hour N_target = 12 pieces, the actual machining time T_actual = T_standard / F_quality is calculated through the current quality optimized feed rate F_quality, wherein T_standard is the standard machining time set to 300 seconds / piece, the actual number of workpieces completed per hour N_actual = 3600 / T_actual is calculated according to the actual machining time, the efficiency balance factor is calculated through the efficiency balance calculation formula η_balance = N_actual / N_target, when η_balance is greater than 1.2, the efficiency balance factor is limited to 1.2, when η_balance is less than 0.8, the efficiency balance factor is limited to 0.8, and the efficiency balance factor is ensured to be in the range of 0.8 to 1.2; the quality optimized feed rate F_quality and the efficiency balance factor η_balance are weighted and fused to calculate, the optimized feed rate coefficient is determined by using the weighted average formula F_optimized = α × F_quality + β × η_balance × F_quality, wherein the quality weight coefficient α is set to 0.6, the efficiency weight coefficient β is set to 0.4, when the calculated F_optimized exceeds the system maximum feed rate limit 1.5, the optimized feed rate coefficient is limited to 1.5, when F_optimized is less than the system minimum feed rate limit 0.3, the optimized feed rate coefficient is limited to 0.3, and finally the optimized feed rate coefficient in the range of 0.3 to 1.5 is obtained.

[0093] The present application realizes fine adjustment and multi-objective optimization of the feed ratio by introducing the dual constraints of workpiece surface quality and processing efficiency, significantly improving the process adaptability and comprehensive performance of the engraving and milling machine under different processing tasks. The surface quality optimization adjustment can ensure that the feed ratio meets the preset roughness standard, suppresses the formation of cutting vibration marks and tool marks, and improves the consistency and precision of the processed surface; the introduction of the efficiency balancing factor realizes the dynamic balance of the contradictory relationship between processing time and surface quality, avoiding the significant decline in efficiency due to excessive pursuit of quality, or sacrificing surface precision due to efficiency improvement; the finally generated optimized feed ratio coefficient comprehensively considers the two core targets of quality and efficiency, so that the processing parameters automatically adapt and intelligently adjust under different process requirements, thereby significantly enhancing the practicality and flexible control ability of the system in high-precision and high-efficiency manufacturing scenarios.

[0094] Preferably, the output of the comprehensive control instruction of each axis servo position controller in the engraving and milling machine based on the corrected position instruction, the thermal deformation compensation amount of each axis and the optimized feed ratio coefficient in step S5 comprises:

[0095] Based on the corrected position instruction and the thermal deformation compensation amount of each axis, data fusion processing is performed to obtain a thermal compensation position instruction;

[0096] Based on the three-dimensional temperature field model, linear guide pair thermal deformation simulation analysis is performed to obtain a guide thermal elongation amount;

[0097] Based on the thermal compensation position instruction and the guide thermal elongation amount, linear guide pair precision compensation is performed to obtain a guide compensation position instruction;

[0098] Based on the guide compensation position instruction and the optimized feed ratio coefficient, the servo position controller speed is modulated to obtain a speed modulation position instruction;

[0099] Based on the thermal deformation amount of the workbench, the thermal displacement compensation of the origin of the workpiece coordinate system is calculated to obtain a workpiece coordinate system thermal compensation value;

[0100] Based on the speed modulation position instruction and the workpiece coordinate system thermal compensation value, the workpiece coordinate is corrected, and multi-axis synchronous time sequence control is performed to obtain a comprehensive control instruction.

[0101] In the embodiment of the present application, the corrected position instruction (X_corrected, Y_corrected, Z_corrected) obtained in step S2 is subjected to numerical addition operation with the thermal deformation compensation amount (ΔX, ΔY, ΔZ) of each axis obtained in step S3, to calculate the thermal compensation position instruction as X_thermal=X_corrected+ΔX, Y_thermal=Y_corrected+ΔY, Z_thermal=Z_corrected+ΔZ, and then the three-dimensional temperature field data in step S3 is input into the linear guide pair thermal deformation calculation program, the guide material is set as 45# steel, the linear thermal expansion coefficient is 1.2×10 ―5 / ℃, the thermal elongation amount L_X=1200×1.2×10 ―5 ×ΔT_X of the X-axis guide rail length 1200mm under the temperature change ΔT_X, the thermal elongation amount L_Y=800×1.2×10 ―5 ×ΔT_Y of the Y-axis guide rail length 800mm, and the thermal elongation amount L_Z=600×1.2×10 ―5X_deltaT_Z, the guide compensation position command X_guide=Y_thermal+L_Y, Z_guide=Z_thermal+L_Z is obtained by superimposing the thermal compensation position command and the guide thermal elongation amount, then the guide compensation position command is multiplied by the optimized feed ratio coefficient F_optimized obtained in step S4, the modulation speed of each axis is calculated through the speed modulation formula V_modulated=V_originalxF_optimized, the speed modulation position command contains position component and speed component, at the same time, the thermal displacement compensation of the workpiece coordinate system origin is calculated according to the worktable thermal deformation amount ΔZ_table in step S3, the workpiece coordinate system origin is adjusted from (0, 0, 0) to (0, 0, ΔZ_table) through the coordinate transformation matrix, the workpiece coordinate system thermal compensation value is (0, 0, ΔZ_table), then the speed modulation position command is operated with the workpiece coordinate system thermal compensation value through coordinate system transformation, the workpiece coordinate correction is carried out through matrix operation [X_final Y_final Z_final]=[X_modulated Y_modulated Z_modulated]+[0 0 ΔZ_table], finally, the modified position command of each axis is time-synchronized according to the interpolation period 1ms through the multi-axis synchronous timing control program, ensuring that the position commands of X axis, Y axis and Z axis are consistent in time, the comprehensive control command is generated through timing synchronization calculation X_sync(t)=X_final*sin(2πt / T), Y_sync(t)=Y_final*sin(2πt / T), Z_sync(t)=Z_final*sin(2πt / T), wherein T is the interpolation period set as 1ms, t is the current time step, finally the comprehensive control command containing position, speed and acceleration information is output.

[0102] The application realizes high-precision dynamic generation of servo control instructions of each axis of the engraving and milling machine through multi-source data fusion and hierarchical thermal compensation strategy, and significantly improves the control accuracy and machining consistency of the system in a complex thermal-force environment. The generation of the thermal compensation position instruction effectively integrates position correction and the influence of thermal deformation of each axis, so that the control instruction is closer to the actual state of the machine tool, and the position drift caused by thermal error is fundamentally reduced; the simulation analysis and compensation processing of the thermal deformation of the linear guide pair further improve the positioning accuracy of the machine tool at the end of the motion chain, and solve the geometric error problem caused by the thermal elongation of the guide; the speed modulation mechanism combined with the optimized feed ratio coefficient enables the servo control system to adaptively adjust the response speed according to the actual load and process state, thereby improving the dynamic performance and trajectory smoothness of the machining; at the same time, the origin of the workpiece coordinate system is corrected for thermal displacement to ensure high consistency between the machining features and the design coordinate system, thereby enhancing the geometric matching of the product size; finally, the timing coordination is realized through multi-axis synchronous control to avoid the coupling amplification of thermal-induced errors under multi-axis linkage, thereby optimizing the execution precision, system stability and machining quality of the engraving and milling machine in the high-speed and high-precision machining process.

[0103] Preferably, the step S5 of constructing the machining trajectory of the engraving and milling machine based on the optimized feed ratio coefficient and the resonance frequency characteristic value and performing optimized interpolation includes:

[0104] The original machining trajectory speed of the engraving and milling machine is calibrated based on the optimized feed ratio coefficient, and the trajectory curvature characteristic data is obtained by analyzing the geometric features of the machining trajectory of the engraving and milling machine based on the original machining trajectory speed;

[0105] The neural network trajectory optimization model is constructed and trained based on the trajectory curvature characteristic data to obtain the trajectory optimization neural network model;

[0106] The machining trajectory intelligent optimization interpolation is performed based on the trajectory optimization neural network model to obtain the preliminary optimized trajectory parameters;

[0107] The vibration avoidance interpolation processing is performed based on the preliminary optimized trajectory parameters and the resonance frequency characteristic value, and the B-spline curve smoothing processing of the trajectory transition point is performed to obtain the B-spline smooth trajectory parameters;

[0108] The frequency response of the bed-spindle of the engraving and milling machine is analyzed based on the resonance frequency characteristic value, and the vibration risk of each trajectory segment of the engraving and milling machine is evaluated based on the analysis result of the frequency response to obtain the vibration risk level;

[0109] The dynamic response analysis of the linear guide pair of each axis of the engraving and milling machine is performed based on the vibration risk level, and the multi-axis coordinated optimization of the motion trajectory of each axis of the engraving and milling machine is performed based on the dynamic response analysis result to obtain the multi-axis coordinated trajectory parameters;

[0110] The motion synchronization of the servo motors of each axis in the engraving and milling machine is optimized based on multi-axis coordinated trajectory parameters to obtain synchronized optimized trajectory parameters.

[0111] The geometric accuracy and motion continuity are verified by the B-spline smooth trajectory parameters and the synchronized optimized trajectory parameters, the processing efficiency and quality are balanced, the spindle feed speed is coordinated, and the ball screw transmission characteristics are optimized to generate smooth trajectory parameters.

[0112] In the embodiment of the present application, first, the optimized feed ratio coefficient generated by step S4 is limited between 0.4 and 1.2, the coefficient is multiplied point by point with the unit feed speed of the original G code trajectory point, the modified processing speed of each trajectory segment is calibrated, then the central difference method is used for coordinate derivative operation on the continuous trajectory points, the curvature radius between adjacent points is calculated, and the curvature characteristic data of the processing path in the three-dimensional space is extracted, and the analysis interval is limited to one group of every 5 trajectory points; then the sliding window is used to count the curvature variation amplitude of each section, the curvature mutation points are identified, and the 10 trajectory points before and after the curvature mutation points are extracted as the high curvature area, and the trajectory reconstruction is performed on the high curvature area, the reconstruction method adopts interpolation smoothing processing, the trajectory is refitted between the points using a cubic B-spline curve, the number of control points of each B-spline is ensured to be not less than 4, and the node spacing is not more than 0.2 mm; in combination with the resonance frequency characteristic value obtained through FFT analysis of the vibration sensor, the trajectory speed spectrum after preliminary reconstruction is compared and analyzed with the resonance frequency, and the trajectory segment having a speed excitation component near the resonance frequency band (within a frequency difference range of ±5%) is interpolated and reconstructed, and the specific method is to increase a speed buffer section in the segment, adjust the acceleration curve to avoid the excitation frequency; the B-spline continuous second derivative processing is applied to all trajectory turning points, so that there is no speed mutation at the turning angle connection, and the smoothness of the trajectory is ensured; then, the frequency response function of the bed-spindle system is used as the basis, the structural dynamics method is used to input the equal-amplitude sinusoidal feed excitation to different trajectory segments and extract the output response, the response amplitude is quantified and divided into low, medium and high three vibration risk levels, and the dynamic response adjustment strategies are respectively set, wherein the high-risk segment needs to insert a speed drop buffer and encrypt the interpolation nodes to 0.1 mm; on this basis, the dynamic response analysis of each axis linear guide pair is performed, the analysis method is based on the simplified model of the finite element guide structure, the velocity and acceleration data of each segment trajectory are input, the stiffness-mass coupled response is evaluated, the timing of each axis feed speed is fine-tuned, and the multi-axis coordinated trajectory parameters are formed; then, the multi-axis coordinate instructions are optimized in synchronization, the method based on time sequence phase alignment is adopted, the time synchronization deviation of each axis to reach the interpolation point is not more than 1 ms, and the synchronized optimized trajectory parameters are generated; finally, the B-spline smooth trajectory parameters and the synchronized optimized trajectory parameters are fused, the weighted average strategy is adopted to integrate the speed, acceleration and position data, and the geometric error and trajectory continuity index are respectively calculated, wherein the geometric error is not more than 0.02 mm, and the maximum jump of the first derivative of the speed curve continuity is not more than 10 mm / s2 After the conditions are met, speed matching correction is performed in combination with the spindle rated speed range (3000-18000 rpm) and the maximum allowable feed speed of each shaft ball screw (not more than 40 m / min), and trajectory speed correction is performed based on the screw drive stiffness to finally generate smooth trajectory parameters for servo position control.

[0113] The present application optimizes the feed ratio and the machine tool resonance characteristics, constructs a high-precision, anti-vibration, continuous smooth machining trajectory, and significantly improves the trajectory control ability and machining quality of the engraving and milling machine under high-speed complex path. Through the combination of trajectory curvature characteristics and neural network model, the trajectory interpolation process has intelligent optimization capability, which can automatically adjust the motion command according to the geometric change of the path, avoiding error accumulation and impact load caused by trajectory mutation; the resonance frequency feature is introduced to realize vibration avoidance and trajectory transition point smoothing, effectively suppressing the excitation response in the trajectory execution process, improving the trajectory continuity and stability; the trajectory segment vibration risk assessment combined with the dynamic response analysis of the guide pair makes the system can optimize the trajectory structure and adjust the motion strategy in advance in the potential resonance section, reducing the risk of vibration interference from the source; through multi-axis trajectory coordination and synchronous optimization control, the motion consistency and response alignment in the process of each axis linkage are ensured, and the spatial path restoration precision is improved; the finally generated smooth trajectory parameters not only realize the optimal balance between geometric precision and dynamic stability, but also dynamically coordinate the spindle feed rate according to the complexity of the path and improve the load transmission characteristics of the ball screw, thereby significantly enhancing the trajectory execution quality, surface forming consistency and overall machining efficiency of the engraving and milling machine.

[0114] Especially important is that the geometric precision and motion continuity are verified through the B-spline smooth trajectory parameters and synchronous optimization trajectory parameters, the machining efficiency and quality are balanced, the spindle feed speed is coordinated, and the ball screw transmission characteristics are optimized, including:

[0115] Based on the B-spline smooth trajectory parameters and the synchronous optimization trajectory parameters, the trajectory fusion processing is performed to obtain the fusion optimization trajectory parameters;

[0116] Based on the fusion optimization trajectory parameters, the trajectory geometric precision and motion continuity are verified and analyzed to obtain trajectory quality evaluation data;

[0117] Based on the preset machining efficiency requirement and the trajectory quality evaluation data, the precision and efficiency balance optimization is performed to obtain the balance optimization trajectory parameters;

[0118] Based on the balance optimization trajectory parameters, the spindle speed and the feed speed of each axis are coordinated and matched to obtain the spindle feed coordination parameters;

[0119] Based on the spindle feed coordination parameters, the ball screw transmission characteristics in the engraving and milling machine are trajectory adapted and optimized to generate smooth trajectory parameters.

[0120] In the embodiment of the application, firstly, the B-spline smooth trajectory parameters and the synchronous optimization trajectory parameters obtained in step S5 are matched in a corresponding manner with equal time steps, and at each time point, three groups of data of position, speed and acceleration are fused and processed by using a weighted average method, the weight coefficients are set to 0.6, 0.3 and 0.1 respectively, and the fused optimization trajectory parameters are generated; then, based on the coordinate continuity and the speed change rate of the trajectory points in each segment of the fused trajectory, geometric accuracy and motion continuity are analyzed, and the specific operation is that the fused trajectory is imported in a CAD / CAM environment, the maximum deviation value in each 100mm trajectory length is calculated segment by segment and is limited to not more than ±0.02mm, and the first-order speed derivative between adjacent trajectory points is calculated and is limited to not more than 15mm / s 2 , thereby obtaining trajectory quality evaluation data; subsequently, the machining efficiency target is set to be not less than 500mm per minute of feeding path length, and the trajectory segment length and the speed stability index in the evaluation data are combined to adjust, linear buffer interpolation processing is performed on the segment with a sharp speed change, and B-spline local reconstruction processing is performed on the segment with a large geometric deviation, and finally balanced optimization trajectory parameters are formed; based on the average feeding speed data of each segment of the trajectory, in combination with the spindle rated output power range of 2.2kW to 5.5kW and the tool machining radius, the spindle speed range is inversely solved according to the cutting linear velocity formula v=π·D·n / 1000, the feeding speed of each segment is matched with the spindle speed, a speed adjustment transition segment is inserted into the paragraph that does not meet the process requirement, and spindle feeding coordination parameters are generated; the spindle feeding coordination parameters and the acceleration and load data of the corresponding trajectory segment of each axis balanced optimization trajectory parameter are combined and input to the controller, the guide rail friction compensation coefficient (limited to 0.015) and the ball screw transmission stiffness (limited to 100-120N / μm) are used for calculation, transmission matching analysis is performed on each trajectory, the speed overshoot and position tracking error caused by the dynamic response lag of the ball screw may be corrected by adjusting the acceleration rising slope and the feeding ratio fine adjustment control, the transmission response delay of each axis in the trajectory execution process is ensured to be not more than 2ms, and finally smooth trajectory parameters are generated.

[0121] The present application realizes the comprehensive improvement of the machining trajectory of the engraving and milling machine in the aspects of geometric precision, motion continuity and execution coordination by fusing trajectory geometric optimization and synchronous motion control strategy. The trajectory fusion processing improves the smoothness of the overall path and the local dynamic matching ability, effectively reduces the impact and speed jitter risk caused by trajectory switching and switching points; through geometric precision and continuity verification, it ensures that the path can still accurately restore the designed contour under high dynamic response conditions, and improves the contour fidelity and surface quality of the final product; the introduction of the precision and efficiency balance mechanism optimizes the machine tool beat while ensuring high-quality trajectory execution, significantly improves the system running efficiency; the coordinated matching of spindle speed and multi-axis feed speed further enhances the motion chain synchronous response performance, makes the spindle cutting stability and feed rhythm consistent, and avoids processing defects caused by speed incoordination; at the same time, the adaptive optimization of the trajectory characteristics to the ball screw transmission characteristics effectively suppresses the nonlinear error of the screw transmission caused by trajectory fluctuation, realizes higher positioning stability and load response consistency, and improves the trajectory execution precision, machining consistency and system robustness of the engraving and milling machine under high speed and high precision conditions.

[0122] Preferably, the coordinated driving control of the servo motors of each axis in the engraving and milling machine based on the comprehensive control instruction and the smoothed trajectory parameters in step S5 comprises:

[0123] Based on the comprehensive control instruction, the position control driving of the ball screw servo motor of each axis in the engraving and milling machine is carried out, and the motor angle instruction data of each axis is obtained;

[0124] Based on the smoothed trajectory parameters, the speed coordination control of the main shaft motor in the engraving and milling machine is carried out, and the main shaft speed synchronization instruction is obtained;

[0125] Based on the motor angle instruction data of each axis and the main shaft speed synchronization instruction, the multi-axis linkage control of the machine tool bed guide is carried out, and the synchronization data of the motion trajectory of each axis is obtained;

[0126] The position feedback signal of the high-precision encoder installed on the ball screw of each axis of the engraving and milling machine is collected, and the encoder pulse feedback data of each axis is obtained;

[0127] Based on the synchronization data of the motion trajectory of each axis and the encoder pulse feedback data of each axis, the actual position coordinates of the workbench of the engraving and milling machine are calculated, and the actual position coordinate data of each axis is obtained;

[0128] The actual position coordinate data of each axis is monitored and analyzed in real time, and the actual position feedback data of each axis is obtained.

[0129] In the embodiment of the present application, firstly, the target position instructions of the X-axis, Y-axis and Z-axis are extracted according to the comprehensive control instructions generated in step S5, the target displacement of each axis and the corresponding timestamp information in the smoothed trajectory parameters are combined, the trajectory is discretized to a sampling period of 1 ms through interpolation, and the expected position, speed and acceleration in each sampling period are calculated based on a third-order position planning curve, which are output to the servo driver by the motion controller to drive the ball screw servo motor of each axis in the engraving and milling machine to generate accurate motor angle instruction data. At the same time, according to the trajectory speed corresponding to the cutting section and the empty section in the smoothed trajectory parameters, the spindle motor control part inversely deduces the synchronous speed that the spindle should reach by using the linear speed conversion formula v = π·D·n / 1000 (where the tool diameter D is limited to Φ6 mm, and v is taken from the average speed of the trajectory section), and controls the slope according to the limitation that the acceleration time constant τ of the spindle does not exceed 0.3 s to form the spindle speed synchronization instruction. Then, the angle instruction data of the three-axis motor and the spindle synchronous speed signal are input to the motion control bus together to build a multi-axis coordinated control scheme with time base as the synchronous reference, and the linkage stepping process is carried out by using the absolute timestamp comparison method to generate synchronization data of the motion trajectory of each axis including position, speed and acceleration. Next, in each sampling period, the high-precision grating encoder (accuracy not less than 0.1 μm) installed at the tail end of the three-axis ball screw outputs a pulse feedback signal, which is converted into the actual angle by differential processing, and then multiplied by the lead of the screw (X-axis 5 mm / r, Y-axis 5 mm / r, Z-axis 4 mm / r) to perform linear displacement conversion to obtain the actual displacement of each axis. Then, the actual displacement data is read synchronously and substituted into the Cartesian coordinate calculation formula to perform coordinate conversion combined with the machine origin offset value and the screw installation direction definition to obtain the actual position coordinate data of each axis at the current time point. Finally, the position coordinate data and the trajectory synchronization data are compared cycle by cycle to calculate the speed difference, position deviation and acceleration change rate, and the motion state is monitored in real time by the servo controller. If any of the three indicators exceeds the set threshold value (where the position error threshold value is ±0.01 mm, the speed difference threshold value is ±5 mm / s, and the acceleration change rate threshold value is ±50 mm / s 2 ), an adjustment signal is immediately sent out and recorded as the actual position feedback data.

[0130] The application realizes high-precision synchronous control of servo motors of each axis of the engraving and milling machine through the coordinated driving of the comprehensive control instruction and the smoothing trajectory parameter, effectively improves the coordination and processing precision of the machine tool movement. The ball screw motor rotation angle instruction based on position control driving ensures the accurate positioning of each axis and improves the execution accuracy of the processing path; the coordinated control of the spindle motor speed realizes the synchronous matching of the spindle and the feeding movement, ensures the stability and processing efficiency of the cutting process; the multi-axis linkage control realizes the smooth movement of the complex space trajectory through the linkage management of the bed guide rail, reduces the mechanical vibration and power impact; the high-precision encoder feedback captures the motion state of each axis in real time, ensures the high responsiveness and accuracy of the position data; combined with the coordinate calculation of the motion trajectory synchronous data and the encoder feedback, the real-time monitoring precision of the actual position is improved; the whole realizes the accurate position control and dynamic state monitoring of the workbench of the engraving and milling machine, enhances the stability, repeat positioning capability and system fault early warning capability of the processing process, and significantly improves the operation reliability and processing quality of the engraving and milling machine in the high-speed and high-precision processing environment.

[0131] Preferably, step S6 comprises the following steps:

[0132] Step S61: Real-time error calculation is performed on the position accuracy of the workbench of the engraving and milling machine based on the actual position feedback data of each axis, and position deviation data of each axis is obtained.

[0133] Step S62: The position deviation data of each axis is classified into systematic error and random error, and a set of error characteristic parameters is obtained.

[0134] Step S63: The comprehensive control instruction is adaptively modified and updated based on the set of error characteristic parameters, and a modified comprehensive control instruction is obtained.

[0135] Step S64: Based on the modified comprehensive control instruction, the torque distribution calculation is performed on the main drive motor and the pre-tightening motor at both ends of the ball screw of each axis in the engraving and milling machine, and the anti-backlash driving torque distribution data is obtained.

[0136] Step S65: The double-drive coordinated control is performed on the main drive motor and the pre-tightening motor of each axis in the engraving and milling machine by using the anti-backlash driving torque distribution data, and a double-drive synchronous control signal is obtained.

[0137] Step S66: Based on the double-drive synchronous control signal, the sub-micron precision compensation control is performed on the magnetic suspension micro-positioner integrated on the guide rail of each axis in the engraving and milling machine, and a final position control instruction is obtained.

[0138] In the embodiment of the present application, first, the actual position coordinate data of each axis obtained by position solution in step S5 is subtracted from the target position data in the smoothed trajectory parameters point by point, and the real-time position deviation data of the X-axis, Y-axis and Z-axis in the whole process is calculated using the difference formula, and the over-limit section is screened with 0.01 mm as the precision threshold; then the discrete Fourier transform is performed on all the deviation data in a trajectory period (set to every 1000 ms) to extract the frequency domain components, the deviation amount with a frequency lower than 1 Hz and periodicity is classified as systematic error, and the deviation amount with a frequency higher than 5 Hz and irregular amplitude change is classified as random error, and the maximum value, mean value and standard deviation of the two types of errors are calculated respectively to form an error characteristic parameter set; then, according to the axis direction corresponding to the maximum value of the systematic error in the error characteristic parameter set, the command position value of the axis in the original comprehensive control command is linearly offset corrected, and the correction value is equal to the negative value of the mean value of the systematic error, and at the same time, for the axis direction with obvious random error, speed buffer points are inserted in the high acceleration section of the trajectory, and the length of each buffer point is limited to not more than 2 mm, thereby forming a corrected comprehensive control command; subsequently, according to the acceleration and friction load parameters of each axis movement section in the corrected comprehensive control command, combined with the lead of the ball screw, the rolling resistance and the reverse clearance value, the output torque distribution ratio of the main drive motor and the pre-tightening motor is calculated, the output ratio of the main drive motor is set to 75%, and the output ratio of the pre-tightening motor is set to 25%, and the output result is the clearance elimination driving torque distribution data; using the data, the dual-drive synchronous control is realized by transmitting the data to each axis servo controller, wherein the maximum response time difference between the two motors is not more than 1 ms, and the output current synchronization error is not more than 3%, thereby forming a dual-drive synchronous control signal; finally, according to the control signal, the magnetic suspension micro-positioner integrated on each axis guide pair (the driving resolution is 0.1 μm, and the response time is less than 0.5 ms) is driven in real time, and the control mode is to follow the correction position drift curve with equal amplitude to perform reverse displacement compensation, so that the final actual position deviation of each axis converges to the range of ±0.5 μm, thereby generating a final position control command.

[0139] The application realizes dynamic monitoring and accurate identification of the worktable position precision of the engraving and milling machine through real-time error calculation and error feature classification, effectively distinguishes systematic errors and random errors, and improves the accuracy of error diagnosis; adaptive control instruction correction based on error features improves the response speed and adjustment accuracy of the servo system to position deviation, and enhances the intelligent level of overall control; reasonable load distribution of the main drive motor and the pre-tightening motor through torque distribution calculation realizes efficient cooperation of the anti-backlash drive, significantly reduces the influence of mechanical backlash on positioning accuracy; double-drive coordinated control ensures the synchronous execution of the main drive and the pre-tightening drive, improves the stability and response consistency of the driving force transmission; combined with the sub-micron precision compensation control of the magnetic suspension micro-positioner, the ultra-high precision adjustment of the guide rail movement is realized, which greatly reduces the micro-vibration and displacement error, and finally improves the positioning stability and repeat positioning capability of the engraving and milling machine in high-precision machining, significantly improves the machining quality and the reliability of equipment operation.

[0140] Preferably, the application further provides a servo position control system applied to an engraving and milling machine, which is used to execute the servo position control method applied to the engraving and milling machine, and the servo position control system applied to the engraving and milling machine comprises:

[0141] A multi-source data acquisition module is configured to acquire real-time temperature data and real-time load data of screws of each axis in the engraving and milling machine, acquire multi-node temperature distribution data of the engraving and milling machine, and acquire cutting force data and resonance frequency characteristic values in the engraving and milling machine.

[0142] An intelligent gap prediction compensation module is configured to construct a screw reverse gap prediction model based on the real-time temperature data and the real-time load data of the screws, and generate a gap compensation value; and perform pre-compensation control based on the gap compensation value before direction transformation of the servo position of each axis, to obtain a corrected position instruction.

[0143] A digital twin thermal deformation simulation module is configured to perform thermal deformation simulation on the engraving and milling machine based on the multi-node temperature distribution data, and generate a thermal deformation compensation amount of each axis.

[0144] An adaptive feed adjustment module is configured to adjust a feed speed of the engraving and milling machine based on the cutting force data, to obtain an optimized feed magnification coefficient.

[0145] A multi-axis coordinated control module is configured to generate a comprehensive control instruction based on the corrected position instruction, the thermal deformation compensation amount of each axis, and the optimized feed magnification coefficient; construct a machining trajectory of the engraving and milling machine based on the optimized feed magnification coefficient and the resonance frequency characteristic values, and perform optimized interpolation to obtain smoothed trajectory parameters; and perform coordinated drive control on the servo motors of each axis based on the comprehensive control instruction and the smoothed trajectory parameters, to obtain actual position feedback data of each axis.

[0146] The precision evaluation feedback optimization module is used for evaluating the control precision of the actual position feedback data of each axis to obtain position error data; the comprehensive control instruction is updated based on the position error data, and the double-drive coordinated control of the engraving and milling machine is performed.

[0147] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the application is not limited by the above description, and therefore all changes falling within the meaning and scope of the equivalent elements of the application file are intended to be included in the application.

[0148] The above description is only a specific embodiment of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A servo position control method applied to a carving and milling machine, characterized by, The method comprises the following steps: Step S1: acquiring real-time temperature data and real-time load data of each shaft in the engraving and milling machine; Collecting multi-node temperature distribution data of the engraving and milling machine; acquiring cutting force data and resonance frequency characteristic value in the engraving and milling machine; Step S2: constructing a ball screw reverse gap prediction model based on the real-time temperature data and real-time load data of the ball screw, and generating a gap compensation value; Based on the gap compensation value, pre-compensation control is performed on the servo position direction of each shaft before transformation, and a corrected position instruction is obtained; Wherein, step S2 comprises the following steps: Step S21: filtering the real-time temperature data of the ball screw to obtain smooth temperature data; normalizing the real-time load data to obtain standardized load data; Step S22: calculating the thermal expansion coefficient of the ball screw based on the smooth temperature data and the standardized load data, and obtaining a thermal expansion influence factor; Step S23: detecting the transmission gap reference value of the ball screw and the ball screw nut pair in each shaft of the engraving and milling machine, and performing load deformation analysis on the ball screw nut pair, to obtain a load deformation coefficient; Step S24: constructing a neural network gap prediction model based on the thermal expansion influence factor and the load deformation coefficient, and training the neural network gap prediction model to obtain the ball screw reverse gap prediction model; Step S25: real-time prediction of the reverse gap of the ball screw of each shaft based on the ball screw reverse gap prediction model, to obtain a dynamic gap prediction value; Step S26: calculating the compensation amount of each shaft based on the dynamic gap prediction value, to obtain a gap compensation value; Step S27: direction discrimination is performed on the gap compensation value, to obtain a forward compensation value and a reverse compensation value; Step S28: based on the forward compensation value and the reverse compensation value, pre-compensation control is performed on the servo position of each shaft before direction transformation, to obtain a corrected position instruction; Step S3: thermal deformation simulation is performed on the engraving and milling machine based on the multi-node temperature distribution data, to generate a thermal deformation compensation amount of each shaft; Step S4: based on the cutting force data, the feed speed of the engraving and milling machine is adjusted to obtain an optimized feed ratio coefficient; Step S5: generating a comprehensive control instruction based on the corrected position instruction, the thermal deformation compensation amount of each shaft and the optimized feed ratio coefficient; constructing a machining trajectory of the engraving and milling machine based on the optimized feed ratio coefficient and the resonance frequency characteristic value, and performing optimized interpolation to obtain a smoothed trajectory parameter; based on the comprehensive control instruction and the smoothed trajectory parameter, coordinated driving control is performed on the servo motor of each shaft, to obtain actual position feedback data of each shaft; Wherein, the generating of the comprehensive control instruction based on the corrected position instruction, the thermal deformation compensation amount of each shaft and the optimized feed ratio coefficient in step S5 comprises: Data fusion processing is performed based on the corrected position instruction and the thermal deformation compensation amount of each shaft, to obtain a thermal compensation position instruction; Based on the three-dimensional temperature field model, thermal deformation simulation analysis is performed on the linear guide pair, to obtain a guide thermal elongation amount; Based on the thermal compensation position instruction and the guide thermal elongation amount, precision compensation is performed on the linear guide pair, to obtain a guide compensation position instruction; Based on the guide compensation position instruction and the optimized feed ratio coefficient, the speed of the servo position controller is modulated, to obtain a speed modulation position instruction; Based on the thermal deformation amount of the workbench, thermal displacement compensation of the origin of the workpiece coordinate system is calculated, to obtain a workpiece coordinate system thermal compensation value; The workpiece coordinate is corrected based on the speed modulation position instruction and the workpiece coordinate system thermal compensation value, and multi-axis synchronous timing control is performed to obtain comprehensive control instructions. The step S5 includes the following steps: The original machining trajectory speed of the milling machine is calibrated based on the optimized feed ratio coefficient, and the geometric characteristics of the milling machine machining trajectory are analyzed based on the original machining trajectory speed to obtain trajectory curvature characteristic data; The neural network trajectory optimization model is constructed and trained based on the trajectory curvature characteristic data to obtain a trajectory optimization neural network model; The machining trajectory intelligent optimization interpolation is performed based on the trajectory optimization neural network model to obtain preliminary optimized trajectory parameters; The vibration avoidance interpolation processing is performed based on the preliminary optimized trajectory parameters and the resonance frequency characteristic value, and the B-spline curve smoothing processing of the trajectory transition point is performed to obtain B-spline smooth trajectory parameters; The frequency response of the milling machine bed-spindle is analyzed based on the resonance frequency characteristic value, and the vibration risk of each trajectory segment of the milling machine is evaluated based on the frequency response analysis result to obtain a vibration risk level; The dynamic response analysis of the linear guide pair of each axis of the milling machine is performed based on the vibration risk level, and the multi-axis coordinated optimization of the motion trajectory of each axis of the milling machine is performed based on the dynamic response analysis result to obtain multi-axis coordinated trajectory parameters; The motion synchronization of the servo motors of each axis of the milling machine is optimized based on the multi-axis coordinated trajectory parameters to obtain synchronized optimized trajectory parameters; The geometric accuracy and motion continuity are verified through the B-spline smooth trajectory parameters and the synchronized optimized trajectory parameters, the processing efficiency and quality are balanced, the spindle feed speed is coordinated, and the ball screw transmission characteristics are optimized to generate smooth trajectory parameters. Step S6: Control accuracy evaluation is performed on the actual position feedback data of each axis to obtain position error data; the comprehensive control instructions are updated based on the position error data, and double-drive coordinated control is performed on the milling machine.

2. The servo position control method for a carving and milling machine according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: The temperatures of the X-axis ball screw, the Y-axis ball screw and the Z-axis ball screw of the milling machine are detected to obtain real-time temperature data of the screws of each axis; Step S12: The axial loads of each axis of the milling machine are detected through a current sensor to obtain real-time load data of each axis; Step S13: The temperatures of the milling machine bed, the column, the spindle box and the workbench are collected through a semiconductor temperature sensor to obtain multi-node temperature distribution data; Step S14: The cutting force of the milling machine is detected through a three-way force sensor to obtain cutting force data; Step S15: The frequency spectrum analysis of the machine tool vibration of the milling machine is performed through a vibration sensor to obtain resonance frequency characteristic values.

3. The servo position control method for a carving and milling machine according to claim 1, wherein Step S3 includes the following steps: Step S31: A three-dimensional digital twin temperature field is constructed based on the multi-node temperature distribution data to obtain a three-dimensional temperature field model; Step S32: Digital twin thermal deformation simulation of the milling machine bed guide rail mounting surface is performed based on the three-dimensional temperature field model to obtain the bed thermal deformation amount; Step S33: Thermal deformation simulation calculation of the connection part of the milling machine column and the bed is performed based on the three-dimensional temperature field model to obtain the column thermal tilt deformation amount; Step S34: thermal displacement simulation of the center line position of the main shaft of the milling machine is performed based on the three-dimensional temperature field model, and a thermal displacement amount of the main shaft is obtained; Step S35: thermal deformation simulation calculation of the flatness of the worktable is performed based on the three-dimensional temperature field model, and a thermal deformation amount of the worktable is obtained; Step S36: X-axis thermal deformation compensation is calculated based on the thermal deformation amount of the bed and the thermal displacement amount of the main shaft, Y-axis thermal deformation compensation is calculated based on the thermal deformation amount of the bed, the thermal tilt deformation amount of the column, and the thermal displacement amount of the main shaft, and Z-axis thermal deformation compensation is calculated based on the thermal tilt deformation amount of the column and the thermal displacement amount of the main shaft, and finally the thermal deformation compensation amounts of the axes are formed.

4. The servo position control method for a carving and milling machine according to claim 1, wherein Step S4 includes the following steps: Step S41: real-time filtering processing is performed on the cutting force data to obtain smoothed cutting force data; the main shaft cutting torque is calculated based on the smoothed cutting force data to obtain the real-time cutting torque of the main shaft; and the cutting state identifier is obtained by identifying the cutting state of the milling machine based on the smoothed cutting force data; Step S42: the temperature and vibration of the main shaft bearing in the milling machine are detected to obtain main shaft bearing state data; the main shaft load coefficient is generated by evaluating the main shaft load based on the real-time cutting torque of the main shaft and the main shaft bearing state data; and the rotational speed and feed speed matching degree is obtained by analyzing the rotational speed and feed speed matching of the main shaft based on the main shaft load coefficient; Step S43: the dynamic response of the bed and main shaft of the milling machine is analyzed based on the rotational speed and feed speed matching degree to obtain a dynamic stiffness coefficient; and the feed speed constraint value is obtained by setting the upper limit of the feed speed of each axis servo motor in the milling machine based on the dynamic stiffness coefficient; Step S44: the tool cutting edge wear degree in the milling machine is evaluated based on the cutting state identifier to obtain a tool wear coefficient; Step S45: cutting vibration frequency domain analysis is performed based on the resonance frequency characteristic value and the smoothed cutting force data to obtain cutting vibration stability data; Step S46: the feed ratio reference value is calculated based on the tool wear coefficient and the cutting vibration stability data to obtain an initial feed ratio coefficient; Step S47: the X-axis, Y-axis, and Z-axis feed speeds are coordinated and optimized based on the initial feed ratio coefficient to obtain axis feed coordination coefficients; and the constraint feed ratio coefficient is obtained by performing feed ratio optimization calculation based on the feed speed constraint value and the axis feed coordination coefficients; Step S48: the surface quality is optimized and adjusted based on the constraint feed ratio coefficient, and efficiency balance is calculated to generate an optimized feed ratio coefficient.

5. The servo position control method for a carving miller as claimed in claim 1, wherein In step S5, the coordinated driving control of the servo motors of each axis based on the comprehensive control instruction and the smoothed trajectory parameters includes: The position control driving of the ball screw servo motors of each axis in the milling machine is performed based on the comprehensive control instruction to obtain motor angle instruction data of each axis; The rotational speed coordination control of the main shaft motor in the milling machine is performed based on the smoothed trajectory parameters to obtain main shaft rotational speed synchronization instructions; Multi-axis linkage control of the bed guide of the milling machine is performed based on the motor angle instruction data of each axis and the main shaft rotational speed synchronization instructions to obtain synchronization data of the movement trajectories of each axis; The position feedback signals of high-precision encoders installed on the ball screws of each axis of the milling machine are collected to obtain encoder pulse feedback data of each axis; The actual position of the working table of the engraving and milling machine is calculated based on the synchronous data of the motion trajectory of each axis and the pulse feedback data of the encoder of each axis to obtain actual position coordinate data of each axis; The actual position coordinate data of each axis is monitored and analyzed in real time to obtain actual position feedback data of each axis.

6. The servo position control method for a carving and milling machine according to claim 1, wherein Step S6 includes the following steps: Step S61: Real-time error calculation is performed on the position accuracy of the working table of the engraving and milling machine based on the actual position feedback data of each axis to obtain position deviation data of each axis; Step S62: The position deviation data of each axis is classified into systematic error and random error to obtain an error characteristic parameter set; Step S63: The comprehensive control instruction is adaptively modified and updated based on the error characteristic parameter set to obtain a modified comprehensive control instruction; Step S64: The torque distribution calculation is performed on the main drive motor and the pre-tightening motor at both ends of the ball screw of each axis of the engraving and milling machine based on the modified comprehensive control instruction to obtain anti-backlash drive torque distribution data; Step S65: The double-drive coordinated control is performed on the main drive motor and the pre-tightening motor of each axis of the engraving and milling machine using the anti-backlash drive torque distribution data to obtain double-drive synchronous control signals; Step S66: The sub-micron precision compensation control is performed on the magnetic suspension micro-positioner integrated on the guide rail of each axis of the engraving and milling machine based on the double-drive synchronous control signals to obtain a final position control instruction.

7. A servo position control system applied to a carving and milling machine, characterized by, The servo position control system applied to the engraving and milling machine includes: A multi-source data acquisition module is configured to acquire real-time temperature data and real-time load data of the screw of each axis of the engraving and milling machine, acquire multi-node temperature distribution data of the engraving and milling machine, and acquire cutting force data and resonance frequency characteristic values of the engraving and milling machine; An intelligent gap prediction compensation module is configured to construct a screw reverse gap prediction model based on the real-time temperature data and the real-time load data of the screw, and generate a gap compensation value, and perform pre-compensation control on each axis before servo position direction transformation based on the gap compensation value to obtain a modified position instruction; A digital twin thermal deformation simulation module is configured to perform thermal deformation simulation on the engraving and milling machine based on the multi-node temperature distribution data to generate a thermal deformation compensation amount of each axis; An adaptive feed adjustment module is configured to adjust the feed speed of the engraving and milling machine based on the cutting force data to obtain an optimized feed ratio coefficient; A multi-axis coordinated control module is configured to generate a comprehensive control instruction based on the modified position instruction, the thermal deformation compensation amount of each axis, and the optimized feed ratio coefficient, construct a machining trajectory of the engraving and milling machine based on the optimized feed ratio coefficient and the resonance frequency characteristic values, and perform optimized interpolation to obtain smoothed trajectory parameters, and perform coordinated drive control on the servo motor of each axis based on the comprehensive control instruction and the smoothed trajectory parameters to obtain actual position feedback data of each axis; An accuracy evaluation and feedback optimization module is configured to evaluate the control accuracy based on the actual position feedback data of each axis to obtain position error data, update the comprehensive control instruction based on the position error data, and perform double-drive coordinated control on the engraving and milling machine.

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