Machine tool feed system comprehensive axial stiffness identification method

Abstract

The present application belongs to the field of parameter identification of numerical control machine tool, and particularly relates to a machine tool feeding system comprehensive axial stiffness identification method, comprising: inputting a stepped excitation signal for identifying real lead screw lead and a stepped excitation signal for identifying mechanical clearance to a numerical control system to obtain the real lead screw lead and the mechanical clearance value of the feeding shaft; inputting an excitation signal for identifying the comprehensive axial stiffness of the lead screw to the numerical control system to calculate the comprehensive axial stiffness identification value K a of the feeding shaft; wherein the mechanical transmission error for calculating K a is an error component Δx error_k related to elastic deformation only, and the value is the mechanical transmission error calculated from the motor encoder data and the grating ruler data minus the error component Δx error_b caused by the mechanical clearance and the error component Δx error_screw caused by the lead screw lead error, the value of Δx error_b is the mechanical clearance value, and the value of Δx error_screw is calculated based on the real lead screw lead. The present application can effectively improve the comprehensive axial stiffness identification precision.

Description

Technical Field

[0001] The present invention belongs to the field of numerical control machine tool parameter identification, and more specifically, relates to a method for identifying the comprehensive axial stiffness of a machine tool feed system. Background Art

[0002] CNC machine tools are highly efficient, high-precision, and highly automated processing equipment widely used in aerospace, automotive, nuclear power, medical, and engineering fields. As the actuator of CNC machine tools, the servo feed system's operational performance directly impacts their machining accuracy. The mechanical transmission systems in servo feed systems are primarily divided into two types: linear motors and ball screws. Ball screws, with their advantages of high rigidity, high load capacity, low cost, reliable operation, and mature processing technology, have been widely used in CNC machine tools.

[0003] Contour error compensation is an effective method to improve the machining accuracy of CNC machine tools. By building a high-precision model of the servo feed system, accurate prediction of the machine tool machining contour can be achieved, providing key data support for contour error compensation. However, due to the complexity of the internal structure and external environment of the actual servo feed system, when establishing the servo feed system model, the accuracy of the model parameters is a key factor affecting the accuracy of the model simulation. Some parameters in the model can be obtained through table lookup, while other parameters need to be determined by designing special parameter identification experiments. The most representative parameter is the comprehensive axial stiffness of the machine tool. Accurately identifying the comprehensive axial stiffness of the machine tool can improve the accuracy of the overall dynamic model, thereby more accurately simulating the dynamic characteristics of the actual machine tool feed system.

[0004] Existing methods for identifying machine tool stiffness mostly rely on various external sensors / devices, such as connecting a torque sensor to the motor output, installing a strain sensor on the screw-nut pair, setting up a laser interferometer on the workbench to collect displacement data, and using exciters and modal analysis equipment to collect data for parameter identification. For example, Li Fuhua et al. designed a double-nut preload device and, using torque sensors and pressure sensors to collect experimental data, identified the axial stiffness of a ball screw feed system. Zhao Wanhua et al. used random excitation signals and a laser interferometer to collect workbench position information. Through continuous iterative parameter testing and frequency domain analysis, they determined the equivalent stiffness of the machine tool. Hu Feng et al. used an exciter to vibrate the workbench, collected workbench displacement information with a laser interferometer, and identified the axial stiffness of each support point in the feed system.

[0005] The above-mentioned parameter identification method based on external sensors / devices requires the purchase of expensive equipment, and often requires a certain degree of modification to the servo feed system when in use. However, for the entire CNC machine tool, its servo feed system has already been assembled and packaged. The above-mentioned parameter identification method will damage the already adjusted state of the machine tool itself, affecting the processing accuracy of the machine tool. In contrast, the parameter identification method based on built-in sensors does not rely on various external sensors, but instead mines information that can be used for parameter identification from the motor encoder, grating scale, and current sensor data built into the CNC machine tool. Chen Guangsheng et al. proposed a stiffness identification method based on closed-loop data. They used a quadratic displacement curve as the feed system excitation signal, collected the displacement difference between the motor encoder and grating scale data, and identified the mechanical stiffness. However, the identification accuracy of this method is not high. Summary of the Invention

[0006] In view of the defects of the prior art and the need for improvement, the present invention provides a method for identifying the comprehensive axial stiffness of a machine tool feed system, the purpose of which is to improve the identification accuracy of the comprehensive axial stiffness of a ball screw mechanical transmission system.

[0007] To achieve the above object, according to one aspect of the present invention, a method for identifying the comprehensive axial stiffness of a machine tool feed system is provided, comprising:

[0008] S1. Input a step-shaped excitation signal for identifying the actual lead of the screw into the numerical control system to control the feed axis to run according to the command position in the excitation signal, collect the motor encoder data and grating scale data during each stop period of the feed axis, and obtain the actual lead of the feed axis through data processing; input a step-shaped excitation signal for identifying the mechanical clearance into the numerical control system to control the feed axis to run according to the command position in the excitation signal, collect the grating scale data during each stop period of the feed axis, and obtain the mechanical clearance value of the feed axis through data processing;

[0009] S2. Input an excitation signal for identifying the integrated axial stiffness of the screw into the CNC system to control the feed axis to run according to the command position in the excitation signal; collect the motor current, motor encoder data and grating scale data of the feed axis, and obtain the integrated axial stiffness identification value K of the feed axis through data processing. a ; In this data processing, it is used to calculate K a The mechanical transmission error is the error component Δx related only to the elastic deformation. error_k , Δx error_k The value is the mechanical transmission error calculated from the motor encoder data and the grating scale data minus the error component Δx caused by the mechanical gap. error_b and the error component Δx caused by the screw lead error error_screw , Δx error_b The mechanical clearance value is taken as Δxerror_screw The value is calculated based on the actual lead of the screw, the nominal lead and the motor encoder data.

[0010] Furthermore, the actual lead of the screw is calculated as follows:

[0011] Based on the motor encoder data of the feed axis during each stop period θ encoder Average value and scale data X ruler The average value, according to the formula X e =θ encoder ·h std -X ruler =Δx error_screw +Δx error_b +Δx error_k Calculate the mechanical transmission error X during each stop period e , where h std is the nominal lead; when the feed axis stops running, Δx error_b and Δx error_k If the values ​​are all 0, the mechanical transmission error X corresponding to the step-shaped excitation signal used to identify the actual lead of the screw is e That is the mechanical transmission error Δx caused by the screw lead error error_screw .

[0012] Plotting command position data versus Δx error_screw Scatter plot, and use the least squares method to fit the slope of the curve;

[0013] Based on the formula Calculate the actual screw lead h real .

[0014] Furthermore, the step-shaped excitation signal for identifying the mechanical clearance specifically causes the feed axis to start running from the stroke zero point, feed in one direction, stop feeding at a preset stroke interval, resume feeding after a preset time interval, and repeat the cycle until it moves to the command stroke end; and then moves from the stroke end to the stroke zero point in the same motion manner;

[0015] Then the mechanical clearance value of the feed axis is calculated as follows:

[0016] Take the average value of the grating ruler data during each stop period as the workbench position of the feed axis corresponding to the stroke in that period;

[0017] The difference between the two worktable positions collected on each stroke is taken as the mechanical clearance of the feed axis at that stroke;

[0018] The average value of the mechanical clearance at all strokes is taken as the final mechanical clearance value of the feed axis.

[0019] Furthermore, the comprehensive axial stiffness identification value K of the feed shaft a The calculation method is as follows:

[0020] Calculating the motor torque based on the motor current; and calculating the equivalent axial force F based on the motor torque and the actual lead screw;

[0021] Based on the equivalent axial force F and the mechanical transmission error Δx caused by elastic deformation error_k Calculate the comprehensive axial stiffness identification value K of the screw under the current excitation signal a ;

[0022] Among them, the mechanical transmission error Δx caused by elastic deformation error_k The calculation method is:

[0023] X e =Δx error_screw +Δx error_b +Δx error_k ;

[0024] Where, X e is the mechanical transmission error, X e =θ encoder ·h std -X ruler ,θ encoder X is the motor encoder data of the feed axis collected under the excitation signal input for identifying the comprehensive axial stiffness of the screw. ruler The grating scale data X is collected under the excitation signal input for identifying the comprehensive axial stiffness of the screw ruler , h std is the nominal lead; Δx error_screw =θ encoder ·(h std -h real ), h real is the actual screw lead.

[0025] Furthermore, the excitation signal for identifying the integrated axial stiffness of the screw is a uniformly accelerated reciprocating motion signal with a constant acceleration amplitude; and when collecting data, only the middle of each uniformly accelerated interval is collected. Motor current, motor encoder data and grating scale data of each time period.

[0026] Furthermore, in step S2, an excitation signal for identifying the integrated axial stiffness of the screw rod is input multiple times to obtain multiple integrated axial stiffness identification values, and the average value is taken as the final integrated axial stiffness identification value.

[0027] The present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute the above-mentioned method for identifying the comprehensive axial stiffness of a machine tool feed system.

[0028] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0029] (1) Compared with the existing CNC machine tool feed system stiffness identification method, the present invention's comprehensive axial stiffness identification method of the ball screw feed system of the machine tool based on the built-in sensor data does not require the purchase of expensive parameter identification equipment and does not rely on external sensors. It only needs to use the motor encoder and grating ruler provided by the machine tool, which is low-cost and has a wider range of applications.

[0030] (2) Compared with the existing CNC machine tool feed system stiffness identification method, the method of the present invention for identifying the comprehensive axial stiffness of the machine tool ball screw feed system based on built-in sensor data does not require any modification to the machine tool and can identify the parameters of the machine tool under actual working conditions, which is more practical.

[0031] (3) Compared with the existing CNC machine tool feed system stiffness identification method, the present invention's integrated axial stiffness identification method for the ball screw feed system of a machine tool based on built-in sensor data takes into account the influence of the errors of all transmission components in the mechanical transmission system, and proposes a mechanical transmission error X e =Δx error_screw +Δx erro _ r b +Δx erro _ r By identifying the actual lead of the screw and the mechanical clearance in advance, decoupling the components in the collected machine tool data, and calculating the size of the component of the mechanical transmission error that is only related to the elastic deformation, a more accurate stiffness value can be identified, avoiding the identification error caused by other factors.

[0032] (4) The excitation signal for identifying the integrated axial stiffness of the screw proposed in the present invention is a uniformly accelerated reciprocating motion signal with a constant acceleration amplitude; and when collecting data, the unstable data at the beginning and end of each uniformly accelerated interval must be eliminated, and only the middle data of each uniformly accelerated interval must be collected. The motor current, motor encoder data and grating scale data of each time period are recorded to ensure data reliability.

[0033] (5) The present invention also proposes to input the excitation signal for identifying the comprehensive axial stiffness of the screw rod multiple times to obtain multiple comprehensive axial stiffness identification values, and take the average as the final comprehensive axial stiffness identification value to further improve the identification accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic flow chart of a method for identifying the comprehensive axial stiffness of a machine tool feed system based on built-in sensor data provided by an embodiment of the present invention;

[0035] Figure 2 A schematic diagram of mechanical transmission error and its components provided by an embodiment of the present invention;

[0036] Figure 3 An excitation signal diagram for identifying the true lead of a machine tool screw provided by an embodiment of the present invention;

[0037] Figure 4 A schematic diagram of the processing results of the feed shaft screw lead identification data of a certain model of machine tool provided by an embodiment of the present invention;

[0038] Figure 5 An excitation signal diagram for identifying the mechanical clearance of a machine tool provided by an embodiment of the present invention;

[0039] Figure 6 A schematic diagram of the processing results of the mechanical clearance identification data of a certain model of machine tool feed axis provided by an embodiment of the present invention;

[0040] Figure 7 An excitation signal diagram for identifying the axial comprehensive stiffness of a machine tool provided by an embodiment of the present invention;

[0041] Figure 8 This is a schematic diagram of the processing results of the comprehensive axial stiffness identification data of a certain model of machine tool feed shaft provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0043] Example 1

[0044] A comprehensive axial stiffness identification method for machine tool feed system, such as Figure 1 As shown, including:

[0045] Step 1: Input a step-shaped excitation signal for identifying the actual lead of the screw into the numerical control system to control the feed axis to run according to the command position in the excitation signal. The motor encoder data and grating scale data are collected during each stop period of the feed axis, and the actual lead of the feed axis is obtained through data processing.

[0046] Step 2: Input a stepped excitation signal for identifying the mechanical clearance into the numerical control system to control the feed axis to run according to the command position in the excitation signal, collect the grating scale data of each stop period of the feed axis, and obtain the mechanical clearance value of the feed axis through data processing;

[0047] Step 3: Input an excitation signal for identifying the integrated axial stiffness of the screw into the CNC system to control the feed axis to run according to the command position in the excitation signal; collect the motor current, motor encoder data and grating scale data of the feed axis, and obtain the integrated axial stiffness identification value K of the feed axis through data processing. a ; In this data processing, it is used to calculate K a The mechanical transmission error is the error component Δx related only to the elastic deformation. error_k , Δx error_k The value is the mechanical transmission error calculated from the motor encoder data and the grating scale data minus the error component Δx caused by the mechanical gap. error_b and the error component Δx caused by the screw lead error error_screw , Δx error_b Take the above mechanical clearance value, Δx error_screw The value is calculated based on the above-mentioned actual screw lead, nominal lead and the above-mentioned motor encoder data.

[0048] The method of this embodiment only needs to use the built-in current sensor, motor encoder, and grating ruler of the CNC machine tool to collect data. By using multiple excitation signals, the real screw lead and mechanical clearance of the feed system are identified in turn. The collected data is decoupled and the mechanical transmission error caused by the axial comprehensive stiffness of the feed system is extracted. This error is only related to the elastic deformation, and the comprehensive axial stiffness value of the feed system is finally accurately identified. Among them, the error component Δx related only to the elastic deformation is error_k The value is the mechanical transmission error calculated from the motor encoder data and the grating scale data minus the error component Δx caused by the mechanical gap. error_b and the error component Δx caused by the screw lead error error_screw , Δx error_b Take the above mechanical clearance value, Δx error_screw The value is calculated based on the above-mentioned actual screw lead, nominal lead and the above-mentioned motor encoder data.

[0049] The theoretical analysis is as follows:

[0050] Collect the motor encoder angle θ of the servo feed system encoder Multiply by the nominal lead h of the screw std The feedback position X of the motor can be obtained encoder_std (Also called the theoretical motor position). The actual table position X of the servo feed system ruler It can be obtained from the grating ruler data. Due to manufacturing errors, elastic deformation and other reasons, there is a deviation between the actual position of the workbench and the theoretical position of the motor. The difference X e It is called the transmission error of the screw-nut pair mechanical system, and its expression is as follows

[0051] X e =X encoder_std -X ruler (1)

[0052] Mechanical transmission error combines the effects of all transmission component errors in the mechanical transmission system, including the mechanical transmission error Δx caused by the screw lead error. error_screw , Mechanical transmission error Δx caused by elastic deformation error_k , Mechanical transmission error Δx caused by mechanical clearance error_b and the mechanical transmission error Δx caused by the periodic contact of the screw-nut pair T .like Figure 2 As shown in Figure 2, when the feed axis moves at a constant speed, decoupling the mechanical transmission error can yield the above-mentioned multiple components. The mechanical transmission error Δx caused by the periodic contact of the screw-nut pair is T It is small (less than 1μm) and can be ignored, so the following formula is obtained.

[0053] X e =Δx error_screw +Δx error_b +Δx error_k (2)

[0054] Based on this information, this embodiment uses specific parameters to identify the excitation signal, eliminating the elastic deformation component of the mechanical transmission error. This allows accurate identification of the screw's true lead and mechanical backlash. Furthermore, based on these identified screw's true lead and backlash, the magnitude of the elastic deformation component of the mechanical transmission error is calculated, allowing accurate identification of the feed shaft's comprehensive axial stiffness.

[0055] Preferably, the excitation signal for identifying the real screw lead in S1 is in a step-like shape, driving the feed axis to start from the stroke zero point and feed in one direction, with a certain stroke Δx interval. step Then stop feeding, and wait for a certain time Δt step The feed is resumed after 1 second and the cycle repeats until the end of the stroke is reached. The data to be collected are the motor encoder and grating ruler data.

[0056] The excitation signal used to identify mechanical backlash described in S1 is step-shaped. This signal drives the feed axis to start from the zero point of travel, feed in one direction, stop at a certain interval, and resume feeding after a certain interval, repeating this cycle until it reaches the end of travel. It then moves from the end of travel to the zero point in the same manner. The data to be collected is the grating scale data.

[0057] The excitation signal for identifying the integrated axial stiffness of the screw described in S3 is a uniformly accelerated reciprocating motion signal with a constant acceleration amplitude. The data of the feed axis position reversal and acceleration reversal phases need to be eliminated, and only the middle data of each uniformly accelerated interval is taken. The motor current, motor encoder and grating ruler data of each time period.

[0058] It should be noted that the selection of the excitation signal for identifying the comprehensive axial stiffness of the screw must ensure that the error component Δx related only to the elastic deformation error_k Not 0.

[0059] In order to better illustrate the method of this embodiment, the following specific examples are given:

[0060] (1) Warm up the machine tool for 2 hours to ensure that the machine tool is in a thermally stable state.

[0061] (2) Input the excitation signal used to identify the actual screw lead to the CNC system, such as Figure 3 This signal drives the feed axis to start from the stroke zero point and feed in one direction with a certain stroke interval Δx step Then stop feeding, and wait for a certain time Δt step After a few seconds, the feed is resumed and the cycle repeats until the end of the stroke is reached. Collect the motor encoder data θ at each stop moment encoder And grating scale data X ruler , at this time there is X ruler =θ encoder ×h real .

[0062] (3) After the excitation signal for identifying the actual lead of the screw is input to the CNC system, at each stopping moment, the mechanical transmission error value only contains the mechanical transmission error component Δx caused by the lead error. error_screw By collecting the motor encoder data and grating ruler data at the stop moment, the X at this time is calculated by formula (1): e , here under static conditions, that is, Δx error_screw Draw X ruler -Δx error_screw Scatter plot, and use the least squares method to fit the slope k and the nominal lead h std Substituting into formula 3, the true lead of the screw can be obtained, as Figure 4shown.

[0063]

[0064] (4) Input the excitation signal for identifying the mechanical gap to the CNC system, such as Figure 5 As shown in Figure 1, this signal drives the feed axis to start from stroke zero, feed in one direction, stop at a certain interval, and resume feeding after a certain interval, repeating this cycle until it reaches the commanded stroke end. It then moves from the stroke end to stroke zero in the same manner. The scale data at each stop is collected, and the average of these data is taken as the worktable position of the feed axis at that stroke.

[0065] Since the feed axis stops at the same travel interval in both the forward and reverse directions, two worktable positions can be collected in each travel. The difference between the two worktable positions can be used to calculate the mechanical clearance of the feed axis at that travel. The average of the mechanical clearance values ​​obtained over the entire travel is used to obtain the mechanical clearance value of the feed axis, which is used as Δx error_b ,like Figure 6 shown.

[0066] The excitation signal used to identify the comprehensive axial stiffness of the screw is input to the motor to drive the feed axis through the machine tool servo system. Figure 7 As shown in FIG, the signal is a uniformly accelerated reciprocating motion signal with a constant acceleration amplitude.

[0067] In order to ensure that the machine tool is in a uniform speed change state when collecting data, it is necessary to eliminate the unstable data at the beginning and end of the reverse and acceleration reverse phases, and only take the middle data of each uniform speed change interval. Motor current I, motor encoder data θ during the period encoder And grating scale data X ruler ,like Figure 7 The motor torque T is calculated using formula 4, where K is the torque constant obtained from the table. The mechanical transmission error value X in the entire period is calculated using formula 1 using the average value of the motor encoder data and the average value of the grating scale data after the entire excitation signal is applied. e , which deducts the mechanical transmission error Δx caused by the screw lead error error_screw and the mechanical transmission error Δx caused by mechanical clearance error_b , the mechanical transmission error Δx caused by elastic deformation is obtained error_k Substituting it into Formula 5 and Formula 6, we can get the comprehensive stiffness K of the screw under this acceleration. a .

[0068] T=K×I (4)

[0069]

[0070]

[0071] By changing the acceleration amplitude, multiple sets of excitation signals of the screw's comprehensive axial stiffness are obtained. Step (7) is repeated to calculate multiple sets of comprehensive stiffness identification values. The average value is the final machine tool comprehensive axial stiffness identification result, as shown in Figure 8 shown.

[0072] In summary, according to the analysis, the influencing factors of mechanical transmission error include the mechanical transmission error Δx caused by the screw lead error error_screw , Mechanical transmission error Δx caused by elastic deformation error_k , Mechanical transmission error Δx caused by mechanical clearance error_b and the mechanical transmission error Δx caused by the periodic contact of the screw-nut pair T After first identifying factors such as mechanical clearance and screw lead, the mechanical transmission error component directly related to elastic deformation can be extracted, and the comprehensive axial stiffness of the ball screw feed system can be accurately calculated. Therefore, the present invention analyzes and decouples the mechanical transmission error component in the position difference component converted between the grating scale and the encoder, and eliminates components unrelated to the comprehensive axial stiffness from the mechanical transmission error, extracting the components directly related to the comprehensive axial stiffness for use in identifying the comprehensive axial stiffness of the ball screw feed system.

[0073] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying the comprehensive axial stiffness of a machine tool feed system, characterized in that: include: S1. Input a step-shaped excitation signal for identifying the actual lead of the screw into the numerical control system to control the feed axis to run according to the command position in the excitation signal. The motor encoder data and grating scale data are collected during each stop period of the feed axis, and the actual lead of the feed axis is obtained through data processing. Input a step-shaped excitation signal for identifying the mechanical gap into the CNC system to control the feed axis to run according to the command position in the excitation signal. Collect the grating scale data of each stop period of the feed axis and obtain the mechanical gap value of the feed axis through data processing. S2. Input an excitation signal for identifying the integrated axial stiffness of the screw into the CNC system to control the feed axis to run according to the command position in the excitation signal; collect the motor current, motor encoder data and grating scale data of the feed axis, and obtain the integrated axial stiffness identification value of the feed axis through data processing. ; In this data processing, it is used to calculate The mechanical transmission error is the error component related only to elastic deformation , The value is the mechanical transmission error calculated from the motor encoder data and the grating scale data minus the error component caused by the mechanical gap. and the error components caused by screw lead error , The value is the mechanical clearance value, The value is calculated based on the actual lead of the screw, the nominal lead and the motor encoder data; The actual lead of the screw is calculated as follows: Feed axis motor encoder data based on each stop period Average value and scale data The average value, according to the formula Calculate the mechanical transmission error during each stop period , where is the nominal lead; When the feed axis stops running, and If the value is 0, it is used to identify the mechanical transmission error corresponding to the step-shaped excitation signal of the actual screw lead. That is, the mechanical transmission error caused by the screw lead error ; Drawing instruction position data and Scatter plot and use the least squares method to fit the slope of the curve ; Based on the formula , calculate the actual screw lead .

2. The method for identifying the comprehensive axial stiffness of a machine tool feed system according to claim 1, characterized in that: The step-shaped excitation signal for identifying the mechanical clearance specifically causes the feed axis to start running from the stroke zero point, feed in one direction, stop feeding at intervals of a preset stroke, resume feeding after an interval of a preset time, and repeat the cycle until it moves to the end of the command stroke; and then moves from the stroke end to the stroke zero point in the same manner; Then the mechanical clearance value of the feed axis is calculated as follows: Take the average value of the grating ruler data during each stop period as the workbench position of the feed axis corresponding to the stroke in that period; The difference between the two worktable positions collected on each stroke is taken as the mechanical clearance of the feed axis at that stroke; The average value of the mechanical clearance at all strokes is taken as the final mechanical clearance value of the feed axis.

3. The method for identifying the comprehensive axial stiffness of a machine tool feed system according to claim 1, wherein: The comprehensive axial stiffness identification value of the feed shaft The calculation method is as follows: Calculating the motor torque based on the motor current; and calculating the equivalent axial force F based on the motor torque and the actual lead screw; Based on the equivalent axial force F and the mechanical transmission error caused by elastic deformation , calculate the comprehensive axial stiffness identification value of the screw under the current excitation signal ; Among them, the mechanical transmission error caused by elastic deformation The calculation method is: ； Where, is the mechanical transmission error, , The motor encoder data of the feed axis collected under the excitation signal input is used to identify the comprehensive axial stiffness of the screw. The grating scale data collected under the excitation signal input is used to identify the comprehensive axial stiffness of the screw , is the nominal lead; , is the actual screw lead.

4. The method for identifying the comprehensive axial stiffness of a machine tool feed system according to claim 1, wherein: The excitation signal for identifying the integrated axial stiffness of the screw is a uniformly accelerated reciprocating motion signal with a constant acceleration amplitude; and when collecting data, only the middle of each uniformly accelerated interval is collected. Motor current, motor encoder data and grating scale data of each time period.

5. The method for identifying the comprehensive axial stiffness of a machine tool feed system according to claim 1, wherein: In step S2, an excitation signal for identifying the integrated axial stiffness of the screw is input multiple times to obtain multiple integrated axial stiffness identification values, and the average value is taken as the final integrated axial stiffness identification value.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute a method for identifying the comprehensive axial stiffness of a machine tool feed system according to any one of claims 1 to 5.

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