A plunger spherical profile degree error compensation processing method

Abstract

The application discloses a kind of plunger spherical profile degree error compensation processing methods, belong to precision ultra-precision machining technical field.First, facing the ultra-precision grinding processing of hydraulic pump plunger part spherical surface, based on process experiment and device error measurement result establishes the processing device error mapping model and grinding cross-scale simulation model of profile error.Then, construct process parameter-device error two-dimensional multivariate nonlinear coupling mathematical model, the contribution of each error influencing factor and its coupling is quantified and screened core item, complete profile error traceability decomposition.Finally, establish the two-dimensional profile error control mode of device error compensation and process parameter optimization coordination, realize the ultra-precision machining of plunger spherical profile.The application cooperates and controls spherical profile degree error by process parameter and device error two dimensions, significantly improves machining precision and precision stability, is applicable to the high efficiency, high stability ultra-precision machining of precision plunger spherical surface in the field such as hydraulic pump.

Description

Technical Field

[0001] This invention belongs to the field of precision and ultra-precision machining technology, specifically involving high-precision machining technology for precision spherical parts such as hydraulic plungers and aero-engine fuel plungers, and particularly involving a two-dimensional error compensation machining method for plunger spherical contour process and device. Background Technology

[0002] Aviation hydraulic pumps are key components of aviation hydraulic actuation systems, and their performance directly affects combat effectiveness and flight safety. With the development of my country's new generation of aviation equipment, aviation hydraulic pumps need to withstand more extreme operating conditions with higher speeds and pressures, posing unprecedented challenges to their manufacturing and assembly precision. As a core component of aviation hydraulic pumps, the quality of the plunger's spherical surface machining is a crucial factor determining the pump's sealing performance, pressure transmission efficiency, and service life. The plunger's spherical surface machining process involves several key steps, requiring rough turning, finish turning, heat treatment, surface hardening, and then ultra-precision grinding to ultimately control its surface shape accuracy. Among these, the spherical profile error has a decisive impact on the hydraulic pump's high-pressure sealing performance and high-speed service stability.

[0003] While my country has achieved a profile accuracy of 1μm in grinding typical spherical parts, the special configuration of plunger spherical surfaces (the ball head and rod are integrated, not an independent complete sphere) limits current processing technology to a profile error control level of 1.5-2μm, which is insufficient to meet equipment requirements. Existing processes primarily focus on roundness control of the plunger ball head, lacking a dedicated method for profile error control. This prevents current profile control from tracing and accurately compensating for all error sources. Furthermore, current grinding accuracy control separates process parameter optimization and machine tool error compensation. Optimal processing parameter combinations, considering only the influence of parameters, become ineffective due to changes in the grinding wheel spindle position, spindle rotation accuracy, and other device conditions. Consequently, combining process parameter optimization and machine tool error compensation fails to achieve optimal profile accuracy control. Therefore, current technologies and processes can only achieve high-precision roundness control, not stable profile accuracy <1μm. A processing method capable of stably tracing and accurately compensating for errors during plunger spherical profile creation is urgently needed to achieve precise profile control. Summary of the Invention

[0004] This invention addresses the existing requirements for controlling the contour error in plunger spherical machining by providing a method for compensating for plunger spherical contour error. It clarifies the sources of error and their influence weights during spherical contour creation. By establishing a nonlinear coupling model between process parameters and machine tool errors during ultra-precision grinding of plunger spherical surfaces, it achieves decoupling identification and collaborative control of device errors and process errors, solving the industry problem of the coupled amplification of these two types of errors. This two-dimensional collaborative approach completes spherical contour error compensation, improving the machining accuracy and batch stability of plunger spherical contours, and achieving ultra-precision and stable machining of plunger spherical contours.

[0005] A complete implementation of this method includes the following steps:

[0006] Step 1: Measure the various errors of the machining equipment according to the standard, input the data into the spherical profile error mapping model, and determine the contribution weight of each error of the machining equipment to the profile error; at the same time, based on the workpiece size, material, and heat treatment process, create a cross-scale numerical simulation model of the spherical profile based on molecular dynamics and finite element simulation, and establish the quantitative influence law of grinding parameters such as grinding wheel spindle speed, workpiece spindle speed, and grinding wheel spindle-workpiece spindle angle on the profile error;

[0007] Step 2: Based on the error mapping model data and numerical simulation data mentioned above, partial least squares regression is used to analyze the multi-factor interaction effect. The geometry of the processing device, assembly error and processing parameters are used as independent variables, and the spherical profile is used as the dependent variable. A multivariate nonlinear coupled mathematical model is constructed. The significant terms of the independent variables are identified by multivariate analysis of variance. The interaction contribution rate of each factor in the two dimensions is quantified based on response surface analysis.

[0008] Step 3: Based on the response surface analysis of the above coupled mathematical model, the core coupling terms of the contour error factors are screened out and decomposed into three categories: process parameter effect, equipment error effect and coupling effect. The influence weight of the coupling effect on each corresponding process parameter and equipment error term is quantified to achieve decoupling identification of process error term, equipment error term and coupling error term.

[0009] Step 4: Based on the above error decoupling results, for the coupled error terms, an error suppression strategy is established from two dimensions: device error directional compensation and grinding process parameter optimization. Corresponding parameters are adjusted in a coordinated manner to eliminate the negative effects of the coupling amplification of the two types of errors. Combined with the compensation parameters of independent error terms, a process parameter tuning scheme and a machine tool error compensation scheme are finally generated, which can be written into the machine tool CNC system. The contour error of the plunger spherical grinding is accurately compensated by preset machining parameters.

[0010] Furthermore, machine tool error data needs to be obtained through measurement and calibration for specific machine tools, including guideway straightness and positioning error, radial rotation error, radial runout, axial runout, and tilting oscillation error of the grinding wheel spindle, workpiece spindle rotation error, radial runout, axial runout, and tilting oscillation error, grinding wheel spindle vibration error, and workpiece spindle vibration error.

[0011] Furthermore, it includes compensation strategies for specific device errors. For spindle rotation errors, dynamic tool path correction parameters are calculated and generated using a built-in tool tip position compensation algorithm; for guideway positioning errors, adjustments are made by calculating pitch error compensation data and backlash compensation data; for spindle rotation errors, the spindle's dynamic and static stiffness are calculated, and stiffness compensation data is also calculated. All data is submitted to the CNC system of the machining equipment either through import or programming.

[0012] Furthermore, a machining error tracing model was constructed based on process experiments and simulation models. The machining error tracing model is constructed based on partial least squares regression and BP deep learning neural network. The input includes device error data, process parameters, and material property parameters. The output is the contribution and quantification value of each process factor to the contour error. Among them, the process parameters include grinding wheel spindle speed, workpiece spindle speed, angle between grinding wheel spindle and workpiece spindle, and grinding pressure; the material parameters include elastic modulus, Poisson's ratio, and surface hardness.

[0013] Furthermore, the collaborative compensation from two dimensions—process parameters and device errors—described in this invention requires an overall influence weight analysis of the profile. When the influence weight of the processing device or machine tool error is large (exceeding a preset threshold), the compensation strategy is adjusted to focus on controlling the device error, while simultaneously optimizing the process parameters based on the error tracing coupling term. When the influence weight of the process parameters is large (exceeding a preset threshold), the compensation strategy is adjusted to focus on the process parameter optimization scheme, while simultaneously compensating or correcting the device error based on the error tracing coupling term.

[0014] Furthermore, after completing the spherical machining of the plunger, the spherical profile data is obtained through a high-precision measuring device to verify the compensation effect. The machining data and error data are then synchronously updated to the process experiment database to complete the iterative optimization of the coupled mathematical model and the error decoupling algorithm.

[0015] Furthermore, this method can be embedded into various existing machine tool CNC systems or used to build entirely new process auxiliary systems to implement the aforementioned plunger spherical contour error compensation machining method. It can be integrated with existing common CNC machine tools through standardized programming languages ​​and software interfaces without modifying the main structure of the machine tool or adding an independent hardware control system.

[0016] The advantages of this invention are:

[0017] 1. This invention is the first to propose considering nonlinear coupling terms for process parameters and machining device errors, which enables precise tracing of the contour error in the ultra-precision grinding process of plunger spherical surfaces. It breaks through the limitations of traditional methods that only consider device error compensation or process optimization, and achieves decoupling identification and precise suppression of device hardware errors and machining process errors. It effectively solves the problem of poor precision control caused by the coupling amplification of the two types of errors.

[0018] 2. The error compensation machining method proposed in this invention can be iteratively optimized on the same machine tool and for the same type of parts. As the number of parts processed increases, the machining accuracy can be further improved, and high machining stability can be maintained.

[0019] 3. This invention establishes a two-dimensional error collaborative control mechanism, which can dynamically adjust the compensation and adaptation strategy weights according to the specific machine tool grinding conditions, error types and proportions, and customize differentiated collaborative strategies to achieve adaptation to different processing devices and machine tools.

[0020] 4. The method of the present invention has strong versatility. It is not only applicable to the precision spherical machining of axial piston pumps / motors, but can also be extended to the ultra-precision machining of various precision spherical parts such as aero-engine spherical valves, precision bearing balls, and ball joints. It has good engineering application value and promotion prospects. Attached Figure Description

[0021] Figure 1 The flowchart of the contour error compensation processing method proposed in this invention is shown.

[0022] Figure 2 This invention illustrates the two-dimensional error tracing and coupling relationship. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this specification as detailed in the appended claims.

[0025] The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of this specification. The singular forms “a,” “the,” and “the” as used in this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0026] It should be understood that although the terms first, second, third, etc., may be used in this specification to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this specification, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0027] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0028] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of the embodiments disclosed in this invention. However, those skilled in the art will recognize that the technical solutions disclosed in this invention can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of the disclosure of this invention.

[0029] This embodiment provides a method for compensating for the spherical profile error of a plunger. It can be implemented using an existing ultra-precision grinding machine or a self-built grinding equipment. The method is for machining the spherical surface of a plunger of a certain type of axial plunger pump. The plunger material is bearing steel GCr15, the nominal diameter of the spherical surface is φ6.3mm, and the design requirements are a spherical profile of <1μm, roundness of ≤0.5μm, surface roughness Ra≤0.05μm, and no subsurface damage.

[0030] As per the instruction manual Figure 1 As shown, the specific steps are as follows:

[0031] Step S1: Measure the various errors of the machining equipment according to the standard, input the data into the spherical profile error mapping model, and determine the contribution weight of each error of the machining equipment to the profile error; at the same time, based on the workpiece size, material, and heat treatment process, create a cross-scale numerical simulation model of the spherical profile based on molecular dynamics and finite element simulation, and establish the quantitative influence law of grinding parameters such as grinding wheel spindle speed, workpiece spindle speed, and grinding wheel spindle-workpiece spindle angle on the profile error;

[0032] Step S11: Based on the characteristics of the processing equipment, instruments such as a laser interferometer, coordinate measuring machine, contact tracking ball, ballbar, eddy current sensor, and built-in sensors of the processing equipment are used to measure the straightness and positioning error of the guide rail, the radial rotation error, radial runout, axial runout, and tilting oscillation error of the grinding wheel shaft, the rotation error, radial runout, axial runout, and tilting oscillation error of the workpiece shaft, the vibration error of the grinding wheel shaft, and the vibration error of the workpiece shaft. The measurement results are then substituted into the mapping model between the geometric / assembly error and the spherical contour error of the processing equipment.

[0033]

[0034] In the formula, This is the overall error of the spherical profile. For the i-th device geometry / assembly error term, is the propagation coefficient of the i-th error, representing the weight of the contribution of this error to the contour accuracy; Let n be the random error term of the model, and n be the total number of device error terms. The propagation path of each device error and its contribution weight to the profile error are obtained through model calculation.

[0035] Step S12: Construct an atomic-level model of a single CBN abrasive grain cutting GCr15 to simulate the evolution of material dislocations, subsurface damage, and cutting force changes during grinding, and obtain the microscopic material removal mechanism. Then, based on the Johnson-Cook constitutive model and molecular dynamics simulation data, establish a grinding finite element model for simulation analysis, and combine process experimental data to fit the influence of grinding parameters such as wheel spindle speed, workpiece spindle speed, and the angle between the wheel spindle and workpiece spindle on the profile error.

[0036] Step S13: Construct a processing error tracing model based on partial least squares regression and BP deep learning neural network, including 10 input layers (containing equipment error data, process parameters, and material property parameters), 2 hidden layers, and 6 output layers (corresponding to the contribution and quantification value of each process factor to the profile error). The ReLU activation function is adopted, and the learning rate is set to 0.001. Pre-training is completed based on process experimental data. Input the data from the previous step to obtain a quantitative evaluation of the process parameters on the profile error.

[0037] Step S2: Based on the error mapping model data and numerical simulation data mentioned above, partial least squares regression is used to analyze the multi-factor interaction effect. The geometry of the processing device, assembly error and processing parameters are used as independent variables, and the spherical profile is used as the dependent variable. A multivariate nonlinear coupled mathematical model is constructed. The significant terms of the independent variables are found by multivariate analysis of variance. The interaction contribution rate of each factor in the two dimensions is quantified based on response surface analysis.

[0038] Step S2.1: Based on the above error measurement and process parameter data, partial least squares regression is used to conduct multi-factor interaction effect analysis. Machining error and grinding process parameters are used as two-dimensional independent variables, and spherical profile error is used as the dependent variable. An error coupling model is established based on the following formula:

[0039]

[0040] In the formula, Let i be the independent variable of the i-th process parameter. Let j be the independent variable representing the device error. For model constants, , The main effect coefficient of the independent variable. The coefficient representing the process-equipment interaction coupling effect. , The coefficient of the quadratic term, denoted as model residual, m as the number of process parameters, and l as the number of device error terms.

[0041] Step S2.2: Based on experience, set parameters for small-batch processing and collect parameter error data as preliminary experimental data. Use the F-statistic of ANOVA to perform significance testing, set the significance level to 0.05, analyze the independent variables that have a significant impact on contour error, and eliminate interfering factors.

[0042] Step S2.3: Based on the above steps, perform response surface methodology and quantify the interaction contribution rate between each factor using the following formula:

[0043]

[0044] In the formula, Let i be the coupling contribution rate between process parameter i and device error j. Let S be the sum of squared deviations from the mean of the coupling terms. This is the sum of squares of the total deviations from the mean.

[0045] Step S3: Based on the response surface analysis of the above coupled mathematical model, the core coupling terms of the contour error factors are screened out and decomposed into three categories: process parameter effect, equipment error effect and coupling effect. The influence weight of the coupling effect on each corresponding process parameter and equipment error term is quantified to achieve decoupling identification of process error term, equipment error term and coupling error term.

[0046] Step S3.1: As Figure 2 As shown, based on the results of the previous step, the core coupling items that contribute more than 5% to the contour error are selected by sorting them from high to low according to the interaction contribution rate. Their impact on the contour error is decomposed into three categories: process parameter effect, equipment error effect, and coupling effect. The influence weight of each process parameter and equipment error item in the coupling effect is quantified.

[0047] Step S3.2: Based on the error decomposition and weight quantification results, clarify the impact of each item on the profile error and the error propagation path. Simultaneously input the measured processing error data, equipment error data, and process parameters into the pre-trained processing error tracing model. Quantify the contribution of each process factor to the profile error through variable projection importance (VIP) value. When the VIP value > 1, the factor is determined to be the dominant process error factor, providing a basis for the generation of process adaptation strategies.

[0048] Step S4: Based on the above error decoupling results, for the coupled error terms, an error suppression strategy is established from two dimensions: device error orientation compensation and grinding process parameter optimization. Corresponding parameters are adjusted in a coordinated manner to eliminate the negative effects of the coupling amplification of the two types of errors. Combined with the compensation parameters of independent error terms, a process parameter tuning scheme and a machine tool error compensation scheme are finally generated, which can be written into the machine tool CNC system. The contour error of the plunger spherical grinding is accurately compensated by preset machining parameters.

[0049] Step S4.1: Considering both process parameters and equipment errors, analyze the overall influence weight of each dimension according to the following formula:

[0050]

[0051] In the formula, For the device compensation strategy weights, As a weight for process optimization strategy, Coefficients are assigned to the coupling effect and dynamically adjusted according to the error type.

[0052] Step S4.2: Calculations show that the device error weight in this embodiment is 80.9%, with the main coupling terms being the grinding wheel spindle speed and spindle rotation error, and the grinding wheel spindle speed and workpiece spindle positioning error. Therefore, a compensation strategy is generated primarily based on device error, referring to the following formula:

[0053]

[0054] In the formula, x, y, z are the theoretical tool tip coordinates, and x', , To correct the tool tip coordinates, The translation transformation matrix is... Let be the rotation transformation matrix. These are the radial / axial error compensation values ​​for the main spindle. , This is the compensation value for the spindle tilt angle oscillation error.

[0055] Step S4.3: Completely write the generated collaborative compensation strategy parameters into the CNC system of the machining equipment, and begin batch processing of subsequent parts. Each batch consists of 200 parts. After each batch is completed, a coordinate measuring machine and a laser confocal microscope are used to measure the contour, roundness, and subsurface damage of the parts in that batch, ensuring that the machining accuracy meets the requirements before proceeding to the next grinding process. Simultaneously, the process parameters, error compensation strategy, and part inspection results for this batch are input into the database for iterative parameter optimization.

[0056] Furthermore, the above figures are merely illustrative of the processes included in the method according to the exemplary embodiments disclosed in this invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0057] It should be understood that the present invention is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for compensating for the profile error of a plunger spherical surface, characterized in that, include: Based on process experimental data, a mapping model between the geometric / assembly error of the processing device and the spherical profile error is constructed to determine the transmission path of the device error and its contribution weight to the profile error. Based on molecular dynamics and finite element simulation, a cross-scale numerical simulation model of the spherical profile is created to establish the quantitative influence law of grinding parameters such as grinding wheel spindle speed, workpiece spindle speed, and grinding wheel spindle-workpiece spindle angle on the profile error. Based on the aforementioned error mapping model and numerical simulation model, a multivariate nonlinear coupled mathematical model is constructed using multifactor interaction effect analysis and partial least squares regression, with geometric / assembly errors of the processing device and processing parameters as independent variables and spherical profile as the dependent variable. The aforementioned process experimental data is input, and multifactor variance analysis is used to identify significant terms of the independent variables. The interaction contribution rate of each factor in the two dimensions is quantified based on response surface analysis. Based on the above-mentioned coupled mathematical model response surface analysis, the core coupling terms of contour error factors are screened out and decomposed into three categories: process parameter effect, equipment error effect, and interactive coupling effect. The influence weight of the coupling effect on each corresponding process parameter and equipment error term is quantified, so as to achieve complete decoupling and identification of process independent error terms, equipment independent error terms, and nonlinear coupled error terms. Based on the above error decoupling results, an error suppression strategy is established for the coupled error terms, which consists of two dimensions: device error orientation compensation and grinding process parameter optimization. Corresponding parameters are adjusted in a coordinated manner to eliminate the negative effects of the coupling amplification of the two types of errors. Combined with the compensation parameters for independent error terms, a process parameter tuning scheme and a machine tool error compensation scheme are finally generated, which can be written into the machine tool CNC system. The contour error of the plunger spherical grinding is accurately compensated by preset machining parameters.

2. The plunger spherical profile error compensation machining method according to claim 1, characterized in that, Machine tool error data needs to be obtained through measurement and calibration for specific machine tools, including guideway straightness and positioning error, radial rotation error, radial runout, axial runout, and tilting oscillation error of grinding wheel spindle, workpiece spindle rotation error, radial runout, axial runout, and tilting oscillation error, grinding wheel spindle vibration error, and workpiece spindle vibration error.

3. The plunger spherical profile error compensation machining method according to claim 1, characterized in that, It includes compensation strategies for specific device errors; for spindle rotation errors, it calculates and generates dynamic tool path correction parameters through a built-in tool tip position compensation algorithm; for guideway positioning errors, it adjusts by calculating pitch error compensation data and backlash compensation data; for spindle rotation errors, it calculates the spindle dynamic and static stiffness and calculates stiffness compensation data; all data is imported into the CNC system of the machining device in the form of import or programming.

4. The plunger spherical profile error compensation machining method according to claim 1, characterized in that, A processing error tracing model was constructed based on process experiments and simulation models. The processing error tracing model is based on partial least squares regression and BP deep learning neural network. The input includes equipment error data, process parameters, and material property parameters. The output is the contribution and quantification value of each process factor to the profile error. The process parameters include the grinding wheel spindle speed, the workpiece spindle speed, the angle between the grinding wheel spindle and the workpiece spindle, and the grinding pressure; the material parameters include the elastic modulus, Poisson's ratio, and surface hardness.

5. The plunger spherical profile error compensation machining method according to claim 1, characterized in that, The compensation of coupled error terms includes the weight analysis of the overall impact of two dimensions: process parameters and equipment errors. When the weight of the error of the processing equipment or machine tool is large (exceeding the preset threshold), the compensation strategy is adjusted to focus on controlling the error of the equipment, and the process parameters are simultaneously optimized according to the error source coupling terms. When the influence of process parameters is significant (exceeding the preset threshold), the compensation strategy is adjusted to focus on the process parameter optimization scheme, while simultaneously compensating for or correcting device errors based on the error tracing coupling term.

6. The plunger spherical profile error compensation machining method according to claim 1, characterized in that, This method can be embedded into various existing machine tool CNC systems or new process auxiliary systems for components to implement the plunger spherical contour error compensation machining method described in any one of claims 1-6, and achieves integration with existing common CNC machine tools through standardized programming voice and software interface.

7. The method for collaborative compensation of plunger spherical profile error according to claim 1, characterized in that, After the plunger spherical surface is machined, the spherical profile data is obtained through a high-precision measuring device to verify the compensation effect. The machining data and error data are then updated synchronously to the process experiment database to complete the iterative optimization of the coupled mathematical model and the error decoupling algorithm.

8. The method for collaborative compensation of plunger spherical profile error according to claim 1, characterized in that, Includes the following steps: S1: Measure various error parameters of the processing equipment, input the data into the mapping model between equipment error and spherical profile error, determine the transmission path of various errors of the processing equipment and their contribution weight to the profile error; for workpiece size, material, and heat treatment process, create a cross-scale numerical simulation model of the spherical profile based on molecular dynamics and finite element simulation, and establish the quantitative influence law of grinding parameters such as grinding wheel spindle speed, workpiece spindle speed, and grinding wheel spindle-workpiece spindle angle on the profile error; S2. Based on the error mapping model data and numerical simulation data mentioned above, the partial least squares regression method is used to analyze the multi-factor interaction effect. The geometry of the processing device, assembly error and processing parameters are used as independent variables, and the spherical profile is used as the dependent variable. A multivariate nonlinear coupled mathematical model is constructed. The multivariate variance analysis method is used to find the significant terms of the independent variables. The interaction contribution rate of each factor in the two dimensions is quantified based on response surface analysis. S3. Based on the above-mentioned response surface analysis of the coupled mathematical model, the core coupling terms of the contour error factors are screened out and decomposed into three categories: process parameter effect, equipment error effect, and coupling effect. The influence weight of the coupling effect on each corresponding process parameter and equipment error term is quantified to achieve the decoupling identification of process error terms, equipment error terms, and dual-dimensional coupling error terms. S4. Based on the above error decoupling results, for the coupled error terms, an error suppression strategy is established from two dimensions: device error orientation compensation and grinding process parameter optimization. Corresponding parameters are adjusted in a coordinated manner to eliminate the negative effects of the coupling amplification of the two types of errors. Combined with the compensation parameters of independent error terms, a process parameter tuning scheme and a machine tool error compensation scheme are finally generated, which can be written into the machine tool CNC system. The contour error of the plunger spherical grinding is accurately compensated by preset machining parameters.

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