A multi-stage mapping machine tool precision degradation characterization and key parameter optimization method

Abstract

This invention discloses a multi-level mapping method for characterizing machine tool accuracy degradation and optimizing its key parameters, belonging to the field of machine tool accuracy retention technology. This method constructs a complete technical framework of forward modeling and prediction followed by reverse parameter optimization, achieving quantitative prediction and proactive control of machine tool accuracy retention throughout its entire lifecycle through four-level mapping. First, based on the small displacement screw method and multibody system theory, a whole-machine spatial accuracy model considering geometric errors at large assembly interfaces is constructed. Second, combining numerical simulation, the Arcard wear model, and the superposition method, a time-varying degradation model of guideway straightness under multi-physics coupling is established. Third, by integrating the straightness and perpendicularity degradation laws, a time-varying equation for whole-machine spatial accuracy is derived, clarifying the mapping relationship of multi-level time-varying factors. Finally, using the accuracy degradation rate as an evaluation index, the machining and assembly process parameters are optimized through reverse derivation, forming a closed-loop optimization system. This invention is of great significance for improving the accuracy retention of high-end CNC machine tools.

Description

Technical Field

[0001] This invention relates to a multi-level mapping method for characterizing machine tool accuracy degradation and optimizing its key parameters, mainly including accuracy modeling, retention prediction and optimization of key parameters driven by it, belonging to the field of machine tool accuracy retention. Background Technology

[0002] High-end CNC machine tools are core equipment for achieving precision manufacturing, and their accuracy retention is a key indicator for measuring a country's manufacturing level. Currently, there is a gap between the accuracy retention of my country's high-end machine tools and international advanced levels. With the continuous deepening of research on machine tool accuracy retention, the time-varying effects of assembly interfaces and the accuracy degradation during service have become another important influencing factor besides the manufacturing accuracy of parts.

[0003] In the field of accuracy modeling and prediction, existing technologies mainly focus on modeling and analyzing the static geometric errors of machine tools. These methods can describe the accuracy state of a machine tool at a given moment, determined by part manufacturing and assembly, but they fail to fully consider the evolution and degradation of accuracy over time. Under the influence of time-varying factors such as stress, wear, vibration, and internal stress relaxation, the accuracy of a machine tool undergoes a dynamic decline. Traditional static models cannot predict how or at what rate accuracy decays, which limits the accuracy optimization and assurance strategies based on static models.

[0004] Specifically, existing research is insufficient in several ways, particularly in its lack of a time dimension: First, at the micro level, there is a lack of quantification of the time-varying patterns of the assembly interface state of basic large components and modeling of its impact on the geometric accuracy of parts; second, at the meso level, there is a lack of methods to map the time-varying geometric accuracy of key components into a dynamic evolution model of six errors in moving parts; and third, at the macro level, there is no integrated predictive model that can reflect the dynamic degradation of the overall machine's spatial accuracy over service time, from the interface to the parts, and then to the components. This limitation of modeling, which "only focuses on space and not time," makes it impossible to achieve design and process optimization for maintaining accuracy throughout the entire life cycle. This invention proposes a multi-level mapping method for characterizing machine tool accuracy degradation and optimizing its key parameters. The core innovation lies in introducing a time parameter and constructing a "time-varying" four-level accuracy degradation prediction framework, namely, "time-varying model of part assembly interface and wear interface state—time-varying model of part accuracy—evolution model of six errors in component motion axis—time-varying model of overall machine spatial accuracy." Summary of the Invention

[0005] To address the lack of systematic modeling and optimization methods for the time-varying accuracy degradation process of CNC machine tools in existing technologies, this invention proposes a multi-level mapping method for characterizing machine tool accuracy degradation and optimizing its key parameters. This method constructs a complete technical framework from the assembly interface to the overall machine accuracy degradation, and then to reverse parameter optimization, aiming to achieve prediction and proactive control of machine tool accuracy retention throughout its entire lifecycle.

[0006] S10: Construct a spatial accuracy model for CNC machine tools that considers geometric errors at large assembly interfaces.

[0007] The small displacement screw method is used to describe the positioning and orientation errors of the assembly interface of large components of machine tool foundation, and a component solid model representation method including the geometric errors of the assembly interface is proposed. By applying the multibody system theory, the geometric errors of each moving part are integrated to construct a spatial accuracy model of the whole CNC machine tool. This model describes the spatial error mapping relationship between the tool and the workpiece.

[0008] S20: Guide rail straightness degradation model considering time-varying characteristics of guide rail mounting surface geometry accuracy

[0009] Numerical simulation was used to study the influence of preload relaxation and residual stress release on the geometric accuracy of the guide rail mounting surface, and a data-driven model was established to characterize the straightness degradation of the guide rail mounting surface. The wear of the rolling elements-raceways of the guide rail pair was calculated based on the Archard model, and the mapping relationship between wear and guide rail straightness was explored. The superposition method was used to construct a time-varying model of linear shaft accuracy under multi-physics coupling.

[0010] S30: Mapping Relationship Between Overall Machine Spatial Accuracy Degradation and Time-Varying Characteristics of Linear Axis Geometric Accuracy

[0011] The influence of assembly preload relaxation and surface residual stress release on the perpendicularity between motion axes was investigated, and a data-driven model characterizing the perpendicularity degradation of each axis of the whole machine was established. The linear axis accuracy degradation model and the perpendicularity degradation model between them were substituted into the whole machine spatial accuracy model to derive the time-varying equation of the whole machine spatial accuracy, and to explore the mapping relationship between the time-varying assembly interface, the time-varying straightness and perpendicularity, and the time-varying spatial accuracy.

[0012] S40: Evaluation of Spatial Accuracy Preservation and Optimization of Key Geometric Parameters Driven by It

[0013] Based on the overall machine spatial accuracy degradation curve, a spatial accuracy retention evaluation method with accuracy degradation rate as the indicator is proposed. The influence of geometric accuracy degradation of each axis on the overall machine spatial accuracy degradation rate is analyzed, and key geometric parameters are determined based on weight analysis. According to the geometric accuracy and stress requirements of the assembly interface and guide rail mounting surface, the machining and assembly process parameters are optimized to form a closed-loop optimization method of "overall machine spatial accuracy degradation rate - linear axis key geometric accuracy degradation - guide rail mounting surface and assembly interface accuracy and stress - machining and process parameter optimization".

[0014] The beneficial effects of this invention are as follows: For the first time, it systematically introduces the time dimension into the entire process of machine tool accuracy modeling, connecting the complete four-level transmission chain of "time-varying wear interface and assembly interface states → part accuracy degradation → component motion error evolution → overall machine accuracy decline," thus achieving quantitative prediction of the long-term accuracy retention of machine tools considering part assembly performance. Based on this prediction model, it innovatively proposes a reverse parameter optimization method targeting long-term performance, realizing a shift from passive accuracy detection and compensation to active accuracy retention design, which is of great significance for improving the accuracy retention of high-end CNC machine tools. Attached Figure Description

[0015] Figure 1 A flowchart for the method of characterizing the accuracy degradation of multi-level mapped machine tools and optimizing key parameters.

[0016] Figure 2 This is a model drawing of a machine tool.

[0017] Figure 3 This is a flowchart illustrating the overall implementation framework.

[0018] Figure 4 This is a sample CNC machine tool topology diagram.

[0019] Figure 5 The time-varying curve of the straightness of the crossbeam guide rail mating surface as assembly stress relaxation occurs.

[0020] Figure 6 The time-varying curve of the straightness of the crossbeam guide rail as residual stress is released. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the following will describe in detail the "a multi-level mapping machine tool accuracy degradation characterization and key parameter optimization method" described in this invention, using a typical machine tool implementation as an example. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are all within the scope of protection of this invention.

[0022] The overall process of this invention follows the logic of "forward modeling and prediction, and reverse step-by-step optimization," and the specific steps are as follows:

[0023] S10 constructs a spatial accuracy model for CNC machine tools that takes into account the geometric errors of large assembly interfaces.

[0024] S101 Model characterization of geometric errors at the assembly interface of large machine tool components based on the small displacement screw method:

[0025] For the assembly of basic large components of machine tools, considering the impact of the assembly interface on assembly accuracy, small displacement rotation is used. This represents the deviation of the elements of the ideal assembly mating surface set, where α represents the small variation in rotation about the x-axis, β represents the small variation in rotation about the y-axis, γ represents the small variation in rotation about the z-axis, x represents the small variation in translation along the x-axis, y represents the small variation in translation along the y-axis, and z represents the small variation in translation along the z-axis.

[0026] The error variation matrix of the mating surface from assembly surface A to assembly surface B is as follows:

[0027] (1)

[0028] In the formula, , , Let x, y, and z represent the error components of the rotation of plane B relative to plane A about the x, y, and z axes, respectively. , , These represent the error components of the translation of plane B relative to plane A along the x, y, and z axes, respectively.

[0029] According to the error formation process ,in, , and These represent the distances from the ideal datum assembly plane A to the actual datum assembly plane. Error variation matrix, actual reference assembly plane To the actual assembly plane The error variation matrix, and the actual assembly plane The error variation matrix to the ideal assembly plane B. All error components in each matrix are minute quantities. The relationship between the joint surface error variation matrix and the part machining error and assembly process error is as follows:

[0030] (2)

[0031] Subscript The corresponding parameters are the error components of plane A; subscript The corresponding parameters are the assembly process error components, subscripts. The corresponding parameters are the error components of plane B.

[0032] S102 Overall Error Modeling Based on Many-Body Theory:

[0033] A topological diagram of the CNC machine tool is established, treating each moving component (bed, column, slide, spindle box, etc.) as a "rigid body," connected by kinematic pairs formed by guide rails and lead screws, and static pairs formed by mating surfaces. Based on the kinematics theory of multibody systems, an ideal motion matrix is ​​established from the workpiece coordinate system to the tool coordinate system.

[0034] By constructing the machine tool topology, describing the geometric error terms of the machine tool motion axes, and establishing homogeneous matrices—namely, the ideal characteristic matrix describing ideal rest and motion, and the error characteristic matrix describing actual rest and motion errors—the comprehensive spatial position error between the actual workpiece forming point PW and the actual tool forming point during actual forming motion is calculated.

[0035] (3)

[0036] In the formula, Ln(j) = v, L is the low-order volume operator, and volume B is the volume operator. j For body B v A higher-order system of order n. When system B v For body B j The adjacent low-order body, or body B j For body B v When the adjacent high-order volume is, we have: L1(j)=v.

[0037] The geometric errors of each component, including the six basic errors of the guide rail pair, and the error variation matrix of the assembly interface obtained in the previous step, are inserted into the overall machine space accuracy model in the form of homogeneous transformation matrix product. Finally, an explicit mathematical model of the machine tool space error E(x, y, z) is obtained. This model is a function of the position of each motion axis and all geometric error terms.

[0038] S20: Establish a guide rail straightness degradation model that considers the time-varying characteristics of the geometric accuracy of the guide rail mounting surface.

[0039] S201 defines the degradation mechanism and input parameters, and clarifies that the main time-varying factors affecting the straightness degradation of the guide rail are: (1) the release of residual stress on the guide rail mounting surface on the bed, beam, column and other basic components; (2) the change of the assembly interface caused by stress relaxation of bolt preload between basic components and guide rail mounting bolts; (3) the wear between the rolling elements and raceway and guide rail in the slider. The input of the model is defined as the initial state of the above factors, namely the initial preload F0, the initial residual stress distribution σ0(x,y), the initial surface roughness R0, the working load P, the running speed v, the time t, etc.

[0040] S202 Multiphysics Numerical Simulation:

[0041] Using Ansys finite element analysis software, a three-dimensional model including the guide rails, mounting bolts, and a portion of the machine bed was created. For example, the machine bed was made of HT300 steel, the guide rails of GCr15 steel, and the bolts of 8.8 grade 35 steel. The density, isotropic elasticity, creep parameters, and tensile yield strength of the bolts, guide rails, and machine bed were set. Contact pressure values ​​were applied to the guide rail mounting surfaces. Static analysis was used to calculate the changes in guide rail straightness under the influence of residual stress release and assembly stress relaxation, respectively. The curves Δ1(t) and Δ2(t) of the macroscopic straightness error of the guide rail as a function of time were obtained.

[0042] Taking one of the basic large components, the crossbeam, as an example, the residual stress inside it is continuously released over time, causing deformation of the mating surface of its guide rails. This leads to a continuous change in straightness. Through simulation calculations and analysis of the straightness at different times, the change shows a trend of first increasing sharply and then rising linearly, indicating a gradual decline in accuracy. The change in the straightness of the crossbeam guide rail mating surface in the plane perpendicular to the guide rails under the release of residual stress over time is as follows: Figure 5 As shown in Equation 4, the nonlinear curve fitting of the scatter plot generated from the simulation-calculated deformation data yields the fitting curve formula for the straightness of the crossbeam guide rail corresponding to the residual stress.

[0043] (4)

[0044] Subsequently, an analysis of the deformation of the guide rail-guide beam mating surface caused by assembly stress relaxation was conducted. Because the initial preload of the bolts connecting the beam and guide rail decreases over time, the assembly stress is continuously released, leading to deformation of the beam-guide rail mating surface. This results in a continuous change in its straightness. Calculations and analysis of the straightness at different times show a trend of initially increasing sharply, then gradually increasing slowly, and finally approaching a stable level. In other words, the accuracy initially decreases rapidly to a certain value and then remains relatively stable. The change in guide rail straightness in the plane perpendicular to the guide rail caused by assembly stress relaxation is shown below. Figure 6 As shown in Equation 5, the fitted curve was obtained by performing nonlinear curve fitting on the scatter plot generated from the simulation data using Matlab.

[0045] (5)

[0046] Finally, the Arcard wear model was applied to calculate the cumulative wear depth process between the balls and the raceway surface.

[0047] According to Arcard's theory, the volumetric wear V of the roller under a contact normal load Q is calculated as shown in Equation 6:

[0048] (6)

[0049] In the formula, KA is the adhesive wear coefficient; L is the sliding distance of the roller in the raceway; and H is the hardness of the softer material.

[0050] The sliding distance L of the roller in the slider raceway can be controlled by the elastic creep coefficient μ. e The distance S traveled by the slider is calculated as shown in Equation 7:

[0051] (7)

[0052] The elastic creep coefficient μe can be calculated using the method proposed by Kalker and Jacobson, as shown in Equation 8:

[0053] (8)

[0054] In the formula, v is the speed of the guide rail pair; w is the angular velocity of the roller; and R0 is the distance from the raceway to the center of the roller. The wear volume WV is shown in Equation 9.

[0055] (9)

[0056] δi represents the combined deformation of the roller and the double-sided raceways caused by the normal contact force Q under normal operating conditions. i The calculation formula is based on the Palmgren formula, as shown in Equation 10. The roller length is l. e , with a radius of R.

[0057] (10)

[0058] The normal wear depth h is shown in Equation 11:

[0059] (11)

[0060] The wear depth distribution is mapped to the effect Δ3(t) on the micro-profile error of the guide rail.

[0061] Creep release of residual stress in the crossbeam, relaxation of assembly stress, and frictional wear are key factors leading to guideway deformation and machine tool accuracy degradation. Simulations were used to quantify the straightness deviation of the crossbeam guideway mating surface caused by residual stress and assembly stress alone, and the change in guideway profile error under wear conditions was calculated. These three types of results were coupled and analyzed to comprehensively evaluate the straightness evolution law under the combined effects of residual stress, assembly stress, and frictional wear, thus systematically revealing its influence mechanism on the maintenance of machine tool geometric accuracy. The overall coupled equation for the change law of the straightness of the crossbeam guideway mating surface under the combined effects of residual stress, assembly stress, and frictional wear is shown in Equation 12.

[0062] (12)

[0063] S30 establishes a mapping relationship between the degradation of overall machine spatial accuracy and the time-varying characteristics of linear axis geometric accuracy.

[0064] This step considers the influence of assembly preload relaxation and surface residual stress release on the perpendicularity between motion axes, establishes a data-driven model characterizing the perpendicularity degradation of each axis of the whole machine, and then combines the whole machine accuracy model considering the assembly mating surface and the time-varying model of linear axis straightness established in the previous two steps to establish a time-varying accuracy degradation model of the whole machine, and sets accuracy degradation index to evaluate the accuracy retention of the CNC machine tool.

[0065] S301 establishes a guide rail straightness degradation model that considers the time-varying characteristics of the geometric accuracy of the guide rail mounting surface.

[0066] As the initial preload of the bolts of the crossbeam and column decreases over time, the assembly stress of the two is released continuously, which causes the joint surface of the crossbeam guide rail to deform to a certain extent. Therefore, the perpendicularity of the y-axis and x-axis of the crossbeam guide rail also changes continuously. In the simulation software, the three-dimensional model of the machine tool is simplified, the part materials are set, the mesh is divided, and reasonable boundary conditions are set. Combined with the bolt preload relaxation curve, the geometric deformation of the crossbeam guide rail at 12 time points from 0h to 1100h is calculated. By comparing the perpendicularity deviation distribution of the guide rail at different times, a quantitative relationship model of its change with time is fitted. After calculating the perpendicularity at different times and summarizing the calculation, it can be seen that the change shows a trend of first increasing sharply and then gradually stabilizing. That is, the accuracy first decreases rapidly to a certain value in the early stage and then basically remains stable in the later stage. The software is used to perform nonlinear curve fitting on the scatter plot generated by the simulation data to obtain the fitting curve formula as shown in Equation (13).

[0067] (13)

[0068] Based on geometric relationships, S302 directly maps the degradation of guide rail straightness and the degradation of perpendicularity between basic components to the corresponding error terms of moving parts. That is, the guide rail straightness degradation curve l(t) and perpendicularity degradation curve p(t) are introduced into the corresponding error terms in the characteristic matrix, thereby obtaining the overall machine accuracy model under time-varying influence.

[0069] Based on actual engineering survey data, S303 sets engineering acceptance standards to ensure the long-term machining stability of CNC machine tools after they are put into use. The evaluation index for accuracy retention is defined as the Accuracy Degradation Rate (ADR). That is, the accuracy degradation rate is used as the evaluation index to transiently describe accuracy in both time and space dimensions. When the accuracy degradation process is non-linear, i.e., the accuracy degradation curve... When the change is nonlinear, the accuracy degradation rate is expressed as the tangential change trend at a certain point in time, and the accuracy degradation rate is:

[0070] ADR= (14)

[0071] The quantitative index for accuracy retention is: after 1000 hours of machine tool operation, the accuracy degradation rate of the entire machine and its key geometric accuracy at this point does not exceed 3%. When the accuracy degradation process is linear, its accuracy degradation rate is:

[0072] ≤3% (15)

[0073] In the formula: Initial accuracy; Transient accuracy; Monitoring time;

[0074] S40 spatial accuracy retention evaluation and its driving optimization of key geometric and physical parameters.

[0075] The required long-term accuracy level for the entire machine is to ensure that the accuracy degradation rate is less than 3% after 1000 hours.

[0076] S401, based on the established time-varying model of overall machine accuracy, reverse-engineers the accuracy requirements that the geometric errors of the component motion axes must meet.

[0077] Based on the required accuracy of the six motion errors of the motion axis, S402 calculates the required accuracy of the parts, namely the allowable upper limit curve of the guide rail straightness degradation.

[0078] S403 uses the upper limit of the allowable degradation curve of the part to deduce the control requirements for the assembly interface and the precision of the part.

[0079] S404 optimizes the initial preload and residual stress control process based on the control requirements of the assembly interface and part accuracy, and improves the machining and assembly process parameters to ensure that the machining and manufacturing of the assembly surface and parts meet the initial accuracy requirements.

[0080] The implementation method constructs a full-link, closed-loop technical system through four progressive implementation steps from S10 to S40, from the analysis of time-varying mechanisms of microscopic interfaces to the prediction of macroscopic machine precision performance, and then to the reverse optimization of machining and assembly process parameters. This fully implements the multi-level mapping machine tool precision degradation characterization method and key parameter optimization strategy proposed in this invention.

Claims

1. A method for characterizing machine tool accuracy degradation through multi-level mapping and optimizing its key parameters, characterized in that, Includes the following steps: S10: Construct a spatial accuracy model for CNC machine tools that takes into account the geometric errors of large assembly interfaces; The small displacement screw method is used to describe the positioning and orientation errors of the assembly interface of large components of machine tool foundation. A component solid model representation method including the geometric errors of the assembly interface is proposed. By applying the multibody system theory, the geometric errors of each moving part are integrated to construct a spatial accuracy model of the whole CNC machine tool. The model describes the spatial error mapping relationship between the tool and the workpiece. S20: Establish a guide rail straightness degradation model that considers the time-varying characteristics of the geometric accuracy of the guide rail mounting surface; Numerical simulation was used to study the influence of preload relaxation and residual stress release on the geometric accuracy of the guide rail mounting surface, and a data-driven model was established to characterize the straightness degradation of the guide rail mounting surface. The wear of the rolling elements-raceways of the guide rail pair was calculated based on the Archard model, and the mapping relationship between the wear and the straightness of the guide rail was explored. The superposition method was used to construct a time-varying model of the straight axis accuracy under the coupling effect of multiple physics fields. S30: Establish the mapping relationship between the overall machine spatial accuracy degradation and the time-varying characteristics of linear axis geometric accuracy; This study investigates the influence of assembly preload relaxation and surface residual stress release on the perpendicularity between motion axes, and establishes a data-driven model characterizing the perpendicularity degradation of each axis of the whole machine. The linear axis accuracy degradation model and the perpendicularity degradation model between them are substituted into the whole machine spatial accuracy model to derive the time-varying equation of the whole machine spatial accuracy, and to explore the mapping relationship between the time-varying assembly interface, the time-varying straightness and perpendicularity, and the time-varying spatial accuracy. S40: Evaluation of spatial accuracy retention and optimization of key geometric and physical parameters driven by it; Based on the overall machine spatial accuracy degradation curve, a spatial accuracy retention evaluation method with accuracy degradation rate as the indicator is proposed; the influence of geometric accuracy degradation of each axis on the overall machine spatial accuracy degradation rate is analyzed, and key geometric parameters are determined based on weight analysis; according to the geometric accuracy and stress requirements of the assembly interface and guide rail mounting surface, the machining and assembly process parameters are optimized to form a closed-loop optimization method.

2. The method for characterizing machine tool accuracy degradation and optimizing its key parameters using multi-level mapping as described in claim 1, characterized in that, In step S10, when the small displacement screw method is used to characterize the geometric error of the assembly interface, the small displacement screw represents the deviation of the elements of the ideal assembly mating surface set, including the small variation α, β, γ of rotation around the x-axis, y-axis, and z-axis, and the small variation x, y, z of translation along the x-axis, y-axis, and z-axis; the relative error between the assembly surfaces is described by the error variation matrix, which is related to the part machining error and the assembly process error.

3. The method for characterizing machine tool accuracy degradation and optimizing its key parameters using multi-level mapping as described in claim 1, characterized in that, In step S10, when constructing the overall machine space accuracy model based on multibody system theory, each moving part of the machine tool is regarded as a rigid body and connected by kinematic pairs and stationary pairs. An ideal motion matrix from the workpiece coordinate system to the tool coordinate system is established. The geometric error and assembly interface error variation matrix of each part are introduced and inserted into the model in the form of homogeneous transformation matrix product to obtain the explicit mathematical model of the machine tool space error E(x, y, z).

4. The method for characterizing machine tool accuracy degradation and optimizing its key parameters using multi-level mapping as described in claim 1, characterized in that, The numerical simulations in steps S20 and S30 use Ansys finite element analysis software to construct a three-dimensional model. The material parameters include density, isotropic elasticity, creep parameters and tensile yield strength. The simulation data are fitted with nonlinear curves using Matlab.

5. The method for characterizing machine tool accuracy degradation and optimizing its key parameters using multi-level mapping according to claim 1, characterized in that, In step S20, the multiphysics coupling effect includes residual stress release, assembly preload relaxation, and frictional wear, and the corresponding guide rail straightness degradation components are as follows: The time evolution formulas of each component are obtained by nonlinear curve fitting, and the comprehensive degradation model is obtained by superposition method. .

6. The method for characterizing machine tool accuracy degradation and optimizing its key parameters using multi-level mapping according to claim 1, characterized in that, In step S20, when calculating the wear amount based on the Archard model, the elastic creep rate is used. Calculate the sliding distance L of the roller in the raceway based on the slider running distance S, and combine this with the adhesive wear coefficient. The normal load Q and the hardness H of the softer material are used to obtain the volumetric wear, which is then mapped to the microscopic profile error of the guide rail. .

7. The method for characterizing machine tool accuracy degradation and optimizing key parameters through multi-level mapping according to claim 1, characterized in that, In step S30, the perpendicularity degradation model obtains the perpendicularity deviation of the motion axis caused by the deformation of the assembly interface due to the relaxation of the assembly stress at the assembly interface of the two connecting parts at different time points through simulation calculation. The perpendicularity change curve is obtained by nonlinear curve fitting. Then, the guide rail straightness degradation curve l(t) and perpendicularity degradation curve p(t) are introduced into the corresponding error terms of the multibody system characteristic matrix to obtain the time-varying whole machine accuracy model.

8. The method for characterizing machine tool accuracy degradation and optimizing its key parameters using multi-level mapping according to claim 1, characterized in that, In step S30, the evaluation criteria for accuracy degradation rate (ADR) are as follows: after the machine tool has been running for 1000 hours, the accuracy degradation rate of the whole machine and key geometric accuracy does not exceed 3%; during nonlinear degradation, ADR is the tangential change rate of the degradation curve, ADR=f'(t). During linear degradation, ,in For initial precision, For transient accuracy, For monitoring time.

9. The method for characterizing machine tool accuracy degradation and optimizing its key parameters using multi-level mapping according to claim 1, characterized in that, The closed-loop optimization method in step S40 follows the reverse derivation logic of: overall machine space accuracy degradation rate - linear axis key geometric accuracy degradation - guide rail mounting surface and assembly interface accuracy and stress - machining and process parameter optimization, and reverse deduces the initial accuracy requirements of part machining, the optimal initial preload force of assembly and residual stress control process.

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