A method for distributing accuracy of a numerically controlled machine tool from a geometric error to manufacturing tolerances
By establishing a manufacturing tolerance-geometric error mapping model in CNC machine tools and combining it with Sobol global sensitivity analysis, the grinding and scraping process of the bottom mounting surface of the guide rail was optimized. This solved the problem of the correlation between geometric error and manufacturing tolerance, achieved a balance between precision design and manufacturing cost, improved the machining accuracy of the machine tool, and reduced costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHEJIANG UNIV
- Filing Date
- 2026-03-17
- Publication Date
- 2026-06-23
AI Technical Summary
Existing technologies make it difficult to establish a clear correlation between geometric errors and manufacturing tolerances in CNC machine tools, making it difficult to implement precision allocation methods. Furthermore, they neglect the elastic deformation of the mating surfaces and the optimization of manufacturing costs, resulting in insufficient economic efficiency.
By establishing a manufacturing tolerance-geometric error mapping model and combining Sobol global sensitivity analysis and optimization algorithms, the grinding and scraping process of the bottom mounting surface of the guide rail is optimized to achieve a balance between precision design and manufacturing cost.
It significantly improves the engineering feasibility of the accuracy allocation results, reduces manufacturing costs, and guides the machining process of the guide rail mounting surface under the overall machine accuracy probability requirements, thereby improving the machining accuracy of the machine tool.
Smart Images
Figure CN122261007A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of precision design and manufacturing tolerance optimization of CNC machine tools, and in particular to a method for precision allocation of CNC machine tools that traces the source of geometric errors to manufacturing tolerances. Background Technology
[0002] High-precision, high-end five-axis machine tools, as the mother machines of industry, directly affect the quality of parts through their machining accuracy. With the advancement of intelligent manufacturing, machine tool accuracy has become a key indicator for improving product reliability and sustainability. The main factors affecting machine tool machining accuracy include geometric errors, thermal errors, dynamic errors, and other errors. Among these, geometric errors account for about 30% of the total error and form the basis of overall machine accuracy, exhibiting repeatability and stability.
[0003] For example, Chinese patent document CN107186548A discloses a method for detecting the geometric error of the rotary axis of a five-axis CNC machine tool, in order to solve the problem of identifying and detecting the geometric error of the rotary axis of the machine tool. Chinese patent document CN106863014A discloses a method for detecting the geometric error of the linear axis of a five-axis CNC machine tool, realizing the measurement of the geometric error of the linear axis of the CNC machine tool.
[0004] In existing technologies, a comprehensive geometric error model for a five-axis machine tool typically includes 41 geometric errors. However, in specific accuracy allocation scenarios, not all geometric errors can be directly traced back to the manufacturing tolerances of the same type of component. For the manufacturing characteristic of the flatness tolerance of the guide rail bottom mounting surface, the 18 geometric error terms related to the linear motion axis are most directly related to it. Therefore, although traditional research can perform error modeling at the level of 41 geometric errors for the entire machine, it still lacks a quantitative correlation with the manufacturing tolerances of specific components, making it difficult to directly guide the manufacturing of machine tool components. Compensating for geometric errors requires complex post-processing procedures, which are cumbersome and cannot completely eliminate errors. Existing methods have the following shortcomings: (1) Remaining only at the level of abstract geometric errors, the methods are too theoretical and difficult to establish a direct and clear connection with the manufacturing tolerances of parts. The mapping relationship between component manufacturing tolerances and machine tool spatial errors is unclear, making it difficult to implement many precision allocation methods; (2) Existing methods typically employ a geometric error-cost model, directly using the abstract geometric error term as a variable in the cost function. This has significant limitations at the manufacturing level: geometric errors are often the overall result after assembly and are difficult to directly correspond to specific component processing steps; (3) Although some studies have attempted to explore the mapping relationship between manufacturing tolerances and machine tool errors, they have neglected the contact elastic deformation of the mating surface, making it difficult to guarantee the reliability of the mapping model; at the same time, they have neglected the quantitative optimization of manufacturing costs, resulting in insufficient economic efficiency of the allocation scheme.
[0005] Therefore, there is an urgent need for a positive precision allocation method that traces the source of geometric errors to manufacturing tolerances in order to achieve a balance between precision design and manufacturing costs. Summary of the Invention
[0006] This invention provides a method for allocating CNC machine tool precision by tracing the source of geometric errors to manufacturing tolerances. This method can reduce manufacturing costs while meeting the precision requirements of the entire machine, achieving a balance between precision design and manufacturing costs. At the same time, the output results can directly guide the grinding and scraping processes of the guide rail mounting surface.
[0007] A method for allocating CNC machine tool precision by tracing the source of geometric errors to manufacturing tolerances includes the following steps: (1) The manufacturing tolerance of the bottom mounting surface of the linear motion axis guide rail of the CNC machine tool is selected as the precision allocation object. Based on the small deformation assumption and the superposition principle, the manufacturing tolerance is characterized as the guide rail slider height disturbance. The influence coefficient of slider height on the geometric error related to the linear motion axis position is solved by finite element analysis. A slider height-geometric error mapping model is constructed. A three-layer mapping model of component manufacturing tolerance-geometric error-machine tool space error is established based on the homogeneous transformation matrix. (2) Feature trajectory sampling is performed on the overall workspace of the machine tool, and the sensitivity index of the total effect of each manufacturing tolerance on the machine tool space error is calculated using the Sobol global sensitivity analysis method; (3) Establish a manufacturing cost assessment model for component manufacturing tolerances, combine the total effect sensitivity index, calculate the cost sensitivity of each component manufacturing tolerance, and determine the ranking constraints for tolerance allocation based on cost sensitivity. (4) With the goal of minimizing manufacturing costs, the optimal allocation value of each manufacturing tolerance is obtained by combining the tolerance sorting constraint, the tolerance domain constraint, and the machine tool space accuracy constraint based on the confidence level, and the optimal allocation value is then used to grind or scrape the bottom mounting surface of the machine tool linear motion axis guide rail.
[0008] This invention takes the flatness tolerance of the bottom mounting surface of the guide rail as the specific object, establishes a manufacturing tolerance-geometric error mapping model by using the small deformation assumption and the superposition principle, and then constructs a three-layer mapping relationship. The influence is quantified by Sobol global sensitivity analysis, and the accuracy allocation is achieved by combining the manufacturing tolerance cost model and optimization algorithm, which ultimately directly guides the component processing procedure.
[0009] In step (1), the CNC machine tool is a five-axis machine tool, including three linear motion axes (X, Y, and Z) and two rotary axes; the manufacturing tolerance is the flatness tolerance of the bottom mounting surfaces of the six guideways corresponding to the dual guideway structure of the three linear motion axes (X, Y, and Z). The change in slider height follows a normal distribution within the flatness tolerance zone. The geometric error consists of 18 position-related geometric error terms for the linear motion axes.
[0010] In step (1), the manufacturing tolerance-geometric error mapping model is based on the following relationship: The height of each slider is relative to the first The linear influence coefficient of the geometric error was obtained through finite element simulation, satisfying the superposition principle: the geometric error under the combined action of multiple sliders is equal to the sum of the individual contributions of each slider; thus, the slider height-geometric error model of the linear motion axis is obtained: ; in, Indicates the first Position-related geometric error term, This represents the influence coefficient matrix, specifically the first... The height of each slider For the The influence coefficient of geometric error.
[0011] In step (1), the expression for the three-layer mapping model is: ; in, No. Flatness tolerance Indicates the first The height of each slider Represents the influence coefficient matrix. This represents the spatial error vector propagated by the homogeneous transformation matrix. The feed coordinates represent the motion axes. Represents various geometric errors; and These are the actual transformation matrix and the ideal transformation matrix, respectively, which contain geometric errors; The coordinates of the knife tip are... The coordinates are the tool axis vector.
[0012] In step (2), the Sobol global sensitivity analysis uses the Monte Carlo sampling method to sample the characteristic trajectories of the entire machine tool workspace, including sampling the volume diagonals of the workspaces of the three linear motion axes and discretely sampling the travel ranges of the two rotary axes, obtaining a number of samples. Given a set of sampling points, calculate the sensitivity index of the total effect of each flatness tolerance: ; in, The total number of sampling points. The sensitivity coefficient of the total effect of the k-th tolerance in the overall workspace; The larger the value, the greater the weight of the manufacturing quality of the guide rail mounting surface on the overall machine precision; The overall effect sensitivity index for each sampling point.
[0013] In step (3), the manufacturing cost assessment model adopts the inverse square model: ; in, Indicates the first Flatness tolerance This is the cost factor, which reflects the length of the guide rail and the difficulty of manufacturing. The total cost is the sum of the costs of each guide rail.
[0014] In step (4), the constraints of the optimization algorithm specifically include: Tolerance domain constraints: ; in, For process limit tolerance, This is the minimum design requirement; Machine tool spatial accuracy constraint: The length of the machine tool spatial error vector does not exceed a given radius R, and the probability of satisfying this constraint is ≥95% confidence level. ; Tolerance sorting constraint: Sort according to cost sensitivity, ensuring that items with high cost sensitivity are assigned smaller tolerance values: ; in, Indicates cost sensitivity; when At that time, it must meet the following conditions. ; and These are tolerance subscript indices, representing respectively as well as Items in Let k be the sensitivity index of the total effect of the k-th tolerance in the overall workspace. This represents the cost coefficient.
[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Compared with the traditional method of allocating only geometric errors, this invention sinks the allocation object to the specific manufacturing feature of the flatness tolerance of the bottom mounting surface of the guide rail, which is machinable, detectable, and adjustable, thus significantly improving the engineering feasibility of the allocation results; it incorporates the elastic deformation of the contact surface into the finite element solution, and establishes the influence coefficient matrix of manufacturing tolerance to position-related geometric errors based on the small deformation assumption and the superposition principle, so that the transmission relationship of manufacturing tolerance-geometric error-spatial error has a clear physical basis, which is different from the scheme that relies only on empirical weights or pure statistical correlation.
[0016] 2. This invention employs Sobol total effect sensitivity analysis oriented towards the overall workspace characteristic trajectory, which can evaluate the contribution of each manufacturing tolerance to the overall machine space error from a global perspective, avoiding the deviation caused by evaluating only a few working points; it constructs a cost sensitivity index by combining the total effect sensitivity with the manufacturing cost model, and completes the optimization solution under the confidence space error constraint, thereby reducing manufacturing costs while meeting the overall machine accuracy probability requirements, and the output results can directly guide the grinding and scraping process of the guide rail mounting surface.
[0017] 3. This invention is applicable to CNC machine tools such as three-axis and five-axis machines; after the corresponding error mapping model is established, it can also be extended to the allocation of manufacturing tolerances related to geometric errors, such as straightness tolerance and radial runout tolerance. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 This is a schematic diagram of the CA rotary table type machine tool in this invention.
[0020] Figure 2 This is the overall flowchart of the method of the present invention.
[0021] Figure 3 This is a schematic diagram of the guide rail-slider of the machine tool in the method of the present invention.
[0022] Figure 4 This is a diagram showing the distribution of the slider within the tolerance range in the method of this invention.
[0023] Figure 5 This is a schematic diagram illustrating the small deformation assumption in the method of the present invention.
[0024] Figure 6 This is a schematic diagram of the superposition principle in the method of the present invention.
[0025] Figure 7 This is a schematic diagram of the kinematic chain of a five-axis machine tool in this invention.
[0026] Figure 8 This is a histogram showing the frequency distribution of machine tool spatial errors at random five-axis coordinate points in this invention.
[0027] Figure 9 This is a distribution diagram of the spatial error of random sample points in this invention. Detailed Implementation
[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0029] It should be noted that, unless otherwise specified, the features in the following embodiments and implementation methods can be combined with each other.
[0030] In this embodiment, a CA rotary table type five-axis CNC machine tool is used as the example for description. This machine tool includes three linear motion axes (X-axis, Y-axis, and Z-axis) and two rotary axes (C-axis and A-axis), as follows: Figure 1 As shown. The accuracy allocation object is selected as the flatness tolerance of the bottom mounting surface of each guide rail in the X, Y, and Z axes. According to... Figure 2 The steps shown lead to the design of the optimal allocation scheme for the flatness tolerance of the bottom mounting surfaces of each guideway of the CA rotary table type five-axis machine tool. The specific process of this invention is as follows: Step (1): Select the manufacturing tolerance of the bottom mounting surface of the linear motion axis guide rail of the CNC machine tool as the accuracy allocation object. Based on the small deformation assumption and the superposition principle, the manufacturing tolerance is characterized as the disturbance of the guide rail slider height. Finite element analysis is used to solve the influence coefficient of the slider height on the geometric error related to the linear motion axis position. A slider height-geometric error mapping model is constructed. Based on the homogeneous transformation matrix, a three-layer mapping model of component manufacturing tolerance-geometric error-machine tool spatial error is established.
[0031] In this step, taking the flatness tolerance of the bottom mounting surface of the linear motion axis guide as an example, the specific construction process of the three-layer mapping model is as follows: (1.1) Mapping of manufacturing tolerances to elastic deformation: Flatness error acts directly on the guide rail reference, causing displacement of the slider mounted on it, such as... Figure 3 As shown, the deformation of the slider caused by the flatness tolerance is approximated as a displacement in the height direction. Since the slider height will vary within the flatness tolerance zone, assuming it follows a normal distribution, the probability distribution of slider height variation is expressed as: ; in, Indicates the first Flatness tolerance of the bottom mounting surface of the guide rail. For example... Figure 4 As shown.
[0032] (1.2) Mapping from elastic deformation to geometric error and theoretical verification: Within the micrometer-level tolerance range, based on the small deformation assumption and superposition principle of mechanics of materials, the geometric error caused by a single slider follows a linear proportional relationship, such as... Figure 5 As shown, the geometric error caused by the combined action of multiple sliders is equal to the algebraic sum of the individual contributions of each slider, such as... Figure 6 As shown.
[0033] To ensure the accuracy and reliability of this mapping model in actual engineering, this example verifies the theoretical assumptions through finite element simulation.
[0034] Linearity verification under the small deformation assumption: Finite element simulations were performed within the conventional micron-level variation range of flatness tolerance. Scatter data of six geometric errors of the slider height and motion axis were extracted and linear fitting was performed. The results show a high degree of linear correlation between slider height variation and geometric errors. The coefficient of determination of all fitted curves exceeded 0.998, and the maximum deviation from linearity was less than 5%. This proves that under small perturbations, the deformation of the machine tool structure caused by the change in the height of a single slider strictly follows a linear proportional relationship. Decoupling verification of the superposition principle: By comparing the "coupled analysis value of simultaneous height changes of multiple sliders" with the "linear superposition value of deformation caused by individual slider changes," the results show that the agreement is extremely high, and the maximum relative error is strictly controlled within 5%. This proves that the deformation caused by each slider is completely decoupled within the allowable range of machine tool structural stiffness.
[0035] Based on the verified physical properties described above, the linear influence coefficient of the change in the height of a single slider on the geometric error of the motion axis was obtained through finite element static simulation. Specifically, a simplified model was established, and self-weight, uniformly distributed load, and bolt preload were applied. The change in slider height was simulated by adjusting the virtual bed height, and the six-degree-of-freedom deformation of the data acquisition unit was extracted. The influence coefficient matrix was obtained by fitting using the least squares method. This allows the establishment of a linear model of the slider height versus geometric error for the linear motion axis of the machine tool. ; in, Indicates the machine tool's first Location-related geometric errors.
[0036] (1.3) Mapping of geometric errors to machine tool spatial errors: Using the homogeneous coordinate transformation matrix (HTM) method, based on the kinematic chain of the five-axis machine tool, such as... Figure 7 As shown, the actual transformation matrix including geometric errors is established. With the ideal transformation matrix Machine tool space error The expression is: ; In the formula, The feed coordinates represent the motion axes. Representing various geometric errors, The coordinates of the knife tip are... The coordinates are the tool axis vector.
[0037] Step (2): Sample the characteristic trajectory for the overall workspace of the machine tool, and use the Sobol global sensitivity analysis method to calculate the sensitivity index of the total effect of each manufacturing tolerance on the machine tool space error.
[0038] To maximize coverage of the machine tool's effective machining range while ensuring computational efficiency, a feature trajectory sampling strategy was adopted instead of uniform full-mesh sampling. For the three linear motion axes, four body diagonals within their workspace were selected as feature paths, and feature points were non-uniformly distributed along these paths (points were taken at 12.5%, 37.5%, 46%, and 54% of the travel). For the two rotary axes, the points were discretized at equal intervals within their travel range. The discretized points of the linear and rotary axes were orthogonally combined to construct an evaluation point set covering the entire machine tool domain. Sobol sensitivity analysis was performed on each coordinate point in the point set based on Monte Carlo sampling. Subsequently, the overall effect sensitivity index of all sampled points was averaged to obtain the [value missing]. The overall sensitivity coefficient of the flatness tolerance within the overall workspace: ； in, Sensitivity index for each sampling point, This represents the total number of sampling points. The larger the value, the higher the weight of the manufacturing quality of the guide rail surface in controlling the overall machine precision.
[0039] Step (3): Establish a manufacturing cost evaluation model for component manufacturing tolerances, combine the sensitivity coefficients, calculate the cost sensitivity of each component manufacturing tolerance, and determine the sorting constraints for tolerance allocation based on cost sensitivity. To address the pain points of grinding and scraping processes for large precision guide rail mounting surfaces—namely, the non-linear and rapid increase in machining difficulty as tolerance requirements tighten—the proposed manufacturing cost assessment model employs a reciprocal square model to accurately simulate the cost surge at the limit tolerance. Its cost function is constructed as follows: ; in, Indicates the first Flatness tolerance This is a cost factor that reflects the length of the guide rail and the difficulty of manufacturing.
[0040] Further calculation of cost sensitivity It is defined as the ratio of the sensitivity coefficient to the cost coefficient: ; based on The size of the tolerance allocation determines the sorting constraint: when At that time, it must meet the following conditions. . and These are tolerance subscript indices, representing respectively as well as The constraint ensures that, when allocating tolerances, items that contribute significantly to accuracy but have low cost are assigned stricter tolerances, thus avoiding cost waste caused by blindly pursuing high accuracy.
[0041] Step (4): With the goal of minimizing manufacturing costs, a machine tool accuracy allocation optimization model is constructed by combining the sorting constraints, tolerance domain constraints, and machine tool spatial accuracy constraints based on confidence levels. Based on the above, the final expression for the standard machine tool accuracy allocation optimization model is as follows: Objective function: ; Constraints include: Tolerance domain constraints: ; in, For process limit tolerance, This represents the minimum design requirements.
[0042] Machine tool spatial accuracy constraints based on confidence levels: ; in, The length of the composite vector of spatial position errors. The threshold radius is set to the spatial accuracy constraint requirement, for example, 0.004. A 95% confidence level can ensure a pass rate while avoiding overly conservative design.
[0043] Tolerance sorting constraint based on cost sensitivity: when When, satisfy .
[0044] Step (5): Solve the precision allocation optimization model using an optimization algorithm to obtain the optimal allocation value for each manufacturing tolerance.
[0045] In practical implementation, the model can be solved using heuristic optimization algorithms such as genetic algorithms. Iterative optimization is performed by setting the population size, maximum number of iterations, and crossover / mutation probabilities.
[0046] Ultimately, the optimal allocation values for the flatness tolerances of the six guide rail bottom mounting surfaces were obtained, achieving a balance between precision design indicators and economic design indicators. The allocation results are shown in Table 1. Design engineers can directly use these optimal flatness tolerance values as drawing tolerances to guide the grinding and manual scraping processes of the bottom mounting surfaces of key guide rails in workshop production, thereby achieving synergistic optimization of CNC machine tool precision and cost at the source of manufacturing.
[0047] Table 1. Flatness tolerance distribution results of the bottom mounting surface of each motion axis guide rail
[0048] To verify the feasibility and effectiveness of the method of the present invention in engineering practice, comparative data on the accuracy allocation effect are provided: Validity verification: 1000 five-axis coordinate points were randomly sampled within the machine tool's working area. Values were taken within the optimized flatness tolerance range and substituted into the model to calculate the spatial error. For example... Figure 8 As shown, the results indicate that the design requirements (space error) are met. Under the premise of ), the probability of random coordinate points meeting the accuracy requirements reached 95.07%, verifying the effectiveness of the accuracy design method.
[0049] Generality verification: At the maximum travel coordinate point of the machine tool, 1000 normally distributed samples within the flatness tolerance range were randomly generated. Calculation results show that even at the worst-case maximum travel point, the probability of the samples meeting the accuracy requirements still reaches 96.2%. Figure 9 As shown, this demonstrates the universal applicability of the allocation scheme across the entire machine tool domain.
[0050] To highlight the advantage of this invention in allocating precision based on cost sensitivity, it is compared with a traditional scheme that does not employ precision allocation but uses a uniform conservative tolerance setting, as shown in Table 2. Specifically: Synergistic optimization of accuracy and cost: Before optimization, the total error is satisfied. The probability of achieving the target accuracy is 92.00%, and the total estimated manufacturing cost is 327,778, both of which are dimensionless relative costs. After optimization using the method of this invention, the probability of meeting the accuracy target is increased to 95.07%, while the total estimated manufacturing cost is reduced to 260,958. Under the premise of meeting or even improving the accuracy confidence level, this invention significantly reduces the estimated manufacturing cost of key machine tool components by 20.4%.
[0051] Simulation verification: The machining of the complex S-shaped specimen was simulated using the allocation schemes before and after optimization. The results show that the average position error of the machining points under the allocation scheme before optimization was 0.0024 mm, while the average position error under the optimized scheme was reduced to 0.0022 mm, a reduction of 8.3%. This fully demonstrates that the method of the present invention effectively improves the overall machining accuracy of the machine tool while reducing manufacturing costs.
[0052] Table 2. Comparison of results before and after optimization
[0053] The embodiments described above provide a detailed explanation of the technical solutions and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, additions, and equivalent substitutions made within the scope of the principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for allocating CNC machine tool precision by tracing the source of geometric errors to manufacturing tolerances, characterized in that, Includes the following steps: (1) The manufacturing tolerance of the bottom mounting surface of the linear motion axis guide rail of the CNC machine tool is selected as the precision allocation object. Based on the small deformation assumption and the superposition principle, the manufacturing tolerance is characterized as the guide rail slider height disturbance. The influence coefficient of slider height on the geometric error related to the linear motion axis position is solved by finite element analysis. A slider height-geometric error mapping model is constructed. A three-layer mapping model of component manufacturing tolerance-geometric error-machine tool space error is established based on the homogeneous transformation matrix. (2) Feature trajectory sampling is performed on the overall workspace of the machine tool, and the sensitivity index of the total effect of each manufacturing tolerance on the machine tool space error is calculated using the Sobol global sensitivity analysis method; (3) Establish a manufacturing cost assessment model for component manufacturing tolerances, combine the total effect sensitivity index, calculate the cost sensitivity of each component manufacturing tolerance, and determine the ranking constraints for tolerance allocation based on cost sensitivity. (4) With the goal of minimizing manufacturing costs, the optimal allocation value of each manufacturing tolerance is obtained by combining the tolerance sorting constraint, the tolerance domain constraint, and the machine tool space accuracy constraint based on the confidence level, and the optimal allocation value is then used to grind or scrape the bottom mounting surface of the machine tool linear motion axis guide rail.
2. The CNC machine tool accuracy allocation method tracing from geometric error to manufacturing tolerance according to claim 1, characterized in that, In step (1), the CNC machine tool is a five-axis machine tool, including three linear motion axes X, Y, and Z and two rotary axes; the manufacturing tolerance is the flatness tolerance of the bottom mounting surface of the six guide rails corresponding to the double guide rail structure of the three linear motion axes X, Y, and Z.
3. The CNC machine tool accuracy allocation method tracing from geometric error to manufacturing tolerance according to claim 2, characterized in that, The change in slider height follows a normal distribution within the flatness tolerance zone.
4. The CNC machine tool accuracy allocation method tracing from geometric error to manufacturing tolerance according to claim 1, characterized in that, In step (1), the manufacturing tolerance-geometric error mapping model is based on the following relationship: The height of each slider is relative to the first The linear influence coefficient of the geometric error was obtained through finite element simulation, satisfying the superposition principle: the geometric error under the combined action of multiple sliders is equal to the sum of the individual contributions of each slider; thus, the slider height-geometric error model of the linear motion axis is obtained: ； in, Indicates the first Position-related geometric error term, This represents the influence coefficient matrix, specifically the first... The height of each slider For the The influence coefficient of geometric error.
5. The CNC machine tool accuracy allocation method tracing from geometric error to manufacturing tolerance according to claim 1, characterized in that, In step (1), the expression for the three-layer mapping model is: ； in, No. Flatness tolerance Indicates the first The height of each slider Represents the influence coefficient matrix. This represents the spatial error vector propagated by the homogeneous transformation matrix. The feed coordinates represent the motion axes. Represents various geometric errors; and These are the actual transformation matrix and the ideal transformation matrix, respectively, which contain geometric errors; The coordinates of the knife tip are... The coordinates are the tool axis vector.
6. The CNC machine tool accuracy allocation method tracing from geometric error to manufacturing tolerance according to claim 1, characterized in that, In step (2), the Sobol global sensitivity analysis uses the Monte Carlo sampling method to sample the characteristic trajectories of the entire machine tool workspace, including sampling the volume diagonals of the workspaces of the three linear motion axes and discretely sampling the travel ranges of the two rotary axes, obtaining a number of samples. Given a set of sampling points, calculate the sensitivity index of the total effect of each flatness tolerance: ； in, The total number of sampling points. The sensitivity index of the total effect of the k-th tolerance in the overall workspace; The larger the value, the greater the weight of the manufacturing quality of the guide rail mounting surface on the overall machine precision; The overall effect sensitivity index for each sampling point.
7. The CNC machine tool accuracy allocation method tracing from geometric error to manufacturing tolerance according to claim 1, characterized in that, In step (3), the manufacturing cost assessment model adopts the inverse square model: ； in, Indicates the first Flatness tolerance This is the cost factor, which reflects the length of the guide rail and the difficulty of manufacturing. The total cost is the sum of the costs of each guide rail.
8. The CNC machine tool accuracy allocation method for tracing geometric errors to manufacturing tolerances according to claim 1, characterized in that, In step (4), the constraints of the optimization algorithm specifically include: Tolerance domain constraints: ； in, For process limit tolerance, This is the minimum design requirement; Machine tool spatial accuracy constraint: The length of the machine tool spatial error vector does not exceed a given radius R, and the probability of satisfying this constraint is ≥95% confidence level. ； Tolerance sorting constraint: Sort according to cost sensitivity, ensuring that items with high cost sensitivity are assigned smaller tolerance values: ； in, Indicates cost sensitivity; when At that time, it must meet the following conditions. ; and These are tolerance subscript indices, representing respectively as well as Items in Let k be the sensitivity index of the total effect of the k-th tolerance in the overall workspace. This represents the cost coefficient.