A dynamic sensitivity analysis method for error terms of CNC machine tools

Abstract

The present invention is applicable to the field of error term analysis of CNC machine tools, and in particular, relates to a dynamic sensitivity analysis method for error terms of CNC machine tools. Compared with the prior art, the present invention introduces the concept of risk equivalent factor to reflect the influence of the mean and variance of the error terms on the total error size and stability of the CNC machine tools, and is used to identify the key error terms that affect the machining accuracy of CNC machine tools. On this basis, the coupling characteristics between the error terms are analyzed, the influence of the dynamic characteristics of the error terms on the actual machining path of the machine tool is studied, and the sensitivity analysis results are used to compensate for position-related errors and position-independent errors, so as to achieve the purpose of improving the machining accuracy of CNC machine tools, and provide method guidance for the accuracy improvement and optimization design of CNC machine tools.

Description

Technical Field

[0001] The invention is applicable to the field of error term analysis of numerically controlled machine tools, and in particular relates to a dynamic sensitivity analysis method of error terms of numerically controlled machine tools. Background Art

[0002] In the reliability analysis of machine tools, sensitivity analysis is a very important task, and its goal is to find out the weaknesses of the system during design and use. Since it is difficult to focus on specific reliability improvement measures for the global reliability evaluation of CNC machine tools, the importance of machine tool components is ranked by quantifying the reliability loss caused by component failure to the system, which can effectively guide the design improvement measures and equipment maintenance and repair decisions of machine tools.

[0003] Due to its good accuracy diagnosis and prediction characteristics, sensitivity analysis of machine tool errors can be used for machine tool error modeling and analysis, and has gradually become a hot topic in machine tool accuracy reliability analysis. The importance analysis of machine tool accuracy mainly conducts sensitivity analysis on the error terms of machine tools. Its purpose is to identify the error terms that have a greater impact on machine tool errors and provide guidance for the design, error compensation and maintenance of machine tools. Commonly used methods for error term sensitivity analysis include matrix differentiation, Sobel method and Extended Fourier Amplitude Sensitivity Test (EFAST). Among them, the matrix differentiation method is simple to calculate and is suitable for linear systems. The error sensitivity analysis results based on the matrix differentiation method are used for machine tool reliability optimization, but the matrix differentiation method is a local sensitivity analysis method and cannot calculate the coupling relationship between the error sources.

[0004] However, the existing methods do not consider the dynamic characteristics of the error terms of CNC machine tools under actual working conditions. The movement of machine tool joints includes two types of kinematic pairs: translation and rotation. Each kinematic pair will produce three spatial displacement errors and three angular errors. In fact, each error term has certain random dynamic characteristics and conforms to the normal distribution law. The existing sensitivity evaluation method directly simplifies and scales each error term and substitutes it into the evaluation model. The evaluation results are obviously inaccurate, and lack analysis of the specific machine tool motion configuration, so the evaluation results lack pertinence.

[0005] Therefore, a new dynamic sensitivity analysis method for error terms of CNC machine tools is urgently needed to solve the above problems. Summary of the invention

[0006] The present invention provides a dynamic sensitivity analysis method for error terms of CNC machine tools, aiming to solve the problem that the dynamic characteristics of error terms under actual working conditions are not considered in the prior art, resulting in inaccurate evaluation results. The method realizes the dynamic evaluation of machine tool error terms and can be used to improve the machining accuracy of machine tools.

[0007] The dynamic sensitivity analysis method comprises the following steps:

[0008] S1. Acquire multiple error term data of a CNC machine tool; the error term data includes a mean value of the error term and a standard deviation of the error term;

[0009] S2, establishing an error term sensitivity evaluation model, using a plurality of error term data as inputs of the error term sensitivity evaluation model for calculation, and obtaining a process capability index of the total error of the CNC machine tool;

[0010] S3. Calculate the process capability index by a preset method to obtain a risk equivalent factor corresponding to each error term data; wherein the risk equivalent factor is used to determine the degree of influence of the error term on the total error of the CNC machine tool;

[0011] S4. Normalize each of the risk equivalent factors and calculate the corresponding sensitivity value;

[0012] S5. Taking the error term data corresponding to the maximum sensitivity value as key error term data, and performing error compensation on the key error term data.

[0013] Preferably, the process capability index satisfies the following calculation formula:

[0014]

[0015] Wherein, Cpk represents the process capability index, σ represents the standard deviation of the total error of the CNC machine tool, and μ represents the mean value of the total error of the CNC machine tool, and n represents the number of data points of the CNC machine tool; x i represents the processing dimension value of the CNC machine tool; T represents the design tolerance range of the CNC machine tool; M represents the median value of the design tolerance band of the CNC machine tool; i represents the number of the error item data.

[0016] Preferably, the preset method is:

[0017] The error value of a single error item data is reduced, the error values ​​of other error item data are kept unchanged, and the process capability index value of the total error of the CNC machine tool is calculated again to obtain the corresponding risk equivalent factor;

[0018] Repeat step S31 until the risk equivalent factor corresponding to each error term data is obtained.

[0019] Preferably, the error term value of the error term data is reduced to 20% of the original value.

[0020] Preferably, the risk equivalence factor satisfies the following calculation formula:

[0021]

[0022] Wherein, REF represents the risk equivalence factor, μ represents the mean value of the total error of the CNC machine tool after the error value of the single error term data is reduced, σ It represents the standard deviation of the total error of the CNC machine tool after the error value of a single error term data is reduced.

[0023] Preferably, the sensitivity value satisfies the following calculation formula:

[0024]

[0025] Wherein, I(i) represents the sensitivity value corresponding to the i-th error term data, REF(i) represents the risk equivalent factor of the i-th error term data; REF max (i) represents the maximum risk equivalent factor in the error term data; REF min (i) represents the minimum risk equivalent factor in the error term data.

[0026] Compared with the prior art, the present invention introduces the concept of risk equivalent factor to reflect the influence of the mean and variance of error terms on the total error size and stability of CNC machine tools, and is used to identify the key error terms that affect the machining accuracy of CNC machine tools. On this basis, the coupling characteristics between the error terms are analyzed, the influence of the dynamic characteristics of the error terms on the actual machining path of the machine tool is studied, and the position-related errors and position-independent errors are compensated using the sensitivity analysis results to achieve the purpose of improving the machining accuracy of CNC machine tools, and provide method guidance for the accuracy improvement and optimization design of CNC machine tools. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The present invention will be described in detail below in conjunction with the accompanying drawings. The above and other aspects of the present invention will become clearer and easier to understand through the detailed description made in conjunction with the following drawings. In the accompanying drawings:

[0028] Figure 1 is a flow chart of a method for dynamic sensitivity analysis of error terms of a numerical control machine tool provided by an embodiment of the present invention;

[0029] Figure 2 Schematic diagram of parameter meanings of process capability index of the dynamic sensitivity analysis method of error terms of numerical control machine tools provided by an embodiment of the present invention;

[0030] Figure 3 It is a schematic diagram of error term sensitivity evaluation results of risk equivalent factors of a dynamic sensitivity analysis method for error terms of a numerical control machine tool provided by an embodiment of the present invention;

[0031] Figure 4 It is a schematic diagram of the influence of error terms on the tool trajectory of a CNC machine tool provided by an embodiment of the present invention.

[0032] Figure 5 It is a schematic diagram comparing actual machining errors before and after error compensation of the dynamic sensitivity analysis method of error terms of CNC machine tools provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0034] Please refer to Figure 1-Figure 4 The present invention provides a dynamic sensitivity analysis method for error terms of a numerically controlled machine tool, the dynamic sensitivity analysis method comprising the following steps:

[0035] S1. Acquire multiple error term data of a CNC machine tool; the error term data includes a mean value of the error term and a standard deviation of the error term;

[0036] S2, establishing an error term sensitivity evaluation model, using a plurality of error term data as inputs of the error term sensitivity evaluation model for calculation, and obtaining a process capability index of the total error of the CNC machine tool;

[0037] In the embodiment of the present invention, in order to reflect the influence of the random characteristics of the error term on the total error of the CNC machine tool, the present invention proposes the concept of Risk Equivalent Factor (REF). "Risk" means the degree of influence on the size and stability of the total error of the machine tool; "equivalent" means the correlation between the error term and the total error, that is, the propagation law of each error factor of the CNC machine tool. The risk equivalent factor actually measures the dynamic influence of the error term on the total error. The calculation formula is based on the Complex Process Capability Index (Cpk),

[0038] The process capability index satisfies the following calculation formula:

[0039]

[0040] Wherein, Cpk represents the process capability index, σ represents the standard deviation of the total error of the CNC machine tool, and μ represents the mean value of the total error of the CNC machine tool, and n represents the number of data points of the CNC machine tool; xi represents the processing dimension value of the CNC machine tool; T represents the design tolerance range of the CNC machine tool; M represents the median value of the design tolerance band of the CNC machine tool; i represents the number of the error term data. The specific physical meaning of each parameter is as follows Figure 2 As shown, Figure 2 Schematic diagram of the meaning of parameters of the process capability index of the dynamic sensitivity analysis method of the error term of a numerical control machine tool provided by an embodiment of the present invention. In the figure, T = ES-es; M = (ES+es) / 2; ES represents the upper deviation; es represents the lower deviation.

[0041] S3. Calculate the process capability index by a preset method to obtain a risk equivalent factor corresponding to each error term data; wherein the risk equivalent factor is used to determine the degree of influence of the error term on the total error of the CNC machine tool;

[0042] In the embodiment of the present invention, the preset method is:

[0043] The error value of a single error item data is reduced, the error values ​​of other error item data are kept unchanged, and the process capability index value of the total error of the CNC machine tool is calculated again to obtain the corresponding risk equivalent factor;

[0044] Repeat step S31 until the risk equivalent factor corresponding to each error term data is obtained.

[0045] In the embodiment of the present invention, the error term value of the error term data is reduced to 20% of the original value.

[0046] In the embodiment of the present invention, under actual working conditions, the error terms are coupled with each other and cause machining errors together. The error terms are random and cannot be completely eliminated. In order to accurately reflect the influence of single error on the total error, the error term data i is reduced to 20% of the original value in this embodiment, and the mean value of the total error is calculated at this time. μ Difference with standard vertebra σ , REF is the difference between the reciprocals of the two Cpk.

[0047] The risk equivalence factor satisfies the following calculation formula:

[0048]

[0049] Wherein, REF represents the risk equivalence factor, μ represents the mean value of the total error of the CNC machine tool after the error value of the single error term data is reduced, σ It represents the standard deviation of the total error of the CNC machine tool after the error value of a single error term data is reduced.

[0050] On the one hand, REF represents the degree of deviation between the mean of the processing dimensions and the mean of the design dimensions caused by the error term. The more the mean deviates, the greater the REF. On the other hand, while excluding the small probability error value, REF represents the influence of the error term on the degree of dispersion of the total error, that is, the greater the change in the standard deviation, the more discrete the total error pattern, and the greater the REF.

[0051] S4. Normalize each of the risk equivalent factors and calculate the corresponding sensitivity value;

[0052] In the embodiment of the present invention, based on the analysis result of the static error of the machine tool, the mean value of the error term is μ i , standard deviation is σ i , i=1,2,...m represents the error term number. Under the action of the error term, the final comprehensive error of the CNC machine tool is Δx 总 , Δy 总 , Δz 总 , total error ΔE 总 for:

[0053]

[0054] Fit the total error mean μ 总 , standard deviation σ 总 , calculate the Cpk value of the total error caused by all error terms. Then calculate the REF of each error term data, and the sensitivity value satisfies the following calculation formula:

[0055]

[0056] Wherein, I(i) represents the sensitivity value corresponding to the i-th error term data, REF(i) represents the risk equivalent factor of the i-th error term data; REF max (i) represents the maximum risk equivalent factor in the error term data; REF min (i) represents the minimum risk equivalent factor in the error term data.

[0057] S5. Taking the error term data corresponding to the maximum sensitivity value as key error term data, and performing error compensation on the key error term data.

[0058] For ease of understanding, the following terms are explained:

[0059] Error terms: Factors that produce errors.

[0060] Error sensitive direction: The direction that has the greatest impact on machining accuracy (i.e., the normal plane of the machining surface passing through the blade) is called the error sensitive direction.

[0061] Dynamic sensitivity: Dynamic sensitivity is an important performance indicator in control systems, which is used to describe the speed and degree of response of the system to changes in input signals.

[0062] Sensitivity evaluation: Sensitivity analysis is an analysis method that allows the model's variables to vary within a certain range to observe the model's behavior or changes. There is always a risk in the evaluation of the model; sensitivity evaluation helps to reduce this risk.

[0063] Four-station time-sharing detection: four base stations are used to perform spatial calibration and measurement on the measuring points of the machine tool at different times.

[0064] Specifically, this embodiment takes the XK200A CNC machine tool as an example to demonstrate the specific implementation steps of the method. The machine tool is composed of moving axes X, Y, Z and a rotating axis A.

[0065] Assume the workpiece coordinate system as the basic coordinate system, establish a serial motion chain from the workpiece to the tool, set the origin of the A-axis coordinate system at the end face of the chuck, and establish the X-axis, Y-axis, bed, Z-axis, spindle and tool coordinate systems according to the improved DH parameter method (Denavit-Hartenberg, DH). The structural parameters of each component of the machine tool are shown in Table 1. The component code indicates the identification of the machine tool component.

[0066] Table 1: Dimensional parameters of components of XK200A CNC machine tool

[0067]

[0068] The XK200A CNC machine tool includes X, Y, Z and A axes, and the errors are shown in Table 2. The symbols mean: workpiece (W), tool (T); d w d A , l A 、a x d y , l y d B , l B d z d S is the size parameter of the machine tool; ε and δ are the angular error and directional error in each direction. The corresponding relationship between the angular error and directional error of each axis of the machine tool and the DH error term data is analyzed. The connecting rod joints that make up the processing chain are: workpiece (W), A axis, X axis, Y axis, bed (B), Z axis and tool (T). Among them, x, y, z, and αα are the theoretical feed amounts of each joint.

[0069] Table 2: XK200A CNC machine tool error table

[0070]

[0071]

[0072] A FARO laser tracker is used, and its standard angle measurement accuracy at 2m is 0.015mm. A four-station time-sharing detection method is used to measure the geometric error of the machine tool. It is assumed that the position of the measurement point in each detection space is the same. The multi-station time-sharing measurement method based on the laser tracker is mainly divided into two steps: base point space position self-calibration and measurement point space coordinate calibration. First, the distance data of a large number of measurement points are measured at each base station position, and the base point position coordinates of different base stations of the laser tracker are calibrated. Then, based on the base point position coordinates, the measurement point space coordinates are calibrated to obtain the actual spatial position coordinates of the measurement point.

[0073] The transmission axes of the XK200A CNC machine tool are made to move according to a certain rule. Based on the error measurement results, the mean and variance of each error term are substituted into the error term sensitivity evaluation model of the machine tool to calculate the mean and standard deviation of the total error. The mean of the error term is reduced by 80% respectively, and the REF value of each error term is calculated, as shown in Table 3. According to the corresponding relationship between the error terms of each axis and the error term data of the improved DH, the sensitivity of each error term data in the X, Y, Z directions and the total error is as follows Figure 3 As shown, Figure 3 It is a schematic diagram of the error term sensitivity evaluation result of the risk equivalent factor of the dynamic sensitivity analysis method of the error term of a CNC machine tool provided in an embodiment of the present invention.

[0074] Table 3: Risk Equivalence Factors for Each Error Term

[0075]

[0076]

[0077] The top six error terms that have the greatest impact on X-axis machining errors are: Δθ A , Δd B , Δa y , Δa x , Δβ x , Δl A ;

[0078] The top six error terms that have the greatest impact on Y-axis machining errors are: Δα A , Δβ A , Δa A , Δa y , Δd B , Δθ B ;

[0079] The top six error items that have the greatest impact on Z-axis machining errors are: Δα x , Δa B , Δθ A, Δβ z , Δα y , Δd z ;

[0080] The first six error terms with the greatest impact on the total error are: Δθ x , Δd z , Δα y , Δβ z , Δl y , Δd x ;

[0081] The geometric error measurement value of the machine tool is used as input. By reducing the proportion of each error term and calculating the influence of each error term on the total error, the influence of the order of magnitude difference between the error terms is avoided, making the analyzed key error terms more targeted. In addition, the risk equivalent factor is used to evaluate the influence of the error term on the total error, which not only takes into account the random characteristics of the actual error term of the machine tool, but also includes the coupling influence between the error terms. The sensitivity analysis results are more practical and clarify the direction for improving the actual machining accuracy of the machine tool.

[0082] The coupling of error terms refers to the interaction between the error terms. In order to study the influence of the coupling of error terms on the machining accuracy of machine tools, the machining trajectory of a cylindrical groove part is taken as an example to simulate the machining error caused by the coupling of error terms.

[0083] Substitute some finishing codes of the part into the kinematic model of the machine tool to draw the ideal spatial trajectory of the tool tip; substitute the measured value of the error term into the error model to draw the actual spatial trajectory of the tool tip. Figure 4 .in Figure 4 (a) represents the actual and ideal trajectory lines of the tool when the error term data is substituted as a constant value; Figure 4 (b) is to substitute only Δθ x , Δd z , Δα y , Δβ z , Δ y , Δd x The key error item data is the actual and ideal trajectory of the tool. By comparison, it can be seen that Figure 4 (b) The deviation is slightly smaller than that in 10(a), and the key error term has a greater impact on the total error; the error term Δd y The sensitivity ranks low, which means the positioning error during Y-axis movement. The tool trajectory line containing only this error term is as follows Figure 4 (c) The actual trajectory is not much different from the theoretical trajectory line, and the other error items ranked later are also in a similar situation, indicating that non-critical error items have little impact on the total error; Figure 4(d) shows that when there are only 6 key error terms and each error term is a random number that conforms to a certain distribution law, the actual trajectory of the tool is a discrete point cloud band in space, and the comprehensive error distribution is (4.9047, 2.7245 2 ).

[0084] When Δθ x The mean value is reduced to 70%, and the comprehensive error is (4.9026, 2.3193 2 ); when Δθ x The mean value is reduced to 30%, and the comprehensive error is (4.8488, 2.5985 2 ); when Δθ x The mean value is reduced to 5%, and the comprehensive error is (4.5541, 2.1699 2 ). When Δθ x With Δβ z The mean value is reduced to 5%, and the comprehensive error is (4.7649, 2.2496 2 ); when Δθ x With Δα y The mean value is reduced to 5%, and the comprehensive error is (4.8354, 2.5017 2 ); when Δθ x , Δβ z With Δα y The mean value is reduced to 5%, and the comprehensive error is (5.0009, 2.4944 2 ). It can be concluded that reducing the mean of a single error term can reduce the mean of the total error. However, if the mean of multiple error terms is reduced at the same time, the mean of the trajectory error may increase, indicating that when the mean of multiple error terms is reduced at the same time, the error of the machining trajectory is irregular. This phenomenon also shows that it is related to the direction of the error term. When all error terms act on the same axis and in the same direction, the error terms can be optimized at the same time to improve the machine tool accuracy; when all error terms act on the same axis but in opposite directions, the superposition of two error terms will reduce the total error; when each error term acts in different axis directions, it is necessary to analyze it specifically according to the kinematic model of the machine tool. Therefore, the coupling effect between error terms is both complicated and cannot be ignored. The total error of the machine tool is jointly affected by the size and direction of each error term. The machine tool accuracy optimization can only determine the optimization variables by specifically analyzing the configuration of the machine tool.

[0085] Since the key error terms play a decisive role in the total error of the machine tool, error compensation can be performed on the key error terms to improve the machining accuracy of the machine tool. According to the relationship between the error terms and the machine tool feed rate, the key error terms of the machine tool are divided into two categories: position-related and position-independent. Different compensation measures are used for the two types of key error terms.

[0086] The error compensation process first determines the driving value range of the machine tool by the processing code, and calculates the value of the key error item; secondly, calculates the total error of the machine tool caused by the key error items related to the position, and performs error decoupling, calculates the compensation amount of each axis, and corrects the driving value of the CNC code; thirdly, for the error items that are not related to the position, the main one in the present invention is the thermal-mechanical coupling error of the electric spindle, and the axial deformation of the end face of the shaft core affects the Z-direction position of the tool tip, which is corrected here by setting the tool length compensation parameters.

[0087] According to the error compensation process, the error compensation values ​​of the X, Y, Z and A axes are used to correct the NC program, and the test piece is processed again using the corrected NC code and machine tool compensation parameters. In order to avoid the influence of machine tool vibration error, tool wear and other errors, the processing parameters of spindle speed 2000rpm, feed speed F300mm / min, cutting depth 0.2mm are still used, and new tools of the same model are replaced. Other processing conditions such as ambient temperature remain stable. The machining errors of the two parts are tested, and the actual comprehensive machining errors after compensation are obtained as follows Figure 5 As shown in the figure. The comprehensive error after compensation is better than that before compensation, and the average processing error of each measuring point is reduced by 52%. It can be seen that only compensating for the key error terms can greatly reduce the processing error of the machine tool. Considering the influence of other non-critical error terms and other error sources, the experimental results prove the correctness of the invention in calculating the multivariate error coupling of the machine tool and analyzing the sensitivity of the error terms.

[0088] Compared with the prior art, the present invention introduces the concept of risk equivalent factor to reflect the influence of the mean and variance of error terms on the total error size and stability of CNC machine tools, which is used to identify the key error terms that affect the machining accuracy of CNC machine tools. On this basis, the coupling characteristics between the error terms are analyzed, the influence of the dynamic characteristics of the error terms on the actual machining path of the machine tool is studied, and the position-related errors and position-independent errors are compensated using the sensitivity analysis results to achieve the purpose of improving the machining accuracy of CNC machine tools, and provide method guidance for the precision improvement and optimization design of CNC machine tools. The dynamic sensitivity evaluation method of error terms is adopted to effectively solve the problem that the dynamic characteristics of error terms are not considered in the prior art, resulting in inaccurate evaluation results, and thus realize the identification of key error terms of machine tools. Substituting the mean and variance values ​​of the error terms into the sensitivity evaluation of the error terms can be used to analyze the machining trajectory error of the machine tool under the action of the error terms. According to the sensitivity analysis results, the error terms with high sensitivity are selected for error compensation, and the method of compensating the position-related errors and the position-independent errors respectively can achieve the improvement of the machining accuracy of the machine tool.

[0089] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0090] The embodiments of the present invention are described above in conjunction with the accompanying drawings. What is disclosed is only the preferred embodiment of the present invention. However, the present invention is not limited to the above-mentioned specific implementation manner. The above-mentioned specific implementation manner is only illustrative rather than restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms and equivalent changes without departing from the scope of protection of the purpose of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A dynamic sensitivity analysis method for error terms of CNC machine tools, characterized in that: The dynamic sensitivity analysis method comprises the following steps: S1. Acquire multiple error term data of a CNC machine tool; the error term data includes a mean value of the error term and a standard deviation of the error term; S2, establishing an error term sensitivity evaluation model, using a plurality of error term data as inputs of the error term sensitivity evaluation model for calculation, and obtaining a process capability index of the total error of the CNC machine tool; S3. Calculate the process capability index by a preset method to obtain a risk equivalent factor corresponding to each error term data; wherein the risk equivalent factor is used to determine the degree of influence of the error term on the total error of the CNC machine tool; S4. Normalize each of the risk equivalent factors and calculate the corresponding sensitivity value; S5, taking the error term data corresponding to the maximum sensitivity value as key error term data, and performing error compensation on the key error term data; The process capability index satisfies the following calculation formula: ； in, represents the process capability index, represents the standard deviation of the total error of the CNC machine tool, and ; represents the mean value of the total error of the CNC machine tool, and ; n Represents the number of data points of the CNC machine tool; Indicates the processing dimension value of the CNC machine tool; Indicates the design tolerance range of the CNC machine tool; represents the median value of the design tolerance band of the CNC machine tool; i The number representing the error term data.

2. The dynamic sensitivity analysis method of the error term of a CNC machine tool according to claim 1, characterized in that: The preset method is: S31, reducing the error value of a single error item data, keeping the error values ​​of other error item data unchanged, and recalculating the process capability index value of the total error of the CNC machine tool to obtain the corresponding risk equivalent factor; S32, repeating step S31 until the risk equivalent factor corresponding to each error term data is obtained.

3. The dynamic sensitivity analysis method of the error term of a CNC machine tool according to claim 2, characterized in that: The error term value of the error term data is reduced to 20% of the original value.

4. The dynamic sensitivity analysis method of the error term of a numerically controlled machine tool according to claim 2, characterized in that: The risk equivalence factor satisfies the following calculation formula: ； in, REF represents the risk equivalence factor, represents the mean value of the total error of the CNC machine tool after the error value of the single error term data is reduced, It represents the standard deviation of the total error of the CNC machine tool after the error value of a single error term data is reduced.

5. The dynamic sensitivity analysis method of the error term of a numerically controlled machine tool according to claim 4, characterized in that: The sensitivity value satisfies the following calculation formula: ； in, represents the sensitivity value corresponding to the i-th error term data, represents the risk equivalent factor of the i-th error term data; represents the maximum risk equivalent factor in the error term data; Represents the minimum risk equivalent factor in the error term data.

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