CN107748539B - Five-axis machine tool multi-axis linkage error tracing method based on RTCP error feature recognition - Google Patents

Abstract

The invention discloses a multi-axis linkage error tracing method for five-axis machine tools based on RTCP detection error feature recognition. The RTCP function of five-axis machine tools is used to measure the error presentation form of five-axis machine tools during linkage—tool tip point error, and an error library is constructed. , and then input the error map into the error and machine tool linkage error category mapping library, use feature recognition technology to trace the machine tool linkage error type, and accurately quantify and trace the linkage error value based on trajectory similarity analysis. The advantage of the present invention is that it can not only evaluate the linkage performance of the machine tool, but also can clarify the influencing factors that affect the multi-axis linkage performance of the machine tool when the multi-axis linkage performance of the machine tool does not meet the requirements, so as to give an optimization scheme of the machine tool, and determine the numerical value of the machine tool. The factors affecting the multi-axis linkage performance of the machine tool are adjusted to meet the high precision requirements of the machine tool.

Description

Error traceability method of multi-axis linkage error of five-axis machine tool based on RTCP error feature recognition

technical field

The invention belongs to the technical field of numerical control machine tools, and in particular relates to a method for tracing the source of multi-axis linkage errors of five-axis machine tools based on RTCP error feature recognition.

Background technique

Five-axis CNC machine tools are mainly used in the processing and manufacturing of complex profile parts such as molds and aerospace. With the continuous improvement of the precision and physical performance requirements of parts, higher requirements are put forward for the multi-axis linkage precision of CNC machine tools. The error factors of CNC machine tools can be divided into two categories: static factors and dynamic factors. The static accuracy is detected under the conditions of no cutting load and the machine tool does not move or the moving speed is very low. Due to the improvement of high-end CNC machine tool manufacturing equipment technology , Static accuracy can only reflect the machining accuracy of high-end machine tools on a limited level, and linkage accuracy is one of the main factors affecting the machining accuracy of high-end CNC machine tools.

At present, the commonly used CNC machine tool linkage performance testing instruments mainly include ballbar and R-Test tester. Ballbar can only be used for two-axis or three-axis linkage performance test, while R-Test can detect multiple motion axes of five-axis CNC machine tools. Machine tool linkage performance during linkage. Ballbar and R-Test manufacturers provide five-axis machine tool linkage performance testing instruments and corresponding application software. These equipment and software can be used to detect machine tool linkage errors during multi-axis linkage of five-axis machine tools, but all manufacturers do not provide them. The method of tracing the error factors causing the multi-axis linkage error. In the five-axis machine tool test standard published by the international standard ISO10791-6, only the detection method of the multi-axis linkage performance of five-axis machine tools is provided, and there is no method for the traceability of machine tool linkage errors. Therefore, at present, international standards and testing equipment manufacturers can only provide testing equipment and testing methods for testing the five-axis linkage performance, and can only point out whether there is an error in the machine tool, and when the testing results fail to meet the requirements, they cannot give a specific The scheme of adjusting the machine factor to improve the machining accuracy of the machine tool.

SUMMARY OF THE INVENTION

The purpose of the present invention is to solve the above problems, and propose a method for tracing the source of errors of five-axis machine tool multi-axis linkage based on RTCP error feature recognition with simple steps and effective traceability.

In order to solve the above-mentioned technical problems, the technical scheme of the present invention is: a five-axis machine tool multi-axis linkage error tracing method based on RTCP error feature identification, comprising the following steps:

S1. According to the structure type of the five-axis machine tool, through the influence of the motion axis error on the linkage error of the five-axis machine tool, analyze and determine the linkage error category of the five-axis machine tool, and detect the multi-axis linkage error of the five-axis machine tool under each linkage error category. ;

S2. Establish a mapping relationship database between the multi-axis linkage error and the five-axis machine tool linkage error category. The five-axis machine tool linkage error is reflected by the displacement error of the tool tip point in the three directions of X, Y, and Z, and through the three-dimensional space The error map is displayed, where X represents the horizontal axis of the tool nose point in the space tool coordinate system, Y represents the vertical axis of the tool nose point in the space tool coordinate system, and Z represents the tool nose point in the space tool coordinate system. vertical axis;

S3. Evaluate the linkage performance of the five-axis machine tool according to the magnitude of the error value of the tool nose point detected in step S2 in the three directions of X, Y, and Z;

S4. If the evaluation result of the linkage performance of the machine tool is poor, use the RTCP detection error feature identification method to trace the source to obtain the error category that affects the linkage performance of the five-axis machine tool;

S5. Based on the similarity of the trajectory, analyze the influence factor of the machine tool linkage error obtained in the step S4 of quantification and traceability, and compare the similarity between the detected actual error and the error in the error database. If the similarity is high, the error in the error database is considered as representative The real error value is the multi-axis linkage error of the currently detected machine tool.

Preferably, the evaluation of the linkage performance of the machine tool in the step S4 is performed by means of image feature recognition to evaluate the linkage performance of the machine tool.

Preferably, the step S4 further includes the following steps:

S41. The tool nose point error result graph is normalized to form a new tool nose point error trajectory graph. The normalization formula is:

Among them, Δ is the set of RTCP detection error values, Δ(i) is the ith RTCP detection error, Δ(i) is _normalized to the ith RTCP detection error after normalization, and max(Δ) is the error value The maximum value in the set, min(Δ) is the minimum value in the error value set;

S42. The tool nose point error trajectory normalized in step S41 is regarded as a tool nose point error trajectory map, and each tool nose point error value is regarded as a pixel in the tool nose point error track map point, the boundary of the trajectory in the image is extracted, and it is represented by the horizontal and vertical coordinates; in the XOY plane, the boundary point coordinate points of the tool nose point error trajectory map can be represented by the point set (x(k), y(k)), k is the number of boundary points of the tool nose point error trajectory map; if these coordinate point sets are placed in the complex UV coordinate system, uv is the letter representation of the complex coordinate system, and the abscissa x(k) corresponds to the complex coordinate system. The real axis coordinate axis of , and the ordinate y(k) corresponds to the imaginary axis coordinate axis in the complex coordinate system, then the coordinate point of the XOY plane can be represented by the displacement of the complex number expression (2):

s(k)=x(k)+jy(k) (2)

In formula (2), s(k) represents the complex expression of the coordinate point of the tool nose point error trajectory in the XOY plane, j is a constant, x(k) represents the tool nose point error trajectory in the XOY plane for the X axis The coordinate value of , y(k) represents the coordinate value of the tool nose point error trajectory for the Y axis in the XOY plane; it is assumed that the point set (x(k), y(k)) of the boundary contains N points in total, and set The starting point of the boundary is (x(0), y(0)), and the end point is (x(N-1), y(N-1)), and they are arranged in the counterclockwise direction from the starting point to the end point, then formula (2) The complex expression shown by ) is a periodic function. According to the Fourier transform theory, the discrete Fourier transform of s(k) is shown in the formula:

where S(u) is the Fourier series coefficient, which is the Fourier descriptor, e is a constant, and the periodic function Fourier series expansion has a unique Fourier descriptor, so the Fourier descriptor As the feature of the tool nose point error trajectory;

S43, trace the linkage error category of the five-axis machine tool, extract the Fourier descriptor in the obtained error trajectory map of the tool nose point, and then compare it with the Fourier descriptor in the error trajectory library constructed in step S42, and obtain The error trajectory graph with the smallest difference; the difference between the Fourier descriptor of the error trajectory graph and the error gallery can be evaluated by accumulating the error, and the formula is:

In formula (4), the letter E represents the accumulation of errors, Z _i represents the i-th Fourier descriptor, Z _{i_ gallery} represents the i-th Fourier descriptor of the error trajectory in the gallery, and n is the error trajectory Fourier The number of leaf descriptors.

Preferably, the normalization in the step S41 means that the tool nose point error trajectory is formed by combining the errors in the three directions of X, Y and Z, and the maximum value of the errors in the three directions is normalized to 1, and the minimum value is The value is normalized to -1, and the remaining error values are normalized by dividing their own value by the maximum value.

Preferably, the degree of similarity in the step S5 refers to the degree of similarity between the error sequence of the tool nose point obtained by detecting the machine tool and the error sequence in the error library.

Preferably, the similarity is assuming that the tool nose point error trajectory data obtained by detection is T=(t ₁ , . . . , t _N ), and the error trajectory data of the error type obtained in step S3 is R _p =(r _p1 , ..., _rpM ), the detected trajectory data is numbered with i=1...N, and the standard trajectory data is numbered with j=1...M The dynamic time warp distance between any points on the two error data can be defined as:

In formula (5), min{D(i-1, j-1), D(i, j-1), D(i-1, j)} represents the minimum value among the three dynamic time warp distances shown, For the detection trajectory data T and the standard trajectory data R, an n×m matrix is constructed, and the (i, j)th element in the matrix is the distance d _ij between the two data points T _i and R _j ; the Euclidean distance is used here. to calculate the distance between two points:

The dynamic twist distance between two elements is the cumulative distance, namely D(i, j), which is the minimum cumulative distance from the elements (T ₁ , R ₁ ) to (T _i , R _i ), and the calculation of the minimum cumulative distance The process is as follows: In the dynamic warping algorithm, in order to find the shortest distance between two sequences, a warped path W=w ₁ , w ₂ , ..., w _K needs to be set; the warped path is composed of some elements on a distance matrix Continuity set, this path defines a mapping between the time series and the time series, and the shortest distance between the two sequences can be obtained by comparing along this path; when calculating the distance between the two error data, the above There are many paths for the condition, but the twisted path here requires a minimum twist cost:

In formula (7), the position loop gain parameters of each motion axis obtained by tracing the source in this embodiment are: (Kpp _X , Kpp _Y , Kpp _Z , Kpp _A , Kpp _B )=(1, 0.95, 1, 1, 0.96) , KPP is the position loop gain parameter, DTW(T, R) is the value of DTW, and the right side of the equal sign in formula (7) represents the minimum value in the twisted path.

The beneficial effects of the present invention are as follows: the method for tracing the source of errors of five-axis machine tool multi-axis linkage based on feature identification provided by the present invention can not only evaluate the linkage performance of the machine tool, but also can clarify when the multi-axis linkage performance of the machine tool fails to meet the requirements. The factors affecting the multi-axis linkage performance of the machine tool are given, and the optimization scheme of the machine tool is given, and the factors affecting the multi-axis linkage performance of the machine tool are adjusted from the quantitative value, so as to meet the high-precision requirements of the machine tool.

Description of drawings

Fig. 1 is the scheme flow chart of the five-axis machine tool multi-axis linkage error tracing method based on feature identification of the present invention;

Fig. 2 is the sub-step diagram of step S4 of the present invention;

Fig. 3 is the test schematic diagram of the present invention;

Fig. 4 is the RTCP detection result figure of the present invention;

Fig. 5 is the RTCP detection result diagram when the linkage error of the present invention is 80% error under the situation of Fig. 4;

Fig. 6 is the normalization diagram of detection result of the present invention;

FIG. 7 is a schematic diagram of boundary extraction of detection results according to the present invention.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments:

As shown in Figure 1, the method for tracing the source of errors of five-axis machine tool multi-axis linkage based on RTCP detection feature identification provided by the present invention comprises the following steps:

S1. According to the structure type of the five-axis machine tool, analyze and determine the linkage error category of the machine tool, and use the machine tool RTCP technology to detect the multi-axis linkage error of the five-axis machine tool under each linkage error category;

In this embodiment, a five-axis linkage machine tool whose linkage performance is to be tested is a domestic five-axis CNC milling machine, which is divided into multi-axis linkage error categories according to the influence of the motion axis error on the machine tool linkage error. The combination of gain parameters is representative to represent the multi-axis linkage error of the machine tool. By using the RTCP function of the five-axis machine tool to measure the error presentation form of the five-axis machine tool linkage - the tool nose point error, RTCP is the existing technology, only a special description is made here, and the corresponding function modules are used in the machine tools of different machine tools. . Use the RTCP function to detect the linkage error of the five-axis machine tool, as shown in Figure 3. Please refer to the international standard ISO10791-6 for the detailed test method and test process of the linkage performance of the five-axis machine tool based on RTCP.

S2. Establish a mapping relationship database between RTCP detection errors and five-axis machine tool linkage error categories;

The multi-axis linkage error of the five-axis machine tool based on RTCP in step S1 will be reflected by the displacement of the tool nose point in the three directions of X, Y, and Z, and then the deviation in the three directions can be expressed in the form of a three-dimensional spatial error graph. The multi-axis linkage error of the machine tool is displayed, as shown in Figure 3. The tool nose point error map and the five-axis machine tool linkage error category are mapped to each other, and the error library is constructed.

S3. According to the RTCP detection error, evaluate the linkage performance of the five-axis machine tool;

The linkage performance of the machine tool is evaluated according to the magnitude of the RTCP detection error in the three directions of X, Y and Z.

S4. If the evaluation result of the linkage performance of the machine tool is poor, use the RTCP detection error feature identification method to trace the source to obtain the error category that affects the linkage performance of the five-axis machine tool; measure the linkage error of the machine tool according to the multi-axis linkage error test method of the five-axis CNC machine tool in step S1 , and then the tool nose point error map can be obtained, and the error map can be input into the mapping relationship library between the detection error and the machine tool linkage error category, and the feature recognition technology can be used to trace the machine tool linkage error type. In this embodiment, the image-based feature recognition technology is used to trace the type of linkage error of the machine tool. The measured value of the linkage error of the five-axis machine tool is shown in Figure 4. As shown in Figure 2, the specific implementation of step S4 includes the following steps:

S41, RTCP detection error result map preprocessing;

Different error values of the same error type will only make the size of the error trajectory different, but will not change the shape of the error trajectory. If the linkage error of the machine tool is 80% of the situation in Fig. 4, the tool nose point error diagram is shown in Fig. 5. The shape of the tool nose point error trajectory in each plane is basically the same as that in Fig. 4, except that there is a certain degree of shape and size. degree of reduction. Therefore, the error trajectory needs to be normalized to form a new tool nose point error trajectory.

The tool nose point error trajectory is a combination of errors in the three directions of X, Y and Z. The maximum value of the errors in the three directions is normalized to 1, the minimum value is normalized to -1, and the rest of the error values are based on their own The value of is divided by the maximum value for normalization, and the normalization formula is:

Among them, Δ is the set of RTCP detection error values, Δ(i) is the ith RTCP detection error, and Δ(i) is _normalized to the ith RTCP detection error after normalization.

The normalized graph of the error trajectory of the XOY plane in Fig. 4 is shown in Fig. 6.

S42, extracting the feature of the detection error result map;

The normalized error trajectory is regarded as an image, and each error value is regarded as a pixel point, and the boundary of the trajectory in the image is extracted and represented by the horizontal and vertical coordinates. The complex number description method can be used to reduce the two-dimensional data to One-dimensional function. Assuming that the boundary of the trajectory is surrounded by k coordinates as shown in Figure 7, in the XOY plane, these coordinate points can be represented by a point set (x(k), y(k)). If these coordinate point sets are placed in the complex UV coordinate system, the abscissa x(k) corresponds to the real axis in the complex coordinate system, and the ordinate y(k) corresponds to the imaginary axis in the complex coordinate system. Then the coordinate point of the XOY plane can be represented by the complex expression (2).

s(k)=x(k)+jy(k) (2)

Suppose that the point set (x(k), y(k)) of the boundary contains N points in total, and the starting point of the boundary is (x(0), y(0)) and the end point is (x(N-1), y(N-1)), arranged counterclockwise from the starting point to the ending point, then the complex expression shown in formula (2) is a periodic function. According to the Fourier transform theory, s(k) is calculated. The discrete Fourier transform is shown in Eq.

Among them, S(u) is the Fourier series coefficient, which is the Fourier descriptor. The Fourier series expansion of the periodic function has a unique Fourier descriptor, so the Fourier descriptor is used as the feature of the tool nose point error trajectory.

S43, based on feature identification and traceability of five-axis machine tool linkage error categories;

When the five-axis machine tool RTCP detects and obtains the error trajectory map of the tool nose point error on the three planes, extracts the Fourier descriptor of the error trajectory map, and compares the Fourier descriptors of the three planes with those in step S42. The Fourier descriptors in the constructed error trajectory library are compared, and the error category of the error trajectory with the smallest difference is the detection error category of the five-axis machine tool. The difference between the Fourier descriptor of the error locus graph and the error gallery can be evaluated by accumulating the error, and its formula is:

In the formula, Z _i represents the ith Fourier descriptor, Z _{i_ Gallery} represents the ith Fourier descriptor of the error trajectory in the gallery, and n is the number of Fourier descriptors of the error trajectory.

The error category obtained by tracing the source in this embodiment is: the five-axis linkage error caused by the error of the Y-axis and the B-axis of the motion axis being larger than the errors of the other three axes.

S5. The machine tool factor obtained in step 4 of the quantification traceability analysis of motion trajectory similarity;

After the five-axis machine tool linkage error type is traced through the feature identification traceability method, the accurate value of the error amount still needs to be accurately analyzed, and the error trajectory image recognition obviously cannot realize the accurate error value traceability. The original error data of the tool nose point in the three directions of X, Y, and Z contains more detailed information, which can provide data support for the accurate traceability of errors. The similarity measure can clearly and accurately represent the difference between the two sets of error data. When the similarity value is small, it means that the difference between the two pieces of data is large. If the similarity value is large, the difference between the two series of numbers smaller. If the measured tool nose point error sequence is very similar to the error sequence in the error library, it can be considered that the five-axis machine tool linkage error value represented by the error sequence in the error library is the multi-axis linkage error of the currently inspected machine tool.

Since the data length of each detected error cannot be guaranteed to be consistent, the DTW distance can be used to represent the similarity between error data.

Assuming that the tool nose point error trajectory data obtained by detection is T=(t ₁ ,...,t _N ), the error trajectory data belonging to the error type obtained in step S3 in the standard error library is R _p =(r _p1 ,...,r _pM ), where p represents the pth error trajectory in the standard error library, and there are k error trajectories in total. The data length of the detection track is N, and the length of the error track data in the standard error library is M. In order to make the following description clearer and clearer, the detected track data is numbered with i=1...N, and the standard track data is numbered with j=1...M. The dynamic time warp distance between arbitrary points on these two error data can be defined as:

For the detection track data T and the standard track data R, an n×m matrix is constructed, and the (i, j)th element in the matrix is the distance d _ij between the two data points T _i and R _j . Here the Euclidean distance is used to calculate the distance between two points:

The dynamic twist distance between two elements is the cumulative distance, ie D(i,j) is the minimum cumulative distance from the elements (T ₁ , R ₁ ) to (T _i , R _j ). The calculation process of the distance is as follows: In the dynamic warping algorithm, the one-to-one correspondence between the points on the two sequences is no longer satisfied. In order to find the shortest distance between the two sequences, a warped path W=w ₁ needs to be set, w ₂ , ..., w _K . A twisted path is a continuum consisting of certain elements on a distance matrix. This path defines a mapping between the time series and . Comparing along this path, the shortest distance between the two sequences can be obtained.

Obviously, when calculating the distance between two error data, there are many paths that meet the above conditions, but the twisted path here requires a minimum twist cost.

In formula (7), the position loop gain parameters of each motion axis obtained by tracing the source in this embodiment are:

(Kpp _X , Kpp _Y , Kpp _Z , Kpp _A , Kpp _B )=(1, 0.95, 1, 1, 0.96), KPP is the position loop gain parameter, DTW(T, R) is the value of DTW, formula (7 ) The right side of the middle sign indicates the minimum value in the twisted path, and then the optimal adjustment of the linkage performance of the machine tool can be guided according to the error parameter.

Those of ordinary skill in the art will appreciate that the embodiments described herein are intended to assist readers in understanding the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations without departing from the essence of the present invention according to the technical teaching disclosed in the present invention, and these modifications and combinations still fall within the protection scope of the present invention.

Claims (5)

1. a five-axis machine tool multi-axis linkage error tracing method based on RTCP error feature identification, is characterized in that, comprises the following steps: S1. According to the structure type of the five-axis machine tool, through the influence of the motion axis error on the linkage error of the five-axis machine tool, analyze and determine the linkage error category of the five-axis machine tool, and detect the multi-axis linkage error of the five-axis machine tool under each linkage error category. ; S2. Establish a mapping relationship database between the multi-axis linkage error and the five-axis machine tool linkage error category. The five-axis machine tool linkage error is reflected by the displacement error of the tool tip point in the three directions of X, Y, and Z, and through the three-dimensional space The error map is displayed, where X represents the horizontal axis of the tool nose point in the space tool coordinate system, Y represents the vertical axis of the tool nose point in the space tool coordinate system, and Z represents the tool nose point in the space tool coordinate system. vertical axis; S3. Evaluate the linkage performance of the five-axis machine tool according to the magnitude of the error value of the tool nose point detected in step S2 in the three directions of X, Y, and Z; S4. If the evaluation result of the linkage performance of the machine tool is poor, use the RTCP detection error feature identification method to trace the source to obtain the error category that affects the linkage performance of the five-axis machine tool; S5. Based on the similarity of the trajectory, analyze the influence factor of the machine tool linkage error obtained in the step S4 of quantification and traceability, and compare the similarity between the detected actual error and the error in the error database. If the similarity is high, the error in the error database is considered as representative The real error value is the multi-axis linkage error of the currently detected machine tool; The degree of similarity in the step S5 refers to the degree of similarity between the error sequence of the tool nose point obtained by detecting the machine tool and the error sequence in the error library. 2. the five-axis machine tool multi-axis linkage error traceability method based on RTCP error feature identification according to claim 1, is characterized in that, in described step S4, machine tool linkage performance evaluation is to carry out machine tool linkage by the method for image feature identification performance evaluation. 3. the five-axis machine tool multi-axis linkage error tracing method based on RTCP error feature identification according to claim 2, is characterized in that, described step S4 also comprises the following steps: S41. The tool nose point error result graph is normalized to form a new tool nose point error trajectory graph. The normalization formula is:

Among them, Δ is the set of RTCP detection error values, Δ(i) is the ith RTCP detection error, Δ(i) is _normalized to the ith RTCP detection error after normalization, and max(Δ) is the error value The maximum value in the set, min(Δ) is the minimum value in the error value set; S42. The tool nose point error trajectory normalized in step S41 is regarded as a tool nose point error trajectory map, and each tool nose point error value is regarded as a pixel in the tool nose point error track map point, extract the boundary of the trajectory in the image, and represent it with abscissa and ordinate; in the XOY plane, the boundary point coordinate point of the tool nose point error trajectory map can be represented by a point set (x(k), y(k)), k is the number of boundary points of the tool nose point error trajectory map; if these coordinate point sets are placed in the complex UV coordinate system, uv is the letter representation of the complex coordinate system, and the abscissa x(k) corresponds to the complex coordinate system. The real axis coordinate axis of , and the ordinate y(k) corresponds to the imaginary axis coordinate axis in the complex coordinate system, then the coordinate point of the XOY plane can be represented by the displacement of the complex number expression (2): s(k)=x(k)+jy(k) (2) In formula (2), s(k) represents the complex expression of the coordinate point of the tool nose point error trajectory in the XOY plane, j is a constant, x(k) represents the tool nose point error trajectory in the XOY plane for the X axis The coordinate value of y(k) represents the coordinate value of the tool nose point error trajectory in the XOY plane for the Y-axis; it is assumed that the point set (x(k), y(k)) of the boundary contains N points in total, and set The starting point of the boundary is (x(0), y(0)), and the end point is (x(N-1), y(N-1)), and they are arranged counterclockwise from the starting point to the end point, then formula (2) The complex expression shown by ) is a periodic function. According to the Fourier transform theory, the discrete Fourier transform of s(k) is shown in the formula:

where S(u) is the Fourier series coefficient, which is the Fourier descriptor, e is a constant, and the periodic function Fourier series expansion has a unique Fourier descriptor, so the Fourier descriptor As the feature of the tool nose point error trajectory; S43, trace the linkage error category of the five-axis machine tool, extract the Fourier descriptor in the obtained error trajectory map of the tool nose point, and then compare it with the Fourier descriptor in the error trajectory library constructed in step S42, and obtain The error trajectory graph with the smallest difference; the difference between the Fourier descriptor of the error trajectory graph and the error gallery can be evaluated by accumulating the error, and the formula is:

In formula (4), the letter E represents the accumulation of errors, Z _i represents the i-th Fourier descriptor, Z _{i_ gallery} represents the i-th Fourier descriptor of the error trajectory in the gallery, and n is the error trajectory Fourier The number of leaf descriptors. 4. the five-axis machine tool multi-axis linkage error traceability method based on RTCP error feature identification according to claim 3, is characterized in that, in described step S41, normalization refers to that the tool nose point error track is composed of X, Y and The errors in the three directions of Z are combined. The maximum value of the errors in the three directions is normalized to 1, the minimum value is normalized to -1, and the remaining error values are normalized by dividing their own values by the maximum value. . 5. the five-axis machine tool multi-axis linkage error traceability method based on RTCP error feature identification according to claim 1, is characterized in that, described similarity is assuming to detect and obtain tool tip point error trajectory data as T=(t ₁ ,...,t _N ), the error track data of the error type obtained in step S3 is R _p =(r _p1 ,...,r _pM ), the detected track data is numbered with i=1...N, and the standard track data is with j=1 … The dynamic time warp distance between any point on two error data with number M can be defined as:

In formula (5), min{D(i-1,j-1), D(i,j-1), D(i-1,j)} represents the minimum value among the three dynamic time warp distances shown, For the detection trajectory data T and the standard trajectory data R, an n×m matrix is constructed, and the (i,j)th element in the matrix is the distance d _ij between the two data points T _i and R _j ; the Euclidean distance is used here. to calculate the distance between two points:

The dynamic twist distance between two elements is the cumulative distance D(i,j), which is the minimum cumulative distance from the elements (T ₁ , R ₁ ) to (T _i , R _j ), and the calculation of the minimum cumulative distance The process is as follows: In the dynamic warping algorithm, in order to find the shortest distance between two sequences, a warped path W=w ₁ ,w ₂ ,...,w _K needs to be set; the warped path is some elements on a distance matrix The continuous set formed by this path defines a mapping between the time series and the time series, and the shortest distance between the two series can be obtained by comparing along this path; when calculating the distance between the two error data, There are many paths that satisfy the above conditions, but the twisted path here requires a minimum twist cost:

In formula (7), the position loop gain parameters of each motion axis obtained by tracing the source are: (Kpp _X , Kpp _Y , Kpp _Z , Kpp _A , Kpp _B )=(1,0.95,1,1,0.96), KPP is the position The loop gain parameter, DTW(T, R) is the value of DTW, and the right side of the equal sign in equation (7) represents the minimum value in the twisted path.

Images