High-precision calibration method for measuring control field in long-term assembly process

Abstract

A high-precision calibration method for the measurement control field during long-term assembly is proposed. First, laser network measurement is used to obtain the measured coordinates of ERS points and the coordinates of additional orientation points before and after movement within the measurement control field. Then, using the current measured coordinates and the original nominal coordinates of a series of ERS points with the same name in the measurement control field, the transformation parameters between the nominal and measured references are calculated. The coordinates of the ERS points and orientation points under the measured reference are aligned to the nominal reference, and the coordinate deviation of each ERS point under the nominal reference is obtained. A coordinate deviation threshold is set according to requirements, and an orientation constraint based on the additional orientation points is constructed. A solution model that minimizes the fitting residual of the ERS points is introduced to form a new transformation parameter optimization solution model with the objective of minimizing the sum of the coordinate deviations of each ERS point. Based on the transformation parameters, the ERS points within the measurement control field are re-assigned globally to achieve calibration of the measurement control field accuracy during long-term assembly, ensuring high-precision transmission of quality data.

Description

Technical Field

[0001] This invention belongs to the field of digital measurement and assembly, and relates to a high-precision calibration method for the measurement control field during long-time domain assembly. Background Technology

[0002] Digital measurement-driven in-situ assembly has become one of the mainstream manufacturing modes for large aerospace components. Accurate measurement and transmission of geometric information are crucial for achieving high-precision assembly of aerospace equipment. Currently, observation data from various local measurement systems and attitude data from tooling / equipment in the aerospace assembly site must be transmitted to the global coordinate system via a "measurement control field." Therefore, the accuracy and reliability of the measurement control field directly affect the final product's assembly quality. However, the ground in aerospace assembly workshops is prone to non-uniform deformation due to loads and natural foundation settlement, leading to inaccuracies in the original calibration information within the measurement control field. Furthermore, temperature differences during long-term, cross-seasonal assembly cause thermal expansion and contraction of the ground. These factors collectively cause significant offsets in the control points (also known as ERS points) within the measurement control field, resulting in the invalidation of their carried global information (nominal coordinate values). If the nominal coordinates of the control points within the measurement control field are still used to solve for the transformation parameters between different references, it easily leads to large reference unification errors, causing a severe decrease in the accuracy of quality data transmission during assembly. In summary, to address the issue of poor accuracy and reliability of the measurement and control field due to factors such as foundation settlement, temperature difference, and large load, a high-precision calibration method for the measurement and control field during long-time-domain assembly is proposed to ensure the accurate transmission of quality data during the assembly process.

[0003] The patent "A Method for Calibration of Aircraft Assembly Measurement Field Points" (patent number CN20201 0353894.8) discloses a method for calibrating aircraft assembly measurement field points. This method first collects data of orientation points and measurement stations using a laser tracker, and then calculates the coordinates of ERS points and TB points using an adjustment algorithm based on the coordinates of the orientation points and measurement stations, and transforms them into the coordinate system of the measurement field to complete the point calibration. The patent "An Automatic Calibration Method, Device, Equipment and Medium for a Measurement Reference Network" (patent number CN202410080738.7) obtains the coordinates of ERS points, uses a station-switching comprehensive error evaluation method to switch the laser tracker between stations, and applies unified spatial measurement network technology to transform the ERS point coordinate values ​​into the theoretical coordinates of ERS points within the measurement reference network.

[0004] The aforementioned methods only consider re-measuring the coordinates of ERS and TB points at the assembly site to complete the point calibration in the assembly measurement field. They do not correct the ERS point coordinates under the nominal datum, resulting in inaccurate conversion parameters between the nominal and measured datums, and a significant datum unification error. Therefore, this invention proposes a high-precision calibration method for the measurement control field at a long-term aerospace assembly site. When obtaining the measured coordinates of the ERS points in the measurement control field, a movable directional point is added to form an directional constraint. Then, the nominal coordinates of the ERS points are iteratively corrected, and the rotation matrix and translation vector between the nominal and measured datums are solved. Finally, all ERS points within the measurement control field are globally reassigned, achieving calibration of the measurement control field accuracy during long-term aerospace assembly, reducing datum unification errors caused by factors such as foundation settlement, and ensuring high-precision transmission of quality data during assembly. This method has good versatility and broad application prospects. Summary of the Invention

[0005] The purpose of this invention is to provide a high-precision calibration method for measuring the control field during long-time-domain assembly.

[0006] The technical solution of the present invention:

[0007] A high-precision calibration method for the measurement control field during long-term assembly is proposed. First, during the long-term assembly of components, laser network measurement is used periodically to obtain the measured coordinates of ERS points in the measurement control field, as well as the measured coordinates of additional orientation points before and after movement. Second, based on the least squares principle, the transformation parameters between the nominal and measured references of the measurement control field are calculated using the current measured coordinates and the original nominal coordinates of a series of ERS points with the same name in the measurement control field. The coordinates of ERS points and orientation points under the measured reference are then aligned to the nominal reference to obtain the coordinate deviation of each ERS point under the nominal reference. Next, considering the actual process requirements of the assembly site, a coordinate deviation threshold is set, and an orientation strength constraint based on the additional orientation points is constructed. This constraint is then introduced into a solution model that minimizes the fitting residual of the ERS points, thus forming a new transformation parameter optimization solution model with the objective of minimizing the sum of the coordinate deviations of each ERS point. This improves the accuracy of the transformation parameter solution between the nominal and measured references of the measurement control field. Finally, based on the above transformation parameters, all ERS points in the measurement control field are re-assigned globally to achieve calibration of the measurement control field accuracy during long-term assembly, thereby ensuring high-precision transmission of quality data. The specific steps are as follows:

[0008] The first step is to measure the network of ERS points and orientation points within the control field.

[0009] First, movable orientation points are added within the measurement control field at the assembly site, and multiple laser trackers are placed within the measurement control field. Through laser network measurement, the measured coordinate values ​​of the ERS points within the measurement control field are obtained, and a dataset Data is constructed, as shown in Equation (1):

[0010]

[0011] In the formula, Let i be the measured coordinates of the i-th ERS point under the measured reference, i = 1, 2, ..., m, where m is the number of ERS points in the measurement control field;

[0012] Secondly, obtain the initial measured coordinates q of the additional movable orientation point. l =(x l ,y l ,z l Then, control the additional movable orientation point to perform orientation movement, and obtain the measured coordinates of the additional orientation point.

[0013] The second step is to solve for the coordinate deviation of the ERS points under the nominal reference of the measurement control field.

[0014] set up Given the nominal coordinates of the i-th ERS point under the nominal datum, based on the least squares principle, we can obtain the nominal coordinates of the ERS point in the nominal datum and the measured datum. Measured coordinates Solve for the transformation parameters between the two references, namely the rotation matrix and the translation vector, as shown in equation (2);

[0015]

[0016] In the formula, R L-G and T L-G The rotation matrix and translation vector for transforming the measured datum to the nominal datum are shown in (3);

[0017]

[0018] In the formula, γ, β, and α represent the rotation angles between the measured datum and the nominal datum, respectively; c and s represent the cosine function and the sine function, respectively.

[0019] Measured coordinates of ERS points in the measured datum Additional movable orientation point starting point measured coordinates q l And additional movable orientation point endpoint measured coordinates Transform to the nominal reference, as shown in equation (4);

[0020]

[0021] In the formula, Let be the measured coordinates of the i-th ERS point in the measurement control field under the nominal reference. The starting coordinates of the movable orientation point are added under the nominal reference. The endpoint coordinates of the movable orientation point are added under the nominal datum; then, the deviation between the measured coordinates and the nominal coordinates of the series of corresponding ERS points under the nominal datum and the displacement before and after the orientation point is moved are calculated by Equation (5);

[0022]

[0023] In the formula, δ i,0 Let δ be the deviation between the measured coordinates and the nominal coordinates of the i-th ERS point under the nominal datum. i,0 =(Δx) i,0 ,Δy i,0 Δz i,0 ) T ε is the displacement of the orientation point before and after moving under the nominal reference.

[0024] The third step is to solve for the transformation parameters based on the iterative correction of the nominal coordinates.

[0025] First, an optimization model for transformation parameters is established with the objective of minimizing the sum of the coordinate deviations of each ERS point. The parameters to be optimized are the nominal coordinate deviation values ​​of the ERS points and the transformation parameters between the nominal reference and the measured reference. Considering the actual measurement process requirements during assembly and to avoid overfitting, a threshold for the nominal coordinate deviation of the ERS points is set. The objective function is shown in Equation (6), and the constraint function is shown in Equation (7).

[0026]

[0027] In the formula, δ is the conversion parameter between the nominal datum and the measured datum. i,j Let δ be the nominal coordinate deviation value obtained by the j-th optimization of the i-th ERS point, where j = 1, 2, ..., n, n is the optimization number, and δ is the value of the deviation. i,j =(Δx) i,j ,Δy i,j Δz i,j ) T a and b are the upper and lower limits of the coordinate deviation threshold, respectively; the nominal coordinates of the ERS point under the nominal datum. Measured coordinates The coordinate deviation ε of the orientation points is calculated using a particle swarm optimization algorithm, with the fitness function as the objective function. The algorithm iteratively corrects the nominal coordinate values ​​of the ERS points, ultimately obtaining the transformation parameters between the nominal and measured reference datums. And the nominal coordinate deviation value δ of the ERS point obtained from the nth optimization.i,n As shown in equation (8);

[0028] δ=[δ 1,n ,δ 2,n ,...,δ i,n ,...,δ m,n (8)

[0029] Secondly, based on the transformation parameters, a laser tracker is used to obtain the measured coordinates of a new set of ERS points within the measurement control field. Where i = 1, 2, ..., k, k is the number of ERS points in the measurement control field obtained this time. Then, based on the transformation parameters obtained by the optimization function, the measured coordinates of the ERS points under the measured datum are transformed to the nominal datum, as shown in (9).

[0030]

[0031] In the formula, That is, the coordinate value of the i-th ERS point transformed to the nominal datum based on the above transformation parameters;

[0032] Then, the measured coordinates of this set of ERS points transformed to the nominal datum are calculated. Nominal coordinates under nominal datum Coordinate deviation E i As shown in (10);

[0033]

[0034] The beneficial effects of this invention are as follows: This invention can accurately calculate the coordinate deviation of ERS points under the nominal reference in the measurement control field, thereby establishing a transformation parameter optimization solution model with the objective of minimizing the sum of coordinate deviations of all ERS points. This improves the accuracy of the transformation parameter solution between the nominal reference and the measured reference in the measurement control field. Finally, based on the above transformation parameters, all ERS points in the measurement control field are re-assigned globally, realizing the calibration of the measurement control field accuracy during long-term assembly and ensuring high-precision transmission of quality data. This method not only has good practicality and versatility, but also good robustness and high accuracy, and has broad application prospects. Attached Figure Description

[0035] Figure 1 This is a flowchart of a high-precision calibration method for measuring the control field during long-time-domain assembly.

[0036] Figure 2 This is a schematic diagram of the measurement and control field during the assembly of the aircraft nose and forward fuselage.

[0037] In the diagram: 1-Aircraft nose; 2-Aircraft forward fuselage; 3-Movable additional orientation point; ERS1~ERS 14For the obtained ERS points; S1 - laser tracker station 1; S2 - laser tracker station 2; S3 - laser tracker station 3. Detailed Implementation

[0038] The specific embodiments of the present invention are described in detail below with reference to the technical solutions and accompanying drawings.

[0039] In this embodiment, the maximum permissible measurement error of the laser tracker is MPE = ±(15μm+6μm / m); 15 1.5-inch laser tracker target balls are selected as the measurement control field ERS points and additional orientation points; the measurement experiment is carried out within a range of 9m×6m.

[0040] like Figure 1 As shown, this method first acquires the measured coordinates of ERS points named p1 to p7 and the additional orientation points before and after movement at the aerospace component assembly site using a laser tracker. Second, based on the least squares principle, the transformation parameters between the measured and nominal datums are calculated using the measured and nominal coordinates of a series of ERS points with the same names in the measurement control field, aligning all the measured coordinates of the ERS points and the orientation points to the nominal datum. Then, a transformation parameter optimization model is established with the objective of minimizing the sum of the coordinate deviations of all ERS points, iteratively correcting the nominal coordinates of the ERS points to improve the accuracy of the transformation parameter calculation between the nominal and measured datums of the measurement control field. Finally, based on the above transformation parameters, all ERS points in the measurement control field are re-assigned globally to achieve calibration of the measurement control field accuracy during long-term assembly. The specific steps of this method are as follows:

[0041] The first step is to measure the network of ERS points and orientation points within the control field.

[0042] like Figure 2 As shown, firstly, additional orientation points are set up within a 9m × 6m measurement control field, and multiple laser trackers are placed simultaneously. The initial measured coordinates q of the additional orientation points are obtained through laser network measurement. l Then change the orientation point position and obtain the measured coordinates of the orientation point again. Simultaneously, the measured coordinate values ​​of the ERS points within the measurement control field are obtained. As shown in Table 1;

[0043] Table 1 ERS point coordinate dataset

[0044]

[0045] The second step is to solve for the coordinate deviation of the ERS points under the nominal datum.

[0046] Based on the least squares principle, the nominal and measured coordinate values ​​of the ERS in the measurement control field are substituted into equation (2) to obtain the rotation matrix R of the transfer station parameters.L-G Translation vector T L-G ;

[0047]

[0048] Then, based on formula (4), the coordinate values ​​of all ERS points and the coordinates of the orientation points measured under the actual datum are transformed to the nominal datum by the rotation matrix and translation vector. At the same time, the coordinate deviation of each ERS point after datum unification is calculated according to formula (5), as shown in Table 2.

[0049] Table 2. ERS point coordinate deviation

[0050]

[0051]

[0052] The coordinate displacements of the additional orientation point before and after movement under the nominal datum are calculated, as shown in Table 3:

[0053] Table 3. Coordinate Deviation of Orientation Points

[0054]

[0055] The third step is to solve for the transformation parameters based on the iterative correction of the nominal coordinates.

[0056] Then, taking the minimum sum of the fitting residuals between the measured and nominal coordinates of the ERS points within the measurement control field as the objective function, and based on the particle swarm optimization algorithm, using the objective function as the fitness function, parameters such as the maximum number of iterations, coordinate deviation range, and particle number are set. This is achieved by using the nominal coordinates of the ERS points under the nominal reference. Measured coordinates The nominal coordinate deviation of the ERS point is obtained by optimizing the data such as the coordinate displacement ε of the orientation point, as shown in Table 4.

[0057] Table 4. Optimized nominal coordinate deviation of ERS points

[0058]

[0059] As shown in Tables 2 and 4, the average registration error in the X direction before correction was 0.232 mm, and the average registration error after correction was 0.004 mm; the average registration error in the Y direction before correction was 0.347 mm, and the average registration error after correction was 0.006 mm; the average registration error in the Z direction before correction was 0.247 mm, and the average registration error after correction was 0.006 mm.

[0060] Based on the nominal coordinates corrected by iteration, the rotation matrix between the nominal datum and the measured datum is solved. and translation vector

[0061]

[0062] Using a laser tracker, the point in the measurement control field named p8–p is obtained. 14 The measured coordinates of the ERS points are obtained. Based on the rotation matrix and translation vector calculated in the above steps, the global values ​​of the ERS points are reassigned, and the coordinate deviation between the measured coordinates and the nominal coordinates under the nominal datum is calculated, as shown in Table 5.

[0063] Table 5. Coordinate deviation of ERS points in the second group.

[0064]

[0065] This invention discloses a high-precision calibration method for the measurement control field during long-term assembly. First, during the long-term assembly of components, laser network measurement is used periodically to obtain the measured coordinates of ERS points in the measurement control field, as well as the measured coordinates of additional orientation points before and after movement. Second, based on the least squares principle, the transformation parameters between the nominal and measured references of the measurement control field are calculated using the current measured coordinates and the original nominal coordinates of a series of ERS points with the same name in the measurement control field. The coordinates of ERS points and orientation points under the measured reference are then aligned to the nominal reference to obtain the coordinate deviation of each ERS point under the nominal reference. Next, considering the actual process requirements of the assembly site, a coordinate deviation threshold is set, and an orientation strength constraint based on the additional orientation points is constructed. This constraint is then introduced into a solution model that minimizes the fitting residual of the ERS points, thus forming a new transformation parameter optimization solution model with the objective of minimizing the sum of the coordinate deviations of each ERS point. This improves the accuracy of the transformation parameter solution between the nominal and measured references of the measurement control field. Finally, based on the above transformation parameters, all ERS points in the measurement control field are re-assigned globally to achieve calibration of the measurement control field accuracy during long-term assembly, thereby ensuring high-precision transmission of quality data.

Claims

1. A high-precision calibration method for measuring control fields during long-time-domain assembly, characterized in that, The specific steps are as follows: The first step is to measure the network of ERS points and orientation points within the control field. First, movable orientation points are added within the measurement control field at the assembly site, and multiple laser trackers are placed within the measurement control field. Through laser network measurement, the measured coordinate values ​​of the ERS points within the measurement control field are obtained, and a dataset is constructed. Data As shown in equation (1): (1) In the formula, For the measured benchmark, the first i Measured coordinates of ERS points , m To measure the number of ERS points within the control field; Secondly, obtain the initial measured coordinates of the additional movable orientation point. Then, control the additional movable orientation point to perform orientation movement, and obtain the measured coordinates of the endpoint of the additional movable orientation point. ; The second step is to solve for the coordinate deviation of the ERS points under the nominal reference of the measurement control field. set up For the first known nominal reference i The nominal coordinates of each ERS point are determined based on the least squares principle, using the nominal coordinates of the ERS points in the nominal datum and the measured datum. Measured coordinates Solve for the transformation parameters between the nominal datum and the measured datum, namely the rotation matrix and translation vector, as shown in equation (2); (2) In the formula, and The rotation matrix and translation vector for transforming the measured datum to the nominal datum are shown in (3); (3) In the formula, , , represent the rotation angle between the measured datum and the nominal datum, respectively; c and s represent the cosine function and the sine function, respectively; Measured coordinates of ERS points in the measured datum Additional movable orientation point initial measured coordinates And additional movable orientation point endpoint measured coordinates Transform to the nominal reference, as shown in equation (4); (4) In the formula, Measurement control field under nominal reference i Measured coordinates of ERS points The starting coordinates of the movable orientation point are added under the nominal reference. The endpoint coordinates of the movable orientation point are added under the nominal datum; then, the deviation between the measured coordinates and the nominal coordinates of the series of corresponding ERS points under the nominal datum and the displacement before and after the orientation point is moved are calculated by Equation (5); (5) In the formula, For the first i The deviation between the measured coordinates and the nominal coordinates of each ERS point under the nominal datum, therefore , The displacement of the orientation point before and after moving under the nominal reference; The third step is to solve for the transformation parameters based on the iterative correction of the nominal coordinates. First, an optimization model for transformation parameters is established with the objective of minimizing the sum of the coordinate deviations of each ERS point. The parameters to be optimized are the nominal coordinate deviation values ​​of the ERS points and the transformation parameters between the nominal reference and the measured reference. Considering the actual measurement process requirements during assembly and to avoid overfitting, a threshold for the nominal coordinate deviation of the ERS points is set. The objective function is shown in Equation (6), and the constraint function is shown in Equation (7). (6) (7) In the formula, , These are conversion parameters between nominal and measured references. For the first i The first ERS point j The nominal coordinate deviation value obtained from the second optimization. , n Optimization times , and These represent the upper and lower limits of the coordinate deviation threshold, respectively; the nominal coordinates of the ERS point under the nominal datum. Measured coordinates and the displacement of the orientation point before and after the nominal reference. The particle swarm optimization algorithm is used for optimization, with the fitness function as the objective function. The nominal coordinates of the ERS points are iteratively corrected to obtain the transformation parameters between the nominal and measured reference points. , and finally the n The nominal coordinate deviation value of the ERS point obtained by the second optimization As shown in equation (8); (8) Secondly, based on the transformation parameters, a laser tracker is used to obtain the measured coordinates of a new set of ERS points within the measurement control field. ,in , k To obtain the number of ERS points in the measurement control field, the measured coordinates of the ERS points under the measured datum are then transformed to the nominal datum based on the obtained transformation parameters, as shown in (9). (9) In the formula, That is, the solution based on the above transformation parameters. i Transform each ERS point to its coordinates under the nominal datum; Then, calculate the measured coordinates of this new set of ERS points transformed to the nominal datum. Nominal coordinates under nominal datum coordinate deviation As shown in (10); (10) 。

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