CN116664889A - Determination Method of Repairing Margin of Aircraft Cover Type Skin - Google Patents

Abstract

The invention discloses a method for determining the repair margin of an aircraft flap type skin, comprising the following steps: obtaining a point cloud data set Q of a factory-made skin and a point cloud data set P of an ideal skin; Set Q for denoising processing, uniformly sample the point cloud data sets Q and P to obtain source point cloud _Q1 and target point cloud _P1 ; determine the key points in source point cloud _Q1 and target point cloud _P1 and calculate FPFH histogram feature descriptors of all key points; coarse registration of source point cloud Q ₁ and target point cloud P ₁ to obtain point cloud set Q ^' ; fine registration of point cloud set Q ^' and target point cloud P ₁ , Get a new transformed point cloud set Q”; extract the boundary line between the point cloud set Q” and P ₁ and calculate the Euclidean distance between the two boundary lines, which is the amount of skin repair. The method for determining the repair margin of aircraft flaps can be completed by a computer, which saves manpower, improves production efficiency, and has more accurate registration results.

Description

Determination Method of Repairing Margin of Aircraft Cover Type Skin

technical field

The invention relates to the field of aircraft skins, and in particular provides a method for determining the repair margin of aircraft flaps.

Background technique

The aircraft skin is a key force-bearing component that affects the aerodynamic shape of the aircraft, and has a vital impact on the stability of the aircraft itself. Therefore, the manufacture of aircraft skins not only has the requirements for shape accuracy and mechanical performance indicators, but also has strict requirements for its installation accuracy. However, the skin is a thin-walled part, and it is easy to deform during processing. Processing according to the theoretical shape will make it difficult to install the skin at the butt joint according to the ideal gap. Therefore, a certain margin is usually left when manufacturing the skin In the later stage, the margin is repaired according to the actual installation size to achieve the purpose of accurate installation.

At present, most domestic aircraft OEMs use manual alignment and marking when assembling the skin, and then use manual trimming and trimming to remove the margin reserved for the skin. However, manual alignment of the skin Accurate, edge trimming and edge trimming require a lot of manpower, high labor costs, and low production efficiency. The processing quality of the skin is very dependent on the experience and proficiency of the workers. The skin processing errors of different workers may be inconsistent, which will lead to It is difficult to guarantee the processing consistency and qualification rate of the skin. When marking the line manually, a marker pen with a width of 1mm is generally used, which is prone to random errors when marking the line. Manual trimming will also lead to low skin registration accuracy and low processing efficiency. Low, the skin boundary quality and contour are difficult to guarantee.

Therefore, it is an urgent problem to provide a method for determining the repair margin of the skin of the aircraft hatch cover to replace the manual alignment and obtain the repair margin of the skin efficiently and accurately.

Contents of the invention

In view of this, the purpose of the present invention is to provide a method for determining the repair margin of aircraft flap skin, so as to solve the problems of low efficiency and low registration accuracy in manual skin registration.

The technical solution provided by the present invention is: a method for determining the repair margin of an aircraft hatch cover, comprising the following steps:

Step 1: Scan the factory-made aircraft skin with a 3D laser scanner to obtain the point cloud data set Q of the factory-made skin;

Step 2: Convert the ideal aircraft skin 3D model into point cloud data according to the data density collected by the 3D laser scanner on the factory-made aircraft skin, and obtain the ideal skin point cloud data set P;

Step 3: Denoise the point cloud data set Q of the factory-made skin, remove noise points that deviate from the outline, and then perform a denoising process on the point cloud data set Q of the factory-made skin and the ideal mask The point cloud data set P of Pi is evenly sampled, and the source point cloud Q ₁ and the target point cloud P ₁ are correspondingly obtained;

Step 4: Calculate the normal vector of each point in the source point cloud Q ₁ and the target point cloud P ₁ and the sum of the mean values of the included angles under different neighborhood radii, and determine the source point cloud Q according to the sum of the mean values of the included angles ₁ and the key points in the target point cloud P ₁ ;

Step 5: Calculate the FPFH histogram feature descriptors of all key points in the source point cloud _Q1 and target point cloud _P1 ;

Step 6: In the source point cloud _Q1, use the KD tree nearest neighbor search algorithm to find points similar to the FPFH features corresponding to the sampling points in the target point cloud _P1 , and use the random sampling consensus algorithm to compare the source point cloud _Q1 and the target The point cloud P ₁ is roughly registered to obtain the point cloud set Q ^' corresponding to the target point cloud P ₁ ;

Step 7: Use the improved point cloud iterative closest point algorithm to fine-register the point cloud set Q ^' and the target point cloud _P1 to obtain a new transformed point cloud set Q";

Step 8: Use the normal estimation extraction boundary algorithm to extract the boundary lines of the new transformed point cloud set Q" and the target point cloud P ₁ respectively, and calculate the Euclidean distance between the two boundary lines, which is the amount of skin repair.

Preferably, in step 4, the method for determining the normal vector of each point in the source point cloud _Q1 or the target point cloud _P1 is as follows:

According to the nearest neighbor search algorithm, find the K nearest neighbors of any point M in the source point cloud _Q1 or the target point cloud _P1 , and use the K nearest neighbors to fit a local plane in the sense of the least squares method, The local plane is the tangent plane of the point M, and the normal of the tangent plane is the straight line where the normal vector of the point M is located. Specifically: use the principal component analysis method to construct the point M and K nearest neighbors covariance matrix and carry out singular value decomposition, seek its minimum eigenvalue, the eigenvector corresponding to the minimum eigenvalue is the straight line where the normal vector of the described point M is;

The straight line where the normal vector is calculated by the principal component analysis method, the final direction of the normal vector needs to be further confirmed, therefore, first set the direction of a normal vector for point M as Traverse all points, if the direction of the normal vector of the next point to be traversed is /> and/> then will /> do a rollover if /> keep So far, the orientation of the normal vectors of all points in the point cloud set is completed.

Further preferably, in step 4, use the following formula to find the sum of the angle mean values of the point cloud normal vectors of two different neighborhood radii:

Among them, K ₁ , K ₂ are the number of points in different neighborhoods, and n _i2 , n _i3 are normal vectors of different neighborhood radii.

Further preferably, in step 4, if the sum of angles θ of point cloud normal vectors of two different neighborhood radii satisfies ε<θ<2ε, and θ>45 at the same time, then determine the point M as a key point, where , threshold ε=1.

Further preferably, in step 5, the feature histogram is established according to the normal vector of the point cloud of the key point in the source point cloud _Q1 and the surface curvature in the K neighborhood thereof, and the FPFH histogram features of all key points in the source point cloud _Q1 are obtained descriptor;

According to the normal vector of the point cloud of the key points in the target point cloud _P1 and the surface curvature in the K neighborhood, the feature histogram is established, and the FPFH histogram feature descriptors of all key points in the target point cloud _P1 are obtained.

Further preferably, in step 6, the method for obtaining the point cloud set Q ^' is as follows:

Input the key points obtained in steps 4 and 5 and the local feature descriptors of the key points, and then randomly sample s points in the target point cloud P ₁ according to the minimum distance greater than d _min , and then, for each sampling point, at the source point In cloud Q ₁ , a group of points with similar FPFH features are found through the K-dtree nearest neighbor search algorithm;

Next, a rigid transformation matrix is determined for each set of corresponding point pairs, and the transformation parameters "distance error and "Registration performance judgment, ie /> Among them, t ₁ is the set threshold, l _i is the distance difference of the i-th group of corresponding points after transformation, select the optimal rigid transformation matrix to minimize the point cloud registration distance error, and complete the random sampling consensus algorithm for point Clouds are roughly registered, and the transformed point cloud set Q ^' is obtained.

Further preferably, step 7 specifically includes the following steps: randomly select key points in the target point cloud _P1 and find the closest corresponding point in the point cloud set Q ^' , obtain the corresponding point pair, obtain the rotation matrix R and the translation matrix T, Make the mean square error _dk between the above corresponding point pairs the smallest, transform the point cloud set Q ^' according to the translation and rotation matrix obtained above to obtain a new transformed point cloud set Q", and calculate the distance error between Q" and _P2 , if the error of the two iterations is less than the set threshold or the maximum number of iterations is greater than the set number of iterations, the iteration ends, the iteration process is repeated, and all points that meet the convergence conditions are traversed, and the improved point cloud iteration closest point algorithm is used to complete Fine registration of point cloud data.

The aircraft flap type skin repair margin determination method provided by the present invention can obtain the skin repair margin by performing data processing on the collected and processed aircraft skin point cloud data and the ideal aircraft model point cloud data. It can greatly save manpower, reduce labor costs, and improve production efficiency. At the same time, because there is no influence of human factors, the registration results are more accurate. Workers can repair the processed aircraft skin according to the amount of skin repair, or they can According to the size of the repair amount, the processing program is output, and the CNC milling of the aircraft skin is completed, which saves the time of aircraft assembly.

Detailed ways

The present invention will be further explained below in conjunction with specific embodiments, but the present invention is not limited thereto.

The invention provides a method for determining the repair margin of an aircraft flap type skin, comprising the following steps:

Step 1: Scan the factory-made aircraft skin with a 3D laser scanner to obtain the point cloud data set Q of the factory-made skin;

Step 2: Convert the ideal aircraft skin 3D model into point cloud data according to the data density collected by the 3D laser scanner on the factory-made aircraft skin, and obtain the ideal skin point cloud data set P;

Step 3: Denoise the point cloud data set Q of the factory-made skin, remove noise points that deviate from the outline, and then perform a denoising process on the point cloud data set Q of the factory-made skin and the ideal mask The point cloud data set P of Pi is uniformly sampled, the number of points in the point cloud data set is reduced, and the source point cloud Q ₁ and the target point cloud P ₁ are correspondingly obtained;

Step 4: Calculate the normal vector of each point in the source point cloud Q ₁ and the target point cloud P ₁ and the sum of the mean values of the included angles under different neighborhood radii, and determine the source point cloud Q according to the sum of the mean values of the included angles ₁ and the key points in the target point cloud P ₁ ;

Among them, the determination method of the normal vector of each point in the source point cloud _Q1 or target point cloud _P1 is as follows:

According to the nearest neighbor search algorithm, find the K nearest neighbors of any point M in the source point cloud _Q1 or the target point cloud _P1 , and use the K nearest neighbors to fit a local plane in the sense of the least squares method, The local plane is the tangent plane of the point M, and the normal of the tangent plane is the straight line where the normal vector of the point M is located. Specifically: use the principal component analysis method to construct the point M and K nearest neighbors covariance matrix and carry out singular value decomposition, seek its minimum eigenvalue, the eigenvector corresponding to the minimum eigenvalue is the straight line where the normal vector of the described point M is;

The straight line where the normal vector is calculated by the principal component analysis method, the final direction of the normal vector needs to be further confirmed, therefore, first set the direction of a normal vector for point M as Traverse all points, if the direction of the normal vector of the next point to be traversed is /> if /> then will /> do a rollover if /> keep /> unchanged, so far the orientation of the normal vectors of all points in the point cloud set is completed;

Use the following formula to find the sum of the mean angles of the point cloud normal vectors of two different neighborhood radii:

Among them, K ₁ , K ₂ are the number of points in different neighborhoods, n _i2 , n _i3 are normal vectors of different neighborhood radii;

If the sum θ of the angles between point cloud normal vectors of two different neighborhood radii satisfies ε<θ<2ε, and θ>45 at the same time, then determine the point M as a key point, where the threshold ε=1;

Step 5: Calculate the FPFH histogram feature descriptors of all key points in the source point cloud _Q1 and target point cloud _P1 ;

Specifically: according to the normal vector of the point cloud of the key point in the source point cloud Q ₁ and the surface curvature in the K neighborhood thereof, the feature histogram is established, and the FPFH histogram feature descriptor of all key points in the source point cloud Q ₁ is obtained;

Establish a feature histogram according to the normal vector of the point cloud of the key point in the target point cloud P ₁ and the surface curvature in the K neighborhood thereof, and obtain the FPFH histogram feature descriptor of all key points in the target point cloud P ₁ ;

Step 6: In the source point cloud _Q1, use the KD tree nearest neighbor search algorithm to find points similar to the FPFH features corresponding to the sampling points in the target point cloud _P1 , and use the random sampling consensus algorithm to compare the source point cloud _Q1 and the target The point cloud P ₁ is roughly registered to obtain the point cloud set Q ^' corresponding to the target point cloud P ₁ ;

Specifically: input the key points obtained in steps 4 and 5 and the local feature descriptors of the key points, and then randomly sample s points in the target point cloud P ₁ according to the minimum distance greater than d _min , and then, for each sampling point, In the source point cloud Q ₁ , a group of similar points of FPFH features are found by the K-dtree nearest neighbor search algorithm, preferably, d _min =0.005;

Next, a rigid transformation matrix is determined for each set of corresponding point pairs, and the transformation parameters "distance error and "Registration performance judgment, ie /> Among them, t ₁ is the set threshold, l _i is the distance difference of the i-th group of corresponding points after transformation, select the optimal rigid transformation matrix to minimize the point cloud registration distance error, and complete the random sampling consensus algorithm for point The cloud is roughly registered to obtain the transformed point cloud set Q ^' ;

Step 7: Use the improved point cloud iterative closest point algorithm (GICP algorithm) to fine-register the point cloud set Q ^' and the target point cloud _P1 to obtain a new transformed point cloud set Q"; compared with the traditional ICP algorithm GICP The algorithm has higher robustness;

Specifically: randomly select key points in the target point cloud P ₁ and find the closest corresponding point in the point cloud set Q ^' to obtain the corresponding point pair, obtain the rotation matrix R and the translation matrix T, so that the above-mentioned corresponding point pair The mean square error d _k is the smallest, and the point cloud set Q ^' is transformed according to the translation and rotation matrix obtained above to obtain a new transformed point cloud set Q", and the distance error between Q" and P ₂ is calculated. If the error of two iterations is less than the set threshold or reaches the maximum number of iterations greater than the set number of iterations, then the iteration ends, repeat the iterative process, traverse all points that meet the convergence conditions, and complete the fine allocation of point cloud data using the improved point cloud iteration closest point algorithm allow;

Step 8: Use the normal estimation extraction boundary algorithm to extract the boundary lines of the new transformed point cloud set Q" and the target point cloud P ₁ respectively, and calculate the Euclidean distance between the two boundary lines, which is the amount of skin repair.

The aircraft flap type skin repair margin determination method can obtain the skin repair margin by processing the collected and processed aircraft skin point cloud data and the ideal aircraft model point cloud data, and the method can greatly improve the It saves manpower, reduces labor costs, and improves production efficiency. At the same time, because there is no influence of human factors, the registration results are more accurate. Workers can repair the processed aircraft skin according to the amount of skin repair, or according to the amount of repair The size of the output processing program, complete the CNC milling of the aircraft skin, saving the time of aircraft assembly.

The embodiments of the present invention have been described in detail above, but the present invention is not limited to the above embodiments, and various changes can be made without departing from the gist of the present invention within the scope of knowledge of those skilled in the art. .

Claims (7)

1. A method for determining an aircraft flap class skin repair margin is characterized in that, comprising the steps: Step 1: Scan the factory-made aircraft skin with a 3D laser scanner to obtain the point cloud data set Q of the factory-made skin; Step 2: Convert the ideal aircraft skin 3D model into point cloud data according to the data density collected by the 3D laser scanner on the factory-made aircraft skin, and obtain the ideal skin point cloud data set P; Step 3: Denoise the point cloud data set Q of the factory-made skin, remove noise points that deviate from the outline, and then perform a denoising process on the point cloud data set Q of the factory-made skin and the ideal mask The point cloud data set P of Pi is evenly sampled, and the source point cloud Q ₁ and the target point cloud P ₁ are correspondingly obtained; Step 4: Calculate the normal vector of each point in the source point cloud Q ₁ and the target point cloud P ₁ and the sum of the mean values of the included angles under different neighborhood radii, and determine the source point cloud Q according to the sum of the mean values of the included angles ₁ and the key points in the target point cloud P ₁ ; Step 5: Calculate the FPFH histogram feature descriptors of all key points in the source point cloud _Q1 and target point cloud _P1 ; Step 6: In the source point cloud _Q1, use the KD tree nearest neighbor search algorithm to find points similar to the FPFH features corresponding to the sampling points in the target point cloud _P1 , and use the random sampling consensus algorithm to compare the source point cloud _Q1 and the target The point cloud P ₁ is roughly registered to obtain the point cloud set Q ^' corresponding to the target point cloud P ₁ ; Step 7: Use the improved point cloud iterative closest point algorithm to fine-register the point cloud set Q ^' and the target point cloud _P1 to obtain a new transformed point cloud set Q"; Step 8: Use the normal estimation extraction boundary algorithm to extract the boundary lines of the new transformed point cloud set Q" and the target point cloud P ₁ respectively, and calculate the Euclidean distance between the two boundary lines, which is the amount of skin repair. 2. according to the said aircraft flap class skin repair margin determination method of claim 1, it is characterized in that: in step 4, the determination method of each point normal vector in source point cloud Q ₁ or target point cloud P ₁ is as follows: According to the nearest neighbor search algorithm, find the K nearest neighbors of any point M in the source point cloud _Q1 or the target point cloud _P1 , and use the K nearest neighbors to fit a local plane in the sense of the least squares method, The local plane is the tangent plane of the point M, and the normal of the tangent plane is the straight line where the normal vector of the point M is located. Specifically: use the principal component analysis method to construct the point M and K nearest neighbors covariance matrix and carry out singular value decomposition, seek its minimum eigenvalue, the eigenvector corresponding to the minimum eigenvalue is the straight line where the normal vector of the described point M is; The straight line where the normal vector is calculated by the principal component analysis method, the final direction of the normal vector needs to be further confirmed, therefore, first set the direction of a normal vector for point M as Traverse all points, if the direction of the normal vector of the next point to be traversed is /> and/> then will /> do a rollover if /> keep /> So far, the orientation of the normal vectors of all points in the point cloud set is completed. 3. according to the said aircraft flap class skin repair margin determining method of claim 1, it is characterized in that: in step 4, utilize following formula to find out the sum of the included angle mean value of the point cloud normal vector of two different neighborhood radii : Among them, K ₁ , K ₂ are the number of points in different neighborhoods, and n _i2 , n _i3 are normal vectors of different neighborhood radii. 4. according to claim 1 said aircraft flap class skin repair margin determination method, it is characterized in that: in step 4, if the sum θ of the point cloud normal vector of two different neighborhood radii satisfies ε<θ <2ε, and satisfying θ>45 at the same time, the point M is determined to be a key point, where the threshold ε=1. 5. according to the said aircraft flap class skin repair margin determination method of claim 1, it is characterized in that: in step 5, according to the normal vector of the point cloud of key point in source point cloud Q ₁ and the surface in K neighborhood thereof The curvature establishes a feature histogram, and obtains the FPFH histogram feature descriptor of all key points in the source point cloud _Q1 ; According to the normal vector of the point cloud of the key points in the target point cloud _P1 and the surface curvature in the K neighborhood, the feature histogram is established, and the FPFH histogram feature descriptors of all key points in the target point cloud _P1 are obtained. 6. according to the aircraft flap class skin repair margin determination method of claim 1, it is characterized in that: in step 6, the method for obtaining point cloud set Q ^' is as follows: Input the key points obtained in steps 4 and 5 and the local feature descriptors of the key points, and then randomly sample s points in the target point cloud P ₁ according to the minimum distance greater than d _min , and then, for each sampling point, at the source point In cloud Q ₁ , a group of points with similar FPFH features are found through the K-dtree nearest neighbor search algorithm; Next, a rigid transformation matrix is determined for each set of corresponding point pairs, and the transformation parameters "distance error and Registration performance judgment, i.e. /> Among them, t ₁ is the set threshold, l _i is the distance difference of the i-th group of corresponding points after transformation, select the optimal rigid transformation matrix to minimize the point cloud registration distance error, and complete the random sampling consensus algorithm for point Clouds are roughly registered, and the transformed point cloud set Q ^' is obtained. 7. according to the said aircraft flap class skin repairing margin determination method of claim 1, it is characterized in that: step 7 specifically comprises the following steps: in target point cloud P ₁ , get key point arbitrarily and find in point cloud collection Q ^' The closest corresponding point, get the corresponding point pair, obtain the rotation matrix R and the translation matrix T, so that the mean square error d _k between the above corresponding point pairs is the smallest, and carry out the point cloud set Q ^' according to the translation and rotation matrix obtained above Transform to obtain a new transformed point cloud set Q", and calculate the distance error between Q" and P ₂ , if the error of the two iterations is less than the set threshold or the maximum number of iterations is greater than the set number of iterations, the iteration ends and repeats The iterative process traverses all points that meet the convergence conditions, and completes the fine registration of point cloud data using the improved point cloud iterative closest point algorithm.