Aircraft wing rib plate numerical control machining method based on structural weight constraint

Abstract

The invention discloses an aircraft wing rib plate numerical control machining method based on structural weight constraints. The method comprises the steps that a three-dimensional model of an aircraft wing rib plate is established, a numerical control machining program is written, and a tool stopping instruction is added to each procedure node; numerical control machining operation is executed, and cutter stopping is conducted according to the cutter stopping instruction; when the cutter is stopped, the weight of the wing rib plate is detected and compared with the theoretical weight, the machining weight deviation is obtained, if the deviation is within the tolerance range and is larger than a preset threshold value, a three-dimensional laser scanner is used for scanning the machining surface of the wing rib plate, actual point cloud data are obtained, and then the actual point cloud data are obtained; noise reduction, sampling and key point extraction are carried out on the actual point cloud data, registration with ideal point cloud data is carried out, the machining allowance is identified and calculated, secondary machining is carried out on the workpiece according to the calculation result or the next procedure is modified, and deviation generated by the previous procedure is offset. According to the machining method, on-machine detection can be achieved, and a manual grinding method can be replaced.

Description

Technical Field

[0001] The invention belongs to the field of aerospace engineering and manufacturing technology, and relates to a numerical control machining method for aircraft wing ribs based on structural weight constraints, which is used to improve the machining weight accuracy of large-scale aviation structural parts. Background Art

[0002] During the aircraft development process, aircraft weight is an important factor affecting flight performance. Aircraft is composed of a large number of parts. Within the tolerance range, parts often have the problem of being too heavy during CNC machining, resulting in the assembly's curb weight exceeding the standard.

[0003] Therefore, it becomes an urgent problem to propose a CNC machining method for aircraft wing ribs to achieve strict control of the machining weight of parts and improve machining efficiency. Summary of the invention

[0004] The purpose of the present invention is to propose an efficient CNC machining method for aviation structural parts based on structural weight constraints to solve the problems existing in the prior art.

[0005] In order to achieve the above-mentioned object, the technical solution adopted by the present invention is as follows: A CNC machining method for aircraft wing ribs based on structural weight constraints comprises the following steps:

[0006] Step 1: Analyze the structural design parameters of the wing ribs, and determine the material and geometric shape of the ribs according to the stress conditions and processing weight requirements of the wing ribs;

[0007] Step 2: Build a 3D model of the aircraft wing rib, write a CNC machining program, and add a stop instruction at each process node;

[0008] Step 3: Execute the CNC machining operation and stop the tool according to the stop instruction; when stopping the tool, detect the weight of the wing rib to obtain the actual weight of the wing rib, then compare the actual weight of the wing rib with the theoretical weight to obtain the machining weight deviation. If the deviation is outside the tolerance range, directly scrap the wing rib, otherwise, determine whether the deviation is greater than a preset threshold, if so, execute step 4, otherwise, continue to the next process;

[0009] Step 4: Scan the processed surface of the wing rib using a 3D laser scanner to obtain actual point cloud data, and then reduce noise on the actual point cloud data to obtain actual point cloud data A;

[0010] Step 5: According to the accuracy of the 3D laser scanner, the theoretical model of the wing rib is converted into ideal point cloud data B having the same density as the actual point cloud data A;

[0011] Step 6: Uniformly sample and extract key points of the actual point cloud data A and the ideal point cloud data B respectively to obtain actual point cloud data A2 and ideal point cloud data B2;

[0012] Step 7: performing rough registration and fine registration on the actual point cloud data A2 and the ideal point cloud data B2, and identifying the machining allowance to obtain the position where the machining deviation occurs on the wing rib;

[0013] Step 8: After extracting the key points, the actual point cloud data A2 and the ideal point cloud data B2 are subjected to boundary point extraction to obtain the actual point cloud data A3 and the ideal point cloud data B3, and then the actual point cloud data A3 and the ideal point cloud data B3 are used to solve the machining allowance. If the machining allowance meets the conditions for secondary machining, the machining program is adjusted to perform secondary machining. If it does not meet the conditions for secondary machining, the next process is modified to offset the deviation caused by the previous process.

[0014] Preferably, in step 3, the weight of the wing rib is detected using a tool with a weighing device, wherein the tool has an automatic chip blowing device.

[0015] Further preferably, in step 4, a radius filtering method is used to reduce noise on the actual point cloud data.

[0016] Further preferably, in step 6, key points are extracted from the actual point cloud data and the ideal point cloud data after uniform sampling based on the local density of the point cloud and the RANSAC fitting space circle.

[0017] Further preferably, in step 7, the actual point cloud data A2 and the ideal point cloud data B2 are roughly aligned using 3DSC feature description + RANSAC algorithm.

[0018] Further preferably, in step 7, the method of precise registration is as follows:

[0019] (1) The point cloud set E after the coordinate transformation after the rough registration is used as the initial point set for the fine registration;

[0020] (2) For all points in the actual point cloud data A2, find their corresponding points Q in the ideal point cloud data B2. k , and form corresponding point pairs;

[0021] (3) Calculate the translation matrix T and the rotation matrix R to minimize the mean square error of the distance between corresponding points, where the mean square error d k It is expressed as:

[0022]

[0023] (4) Set the threshold ε and the maximum number of iterations m to determine d k-1 -d kThe relationship with the set threshold ε is that if it is less than the threshold ε, the transformation matrix is ​​obtained and the precise registration of the point cloud is completed; if it is greater than or equal to the threshold ε, the iteration continues until the maximum number of iterations is reached to complete the precise registration of the point cloud, otherwise the above steps are repeated until the convergence conditions are met.

[0024] Further preferably, in step 8, the machining allowance is solved using a minimum bounding box method.

[0025] The invention provides a method for numerically controlling the machining of aircraft wing ribs based on structural weight constraints, which can realize on-machine detection and eliminate errors caused by multiple clamping. It can also replace the method of using manual grinding to deal with the upward deviation of the machining quality of finished parts. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 A flow chart of the numerical control machining method for aircraft wing ribs based on structural weight constraints provided by the present invention;

[0028] Figure 2 This is a flowchart of the algorithm for extracting key points based on the local density of point clouds and RANSAC fitting of space circles;

[0029] Figure 3 It is the feature space partition diagram of 3DSC. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0031] like Figure 1 As shown, the present invention provides a CNC machining method for aircraft wing ribs based on structural weight constraints, comprising the following steps:

[0032] Step 1: Analyze the structural design parameters of the wing ribs, and determine the material and geometric shape of the ribs according to the stress conditions and processing weight requirements of the wing ribs;

[0033] Step 2: Build a 3D model of the aircraft wing rib, write a CNC machining program, and add a stop instruction at each process node;

[0034] Step 3: Execute the CNC machining operation and stop the tool according to the stop instruction; when stopping the tool, detect the weight of the wing rib to obtain the actual weight of the wing rib, then compare the actual weight of the wing rib with the theoretical weight to obtain the machining weight deviation. If the deviation is outside the tolerance range, directly scrap the wing rib, otherwise, determine whether the deviation is greater than a preset threshold, if so, execute step 4, otherwise, continue to the next process;

[0035] The weight of the wing rib is detected by using a tool with a weighing device, the tool has an automatic chip blowing device for timely removing processing waste chips and coolant to ensure accurate weighing, and the weighing device can be a weight sensor;

[0036] Step 4: Scan the processed surface of the wing rib using a 3D laser scanner to obtain actual point cloud data, and then reduce noise on the actual point cloud data to obtain actual point cloud data A;

[0037] Among them, the radius filtering method is used to reduce the noise of the actual point cloud data. The specific steps are as follows:

[0038] An outlier point determination is performed on each point in the actual point cloud data, wherein the method for determining whether point a is an outlier point is as follows: a filter radius is set, and the number of points within a range with point a as the center and the filter radius as the radius is calculated. If the calculated number of points does not meet the set number requirement, point a is determined to be an outlier point;

[0039] All outliers are removed to obtain the actual point cloud data A after noise reduction.

[0040] Step 5: According to the accuracy of the 3D laser scanner, the theoretical model of the wing rib is converted into ideal point cloud data B with the same density as the actual point cloud data A using cloudcompare software;

[0041] Step 6: Uniformly sample and extract key points of the actual point cloud data A and the ideal point cloud data B respectively to obtain actual point cloud data A2 and ideal point cloud data B2;

[0042] Among them, based on the local density of the point cloud and the RANSAC fitting space circle, the key points of the actual point cloud data A1 and the ideal point cloud data B1 after uniform sampling are extracted, as shown in Figure 2 As shown, the steps are as follows:

[0043] (1) Initialize the neighborhood search radius r and density threshold ρ ε, fitting times C, initial internal point set Sin, distance threshold T and iteration times I;

[0044] (2) Construct a kd tree data structure for the point cloud data. Take any point pi, search for the number of points in the neighborhood with a radius of r according to the neighborhood search radius, and calculate the local density ρ of the point. i ;

[0045] (3) If the local density of the point is greater than or equal to the set density threshold, that is, ρ i ≥ρ ε , then keep it, when i < number of point clouds, continue to calculate until all points are traversed;

[0046] (4) Using the breadth-first search algorithm, points with equal local density are saved in the same file, and the number of point cloud data points in the file is limited. If n∈(n min ,n max ), then save the file; the point number limitation is to provide a good basis for RANSAC to fit the space circle;

[0047] (5) Merge the point cloud data in each file in the previous step and save them as one point cloud data;

[0048] (6) Set the clustering radius and the minimum number of clustering points, and perform Euclidean clustering on the point cloud data saved in the previous step;

[0049] (7) Perform RANSAC fitting of spatial circles on the clustered point cloud data, and limit the radius and number of points of the fitted circle to prevent fitting of circles that are too large, too small, or incomplete. At this time, multiple circles can be fitted and located in different files;

[0050] (8) Extract and save the points of the fitted circles of each file in the previous step, merge the point clouds, and finally output them as point cloud key points.

[0051] Step 7: performing rough registration and fine registration on the actual point cloud data A2 and the ideal point cloud data B2, and identifying the machining allowance to obtain the position where the machining deviation occurs on the wing rib;

[0052] Among them, the 3DSC feature description + RANSAC algorithm is used to perform coarse alignment on the actual point cloud data A2 and the ideal point cloud data B2 to obtain the point cloud set E; the point cloud set E after coarse alignment is used as the input point cloud for ICP fine alignment to obtain the point cloud set E1.

[0053] The steps of the 3DSC context descriptor are as follows: first, the normal vector is calculated for each query point p i , with p i is the center of the sphere, R 3dsc_frameis the radius, the surface normal vector N i As the North Pole direction, a local support area is established. Then the sub-area division and radius calculation are performed. 3dsc_frame = {R0, R1, …, R J}, take the logarithm of J+1 intervals, along the azimuth ψ={ψ0,ψ1,…,ψ L} is divided into L+1 intervals, along the pitch angle θ={θ0,θ1,…,θ K} is divided into k+1 intervals, with a total of J×L×K levels. Since the intervals are divided logarithmically on the radius, if the center p i If the nearby area is too small, it is easily disturbed by noise. Figure 3 It is the feature space partition diagram of 3DSC.

[0054] Let R0 be the minimum radius r min , R J As the maximum radius r max , then the radius R j The calculation formula can be expressed as:

[0055]

[0056] Secondly, the sub-regions are weighted and the weight ω(P i ), as shown in the following formula, where V(j,k,l) ​​represents the volume of the jth radius, kth azimuth direction area and lth elevation direction area, ρ i is the local density of the point.

[0057]

[0058] Finally, the feature vector is synthesized using ω(P i ) form the feature vector It can be expressed as:

[0059]

[0060] In the formula, k = {1, 2,…, n}, n = j×k×l.

[0061] At this point, the vector has the 3DCS shape context information around the point.

[0062] Fine registration is to use the point cloud set E after rough registration as the input point cloud for ICP fine registration. The specific implementation process is as follows:

[0063] (1) The point cloud set E after the coordinate transformation after the rough registration is used as the initial point set for the fine registration;

[0064] (2) For all points in the actual point cloud data A2, find their corresponding points Q in the ideal point cloud data B2. k , and form corresponding point pairs;

[0065] (3) Calculate the translation matrix T and the rotation matrix R to minimize the mean square error of the distance between corresponding points, where the mean square error d k It can be expressed as:

[0066]

[0067] (4) Set the threshold ε and the maximum number of iterations m to determine d k-1 -d k The relationship with the set threshold ε is that if it is less than the threshold ε, the transformation matrix is ​​obtained and the precise registration of the point cloud is completed; if it is greater than or equal to the threshold ε, the iteration continues until the maximum number of iterations is reached and the precise registration of the point cloud is completed, otherwise the above steps are repeated until the convergence condition is met;

[0068] Step 8: After extracting the key points, the actual point cloud data A2 and the ideal point cloud data B2 are subjected to boundary point extraction to obtain the actual point cloud data A3 and the ideal point cloud data B3, and then the actual point cloud data A3 and the ideal point cloud data B3 are used to solve the machining allowance. If the machining allowance meets the conditions for secondary machining, the machining program is adjusted to perform secondary machining. If it does not meet the conditions for secondary machining, the next process is modified to offset the deviation caused by the previous process.

[0069] Among them, the minimum bounding box method is used to solve the machining allowance. The basic idea is to regard each corresponding boundary of the point cloud data model as parallel to each other, and construct an OBB minimum bounding box for it respectively. At this time, each boundary of the bounding box can be regarded as the boundary lines of the point cloud data model. Then, the midpoint coordinates of each boundary line, that is, the midpoint coordinates of the bounding box boundary, are calculated. Finally, the solution of the allowance becomes the distance between the center points of the corresponding boundary lines.

[0070] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0071] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features applied for herein.

Claims

1. A method for numerically controlling the machining of aircraft wing ribs based on structural weight constraints, characterized in that: include: Step 1: Analyze the structural design parameters of the wing ribs, and determine the material and geometric shape of the ribs according to the stress conditions and processing weight requirements of the wing ribs; Step 2: Build a 3D model of the aircraft wing rib, write a CNC machining program, and add a stop instruction at each process node; Step 3: Execute the CNC machining operation and stop the tool according to the stop instruction; when stopping the tool, detect the weight of the wing rib to obtain the actual weight of the wing rib, then compare the actual weight of the wing rib with the theoretical weight to obtain the machining weight deviation. If the deviation is outside the tolerance range, directly scrap the wing rib, otherwise, determine whether the deviation is greater than a preset threshold, if so, execute step 4, otherwise, continue to the next process; Step 4: Scan the processed surface of the wing rib using a 3D laser scanner to obtain actual point cloud data, and then reduce noise on the actual point cloud data to obtain actual point cloud data A; Step 5: According to the accuracy of the 3D laser scanner, the theoretical model of the wing rib is converted into ideal point cloud data B having the same density as the actual point cloud data A; Step 6: Uniformly sample and extract key points of the actual point cloud data A and the ideal point cloud data B respectively to obtain actual point cloud data A2 and ideal point cloud data B2; Step 7: performing coarse registration and fine registration on the actual point cloud data A2 and the ideal point cloud data B2, and identifying the machining allowance; Step 8: After extracting the key points, the actual point cloud data A2 and the ideal point cloud data B2 are subjected to boundary point extraction to obtain the actual point cloud data A3 and the ideal point cloud data B3, and then the actual point cloud data A3 and the ideal point cloud data B3 are used to solve the machining allowance. If the machining allowance meets the conditions for secondary machining, the machining program is adjusted to perform secondary machining. If it does not meet the conditions for secondary machining, the next process is modified to offset the deviation caused by the previous process.

2. The method for numerically controlling the machining of aircraft wing ribs based on structural weight constraints according to claim 1, characterized in that: In step 3, the weight of the wing rib is detected using a tool with a weighing device, wherein the tool has an automatic chip blowing device.

3. The method for numerically controlling the machining of aircraft wing ribs based on structural weight constraints according to claim 1, characterized in that: In step 4, the radius filtering method is used to reduce noise on the actual point cloud data.

4. The method for numerically controlling the machining of aircraft wing ribs based on structural weight constraints according to claim 1, characterized in that: In step 6, key points are extracted from the actual point cloud data and the ideal point cloud data after uniform sampling based on the local density of the point cloud and the RANSAC fitting space circle.

5. The method for numerically controlling the machining of aircraft wing ribs based on structural weight constraints according to claim 1, characterized in that: In step 7, the actual point cloud data A2 and the ideal point cloud data B2 are roughly aligned using the 3DSC feature description + RANSAC algorithm.

6. The method for numerically controlling the machining of aircraft wing ribs based on structural weight constraints according to claim 1, characterized in that: In step 7, the method of fine registration is as follows: (1) The point cloud set E after the coordinate transformation after the rough registration is used as the initial point set for the fine registration; (2) For all points in the actual point cloud data A2, find their corresponding points Q in the ideal point cloud data B2. k , and form corresponding point pairs; (3) Calculate the translation matrix T and the rotation matrix R to minimize the mean square error of the distance between corresponding points, where the mean square error d k It is expressed as: (4) Set the threshold ε and the maximum number of iterations m to determine d k-1 -d k The relationship with the set threshold ε is that if it is less than the threshold ε, the transformation matrix is ​​obtained and the precise registration of the point cloud is completed; if it is greater than or equal to the threshold ε, the iteration continues until the maximum number of iterations is reached to complete the precise registration of the point cloud, otherwise the above steps are repeated until the convergence conditions are met.

7. The method for numerically controlling the machining of aircraft wing ribs based on structural weight constraints according to claim 1, characterized in that: In step 8, the minimum bounding box method is used to solve the machining allowance.

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