Adaptive trimming method for thin-walled parts based on flexible clamping

Abstract

The present invention relates to a thin-walled part trimming method based on flexible clamping, comprising the following steps: recording a theoretical grid data model of the thin-walled part as a first model, and recording a grid data model of a finished thin-walled part as a second model; aligning the first model with the second model; forming a third model by projections of all grids on the first model on the second model; making each grid node in the third model slide along its node tangent plane, calculating a grid data model with the minimum global strain energy by an iterative method, and recording it as a fourth model; extracting the contour line of the fourth model, and trimming the thin-walled part according to the new contour line. The present invention can quickly predict the trimming line size and shape of the thin-walled part, and ensure that the contour shape of the thin-walled part processed under the trimming line is accurate, greatly saving the trimming line prediction time, saving the number of tests and experimental costs, and shortening the research and development cycle.

Description

Technical Field

[0001] The invention relates to a thin-walled part adaptive trimming method based on flexible clamping, belonging to the technical field of thin-walled part processing and forming. Background Art

[0002] In the aerospace manufacturing industry, the extensive use of thin-walled parts can greatly reduce the weight of aircraft and spacecraft, and increase their strength and maneuverability. Therefore, thin-walled parts are widely used in key parts such as fuselages, wings and engines of aircraft and spacecraft.

[0003] In order to ensure that the thin-walled parts have an accurate contour after processing, the manufacturing process cannot be separated from the trimming process. The trimming line is the cutting limit determined to obtain the inner and outer contours of the parts, and is used to cut off the excess parts outside the reserved parts. Obviously, finding the best trimming line is the key to ensuring that the parts have an accurate shape in subsequent processes, but due to the relatively large size of thin-walled parts on aircraft, and the complex curved surface structure, the thin wall structure makes the stiffness of the workpiece generally low.

[0004] Traditional thin-walled parts manufacturing and processing methods, in order to ensure the reasonable shape of the parts, are mostly milled according to theoretical digital models. Although the deformation caused by the flexible fixture clamping can be adjusted by optimizing the layout of the fixture and the position of the support body, on the one hand, the flexible fixture has poor fixation and the number of support brackets is limited, making it difficult to adsorb and fix all the positions covered by the parts, which will have a certain impact on the later processing accuracy; on the other hand, the large rebound deformation of thin-walled parts generally causes large errors in the spacing of the positioning holes. Therefore, in the actual processing process, due to the clamping force of the flexible fixture and other factors, the workpiece is very easy to produce processing deformation. It is precisely because of the influence of the deformation of the thin-walled structure that the existing trimming line method often cannot meet the product trimming and cutting processing requirements. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a thin-walled part trimming method that allows the thin-walled part to have an accurate contour shape. The method intelligently analyzes the trimming of the thin-walled part based on the consideration of the deformation of the thin-wall structure, thereby obtaining an accurate three-dimensional trimming line.

[0006] In order to solve the above technical problems, the technical solution proposed by the present invention is: a method for adaptive trimming of thin-walled parts based on flexible clamping, comprising the following steps:

[0007] Step 1: record the theoretical mesh data model of the thin-walled part as the first model, and obtain a mesh data model of a finished thin-walled part as the second model;

[0008] Step 2: aligning the first model with the second model;

[0009] Step 3: Project any mesh on the first model onto the second model to obtain the projection of the mesh on the second model, and the projections of all meshes on the second model constitute a third model; wherein the projection of any mesh on the first model on the second model is: a new mesh formed by sequentially connecting the normal direction of each node of the mesh with the intersection points of the second model;

[0010] Step 4: According to the local displacement of each mesh node in the third model relative to the first model, a linear elastic model is used to represent the corresponding stress-strain relationship, and the unit deformation energy of each mesh is calculated, and the unit deformation energies of all meshes are accumulated to obtain the global strain energy;

[0011] Each mesh node in the third model is made to slide along its node tangent plane, and a mesh data model with the minimum global strain energy is obtained by an iterative method, which is recorded as the fourth model;

[0012] Step 5: Extract the contour line of the fourth model, and trim the thin-walled part according to the new contour line.

[0013] The present invention aims at correcting the processing errors caused by factors such as deformation, springback, and processing errors caused by the clamping force of flexible clamps during the processing of thin-walled parts in the prior art. That is, the thin-walled part trimming method of the present invention adopts different deformation amounts for different thin-walled parts, so that the trimming process of thin-walled parts is adaptive, which reduces the errors caused by directly using the same theoretical model to process thin-walled parts with different deformation amounts in the traditional method, and improves the trimming accuracy.

[0014] The present invention can quickly predict the trimming line size and shape of thin-walled parts, and ensure that the contour of the thin-walled parts processed under the trimming line is accurate, greatly saving the trimming line prediction time, saving the number of tests and experimental costs, and shortening the research and development cycle. Further beneficial effects of the present invention can be found in the specific implementation method section. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be further described below in conjunction with the accompanying drawings.

[0016] Figure 1 is a schematic diagram of a second model of an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of the second model after preprocessing.

[0018] Figure 3 Schematic diagram for comparing the first model and the second model.

[0019] Figure 4 Schematic diagram of the projection process of a triangular mesh unit.

[0020] Figure 5 Schematic diagram of the trimming line after adaptive correction. DETAILED DESCRIPTION Example

[0021] This embodiment is applicable to thin-walled parts made of metal materials and thin-walled parts made of composite materials. The present invention will be further described below by taking a certain large thin-walled part as an example.

[0022] The method for adaptive trimming of thin-walled parts based on flexible clamping of this embodiment includes the following steps:

[0023] Step 1: record the theoretical mesh data model of the thin-walled part as the first model. Fix a formed thin-walled part by a flexible clamp to obtain a finished thin-walled part; scan the finished thin-walled part by three-dimensional measurement technology to obtain a mesh data model of the finished thin-walled part, which is recorded as the second model.

[0024] When scanning finished thin-walled parts, a laser scanner may be preferably used for scanning, and then a mesh data model of the thin-walled parts is generated after data processing, which is the second model. Figure 1 shown.

[0025] In order to reduce the size of the point cloud data, in this embodiment, the point cloud data of the second model is preferably preprocessed to reduce the number of meshes obtained by 3D reconstruction. At the same time, the lug structure is removed, and the irregular holes are optimized into round holes to simplify the model structure, which is convenient for subsequent calculations. Figure 2 As shown. The comparison between the first model and the second model is Figure 3 As shown, the model on the lower side is the first model, and the model on the upper side is the second model.

[0026] Step 2: align the first model with the second model; there are many classic algorithms for aligning 3D models, such as the ICP (Iterative Closest Point) algorithm, which is one of the most widely used 3D point cloud alignment algorithms. It uses Euclidean transformation to solve the rotation and translation matrices of two point clouds and the corresponding alignment errors. In order to improve the robustness of the ICP algorithm, there are also a series of variant ICP algorithms in the prior art.

[0027] However, existing registration algorithms are often complex. In order to improve the efficiency and accuracy of registration, this embodiment optimizes the mesh data model of thin-walled parts, selects at least three first feature points on the first model, and selects second feature points in the second model that correspond one-to-one to the first feature points. The alignment of the models is achieved through feature point registration, which ensures the accuracy and precision of subsequent surface mapping, thereby facilitating improving the accuracy of trimming.

[0028] In this embodiment, five first feature points are selected in the first model, and five second feature points are selected in the second model (such as Figure 2 ), the first feature point corresponds to the second feature point one by one, and the ideal state after registration is that the five first feature points are aligned with the corresponding second feature points of the second model in the normal direction of the first model, but this ideal state is difficult to achieve due to the influence of deformation and rebound deformation caused by the clamping force of the flexible clamp. In order to be as close to the ideal state as possible, this embodiment preferably adopts the following method for registration: move the position of the second model, calculate the mean square error of the distance between the first feature point and the corresponding second feature point, find the position of the second model when the mean square error is the smallest, and complete the registration of the second model with the first model.

[0029] Step 3: Project any mesh on the first model onto the second model to obtain the projection of the mesh on the second model, and the projections of all meshes on the second model constitute a third model; wherein the projection of any mesh on the first model on the second model is: a new mesh formed by sequentially connecting the normal direction of each node of the mesh with the intersection points of the second model.

[0030] Assume that the finite element mesh of the first model is C t , the finite element mesh of the second model is C s According to the actual clamping and positioning of the thin-walled parts, the finite element mesh C of the first model is t The nodes at the corresponding positions on the grid are defined to ensure the accuracy of the projection. t Any node P on t Project along the normal direction to the second model C s The projection point P is obtained s , C t Project all nodes on C s After the mesh is loaded, a new mesh data model C is generated due to the overlap and deformation of the mesh. m , that is, the third model.

[0031] like Figure 4 A triangular mesh unit is shown by mesh C t Mapping to Grid C s The process above, where the OXYZ coordinate system is the global coordinate system, ΔABC is the first model C t The dotted line unit ΔA0B0C0 is the projection of the triangular mesh unit ΔABC along the normal to C. s A local coordinate system O is established on the triangular mesh units corresponding to the first model and the second model respectively. m x m y m z m and On x n y n z n , the local coordinate numbers of the nodes in their respective local coordinate systems are 1, 2, and 3, corresponding to the global node numbers A (A0), B (B0), and C (C0), respectively. The origin of the local coordinate system is set at node 1, and the x-axis is along line segment 12, and the y-axis is perpendicular to the x-axis and points to the side of node 3. m (i) y m (j) and x n (i) y n (j) is the local coordinate of the triangular mesh unit node in its respective local coordinate system.

[0032] Step 4: According to the displacement of each grid node in the third model relative to the first model, a linear elastic model is used to represent the corresponding stress-strain relationship, the unit deformation energy of each grid is calculated, and the unit deformation energies of all grids are accumulated to obtain the global strain energy.

[0033] In specific implementation, Figure 4 As shown in the figure, assuming that linear elastic reverse deformation occurs during mesh projection, the displacement vector {q e} can be simply expressed as:

[0034]

[0035] The stress and strain of the unit can be calculated based on the unit node displacement in the local coordinate system, and then converted into the stress and strain in the global coordinate system. The linear elastic model is used to represent the stress-strain relationship in the calculation, so as to calculate the unit deformation energy of each grid.

[0036] Since the assumed linear elastic reverse deformation will cause node internal forces and the influence of external loads is ignored, the finite element equations established for the third model are not balanced. Therefore, in order to achieve equilibrium, it is necessary to use the Newton-Raphson iteration method for iterative solution, and the iterative convergence is judged by the displacement convergence criterion, that is, the norm of the displacement vector of the unit node is calculated. If the norm is less than the given iterative convergence factor after multiple iterations, the iteration converges, that is: each mesh node in the third model slides along its node tangent plane, and the mesh data model with the minimum global strain energy is calculated by the iterative method, which is recorded as the fourth model. The mesh is optimized by this method. After iterative convergence, the unit global strain reaches the minimum, and the sum of the distortion energy of all units reaches the minimum, so that the optimized mesh model after mapping can be constructed.

[0037] Step 5: Extract the contour line of the fourth model, such as Figure 5As shown, the thin-walled part is trimmed according to the new contour line, that is, the contour line is exported and then imported into the control system of the flexible fixture, and the thin-walled part is accurately trimmed according to the new contour line.

[0038] The present invention is not limited to the specific technical solutions described in the above embodiments. In addition to the above embodiments, the present invention may also have other implementation modes. For those skilled in the art, any technical solutions formed by any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for adaptive trimming of thin-walled parts based on flexible clamping, comprising the following steps: Step 1: Record the theoretical grid data model of the thin-walled part as the first model; fix the formed thin-walled part by a flexible clamp to obtain a finished thin-walled part, and scan the finished thin-walled part by using a three-dimensional measurement technology to obtain a second model; Step 2: aligning the first model with the second model; Step 3: Project any mesh on the first model onto the second model to obtain the projection of the mesh on the second model, and the projections of all meshes on the second model constitute a third model; wherein the projection of any mesh on the first model on the second model is: a new mesh formed by sequentially connecting the normal direction of each node of the mesh with the intersection points of the second model; Step 4: According to the local displacement of each mesh node in the third model relative to the first model, a linear elastic model is used to represent the corresponding stress-strain relationship, and the unit deformation energy of each mesh is calculated, and the unit deformation energies of all meshes are accumulated to obtain the global strain energy; Each mesh node in the third model is made to slide along its node tangent plane, and a mesh data model with the minimum global strain energy is obtained by calculating the Newton-Raphson iteration method, which is recorded as the fourth model; Step 5: Extract the contour line of the fourth model, and trim the thin-walled part according to the new contour line.

2. The method for adaptive trimming of thin-walled parts based on flexible clamping according to claim 1 is characterized in that: In step 1, the point cloud data of the second model is preprocessed to reduce the number of meshes obtained by 3D reconstruction, while the lug structure is removed and the irregular holes are optimized into round holes.

3. The method for adaptive trimming of thin-walled parts based on flexible clamping according to claim 1 or 2, characterized in that: In step 2, at least three first feature points are selected on the first model, and second feature points corresponding to the first feature points are selected in the second model; the position of the second model is moved, the mean square error of the distance between the first feature point and the corresponding second feature point is calculated, and the position of the second model when the mean square error is minimized is found, thereby completing the alignment of the second model with the first model.

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