Multi-screw tightening dynamic optimization method for minimum assembly clearance

Abstract

Most of the existing radar antenna assembly methods adopt assembly process profile parameters from theoretical calculation, and the profile parameters obtained in this way often have certain errors with the actual product profile, and the errors greatly influence the accuracy of an assembly clearance prediction result. The invention provides a multi-screw tightening dynamic optimization method for the minimum assembly clearance aiming at the requirements of complex mechanical and electrical products such as radar antennas on high-quality electric signal transmission, the influence of the assembly process on interlayer clearances is accurately predicted based on measured data, the screw tightening sequence and loading times are optimized by taking the minimum assembly clearance as a target, and the multi-screw tightening dynamic optimization method for the minimum assembly clearance is realized. A screw tightening sequence is planned in a mode of repeatedly collecting three-dimensional point cloud data of a workpiece to reconstruct a model, searching adjacent points of hole sites and calculating gaps, and loading times are planned by calculating and comparing average gaps through finite elements, so that the process parameters are optimized to obtain the minimum assembly gap, and high-quality transmission of electric signals among all layers of units of a radar antenna is ensured.

Description

Technical Field

[0001] The invention relates to the field of fastener assembly, and in particular to a multi-screw tightening dynamic optimization method oriented to minimum assembly gap. Background Art

[0002] Complex electromechanical products represented by radar antennas have the characteristics of strong electromechanical coupling. Such products are usually composed of layered parts, and various electrical connectors are used between layers to transmit electrical signals. The gap between assembly layers is the decisive factor in the quality of electrical signal transmission of such products, which is mainly affected by the processing accuracy of layered parts and the product assembly process. At present, such products are gradually developing in the direction of high integration and complex surface. Therefore, some of their layers are produced by additive manufacturing processes of polymer materials and composite materials. This production process can produce products with complex surfaces, but due to its manufacturing principle, the product often warps due to the accumulation of residual stress, which leads to large and uneven gaps between layers after assembly. The existing assembly process planning method predicts the surface state of the product during assembly by theoretical calculation and simulation, and then plans the tightening process of threaded connectors according to the predicted product surface.

[0003] Most of the existing methods use assembly process surface parameters from theoretical calculations. The surface parameters obtained in this way often have certain errors with the actual product surface, which will greatly affect the accuracy of the assembly gap prediction results. Therefore, it is of great significance to explore a simple, efficient, and accurate assembly gap prediction method for assembly process optimization. Summary of the invention

[0004] In response to the demand for high-quality electrical signal transmission for complex electromechanical products such as radar antennas, the present invention proposes a dynamic optimization method for tightening multiple screws for minimum assembly gap. Based on measured data, the method accurately predicts the impact of the assembly process on the interlayer gap, and optimizes the screw tightening sequence and loading times with the minimum assembly gap as the goal. According to the research needs, this method plans the screw tightening sequence by repeatedly collecting three-dimensional point cloud data of the workpiece to reconstruct the model, searching for points adjacent to the hole and calculating the gap, and plans the loading times by finite element calculation and comparison of the average gap, thereby optimizing these process parameters to obtain the minimum assembly gap and ensure high-quality transmission of electrical signals between each layer of the radar antenna. Specifically, it includes the following steps:

[0005] Step S1, obtaining point cloud data: using a point cloud camera to repeatedly scan the antenna to obtain surface point cloud data.

[0006] Step S2, reconstructing the product model: using the PCL library to filter the point cloud data, register the point cloud data, and reconstruct the model based on the point cloud data.

[0007] Step S3, determine the maximum preload force and loading times: determine the maximum preload force, preset the loading times to N, simulate the assembly process of a certain tightening sequence under N loading times by finite element method, and output the product assembly gap field quantity; determine the assembly loading times according to the mean of the assembly gap at all grid nodes.

[0008] Step S4: Determine the position of the screws to be tightened: divide the reconstructed model obtained in step S2 into a grid, calculate the gap between the upper and lower layers without applying a load, and output the position parameters and gap values ​​of all nodes on the contact surface; establish a gap database A after tightening x screws = {A1,KA n}, where n is the total number of screws to be installed, A n ={x n ,y n ,z n ,d n} is the node gap vector, including the node coordinate information (x, y, z) and the gap value d of the node position; set the number of neighboring points to a, and establish the neighboring point library L = {L1, K, L j}, where j = nx is the number of unmounted screws of the product to be installed; find the hole i without screws in the gap database A, 1≤i≤j, and the node with the closest three-dimensional Euclidean distance to the center of the circle intersecting the assembly contact surface, extract it from the gap database and add it to its neighboring point library L i Repeat the search until the point L is close to the i When the number of midpoints reaches a, the neighboring point library L i Data addition completed; continue to add neighboring point library L for screw hole i+1 that is not installed i+1 Add neighboring points. When all j neighboring point libraries are added, calculate the average gap d of all points in each neighboring point library. i , then the largest value d among the j average gap values max =Max{d1,K,d j}, the corresponding hole position is the hole position of the threaded connector to be installed; in this way, the screw tightening sequence is obtained.

[0009] Step S5: Complete assembly: Repeat steps S1, S2, and S4 until all screws have completed the first loading, and then continue to complete subsequent loading in the order of the first loading.

[0010] Furthermore, in step S2, the PCL library is used to filter the point cloud data in the following ways: through filtering and statistical filtering. The 3D-NDT algorithm is used to align the partitioned point cloud data. The digital surface editor DSE and the rapid surface reconstruction module QSR in CATIA are used as the reverse modeling platform to reconstruct the model.

[0011] Furthermore, step S3 also includes setting the contact type between the lower surface of the upper layer of the antenna and the upper surface of the lower layer to surface-to-surface contact, setting the sliding friction coefficient to 0.2, applying a fixed constraint to the side of the lower layer, applying a linear force between the centers of the screw holes corresponding to the upper surface of the upper layer and the lower surface of the lower layer, and adding a coupling constraint between the center and the corresponding inner wall of the hole.

[0012] Furthermore, in step S4, the finite element analysis method is used to divide the mesh of the model reconstructed in step S2.

[0013] Furthermore, in step S4, the number of neighboring points a is related to the model meshing accuracy and satisfies a≥(m / δ) 2 , where m is the distance between adjacent holes and δ is the meshing accuracy.

[0014] The beneficial effects of the present invention are

[0015] 1. By repeatedly collecting 3D point cloud data to reconstruct the product model, the tightening process of threaded connectors can be planned based on the measured product surface parameters during the assembly process, thereby improving the accuracy of assembly clearance prediction results.

[0016] 2. The tightening sequence of threaded connectors is planned by searching for adjacent points of the hole and calculating the clearance. The finite element method is used to calculate and compare the average clearance planning loading times, which can effectively solve the tightening process planning problem of threaded connectors for minimum assembly clearance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of a radar antenna threaded fastener and its structure.

[0018] Figure 2 Flow chart of the tightening process for threaded fasteners.

[0019] Figure 3 This is a picture of the product surface taken by a 3D point cloud camera.

[0020] Figure 4 This is the finite element calculation result - assembly clearance cloud diagram.

[0021] The meanings of the reference numerals are as follows: 1- screw; 2- liquid cooling layer; 3- radiation feeding layer. DETAILED DESCRIPTION

[0022] The following will be combined with the attached Figure 1-4 The technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 The threaded connection structure includes a screw 1, a lower layer is a liquid cooling layer 3, and an upper layer is a radiation feed layer 2. The liquid cooling layer is a machined metal part fixed by a clamp, and its spatial position remains unchanged. The radiation feed layer is composed of multiple layers of heterogeneous parts stacked together and is fixed to the liquid cooling layer 1 by a screw 1.

[0024] See also Figure 2 , which is a multi-screw tightening dynamic optimization method for minimum assembly gap of the present invention, and the specific implementation steps are combined with Figure 1 The threaded connection shown is described as follows:

[0025] Step S1: Obtain point cloud data

[0026] See also Figure 3 , use the LMI Gocator3210 snapshot camera to take pictures of the upper 10 partitions to obtain point cloud data, use the web page provided by LMI and the point cloud processing tool CsvConverter to record all partition point cloud data, and export the point cloud data to txt text format.

[0027] Step S2: Fitting the upper surface

[0028] Based on the PCL (Point Cloud Library) tool, a point cloud processing program was written to obtain high-quality point cloud data by performing direct filtering and statistical filtering on all the partitioned point cloud data. The 3D-NDT algorithm was used to register the partitioned point cloud data to obtain the overall point cloud data of the upper surface. The digital surface editor DSE (Digitized Shape Editor) and the quick surface reconstruction module QSR (Quick Surface Reconstruction) in the modeling software CATIA were used as the reverse modeling platform to reconstruct the upper model.

[0029] Step S3: Preload force and loading times planning

[0030] Determine the maximum preload force based on engineering experience. Select Abaqus as the finite element analysis software, import the reconstructed model into the software, define material properties for it according to the actual working conditions, use engineering constants and solid unit plies to define its material properties for the upper layer, define the elastic modulus and Poisson's ratio for the lower layer, and establish a linear elastic model. The mesh type is C3D8R. Set the contact type between the lower surface of the upper layer and the upper surface of the lower layer to surface-to-surface contact, and set the sliding friction coefficient to 0.2. Apply a fixed constraint to the side of the lower layer, apply a linear force between the centers of the screw holes corresponding to the upper surface of the upper layer and the lower surface of the lower layer, and add a coupling constraint between the center of the circle and the corresponding inner wall of the hole. Select a tightening sequence, for 1 direct loading; 2 loadings, each loading 50% of the maximum preload; 3 loadings, each loading 30%, 30%, 40% of the maximum preload; 4 loadings, each loading 25% of the maximum preload; 5 loadings, each loading 20% ​​of the maximum preload; five process setting analysis steps, respectively, five assembly process simulations, and output the gap field quantity COPEN between the upper and lower layers. Determine the number of loadings based on the average gap value of all high-value points and the comprehensive assembly cost factor.

[0031] Step S4: Determine the screw to be tightened

[0032] Import the reconstructed model obtained in step S2 into ABAQUS, set the same material properties, boundary conditions, contact mode and mesh as in step S3, and output the gap field COPEN between the upper and lower layers without applying load. Set the number of adjacent points to 15, use Visual studio 2019 to write an adjacent point query program, find the 15 nearest mesh nodes in the Euclidean space for each unscrewed hole in the gap field COPEN obtained in this step, and record them as the adjacent points of the corresponding hole, calculate the average assembly gap of each hole adjacent point, and refer to the simulation structure results. Figure 4 The hole with the largest average gap is selected as the screw that needs to be tightened currently, and the screw is installed in the hole and tightened according to the pre-tightening force set in step S3.

[0033] Repeat steps S1, S2, and S4 until all holes have been screwed, and the first loading is completed. Complete the remaining loading times according to the first loading sequence, and the product assembly is completed. Use industrial CT scanning to detect the product assembly gap, compare it with product quality requirements, and determine the effectiveness of the planned assembly process.

[0034] The present invention is not limited to the above specific implementations, and the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made to the above implementations based on the technical essence of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-screw tightening dynamic optimization method for minimum assembly clearance, characterized in that: The specific steps include: Step S1, obtain point cloud data Use the point cloud camera to repeatedly scan the antenna to obtain surface point cloud data; Step S2: Reconstruct the product model Use the PCL library to filter the point cloud data, register the point cloud data, and reconstruct the model based on the point cloud data; Step S3, determine the maximum preload force and loading times Determine the maximum preload force, preset the number of loading schemes as N, simulate the assembly process of a certain tightening sequence under N number of loading schemes by finite element method, and output the product assembly gap field quantity; determine the assembly loading number according to the mean value of the assembly gap at all grid nodes; Step S4: Determine the position of the screw to be tightened The reconstructed model obtained in step S2 is meshed, the gap between the upper and lower layers is calculated without applying a load, and the position parameters and gap values ​​of all nodes on the contact surface are output; a gap database A = {A1, KA n }, where n is the total number of screws to be installed, A n ={x n ,y n ,z n ,d n } is the node gap vector, including the node coordinate information (x, y, z) and the gap value d of the node position; set the number of neighboring points to a, and establish the neighboring point library L = {L1, K, L j }, where j = nx is the number of unmounted screws of the product to be installed; find the hole i without screws in the gap database A, 1≤i≤j, and the node with the closest three-dimensional Euclidean distance to the center of the circle intersecting the assembly contact surface, extract it from the gap database and add it to its neighboring point library L i Repeat the search until the point L is close to the i When the number of midpoints reaches a, the neighboring point library L i Data addition completed; continue to add neighboring point library L for screw hole i+1 that is not installed i+1 Add neighboring points. When all j neighboring point libraries are added, calculate the average gap d of all points in each neighboring point library. i , then the largest value d among the j average gap values max =Max{d1,K,d j }, the corresponding hole position is the hole position of the threaded connector to be installed; in this way, the screw tightening sequence is obtained; Step S5: Complete assembly Repeat step S1, step S2, and step S4 until all the screws have completed the first loading, and then continue to complete the subsequent loading in the order of the first loading.

2. The method for dynamic optimization of multiple screw tightening for minimum assembly clearance according to claim 1, characterized in that: In step S2, the PCL library is used to filter the point cloud data in the form of through filtering and statistical filtering. The 3D-NDT algorithm is used to align the partitioned point cloud data. The digital surface editor DSE and the rapid surface reconstruction module QSR in CATIA are used as the reverse modeling platform to reconstruct the model.

3. The method for dynamic optimization of multiple screw tightening for minimum assembly clearance according to claim 1, characterized in that: Step S3 also includes setting the contact type between the lower surface of the upper layer of the antenna and the upper surface of the lower layer to surface-to-surface contact, setting the sliding friction coefficient to 0.2, applying a fixed constraint to the side of the lower layer, applying a linear force between the centers of the screw holes corresponding to the upper surface of the upper layer and the lower surface of the lower layer, and adding a coupling constraint between the center and the corresponding inner wall of the hole.

4. The method for dynamic optimization of multiple screw tightening for minimum assembly clearance according to claim 1, characterized in that: There are 5 loading schemes, including: 1 direct loading; 2 loadings, each loading is 50% of the maximum preload; 3 loadings, each loading is 30%, 30%, and 40% of the maximum preload respectively; 4 loadings, each loading is 25% of the maximum preload; 5 loadings, each loading is 20% of the maximum preload.

5. The method for dynamic optimization of multiple screw tightening for minimum assembly clearance according to claim 1, characterized in that: In step S4, the model reconstructed in step S2 is meshed using finite element analysis.

6. The method for dynamic optimization of multiple screw tightening for minimum assembly clearance according to claim 1, characterized in that: In step S4, the number of neighboring points a is related to the model meshing accuracy and satisfies a≥(m / δ) 2 , where m is the distance between adjacent holes and δ is the meshing accuracy.

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