Part matching quality analysis method and device, electronic equipment and storage medium

Abstract

The present application relates to the technical field of part matching, and particularly relates to a part matching quality analysis method and device, electronic equipment and storage medium, the method comprising: determining a theoretical coordinate matching point on a theoretical simulation model of a preset assembly comprising a plurality of parts; measuring a physical sample of the preset assembly according to the theoretical coordinate matching point to obtain an actual coordinate matching point; calculating virtual offset coordinates of the plurality of parts in the preset assembly according to the actual coordinate matching point and a preset conversion formula, performing point cloud processing to obtain point cloud data of the virtual offset coordinates; constructing a preset assembly deviation visualization model according to the theoretical coordinate matching point and the point cloud data of the virtual offset coordinates, and outputting matching quality between the plurality of parts in the preset assembly and deviation evaluation results of each part. The present application can quickly judge the matching quality of the preset assembly and the deviation results of each part, and provide quantitative guidance data for part matching precision optimization.

Description

Technical Field

[0001] This invention relates to the field of component matching technology, and specifically to a component matching quality analysis method, apparatus, electronic device, and storage medium. Background Technology

[0002] In recent years, competition in the automotive industry has become increasingly fierce. As the competition in the automotive market has entered a white-hot stage, in order to shed the image of cheapness and achieve high-end quality without being overpriced, automakers have gone to great lengths to refine the exterior dimensions of their vehicles. However, refining exterior dimensions undoubtedly increases costs significantly and extends the production cycle. During the automotive manufacturing process, ensuring the precision of the body-in-white quickly and effectively, especially the step differences and gaps of the four doors that affect the vehicle's appearance, has become a key factor directly impacting vehicle quality.

[0003] CN103440388A discloses a method for evaluating the matching quality of a passenger car using virtual assembly, including: a parts scanning step, a virtual assembly step, a seam and flatness analysis step, and an adjustment and optimization step. After parts are scanned and virtually assembled, using a theoretical car body as a benchmark, the flatness and seams of each part are analyzed according to the theoretical seam and flatness requirements of the virtually assembled car body. Part measurement reports and assembly flatness and seam measurement reports are generated. By adjusting different assembly positions of the parts, candidate virtual assembly schemes are provided to solve the problem, and the optimal virtual assembly scheme is selected from the candidate schemes to achieve the best matching quality. CN114792062A discloses a method and apparatus for finding key factors of welding springback in sheet metal assemblies. The method includes: establishing a theoretical data model of the sheet metal assembly; obtaining vehicle coordinate fitting data for the sheet metal assembly; importing the vehicle coordinate fitting data into the theoretical data model to obtain the actual welding deviation of the sheet metal assembly in the theoretical data model; resetting the actual welding deviation and calculating the springback amount; calculating the contribution ratio based on the springback amount and a preset contribution ratio equation; and sorting the contribution ratios to obtain the key factors of welding springback in the sheet metal assembly. Both are beneficial attempts in this field. Summary of the Invention

[0004] The purpose of this invention is to provide a component matching quality analysis method, device, electronic device and storage medium, which can quickly determine the matching quality between several components in a preset assembly and the deviation results of each component itself, providing quantitative guidance data for optimizing component matching accuracy.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] Firstly, this application provides a method for analyzing the matching quality of components, which includes:

[0007] On the theoretical simulation model of the pre-defined assembly including several components, the theoretical coordinate matching points of several components are determined;

[0008] The actual coordinate matching points of several components in the preset assembly are obtained by measuring the physical sample of the preset assembly based on the theoretical coordinate matching points.

[0009] The actual coordinate matching points are calculated according to the preset transformation formula to obtain the virtual offset coordinates of several components in the preset assembly, and the virtual offset coordinates are processed into point cloud to obtain the point cloud data of the virtual offset coordinates.

[0010] A preset assembly deviation visualization model is constructed based on the point cloud data of theoretical coordinate matching points and virtual offset coordinates, and the matching quality between several components in the preset assembly and the deviation evaluation results of each component are output.

[0011] By adopting the above technical solution, theoretical coordinate matching points of several components are directly determined on the theoretical simulation model of a pre-designed assembly including several parts. Then, based on these theoretical coordinate matching points, the actual coordinate matching points of several components in the pre-designed assembly are measured to obtain the corresponding actual coordinate matching points. The actual coordinate matching points are then calculated using a preset transformation formula to obtain virtual offset coordinates, which are then processed into point cloud data. Finally, a deviation visualization model of the pre-designed assembly is constructed based on the point cloud data of the theoretical coordinate matching points and the virtual offset coordinates. From this model, the matching gap between preset positions (measurement points) of the components in the pre-designed assembly can be directly obtained, and this gap characterizes the matching quality between the components. Furthermore, the theoretical and actual offset of each component in the welded assembly can be obtained, thus yielding and outputting the evaluation results of the matching quality between several components in the pre-designed assembly and the deviation of each component itself. This allows for a rapid and intuitive evaluation and verification of the matching quality of the components in the pre-designed assembly, providing quantitative guidance data for subsequent optimization of the matching accuracy of the pre-designed assembly.

[0012] In some embodiments, the preset assembly is a welded assembly with weld point data. The determination of the theoretical coordinate matching point specifically includes: opening the digital model data of the welded assembly, extracting the surfaces of several components in the welded assembly according to the actual three-coordinate measurement direction of the components, projecting the weld point data onto the extracted surfaces of several components in the welded assembly, setting the projection type to normal, and obtaining all 3D measurement points of several components; measuring all 3D measurement points of several components respectively, and obtaining the coordinate values ​​of all 3D measurement points, which are the theoretical coordinate matching points.

[0013] In some implementations, the actual coordinate matching points of several components in the preset assembly are obtained by measuring a physical sample of the preset assembly based on theoretical coordinate matching points. Specifically, this includes:

[0014] Import the theoretical coordinate matching points, the preset assembly 3D model data, and the fixture model data into the coordinate measuring software. Establish a coordinate system based on the fixture model data. Identify the theoretical coordinate matching point data in the coordinate measuring software and perform coordinate measurements on several physical samples of components in the preset assembly to obtain the actual coordinates of the actual measured points corresponding to the theoretical coordinate matching points, which are the actual coordinate matching points.

[0015] In some implementations, the preset conversion formula is: T×M+N, where T is the deviation value of the normal direction of a certain component or the difference between the deviation values ​​of the normal directions of two components, M is the unit offset, and N is the coordinate of the theoretical coordinate matching point.

[0016] In some implementations, when T is the deviation value of the normal direction of a certain component, its calculation formula is:

[0017] T= In the formula, X 实际 Y 实际 Z 实际 These are the actual X, Y, and Z coordinates of the k-th measuring point of a certain component; X 理论 Y 理论 Z 理论 These are the theoretical coordinates of the X, Y, and Z axes of the k-th measuring point of a certain component;

[0018] When T is the difference in the deviation values ​​of the normal directions of two components, its calculation formula is: In the formula, T1 is the deviation value of the normal direction of the first component, and T2 is the deviation value of the normal direction of the second component.

[0019] In some implementations, determining the unit offset M includes: opening the digital model data of the preset assembly, extracting the surfaces of several components in the welding assembly according to the actual coordinate measurement direction of the components, and determining all 3D measurement points on the extracted surfaces of the several components.

[0020] Reopen the digital model data of the welding assembly, offset it by a preset value along a preset direction, and project all 3D measurement points of several components onto the extracted surface of the offset components along the preset direction. Subtract the coordinate values ​​of the 3D measurement points on the extracted surface of the component before offset from the coordinate values ​​of the 3D measurement points on the extracted surface of the component after offset. That is, the unit offset M includes the unit offset in the X-axis direction. Y-axis unit offset and unit offset in the Z-axis direction ;

[0021] =X 偏移 -X 理论 ;

[0022] =Y 偏移 -Y 理论 ;

[0023] =Z 偏移 -Z 理论 ;

[0024] In the formula, X 偏移 Y 偏移 Z 偏移 These are the offset coordinates of the X, Y, and Z axes of a component after the k-th measuring point has been offset; that is, the 3D measuring point coordinates on the extracted surface of the component after offset. 理论 Y 理论 Z 理论 These are the theoretical coordinates of the X, Y, and Z axes of the kth measuring point of a certain component, which are the 3D measuring point coordinates on the extraction surface of the component before offset.

[0025] In some implementations, point cloud processing of virtual offset coordinates specifically includes: copying the virtual offset coordinate data into a text document and changing the file extension of the text document to .asc;

[0026] In the Digital Surface Editing module of CATIA software, create a new part of the geometry set, import the text document with the .asc extension, convert it into three-dimensional solid points, and save the three-dimensional solid points as point cloud data in STL format.

[0027] In some implementations, a preset assembly deviation visualization model is constructed based on point cloud data of theoretical coordinate matching points and virtual offset coordinates. The output of the matching quality between several components in the preset assembly and the deviation evaluation results of each component specifically include:

[0028] Import the point cloud data of theoretical coordinate matching points and virtual offset coordinates into the point cloud software. Copy the theoretical coordinate matching point data into a text document. Based on the text document, create the first point cloud image of the welding assembly simulation model. Combine the point cloud data of the virtual offset coordinates to generate the second point cloud image. Compare the first point cloud image and the second point cloud image to obtain and output the evaluation results of the matching quality between several components in the preset assembly and the deviation of each component itself.

[0029] In some implementations, color labels are set in the point cloud software, with different colors representing different deviation ranges.

[0030] In some implementations, the method further includes, after obtaining the evaluation results of the matching quality between several components in the preset assembly and the deviation of each component itself, calculating the virtual matching gap value range data and the range data of each component based on the evaluation results of the matching quality between several components in the preset assembly and the deviation of each component itself, determining the problem location of the matching components and the fixture adjustment amount data, and formulating a component matching adjustment plan.

[0031] Secondly, this application provides a component matching quality analysis device capable of performing the aforementioned component matching quality analysis method, comprising a first determining module, a second determining module, a conversion module, a point cloud processing module, and an output module:

[0032] The first determining module is used to determine the theoretical coordinate matching points of several components on the theoretical simulation model of the pre-defined assembly that includes several components.

[0033] The second determining module measures the physical sample of the preset assembly based on the theoretical coordinate matching points to obtain the actual coordinate matching points of several components in the preset assembly;

[0034] The conversion module is used to calculate the virtual offset coordinates of several components in a preset assembly by matching the actual coordinates of the points according to a preset conversion formula.

[0035] The point cloud processing module is used to process the virtual offset coordinates into point cloud data to obtain the virtual offset coordinates.

[0036] The output module constructs a preset assembly deviation visualization model based on the point cloud data of theoretical coordinate matching points and virtual offset coordinates, and outputs the matching quality between several components in the preset assembly and the deviation evaluation results of each component itself.

[0037] Thirdly, this application provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, characterized in that the processor executes the computer program stored in the memory to implement the steps in the above-mentioned component matching quality analysis method.

[0038] Fourthly, this application provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the steps in the above-described component matching quality analysis method.

[0039] The beneficial effects of this invention are as follows: The component matching quality analysis method described in this application combines simulation analysis and physical measurement. Based on the theoretical coordinate matching points of the components on the theoretical simulation model, the actual coordinate matching points corresponding to the theoretical coordinate matching points are determined through physical sample measurement. Then, the actual coordinate matching point cloud is processed and combined with the theoretical simulation model to construct a preset assembly deviation visualization model. This model intuitively displays the matching deviations between components in the preset assembly, as well as the deviations of the components themselves. On the one hand, it advances the analysis and countermeasures for new vehicle matching problems from the mold return stage to the component sampling stage, greatly shortening the product maturity cycle of the actual vehicle. On the other hand, by combining actual measurement with the theoretical model, the accuracy of the analysis results is improved. Furthermore, the deviation results can be quantitatively output, thus providing quantitative guidance data for subsequent component matching accuracy optimization, shortening the optimization time, and improving the efficiency of achieving matching accuracy. Attached Figure Description

[0040] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention.

[0041] Figure 1 A flowchart of a component matching quality analysis method provided in this application embodiment;

[0042] Figure 2 A visualization of the matching gap between the front door inner panel and the front door anti-collision beam provided in an embodiment of this application;

[0043] Figure 3 A visualization of deviation data for the front door inner panel provided in an embodiment of this application;

[0044] Figure 4 A visualization of the deviation data of the front door anti-collision beam provided in the embodiments of this application;

[0045] Figure 5 A flowchart illustrating the method for achieving precision in the welding assembly of the front door inner panel provided in this application embodiment;

[0046] Figure 6 A functional unit block diagram of a component matching quality analysis device provided in this application embodiment;

[0047] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0048] This application describes several embodiments, but these descriptions are exemplary and not restrictive, and it will be apparent to those skilled in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with, or may replace, any feature or element of any other embodiment.

[0049] This application includes and contemplates combinations of features and elements known to those skilled in the art. The embodiments, features, and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive scheme as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive schemes to form another unique inventive scheme as defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any suitable combination. Therefore, the embodiments are not limited except by the limitations imposed by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.

[0050] Furthermore, in describing representative embodiments, the specification may have presented methods and / or processes as a specific sequence of steps. However, the method or process should not be limited to the specific order of steps described herein, to the extent that it does not depend on such a specific order. As will be understood by those skilled in the art, other sequences of steps are also possible. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation of the claims. Moreover, the claims concerning the method and / or process should not be limited to the steps performed in the written order, and those skilled in the art will readily understand that these orders can be varied and still remain within the spirit and scope of the embodiments of this application.

[0051] The precision of the body-in-white, especially the step differences and gaps of the four doors that affect the vehicle's appearance, has become a key factor directly affecting the quality of the car. Achieving precision in the four doors is particularly difficult, with numerous influencing factors, including a large number of matching parts, long matching cycles, a large amount of matching work, low rigidity of the inner panels, the need to adjust the welding sequence according to the actual matching, and the need for welding fixtures to be adjusted in conjunction with the inner panels, etc. Currently, the automotive industry mainly uses two methods to achieve assembly precision.

[0052] The first type involves developing a door inner panel welded assembly matching fixture (PCF). This fixture relies on a positioning device to achieve the overlap between parts, verifying and measuring the overlap accuracy and matching status. Its function is similar to a screw-operated machine fixture, simulating the actual clamping process to verify product tolerance design, welding procedures, and positioning benchmarks, and guiding the correction of parts. However, this fixture is costly, has many assembly blocks, making it difficult to guarantee accuracy during switching, cannot cover all matching areas, and interference between parts can cause deformation and data distortion. Furthermore, the data is unstable due to continuous corrections of inner panel parts and matching parts, making it impossible to perform this work repeatedly and implement full-process matching tracking. Given these obvious drawbacks, this assembly matching fixture has been gradually phased out by various automakers.

[0053] The second method involves directly matching parts on a welding fixture, which has a positioning system for each part. Compared to PCF (Precast Concrete Formwork) fixtures, this method has the same functions and disadvantages, but saves on the development costs of PCF fixtures. Therefore, this method is used for most vehicle models. However, this method is affected by the following three aspects: 1. Matching cannot be performed in the early stages of a project when there are no fixtures available; 2. Matching cannot be performed during workshop production; 3. On some vehicle production lines, doors are placed with latches, making matching measurements impossible.

[0054] Based on this, such as Figure 1 As shown in the embodiment of this application, a component matching quality analysis method includes the following steps:

[0055] S1. On the theoretical simulation model of the pre-defined assembly including several components, determine the theoretical coordinate matching points of several components.

[0056] S2, Measure the physical sample of the preset assembly based on the theoretical coordinate matching point to obtain the actual coordinate matching points of several parts in the preset assembly corresponding to the theoretical coordinate matching point;

[0057] S3, calculate the virtual offset coordinates of several components in the preset assembly by matching the actual coordinates according to the preset transformation formula, and process the virtual offset coordinates into a point cloud to obtain the point cloud data of the virtual offset coordinates;

[0058] S4 constructs a preset assembly deviation visualization model based on the point cloud data of theoretical coordinate matching points and virtual offset coordinates, and outputs the matching quality between several parts in the preset assembly and the deviation evaluation results of each part itself.

[0059] This application directly determines the theoretical coordinate matching points of several components on a theoretical simulation model of a pre-designed assembly, which includes several parts. Then, based on these theoretical coordinate matching points, a physical sample of the welded assembly is measured to obtain the actual coordinate matching points of several components in the pre-designed assembly corresponding to the theoretical coordinate matching points. The actual coordinate matching points are then used to calculate virtual offset coordinates using a preset transformation formula, and point cloud processing is performed to obtain the corresponding point cloud data. Finally, a deviation visualization model of the pre-designed assembly is constructed based on the point cloud data of the theoretical coordinate matching points and the virtual offset coordinates. From this deviation visualization model, the matching gap between preset positions (i.e., measurement points) between components in the pre-designed assembly can be directly obtained, and this matching gap characterizes the matching quality between the components in the pre-designed assembly. Furthermore, the theoretical and actual offset of each component in the welded assembly can be obtained, thus obtaining and outputting the matching quality between several components in the pre-designed assembly and the deviation evaluation results of each component. This allows for a rapid and intuitive evaluation and verification of the matching quality of the components in the pre-designed assembly, providing quantitative guidance data for subsequent optimization of the matching accuracy of the pre-designed assembly.

[0060] In one possible implementation, the preset assembly is a welded assembly with weld point data. The determination of the theoretical coordinate matching point specifically includes: opening the digital model data of the welded assembly, extracting the surfaces of several components in the welded assembly according to the actual three-coordinate measurement direction of the components, projecting the weld point data onto the extracted surfaces of several components in the welded assembly, setting the projection type to normal, and obtaining all 3D measurement points of several components; measuring all 3D measurement points of several components respectively, and obtaining the coordinate values ​​of all 3D measurement points, which are the theoretical coordinate matching points.

[0061] It should be noted that the theoretical coordinate matching points can also be manually set according to actual needs, and are not limited to the welding point projection method of the welding assembly.

[0062] For example, taking the front door inner panel and the front door anti-collision beam, which have a welding matching relationship, as an example, the steps for obtaining the theoretical coordinate matching points of the front door inner panel and the front door anti-collision beam specifically include:

[0063] 1) Open the automotive four-door inner panel welding assembly with weld point data: Open the door assembly CAD model data in CATProduct format with CATIA, hide the door outer panel in the CAD model data, and the automotive four-door inner panel welding assembly CAD model with weld point data will be displayed. At the same time, you can find the front door inner panel and the front door anti-collision beam.

[0064] 2) Extract the surfaces of the inner door panel and each matching part separately, and select the inner or outer side of the vehicle body according to the actual coordinate measuring direction of the parts. In this embodiment, since the coordinate measuring machine detects the front door inner panel and the front door anti-collision beam from the outside in, the outer side of the vehicle body is extracted for both the front door inner panel and the front door anti-collision beam, and the extraction method is tangential continuous method.

[0065] 3) Project the weld point data onto the surfaces extracted in step 2) of the front door inner panel and the front door anti-collision beam respectively. Use the projection command in CATIA and set the projection type to normal to obtain the 3D measurement point data of the front door inner panel and the front door anti-collision beam.

[0066] 4) Open the 3D measurement point data of the front door inner panel or the front door anti-collision beam in CATIA respectively, and use the measurement command in CATIA to measure all the 3D measurement points of the front door inner panel or the front door anti-collision beam respectively.

[0067] 5) In CATIA, start the design table, select "Create design table using current parameter values", choose horizontal for the direction, and finally click OK;

[0068] 6) In the pop-up selection dialog box, use the measurement points as filter points. The left-hand list will then only display the parameters of the measurement points, including the coordinates of the X, Y, and Z axes. Click the first parameter, hold down Shift and select the last parameter to select all parameters. Click the middle arrow to move the X, Y, and Z axis coordinate information to the right-hand list. After clicking OK, select the directory, set the file name, and the theoretical coordinate matching point data for the front door inner panel or front door anti-collision beam will be output to an Excel spreadsheet.

[0069] 6) Arrange the measurement points of the front door inner panel or front door anti-collision beam in the same order in EXCEL to ensure that the theoretical matching points of the door inner panel and the anti-collision beam correspond one-to-one.

[0070] In one possible implementation, the actual coordinate matching points of several components in the preset assembly are obtained by measuring the physical sample of the preset assembly based on the theoretical coordinate matching points. Specifically, this includes: importing the theoretical coordinate matching points, the three-dimensional model data of the preset assembly, and the fixture model data into a coordinate measuring machine (CMM) software; establishing a coordinate system based on the fixture model data; identifying the theoretical coordinate matching point data in the CMM software; and performing CMM measurements on the physical samples of several components in the preset assembly to obtain the actual coordinates of the actual measured points corresponding to the theoretical coordinate matching points, which are the actual coordinate matching points.

[0071] For example, taking the welded assembly of the front door inner panel and the front door bumper beam as an example, the theoretical coordinate matching point data of the front door inner panel or the front door bumper beam, outputting an Excel spreadsheet, is sent to the physical production plant of the component, and included in the measurement range of each batch of samples. The 3D data of the part file of the front door inner panel or the front door bumper beam, along with the digital model of the inspection bracket or fixture, are imported into the factory's coordinate measuring machine (CMM) software. A coordinate system is established based on the reference of the inspection bracket or fixture digital model. The theoretical coordinate matching point data is identified in the CMM software, and the physical measurements of each component are performed to obtain the X, Y, Z, and T coordinates. X, Y, and Z are the actual coordinate values ​​of the X, Y, and Z axes of a measurement point on a certain component, respectively, and T is the deviation value in the normal direction of the component. The coordinate data of the actual matching points are then exported as an Excel file using the CMM software.

[0072] In one possible implementation, the preset conversion formula is: T×M+N, where T is the deviation value of the normal direction of a certain component or the difference between the deviation values ​​of the normal directions of two components, M is the unit offset, and N is the coordinate of the theoretical coordinate matching point.

[0073] In one possible implementation, when T is the deviation value of the normal direction of a certain component, its calculation formula is:

[0074] T= In the formula, X 实际 Y 实际 Z 实际 These are the actual X, Y, and Z coordinates of the k-th measuring point of a certain component, respectively. 理论 Y 理论 Z 理论 These are the theoretical coordinates of the X, Y, and Z axes of the k-th measuring point of a certain component.

[0075] When T is the difference in the deviation values ​​of the normal directions of two components, its calculation formula is: In the formula, T1 is the deviation value of the normal direction of the first component, and T2 is the deviation value of the normal direction of the second component.

[0076] In one possible implementation, the determination of the unit offset M includes: opening the digital model data of the preset assembly, extracting the surfaces of several components in the welding assembly according to the actual three-coordinate measurement direction of the components, and determining all 3D measurement points on the extracted surfaces of the several components.

[0077] Reopen the digital model data of the welding assembly, offset it by a preset value along a preset direction, and project all 3D measurement points of several components onto the extracted surface of the offset components along the preset direction. Subtract the coordinate values ​​of the 3D measurement points on the extracted surface of the component before offset from the coordinate values ​​of the 3D measurement points on the extracted surface of the component after offset. That is, the unit offset M includes the unit offset in the X-axis direction. Y-axis unit offset and unit offset in the Z-axis direction ;

[0078] =X 偏移 -X 理论 ;

[0079] =Y 偏移 -Y 理论 ;

[0080] =Z 偏移 -Z 理论 ;

[0081] In the formula, X 偏移 Y 偏移 Z 偏移 These are the offset coordinates of the X, Y, and Z axes of a component after the k-th measuring point has been offset; that is, the 3D measuring point coordinates on the extracted surface of the component after offset. 理论 Y 理论 Z 理论 These are the theoretical coordinates of the X, Y, and Z axes of the kth measuring point of a certain component, which are the 3D measuring point coordinates on the extraction surface of the component before offset.

[0082] For example, again taking the welded assembly of the front door inner panel and the front door anti-collision beam as an example, the acquisition of the unit offset includes the following steps:

[0083] ① Open the extracted 3D model data of the four-door inner panel parts in CATIA, use the offset command in CATIA, set the offset value to 1mm, select the outward direction of the vehicle, and finally click OK;

[0084] ② In CATIA, open the 3D measurement point matching data projected onto the inner panel of the door. Use the projection command in CATIA, set the projection type to normal, select the 3D measurement point data on the inner panel of the front door as the content to be projected, select the face offset by 1mm as mentioned above as the supported face, and finally click OK.

[0085] ③ Export the 3D measurement point data projected onto the inner panel of the front door as specific coordinate data (X, Y, Z) in an Excel document using CATIA software;

[0086] ④ In Excel, subtract the X, Y, and Z values ​​of the 3D measurement point data on the inner plate before offset from the X, Y, and Z values ​​of the 3D measurement point data on the inner plate after offset, respectively, to obtain the result under the condition of a 1mm change in the normal direction. , , The value is used as the unit offset.

[0087] This application uses a coordinate change of 1mm as the unit offset, which facilitates subsequent conversion calculations. It can directly use the deviation value of the normal direction of a certain component or the difference of the deviation values ​​of the normal directions of two components multiplied by the unit offset, without the need for additional conversion, which is convenient, fast and reduces the amount of calculation.

[0088] In one possible implementation, the point cloud processing of virtual offset coordinates specifically includes: copying the virtual offset coordinate data into a text document and changing the file extension of the text document to .asc; creating a new part of a geometry set in the digital surface editing module of CATIA software, and importing the text document with the .asc extension; running the Cloud / Points command in CATIA, selecting the imported text document and converting it into 3D solid points, and saving the 3D solid points as point cloud data in STL format.

[0089] In one possible implementation, a preset assembly deviation visualization model is constructed based on point cloud data of theoretical coordinate matching points and virtual offset coordinates. The output results include the matching quality between several components in the preset assembly and the deviation evaluation results of each component itself.

[0090] Import the point cloud data of theoretical coordinate matching points and virtual offset coordinates into the point cloud software. Copy the theoretical coordinate matching point data into a text document. Based on the text document, create the first point cloud image of the welding assembly simulation model. Combine the point cloud data of the virtual offset coordinates to generate the second point cloud image. Compare the first point cloud image and the second point cloud image to obtain and output the evaluation results of the matching quality between several components in the preset assembly and the deviation of each component itself.

[0091] For example, taking the welded assembly of the front door inner panel and the front door anti-collision beam as an example, the specific steps for obtaining the welding assembly matching quality evaluation results include the following:

[0092] A1. Copy the theoretical coordinate matching point data of the front door inner panel into a text document, and import it into Polyworks software along with the point cloud data of the virtual offset coordinate transformation between the front door inner panel and the front door anti-collision beam.

[0093] A2, click the "Create Surface Comparison Point" command in the Polyworks software, select "Create from Text File" as the method, select the text document containing the theoretical coordinate matching point data coordinate values ​​of the front door inner panel, and obtain the first point cloud image of the front door inner panel.

[0094] A3. In Polyworks software, locate the reference point of the front door bumper beam in the point cloud data of the virtual offset coordinate transformation between the front door inner panel and the front door bumper beam. Select the reference point positions with three relatively small absolute gap values, choose Alignment—Best Fit Measurement Object—Start, and wait for the software to automatically calculate and generate the second point cloud image. See [link / reference]. Figure 2 As shown, by combining the first point cloud image and the second point cloud image, a visualization model of welding assembly deviation is constructed, namely a visualization diagram of the matching gap between the front door inner panel and the front door anti-collision beam, and the display order of the points in the diagram is adjusted.

[0095] A4. In the point cloud software Polyworks, set color labels to represent different deviation ranges. A deviation greater than a first preset value is set to red; a deviation greater than or equal to a second preset value is set to green; and a deviation less than the second preset value is set to blue. The first preset value should be greater than the second preset value, and both the first and second preset values ​​should be adjusted appropriately based on actual conditions. See also... Figure 2 As shown, in this embodiment, the first preset value is 0.5 and the second preset value is -0.5.

[0096] As can be seen, by generating a visualization diagram of the matching gap between the front door inner panel and the front door anti-collision beam in this embodiment, the matching quality between the front door anti-collision beam and the front inner panel can be intuitively obtained. This method only requires a welding assembly simulation model with weld point data and actual measured three-coordinate data of the parts to perform matching analysis. It breaks the constraint of traditional welding quality analysis methods that must use physical fixtures or welding assembly matching inspection tools. Thus, the analysis and countermeasures time for matching problems of new models can be advanced from the mold return to the factory stage to the parts prototyping stage, which greatly shortens the product maturity cycle of the actual vehicle.

[0097] For example, obtaining the deviation evaluation results of the front door inner panel specifically includes the following steps:

[0098] B1. Copy the theoretical coordinate matching point data of the front door inner panel into a text document, and import it into Polyworks software along with the point cloud data of the virtual offset coordinate transformation of the front door inner panel itself.

[0099] B2, click the "Create Surface Comparison Point" command in the Polyworks software, select "Create from Text File" as the method, select the text document containing the theoretical coordinate matching point data coordinate values ​​of the front door inner panel, and obtain the first point cloud image of the front door inner panel.

[0100] B3, in Polyworks software, generates a second point cloud image based on the point cloud data transformed from the virtual offset coordinates of the front door inner panel. (See [link]) Figure 3 As shown, by combining the first point cloud image and the second point cloud image, a visualization model of welding assembly deviation is constructed, namely a visualization diagram of deviation data of the front door inner panel, and the display order of the points in the diagram is adjusted.

[0101] B4. In the point cloud software Polyworks, set color labels to represent different deviation ranges. A deviation greater than a first preset value is set to red; a deviation greater than or equal to a second preset value is set to green; and a deviation less than the second preset value is set to blue. The first preset value should be greater than the second preset value, and both the first and second preset values ​​should be adjusted appropriately based on actual conditions. See also... Figure 3 As shown, in this embodiment, the first preset value is 0.5 and the second preset value is -0.5.

[0102] This method can quickly obtain the offset of the components themselves, providing data support for subsequent rectification.

[0103] Similarly, see Figure 4 As shown, a visualization of the deviation data of the front door anti-collision beam can be obtained.

[0104] In one possible approach, based on the evaluation results of the welding assembly matching quality and the deviation of each component, the virtual matching gap value range data and the range data of each component are calculated. The problem locations of the matching components and the fixture adjustment amount data are determined, and a welding point sequence adjustment plan is formulated, thereby accurately, comprehensively, and systematically formulating the path to achieve welding assembly accuracy.

[0105] For example, such as Figure 5 As shown in the embodiment of this application, the method for achieving the welding accuracy of the front door inner panel assembly includes the following steps:

[0106] C1, determine whether the matching range is within the preset range: according to Figure 2 The visualization of the gap between the front door inner panel and the front door bumper beam is shown. First, subtract the minimum value from the maximum value to determine if the range is within the standard 0.5 range. If it is, the matching is acceptable; if not, the influencing points need to be identified and circled. In this case, as shown... Figure 2 The matching graph shows a range exceeding the tolerance at the point circled in red, requiring further rectification and assessment.

[0107] C2, Develop a solder joint sequence plan: Based on the matching diagram, if there are matching points exceeding ±0.3, a temporary solder joint sequence plan needs to be developed. First, weld the area with a gap value within ±0.3, then weld the area within ±0.5 (if any), and finally weld the solder joints that exceed the tolerance range (if any). If all are within ±0.3, the solder joint sequence should be developed in order of gap value from smallest to largest.

[0108] C3. Formulate a fixture adjustment plan: Based on the visualization of the deviation data of the inner door panel, find the support point of the welding fixture. Using the data of the inner door panel as a reference, formulate a temporary or permanent fixture adjustment plan according to the gap value data to compensate for the fixture.

[0109] C4, Component Rectification Plan: Based on the component deviation data visualization chart, subtract the minimum value from the maximum value for each component to determine if the range is within the standard 0.5 range. If the range of the front door inner panel exceeds 0.5, the range of the front door inner panel needs to be controlled within 0.5 before formulating a rectification plan for the matching parts of the front door inner panel. If the range of the front door inner panel is within 0.5, the front door inner panel does not need to be rectified; instead, the matching parts, namely the front door anti-collision beam, need to be rectified. The rectification of the front door inner panel is a supplementary measure. In this case, the P-value of the front door anti-collision beam is unqualified. Locate the circled position on the matching diagram, and correct and rectify the points at the same position on the front door anti-collision beam according to the matching P-value data.

[0110] It should be noted that the tolerance range and range of gap values ​​matched in the above examples can be extended to the development of new products of the same type and from the same company using the same process. Different companies and different products can establish tolerance ranges that conform to their own characteristics using the method of this invention.

[0111] Figure 6 This application provides a functional unit block diagram of a component matching quality analysis device. The component matching quality analysis device 100 is used to determine the matching quality of welded assembly components and the offset of the components themselves. It can execute the above-mentioned component matching quality analysis method and includes a first determination module 101, a second determination module 102, a conversion module 103, a point cloud processing module 104, and an output module 105.

[0112] The first determining module 101 is used to determine the theoretical coordinate matching points of several components on the theoretical simulation model of the pre-defined assembly that includes several components.

[0113] The second determining module 102 measures the physical sample of the preset assembly based on the theoretical coordinate matching point to obtain the actual coordinate matching points of several components in the preset assembly corresponding to the theoretical coordinate matching point.

[0114] The conversion module 103 calculates the virtual offset coordinates of several components in the preset assembly by matching the actual coordinates with the actual coordinates according to the preset conversion formula.

[0115] The point cloud processing module 104 is used to perform point cloud processing on the virtual offset coordinates to obtain point cloud data of the virtual offset coordinates.

[0116] The output module 105 is used to construct a preset assembly deviation visualization model based on the point cloud data of theoretical coordinate matching points and virtual offset coordinates, and output the matching quality between several parts in the preset assembly and the deviation evaluation results of each part itself.

[0117] In the exemplary embodiments of this application, any of the embodiments in the foregoing component matching quality analysis method embodiments are applicable to the device embodiments, and will not be described in detail here.

[0118] As can be seen from the embodiments of this application, the component matching quality analysis device combines simulation analysis and physical measurement. The first determining module 101 determines the theoretical coordinate matching points of the components on the theoretical simulation model. The second determining module determines the actual coordinate matching points corresponding to the theoretical coordinate matching points based on the theoretical coordinate matching points on the theoretical simulation model. The actual coordinate matching point is then determined through physical sample measurement. The actual coordinate matching point cloud is processed by the conversion module 103 and the point cloud processing module 104. Finally, the output module 105 constructs a preset assembly deviation visualization model based on the point cloud data of the theoretical coordinate matching points and the virtual offset coordinates, intuitively displaying the matching deviations between components in the preset assembly and the deviations of the components themselves. On the one hand, this allows the analysis and countermeasures for new vehicle model matching problems to be moved from the mold return stage to the component sampling stage, greatly shortening the product maturity cycle of the actual vehicle. On the other hand, combining actual measurement with the theoretical model improves the accuracy of the analysis results. Furthermore, the deviation results can be quantitatively output, providing quantitative guidance data for subsequent component matching accuracy optimization, shortening the optimization time, and improving the efficiency of achieving matching accuracy.

[0119] This application provides an electronic device, including a processor and a memory. The memory stores a computer program, and the processor executes the computer program stored in the memory to implement some or all of the steps in the piston pin bore profile design optimization method described above.

[0120] This application provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement some or all of the steps in the piston pin bore profile design optimization method described above. The computer-readable storage medium can be transient or non-transient.

[0121] This application provides a computer program including computer-readable code. When the computer-readable code is run in an electronic device, the processor in the electronic device executes some or all of the steps in the above-described piston pin bore profile design optimization method.

[0122] This application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program. When the computer program is read and executed by a computer, it implements some or all of the steps in the piston pin bore profile design optimization method described above. This computer program product can be implemented specifically through hardware, software, or a combination thereof. In some embodiments, the computer program product is specifically embodied as a computer storage medium; in other embodiments, the computer program product is specifically embodied as a software product, such as a software development kit (SDK), etc.

[0123] It should be noted that the descriptions of the various embodiments above tend to emphasize the differences between them, while their similarities or commonalities can be referred to interchangeably. The descriptions of the above embodiments of the device, storage medium, computer program, and computer program product are similar to the descriptions of the above method embodiments and have similar beneficial effects. For technical details not disclosed in the embodiments of the device, storage medium, computer program, and computer program product of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0124] It should be noted that, Figure 7 This is a schematic diagram of a hardware entity of an electronic device in an embodiment of this application, such as... Figure 7 As shown, the hardware entity of the electronic device 200 includes: a processor 201, a memory 202, and a communication interface 203, wherein:

[0125] The processor 201 typically controls the overall operation of the electronic device 200.

[0126] The memory 202 is configured to store instructions and applications executable by the processor 201, and can also cache data to be processed or already processed by the processor 201 and various modules in the electronic device 200, such as image data, audio data, voice communication data and video communication data, which can be implemented by flash memory or random access memory (RAM).

[0127] Communication interface 203 enables electronic devices to communicate with other terminals or servers via a network.

[0128] Data can be transferred between the processor 201, memory 202 and communication interface 203 via bus 204.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0130] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0131] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0132] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0133] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence or the part that contributes to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, magnetic disks, or optical disks.

Claims

1. A method for analyzing the matching quality of components, characterized in that, include: On the theoretical simulation model of the pre-defined assembly including several components, the theoretical coordinate matching points of several components are determined; The physical sample of the preset assembly is measured based on the theoretical coordinate matching point to obtain the actual coordinate matching points of several components in the preset assembly corresponding to the theoretical coordinate matching point; The virtual offset coordinates of several components in a preset assembly are calculated using a preset transformation formula based on the actual coordinate matching points. These virtual offset coordinates are then processed into a point cloud to obtain point cloud data of the virtual offset coordinates. The preset transformation formula is: T×M+N, where T is the deviation value of the normal direction of a component or the difference between the deviation values ​​of the normal directions of two components, M is the unit offset, and N is the coordinate of the theoretical coordinate matching point. When T is the deviation value of the normal direction of a component, its calculation formula is: T= In the formula, X 实际 Y 实际 Z 实际 These are the actual X, Y, and Z coordinates of the k-th measuring point of a certain component, respectively. 理论 Y 理论 Z 理论 These are the theoretical coordinates of the X, Y, and Z axes of the k-th measuring point of a certain component; When T is the difference in the deviation values ​​of the normal directions of two components, its calculation formula is: In the formula, T1 is the deviation value of the normal direction of the first component, and T2 is the deviation value of the normal direction of the second component. The determination of the unit offset M includes: opening the digital model data of the preset assembly, extracting the surfaces of several components in the welding assembly according to the actual three-coordinate measurement direction of the components, and determining all 3D measurement points on the extracted surfaces of the several components. Reopen the digital model data of the welding assembly, offset it by 1mm along the preset direction, and project all 3D measurement points of several components onto the extraction surface of the offset components along the normal direction. Subtract the coordinate values ​​of the 3D measurement points on the extraction surface of the offset components from the coordinate values ​​of the 3D measurement points on the extraction surface of the original components; that is, the unit offset M includes the unit offset in the X-axis direction. Y-axis unit offset and unit offset in the Z-axis direction ; =X 偏移 -X 理论 ; =Y 偏移 -AND 理论 ; =Z 偏移 -WITH 理论 ; In the formula, X 偏移 Y 偏移 Z 偏移 These are the offset coordinates of the X, Y, and Z axes of a component after the k-th measuring point has been offset; that is, the 3D measuring point coordinates on the extracted surface of the component after offset. 理论 Y 理论 Z 理论 These are the theoretical coordinates of the X, Y, and Z axes of the k-th measuring point of a certain component, which are the coordinates of the 3D measuring point on the extraction surface of the component before offset. A preset assembly deviation visualization model is constructed based on the point cloud data of theoretical coordinate matching points and virtual offset coordinates, and the matching quality between several components in the preset assembly and the deviation evaluation results of each component are output.

2. The component matching quality analysis method according to claim 1, characterized in that, The preset assembly is a welded assembly with weld point data, and the determination of the theoretical coordinate matching point specifically includes: Open the digital model data of the welding assembly, extract the surface of several components in the welding assembly according to the actual three-coordinate measurement direction of the components, project the weld point data onto the extracted surface of several components in the welding assembly, set the projection type to normal, and obtain all 3D measurement points of several components. Measure all 3D measurement points of several components respectively, and obtain the coordinate values ​​of all 3D measurement points, which are the theoretical coordinate matching points.

3. The component matching quality analysis method according to claim 1, characterized in that, Based on the theoretical coordinate matching points, the physical sample of the preset assembly is measured to obtain the actual coordinate matching points of several components in the preset assembly, including: Import the theoretical coordinate matching points, the preset assembly 3D model data, and the fixture model data into the coordinate measuring software. Establish a coordinate system based on the fixture model data. Identify the theoretical coordinate matching point data in the coordinate measuring software and perform coordinate measurements on several physical samples of components in the preset assembly to obtain the actual coordinates of the actual measured points corresponding to the theoretical coordinate matching points, which are the actual coordinate matching points.

4. The component matching quality analysis method according to claim 1, characterized in that, The point cloud processing of virtual offset coordinates specifically includes: copying the virtual offset coordinate data into a text document and changing the file extension of the text document to .asc; In the Digital Surface Editing module of CATIA software, create a new part of the geometry set, import the text document with the .asc extension, convert it into three-dimensional solid points, and save the three-dimensional solid points as point cloud data in STL format.

5. The component matching quality analysis method according to claim 1, characterized in that, Based on point cloud data of theoretical coordinate matching points and virtual offset coordinates, a preset assembly deviation visualization model is constructed. The output results include the matching quality between several components in the preset assembly and the deviation evaluation results of each component itself. Import the point cloud data of theoretical coordinate matching points and virtual offset coordinates into the point cloud software. Copy the theoretical coordinate matching point data into a text document. Based on the text document, create the first point cloud image of the welding assembly simulation model. Combine the point cloud data of the virtual offset coordinates to generate the second point cloud image. Compare the first point cloud image and the second point cloud image to obtain and output the evaluation results of the matching quality between several components in the preset assembly and the deviation of each component itself.

6. The component matching quality analysis method according to claim 5, characterized in that: Color labels are set in point cloud software to represent different deviation ranges using different colors.

7. The component matching quality analysis method according to claim 1, characterized in that: It also includes, after obtaining the evaluation results of the matching quality between several components in the preset assembly and the deviation of each component itself, calculating the virtual matching gap value range data and the range data of each component based on the evaluation results of the matching quality between several components in the preset assembly and the deviation of each component itself, determining the problem location of the matching components and the fixture adjustment amount data, and formulating a component matching adjustment plan.

8. A component matching quality analysis device, characterized in that, Capable of performing the component matching quality analysis method as described in any one of claims 1 to 7, comprising: The first determining module is used to determine the theoretical coordinate matching points of several components on the theoretical simulation model of the pre-defined assembly that includes several components. The second determining module measures the physical sample of the preset assembly based on the theoretical coordinate matching points to obtain the actual coordinate matching points of several components in the preset assembly; The conversion module is used to calculate the virtual offset coordinates of several components in a preset assembly by converting the actual coordinate matching points according to a preset conversion formula. The preset conversion formula is: T×M+N, where T is the deviation value of the normal direction of a component or the difference between the deviation values ​​of the normal directions of two components, M is the unit offset, and N is the coordinate of the theoretical coordinate matching point. When T is the deviation value of the normal direction of a component, its calculation formula is: T= In the formula, X 实际 Y 实际 Z 实际 These are the actual X, Y, and Z coordinates of the k-th measuring point of a certain component, respectively. 理论 Y 理论 Z 理论 Let X, Y, and Z be the theoretical coordinates of the k-th measuring point of a component, respectively; and T be the difference in the deviation of the normal directions of two components, then the calculation formula is: In the formula, T1 is the deviation value of the normal direction of the first component, and T2 is the deviation value of the normal direction of the second component; the determination of the unit offset M includes: opening the digital model data of the preset assembly, extracting the surface of several components in the welding assembly according to the actual three-coordinate measurement direction of the components, and determining all 3D measurement points on the extracted surfaces of several components. Reopen the digital model data of the welding assembly, offset it by 1mm along the preset direction, and project all 3D measurement points of several components onto the extraction surface of the offset components along the normal direction. Subtract the coordinate values ​​of the 3D measurement points on the extraction surface of the offset components from the coordinate values ​​of the 3D measurement points on the extraction surface of the original components; that is, the unit offset M includes the unit offset in the X-axis direction. Y-axis unit offset and unit offset in the Z-axis direction ; =X 偏移 -X 理论 ; =Y 偏移 -AND 理论 ; =Z 偏移 -WITH 理论 ; In the formula, X 偏移 Y 偏移 Z 偏移 These are the offset coordinates of the X, Y, and Z axes of a component after the k-th measuring point has been offset; that is, the 3D measuring point coordinates on the extracted surface of the component after offset. 理论 Y 理论 Z 理论 These are the theoretical coordinates of the X, Y, and Z axes of the k-th measuring point of a certain component, which are the coordinates of the 3D measuring point on the extraction surface of the component before offset. The point cloud processing module is used to process the virtual offset coordinates into point cloud data to obtain the virtual offset coordinates. The output module constructs a preset assembly deviation visualization model based on the point cloud data of theoretical coordinate matching points and virtual offset coordinates, and outputs the matching quality between several components in the preset assembly and the deviation evaluation results of each component itself.

9. An electronic device comprising a processor and a memory, the memory storing a computer program, characterized in that, The processor executes the computer program stored in the memory to implement the steps in the component matching quality analysis method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the steps in the component matching quality analysis method as described in any one of claims 1 to 7.

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