A method and device for finding key factors of sheet metal assembly welding springback

Abstract

The present application discloses a method and device for finding key factors for springback in the welding of sheet metal assemblies. The method includes: establishing a theoretical data model for 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, and obtaining the actual welding deviation of the sheet metal assembly from 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 key factors for springback in the welding of the sheet metal assembly. This embodiment enables rapid and accurate identification of key factors causing springback in the sheet metal assembly welding process, reducing the number of experimental matching times and saving time and resources.

Description

Technical Field

[0001] The embodiments of the present application relate to sheet metal assembly welding technology, and more particularly to a method and device for finding key factors of springback in sheet metal assembly welding. Background Art

[0002] To improve the body-in-white dimensional engineers' understanding of the matching process and accelerate the improvement of the qualified rate of the assembly after the sheet metal welding is completed, it is necessary to find the key factors that cause the rebound of the sheet metal assembly.

[0003] The current method for finding the key factors causing springback is physical matching verification. Physical matching has the following weaknesses:

[0004] 1. The physical matching verification cycle is long and requires the sheet metal parts, fixtures, welding guns, and workers to be present on site;

[0005] 2. When springback occurs during physical matching, the cause of the springback can only be determined based on subjective experience. However, subjective experience is often inaccurate, which will cause the springback to continue to occur, wasting time and resources. Summary of the Invention

[0006] The embodiments of the present application provide a method and device for searching for key factors of springback in sheet metal assembly welding, which can quickly and accurately find the key factors causing springback in sheet metal assembly welding, reduce the number of experimental matching times, and save time and resources.

[0007] The present application provides a method for finding a key factor of sheet metal assembly welding springback, which may include:

[0008] Establish theoretical data model for sheet metal assembly;

[0009] Obtain vehicle coordinate fitting data of sheet metal parts;

[0010] Importing the vehicle coordinate fitting data into the theoretical data model, and obtaining the actual welding deviation of the sheet metal assembly in the theoretical data model;

[0011] Resetting the actual welding deviation and calculating the springback amount;

[0012] Calculating a contribution ratio according to the rebound amount and a preset contribution ratio equation;

[0013] The contribution ratios are sorted to obtain the key factors of sheet metal assembly welding springback.

[0014] In an exemplary embodiment of the present application, establishing a theoretical data model for sheet metal assembly welding may include:

[0015] The CATIA theoretical data of sheet metal assembly welding is meshed using the CATIA algorithm and the HyperMesh algorithm to generate a theoretical mid-surface finite element mesh;

[0016] The theoretical mid-surface finite element mesh is imported into RD&T software to establish the theoretical data model.

[0017] In an exemplary embodiment of the present application, the step of obtaining vehicle coordinate fitting data of a sheet metal part may include:

[0018] Scan sheet metal parts and generate measurement data of point cloud triangulated mesh;

[0019] In PolyWorks, the measurement data of the triangulated point cloud mesh is fitted into the vehicle coordinates to obtain the vehicle coordinate fitting data of the sheet metal part.

[0020] In an exemplary embodiment of the present application, the step of importing the vehicle coordinate fitting data into the theoretical data model and obtaining the actual welding deviation of the sheet metal assembly in the theoretical data model may include:

[0021] In RD&T software, the point cloud obtained by scanning the sheet metal part is combined with the theoretical mid-surface finite element mesh;

[0022] The distance between the finite element nodes in the theoretical mid-surface finite element mesh and the point cloud triangulated mesh surface in the vector direction is calculated, and the actual welding deviation is generated on the finite element nodes.

[0023] In an exemplary embodiment of the present application, resetting the actual welding deviation and calculating the springback amount may include:

[0024] Reset each actual deviation corresponding to the finite element node to 0, and calculate the springback amount corresponding to the actual deviation at that location according to the preset calculation formula.

[0025] In an exemplary embodiment of the present application, the actual welding deviation may include: a first deviation between the sheet metal part and the fixture, and a second deviation between the nodes of the mating surfaces of the sheet metal parts;

[0026] Resetting the actual deviation of each location corresponding to the finite element node to 0 and calculating the springback amount corresponding to the actual deviation at that location according to a preset calculation formula may include:

[0027] Resetting each first deviation corresponding to the finite element node to 0, and calculating a first springback corresponding to the first deviation at the location according to a first preset calculation formula;

[0028] The second deviation at each location corresponding to the finite element node is reset to 0, and the second springback amount corresponding to the second deviation at the location is calculated according to a second preset calculation formula.

[0029] In an exemplary embodiment of the present application, resetting each first deviation corresponding to the finite element node to 0 and calculating the first springback corresponding to the first deviation at the location according to a first preset calculation formula may include:

[0030] Reset any first deviation to 0, calculate the first rebound amount corresponding to the first deviation, and then restore the first deviation at that location; reset the next first deviation to zero, and calculate the first rebound amount corresponding to the next first deviation;

[0031] Resetting each second deviation corresponding to the finite element node to 0 and calculating the second springback corresponding to the second deviation at the location according to a second preset calculation formula may include:

[0032] After resetting any second deviation to 0 and calculating the second rebound amount corresponding to the second deviation at that location, the second deviation at that location is restored; resetting the next second deviation to zero and calculating the second rebound amount corresponding to the next second deviation at that location.

[0033] In an exemplary embodiment of the present application, the contribution ratio equation may include: P=1-(Un-V) / (Ut-V);

[0034] Wherein, P is the contribution ratio, Un is the springback amount calculated after resetting the actual deviation, Ut is the springback amount calculated before resetting the actual deviation, and V is the deviation of the sheet metal part before welding.

[0035] In an exemplary embodiment of the present application, sorting the contribution ratios to obtain the key factors of sheet metal assembly welding springback may include:

[0036] sorting the first contribution ratios calculated based on the first offset corresponding to each first deviation and the second contribution ratios calculated based on the second offset corresponding to each second deviation;

[0037] Obtaining first contribution ratios and second contribution ratios whose values ​​are greater than or equal to a preset threshold value from among all the sorted first contribution ratios and second contribution ratios;

[0038] A first deviation corresponding to a first contribution ratio whose value is greater than or equal to a preset threshold and a second deviation corresponding to a second contribution ratio whose value is greater than or equal to the preset threshold are used as key factors for the welding springback of the sheet metal assembly.

[0039] An embodiment of the present application also provides a device for searching for key factors of welding springback of a sheet metal assembly, comprising a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and is characterized in that when the instructions are executed by the processor, the method for searching for key factors of welding springback of a sheet metal assembly is implemented.

[0040] Compared to related technologies, the present embodiment can include: establishing a theoretical data model for a sheet metal assembly; obtaining vehicle coordinate fitting data for the sheet metal assembly; importing the vehicle coordinate fitting data into the theoretical data model, and obtaining the actual welding deviation of the sheet metal assembly from 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 for the sheet metal assembly's welding springback. This embodiment can quickly and accurately identify the key factors causing springback in the sheet metal assembly welding process, reducing the number of experimental matching experiments and saving time and resources.

[0041] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. Other advantages of the present application can be realized and obtained by the solutions described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are used to provide an understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0043] Figure 1 A flow chart of a method for finding key factors of sheet metal assembly welding springback in an embodiment of the present application;

[0044] Figure 2 This is a block diagram of the device for searching for key factors of welding springback of sheet metal assemblies according to an embodiment of the present application. DETAILED DESCRIPTION

[0045] This application describes multiple embodiments, but this description is exemplary rather than restrictive, and it will be apparent to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described herein. Although many possible feature combinations 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 any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0046] This application includes and contemplates combinations of features and elements known to those of ordinary skill 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 solution defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other inventive solutions to form another unique inventive solution 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 appropriate combination. Therefore, except for the limitations made according to the appended claims and their equivalents, the embodiments are not subject to other limitations. In addition, various modifications and changes may be made within the scope of protection of the appended claims.

[0047] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps in the specific order described. As will be understood by those skilled in the art, other orders of steps are also possible. Therefore, the specific order of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to performing their steps in the order written, and those skilled in the art can readily understand that these orders can be changed and still remain within the spirit and scope of the embodiments of the present application.

[0048] The embodiment of the present application provides a method for finding the key factors of sheet metal assembly welding springback, such as Figure 1 As shown, the method may include steps S101-S106:

[0049] S101. Establishing a theoretical data model for sheet metal assembly;

[0050] S102, obtaining vehicle coordinate fitting data of the sheet metal part;

[0051] S103, importing the vehicle coordinate fitting data into the theoretical data model, and obtaining the actual welding deviation of the sheet metal assembly in the theoretical data model;

[0052] S104, resetting the actual welding deviation and calculating the springback amount;

[0053] S105, calculating a contribution ratio according to the rebound amount and a preset contribution ratio equation;

[0054] S106. Sort the contribution ratios to obtain key factors for welding springback of the sheet metal assembly.

[0055] In an exemplary embodiment of the present application, CATIA, Hypermesh, PolyWorks, and RD&T software are used to develop a method for finding key factors causing springback of sheet metal welding assemblies by using triangulated mesh data from scanned point clouds.

[0056] In an exemplary embodiment of the present application, establishing a theoretical data model for sheet metal assembly welding may include:

[0057] The CATIA theoretical data of sheet metal assembly welding is meshed using the CATIA algorithm and the HyperMesh algorithm to generate a theoretical mid-surface finite element mesh;

[0058] The theoretical mid-surface finite element mesh is imported into RD&T software to establish the theoretical data model.

[0059] In an exemplary embodiment of the present application, the theoretical data model includes positioning, welding points, welding point sequence, welding angles, and fitting technology, wherein the fitting technology is used to prevent the occurrence of penetration of sheet metal matching surfaces in the theoretical data model.

[0060] In an exemplary embodiment of the present application, the theoretical data model can establish the springback amount {μ a Deviation from sheet metal parts {μ p}, that is:

[0061] {μ a}=[S]{μ p} (1)

[0062] Where [S] is the sensitivity matrix.

[0063] In an exemplary embodiment of the present application, the step of obtaining vehicle coordinate fitting data of a sheet metal part may include:

[0064] Scan sheet metal parts and generate measurement data of point cloud triangulated mesh;

[0065] In PolyWorks, the measurement data of the triangulated point cloud mesh is fitted into the vehicle coordinates to obtain the vehicle coordinate fitting data of the sheet metal part.

[0066] In an exemplary embodiment of the present application, a point cloud triangulated mesh may be generated by scanning a physical sheet metal part; and the generated point cloud triangulated mesh may be fitted with the vehicle body coordinates using PolyWorks.

[0067] In an exemplary embodiment of the present application, the step of importing the vehicle coordinate fitting data into the theoretical data model and obtaining the actual welding deviation of the sheet metal assembly in the theoretical data model may include:

[0068] In RD&T software, the point cloud obtained by scanning the sheet metal part is combined with the theoretical mid-surface finite element mesh;

[0069] The distance between the finite element nodes in the theoretical mid-surface finite element mesh and the point cloud triangulated mesh surface in the vector direction is calculated, and the actual welding deviation is generated on the finite element nodes.

[0070] In an exemplary embodiment of the present application, resetting the actual welding deviation and calculating the springback amount may include:

[0071] Reset each actual deviation corresponding to the finite element node to 0, and calculate the springback amount corresponding to the actual deviation at that location according to the preset calculation formula.

[0072] In the exemplary embodiment of the present application, the springback corresponding to the actual deviation at each location can be calculated according to the above linear matrix relationship (1); wherein the actual deviation at each location is the sheet metal single piece deviation {μ p}.

[0073] In an exemplary embodiment of the present application, the actual welding deviation may include: a first deviation between the sheet metal part and the fixture, and a second deviation between nodes of matching surfaces of the sheet metal parts.

[0074] In the exemplary embodiment of the present application, the factors causing the springback of the sheet metal assembly are mainly the deviation of the sheet metal itself and the deviation between the sheet metal and the fixture. When matching the theoretical data model, these two factors are reflected in the deviation dc (i.e., the second deviation) between multiple sheet metal matching surface nodes (i.e., finite element nodes) and the deviation dp (i.e., the first deviation) between the sheet metal and the fixture, both of which are not 0. The areas where multiple dc and dp are not 0 can be defined as influencing factors.

[0075] In an exemplary embodiment of the present application, resetting each actual deviation corresponding to a finite element node to 0 and calculating the springback amount corresponding to the actual deviation at that location according to a preset calculation formula may include:

[0076] Resetting each first deviation corresponding to the finite element node to 0, and calculating a first springback corresponding to the first deviation at the location according to a first preset calculation formula;

[0077] The second deviation at each location corresponding to the finite element node is reset to 0, and the second springback amount corresponding to the second deviation at the location is calculated according to a second preset calculation formula.

[0078] In an exemplary embodiment of the present application, resetting each first deviation corresponding to the finite element node to 0 and calculating the first springback corresponding to the first deviation at the location according to a first preset calculation formula may include:

[0079] Reset any first deviation to 0, calculate the first rebound amount corresponding to the first deviation, and then restore the first deviation at that location; reset the next first deviation to zero, and calculate the first rebound amount corresponding to the next first deviation;

[0080] Resetting each second deviation corresponding to the finite element node to 0 and calculating the second springback corresponding to the second deviation at the location according to a second preset calculation formula may include:

[0081] After resetting any second deviation to 0 and calculating the second rebound amount corresponding to the second deviation at that location, the second deviation at that location is restored; resetting the next second deviation to zero and calculating the second rebound amount corresponding to the next second deviation at that location.

[0082] In an exemplary embodiment of the present application, the first deviation dp corresponding to the influencing factor between the sheet metal and the fixture can be reset to 0 first, and the first deviation of the influencing factor can be reset at one time, and the first rebound amount corresponding to the deviation can be calculated and recorded; then the first deviation dp corresponding to the previous influencing factor can be restored, and the first deviation dp corresponding to another influencing factor can be reset to 0, and the corresponding first rebound amount can be calculated and recorded, until the first deviations corresponding to the influencing factors between all sheet metal parts and the fixture are offset and restored.

[0083] In an exemplary embodiment of the present application, the steps of resetting the second deviation dc between the nodes of the sheet metal matching surfaces and obtaining the second springback amount are the same as the steps of resetting dp and recording the second springback amount, and are not repeated here.

[0084] In an exemplary embodiment of the present application, the contribution ratio equation may include: P=1-(Un-V) / (Ut-V);

[0085] Wherein, P is the contribution ratio, Un is the springback amount calculated after resetting the actual deviation, Ut is the springback amount calculated before resetting the actual deviation, and V is the deviation of the sheet metal part before welding.

[0086] In an exemplary embodiment of the present application, multiple contribution ratios are obtained using the contribution ratio equation and can be stored in a preset mapping table, as shown in Table 1:

[0087] Table 1

[0088] Impact Factor dp / dc before reset dp / dc after reset Rebound amount Un P 1 dp1 0 Un-p1 P-p1 2 dp2 0 Un-p2 P-p2 … … 0 … 4 dc1 0 Un-c1 P-c1 5 dc2 0 Un-c2 P-c2 6 … 0 …

[0089] In an exemplary embodiment of the present application, sorting the contribution ratios to obtain the key factors of sheet metal assembly welding springback may include:

[0090] sorting the first contribution ratios calculated based on the first offset corresponding to each first deviation and the second contribution ratios calculated based on the second offset corresponding to each second deviation;

[0091] Obtaining first contribution ratios and second contribution ratios whose values ​​are greater than or equal to a preset threshold value from among all the sorted first contribution ratios and second contribution ratios;

[0092] A first deviation corresponding to a first contribution ratio whose value is greater than or equal to a preset threshold and a second deviation corresponding to a second contribution ratio whose value is greater than or equal to the preset threshold are used as key factors for the welding springback of the sheet metal assembly.

[0093] In an exemplary embodiment of the present application, P-p1, P-p2, ..., P-c1, P-c2, ... can be uniformly sorted to obtain a contribution ratio that meets the preset requirements, thereby obtaining the key factor of sheet metal assembly welding springback.

[0094] In the exemplary embodiments of the present application, at least the following advantages are included:

[0095] 1. This method allows the meshing of CATIA theoretical data and the establishment of a theoretical data model based on RD&T to be completed before the sheet metal parts arrive on site. The supplier can scan the point cloud triangulated mesh of the physical sheet metal parts and fit the point cloud triangulated mesh to the vehicle body coordinates. Therefore, the search for key factors can be completed before the sheet metal parts arrive on site, improving work efficiency.

[0096] 2. Through the key factor search method, the key factors that cause the springback of sheet metal welding assembly can be accurately found, reducing the number of experimental matching times and saving time and resources.

[0097] The embodiment of the present application also provides a device 1 for searching the key factors of sheet metal assembly welding springback, such as Figure 2 As shown, it includes a processor 11 and a computer-readable storage medium 12, wherein the computer-readable storage medium 12 stores instructions. When the instructions are executed by the processor 11, the method for searching the key factors of the sheet metal assembly welding springback is implemented.

[0098] In the exemplary embodiments of the present application, any of the aforementioned embodiments of the method for searching for the key factors of sheet metal assembly welding springback are applicable to the device embodiment and will not be described in detail here.

[0099] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media generally embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A method for finding key factors of sheet metal assembly welding springback, characterized in that: The method comprises: Establish theoretical data model for sheet metal assembly; Obtain vehicle coordinate fitting data of sheet metal parts; Importing the vehicle coordinate fitting data into the theoretical data model, and obtaining the actual welding deviation of the sheet metal assembly in the theoretical data model; the actual welding deviation includes: a first deviation between the sheet metal and the fixture, and a second deviation between the nodes of the sheet metal matching surfaces; Resetting the actual welding deviation and calculating the springback amount; Calculating a contribution ratio according to the rebound amount and a preset contribution ratio equation; Sorting the contribution ratios to obtain key factors of sheet metal assembly welding springback; The resetting of the actual welding deviation and calculation of the springback amount include: Reset the actual deviation of each finite element node to 0, and calculate the springback corresponding to the actual deviation according to the preset calculation formula, including: Resetting each first deviation corresponding to the finite element node to 0, and calculating a first springback corresponding to the first deviation at the location according to a first preset calculation formula; The second deviation at each location corresponding to the finite element node is reset to 0, and the second springback amount corresponding to the second deviation at the location is calculated according to a second preset calculation formula.

2. The method for finding the key factors of sheet metal assembly welding springback according to claim 1, characterized in that: The establishment of a theoretical data model for sheet metal assembly welding includes: The CATIA theoretical data of sheet metal assembly welding is meshed using the CATIA algorithm and the HyperMesh algorithm to generate a theoretical mid-surface finite element mesh; The theoretical mid-surface finite element mesh is imported into RD&T software to establish the theoretical data model.

3. The method for finding the key factor of sheet metal assembly welding springback according to claim 2, characterized in that: The step of obtaining vehicle coordinate fitting data of the sheet metal part includes: Scan sheet metal parts and generate measurement data of point cloud triangulated mesh; In PolyWorks, the measurement data of the triangulated point cloud mesh is fitted into the vehicle coordinates to obtain the vehicle coordinate fitting data of the sheet metal part.

4. The method for finding the key factor of sheet metal assembly welding springback according to claim 3, characterized in that: The step of importing the vehicle coordinate fitting data into the theoretical data model and obtaining the actual welding deviation of the sheet metal assembly in the theoretical data model includes: In RD&T software, the point cloud obtained by scanning the sheet metal part is combined with the theoretical mid-surface finite element mesh; The distance between the finite element nodes in the theoretical mid-surface finite element mesh and the point cloud triangulated mesh surface in the vector direction is calculated, and the actual welding deviation is generated on the finite element nodes.

5. The method for finding the key factor of sheet metal assembly welding springback according to claim 1, characterized in that: The resetting of each first deviation corresponding to the finite element node to 0 and calculating the first springback corresponding to the first deviation at the location according to a first preset calculation formula includes: Reset any first deviation to 0, calculate the first rebound amount corresponding to the first deviation, and then restore the first deviation at that location; reset the next first deviation to zero, and calculate the first rebound amount corresponding to the next first deviation; The resetting of each second deviation corresponding to the finite element node to 0 and calculating the second springback corresponding to the second deviation at the location according to a second preset calculation formula includes: After resetting any second deviation to 0 and calculating the second rebound amount corresponding to the second deviation at that location, the second deviation at that location is restored; resetting the next second deviation to zero and calculating the second rebound amount corresponding to the next second deviation at that location.

6. The method for finding the key factor of sheet metal assembly welding springback according to any one of claims 1 to 5, characterized in that: The contribution ratio equation includes: P=1-(Un-V) / (Ut-V); Wherein, P is the contribution ratio, Un is the springback amount calculated after resetting the actual deviation, Ut is the springback amount calculated before resetting the actual deviation, and V is the deviation of the sheet metal part before welding.

7. The method for finding the key factor of sheet metal assembly welding springback according to claim 1, characterized in that: Sorting the contribution ratios to obtain the key factors of sheet metal assembly welding springback includes: sorting the first contribution ratios calculated based on the first offset corresponding to each first deviation and the second contribution ratios calculated based on the second offset corresponding to each second deviation; Obtaining first contribution ratios and second contribution ratios whose values ​​are greater than or equal to a preset threshold value from among all the sorted first contribution ratios and second contribution ratios; A first deviation corresponding to a first contribution ratio whose value is greater than or equal to a preset threshold and a second deviation corresponding to a second contribution ratio whose value is greater than or equal to the preset threshold are used as key factors for the welding springback of the sheet metal assembly.

8. A device for searching for key factors of springback in welding of sheet metal parts, comprising a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, characterized in that: When the instruction is executed by the processor, the method for searching the key factor of welding springback of a sheet metal assembly according to any one of claims 1 to 7 is implemented.

Images