Welding deformation analysis and prediction method based on CAE
Through the CAE-based welding deformation analysis and prediction method, combined with physical welding and measurement data, the CAE model is established and corrected, and the problem of inaccurate welding deformation prediction in the prior art is solved, achieving higher simulation accuracy and production efficiency.
Patent Information
- Application Number
- CN202411963093.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-30
- Publication Date
- 2025-03-21
AI Technical Summary
The prior art is difficult to accurately predict welding deformation during laser welding, resulting in a long time-consuming and costly debugging of the welding process, and insufficient accuracy of the simulation model.
Using CAE-based welding deformation analysis and prediction methods, temperature and time data and heat-affected zone organization performance parameters are obtained through physical welding and measurement, local and overall CAE models are established, and heat source parameters are adjusted, so that the simulation model is basically consistent with the physical welding results, thereby improving simulation accuracy.
Through two benchmarking corrections, the accuracy of the simulation model is improved, the on-site process debugging process is simplified, the deviation of welding deformation is reduced, and the production efficiency and product quality are improved.
Smart Images
Figure CN119658125A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the field of laser welding, and in particular to a CAE-based welding deformation analysis and prediction method. Background Art
[0002] Thermal deformation caused by laser welding has always been one of the important factors affecting the quality of slide rail products. Especially in the context of the rapid development of new energy vehicles, seat slide rails usually need to use high-strength or even ultra-high-strength steel to meet the safety of drivers and passengers in different scenarios, which is more sensitive to thermal deformation caused by laser welding. Therefore, how to more clearly understand the relationship between welding process parameters and welding deformation is of great significance to the control of the final part size and design optimization.
[0003] One of the existing methods is to design a relatively reasonable welding method through continuous debugging and testing during the actual welding process. However, this method has a large workload, a long debugging time, and seriously affects the production rhythm, has poor robustness and high cost.
[0004] Another option is to use CAE modeling and simulation. However, since the laser weld and heat-affected zone are very small, it is difficult to truly and objectively understand the microstructure and properties of the weld and heat-affected zone. The simulation model is usually unable to accurately and effectively assign materials to them, resulting in large deviations in the simulation results.
[0005] Chinese invention patent announcement number CN104008219B discloses a welding deformation analysis prediction method, which predicts the welding deformation through physical welding, testing, local modeling fitting correction, and finally overall application. This prediction method takes into account that the overall structure and weld size of the automobile torsion bar beam rear axle structure are larger than ordinary parts (especially compared with the metal frame of the automobile seat), resulting in a large amount of heat released during welding. Therefore, it chooses to start from the residual stress direction and fit the simulation. Summary of the invention
[0006] In order to overcome the above-mentioned defects of the prior art, the object of the present invention is to provide a CAE-based welding deformation analysis and prediction method to simplify the on-site process debugging process.
[0007] In order to achieve the above object, the technical solution adopted by the present invention is:
[0008] CAE-based welding deformation analysis and prediction methods include:
[0009] S1 uses the existing welding process to weld the sheet and records the temperature and time data of the corresponding process;
[0010] S2 measures the microstructure and performance parameters of the heat affected zone of the sheet;
[0011] S3 performs computer-aided engineering (CAE) analysis on the sheet, establishes a local CAE model, applies material to the local CAE model according to the microstructure and performance parameters of the heat-affected zone, and adjusts the heat source parameters so that the deformation trend of the local CAE model is substantially consistent with that of the sheet;
[0012] S4 analyzes the whole part, expands and applies the blank analysis method to establish a whole CAE model of the whole part, and adjusts the heat source parameters so that the whole CAE model is substantially consistent with the deformation trend of the whole part;
[0013] S5 predicts each welding deformation based on the CAE simulation model of the overall part.
[0014] In a preferred embodiment of the present invention, it also includes S6: when a specific critical dimension of the CAE simulation model of the integral part is greater than a set value, the existing welding process is improved again until the critical dimension is less than or equal to the set value.
[0015] In a preferred embodiment of the present invention, the temperature is a peak temperature, and the time is a cooling time at the peak temperature.
[0016] In a preferred embodiment of the present invention, the microstructure and performance parameters of the heat-affected zone are the fracture mechanics properties of the sheet after welding.
[0017] In a preferred embodiment of the present invention, the fracture mechanical properties are yield strength and tensile strength.
[0018] In a preferred embodiment of the present invention, the heat source parameters are adjusted according to the degree of fit between the weld joint microstructure morphology and hardness of the sheet and / or the integral part and the local CAE model and / or the overall CAE model.
[0019] In a preferred embodiment of the present invention, the welding process is a laser welding process.
[0020] In a preferred embodiment of the present invention, the step S21 is also included to obtain the thermal physical properties and high temperature mechanical properties of the above-mentioned sheet through simulation calculation or actual measurement.
[0021] In a preferred embodiment of the present invention, the thermophysical properties and high-temperature mechanical properties of the material sheet include thermal expansion coefficient, Poisson's ratio, elastic modulus, latent heat of phase change, yield strength index, and hardening index.
[0022] In a preferred embodiment of the present invention, the computer-aided engineering (CAE) analysis is a finite element analysis model of a three-dimensional solid unit.
[0023] In a preferred embodiment of the present invention, the sheet and the integral part are both thin-plate carbon steel parts.
[0024] In a preferred embodiment of the present invention, the integral part is a car seat frame.
[0025] The beneficial effects of the present invention are:
[0026] The CAE-based welding deformation analysis and prediction method provided by the present invention calibrates the simulation model based on the actual results, expands from local analysis to overall application, improves the accuracy of the simulation model through two benchmarking calibrations, and simplifies the on-site process debugging process. BRIEF DESCRIPTION OF THE DRAWINGS
[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.
[0028] Figure 1 It is a flow chart of the present invention.
[0029] Figure 2 It is a structural diagram of the slide rail and its components.
[0030] Figure 3 It is a schematic diagram of welding sheet connection.
[0031] Figure 4 It is the temperature curve of the weldment under the welding thermal cycle process.
[0032] Figure 5 This is a comparison chart of the hardness performance of simulated and actual welded joints.
[0033] Figure 6 This is a comparison chart of the morphology of the simulated and actual welded joints. DETAILED DESCRIPTION
[0034] Introduced below are some of the multiple possible embodiments of the present invention, which are intended to provide a basic understanding of the present invention, and are not intended to confirm the key or decisive elements of the present invention or to limit the scope of protection. It is easy to understand that according to the technical solution of the present invention, without changing the essential spirit of the present invention, a person of ordinary skill in the art can propose other interchangeable implementations. Therefore, the following specific embodiments and the accompanying drawings are only exemplary descriptions of the technical solution of the present invention, and should not be regarded as the whole of the present invention or as a limitation or restriction to the technical solution of the present invention.
[0035] The following will describe exemplary embodiments of the present application with reference to the accompanying drawings. However, it should be understood that the present application can be presented in a variety of different ways and is not limited to the embodiments described below. It should also be understood that the embodiments disclosed herein can be combined in various ways to provide more additional embodiments. In all drawings, the same reference numerals represent the same or functionally identical elements.
[0036] Figure 1 The schematic diagram of the welding deformation analysis and prediction method flow based on the seat embodiment is shown. The present invention corrects the simulation model based on the actual results, expands from local analysis to overall application, improves the accuracy of the simulation model, and simplifies the on-site process debugging process. Furthermore, the welding deformation analysis and prediction method of the present invention can also be applied to other thin plate low-carbon steel welding workpieces, especially in laser welding.
[0037] Combination Figure 2 The slide rail part of a car seat metal frame is shown, including upper rail 1, front bracket 3, rear bracket 5 and other components. In order to achieve fixed connection between the components and ensure their bearing capacity, one or more welds need to be welded at each welding station. This deformation analysis prediction method mainly considers the influence of unit material in finite element analysis on simulation results during computer modeling, and then iteratively improves the welding method.
[0038] First, in step S1, the material is welded using an existing method, and the microstructure and properties of the heat-affected zone of the material under different welding process parameters are collected.
[0039] Figure 3 The welding pieces are shown. The selected welding pieces are pieces rather than integral parts. Considering the small size of the target welded assembly parts and the small laser welding weld, the selection of pieces has the advantage of being easy to implement in batches. Metallographic structure analysis and mechanical property testing are used to obtain the structure and properties of the heat affected zone under different welding process parameters. For example, in the welding implementation of the upper rail 1 and the front bracket 3, the material used for the upper rail 1 is CP980, and the front bracket 3 is S500MC. The thermal cycle process is set according to the welding parameters. Considering that the phase change range of steel materials is mainly concentrated in the range of 500°C to 800°C, the cooling rate in this range has the greatest thermal impact on welding. Therefore, different peak temperatures are selected and the t8 / 5 time is recorded ( Figure 4 , cooling time is the time taken for the material temperature to drop from 800° C. to 500° C.). In this embodiment, the sampling device used is a Gleeble-3500 thermal simulation test machine.
[0040] In step S2, the fracture mechanical properties of the above-mentioned solder sheet are tested. Specifically, the solder sheet is fixed on a tensile machine and gradually loaded until it breaks to obtain the yield strength and / or tensile strength MPa. Taking CP980 as an example, the measurement results are shown in Table 1 below, and a total of 9 sets of data are taken. In order to improve accuracy, multiple identical solder sheets can also be selected to repeat the measurement and take the average value.
[0041]
[0042] Table 1
[0043] The above steps S1 and S2 are all completed by actual welding and measurement on site, which are physical measurement steps.
[0044] Furthermore, step S21 is also included in which the thermal physical properties and high-temperature mechanical properties of the above-mentioned sheet are obtained through simulation calculation or actual measurement, including but not limited to: thermal expansion coefficient, Poisson's ratio, elastic modulus, latent heat of phase change, change of yield strength with temperature, change of hardening index with temperature and other material parameters.
[0045] In step S3, CAE modeling is performed on the above-mentioned welding piece. The simulation welding result of the piece is obtained, and the measured Thermophysical properties and / or high temperature mechanical properties Calibrate the material in the CAE model and calibrate the material-specific properties assigned to the unit grid in the three-dimensional finite element model, which can effectively improve the simulation accuracy of the CAE model. Finally, adjust the heat source parameters in the CAE model by comparing the physical appearance. In this embodiment, SIMUFACT software is used to simulate the CAE three-dimensional unit finite element model. Figure 5 and Figure 6 ,By comparing the morphology and hardness performance of the simulation model and the actual welding joint, the heat source parameters are adjusted to make the morphology and hardness performance of the two consistent, and the simulation model deviation is calibrated.
[0046] In step S4, the local model calibrated in step S3 is extended and applied to the CAE modeling of the overall part.
[0047] The overall simulation model was analyzed, and the deformation model obtained by simulation was compared with the actual welding assembly. The simulation heat source parameters were further adjusted to make the morphology and hardness performance of the two consistent, completing the overall modeling. The two benchmarking adjustments further increased the model accuracy.
[0048] In step S5, the deformation is analyzed using the overall simulation model. In one embodiment, the deformation at key locations (such as the front bracket, rear bracket, and seat belt connection point) of the simulation model and the physical welding assembly are recorded as shown in Table 2 below.
[0049]
[0050] Table 2
[0051] In step S6, it is determined whether the deformation recorded in step S5 is greater than the set value. In one embodiment, the deformation requirement of the seat belt bracket mounting hole of the rear bracket 5 is not more than 0.6mm. If the welding deformation at the mounting hole position reaches 0.8mm, it does not meet the welding deformation requirement, and the welding process needs to be optimized. Therefore, step S7 is entered to re-optimize the process. When the welding deformation of the slide rail meets the predetermined requirements, step S8 is entered, that is, no improvement is made to the established welding method.
[0052] If it is determined to be unqualified in step S6, the welding process is further adjusted and optimized in step S7. Factors affecting laser welding deformation include: welding method, including welding sequence (welding sequence between multiple welds), welding direction (welding direction of the same weld), etc.; welding process parameters (such as power, speed), the welding method here mainly refers to the broad welding method; welding equipment; weldment structure. When the structure of the weldment remains unchanged, the established welding method can be improved to reduce welding deformation. Preferably, the welding deformation is reduced by simulating the change of the welding sequence. After the process is improved, enter S5 to re-analyze the deformation, and then judge whether it meets the set value in step S6.
[0053] If it is determined to be qualified in step S6, the prediction method ends. The above embodiment studies the welding of a typical seat slide rail upper rail and adopts a physical simulation method to obtain the influence of the organizational evolution of the weld and the heat-affected zone on the deformation, which helps FEA to achieve accurate materialization of the cold wind and heat-affected zone. Subsequently, a simulation method for local characteristic welding is established in combination with numerical simulation and the heat source parameters are adjusted. This method is then applied to the welding deformation simulation of the entire slide rail upper rail, so that the welding deformation can be analyzed and predicted. It should be understood that this method is particularly suitable for the laser welding process of thin-plate carbon steel parts. This welding process is different from other conventional welding methods, and the weld and heat-affected zone are very small.
[0054] Furthermore, based on this analysis and prediction method, when the welding deformation at the key feature position exceeds the requirement, the laser welding method can be optimized, thereby making the welding debugging process simpler and more effective and reducing the welding deformation more effectively.
[0055] The above examples mainly illustrate the welding deformation analysis prediction method of the present invention. Although only some of the embodiments of the present invention are described, it should be understood by those skilled in the art that the present invention can be implemented in many other forms without departing from its subject matter and scope. Therefore, the examples and embodiments shown are regarded as illustrative rather than restrictive, and the present invention may cover various modifications and substitutions without departing from the spirit and scope of the present invention as defined in the appended claims.
[0056] It should be understood that the above welding deformation analysis and prediction method is schematically illustrated based on the slide rail in the seat frame. According to the above teachings and / or inspirations, technical personnel in this field can generalize and apply this design method to the welding deformation analysis and prediction of other welding workpieces to effectively reduce the welding deformation of their welding stations.
Claims
1. CAE-based welding deformation analysis and prediction method, characterized in that: include: S1 uses the existing welding process to weld the sheet and records the temperature and time data of the corresponding process; S2 measures the microstructure and performance parameters of the heat affected zone of the sheet; S3 performs computer-aided engineering (CAE) analysis on the sheet, establishes a local CAE model, applies material to the local CAE model according to the microstructure and performance parameters of the heat-affected zone, and adjusts the heat source parameters so that the deformation trend of the local CAE model is substantially consistent with that of the sheet; S4 analyzes the whole part, expands and applies the blank analysis method to establish a whole CAE model of the whole part, and adjusts the heat source parameters so that the whole CAE model is substantially consistent with the deformation trend of the whole part; S5 predicts each welding deformation based on the CAE simulation model of the overall part.
2. The CAE-based welding deformation analysis and prediction method according to claim 1, characterized in that: The method further includes S6, when a specific critical dimension of the CAE simulation model of the integral part is greater than a set value, re-improving the existing welding process until the critical dimension is less than or equal to the set value.
3. The CAE-based welding deformation analysis and prediction method according to claim 1, characterized in that: The temperature is the peak temperature, and the time is the cooling time at the peak temperature.
4. The CAE-based welding deformation analysis and prediction method according to claim 1, characterized in that: The microstructure and performance parameters of the heat-affected zone are the fracture mechanics properties of the sheet after welding.
5. The CAE-based welding deformation analysis and prediction method according to claim 4, characterized in that: The fracture mechanical properties are yield strength and tensile strength.
6. The CAE-based welding deformation analysis and prediction method according to claim 1, characterized in that: The heat source parameters are adjusted according to the degree of fit between the weld joint microstructure morphology and hardness of the blank and / or the integral part and the local CAE model and / or the overall CAE model.
7. The CAE-based welding deformation analysis and prediction method according to claim 1, characterized in that: The welding process is a laser welding process.
8. The CAE-based welding deformation analysis and prediction method according to claim 1, characterized in that: The step S21 is also included to obtain the thermal physical properties and high temperature mechanical properties of the above-mentioned sheet through simulation calculation or actual measurement.
9. The CAE-based welding deformation analysis and prediction method according to claim 8, characterized in that: The thermal physical properties and high temperature mechanical properties of the material sheet include thermal expansion coefficient, Poisson's ratio, elastic modulus, latent heat of phase change, yield strength index, and hardening index.
10. The CAE-based welding deformation analysis and prediction method according to any one of claims 1 to 9, characterized in that: The computer-aided engineering (CAE) analysis is a finite element analysis model of a three-dimensional solid unit.
11. The CAE-based welding deformation analysis and prediction method according to any one of claims 1 to 9, characterized in that: The blank and the integral part are both thin-plate carbon steel parts.
12. The CAE-based welding deformation analysis and prediction method according to claim 11, characterized in that: The integral part is a car seat frame.
Citation Information
Patent Citations
A Welding Deformation Analysis and Prediction Method
CN104008219B