Finite element-based dissimilar steel welding parameter optimization method
By constructing a three-dimensional model of dissimilar steel welding and performing finite element simulation, the problem of parameter optimization in dissimilar steel welding was solved, and efficient and low-cost welding parameter optimization was achieved.
Patent Information
- Application Number
- CN202510843656.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-23
- Publication Date
- 2025-10-17
AI Technical Summary
During the welding process of dissimilar steels, differences in material composition and thermophysical properties lead to severe temperature gradients and residual stress concentration. Traditional processes make it difficult to quantify the dynamic relationship between heat input, welding speed and joint quality, resulting in long process development cycles and high costs.
By constructing a three-dimensional model of dissimilar steel welding, performing finite element simulation, establishing a welding heat source model, obtaining simulated welding data, and comparing it with actual data, the welding parameters are optimized.
The accurate simulation of the welding process of dissimilar steels is achieved, which improves work efficiency and reduces experimental costs.
Smart Images
Figure CN120805554A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of welding, in particular to a heterogeneous steel welding parameter optimization method based on finite elements. BACKGROUND
[0002] Heterogeneous steel welding is prone to produce severe temperature gradient and residual stress concentration during the welding process due to the significant differences in material composition, thermal physical properties (such as thermal conductivity, expansion coefficient), which directly affects the mechanical properties and service life of the joint. However, in the traditional process, the setting of heterogeneous steel welding parameters is mostly dependent on empirical formulas or trial and error experiments, which is difficult to quantify the dynamic relationship between heat input, welding speed and joint quality, resulting in long process development cycle and high cost.
[0003] In recent years, the welding process parameter optimization of the same kind of metal based on finite elements has achieved certain results, but compared with the optimization of the same kind of steel welding process parameters, the current heterogeneous steel welding process parameter optimization lacks research on combined parameters.
[0004] Therefore, there is an urgent need for a heterogeneous steel welding parameter optimization analysis method that can accurately obtain welding pool morphology, post-weld residual stress and other data, and optimize the welding process parameters of heterogeneous steel. SUMMARY
[0005] The present application aims to provide a heterogeneous steel welding parameter optimization method that can realize accurate simulation of heterogeneous steel welding for welding parameter optimization, to solve the problem of the blank of the existing heterogeneous steel welding process parameter optimization. The present application realizes accurate simulation of the heterogeneous steel welding process, and optimizes the welding parameters based on the simulation results, improving work efficiency while reducing experimental cost.
[0006] In order to achieve the above purpose, the basic scheme of the present application is as follows: a heterogeneous steel welding parameter optimization method based on finite elements, comprising the following steps: constructing a heterogeneous steel welding three-dimensional model through a three-dimensional software, and transmitting it to a finite element software for meshing to obtain a finite element model; defining the welding material properties of the welded part and the base material, and constructing a heterogeneous steel welding material database based on the welding material properties; establishing a heterogeneous steel welding heat source model, and checking the welding heat source model according to the actual welding parameters, obtaining a target welding heat source model after the checking; simulating the welding process based on the finite element parameterized design language with the finite element model, welding material properties and target welding heat source model as conditions, obtaining simulation welding data of the heterogeneous steel; extracting the simulation molten pool morphology and simulation welding data of different heat sources, and comparing them with the corresponding actual molten pool morphology and welding data to verify the effective simulation welding data; obtaining the effective simulation welding data under different welding parameters, and comparing and analyzing the effective simulation welding data, obtaining the optimized target welding parameters according to the analysis results.
[0007] Further, the three-dimensional model of dissimilar steel welding is constructed by three-dimensional software, comprising: constructing the three-dimensional model of dissimilar steel welding in the three-dimensional software according to the geometric structure of the welding piece and the splicing requirements, the splicing requirements comprising one or more combinations of the groove angle, the joint form, the welding layer number and the welding gap.
[0008] Further, the welding parameters comprise welding current, welding voltage and welding speed.
[0009] Further, the grid division comprises: dividing the welding seam and the heat-affected zone thereof into dense grids, and dividing other regions into sparse grids.
[0010] Further, the welding material properties comprise thermal conductivity, specific heat capacity, thermal expansion coefficient, density, elastic modulus, Poisson's ratio and yield strength of the welding material at different temperatures.
[0011] Further, the welding heat source model is constructed by using a double-ellipsoid heat source model.
[0012] Further, the comparative analysis comprises comparative analysis of temperature, residual stress and welding deformation.
[0013] Compared with the prior art, the advantages and beneficial effects of the present application are that: a three-dimensional model of dissimilar steel welding is constructed by three-dimensional software and transmitted to finite element software for grid division to obtain a finite element model, welding material properties of the welding piece and the base material are defined, a dissimilar steel welding material database is constructed based on the welding material properties to facilitate subsequent welding material property acquisition, a dissimilar steel welding heat source model is established, and the welding heat source model is checked according to actual welding parameters to obtain a target welding heat source model after the check, thereby ensuring the accuracy of the welding heat source model; the welding process is simulated based on the finite element parameterized design language under the conditions of the finite element model, the welding material properties and the target welding heat source model, simulated welding data of the dissimilar steel are obtained, the simulation molten pool morphology and the simulated welding data of different heat sources are extracted, and are compared with the corresponding actual molten pool morphology and welding data to verify the effective simulated welding data, the effectiveness of the simulated welding data is verified, the accuracy of the heat source model parameters is ensured, the effective simulated welding data under different welding parameters are obtained, the effective simulated welding data are compared and analyzed, the optimized target welding parameters are obtained according to the analysis results, the dissimilar steel welding process can be accurately and reliably simulated, thereby realizing optimization of the dissimilar steel welding parameters, greatly improving the work efficiency and reducing the experimental cost. BRIEF DESCRIPTION OF DRAWINGS
[0014] Figure 1 is a flowchart of a dissimilar steel welding parameter optimization method based on finite elements in one embodiment;
[0015] Figure 2 A structural schematic diagram of a dissimilar steel welding three-dimensional model in an embodiment;
[0016] Figure 3 A schematic diagram of a welding heat source model compared with an actual welding joint in an embodiment;
[0017] Figure 4 A comparison curve of transverse residual stress of a simulation and an experiment of a center section perpendicular to a welding direction in dissimilar steel welding in an embodiment;
[0018] Figure 5 A comparison curve of longitudinal residual stress of a simulation and an experiment of a center section perpendicular to a welding direction in dissimilar steel welding in an embodiment;
[0019] Figure 6 A temperature field distribution cloud chart of a dissimilar steel welding process in an embodiment;
[0020] Figure 7 A temperature field distribution cloud chart of a dissimilar steel welding post-weld cooling process in an embodiment;
[0021] Figure 8 A dissimilar steel post-weld residual stress distribution cloud chart in an embodiment;
[0022] Figure 9 A dissimilar steel post-weld center section transverse residual stress distribution curve perpendicular to a welding direction in an embodiment;
[0023] Figure 10 A dissimilar steel post-weld center section longitudinal residual stress distribution curve parallel to a welding direction in an embodiment. DETAILED DESCRIPTION
[0024] In order to make the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application.
[0025] As shown in Figure 1 , a finite element-based dissimilar steel welding parameter optimization method is provided, comprising the following steps:
[0026] Step S110, a dissimilar steel welding three-dimensional model is constructed through three-dimensional software, and is transmitted to finite element software for meshing to obtain a finite element model.
[0027] Specifically, according to the geometric structure and splicing requirements of the real welding piece, a dissimilar steel welding three-dimensional model is constructed in a three-dimensional software, such as SOLIDWORKS, and the dissimilar steel welding three-dimensional model is transmitted to a finite element software, such as ANSYS, for meshing to obtain a dissimilar steel welding finite element model, as shown in Figure 2
[0028] In the construction of the dissimilar steel welding three-dimensional model, the geometric structure and splicing requirements of the welding piece are considered, and the splicing requirements include one or more combinations of the groove angle, joint form, welding layer number and welding gap.
[0029] Specifically, in the construction of the dissimilar steel welding three-dimensional model, the geometric structure and splicing requirements of the welding piece are considered, and the splicing requirements include one or more combinations of the groove angle, joint form, welding layer number and welding gap, etc. For example, the size of the welding piece is 150mmx70mmx10mm, the groove form is X type, the groove angle is 90°, the root face is 2mm, there is no gap, and the welding layer number is 2, so as to realize the accurate construction of the three-dimensional model.
[0030] In the meshing, the weld and its heat-affected zone are divided into dense meshes, and other regions are divided into sparse meshes.
[0031] Specifically, in the meshing, the weld and its heat-affected zone are divided into dense meshes, so as to facilitate high-precision calculation of this region, and other regions are divided into sparse meshes, so as to improve the model calculation efficiency.
[0032] In step S120, the welding material properties of the welding piece and the base material are defined, and a dissimilar steel welding material database is constructed based on the welding material properties.
[0033] Specifically, the welding material properties of the welding piece and the base material are determined, for example, the welding material is Q345 low carbon steel and 316L stainless steel, and the thermal physical parameters of the two are shown in Table 1 and Table 2 respectively. When defining the material properties, the welding base material along the positive direction of the x-axis is defined as Q345, the welding base material along the negative direction of the x-axis is defined as 316L, and the weld material is defined as 316L.
[0034] Table 1 Thermal physical parameters table of Q345 low carbon steel
[0035]
[0036]
[0037] Table 2 Thermal physical parameters table of 316L stainless steel
[0038]
[0039] In addition, a dissimilar steel welding material database can also be constructed based on the welding material attributes to facilitate subsequent acquisition of welding material attribute data.
[0040] The welding material attributes include thermal conductivity, specific heat capacity, thermal expansion coefficient, density, elastic modulus, Poisson's ratio, and yield strength of the welding material at different temperatures.
[0041] Step S130, a dissimilar steel welding heat source model is established, and the welding heat source model is checked according to the actual welding parameters. The target welding heat source model is obtained after the checking is passed.
[0042] The welding heat source model is constructed using a double-ellipsoid heat source model.
[0043] Specifically, according to the heat source characteristics of gas shielded welding, a double-ellipsoid heat source model is used to construct the welding heat source model, so as to realize accurate simulation of the welding process through the welding heat source model.
[0044] The welding parameters include welding current, welding voltage, and welding speed.
[0045] Specifically, the heat source equation is determined according to the actual welding parameters and the double-ellipsoid molten pool size. The heat flux density function formula of the front hemisphere of the double-ellipsoid heat source is:
[0046]
[0047] The heat flux density function formula of the rear hemisphere of the double-ellipsoid heat source is:
[0048]
[0049] Where x, y, and z are the coordinates of the heat source center; a and b are the width and depth of the heat source; c f and c r are the geometric dimensions of the heat source along the welding direction; f f and f r are heat flux density distribution functions, which are used to represent the proportional relationship of the front and rear ellipsoid heat inputs, f f +f r = 2; u is the welding voltage, I is the welding current, and η is the thermal efficiency.
[0050] Specifically, after the dissimilar steel welding heat source model is constructed, the actual welding parameters can also be used to check the welding heat source model. The target welding heat source model is obtained after the checking is passed. When checking, the numerical model results can be compared with the actual welding results to ensure the accuracy and reliability of the welding heat source model.
[0051] Step S140, based on the finite element parametric design language, simulate the welding process under the condition of the finite element model, welding material properties and target welding heat source model, and obtain the simulation welding data of the dissimilar steel.
[0052] Specifically, based on the finite element parametric design language, simulate the welding process under the condition of the finite element model, welding material properties and target welding heat source model, and obtain the simulation welding data of the dissimilar steel, including the simulation results of the welding temperature field and the welding stress field.
[0053] Wherein, when simulating the welding process, the movement of the heat source is simulated by ANSYS workbench APDL, and the specific implementation steps are as follows: set the parameters of the double-ellipsoid heat source, including voltage, current, welding speed and heat source geometric parameters, etc.; set the welding time, cooling time, welding sequence and initial temperature; refine the welding time into several time sub-steps, and multiply the time sub-steps by the welding speed to obtain the heat source center coordinates at each time step; realize the loading of the heat source by moving the heat source center.
[0054] Through the simulation of the welding temperature field and the stress field by step S140, the welding temperature field simulation is carried out by transient thermal analysis, the convection heat transfer and thermal radiation parameters of the welding part surface and the environment are set, and the loading of the heat source is realized by ANSYS workbench APDL to obtain the simulation results of the temperature field; the simulation results of the temperature field are imported into the structure module for stress field analysis to obtain the simulation results of the stress field; and the grid points far away from the welding position are subjected to displacement constraint.
[0055] Step S150, extract the simulation molten pool morphology and simulation welding data of different heat sources, and compare them with the corresponding actual molten pool morphology and welding data to verify the effective simulation data.
[0056] Specifically, the molten pool morphology of different heat sources is extracted by the post-processing tool of ANSYS workbench software, and compared with the actual morphology to determine the accurate welding heat source parameters, and the comparison of the heat source molten pool and the actual molten pool is as shown in Figure 3 .
[0057] The simulation welding data includes welding residual stress and welding temperature field, and the effectiveness of the simulation welding data can be verified by welding residual stress. The residual stress of the actual welded steel plate is measured by the blind hole method (a blind hole is punched on the workpiece, the stress change before and after punching is measured to obtain the residual stress, which is suitable for various materials and simple operation), and the welding residual stress of the corresponding position on the surface of the steel plate weld is obtained. The simulation results of the welding residual stress at the measured position are extracted by the post-processing tool of ANSYS workbench software, and compared with the actual test results to verify the accuracy of the simulation results. The transverse and longitudinal residual stress results of welding simulation and welding test are compared as shown in Figure 4 and Figure 5 , wherein the red dots represent the residual stress of the actual welding test, and the black curves represent the residual stress of the welding simulation.
[0058] In step S160, the effective simulation welding data under different welding parameters is obtained, and the effective simulation welding data is compared and analyzed, and the optimized target welding parameters are obtained according to the analysis results.
[0059] Specifically, according to the welding process specification and technical condition requirements, the welding parameters (voltage, current, welding speed, etc.) are adjusted, the target welding heat source model is used to simulate the welding process under different welding parameters, and the corresponding effective simulation welding data is obtained, wherein the temperature field distribution cloud diagram of the dissimilar steel welding process is as shown in Figure 6 , the temperature field distribution cloud diagram of the post-weld cooling process is as shown in Figure 7 , and the post-weld residual stress cloud diagram is as shown in Figure 8 .
[0060] The residual stress results under different welding parameters are compared. The welding residual stress of the center section perpendicular to the weld direction and the center section parallel to the weld direction is analyzed, and the transverse and longitudinal residual stress distribution curves are obtained, as shown in Figure 9 and Figure 10 . By comparing the welding residual stress under different parameters, the welding parameters corresponding to the welding residual stress closest to the actual welding residual stress are selected, the welding parameters are optimized, and the optimized target welding parameters are obtained.
[0061] Among them, the comparative analysis includes the comparative analysis of temperature, residual stress and welding deformation.
[0062] Specifically, the temperature of different welding parameters is compared and analyzed by the temperature field distribution cloud diagram, the residual stress of different welding parameters is compared and analyzed according to the residual stress distribution curve, and the welding deformation can be compared and analyzed by three-dimensional scanning or human eye judgment.
[0063] Through the above steps, the welding pool morphology, welding temperature field, welding residual stress and other related welding data can be intuitively displayed, and the experimental cost is reduced; by comparing the welding simulation pool and the actual container morphology, the accuracy of the heat source model parameters is ensured; by comparing the simulation and experimental welding residual stress distribution, the reliability of the simulation results is ensured.
[0064] In the embodiment, a three-dimensional model of dissimilar steel welding is constructed by three-dimensional software and transmitted to finite element software for meshing to obtain a finite element model, welding material properties of the welded part and base material are defined, and a dissimilar steel welding material database is constructed based on the welding material properties to facilitate subsequent welding material property acquisition, a dissimilar steel welding heat source model is established, and the welding heat source model is checked according to actual welding parameters to obtain a target welding heat source model after the check, thereby ensuring the accuracy of the welding heat source model; the finite element model, the welding material properties and the target welding heat source model are taken as conditions, the welding process is simulated based on the parameterized design language of the finite element, the simulation welding data of the dissimilar steel are obtained, the simulation pool morphology and the simulation welding data of different heat sources are extracted, and the effective simulation welding data are verified by comparing with the corresponding actual pool morphology and welding data, thereby verifying the effectiveness of the simulation welding data, ensuring the accuracy of the heat source model parameters, obtaining the effective simulation welding data under different welding parameters, comparing and analyzing the effective simulation welding data, obtaining the optimized target welding parameters according to the analysis results, accurately and reliably simulating the dissimilar steel welding process, and thus realizing the optimization of the dissimilar steel welding parameters, greatly improving the work efficiency and reducing the experimental cost.
[0065] It should be noted that in this document, the terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.
[0066] The above is only an embodiment of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. Ordinary technicians in the field are aware of all common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the field can improve and implement this scheme in combination with their own abilities under the inspiration given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.
Claims
1. A finite element-based method for optimizing welding parameters of dissimilar steels, characterized in that: The following steps are involved: A 3D model of dissimilar steel welding is constructed using 3D software and transferred to finite element software for meshing to obtain a finite element model; defining welding material properties of the weldment and the base material, and constructing a dissimilar steel welding material database based on the welding material properties; Establishing a heat source model for dissimilar steel welding, and calibrating the welding heat source model according to actual welding parameters, and obtaining a target welding heat source model after the calibration passes; The welding process is simulated based on the finite element parametric design language using the finite element model, welding material properties, and target welding heat source model as conditions to obtain simulated welding data of dissimilar steels; Extract the simulated molten pool morphology and simulated welding data of different heat sources, and compare them with the corresponding actual molten pool morphology and welding data to verify the effective simulated welding data; Effective simulated welding data under different welding parameters are obtained, and the effective simulated welding data are compared and analyzed, and optimized target welding parameters are obtained according to the analysis results.
2. The finite element-based dissimilar steel welding parameter optimization method according to claim 1, characterized in that: The method of constructing a three-dimensional model of dissimilar steel welding by using three-dimensional software includes: A 3D model of dissimilar steel welding is constructed in 3D software based on the geometric structure and splicing requirements of the weldment, wherein the splicing requirements include one or more combinations of groove angle, joint form, number of welding layers and welding gap.
3. The method for optimizing welding parameters of dissimilar steels based on finite element method according to claim 1, characterized in that: The welding parameters include welding current, welding voltage and welding speed.
4. The method for optimizing welding parameters of dissimilar steels based on finite element method according to claim 1, characterized in that: The grid division includes: The weld and its heat-affected zone are divided into dense grids, and other areas are divided into sparse grids.
5. The method for optimizing welding parameters of dissimilar steels based on finite element method according to claim 1, characterized in that: The welding material properties include thermal conductivity, specific heat capacity, thermal expansion coefficient, density, elastic modulus, Poisson's ratio and yield strength of the welding material at different temperatures.
6. The method for optimizing welding parameters of dissimilar steels based on finite element method according to claim 1, characterized in that: The welding heat source model is constructed using a double ellipsoid heat source model.
7. The method for optimizing welding parameters of dissimilar steels based on finite element method according to claim 1, characterized in that: The comparative analysis includes comparative analysis of temperature, residual stress and welding deformation.