A high-precision simulation and prediction method for temperature field of aluminum alloy welded joints

Abstract

This invention discloses a high-precision simulation and prediction method for the temperature field of aluminum alloy welded joints. Compared with existing technologies, before finite element modeling, a simple weld measurement experiment method is used. This method not only measures the weld length but also calibrates the weld morphology parameters of the welded joint in detail, such as weld width, weld depth, and weld toe depth of stiffeners and wall plates. The detailed dimensional contour of the weld is highly reproduced and applied to the simulation model, achieving a correlation accuracy of over 88% between the simulated heat source and the experimental weld morphology. The simulation calculation highly reflects the dynamic process of heat source movement during welding, avoiding the problem of inaccurate heat source model movement in welding simulation analysis. This achieves an accurate description of the temperature field distribution of the welded joint, enabling the simulation prediction accuracy of the weld temperature field in both the horizontal and vertical directions to reach over 95%.

Description

Technical Field

[0001] This invention relates to the field of metal material connection performance analysis technology, specifically to a high-precision method for temperature field simulation and prediction of aluminum alloy welded joints. Background Technology

[0002] Aluminum alloys are widely used in the automotive industry due to their high specific strength, light weight, good formability, and excellent extrusion performance. Aluminum alloy welding technology, as one of the main connection methods for automobile bodies, is a prerequisite for the large-scale application of aluminum alloy structural components in new energy vehicles. However, due to the complex physicochemical reactions involved in the welding process and the unique physical properties of aluminum alloys, such as high thermal conductivity, predicting the welding temperature field is extremely difficult. Currently, conventional predictions of aluminum alloy welding temperature fields mainly rely on a large amount of experimental data, based on which curve fitting is used to establish empirical models. Therefore, there is an urgent need to develop an accurate method for predicting the welding temperature field in aluminum alloy welding.

[0003] Today, the finite element method for welding is widely used in engineering applications. Welding numerical simulation software can effectively reduce the number of experiments, save time, lower costs, and provide technical guidance and theoretical basis for actual production. One existing commercial software is specifically designed for welding simulation analysis. Its user interface is user-friendly, allowing users to drag and drop the required model and corresponding physical data into the process tree using the mouse. Because of its intuitive user interface and rapid response, this software effectively helps CAE engineers save on development costs and has therefore been widely used in the automotive industry.

[0004] Currently, in aluminum alloy welding simulation analysis, most CAE engineers simply set the welding process parameters, such as welding current, voltage, and welding speed, within the welding software. However, the parameters of the welding heat source ellipsoid are usually set to default values, and it is rare to go to the welding site to calibrate the detailed weld morphology parameters by cutting through the weld seam dimensions. This approach results in the welding heat source movement and temperature conduction not matching reality, leading to low accuracy in welding simulation analysis results and an inability to accurately predict the temperature field distribution in aluminum alloy welding. Summary of the Invention

[0005] The purpose of this invention is to solve the problems mentioned above in the background technology and to propose a high-precision method for simulating and predicting the temperature field of aluminum alloy welded joints.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A high-precision method for simulating and predicting the temperature field of aluminum alloy welded joints includes the following steps:

[0008] S1: Prepare welding test plates and use an electric couple to collect real-time temperature curves of the wall panels and stiffeners of the welding test plates;

[0009] S2: Take weld samples and prepare weld morphology samples using grinding equipment;

[0010] S3: Use penetration depth testing equipment to observe the arc termination length and weld cross-sectional morphology of the joint, and use image processing software to perform detailed dimensional calibration of the joint weld morphology:

[0011] S4: Establish a three-dimensional assembly model of the test plate and welding fixture using three-dimensional software modeling, and import it into finite element preprocessing software for detailed mesh generation;

[0012] S5: Detect the chemical composition of the aluminum alloy, and then import it into the material performance simulation software to calculate the curve of the aluminum alloy material parameters changing with temperature;

[0013] S6: Import the finite element mesh model obtained in S4 into the welding finite element analysis software; import the thermal property parameter curves of the aluminum alloy material obtained in S5 into the software material library, and define the material properties and boundary conditions of the test plate and tooling.

[0014] S7: The double ellipsoidal heat source model is selected as the heat source for welding simulation;

[0015] S8: Set the actual values ​​of the weld morphology parameters measured in S4 in the software;

[0016] S9: Establish a set of nodes for the movement trajectory of the welding heat source and define the movement trajectory of the welding heat source;

[0017] S10: Establish a monitoring node for the welding temperature field;

[0018] S11: Select the transient solver to perform welding temperature field simulation calculations;

[0019] S12: Output the calculation results.

[0020] As a further aspect of the present invention, the thermocouple is specifically a K-type thermocouple.

[0021] As a further aspect of the present invention: in S8, the actual values ​​of the weld morphology parameters measured in S4, such as joint weld length, weld width, weld depth, and overall weld profile, are used.

[0022] As a further aspect of the present invention: the heat source model distribution function in S7 is:

[0023] Expression for the heat source of the first half-ellipsoid:

[0024]

[0025] Expression for the heat source in the posterior hemispheric region:

[0026]

[0027] Where Q is the instantaneous welding heat from the heat source, and a, b, c f c r Let f1 and f2 be the shape parameters of the heat source, and f1 and f2 be the energy distribution coefficients of the front and rear ellipsoids of the heat source model, respectively.

[0028] The beneficial effects of this invention are:

[0029] This invention discloses a high-precision simulation and prediction method for the temperature field of aluminum alloy welded joints. Compared with existing technologies, before finite element modeling, a simple weld measurement experiment method is used. This method not only measures the weld length but also calibrates the weld morphology parameters of the welded joint in detail, such as weld width, weld depth, and weld toe depth of stiffeners and wall plates. The detailed dimensional contour of the weld is highly reproduced and applied to the simulation model, achieving a correlation accuracy of over 88% between the simulated heat source and the experimental weld morphology. The simulation calculation highly reflects the dynamic process of heat source movement during welding, avoiding the problem of inaccurate heat source model movement in welding simulation analysis. This achieves an accurate description of the temperature field distribution of the welded joint, enabling the simulation prediction accuracy of the weld temperature field in both the horizontal and vertical directions to reach over 95%. Attached Figure Description

[0030] The invention will now be further described with reference to the accompanying drawings.

[0031] Figure 1 This is a diagram showing the weld morphology parameters of the present invention;

[0032] Figure 2 This is a curve showing the change of the thermophysical parameters of the material of this invention with temperature;

[0033] Figure 3 This is the double ellipsoidal heat source model of the present invention;

[0034] Figure 4 This is a comparison chart of temperature change curves at welding simulation and experimental monitoring points in this invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Please see Figure 1-4 As shown, this invention provides a high-precision method for simulating and predicting the temperature field of aluminum alloy welded joints.

[0037] S1: T-shaped welding test plates are prepared using an automated welding robot platform, and K-type thermocouples are used to collect real-time temperature curves of the welding test plates.

[0038] Before the test, the test plate was polished to a metallic luster and fixed with a self-designed welding fixture. Holes were drilled in the test plate wall and stiffening plate areas using a 1mm diameter drill bit to facilitate the placement of K-type thermocouple temperature monitoring points and to collect real-time temperature curves of the welding temperature field.

[0039] S2: Take a sample of the weld seam in the middle part of the T-joint and prepare a weld seam morphology sample by grinding with a grinding machine;

[0040] Weld samples were cut using an electrical discharge wire cutting machine and then ground using 600-grit, 100-grit, and 2000-grit sandpaper to prepare weld morphology samples.

[0041] S3: Use a penetration depth measurement device to observe the weld cross-sectional morphology and use image processing software to perform detailed dimensional calibration of the weld morphology;

[0042] Import the observed weld cross-sectional morphology images into the image processing software. First, calibrate the T-joint in the options to confirm the weld dimensions are correct. Then, enter the measurement interface and confirm the measurement unit, arrow type, color, and line type. Select the measurement mode to perform detailed dimensional calibration of the weld cross-sectional length, weld width, weld depth, and weld toes of the stiffeners and wall panels. After completing the measurement of the weld morphology images, export and save them.

[0043] S4: Create an assembly model of the T-shaped test plate and welding fixtures and a cross-sectional model of the T-shaped joint weld using 3D software modeling, and import them into finite element preprocessing software for detailed mesh generation;

[0044] A 3D model was created using software and imported into finite element preprocessing software for detailed mesh generation. A hexahedral mesh was used for model meshing. Different components were created based on the distribution of the weld heat-affected zone, and the mesh density was adjusted. The closer to the weld, the smaller the mesh size, with the minimum mesh size being 1mm×1mm×1mm. The total number of nodes was 77,842, and the total number of elements was 52,328.

[0045] S5: Detect the chemical composition of the aluminum alloy, and then import it into the material performance simulation software to calculate the curve of the aluminum alloy material parameters changing with temperature;

[0046] Aluminum alloy material was cut into small pieces, placed in a beaker, and a solution was poured in. The chemical composition of the aluminum alloy was determined by inductively coupled plasma atomic emission spectrometry (ICP-AES). The chemical composition of the aluminum alloy was then input into a material performance simulation software, and the curves of the aluminum alloy material parameters changing with temperature were calculated.

[0047] S6: Import the finite element mesh model obtained in S4 into the welding finite element analysis software; import the thermal property parameter curves of the aluminum alloy material obtained in S5 into the software material library, and define the material properties and boundary conditions of the test plate and tooling.

[0048] Export the finite element mesh model from the finite element preprocessing software, save it, and then import it into the welding finite element analysis software, dragging and dropping it onto different components one by one. Import the thermophysical property parameter curves of the aluminum alloy material into the software's material library, establish new material properties, and assign them to the welding test plate. Based on the welding site environment, recreate and define the boundary conditions between the test plate and the tooling as much as possible.

[0049] S7: A double ellipsoidal heat source model is selected as the heat source for welding simulation. Its heat source model distribution function is:

[0050] Expression for the heat source of the first half-ellipsoid:

[0051]

[0052] Expression for the heat source in the posterior hemispheric region:

[0053]

[0054] Where Q is the instantaneous welding heat from the heat source, and a, b, c f c r Here, f1 and f2 are the shape parameters of the heat source, and f1 and f2 are the energy distribution coefficients of the front and rear ellipsoids of the heat source model, respectively.

[0055] S8: Set the detailed weld morphology parameters obtained in S4, such as weld width, weld depth and weld outline, in the heat source parameters of the welding software.

[0056] Enter the welding software's heat source parameter setting interface, and restore the welding heat source morphology parameters as much as possible based on the detailed parameter values ​​of the weld morphology.

[0057] S9: Establish a set of nodes for the movement trajectory of the welding heat source and define the movement trajectory of the welding heat source;

[0058] Create a new node set in the welding software collection section, define the welding heat source movement trajectory, and let the welding heat source move from the start of the arc to the end of the arc on the mesh surface.

[0059] S10: Establish welding temperature field monitoring nodes to facilitate the export of simulation results after calculation;

[0060] Create a new node set in the welding software collection section, and detect the node temperature at 5mm, 10mm, 15mm, and 20mm from the fusion line in the arc initiation, middle, and arc termination areas.

[0061] S11: Select the transient solver to perform welding temperature field simulation calculations;

[0062] Select the transient solver to perform thermal calculations on the model, and perform welding simulation calculations based on the server configuration.

[0063] S12: Output the calculation results.

[0064] View the results in the welding software. Double-click the temperature area to adjust the color legend and view the welding simulation temperature distribution cloud map. Click the results area to enter the welding monitoring section and view the real-time dynamics of the welding simulation heat source. Use image processing software to calibrate the weld cross-sectional morphology of the simulated heat source, and compare the results with the actual weld morphology parameter values, as shown in Table 1.

[0065] The results show that, based on standard GMW-14058, both the simulation and experimental results meet the standard. The accuracy of the simulation modeled weld morphology parameters compared with the experimental parameters is over 88%, proving that the selection of weld morphology parameters for the welding heat source is reasonable.

[0066] Table 1. Measurement results of weld cross-sectional morphology in simulation and experiment.

[0067]

[0068] Click on the tracking point to enter the results display panel, export the simulation calculation results of the welding temperature field, and compare the simulation and experimental data in the data processing software.

[0069] By comparing the simulation calculations and experimental results of the welding temperature field in the horizontal and vertical directions, it was found that the heating rate, peak temperature and cooling time of the temperature curves were in good agreement, and the simulation accuracy reached more than 95%. The detailed temperature values ​​of the simulated and experimental welding temperature fields are shown in Table 2.

[0070] Table 2 Detailed Temperature Values ​​of Simulated and Experimental Welding Temperature Fields

[0071]

[0072] For inventions of mechanical products, the composition of the invention should be described in conjunction with the accompanying drawings, including the relative positions and assembly connections between the various parts or circuit components, and the role and function of each part; the working principle and process should also be described in conjunction with the accompanying drawings and the movement of each part, i.e., a static description should be given first, followed by a dynamic explanation.

[0073] This invention calibrates detailed parameters of the weld morphology of welded joints using a simple weld penetration measurement instrument, such as weld width, weld depth, and weld toes of stiffeners and wall plates. In simulations, the weld morphology achieves a calibration accuracy of over 88% compared to experimental results, highly reproducing the dynamic process of welding heat source movement and resulting in a welding temperature field prediction accuracy of over 95%. Compared with existing methods, this invention offers the following advantages:

[0074] Advantage 1: The method of this invention uses a penetration depth measuring instrument and image processing software to calibrate the weld morphology parameters in detail, which has the advantages of simple measuring equipment, reliable method and high efficiency;

[0075] Advantage 2: The method of this invention applies detailed dimensions such as weld width, weld depth, and weld toe of stiffener and wall plate to the simulation model. The weld morphology in the simulation is 88% accurate to the experimental model, which makes the prediction accuracy of the welding temperature field in the transverse and longitudinal directions reach more than 95%.

[0076] Advantage 3: The method of this invention can efficiently and accurately predict the temperature distribution of the heat-affected zone of welding, thereby accurately studying the attenuation effect of welding thermal shock on the heat-affected zone of aluminum alloy, eliminating the need for temperature field monitoring tests and effectively reducing the workload of testing.

[0077] Advantage 4: The method of this invention can also provide a theoretical reference for the efficient and high-precision prediction of the temperature field of welded joints of other metal materials.

[0078] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A high-precision method for simulating and predicting the temperature field of aluminum alloy welded joints, characterized in that, Includes the following steps: S1: Prepare welding test plates and use an electric couple to collect real-time temperature curves of the wall panels and stiffeners of the welding test plates; S2: Take weld samples and prepare weld morphology samples using grinding equipment; S3: Use penetration depth testing equipment to observe the arc termination length and weld cross-sectional morphology of the joint, and use image processing software to perform detailed dimensional calibration of the joint weld morphology: S4: Establish a three-dimensional assembly model of the test plate and welding fixture using three-dimensional software modeling, and import it into finite element preprocessing software for detailed mesh generation; S5: Detect the chemical composition of the aluminum alloy, and then import it into the material performance simulation software to calculate the curve of the aluminum alloy material parameters changing with temperature; S6: Import the finite element mesh model obtained in S4 into the welding finite element analysis software; Import the thermophysical property parameter curves of aluminum alloy obtained by S5 into the software material library, and define the material properties and boundary conditions of the test plate and tooling. S7: The double ellipsoidal heat source model is selected as the heat source for welding simulation; S8: Set the actual values ​​of the weld morphology parameters measured in S4 in the software; S9: Establish a set of nodes for the movement trajectory of the welding heat source and define the movement trajectory of the welding heat source; S10: Establish a monitoring node for the welding temperature field; S11: Select the transient solver to perform welding temperature field simulation calculations; S12: Output the calculation results; The heat source model distribution function in S7 is: Expression for the heat source of the first half-ellipsoid: Expression for the heat source in the posterior ellipsoid: Where Q is the instantaneous welding heat from the heat source, and a, b, c f c r Let f1 and f2 be the shape parameters of the heat source, and f1 and f2 be the energy distribution coefficients of the front and rear ellipsoids of the heat source model, respectively.

2. The high-precision aluminum alloy welded joint temperature field simulation and prediction method according to claim 1, characterized in that, The thermocouple is specifically a type K thermocouple.

3. The high-precision aluminum alloy welded joint temperature field simulation and prediction method according to claim 1, characterized in that, In S8, the actual values ​​of the weld morphology parameters measured in S4, such as joint weld length, weld width, weld depth, and overall weld profile, are used.

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