An aluminum alloy welded joint fracture prediction method based on an improved GTN damage model

Abstract

The application discloses a kind of aluminum alloy welded joint fracture prediction method based on improved GTN damage model, comprising: preparing aluminum alloy welded joint sample;Tensile test is carried out to aluminum alloy welded joint sample, and the stress-strain curve of weld zone, heat-affected zone and base material of aluminum alloy welded joint is obtained respectively;According to the stress-strain curve of weld zone, heat-affected zone and base material zone, the damage parameters of improved GTN damage model are solved respectively;The damage parameters corresponding to weld zone, the damage parameters of heat-affected zone and the damage parameters of base material zone are respectively given to improved GTN damage model, and the improved GTN damage model of weld zone, the improved GTN damage model of heat-affected zone and the improved GTN damage model of base material zone are obtained;According to the improved GTN damage model of weld zone, the improved GTN damage model of heat-affected zone and the improved GTN damage model of base material zone, the Mises equivalent stress and true strain when fracture failure occurs at different positions of welded joint are predicted by simulation software.

Description

Technical Field

[0001] This invention belongs to the technical field of fracture performance analysis of aluminum alloy welded joints, and specifically relates to a fracture prediction method for aluminum alloy welded joints based on an improved GTN damage model. Background Technology

[0002] Welding of metallic materials involves heating metals to melt them and joining them together in their molten state to form a strong joint. This process includes cleaning the surfaces, heating the metal above its melting point, adding filler material, and cooling. The core idea of ​​welding is to use thermal energy to melt metals, making them malleable and allowing them to bond together, thus achieving a connection between materials. As a commonly used method of joining metals, welding is widely applied in metal processing and manufacturing in many fields, such as aerospace, automotive, and shipbuilding, due to its advantages of high joint strength, durability, good sealing, and strong corrosion resistance.

[0003] During the welding process of aluminum alloys, the heat input during welding causes gradient changes in the microstructure and mechanical properties of different regions of the weld joint, resulting in non-uniform characteristics that negatively impact the strength of the welded aluminum alloy structure. It is essential to fully consider these non-uniform characteristics when analyzing the fracture and other mechanical properties of the weld joint. However, studying these non-uniform characteristics in tensile fracture tests typically requires cutting the weld joint into micro-samples and testing the mechanical properties of each sample. This method is not only difficult to implement but also time-consuming to adjust welding parameters. Simulation methods can quickly and accurately study the mechanical properties of different regions of the weld joint, and further predict and analyze the fracture and other mechanical properties of the entire weld joint. Currently, the methods for simulating and evaluating the fracture performance of weld joints are generally similar to those described in the paper "A Simulation Analysis Method for the Strength of Laser Welded Joints Based on Tensile Fracture Evaluation," which involves establishing a mesh model of the welded sample, assigning material properties to each region, setting displacement boundaries, and performing static or dynamic calculations. However, this method only considers the differences in material parameters in each region and does not take into account the impact of differences in damage model parameters in each welding region. For the fracture performance analysis of welded joints, selecting the correct damage model is equally important for the accuracy of simulation results. Summary of the Invention

[0004] The purpose of this invention is to provide a fracture prediction method for aluminum alloy welded joints based on an improved GTN damage model. This method assigns differentiated damage model parameters to the weld, heat-affected zone, and base material of the welded joint, which can improve the accuracy of fracture simulation calculation results.

[0005] The technical solution provided by this invention is as follows:

[0006] A method for predicting fracture of aluminum alloy welded joints based on an improved GTN damage model includes the following steps:

[0007] Step 1: Prepare aluminum alloy welded joint samples;

[0008] Step 2: Perform tensile tests on the aluminum alloy welded joint specimens to obtain the stress-strain curves of the weld zone, heat-affected zone, and base material of the aluminum alloy welded joint.

[0009] Step 3: Solve the damage parameters of the improved GTN damage model based on the stress-strain curves of the weld zone, heat-affected zone and base metal zone respectively, to obtain the damage parameters of the weld zone, the heat-affected zone and the base metal zone.

[0010] Step 4: Assign the damage parameters corresponding to the weld zone, the heat-affected zone, and the base metal zone to the improved GTN damage model to obtain the improved GTN damage model of the weld zone, the improved GTN damage model of the heat-affected zone, and the improved GTN damage model of the base metal zone.

[0011] Step 5: Using simulation software, predict the Mises equivalent stress and true strain when fracture failure occurs at different locations of the weld joint, based on the improved GTN damage model of the weld zone, the improved GTN damage model of the heat-affected zone, and the improved GTN damage model of the base metal zone.

[0012] Preferably, the expression for the improved GTN damage model is:

[0013]

[0014] Where φ represents the yield surface, σ e Represents the MISES equivalent stress, σ m σ represents hydrostatic stress, q1 and q2 represent material-related characteristic damage parameters, and σ represents hydrostatic stress. y It is the flow stress of the matrix material, f * Let D be the volume of the hole. s This represents the cumulative shear damage factor.

[0015] Preferably, the formula for calculating the cavity volume is:

[0016]

[0017] Among them, f c f represents the volume fraction of pores during pore aggregation. f The volume fraction of voids when the material fractures is represented by f, where f represents the volume fraction of voids.

[0018] Preferably, the formula for calculating the cumulative shear damage factor is:

[0019]

[0020] Where n is the weakening factor, used to characterize the growth rate of shear damage; For equivalent plastic strain; ε f φ(θ,η) is the material fracture strain; φ(θ,η) is a function of the Lode angle and the stress triaxiality, where θ is the Lode angle and η is the stress triaxiality.

[0021] Preferably, in step two, the tensile test is performed on a universal tensile testing machine with a loading rate of 2 mm / min and a sampling frequency of 20 Hz.

[0022] Preferably, in step three, the damage parameters include: material-related characteristic damage parameters q1 and q2, and the pore volume fraction f during pore aggregation. c The volume fraction of voids f when the material fractures f and material fracture strain δ f .

[0023] Preferably, in step three, the damage parameters of the weld zone, heat-affected zone, and base metal zone are determined by using a particle swarm optimization algorithm, including:

[0024] Initialize the damage parameter set for each region:

[0025] X = (x1, x2, x3);

[0026]

[0027] Where x1, x2, and x3 represent the damage parameter sets for the weld zone, heat-affected zone, and base metal zone, respectively; j represents any region among the weld zone, heat-affected zone, and base metal zone; x j The parameter set representing region j, An array of parameters q1 for region j; An array of parameters q2 for region j; The parameter f for region j c Array; The parameter f for region j f Array; δ is the parameter of region j f Array;

[0028] The objective function of the particle swarm optimization algorithm is determined as follows:

[0029]

[0030] Where, σ inum The stress value output by the simulation, σ iexpThe stress value collected in the experiment is i, where i is any sampling point within the optimization region, and N is the number of sampling points.

[0031] Using the minimum objective function value as the optimization objective, the damage parameter group of each region is iteratively optimized, and the global optimal solution is obtained in each iteration.

[0032] The iteration process ends when the fitness of the global optimal solution obtained by iterative optimization meets the set threshold or the number of iterations reaches the maximum number of iterations.

[0033] The global optimal solution obtained in the last iteration for each region is used as the damage parameter value for that region.

[0034] Preferably, the number of particles in the damage parameter particle swarm in each region is set to at least 100, and the maximum number of iterations is set to at least 500.

[0035] Preferably, in step five, the simulation software used is the nonlinear finite element software ABAQUS.

[0036] Preferably, fine meshes are generated for the weld zone, heat-affected zone, and base material zone of the aluminum alloy welded joint in the nonlinear finite element analysis software, and the mesh generation property is set to an eight-node hourglass control unit in the reduced integral form under the display solver.

[0037] The mesh size increases in that order: in the weld zone, heat-affected zone, and base metal zone.

[0038] The beneficial effects of this invention are:

[0039] The present invention provides a method for predicting the fracture of aluminum alloy welded joints based on an improved GTN damage model. When predicting the fracture performance of welded joints, the method takes into account the non-uniform mechanical properties of the welded joints caused by the welding heat input. Different mechanical property material parameters are assigned to the weld, heat-affected zone and base material of the welded joint, and different damage model parameters are assigned to each region. This makes the fracture calculation process of the tensile specimen of the welded joint more consistent with reality and the calculation results more accurate.

[0040] The present invention provides a method for predicting the fracture of aluminum alloy welded joints based on an improved GTN damage model. The method uses a particle swarm optimization algorithm to solve for each parameter of the damage model, which significantly improves the calculation accuracy of each parameter and improves the simulation prediction efficiency. Attached Figure Description

[0041] Figure 1 This is a flowchart of the simulation analysis method for fracture performance of aluminum alloy welded joints based on the improved GTN damage model described in this invention.

[0042] Figure 2This is a schematic diagram of the structure of the mesh model described in this invention.

[0043] Figure 3 This is a Mises equivalent stress distribution diagram when the aluminum alloy welded joint described in this invention experiences fracture failure.

[0044] Figure 4 This is a stress distribution diagram of the aluminum alloy welded joint described in this invention when it experiences fracture failure.

[0045] Figure 5 This is a strain distribution diagram of the aluminum alloy welded joint described in this invention when it undergoes fracture failure. Detailed Implementation

[0046] The present invention will now be described in further detail with reference to the accompanying drawings, so that those skilled in the art can implement it based on the description.

[0047] This invention provides a method for predicting the fracture of aluminum alloy welded joints based on an improved GTN damage model. Based on finite element analysis, the method first involves welding and wire cutting tests on the aluminum alloy, followed by cutting standard tensile test specimens according to GB / T2651-2008 "Tension Test Method for Welded Joints". Tensile tests are then performed on the specimens to obtain stress-strain curves. The damage parameters of the weld zone, heat-affected zone, and base metal zone in the improved GTN model are solved using the obtained stress-strain curve data. Different material properties and damage model parameters are then assigned to the weld zone, heat-affected zone, and base metal zone respectively. Finally, an explicit solver is used to analyze and obtain the tensile fracture performance of the welded joint.

[0048] like Figure 1 As shown, the specific implementation process of the simulation analysis method for fracture performance of aluminum alloy welded joints based on the improved GTN damage model provided by this invention is as follows:

[0049] I. Preparation of standard tensile test specimens:

[0050] Aluminum alloy welding test: Aluminum alloy welded joints are obtained by welding tests.

[0051] Wire EDM test: The aluminum alloy welded joint is cut into standard tensile test specimens using wire EDM.

[0052] II. Determining the parameters of the improved GTN damage model:

[0053] (1) Mechanical property test: The standard tensile test specimen of the welded aluminum alloy joint was dynamically measured by performing a tensile test on a universal tensile testing machine at a loading rate of 2 mm / min and a sampling frequency of 20 Hz.

[0054] (2) Engineering stress-strain extraction: Based on the test results of the tensile test, the engineering stress-strain of the weld zone, heat-affected zone and base material zone of the aluminum alloy welded joint is extracted and calculated according to the standard tensile test.

[0055] (3) Parameter Solving: Using the obtained engineering stress-strain values ​​for each region, the particle swarm optimization algorithm is used to optimize and solve the damage parameters of the weld zone, heat-affected zone, and base metal zone of the welded joint, respectively, to obtain the damage parameters of the weld zone, heat-affected zone, and base metal zone of the improved GTN damage model. The formula for the improved GTN damage model is as follows:

[0056]

[0057] Where φ represents the yield surface, σ e Represents the MISES equivalent stress, σ m σ represents hydrostatic stress, q1 and q2 represent material-related characteristic damage parameters, and σ represents hydrostatic stress. y It is the flow stress of the matrix material, f * Let D be the volume of the hole. s This represents the cumulative shear damage factor.

[0058] The specific description is as follows:

[0059]

[0060] In the formula, f c f represents the volume fraction of pores during pore aggregation. f This represents the volume fraction of pores when the material fractures; when the volume fraction of pores in the matrix reaches f... f It was assumed that the material had fractured.

[0061] The variable Ds is the cumulative shear damage factor, which is described in detail below:

[0062]

[0063] In the formula, n is the weakening factor, used to characterize the growth rate of shear damage; ε f The material fracture strain is denoted by φ(θ,η); φ(θ,η) is a function of the Lode angle and the stress triaxiality, used to characterize the current stress state.

[0064]

[0065] In the formula, T k The weighting factor is used for triaxiality under negative stress; the expression for the weighting function g0 is:

[0066]

[0067] In the formula, θ is the Lode angle, and its expression is:

[0068]

[0069] In the formula, μ0 is the Lode parameter, and its expression is:

[0070]

[0071] In the formula, σ1, σ2, and σ3 represent the first, second, and third principal stresses, respectively.

[0072] Among them, it is necessary to solve for various damage parameters of the improved GTN damage model, including q1, q2, and the pore volume fraction f during pore aggregation. c The volume fraction of voids f when the material fractures f Material fracture strain ε f .

[0073] The process of optimizing and solving the damage parameters for each region using the particle swarm optimization algorithm is as follows:

[0074] The undetermined parameter groups for the weld zone, heat-affected zone, and base metal zone are defined as follows:

[0075] X = (x1, x2, x3);

[0076] Where x1, x2, and x3 represent the sets of undetermined parameters for the weld zone, heat-affected zone, and base metal zone, respectively.

[0077]

[0078] The "velocity" of each parameter is defined as follows:

[0079] v j =(v1,v2,v3)

[0080] Where v1, v2, and v3 represent the "velocity" of each parameter in the parameter sets x1, x2, and x3, respectively.

[0081] The individual extreme value of the currently searched optimal position is defined as:

[0082] p best =(p j1 ,p j2 ,...,p j5 j = 1, 2, 3

[0083] The global extremum of the optimal position obtained after the search for all parameters is defined as:

[0084] g best =(p j1 ,p j2 ,...,p j5 j = 1, 2, 3

[0085] The number of particles in each parameter group is set to 100, and the maximum number of iterations is 500.

[0086] The objective function in the particle swarm optimization algorithm is expressed as follows:

[0087]

[0088] Where, σ inum The stress value output by the simulation, σ iexp The stress value collected in the experiment is denoted as i, where i is any sampling point within the optimization region, and N is the number of sampling points.

[0089] The particle swarm optimization algorithm is used for iterative calculations, with the goal of minimizing the objective function value.

[0090] Calculate the individual optimal solution for each particle;

[0091] Calculate the individual optimal solution T for each particle. best The calculation formula is as follows:

[0092]

[0093] Let the smallest individual optimal solution obtained from the calculation be set as the global optimal solution G of the particle swarm at this time. best .

[0094] Iteratively update the particle's velocity and position information, and iteratively update the particle's velocity v. j and location information x j

[0095] The velocity relationship of a certain particle y under the kth iteration is as follows:

[0096]

[0097] Where k-1 and k represent the k-th and (k-1)-th iterations of the particle swarm optimization algorithm.

[0098] x represents the position corresponding to the individual's optimal solution. y G represents the position of the different particles. best This represents the global optimal solution, with a default time step of 1. ω represents the inertia weight, c1 represents the individual experience weight, and c2 represents the social experience weight.

[0099] The termination condition of the particle swarm optimization algorithm:

[0100] Calculate the global optimal objective function fitness T of the particle swarm at the current iteration number k. k(i.e., the objective function value corresponding to the global optimal solution at the k-th iteration), determine whether the termination condition is met. If the termination condition is met, stop the iteration and output the parameter values ​​and error to be calibrated. If the termination condition is not met, continue the iteration until it is met.

[0101] Specifically, when the number of iterations k is greater than 500 or when the objective function T k Less than a certain self-defined value T min When the time is reached, the iterative calculation is terminated.

[0102] The above iterative calculations were performed on each region of the welded joint using Matlab 2023. After 421 iterations, the iteration termination condition was met, and the damage parameters of the final weld, heat-affected zone, and base material were obtained, as shown in Table 1.

[0103] Table 1 Damage parameters of weld, heat-affected zone and base metal

[0104]

[0105] III. Fracture Performance Analysis:

[0106] (1) Refined Mesh Generation for Each Region: In the nonlinear finite element analysis software, a mesh generation method with progressively larger mesh sizes was used for the weld zone, heat-affected zone, and base metal zone of the aluminum alloy welded joint. The mesh size increased sequentially in the weld zone, heat-affected zone, and base metal zone, reducing the overall mesh size and creating a refined mesh. The resulting mesh model is shown below. Figure 2 As shown. All meshes are hexahedral meshes and gradient algorithms are used to calculate stress. Each element contains multiple stress and strain integration points to accurately describe stress and strain gradient changes. The mesh generation property is set to an eight-node hourglass control unit with reduced integration form under the solver to improve the convergence of the calculation. The nonlinear finite element analysis software is ABAQUS.

[0107] (2) Assignment of material parameters for each region: When dividing the mesh, the weld, heat-affected zone and base material were distinguished. Since the material properties of different regions of the weld joint are different due to the heat input during welding, the mesh of the weld area, heat-affected zone and base material were assigned corresponding material properties, including elastic modulus, Poisson's ratio, yield stress, hardening coefficient and hardening index.

[0108] (3) Assign different improved GTN damage model parameters to each region: Assign the improved GTN damage model parameters determined in step (2) to the weld zone, heat-affected zone and base material zone respectively to determine the corresponding improved GTN model for each region. Assign the damage model to each region of the welded joint through ABAQUS user subroutines.

[0109] (4) Set the fracture tensile process analysis step: Set the analysis step of the tensile process to explicit dynamic analysis, set the step size to dynamic step size, and set the mass amplification factor while ensuring that the calculation results are not distorted. The explicit solver does not need to perform equilibrium iteration, and the calculation speed is fast and easy to converge.

[0110] (5) Set tensile boundary conditions: Set the tensile fracture condition of the aluminum alloy welded joint in the mesh model, fully constrain one end of the tensile mesh model, and set the displacement boundary at the other end, i.e., unilateral tension.

[0111] (6) Solution Analysis: Create and submit an analysis task for the tensile process, obtain simulation results, and view the Mises equivalent stress distribution of the aluminum alloy welded joint when fracture failure occurs, such as... Figure 3 As shown, the average value (Avg) of the Mises equivalent stress (S, Mises) is 75%, and the Mises equivalent stress is the largest at the interface between the weld and the heat-affected zone, with a maximum value (Max) of 461.2 MPa. Meanwhile, the distribution of stress (S) at the point of fracture failure in the aluminum alloy welded joint is as follows... Figure 4 As shown, the average stress (S) (Avg) is 75%, with the highest stress (Max) at the interface between the weld and the heat-affected zone, reaching 435.2 MPa. From the perspective of true strain (LE), the true strain distribution at fracture is as follows... Figure 5 As shown, the average true strain (LE) (Avg) is 75%, and the true strain is the largest at the interface between the weld and the heat-affected zone, with a maximum value (Max) of 0.2185. Based on the above analysis, fracture is most likely to occur in this region.

[0112] The simulation analysis method for fracture performance of aluminum alloy welded joints provided by this invention takes into account the non-uniform mechanical properties of the welded joint caused by the heat input during the welding process. It not only distinguishes the material properties of each region, including the weld, heat-affected zone, and base metal joint, but also confirms the parameters of the improved GTN damage model selected for the welded joint based on the regional differences. By considering the influence of the non-uniform mechanical properties of aluminum alloy welded joints from two perspectives, the simulation model is more consistent with reality, and the results of the simulation analysis of fracture performance of aluminum alloy welded joints are more consistent with the actual fracture performance.

[0113] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A method for predicting fracture of aluminum alloy welded joints based on an improved GTN damage model, characterized in that, Includes the following steps: Step 1: Prepare aluminum alloy welded joint samples; Step 2: Perform tensile tests on the aluminum alloy welded joint specimens to obtain the stress-strain curves of the weld zone, heat-affected zone, and base material of the aluminum alloy welded joint. Step 3: Solve the damage parameters of the improved GTN damage model based on the stress-strain curves of the weld zone, heat-affected zone and base metal zone respectively, to obtain the damage parameters of the weld zone, the heat-affected zone and the base metal zone. Step 4: Assign the damage parameters corresponding to the weld zone, the heat-affected zone, and the base metal zone to the improved GTN damage model to obtain the improved GTN damage model of the weld zone, the improved GTN damage model of the heat-affected zone, and the improved GTN damage model of the base metal zone. The expression for the improved GTN damage model is: ； in, Indicates the yield surface. Represents the equivalent stress of MISES. Represents hydrostatic stress. , Represents material-related characteristic damage parameters. It is the flow stress of the matrix material. The volume of the cavity. It is the cumulative shear damage factor; Step 5: Using simulation software, predict the Mises equivalent stress and true strain when fracture failure occurs at different locations of the weld joint, based on the improved GTN damage model of the weld zone, the improved GTN damage model of the heat-affected zone, and the improved GTN damage model of the base metal zone.

2. The method for predicting fracture of aluminum alloy welded joints based on the improved GTN damage model according to claim 1, characterized in that, The formula for calculating the volume of the cavity is: ； in, This represents the volume fraction of pores during pore aggregation. This indicates the volume fraction of voids when the material fractures. This represents the volume fraction of the pores.

3. The method for predicting fracture of aluminum alloy welded joints based on the improved GTN damage model according to claim 2, characterized in that, The formula for calculating the cumulative shear damage factor is as follows: ； Where n is the weakening factor, used to characterize the growth rate of shear damage; Equivalent plastic strain; For material fracture strain; It is a function of the Lode angle and stress triaxiality. Lode angle, It is a stress triaxiality.

4. The method for predicting fracture of aluminum alloy welded joints based on the improved GTN damage model according to claim 2 or 3, characterized in that, In step two, the tensile test is performed on a universal tensile testing machine with a loading rate of 2 mm / min and a sampling frequency of 20 Hz.

5. The method for predicting fracture of aluminum alloy welded joints based on the improved GTN damage model according to claim 4, characterized in that, In step three, the damage parameters include: material-related characteristic damage parameters q1 and q2, and the pore volume fraction during pore aggregation. The volume fraction of pores when the material fractures and material fracture strain .

6. The method for predicting fracture of aluminum alloy welded joints based on the improved GTN damage model according to claim 5, characterized in that, In step three, the particle swarm optimization algorithm is used to solve for and determine the damage parameters of the weld zone, heat-affected zone, and base metal zone, including: Initialize the damage parameter set for each region: ； ； in, These represent the sets of damage parameters for the weld zone, heat-affected zone, and base metal zone, respectively. It represents any one of the weld zone, heat-affected zone, and base metal zone; Indicates the region The set of parameters, For the region parameters Array; For the region parameters Array; For the region parameters Array; For the region parameters Array; For the region parameters Array; The objective function of the particle swarm optimization algorithm is determined as follows: ； in, The stress value output from the simulation. The stress values ​​collected during the experiment, To optimize any sampling point within the region, The number of sampling points; Using the minimum objective function value as the optimization objective, the damage parameter group of each region is iteratively optimized, and the global optimal solution is obtained in each iteration. The iteration process ends when the fitness of the global optimal solution obtained by iterative optimization meets the set threshold or the number of iterations reaches the maximum number of iterations. The global optimal solution obtained in the last iteration for each region is used as the damage parameter value for that region.

7. The method for predicting fracture of aluminum alloy welded joints based on the improved GTN damage model according to claim 6, characterized in that, The number of particles in the damage parameter particle swarm for each region should be set to at least 100, and the maximum number of iterations should be set to at least 500.

8. The method for predicting fracture of aluminum alloy welded joints based on the improved GTN damage model according to claim 7, characterized in that, In step five, the simulation software used is the nonlinear finite element software ABAQUS.

9. The method for predicting fracture of aluminum alloy welded joints based on the improved GTN damage model according to claim 8, characterized in that, In the nonlinear finite element analysis software, fine meshes were generated for the weld zone, heat-affected zone, and base material zone of the aluminum alloy welded joint, and the mesh generation property was set to an eight-node hourglass control unit in the reduced integral form under the display solver. The mesh size increases in that order: in the weld zone, heat-affected zone, and base metal zone.

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