Calibration method of advanced high-strength steel rebound prediction material card

Abstract

The invention relates to a calibration method of an advanced high-strength steel springback prediction material card. The calibration method comprises the following steps: S1, carrying out mechanical behavior tests under different loading path conditions; s2, establishing an elastic behavior criterion model considering an elastic behavior change rule; s3, a yield behavior criterion considering isotropic / anisotropic behaviors of the researched material is described and established; s4, establishing a material mixing and hardening model under the dynamic strain path; s5, establishing an advanced high-strength steel material card and optimizing parameters; and S6, verifying the accuracy of the advanced high-strength steel material card model. Through a series of experiments and simulation analysis, key physical parameters such as flow stress and elastic modulus of advanced high-strength steel are accurately measured, and a rebound prediction model suitable for the material is established based on the parameters. The model can describe the stress-strain relation and the springback behavior of the advanced high-strength steel more accurately, so that the accuracy and the reliability of springback prediction are improved, and the problem of springback prediction of the advanced high-strength steel in the forming process is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of springback prediction of advanced high-strength steel, and particularly to a calibration method for a springback prediction material card of advanced high-strength steel. Background Art

[0002] Due to environmental concerns and collision safety considerations, the automotive industry is facing the challenge of lightweighting. Advanced high-strength steel (AHSS) has become an important choice for body lightweighting materials due to its high strength-plasticity matching, yield strength, and ultimate tensile strength. Springback is a difficulty and challenge in the forming process of advanced high-strength steel. The springback behavior depends to a large extent on the elastic modulus, so there are certain limitations in improvement measures from the material aspect. The springback problem not only affects the dimensional accuracy of the formed parts but also may increase the number of die debugging times and production costs.

[0003] To accurately predict springback, it is necessary to establish a material model suitable for advanced high-strength steel. These models need to consider the isotropy / anisotropy of the material, dynamic hardening behavior, friction factors, and complex rheological behaviors (such as the Bauschinger effect), etc. A series of experimental means, such as uniaxial tensile tests, biaxial bulging experiments, and loading / unloading experiments, etc., are required to accurately measure the rheological stress, elastic modulus, and other physical properties of advanced high-strength steel. These parameters are the basis for establishing a springback prediction model. Based on the measured material parameters, a springback prediction model suitable for advanced high-strength steel needs to be established. At the same time, it is necessary to continuously optimize and adjust the model parameters by comparing with the actual measurement results to improve the accuracy and reliability of springback prediction. Springback compensation needs to be added in the die design stage to reduce the dimensional error of the formed parts. However, for experienced die designers, determining the compensation amount is also a difficult problem. Therefore, it is necessary to develop a springback prediction material card for advanced high-strength steel to reduce the number of die debugging times and production costs. A high-precision springback prediction material card for advanced high-strength steel needs to study the applicability of different material models in springback prediction of advanced high-strength steel and determine which model can better describe the springback behavior of the material. At the same time, the stability and accuracy of the material model under different deformation conditions also need to be considered.

[0004] Therefore, the key problems that need to be solved by the calibration method of the springback prediction material card for advanced high-strength steel include the accurate determination of material parameters, the establishment and optimization of the springback prediction model, and the applicability of the material model. By solving these problems, the dimensional accuracy and production efficiency of formed parts made of advanced high-strength steel can be further improved. Summary of the Invention

[0005] In view of the above problems, the purpose of the present invention is to provide a calibration method for a springback prediction material card of advanced high-strength steel. Through a series of experiments and simulation analyses, key physical parameters such as the flow stress and elastic modulus of advanced high-strength steel are accurately measured, and a springback prediction model applicable to this material is established based on these parameters. This model can more precisely describe the stress-strain relationship and springback behavior of advanced high-strength steel, thereby improving the accuracy and reliability of springback prediction and effectively solving the problem of springback prediction during the forming process of advanced high-strength steel.

[0006] The technical solution adopted by the present invention is as follows:

[0007] A calibration method for a springback prediction material card of advanced high-strength steel proposed by the present invention specifically includes the following steps:

[0008] S1. Conduct mechanical behavior tests under different loading path conditions;

[0009] S2. Establish an elastic behavior criterion model considering the change law of elastic behavior;

[0010] S3. Describe and establish a yield behavior criterion considering the isotropic / anisotropic behavior of the material under study;

[0011] S4. Establish a material mixed hardening model under dynamic strain paths;

[0012] S5. Establish an advanced high-strength steel material card and parameter optimization;

[0013] S6. Verify the accuracy of the advanced high-strength steel material card model.

[0014] Furthermore, in the step S1, the mechanical behavior tests include unidirectional tensile experiments, sheet metal cyclic loading / unloading experiments, biaxial tensile experiments, equi-proportion / non-equi-proportion biaxial tensile experiments on cruciform specimens, and uniaxial cyclic tension-compression experiments to obtain the mechanical behavior of the sheet metal under complex loading conditions and obtain the Bauschinger behavior curve of the sheet metal.

[0015] Furthermore, for the unidirectional tensile experiment: conduct unidirectional tensile tests at angles of 0°, 15°, 30°, 45°, 60°, 75°, and 90° with respect to the rolling direction of the sheet metal. After data processing, the engineering stress-strain curve and true stress-strain curve of the advanced high-strength steel are obtained;

[0016] For the sheet metal cyclic loading-unloading experiment: conduct a ten-cycle cyclic loading-unloading experiment with a cyclic interval of 1% along the rolling direction of the sheet metal. After processing, the stress-strain curve is obtained;

[0017] The uniaxial cyclic tension-compression experiment requires the use of a compression experiment mold. The tensile mold cannot precisely meet the requirement of the compression force being centered vertically during compression, and buckling instability is likely to occur, making it impossible to achieve the required pre-strain. Therefore, a compression experiment mold is needed for the tension-compression experiment;

[0018] The biaxial tension test: Conduct equal-proportion and non-equal-proportion loading experiments on cruciform specimens, specifically: nine groups of experiments of 4:0, 4:1, 4:2, 4:3, 4:4, 3:4, 2:4, 1:4, 0:4; among them, the two groups of experiments of 4:0 and 0:4 are replaced by uniaxial tension experiments in the 0° and 90° directions.

[0019] Furthermore, in step S2, the elastic modulus variation behavior of advanced high-strength steel can be obtained from the cyclic loading-unloading experiment. The elastic modulus decay model using the following formula:

[0020]

[0021] where E0 is the initial elastic modulus of the material, E a is the stable value of the unloading elastic modulus after the sheet has undergone multiple deformations, ξ is the decay gradient value of the material's elastic modulus, and ε p is the true plastic strain;

[0022] The fitting diagram obtained by fitting the data points obtained from the experiment with the elastic modulus decay model; thus, the elastic modulus decay model of advanced high-strength steel considering the change law of elastic behavior at a 1% unloading interval is obtained, and the stable value E of the unloading elastic modulus a and the material elastic modulus decay coefficient ξ are determined.

[0023] Furthermore, in step S3, the uniaxial tension experiment at multiple angles can determine whether advanced high-strength steel has anisotropy. If anisotropy exists, a low-order anisotropic yield criterion is adopted. If strong anisotropy exists, a higher-order anisotropic yield criterion is adopted.

[0024] Furthermore, step S4 includes: combining the saturated hardening model and the non-saturated hardening model to construct a new non-saturated superimposed hardening model. The expression of the non-saturated superimposed hardening model is:

[0025]

[0026] where α is the weight coefficient;

[0027] The 7 parameters to be determined in the non-saturated superimposed hardening model are α, C, ε0, m, σ sat , σ i , p; preliminary fitting of the seven parameters can obtain the initial values of the parameters of the non-saturated superimposed hardening model of advanced high-strength steel.

[0028] Furthermore, by using Isight to compare the data obtained from the initial values of the parameters of the non-saturated superimposed hardening model with the experimental curve and gradually approaching the experimental curve through iteration, the 7 parameter values of the more accurate non-saturated superimposed hardening model can be obtained.

[0029] Furthermore, step S5 includes: establishing an advanced high-strength steel material card including elastic behavior, yield behavior, and hardening behavior according to steps S1 - S4.

[0030] Furthermore, step S6 includes: selecting the following 4 combinations of constitutive models to conduct 90-degree V-bending finite element simulations along the rolling direction to compare the simulation accuracy, and selecting the one with the optimal simulation accuracy as the calibration method for establishing the advanced high-strength steel material card;

[0031] 1. Constant elastic modulus + Barlat2000 - 2D yield + optimized Swift&H - S hardening;

[0032] 2. Elastic modulus decay + Hill48 yield criterion + optimized Swift&H - S hardening;

[0033] 3. Elastic modulus decay + Barlat2000 - 2D yield + unoptimized Swift&H - S hardening;

[0034] 4. Elastic modulus decay + Barlat2000 - 2D yield + optimized Swift&H - S hardening.

[0035] The present invention has the following beneficial effects compared with the prior art:

[0036] 1. Through the accurate material card calibration method, the mechanical properties of advanced high-strength steel under changing strain paths can be more accurately reflected, including the hardening behavior of the material, changes in elastic modulus, etc. It helps to improve the accuracy of finite element simulation in springback prediction, making the simulation results closer to the springback situation in actual production;

[0037] 2. Accurate springback prediction can provide strong support for production processes and die design. In the die design stage, by simulating the springback situation, the die shape and size can be optimized to reduce the springback amount in actual production and improve the dimensional accuracy and surface quality of the product;

[0038] 3. Accurate springback prediction can also reduce the number of die modifications and adjustments, further improving production efficiency;

[0039] 4. As an ideal material to meet this requirement, the accuracy of springback prediction of advanced high-strength steel directly affects the quality and performance of the product. By adopting the accurate material card calibration method, the dimensional accuracy and surface quality of the product can be improved, thus enhancing the competitiveness of the product. Description of the Drawings

[0040] Figure 1 It is a schematic diagram of the fitting curve of the elastic modulus decay of CP980 steel;

[0041] Figure 2 It is a schematic diagram of the yield locus of CP980 steel YLD-2000;

[0042] Figure 3 It is a schematic diagram of the yield locus of CP980 steel BBC-2005;

[0043] Figure 4 It is a schematic diagram of the comparison between the theoretically predicted yield stress and the actual yield stress of Barlat2000-2D;

[0044] Figure 5 It is a schematic diagram of the comparison between the theoretically predicted r value and the actual r value of Barlat2000-2D;

[0045] Figure 6 It is a schematic diagram of the comparison of the accuracy of the material cards of four constitutive models for advanced high-strength steel. Detailed Implementation Manner

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0047] A calibration method for an advanced high-strength steel springback prediction material card proposed by the present invention specifically includes the following steps:

[0048] Step S1: Conduct mechanical behavior tests under different loading path conditions.

[0049] The mechanical behavior tests include: unidirectional tensile experiments, sheet metal cyclic loading / unloading experiments, biaxial tensile experiments, conducting equi-proportion / non-equi-proportion biaxial tensile experiments on cruciform specimens, and uniaxial cyclic tension-compression experiments to obtain the mechanical behavior of the sheet metal under complex loading conditions and obtain the Bauschinger behavior curve of the sheet metal.

[0050] Unidirectional tensile experiment: Unidirectional tensile tests were carried out at angles of 0°, 15°, 30°, 45°, 60°, 75°, and 90° with respect to the rolling direction of the sheet metal. After data processing, the engineering stress-strain curve and true stress-strain curve of the advanced high-strength steel were obtained.

[0051] Plate cyclic loading-unloading experiment: A cyclic loading-unloading experiment with a 1% cycle interval was carried out along the rolling direction of the plate for ten cycles, and the stress-strain curve obtained after treatment.

[0052] Uniaxial cyclic tension-compression experiment: The tension-compression experiment requires the application of a compression experiment die. The tensile die cannot precisely meet the requirement that the compression force is centered vertically during compression, and buckling instability is likely to occur, making it impossible to achieve the required pre-strain. Therefore, the compression experiment die needs to be applied for the tension-compression experiment.

[0053] Biaxial tensile test: Equal-proportion and non-equal-proportion loading experiments were carried out on cruciform specimens, specifically: nine groups of experiments of 4:0, 4:1, 4:2, 4:3, 4:4, 3:4, 2:4, 1:4, 0:4; among them, the two groups of experiments of 4:0 and 0:4 were replaced by uniaxial tensile experiments in the 0° and 90° directions.

[0054] Step S2: Establish an elastic behavior criterion model considering the variation law of elastic behavior.

[0055] The elastic modulus variation behavior of advanced high-strength steel plates can be obtained from the cyclic loading-unloading experiment. An elastic modulus decay model with the following formula is used.

[0056]

[0057] Among them, E0 is the initial elastic modulus of the material, E a is the stable value of the unloading elastic modulus after the plate has undergone multiple deformations, ξ is the decay gradient value of the material's elastic modulus, and ε p is the true plastic strain. The fitting diagram obtained by fitting the data points obtained from the experiment with the elastic modulus decay model. Thus, the elastic modulus decay model of advanced high-strength steel considering the variation law of elastic behavior at a 1% unloading interval is obtained, and the stable value E of the unloading elastic modulus a and the material elastic modulus decay coefficient ξ are determined.

[0058] Step S3: Describe and establish a yield behavior criterion considering the isotropic / anisotropic behavior of the material under study.

[0059] The anisotropy of advanced high-strength steel can be determined from uniaxial tensile experiments at multiple angles. If anisotropy exists, a low-order anisotropic yield criterion (such as the Hill48 yield criterion) is used. If strong anisotropy exists, a higher-order anisotropic yield criterion (the Barlat-YLD2000-2D yield criterion or the BBC-2005 yield criterion) is used. Among them,

[0060] Determination of anisotropic parameters of Barlat-YLD2000-2D yield criterion: In the Barlat-YLD2000-2D yield criterion, α1 to α8 are 8 anisotropic parameters. These 8 anisotropic parameters need to be obtained from the experimental data of σ0, σ 45 , σ 90 , σ b , σ 45 , γ0, γ 90 , γ b obtained from uniaxial tensile tests and biaxial tensile tests. The values of 8 anisotropic parameters of advanced high-strength steel were obtained through Matlab algorithm iteration. Substituting the 8 Barlat-YLD2000-2D anisotropic parameters into the yield function, the yield locus of advanced high-strength steel was obtained.

[0061] Determination of anisotropic parameters of BBC-2005 yield criterion: The BBC-2005 yield criterion also has 8 material parameters. These 8 anisotropic parameters are still obtained from the experimental data of σ0, σ 45 , σ 90 , σ b , γ0, γ 45 , γ 90 , γ b obtained from uniaxial tensile tests and biaxial tensile tests. In Matlab, the Newton-Raphson iteration method was used to solve the 8 parameters, and the numerical values of the 8 parameters were obtained. Substituting the 8 material parameters into the BBC-2005 yield model, the yield locus of advanced high-strength steel was obtained.

[0062] After comparative analysis, both the Barlat-YLD2000-2D yield criterion and the BBC-2005 yield criterion can well describe the yield behavior of advanced high-strength steel. Both the Barlat-YLD2000-2D and BBC-2005 yield models can make good predictions of the yield locus of advanced high-strength steel. Here, the Barlat-YLD2000-2D yield criterion is selected to predict the theoretical yield stress and theoretical r value of advanced high-strength steel.

[0063] Step S4: Establish a material mixed hardening model under dynamic strain paths.

[0064] Combining the saturated hardening model and the non-saturated hardening model to construct a new non-saturated superposition hardening model, the expression of the Swift-Hockeet-Sherby hardening model is:

[0065]

[0066] where α is the weight coefficient.

[0067] The seven parameters to be determined in the Swift-Hockeet-Sherby hardening model are α, C, ε0, m, σ sat , σ i , and p. The initial values of the Swift-Hockeet-Sherby hardening model parameters of advanced high-strength steel were obtained through preliminary fitting of the seven parameters.

[0068] The accuracy of the parameters obtained by preliminary fitting can meet the general simulation requirements. However, for more accurate simulation results, the hardening model parameters were optimized here. By using Isight to compare the data obtained from the initial parameters with the experimental curve and gradually approaching the experimental curve through iteration, the seven parameter values of the more accurate Swift-Hockeet-Sherby hardening model were obtained.

[0069] Step S5: Establish the material card of advanced high-strength steel and parameter optimization.

[0070] According to steps S1 - S4, a material card of advanced high-strength steel including elastic behavior, yield behavior, and hardening behavior was established. The uniaxial tensile finite element simulation was carried out using the mixed hardening model with the best root mean square error and goodness of fit. It was found that there was a large error between the simulation curve and the experimental curve in the second half. Then, Isight was used to optimize the model parameters of the mixed hardening model with the minimum difference between the experimental curve and the simulation curve as the goal. After optimization, the simulation curve was in good agreement with the experimental curve.

[0071] Step S6: Verify the accuracy of the advanced high-strength steel material card model.

[0072] Four combinations of constitutive models were selected for 90-degree V-bending finite element simulation along the rolling direction to compare the simulation accuracy:

[0073] 1. Constant elastic modulus + Barlat2000-2D yield + optimized Swift&H-S hardening;

[0074] 2. Elastic modulus decay + Hill48 yield criterion + optimized Swift&H-S hardening;

[0075] 3. Elastic modulus decay + Barlat2000-2D yield + unoptimized Swift&H-S hardening;

[0076] 4. Elastic modulus decay + Barlat2000-2D yield + optimized Swift&H-S hardening;

[0077] Comparing with the experimental results, it was found that the combined model (Scheme 4) with optimized hardening function, higher-order yield criterion, and elastic modulus decay had the best simulation accuracy. Therefore, Scheme 4 was selected as the calibration method for establishing the advanced high-strength steel material card.

[0078] The present invention will be further described below through specific embodiments:

[0079] Combined with the attached Figure 1-6 , a calibration method for an advanced high-strength steel springback prediction material card proposed in this embodiment, taking CP980 steel as an example, the specific implementation steps are as follows:

[0080] Step S1: Conduct mechanical behavior tests under different loading paths.

[0081] Step S2: Establish an elastic behavior criterion model considering the change law of elastic behavior.

[0082] The fitting diagram (such as Figure 1 ) and formula obtained by fitting the data points obtained from the experiment with the elastic modulus decay model are as follows:

[0083]

[0084] Thus, an elastic modulus decay model of CP980 high-strength steel considering the change law of elastic behavior at a 1% unloading interval is obtained, where the stable value E of the unloading elastic modulus a = 176394.6 MPa, and the material elastic modulus decay coefficient ξ = 57.84.

[0085] Step S3: Establish a yield behavior criterion considering the isotropic / anisotropic behavior of the material under study.

[0086] 1. Determination of anisotropic parameters of the Barlat-YLD2000-2D yield criterion. In the Barlat-YLD2000-2D yield criterion, α1 to α8 are 8 anisotropic parameters, and these 8 anisotropic parameters need to be obtained from the 8 experimental data of σ0, σ 45 , σ 90 , σ b , γ0, γ 45 , γ 90 , γ b calculated through uniaxial tensile experiments and biaxial tensile experiments. Through Matlab algorithm iteration, 8 anisotropic parameter values of CP980 steel are obtained, which are: α1 = 0.986011, α2 = 0.932247, α3 = 0.949883, α4 = 0.948232, α5 = 0.993303, α6 = 0.890688, α7 = 0.996875, α8 = 0.992066. Substituting the 8 Barlat-YLD2000-2D anisotropic parameters into the yield function, the yield locus of CP980 as shown in Figure 2 is obtained.

[0087] 2. Determination of anisotropic parameters of the BBC-2005 yield criterion. The BBC-2005 yield criterion also has 8 material parameters. These 8 anisotropic parameters are still calculated from the experimental data of uniaxial tension test and biaxial tension test, including σ0, σ 45 , σ 90 , σ b , γ0, γ 45 , γ 90 , γ b . In Matlab, the Newton-Raphson iteration method is used to solve the 8 parameters, and the numerical values of the 8 parameters are: a = 0.47662; b = 0.490693; L = 0.518424; M = 0.443158; N = 0.517315; P = 0.512116; Q = 0.453811; R = 0.441033. Similarly, substituting the 8 material parameters into the BBC-2005 yield model, the yield locus of CP980 as shown in Figure 3 is obtained.

[0088] Both the Barlat-YLD2000-2D and BBC-2005 yield models can make good predictions on the yield locus of CP980. Here, the Barlat-YLD2000-2D yield criterion is selected to predict the theoretical yield stress and theoretical r value of CP980 steel, and the predicted values and experimental values are as shown in Figure 4 and 5 . By comparing the theoretical predicted values and experimental values, it can be obtained that the Barlat-YLD2000-2D yield criterion has strong prediction ability for the yield stress and plastic strain ratio of CP980, indicating that the Barlat-YLD2000-2D yield criterion can accurately describe the yield behavior of CP980 steel.

[0089] Step S4: Establish a material mixed hardening model under dynamic strain path.

[0090] The 7 parameters to be determined in the Swift-Hockeet-Sherby hardening model are α, C, ε0, m, σ sat , σ i , p. Preliminary fitting of the seven parameters is carried out to obtain the initial values of the Swift-Hockeet-Sherby hardening model parameters of advanced high-strength steel.

[0091] Preliminary fitting of the seven parameters is carried out to obtain the initial values of the Swift-Hockeet-Sherby hardening model parameters of CP980 steel, which are: α = 3.5, C = 1305, ε0 = 0.00259, m = 0.0709, σ sat = 1238, σ i = 821.4, p = 0.475, and the weight coefficient is 0.25.

[0092] The accuracy of the parameters obtained by the preliminary fitting can meet the general simulation requirements. However, for more accurate simulation results, the hardening model parameters are optimized here. By using Isight to compare the data obtained from the initial parameters with the experimental curve, and through iteration to gradually approximate the experimental curve, seven parameter values of the more accurate Swift-Hockeet-Sherby hardening model are obtained.

[0093] Step S5: Establish an advanced high-strength steel material card and parameter optimization.

[0094] According to steps S1 - S4, an advanced high-strength steel material card including elastic behavior, yield behavior, and hardening behavior is established. The mixed hardening model with the best root mean square error and goodness of fit is used for uniaxial tensile finite element simulation. It is found that there is a large error between the simulation curve and the experimental curve in the second half. Then, Isight is used to optimize the model parameters of the mixed hardening model with the minimum difference between the experimental curve and the simulation curve as the goal. After optimization, the simulation curve fits well with the experimental curve.

[0095] Step S6: Verify the accuracy of the advanced high-strength steel material card model.

[0096] Four combinations of constitutive models are selected for 90-degree V-bending finite element simulation along the rolling direction to compare the simulation accuracy. The comparison with the experimental results is shown in Figure 6 , and the combined model (Scheme 4) using the optimized hardening function, higher-order yield criterion, and elastic modulus decay has the best simulation accuracy. Therefore, Scheme 4 is selected as the calibration method for establishing the advanced high-strength steel material card.

[0097] All matters not detailed in the present invention are well-known technologies.

[0098] The embodiments described above are only used to describe the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A calibration method for an advanced high-strength steel springback prediction material card, characterized in that: The method comprises the following steps: S1. Carry out mechanical behavior tests under different loading path conditions; S2. Establish an elastic behavior criterion model that takes into account the changing laws of elastic behavior; S3. Describe and establish yield behavior criteria that take into account the isotropic / anisotropic behavior of the studied material; S4, establish a material hybrid hardening model under dynamic strain path; S5. Establish advanced high-strength steel material card and parameter optimization; S6. Verify the accuracy of the advanced high-strength steel material card model.

2. The method for calibrating a material card for predicting springback of advanced high-strength steel according to claim 1, characterized in that: In step S1, the mechanical behavior test includes uniaxial tensile test, plate cyclic loading / unloading test, biaxial tensile test, and proportional / non-proportional biaxial tensile test and uniaxial cyclic tensile-compression test of cross-shaped specimens to obtain the mechanical behavior of the plate under complex loading conditions and obtain the Bauschinger behavior curve of the plate.

3. The method for calibrating a material card for predicting springback of advanced high-strength steel according to claim 2, characterized in that: The uniaxial tensile test: a uniaxial tensile test is performed at angles of 0°, 15°, 30°, 45°, 60°, 75°, and 90° to the rolling direction of the plate, and the engineering stress-strain curve and the true stress-strain curve of the advanced high-strength steel are obtained after data processing; The plate cyclic loading-unloading experiment: a cyclic loading-unloading experiment is performed along the rolling direction of the plate for ten cycles with a cycle interval of 1%, and a stress-strain curve is obtained after processing; The uniaxial cyclic tension-compression test requires the use of a compression test mold. The tension mold cannot accurately meet the compression force during compression, and is prone to buckling instability, and cannot achieve the required pre-strain. Therefore, the tension-compression test requires the use of a compression test mold; The biaxial tensile test: proportional and non-proportional loading experiments are carried out on cross-shaped specimens, specifically nine groups of experiments: 4:0, 4:1, 4:2, 4:3, 4:4, 3:4, 2:4, 1:4, and 0:4; among which the 4:0 and 0:4 groups of experiments are replaced by single pulling experiments in the 0° and 90° directions.

4. The method for calibrating a material card for predicting springback of advanced high-strength steel according to claim 3, characterized in that: In step S2, the elastic modulus variation behavior of the advanced high-strength steel plate can be obtained by the cyclic loading-unloading experiment, and the elastic modulus attenuation model of the following formula is adopted: Among them, E0 is the initial elastic modulus of the material, E a is the stable value of the elastic modulus of the plate after multiple deformations, ξ is the attenuation gradient value of the elastic modulus of the material, and ε p is the true plastic strain; The fitting graph is obtained by fitting the experimental data points with the elastic modulus attenuation model; thus, the elastic modulus attenuation model of advanced high-strength steel considering the change law of elastic behavior under 1% unloading interval is obtained, and the stable value of the unloading elastic modulus E is determined. a and the material elastic modulus attenuation coefficient ξ.

5. The method for calibrating a material card for predicting springback of advanced high-strength steel according to claim 3, characterized in that: In step S3, the multi-angle uniaxial tensile test can determine whether the advanced high-strength steel has anisotropy. If anisotropy exists, a low-order anisotropic yield criterion is adopted. If strong anisotropy exists, a higher-order anisotropic yield criterion is adopted.

6. The method for calibrating a material card for predicting springback of advanced high-strength steel according to claim 1, characterized in that: The step S4 includes: combining the saturated hardening model and the unsaturated hardening model to construct a new unsaturated superposition hardening model, and the unsaturated superposition hardening model is expressed as: Among them, α is the weight coefficient; The seven parameters to be determined in the unsaturated superposition hardening model are α, C, ε0, m, σ sat , σ i , p; The initial values ​​of the unsaturated superposition hardening model parameters of advanced high-strength steel can be obtained by preliminary fitting of the seven parameters.

7. The method for calibrating a material card for predicting springback of advanced high-strength steel according to claim 6, characterized in that: The data obtained from the initial values ​​of the parameters of the unsaturated superimposed hardening model are compared with the experimental curve through Isight. By gradually approaching the experimental curve through iteration, more accurate values ​​of the seven parameters of the unsaturated superimposed hardening model can be obtained.

8. The method for calibrating a material card for predicting springback of advanced high-strength steel according to claim 1, characterized in that: The step S5 comprises: establishing an advanced high-strength steel material card including elastic behavior, yield behavior, and hardening behavior according to steps S1 to S4.

9. The method for calibrating a material card for predicting springback of advanced high-strength steel according to claim 1, characterized in that: The step S6 comprises: selecting the following four combined constitutive models to perform 90-degree V-bend finite element simulation along the rolling direction to compare the simulation accuracy, and selecting the one with the best simulation accuracy as the calibration method for establishing the advanced high-strength steel material card; 1. Constant elastic modulus + Barlat2000-2D yield + optimized Swift&H-S hardening; 2. Elastic modulus decay + Hill48 yield criterion + optimized Swift & H-S hardening; 3. Elastic modulus decay + Barlat2000-2D yielding + unoptimized Swift&H-S hardening; 4. Elastic modulus decay + Barlat2000-2D yielding + optimized Swift&H-S hardening.

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