Parameter Optimization Method of Ohno-WangⅠ Constitutive Model of Dual-Phase Steel Based on Pattern Search Method

Abstract

The present invention relates to a method for optimizing the parameters of a dual-phase steel Ohno-WangⅠ constitutive model based on a pattern search method, which comprises the following steps: step one: establishing an Ohno-WangⅠ constitutive model of a dual-phase steel material; step two: establishing a uniaxial tensile curve relationship according to a stress-strain model of the dual-phase steel material, and obtaining initial values ​​of constitutive model parameters; step three: using a dual-phase steel material constitutive model optimization platform built based on Isight software, and obtaining accurate constitutive model parameters using a pattern search method; step four: determining all optimized constitutive model parameters, and establishing an accurate Ohno-WangⅠ constitutive model. The present invention establishes a constitutive model through material flow laws, yield functions, and back stress equations, determines the initial values ​​of constitutive model parameters through uniaxial tensile curves, and uses a pattern search method to achieve optimization of constitutive model parameters; the application of the present invention solves the problem of having too many constitutive model parameters and insufficient optimization accuracy, and achieves accurate analysis of the properties of dual-phase steel materials.

Description

Technical Field

[0001] The present application relates to the field of dual-phase steel materials, and in particular to a method for optimizing parameters of a dual-phase steel Ohno-Wang I constitutive model based on a pattern search method. Background Art

[0002] Metal materials always show the Bauschinger effect during cyclic deformation, and the yield point in the reverse direction of the hysteresis loop is lower than the yield point in the positive direction in the cyclic hysteresis loop diagram. In order to describe this phenomenon, the kinematic hardening model was proposed and widely developed. Among them, the most widely used is the AF model (AF, Chaboche, Ohno-Wang, Jiang-Sehitoglu, etc.). However, the AF model has many parameters. As the number of back stress decompositions increases, the number of parameters increases exponentially, the constitutive model becomes more and more complex, and the number of parameters required also increases. There are often complex correlations between the parameters, and the nonlinearity of the constitutive model is high. It becomes difficult to solve the parameters using traditional methods. Therefore, people look for various optimization algorithms to optimize the parameters.

[0003] Due to the complexity of the mechanical behavior of materials (creep, relaxation, cyclic hardening and softening, Bauschinger effect, etc.), many parameters are implicitly included in the constitutive model of the material. According to different experimental data, load conditions and control methods, it is a big challenge to find these parameters. At the same time, due to the lack of physical description of the parameters, in simple experiments (such as tension and compression tests), multiple parameters will also couple with each other, thus affecting the mechanical response of the material. As the constitutive model becomes more and more complex, the constitutive model function carried by the finite element software (such as ABAQUS, etc.) is insufficient, so it is necessary to write UMAT subroutines to describe the constitutive model, which adds a lot of difficulties to the optimization process of the constitutive model parameters. It is often necessary to write many additional programs to optimize the combination of software and finite element software, which is time-consuming and labor-intensive. This limits the optimization technology of constitutive model parameters and the development of constitutive models. In this paper, the Ohno-Wang I constitutive model is established, and the Ohno-Wang I constitutive model parameters are optimized by pattern search method in combination with Isight software. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the present invention establishes the Ohno-Wang Ⅰ constitutive model of dual-phase steel material through material flow law, yield function and back stress equation group, determines the initial values ​​of the constitutive model parameters through the uniaxial tensile curve, and finally uses the optimization platform and pattern search method established based on Isight (product lifecycle management and optimization software) software to achieve the optimization of the constitutive model parameters, solving the problem of too many constitutive model parameters and insufficient optimization speed and accuracy.

[0005] To achieve the above purpose, the solution adopted by the present invention is:

[0006] A method for optimizing parameters of a dual-phase steel Ohno-Wang Ⅰ constitutive model based on a pattern search method comprises the following steps:

[0007] Step 1: Establish the Ohno-Wang Ⅰ constitutive model of dual-phase steel material;

[0008] The Ohno-Wang I constitutive model of the dual-phase steel material includes a material flow law, a material yield function and a material back stress equation group;

[0009] Step 2: Establish a uniaxial tensile curve relationship based on the stress-strain model of the dual-phase steel material to obtain the initial values ​​of the constitutive model parameters;

[0010] The equation group for solving the initial values ​​of the constitutive model parameters of the dual-phase steel material determined according to the uniaxial tension curve is as follows:

[0011]

[0012] Where: γ i represents the first parameter of the constitutive model corresponding to the i-th back stress component; r i represents the second parameter of the constitutive model corresponding to the i-th back stress component; σ i and are the stress and plastic strain values ​​corresponding to the point on the i-th uniaxial tension curve selected; σ0 represents the stress value when the plastic strain value on the uniaxial tension curve is 0; σ max represents the peak stress value of the uniaxial tensile curve; M represents the number of back stress components; i represents the number of the back stress component; r1 represents the second parameter of the model of the first back stress component;

[0013] Step 3: Use the dual-phase steel material constitutive model optimization platform built on the Isight software and use the pattern search method to obtain accurate constitutive model parameters;

[0014] Step 31: Obtain the initial values ​​of the constitutive model parameters obtained in step 2, build a dual-phase steel material constitutive model optimization platform based on Isight software, and solve the optimal solution of the constitutive model parameters; the optimization process needs to compare the time-stress curve generated according to the initial value of the parameter and the time-stress curve generated according to the optimized value. The objective function calculation formula based on the least squares method is as follows:

[0015]

[0016] Where: L represents the sum of the squares of the stress differences between the target curve and the curve to be optimized; t jRepresents the time at the jth time-stress curve point, i.e., the horizontal coordinate in the curve; Represents the stress value of the midpoint of the jth target curve, i.e., the ordinate of the curve; represents the stress value of the jth curve point to be optimized; N represents the number of points in the time-stress curve; j represents the segment number of the target curve;

[0017] Step 32: Assign the initial values ​​of the constitutive model parameters to x (1) As the initial value of the optimization process; detection step d, acceleration factor μ, μ>0, reduction factor β, n detection directions e1, e2, e3, ..., e n , calculate the accuracy ε, set k = 1; determine the iteration reference value y:

[0018] y=x k ;

[0019] Where: y represents the iterative reference value of the parameter; x k represents the process parameters of the kth iteration;

[0020] Step 33: Starting from the reference values ​​of the constitutive model parameters, calculate along the directions e1,...,e n , perform calculations in both positive and negative directions;

[0021] Detection in the positive direction: If the objective function value satisfies L(y+de)<L(y), the iterative reference value is updated as:

[0022] y=y+de;

[0023] Where: d represents the detection step of the parameter; e represents the detection direction of the parameter;

[0024] If L(y+de)≥L(y), perform negative direction detection;

[0025] Detection in the negative direction: If the objective function value L(y-de) < L(y), the iterative reference value update is as follows:

[0026] y=y-de;

[0027] If L(y-de)≥L(y), let y=y;

[0028] Through the above detection, we can obtain the updated reference value y and the process parameter x of the k+1th iteration. k+1 =y;

[0029] Step 34: Perform accelerated calculation along the direction of the optimal solution of the constitutive model parameters. The process is as follows:

[0030] The calculation formula for detecting the acceleration direction is as follows:

[0031] p k =x k+1 -x k ;

[0032] Where: p k Indicates the detection acceleration direction of the calculation process;

[0033] If the objective function value L(x k+1 )<L(x k ), the number of iterations is updated as follows:

[0034]

[0035] Where: μ represents the detection acceleration factor of the calculation process; k represents the number of iterations of the calculation process;

[0036] Otherwise, shorten the step length d, even if d = dβ, and determine whether the iteration termination condition d≤ε is met. If it is met, terminate the iteration; if not, go to step 33 to continue detection;

[0037] Step 4: Determine all optimized constitutive model parameters and establish an accurate Ohno-Wang Ⅰ constitutive model;

[0038] According to the optimization process of step 3, the first parameters of M constitutive models and the second parameters of the constitutive model are optimized to obtain the optimal values ​​of all constitutive model parameters, and an accurate Ohno-Wang Ⅰ constitutive model of dual-phase steel material is established to realize accurate analysis of the properties of dual-phase steel material.

[0039] Preferably, the material flow rule in step 1 is specifically:

[0040] The material flow is subject to flow laws as follows:

[0041]

[0042] Where: dε p represents the plastic strain increment of the material; dλ represents the plastic multiplier increment of the material; f represents the yield surface function of the material; σ represents the stress value of the material; Indicates the direction of plastic strain evolution of the material.

[0043] Preferably, the yield function of the material in step 1 is as follows:

[0044] The yield function obeys the von Mises yield criterion, and the method for obtaining the yield function is as follows:

[0045]

[0046] Where: represents the deviatoric stress tensor of the yield function; α represents the back stress of the material; I represents the unit deviatoric stress tensor; tr(σ) represents the trace of the deviatoric stress tensor; σ y Represents the yield stress of the material, that is, the size of the yield surface;

[0047] According to the von Mises yield criterion, the plastic multiplier increment is equal to the cumulative plastic strain increment as shown below:

[0048]

[0049] Where: dp represents the cumulative plastic strain increment of the material.

[0050] Preferably, the back stress equations of the material in step 1 are specifically:

[0051] The formula of the back stress equation group is as follows:

[0052]

[0053] Where: α i represents the i-th back stress component; dα represents the back stress increment of the material; ||α i || represents the modulus of the material back stress component, and represents the back stress component value; H represents the Heaviside step function; K i represents the direction of the i-th back stress component; m i represents the critical state of the i-th back stress component; <> represents the Macauley bracket, when x>0, 〈x〉=x, when x≤0, 〈x〉=0.

[0054] Preferably, the Heavisied function is specifically:

[0055] The calculation result of the Heavisied function in the Ohno-Wang Ⅰ constitutive model is related to the modulus of the back stress and the second parameter of the model. The method of obtaining it is as follows:

[0056]

[0057] Where: ||α i || is the modulus of the back stress component; r i The second parameter of the model representing the i-th backstress component.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] (1) The present invention establishes the Ohno-Wang I constitutive model of dual-phase steel material through material flow law, yield function and back stress equation group, determines the initial value of constitutive model parameters from the uniaxial tensile curve, and finally optimizes the constitutive model parameters using an optimization platform and pattern search method established by Isight software, obtains accurate constitutive model parameters, and establishes an accurate Ohno-Wang I constitutive model, which provides a premise for the analysis of the properties of dual-phase steel materials;

[0060] (2) The application of the present invention solves the problem of relatively large parameters and low precision of the Ohno-Wang Ⅰ constitutive model of dual-phase steel materials. The optimization speed and calculation accuracy of the constitutive model parameters are improved through the pattern search method, which provides a new technical solution and solution idea for solving the problem of many correlations in the constitutive model parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a control flow chart of a method for optimizing parameters of a dual-phase steel Ohno-Wang Ⅰ constitutive model based on a pattern search method according to an embodiment of the present invention;

[0062] Figure 2 A comparison diagram of a target curve and a curve to be optimized according to an embodiment of the present invention;

[0063] Figure 3 A comparison diagram of the target curve and the optimized final result curve of an embodiment of the present invention;

[0064] Figure 4 It is a comparison diagram of the final optimization result, the curve to be optimized and the target curve of the embodiment of the present invention. DETAILED DESCRIPTION

[0065] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0066] The embodiment of the present invention establishes the Ohno-Wang Ⅰ constitutive model of the dual-phase steel material through the material flow law, yield function and back stress equation group, determines the initial values ​​of the constitutive model parameters through the uniaxial tensile curve, and finally uses the optimization platform and pattern search method established by Isight software to achieve the optimization of all parameters of the Ohno-Wang Ⅰ constitutive model, and establishes an accurate Ohno-Wang Ⅰ constitutive model, such as Figure 1 Shown is a control flow chart of a method for optimizing parameters of a dual-phase steel Ohno-Wang Ⅰ constitutive model based on a pattern search method according to an embodiment of the present invention.

[0067] The embodiment of the present invention provides a method for optimizing the parameters of the Ohno-Wang I constitutive model of dual-phase steel based on a pattern search method. In order to prove the applicability of the present invention, the method is applied to an example, which specifically comprises the following steps:

[0068] S1: Establish the Ohno-Wang Ⅰ constitutive model of dual-phase steel;

[0069] The Ohno-Wang Ⅰ constitutive model of dual-phase steel includes material flow law, material yield function and material back stress equations;

[0070] Material flow is subject to flow laws as follows:

[0071]

[0072] Where: dε p represents the plastic strain increment; dλ represents the plastic multiplier increment; f represents the yield surface function; σ represents the stress value; Indicates the direction of plastic strain evolution.

[0073] The yield function of the material obeys the von Mises yield criterion, and the method for obtaining the yield function is as follows:

[0074]

[0075] Where: represents the deviatoric stress tensor; α represents the back stress; I represents the unit tensor; tr(σ) represents the trace of the stress tensor; σ y represents the yield stress, i.e. the size of the yield surface;

[0076] According to the von Mises yield criterion, the plastic multiplier increment is equal to the cumulative plastic strain increment as shown below:

[0077]

[0078] Where: dp represents the cumulative plastic strain increment.

[0079] The formula for the back stress equations for the material is as follows:

[0080]

[0081] Where: α i represents the i-th back stress component; dα represents the back stress increment; ||α i || represents the modulus of the back stress component, represents the value of the back stress component; H represents the Heaviside step function; K i represents the direction of the i-th back stress component; m i Indicates the critical state of the i-th back stress component; <> indicates Macauley brackets, when x>0 <x>=x, when x≤0 <x>=0.

[0082] The calculation result of the Heavisied function in the Ohno-Wang Ⅰ constitutive model is related to the modulus of the back stress and the second parameter of the model. The method of obtaining it is as follows:

[0083]

[0084] Where: ||α i || is the modulus of the back stress component; r i The second parameter of the model representing the i-th backstress component.

[0085] S2: Establish the uniaxial tensile curve relationship based on the stress-strain model of the dual-phase steel material and obtain the initial values ​​of the constitutive model parameters;

[0086] The equations for solving the initial values ​​of the constitutive model parameters established based on the uniaxial tension curve are as follows:

[0087]

[0088] Where: γ i represents the first model parameter of the i-th back stress component; r i The second parameter of the model representing the i-th back stress component; σ i and are the stress and plastic strain values ​​corresponding to the point on the i-th uniaxial tension curve selected; σ0 represents the stress value when the plastic strain value on the uniaxial tension curve is 0; σ max represents the peak stress value of the uniaxial tensile curve; M represents the number of back stress components; i represents the i-th back stress component; r1 represents the second parameter of the model of the first back stress component;

[0089] S3: Use the dual-phase steel material constitutive model optimization platform built on Isight software and use the pattern search method to obtain accurate constitutive model parameters;

[0090] S31: Obtain the initial values ​​of the constitutive model parameters obtained in S2, build a dual-phase steel material constitutive model optimization platform based on Isight software, and solve the optimal solution of the constitutive model parameters; the optimization process requires comparing the time-stress curve generated according to the initial value of the parameter with the time-stress curve generated according to the optimized value, such as Figure 2 The target curve and the curve to be optimized are compared in the embodiment of the present invention. The objective function calculation formula based on the least squares method is as follows:

[0091]

[0092] Where: L represents the sum of the squares of the stress differences between the target curve and the curve to be optimized; t j Represents the time at the jth time-stress curve point, i.e., the horizontal coordinate in the curve; Represents the stress value of the midpoint of the jth target curve, i.e., the ordinate of the curve; represents the stress value of the jth curve point to be optimized; N represents the number of points in the time-stress curve; j represents the target curve number.

[0093] S32: Assign the initial values ​​of the constitutive model parameters to x (1) As the initial value of the optimization process; detection step d, acceleration factor μ, μ>0, reduction factor β, n detection directions e1, e2, e3, ..., e n , calculate the accuracy ε, set k = 1; determine the iteration reference value y as follows:

[0094] y=x k ;

[0095] Where: y represents the iteration reference value; x k represents the process parameters of the kth iteration;

[0096] S33: Starting from the reference value of the constitutive model parameters, the calculation is carried out in the directions e1,...,e n , perform calculations in both positive and negative directions;

[0097] Detection in the positive direction: If the objective function value satisfies L(y+de)<L(y), the iterative reference value is updated as follows:

[0098] y=y+de;

[0099] Where: d represents the detection step length; e represents the detection direction;

[0100] If L(y+de)≥L(y), perform negative direction detection;

[0101] Detection in the negative direction: If the objective function value L(y-de) < L(y), the iterative reference value is updated as follows:

[0102] y=y-de;

[0103] If L(y-de)≥L(y), let y=y;

[0104] Through the above detection, we obtain the new reference value y and the process parameter x of the k+1th iteration. k+1 =y;

[0105] S34: Accelerate the calculation along the direction of the optimal solution of the constitutive model parameters. The process is as follows:

[0106] Detect the acceleration direction as follows:

[0107] p k =x k+1 -x k ;

[0108] Where: p k Indicates the detection acceleration direction;

[0109] If the objective function value L(x k+1 )<L(x k ), the number of iterations is updated as follows:

[0110]

[0111] Where: μ represents the detection acceleration factor; k represents the number of iterations;

[0112] Otherwise, shorten the step length d, even if d = dβ, and determine whether the iteration termination condition d≤ε is met. If it is met, terminate the iteration; if not, go to S33 to continue detection;

[0113] S4: Determine all optimized constitutive model parameters and establish an accurate Ohno-Wang Ⅰ constitutive model;

[0114] According to the optimization process of step 3, the first parameters of M constitutive models and the second parameters of the constitutive model are optimized to obtain the optimal values ​​of all constitutive model parameters, and an accurate Ohno-Wang Ⅰ constitutive model of dual-phase steel material is established to realize accurate analysis of the properties of dual-phase steel material.

[0115] Figure 3 The figure shows a comparison diagram of the target curve and the optimized final result curve of the embodiment of the present invention; Figure 4 The figure shows the final optimization result, the curve to be optimized and the target curve comparison of the embodiment of the present invention. Through the analysis of the curves in the figure, it can be seen that the optimization effect and accuracy of the method can meet the actual needs and have a good application effect.

[0116] In summary, the calculation results of the parameter optimization of the Ohno-Wang Ⅰ constitutive model of dual-phase steel based on the pattern search method in this case proved to have a good effect.

[0117] (1) The embodiment of the present invention establishes the Ohno-Wang I constitutive model of the dual-phase steel material through the material flow law, yield function and back stress equation group, determines the initial value of the constitutive model parameter from the uniaxial tensile curve, and finally uses the optimization platform and pattern search method established by Isight software to achieve the optimization of the constitutive model parameters; obtains accurate constitutive model parameters, and establishes an accurate Ohno-Wang I constitutive model; the accompanying drawings can prove that the method can achieve the optimization function and has a good practical application effect;

[0118] (2) The application of the present invention solves the problem of too many parameters and low precision of the Ohno-Wang Ⅰ constitutive model of dual-phase steel materials, improves the optimization speed and calculation accuracy of the constitutive model parameters, and proves through analysis of the attached drawings that the calculation accuracy and speed of the present method can meet the needs of practical applications, and can create a more accurate Ohno-Wang Ⅰ constitutive model, accurately analyze the properties of bidirectional steel materials, and provide a new technical scheme and solution to solve the problem of too many correlations in the constitutive model parameters.

[0119] The embodiments described above are only descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.< / x> < / x>

Claims

1. A method for optimizing the parameters of the Ohno-Wang Ⅰ constitutive model of dual-phase steel based on a pattern search method, characterized in that: It includes the following steps: Step 1: Establish the Ohno-Wang Ⅰ constitutive model of dual-phase steel material; The Ohno-Wang I constitutive model of the dual-phase steel material includes a material flow law, a material yield function and a material back stress equation group; Step 2: Establish a uniaxial tensile curve relationship based on the stress-strain model of the dual-phase steel material to obtain the initial values ​​of the constitutive model parameters; The equation group for solving the initial values ​​of the constitutive model parameters of the dual-phase steel material determined according to the uniaxial tension curve is as follows: Where: γ i represents the first parameter of the constitutive model corresponding to the i-th back stress component; r i represents the second parameter of the constitutive model corresponding to the i-th back stress component; σ i and are the stress and plastic strain values ​​corresponding to the point on the i-th uniaxial tension curve selected; σ0 represents the stress value when the plastic strain value on the uniaxial tension curve is 0; σ max It represents the peak stress of uniaxial tension curve; M represents the number of back stress components; i represents the number of back stress components; r1 represents the second parameter of the model for the first back stress component; Step 3: Use the dual-phase steel material constitutive model optimization platform built on the Isight software and use the pattern search method to obtain accurate constitutive model parameters; Step 31: Obtain the initial values ​​of the constitutive model parameters obtained in step 2, build a dual-phase steel material constitutive model optimization platform based on Isight software, and solve the optimal solution of the constitutive model parameters; The optimization process requires comparing the time-stress curve generated according to the initial parameter value and the time-stress curve generated according to the optimized value. The objective function calculation formula based on the least squares method is as follows: Where: L represents the sum of the squares of the stress differences between the target curve and the curve to be optimized; t j Represents the time at the jth time-stress curve point, i.e., the horizontal coordinate in the curve; Represents the stress value of the midpoint of the jth target curve, i.e., the ordinate of the curve; represents the stress value of the jth curve point to be optimized; N represents the number of points in the time-stress curve; j represents the segment number of the target curve; Step 32: Assign the initial values ​​of the constitutive model parameters to x (1) As the initial value of the optimization process; detection step d, acceleration factor μ, μ>0, reduction factor β, n detection directions e1, e2, e3, ..., e n , calculate the accuracy ε, set k = 1; determine the iteration reference value y: y=x k ; Where: y represents the iterative reference value of the parameter; x k represents the process parameters of the kth iteration; Step 33: Starting from the reference values ​​of the constitutive model parameters, calculate along the directions e1,...,e n , perform calculations in both positive and negative directions; Detection in the positive direction: If the objective function value satisfies L(y+de)<L(y), the iterative reference value is updated as: y=y+de; Where: d represents the detection step of the parameter; e represents the detection direction of the parameter; If L(y+de)≥L(y), perform negative direction detection; Detection in the negative direction: If the objective function value L(y-de) < L(y), the iterative reference value update is as follows: y=y-de; If L(y-de)≥L(y), let y=y; Through the above detection, we can obtain the updated reference value y and the process parameter x of the k+1th iteration. k+1 =y; Step 34: Perform accelerated calculation along the direction of the optimal solution of the constitutive model parameters. The process is as follows: The calculation formula for detecting the acceleration direction is as follows: p k =x k+1 -x k ; Where: p k Indicates the detection acceleration direction of the calculation process; If the objective function value L(x k+1 )<L(x k ), the number of iterations is updated as follows: Where: μ represents the detection acceleration factor of the calculation process; k represents the number of iterations of the calculation process; Otherwise, shorten the step length d, even if d = dβ, and determine whether the iteration termination condition d≤ε is met. If it is met, terminate the iteration; if not, go to step 33 to continue detection; Step 4: Determine all optimized constitutive model parameters and establish an accurate Ohno-Wang I constitutive model; According to the optimization process of step 3, the first parameters of M constitutive models and the second parameters of the constitutive model are optimized to obtain the optimal values ​​of all constitutive model parameters, and an accurate Ohno-Wang I constitutive model of dual-phase steel material is established to realize accurate analysis of the properties of dual-phase steel material.

2. The method for optimizing the parameters of the Ohno-WangⅠ constitutive model of dual-phase steel based on the pattern search method according to claim 1, characterized in that: The material flow rule in step 1 is specifically: The material flow is subject to flow laws as follows: Where: dε p represents the plastic strain increment of the material; dλ represents the plastic multiplier increment of the material; f represents the yield surface function of the material; σ represents the stress value of the material; Indicates the direction of plastic strain evolution of the material.

3. The method for optimizing the parameters of the Ohno-WangⅠ constitutive model of dual-phase steel based on the pattern search method according to claim 1, characterized in that: The yield function of the material in step 1 is as follows: The yield function obeys the von Mises yield criterion, and the method for obtaining the yield function is as follows: Where: represents the deviatoric stress tensor of the yield function; α represents the back stress of the material; I represents the unit deviatoric stress tensor; tr(σ) represents the trace of the deviatoric stress tensor; σ y Represents the yield stress of the material, that is, the size of the yield surface; According to the vonMises yield criterion, the plastic multiplier increment is equal to the cumulative plastic strain increment as shown below: Where: dp represents the cumulative plastic strain increment of the material.

4. The method for optimizing the parameters of the Ohno-WangⅠ constitutive model of dual-phase steel based on the pattern search method according to claim 1, characterized in that: The back stress equations of the material in step 1 are specifically: The formula of the back stress equation group is as follows: Where: α i represents the i-th back stress component; dα represents the back stress increment of the material; ||α i || represents the modulus of the material back stress component, and represents the back stress component value; H represents the Heaviside step function; K i represents the direction of the i-th back stress component; m i Indicates the critical state of the i-th back stress component; <> indicates Macauley brackets, when x>0 <x>=x, when x≤0 <x> ＝0。< / x> < / x> 5. The method for optimizing parameters of the Ohno-WangⅠ constitutive model of dual-phase steel based on pattern search method according to claim 4, characterized in that: The Heavisied function is specifically: The calculation result of the Heavisied function in the Ohno-Wang Ⅰ constitutive model is related to the modulus of the back stress and the second parameter of the model. The method of obtaining it is as follows: Where: ||α i || is the modulus of the back stress component; r i The second parameter of the model representing the i-th backstress component.

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