A method and system for determining parameters of a creep relaxation aging macro-micro unified constitutive model

Abstract

The present invention discloses a method and system for determining parameters of a creep relaxation aging macro-micro unified constitutive model. The method comprises first fitting the creep amount-time curve under a single process condition, calculating the corresponding weighted average and arithmetic mean, and then fitting the creep amount-time curve under multiple groups of process conditions with the weighted average and arithmetic mean to obtain a first fitting error and a second fitting error, comparing the first fitting error and the second fitting error, updating the range of each material parameter until the overall fitting error meets the preset expected value, and obtaining the optimal material parameter. The solution provided by the present invention updates the range of material parameters by dichotomy, which can greatly reduce the number of fitting times and shorten the fitting time.

Description

Technical Field

[0001] The invention relates to the technical field of creep relaxation aging forming of thin-walled components, and in particular to a method and system for determining parameters of a creep / stress relaxation aging macro-micro unified constitutive model. Background Art

[0002] Creep / stress relaxation aging forming technology is an advanced forming technology that meets the high performance requirements and low-cost manufacturing of large-scale integral complex thin-walled components. The creep / stress relaxation aging forming technology is used to achieve coordinated manufacturing of component shape. The key lies in integrating the creep characteristics of the material and the shape evolution law during the creep / stress relaxation process of the material into a unified macro-micro constitutive model, and using the constitutive model to realize the application of creep / stress relaxation aging forming technology in engineering practice.

[0003] However, the difficulty in solving the creep / stress relaxation aging macro-micro unified constitutive model lies in the fact that there are many material parameters, often involving 7-30 parameters, which makes it difficult to optimize using intelligent algorithms; on the other hand, there are complex coupling relationships between the various equations in the model. Generally, only the model parameters involving micro-evolution are easy to solve, but the micro-evolution law is integrated into the creep amount change model. The parameter fitting of multiple creep amount (or stress relaxation amount)-time curves is cumbersome and time-consuming (generally fitting takes 2-10 days). Therefore, it is necessary to propose a method that can quickly solve the parameters of the creep / stress relaxation aging macro-micro unified constitutive model, so as to realize the application of creep / stress relaxation aging forming technology in engineering practice.

[0004] In view of the difficulty in fitting constitutive parameters, the existing technology has proposed a variety of solutions, but there is a technical problem that the model parameter fitting is cumbersome and time-consuming. Therefore, for the engineering application of creep / stress relaxation aging forming technology, it is necessary to propose a method that can quickly solve the creep / stress relaxation aging macro-micro unified constitutive model parameters. Summary of the invention

[0005] In order to solve the technical problem that the existing creep constitutive model parameter fitting is complicated and time-consuming, an embodiment of the present invention provides a method and system for determining parameters of a creep relaxation aging macro-micro unified constitutive model.

[0006] The technical solution of the embodiment of the present invention is achieved as follows:

[0007] The embodiment of the present invention provides a method for determining parameters of a creep relaxation aging macro-micro unified constitutive model, the method comprising: obtaining a creep relaxation aging macro-micro unified constitutive model; obtaining multiple groups of creep amount-time curve data under different process parameter conditions and corresponding microstructure content change data; the process parameter conditions include aging temperature, stress level and aging time; and obtaining the creep amount of the material entering the steady-state creep stage or the stress value entering the steady-state relaxation stage under different process parameter conditions; determining the initial material parameter range of the creep relaxation aging macro-micro unified constitutive model; within the initial material parameter range, fitting the creep relaxation aging macro-micro unified constitutive model using the multiple groups of creep amount-time curve data under different process parameter conditions and corresponding microstructure content change data to obtain multiple groups of material parameters; each group of process parameter conditions corresponds to a group of material parameters; calculating the weighted average value and the arithmetic mean value of the multiple groups of material parameters; bringing the weighted average value into the creep relaxation aging macro-micro unified constitutive model, and using the mean square error method to evaluate the creep relaxation aging macro-micro unified constitutive model after bringing it into the model. The method comprises the following steps: calculating the fitting error of the creep relaxation aging macro-micro unified constitutive model to obtain a first fitting error; and bringing the arithmetic mean into the creep relaxation aging macro-micro unified constitutive model, and using the mean square error method to evaluate the fitting error of the creep relaxation aging macro-micro unified constitutive model after bringing it into the model to obtain a second fitting error; judging whether the fitting errors of the multiple sets of material parameters reach a preset expected value according to the first fitting error and the second fitting error; if the fitting errors of the multiple sets of material parameters reach the preset expected value, determining the multiple sets of material parameters as the final material parameters; if the fitting errors of the multiple sets of material parameters do not reach the preset expected value, adjusting the material parameter range according to the first fitting error and the second fitting error, and fitting the creep relaxation aging macro-micro unified constitutive model with the multiple sets of creep amount-time curve data under different process parameter conditions and their corresponding microstructure content change data under the adjusted material parameter range to obtain multiple sets of material parameters; and judging whether the fitting errors of the multiple sets of material parameters reach the preset expected value; until the fitting errors of the multiple sets of material parameters reach the preset expected value.

[0008] In one embodiment, the creep relaxation aging macro-micro unified constitutive model is fitted using the multiple groups of creep amount-time curve data under different process parameter conditions and their corresponding microstructure content change data to obtain multiple groups of material parameters, including: fitting the microstructure content change equation in the creep relaxation aging macro-micro unified constitutive model using the microstructure content change data under different process parameter conditions to obtain the material parameters in the microstructure content change equation; fitting the creep amount evolution model in the creep relaxation aging macro-micro unified constitutive model under each process parameter condition using the material parameters in the microstructure content change equation and the creep amount-time curve data under each process parameter condition to obtain the material parameters under each process parameter condition; and obtaining multiple groups of material parameters based on the material parameters under each process parameter condition.

[0009] In one embodiment, calculating the weighted average values ​​of the plurality of sets of material parameters includes: calculating the weighted average values ​​of the plurality of sets of material parameters using the following calculation formula:

[0010] Cs p = or Cs p = or Cs p = ;

[0011] Where ci is the creep amount of the material entering the steady-state creep stage under the conditions of the i-th set of process parameters, is the stress value of the material entering the steady-state relaxation stage under the conditions of the i-th set of process parameters; x p ,y p is the material parameter range, p is the number of material parameters, and m is the total number of groups of process parameter conditions.

[0012] In one embodiment, the fitting errors of the multiple sets of material parameters are determined as follows: if the second fitting error is greater than the first fitting error, the fitting errors of the multiple sets of material parameters are the first fitting error; if the second fitting error is less than the first fitting error, the fitting errors of the multiple sets of material parameters are the second fitting error.

[0013] In one embodiment, adjusting the material parameter range according to the first fitting error and the second fitting error includes: when the second fitting error is greater than the first fitting error, calculating a first value; and judging whether the weighted average value is greater than the first value; if the weighted average value is greater than the first value, replacing the minimum value in the material parameter range with the first value; if the weighted average value is not greater than the first value, replacing the maximum value in the material parameter range with the first value; when the second fitting error is less than the first fitting error, calculating a first value; and judging whether the arithmetic mean value is greater than the first value; if the arithmetic mean value is greater than the first value, replacing the minimum value in the material parameter range with the first value; if the arithmetic mean value is not greater than the first value, replacing the maximum value in the material parameter range with the first value;

[0014] The calculation formula of the first value is:

[0015] G= ;

[0016] Among them, G is the first value, x p is the minimum value in the material parameter range, y p is the maximum value in the material parameter range.

[0017] The embodiment of the present invention also provides a creep relaxation aging macro-micro unified constitutive model parameter determination system, comprising: a processor and a memory for storing a computer program that can be run on the processor; wherein the processor is used to execute the following steps when running the computer program: obtaining a creep relaxation aging macro-micro unified constitutive model; obtaining multiple groups of creep amount-time curve data and corresponding microstructure content change data under different process parameter conditions; the process parameter conditions include aging temperature, stress level and aging time; and obtaining creep of the material entering the steady-state creep stage under different process parameter conditions. variable or the stress value entering the steady-state relaxation stage; determining the initial material parameter range of the creep relaxation aging macro-micro unified constitutive model; within the initial material parameter range, fitting the creep relaxation aging macro-micro unified constitutive model using the creep amount-time curve data under the multiple groups of different process parameter conditions and the corresponding microstructure content change data to obtain multiple groups of material parameters; each group of process parameter conditions corresponds to a group of material parameters; calculating the weighted average and arithmetic mean of the multiple groups of material parameters; bringing the weighted average into the creep relaxation aging macro-micro unified constitutive model and using the mean square error method to evaluate the fitting error of the creep relaxation aging macro-micro unified constitutive model to obtain a first fitting error; and bringing the arithmetic mean into the creep relaxation aging macro-micro unified constitutive model, and using the mean square error method to evaluate the fitting error of the creep relaxation aging macro-micro unified constitutive model to obtain a second fitting error; judging whether the fitting errors of the multiple sets of material parameters reach a preset expected value based on the first fitting error and the second fitting error; if the fitting errors of the multiple sets of material parameters reach a preset expected value, then the multiple sets of material parameters are The final material parameters are determined; if the fitting errors of the multiple groups of material parameters do not reach the preset expected value, the material parameter range is adjusted according to the first fitting error and the second fitting error, and under the adjusted material parameter range, the creep relaxation aging macro-micro unified constitutive model is fitted using the multiple groups of creep amount-time curve data under different process parameter conditions and their corresponding microstructure content change data to obtain multiple groups of material parameters; and it is determined whether the fitting errors of the multiple groups of material parameters reach the preset expected value; until the fitting errors of the multiple groups of material parameters reach the preset expected value.

[0018] In one embodiment, when the processor is used to run the computer program, it also performs the following steps: using the microstructure content change data under different process parameter conditions to fit the microstructure content change equation in the creep relaxation aging macro-micro unified constitutive model, and obtain the material parameters in the microstructure content change equation; using the material parameters in the microstructure content change equation and the creep amount-time curve data under each process parameter condition, the creep amount evolution model in the creep relaxation aging macro-micro unified constitutive model under each process parameter condition is fitted to obtain the material parameters under each process parameter condition; according to the material parameters under each process parameter condition, multiple groups of material parameters are obtained.

[0019] In one embodiment, when the processor is used to run the computer program, it further performs the following steps: calculating the weighted average of the multiple sets of material parameters using the following calculation formula:

[0020] Cs p = or Cs p = or Cs p = ;

[0021] Where ci is the creep amount of the material entering the steady-state creep stage under the conditions of the i-th set of process parameters, is the stress value of the material entering the steady-state relaxation stage under the conditions of the i-th set of process parameters; x p ,y p is the material parameter range, p is the number of material parameters, and m is the total number of groups of process parameter conditions.

[0022] In one embodiment, when the processor is used to run the computer program, it also performs the following steps: if the second fitting error is greater than the first fitting error, the fitting error of the multiple sets of material parameters is the first fitting error; if the second fitting error is less than the first fitting error, the fitting error of the multiple sets of material parameters is the second fitting error.

[0023] In one embodiment, when the processor is used to run the computer program, it further performs the following steps: when the second fitting error is greater than the first fitting error, calculate the first value; and determine whether the weighted average value is greater than the first value; if the weighted average value is greater than the first value, replace the minimum value in the material parameter range with the first value; if the weighted average value is not greater than the first value, replace the maximum value in the material parameter range with the first value; when the second fitting error is less than the first fitting error, calculate the first value; and determine whether the arithmetic mean value is greater than the first value; if the arithmetic mean value is greater than the first value, replace the minimum value in the material parameter range with the first value; if the arithmetic mean value is not greater than the first value, replace the maximum value in the material parameter range with the first value;

[0024] The calculation formula of the first value is:

[0025] G= ;

[0026] Among them, G is the first value, x p is the minimum value in the material parameter range, y p is the maximum value in the material parameter range.

[0027] This embodiment has the following beneficial effects:

[0028] In this embodiment, the creep amount (or stress relaxation amount)-time curve under a single process condition is first fitted respectively, and the corresponding weighted average value csp and arithmetic average value cs1p are calculated. Then, the weighted average material parameters csp and cs1p are used to fit the creep amount (or stress relaxation amount)-time curve under multiple groups of process conditions to obtain the first fitting error MSE and the second fitting error MSE1. MSE1 is compared with MSE, and the range of each material parameter is updated by dichotomy until the overall fitting error ZMSE meets the preset expected value. This can greatly reduce the number of fitting times, shorten the fitting time, and obtain the optimal material parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of a method for determining parameters of a macro-micro unified constitutive model for creep relaxation aging according to an embodiment of the present invention;

[0030] Figure 2 It is a schematic diagram of a specific application process of a method for determining parameters of a macro-micro unified constitutive model for creep relaxation aging according to an embodiment of the present invention;

[0031] Figure 3 (a) is a schematic diagram of the fitting curve of creep amount and relative increase of dislocation;

[0032] Figure 3 (b) is a schematic diagram of the fitting curve of creep time and creep amount;

[0033] Figure 3 (c) is a schematic diagram of the fitting curve of creep amount and relative volume fraction of precipitated phase;

[0034] Figure 4 1 is a diagram showing the internal structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0036] The embodiment of the present invention provides a method for determining parameters of a creep relaxation aging macro-micro unified constitutive model, such as Figure 1 As shown, the method includes:

[0037] Step 101: Obtaining a creep relaxation aging macro-micro unified constitutive model;

[0038] Step 102: obtaining multiple groups of creep amount-time curve data under different process parameter conditions and corresponding microstructure content change data; the process parameter conditions include aging temperature, stress level and aging time; and obtaining the creep amount of the material entering the steady-state creep stage or the stress value entering the steady-state relaxation stage under different process parameter conditions;

[0039] Step 103: determining the initial material parameter range of the creep relaxation aging macro-micro unified constitutive model; within the initial material parameter range, fitting the creep relaxation aging macro-micro unified constitutive model using the multiple groups of creep amount-time curve data under different process parameter conditions and their corresponding microstructure content change data to obtain multiple groups of material parameters; each group of process parameter conditions corresponds to a group of material parameters;

[0040] Step 104: Calculating the weighted average and arithmetic average of the plurality of sets of material parameters;

[0041] Step 105: Substitute the weighted average value into the creep relaxation aging macro-micro unified constitutive model, and use the mean square error method to evaluate the fitting error of the creep relaxation aging macro-micro unified constitutive model after the weighted average value is introduced, so as to obtain a first fitting error; and substitute the arithmetic average value into the creep relaxation aging macro-micro unified constitutive model, and use the mean square error method to evaluate the fitting error of the creep relaxation aging macro-micro unified constitutive model after the weighted average value is introduced, so as to obtain a second fitting error;

[0042] Step 106: judging whether the fitting errors of the multiple groups of material parameters reach a preset expected value according to the first fitting error and the second fitting error; if the fitting errors of the multiple groups of material parameters reach the preset expected value, determining the multiple groups of material parameters as final material parameters; if the fitting errors of the multiple groups of material parameters do not reach the preset expected value, adjusting the material parameter range according to the first fitting error and the second fitting error, and fitting the creep relaxation aging macro-micro unified constitutive model under the adjusted material parameter range using the multiple groups of creep amount-time curve data under different process parameter conditions and their corresponding microstructure content change data to obtain multiple groups of material parameters; and judging whether the fitting errors of the multiple groups of material parameters reach the preset expected value; until the fitting errors of the multiple groups of material parameters reach the preset expected value.

[0043] Specifically, the creep / stress relaxation aging macro-micro unified constitutive model in this embodiment is:

[0044]

[0045] Where A, B, , , m0, m1, m2, m3, m4, m5, n1, n2, n3, n4, n5, n6, n7, n8, k0, k1, k2, k3, C ppt , C ss , C dis ,C1,C2,C3,C4,C5,a,b, etc. are all material parameters.

[0046] The material meanings of the above material parameters are as follows:

[0047] Creep rate-related material constants ;

[0048] Material constants related to the effect of stress on creep rate ;

[0049] Threshold stress σ m (MPa);

[0050] C1, m1, m2, k2, n3, etc. all represent material constants related to the volume of the precipitated phase;

[0051] C2, m3, k3, n4, a, b, etc. are all material constants related to the length of the material precipitation phase;

[0052] C3, k4, n5, n6 represent material constants related to the shape factor of the material precipitation phase. It indicates the peak aging time;

[0053] C4, n0, C5, n7, etc. all express material constants related to the evolution of dislocation density inside the material;

[0054] C ppt , m5, n1 are all material constants related to precipitation strengthening;

[0055] C dis , n2 are material constants related to material dislocation strengthening;

[0056] C ss , n3 are material parameters related to solid solution strengthening;

[0057] In the above-mentioned creep / stress relaxation aging macro-micro unified constitutive model, the following is the microstructure content change equation in the creep / stress relaxation aging macro-micro unified constitutive model (that is, the creep / stress relaxation aging macro-micro unified constitutive model includes the creep amount / stress relaxation amount evolution model, the yield strength calculation model and the microstructure content change equation composed of the following model).

[0058] Before introducing the scheme of this embodiment, a method for determining the parameters of the traditional creep relaxation aging macro-micro unified constitutive model is first introduced.

[0059] The existing creep / stress relaxation aging macro-micro unified constitutive model parameters are mainly determined by the following methods:

[0060] S1. Collect and compile statistical experimental data, obtain multiple groups (referred to as m groups) of creep (or stress relaxation)-time curve data under different process parameters and their corresponding microstructure content changes, and calculate the creep amount ci (i=1.....m) of the material entering the steady-state creep stage under different process parameters.

[0061] Among them, the creep amount (or stress relaxation amount)-time curve data is measured under at least three sets of different process parameter conditions. Specific process parameters may include aging temperature (160℃-180℃), stress level (200MPa-400MPa), aging time (8h-12h), etc., and each set of data must record the complete curve of creep amount changing with time to ensure that it covers the initial creep stage and steady-state creep stage.

[0062] Through scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD) or other characterization techniques, combined with quantitative analysis methods, the content of the internal microstructure of the material before, during and after creep aging of each process parameter is obtained. The microstructure changes include but are not limited to: changes in dislocation density, changes in the volume fraction of precipitated phases, precipitated phase size, precipitated phase shape, etc.

[0063] The acquisition nodes in the creep aging process need to be determined in combination with the creep amount-time curve. The specific acquisition node is the end of the initial creep stage. The characteristics of the initial creep stage are that the creep amount increases rapidly over time and the creep strain rate decays rapidly; the steady-state stage is usually characterized by a constant creep amount change rate and no significant change in the creep amount over time. During stress relaxation, the stress value yi (i=1.....m) entering the steady-state relaxation stage should be recorded.

[0064] S2. Establish a unified macro-micro constitutive model of creep / stress relaxation aging suitable for the process conditions.

[0065] S3. First fit the microstructure content change equation to obtain the corresponding material parameters related to the microstructure content change.

[0066] Initialize the parameter ranges (xp, yp) of each material (i.e., the initial material parameter range) based on the physical meaning, where p is the number of material parameters. First, fit the equation for the change of microstructure content during the creep aging process of the material, and use the intelligent algorithm to fit the microstructure evolution equations and material strengthening models under different process parameter conditions.

[0067] S4. Fit the creep amount-time curve under single process parameter conditions separately.

[0068] The microstructure evolution model parameters fitted in S3 are brought into the creep (or stress relaxation) macro-micro unified constitutive model, and the creep amount (or stress relaxation amount) evolution model under single process parameter conditions is fitted respectively to obtain m groups of material parameters cs1ip (i=1.....m, p is the number of material parameters).

[0069] S5. Modify the range of each material parameter (xp, yp) based on experience, and then repeat S3 to S4 until the fitting result meets the preset expected value.

[0070] Since the existing method for determining the parameters of the creep / stress relaxation aging macro-micro unified constitutive model requires manual adjustment of the material parameter range multiple times based on experience, the fitting time is relatively long, generally requiring 2-10 days of fitting, which is a long time.

[0071] Based on the above problems of the existing creep / stress relaxation aging macro-micro unified constitutive model parameter determination method, this embodiment provides a model parameter determination method, which updates the range of material parameters by dichotomy, which can greatly reduce the number of fitting times and shorten the fitting time.

[0072] Specifically, see Figure 2 The specific contents of the method in this embodiment are as follows:

[0073] S1. Collect and compile statistical experimental data, obtain multiple groups (referred to as m groups) of creep (or stress relaxation)-time curve data under different process parameters and their corresponding microstructure content changes, and calculate the creep amount ci (i=1.....m) of the material entering the steady-state creep stage under different process parameters.

[0074] Among them, the creep amount (or stress relaxation amount)-time curve data is measured under at least three sets of different process parameter conditions. Specific process parameters may include aging temperature (160℃-180℃), stress level (200MPa-400MPa), aging time (8h-12h), etc., and each set of data must record the complete curve of creep amount changing with time to ensure that it covers the initial creep stage and steady-state creep stage.

[0075] Through scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD) or other characterization techniques, combined with quantitative analysis methods, the content of the internal microstructure of the material before, during and after creep aging of each process parameter is obtained. The microstructure changes include but are not limited to: changes in dislocation density, changes in the volume fraction of precipitated phases, precipitated phase size, precipitated phase shape, etc.

[0076] The acquisition nodes in the creep aging process need to be determined in combination with the creep amount-time curve. The specific acquisition node is the end of the initial creep stage. The characteristics of the initial creep stage are that the creep amount increases rapidly over time and the creep strain rate decays rapidly; the steady-state stage is usually characterized by a constant creep amount change rate and no significant change in the creep amount over time. During stress relaxation, the stress value yi (i=1.....m) entering the steady-state relaxation stage should be recorded.

[0077] S2. Establish a unified macro-micro constitutive model of creep / stress relaxation aging suitable for the process conditions.

[0078] The creep / stress relaxation aging macro-micro unified constitutive model can be found in the previous introduction. The creep / stress relaxation aging macro-micro unified constitutive model of this embodiment takes into account the comprehensive effects of microstructures such as dislocation evolution, precipitate size, shape evolution, and precipitate content change:

[0079] ;

[0080] ;

[0081] Where A, B, , h, m0, and m1 are all material constants. is the threshold stress.

[0082] 2) The evolution law of dislocation density can generally be expressed as the following formula, and the specific expression needs to be determined in combination with the law of dislocation change:

[0083] ;

[0084] k1, k2, k3: constants, reflecting the effects of stress, dislocation annihilation and slip on dislocation density.

[0085] 3) Evolution of volume fraction of precipitated phase: The specific expression needs to be determined in combination with the variation law of the volume fraction of the precipitated phase:

[0086] : Precipitation kinetics equation, depends on temperature, time and stress conditions.

[0087] 4) Evolution of precipitate size: The specific expression needs to be determined in combination with the evolution law of the precipitate phase size:

[0088] : equilibrium size of the precipitated phase; : Size growth rate coefficient.

[0089] 5) Evolution of precipitate shape: using equivalent aspect ratio r p Description, the shape evolution law can be expressed as:

[0090] , Shape evolution function fitted from experimental data.

[0091] S3. First fit the microstructure content change equation to obtain the corresponding material parameters related to the microstructure content change.

[0092] Initialize the parameter range of each material (xp, yp) based on the physical meaning, where p is the number of material parameters. First, fit the equation for the change of the microstructure during the creep aging process of the material, and use the intelligent algorithm to fit the microstructure evolution equations and material strengthening models under different process parameter conditions.

[0093] Among them, the microstructure evolution equations include but are not limited to dislocation evolution, precipitate phase volume fraction evolution, precipitate phase size evolution and precipitate phase shape evolution. The material strengthening model includes but is not limited to precipitation strengthening model, dislocation strengthening model, solid solution strengthening model and matrix strengthening model. The algorithms include particle swarm algorithm, genetic algorithm, simulated annealing algorithm, etc.

[0094] S4. Fit the creep amount-time curve under single process parameter conditions separately.

[0095] Substitute the parameters of the microstructure evolution model obtained by fitting in S3 into the macro-micro unified constitutive model of creep (or stress relaxation), and respectively fit the evolution models of creep strain (or stress relaxation amount) under the condition of a single process parameter to obtain m groups of material parameters cs1ip, (i = 1.....m, p is the number of material parameters).

[0096] S5. Calculate the arithmetic mean and weighted mean of the multiple groups of material parameters obtained in S4, and then substitute them into the creep strain-time curve fitting under multiple groups of process parameters, and respectively evaluate their fitting errors.

[0097] Calculate the corresponding weighted mean csp and arithmetic mean cs1p of the m groups of corresponding material parameters cs1ip (i = 1.....m, p is the number of material parameters) respectively. The calculation formula for the weighted mean csp of each group of parameters can be (i = 1.....m) or (i = 1.....m) or (p is the number of material parameters). Substitute csp and cs1p into the overall creep strain (or stress relaxation amount) evolution model respectively, and evaluate the fitting error of each curve with the mse error to obtain the first fitting error MSE and the second fitting error MSE1.

[0098] S6. Compare the magnitudes of MSE and MSE1, and update the value range of the material parameters.

[0099] S6-1. If MSE1 > MSE, then the overall fitting error ZMSE = MSE. Further compare the magnitudes of csp and If csp > , update the corresponding parameter range to ( , yp), otherwise update the parameter range to (xp, ).

[0100] S6-2. If MSE1 < MSE, then the overall fitting error ZMSE = MSE1. Further compare the magnitudes of cs1p and If cs1p > , update the corresponding parameter range to ( , yp), otherwise update the parameter range to (xp, ).

[0101] S7. Repeat S4 - S6. When ZMSE reaches the expected value, the current optimal material parameters can be obtained.

[0102] In this embodiment, the creep amount (or stress relaxation amount)-time curve under a single process condition is first fitted respectively, and the corresponding weighted average value csp and arithmetic average value cs1p are calculated. Then, the weighted average material parameters csp and cs1p are used to fit the creep amount (or stress relaxation amount)-time curve under multiple groups of process conditions to obtain the fitting errors MSE and MSE1. The size of MSE1 is compared with MSE, and the range of each material parameter is updated until ZMSE meets the expected value, so as to obtain the optimal material parameters.

[0103] The following table lists the material parameters obtained after fitting using the method of this embodiment in a specific scenario:

[0104] Table 1

[0105]

[0106] The fitting results are shown in Figure 3(a) to Figure 3(c).

[0107] The method of this embodiment can not only greatly improve the efficiency of parameter solution, but also ensure the accuracy and applicability of model prediction, especially when dealing with creep or stress relaxation aging forming problems of complex thin-walled components. This embodiment can be widely used in the design and manufacture of complex thin-walled structural parts in the fields of aerospace, automobile manufacturing, energy equipment, etc.

[0108] In order to implement the method of an embodiment of the present invention, an embodiment of the present invention also provides a creep relaxation aging macro-micro unified constitutive model parameter determination system, including: a processor and a memory for storing a computer program that can be run on the processor; wherein, when the processor is used to run the computer program, the steps of the above-mentioned method are executed.

[0109] The above-mentioned system provided in this embodiment belongs to the same concept as the above-mentioned method embodiment. The specific implementation process thereof is detailed in the method embodiment and will not be repeated here.

[0110] In order to implement the method of the embodiment of the present invention, the embodiment of the present invention also provides a computer program product, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps of the above method.

[0111] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiment of the present invention, the embodiment of the present invention further provides an electronic device (computer device). Specifically, in one embodiment, the computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, the method of any one of the above embodiments is implemented. The display screen A04 of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device A05 of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0112] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0113] The device provided by the embodiment of the present invention includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the method of any one of the above embodiments is implemented.

[0114] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0115] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0118] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0119] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0120] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0121] It can be understood that the memory of the embodiment of the present invention can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAMbus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memories.

[0122] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0123] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining parameters of a creep relaxation aging macro-micro unified constitutive model, characterized in that: The method comprises: Obtain the unified macro-micro constitutive model of creep relaxation aging; Acquire multiple groups of creep amount-time curve data and corresponding microstructure content change data under different process parameter conditions; the process parameter conditions include aging temperature, stress level and aging time; and obtain the creep amount of the material entering the steady-state creep stage or the stress value entering the steady-state relaxation stage under different process parameter conditions; Determine the initial material parameter range of the creep relaxation aging macro-micro unified constitutive model; within the initial material parameter range, fit the creep relaxation aging macro-micro unified constitutive model using the multiple groups of creep amount-time curve data under different process parameter conditions and their corresponding microstructure content change data to obtain multiple groups of material parameters; each group of process parameter conditions corresponds to a group of material parameters; Calculating weighted average values ​​and arithmetic average values ​​of the plurality of sets of material parameters; Substituting the weighted average value into the creep relaxation aging macro-micro unified constitutive model, and using the mean square error method to evaluate the fitting error of the creep relaxation aging macro-micro unified constitutive model after the weighted average value is brought in, so as to obtain a first fitting error; and substituting the arithmetic average value into the creep relaxation aging macro-micro unified constitutive model, and using the mean square error method to evaluate the fitting error of the creep relaxation aging macro-micro unified constitutive model after the weighted average value is brought in, so as to obtain a second fitting error; According to the first fitting error and the second fitting error, it is judged whether the fitting errors of the multiple groups of material parameters reach the preset expected value; if the fitting errors of the multiple groups of material parameters reach the preset expected value, the multiple groups of material parameters are determined as the final material parameters; if the fitting errors of the multiple groups of material parameters do not reach the preset expected value, the material parameter range is adjusted according to the first fitting error and the second fitting error, and under the adjusted material parameter range, the creep relaxation aging macro-micro unified constitutive model is fitted using the multiple groups of creep amount-time curve data under different process parameter conditions and their corresponding microstructure content change data to obtain multiple groups of material parameters; and it is judged whether the fitting errors of the multiple groups of material parameters reach the preset expected value; until the fitting errors of the multiple groups of material parameters reach the preset expected value; Wherein, adjusting the material parameter range according to the first fitting error and the second fitting error includes: When the second fitting error is greater than the first fitting error, a first value is calculated; and whether the weighted average value is greater than the first value is determined; if the weighted average value is greater than the first value, the minimum value in the material parameter range is replaced by the first value; if the weighted average value is not greater than the first value, the maximum value in the material parameter range is replaced by the first value; When the second fitting error is less than the first fitting error, a first value is calculated; and whether the arithmetic mean is greater than the first value is determined; if the arithmetic mean is greater than the first value, a minimum value in the material parameter range is replaced by the first value; if the arithmetic mean is not greater than the first value, a maximum value in the material parameter range is replaced by the first value; The calculation formula of the first value is: G= ； Among them, G is the first value, x p is the minimum value in the material parameter range, y p is the maximum value in the material parameter range.

2. The method for determining parameters of the creep relaxation aging macro-micro unified constitutive model according to claim 1, characterized in that: The creep relaxation aging macro-micro unified constitutive model is fitted using the creep amount-time curve data under the conditions of the multiple sets of different process parameters and the corresponding microstructure content change data to obtain multiple sets of material parameters, including: Using the data of microstructure content change under different process parameters, the microstructure content change equation in the creep relaxation aging macro-micro unified constitutive model is fitted to obtain material parameters in the microstructure content change equation; Using the material parameters in the microstructure content variation equation and the creep amount-time curve data under each process parameter condition, the creep amount evolution model in the creep relaxation aging macro-micro unified constitutive model under each process parameter condition is fitted to obtain the material parameters under each process parameter condition; According to the material parameters under each process parameter condition, multiple groups of material parameters are obtained.

3. The method for determining parameters of the creep relaxation aging macro-micro unified constitutive model according to claim 1, characterized in that: Calculating the weighted average values ​​of the plurality of sets of material parameters comprises: The weighted average values ​​of the multiple groups of material parameters are calculated using the following formula: Cs p = or Cs p = or Cs p = ; Where ci is the creep amount of the material entering the steady-state creep stage under the conditions of the i-th set of process parameters, yi is the stress value of the material entering the steady-state relaxation stage under the conditions of the i-th set of process parameters; x p is the minimum value in the material parameter range, y p is the maximum value in the material parameter range, p is the number of material parameters, i=1.....m, and m is the total number of groups of process parameter conditions.

4. The method for determining parameters of the creep relaxation aging macro-micro unified constitutive model according to claim 1, characterized in that: The fitting errors of the multiple sets of material parameters are determined as follows: If the second fitting error is greater than the first fitting error, the fitting error of the multiple sets of material parameters is the first fitting error; If the second fitting error is smaller than the first fitting error, the fitting error of the multiple sets of material parameters is the second fitting error.

5. A creep relaxation aging macro-micro unified constitutive model parameter determination system, characterized in that: include: A processor and a memory for storing a computer program that can be run on the processor; wherein the processor, when used to run the computer program, performs the following steps: Obtain the unified macro-micro constitutive model of creep relaxation aging; Acquire multiple groups of creep amount-time curve data and corresponding microstructure content change data under different process parameter conditions; the process parameter conditions include aging temperature, stress level and aging time; and obtain the creep amount of the material entering the steady-state creep stage or the stress value entering the steady-state relaxation stage under different process parameter conditions; Determine the initial material parameter range of the creep relaxation aging macro-micro unified constitutive model; within the initial material parameter range, fit the creep relaxation aging macro-micro unified constitutive model using the multiple groups of creep amount-time curve data under different process parameter conditions and their corresponding microstructure content change data to obtain multiple groups of material parameters; each group of process parameter conditions corresponds to a group of material parameters; Calculating weighted average values ​​and arithmetic average values ​​of the plurality of sets of material parameters; Substituting the weighted average value into the creep relaxation aging macro-micro unified constitutive model, and using the mean square error method to evaluate the fitting error of the creep relaxation aging macro-micro unified constitutive model after the weighted average value is brought in, so as to obtain a first fitting error; and substituting the arithmetic average value into the creep relaxation aging macro-micro unified constitutive model, and using the mean square error method to evaluate the fitting error of the creep relaxation aging macro-micro unified constitutive model after the weighted average value is brought in, so as to obtain a second fitting error; According to the first fitting error and the second fitting error, it is judged whether the fitting errors of the multiple groups of material parameters reach the preset expected value; if the fitting errors of the multiple groups of material parameters reach the preset expected value, the multiple groups of material parameters are determined as the final material parameters; if the fitting errors of the multiple groups of material parameters do not reach the preset expected value, the material parameter range is adjusted according to the first fitting error and the second fitting error, and under the adjusted material parameter range, the creep relaxation aging macro-micro unified constitutive model is fitted using the multiple groups of creep amount-time curve data under different process parameter conditions and their corresponding microstructure content change data to obtain multiple groups of material parameters; and it is judged whether the fitting errors of the multiple groups of material parameters reach the preset expected value; until the fitting errors of the multiple groups of material parameters reach the preset expected value; Wherein, when the processor is used to run the computer program, it further performs the following steps: When the second fitting error is greater than the first fitting error, a first value is calculated; and whether the weighted average value is greater than the first value is determined; if the weighted average value is greater than the first value, the minimum value in the material parameter range is replaced by the first value; if the weighted average value is not greater than the first value, the maximum value in the material parameter range is replaced by the first value; When the second fitting error is less than the first fitting error, a first value is calculated; and whether the arithmetic mean is greater than the first value is determined; if the arithmetic mean is greater than the first value, a minimum value in the material parameter range is replaced by the first value; if the arithmetic mean is not greater than the first value, a maximum value in the material parameter range is replaced by the first value; The calculation formula of the first value is: G= ； Among them, G is the first value, x p is the minimum value in the material parameter range, y p is the maximum value in the material parameter range.

6. The creep relaxation aging macro-micro unified constitutive model parameter determination system according to claim 5, characterized in that: When the processor is used to run the computer program, the processor further performs the following steps: Using the data of microstructure content change under different process parameters, the microstructure content change equation in the creep relaxation aging macro-micro unified constitutive model is fitted to obtain material parameters in the microstructure content change equation; Using the material parameters in the microstructure content variation equation and the creep amount-time curve data under each process parameter condition, the creep amount evolution model in the creep relaxation aging macro-micro unified constitutive model under each process parameter condition is fitted to obtain the material parameters under each process parameter condition; According to the material parameters under each process parameter condition, multiple groups of material parameters are obtained.

7. The creep relaxation aging macro-micro unified constitutive model parameter determination system according to claim 5, characterized in that: When the processor is used to run the computer program, the processor further performs the following steps: The weighted average values ​​of the multiple groups of material parameters are calculated using the following formula: Cs p = or Cs p = or Cs p = ; Where ci is the creep amount of the material entering the steady-state creep stage under the conditions of the i-th set of process parameters, yi is the stress value of the material entering the steady-state relaxation stage under the conditions of the i-th set of process parameters; x p is the minimum value in the material parameter range, y p is the maximum value in the material parameter range, p is the number of material parameters, i=1.....m, and m is the total number of groups of process parameter conditions.

8. The creep relaxation aging macro-micro unified constitutive model parameter determination system according to claim 5, characterized in that: When the processor is used to run the computer program, the processor further performs the following steps: If the second fitting error is greater than the first fitting error, the fitting error of the multiple sets of material parameters is the first fitting error; If the second fitting error is smaller than the first fitting error, the fitting error of the multiple sets of material parameters is the second fitting error.

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