A collaborative prediction method for creep-aging and shape-property of aluminum alloy considering temperature history

Abstract

This invention discloses a method for collaboratively predicting the creep and aging properties of aluminum alloys, taking into account the temperature history. This method studies the characteristics and quantitative data of stress components and activation energy variations at different temperatures, stress levels, and time, and constructs a creep deformation model for aluminum alloys that can account for complex temperature histories. Furthermore, based on the theory of aging-precipitation kinetics, a model is constructed to predict the strengthening properties of aluminum alloys under complex temperature histories, coupled with creep or stress relaxation-aging. This method enables collaborative prediction of the properties of aluminum alloys during the entire heating-holding-cooling CAF process. This method overcomes the limitation of existing methods, which can only predict the creep aging of aluminum alloys at a single constant temperature. This method has important guiding significance for the accurate prediction of the entire CAF process, its improved high-efficiency variable-temperature CAF process, and the numerical simulation of component manufacturing.

Description

Technical Field

[0001] The present invention relates to the field of basic theory of plastic forming and modeling technology, and in particular to a method for collaboratively predicting creep-aging and shape-properties of aluminum alloys taking temperature history into consideration. Background Art

[0002] The creep aging forming (CAF) process deforms and loads a heat-treatable aluminum alloy component, then heats it to the aging temperature and holds it there for a period of 10-20 hours. During this process, the material undergoes creep deformation and aging hardening, simultaneously achieving final component formation and improved performance. This process is extremely suitable for the precise manufacture of thin-walled components with small deformations, such as large aerospace skins and panels. Because only a portion of the elastic deformation during creep is converted into creep (plastic deformation), the process exhibits significant springback. Furthermore, the precipitation and dislocation proliferation within the aluminum alloy during the forming process are coupled by stress, temperature, and time, resulting in significant variations in strength and performance. Coordinated prediction of deformation and performance during the forming process is key to achieving process design and application.

[0003] During the actual CAF process, components undergo heating, insulation, and cooling stages after loading. This complex temperature history leads to complex internal creep and precipitation evolution. Existing models describing creep aging behavior only consider the creep deformation and aging strengthening evolution of the material at a single temperature, and can only predict the deformation and performance evolution of the CAF process during the insulation process at a single temperature. They lack consideration of the heating and cooling stages. At the same time, if the forming temperature is changed, the relevant models and parameters will no longer be applicable. Existing research shows that the heating and cooling processes in the CAF process have a significant impact on material deformation and aging, and the deformation impact can exceed 20%. At the same time, to improve process efficiency, existing research has also proposed a new CAF process path under variable temperature. However, the existing creep aging material constitutive model cannot meet the requirements of the complete CAF process flow and the effective prediction of component deformation and performance evolution during the new variable temperature CAF process, which greatly restricts the future promotion and application of this process in the aerospace field.

[0004] Therefore, a collaborative prediction method of creep-aging and shape-property of aluminum alloy considering temperature history is proposed to solve the above problems. Summary of the Invention

[0005] The present invention aims to provide a method for collaboratively predicting the creep-aging and morphological properties of aluminum alloys taking into account the temperature history, so as to solve or improve at least one of the above-mentioned technical problems.

[0006] In view of this, a first aspect of the present invention is to provide a method for collaboratively predicting creep-aging and shape-properties of aluminum alloys taking temperature history into consideration.

[0007] The first aspect of the present invention provides a collaborative prediction method for creep aging properties of aluminum alloys considering temperature history, comprising the following steps: S1, on a uniaxial tensile testing machine with a heating furnace, at different temperatures, performing uniaxial tension-continuous short-time stress relaxation tests on the aluminum alloy material, respectively, to obtain short-time stress relaxation curve data of the material in the initial state at the corresponding temperature; S2, on a uniaxial tensile testing machine with a heating furnace, at different temperatures, performing stress relaxation tests and uniaxial tension-continuous short-time stress relaxation tests on the aluminum alloy material, respectively, to obtain continuous short-time stress relaxation experimental data of the aluminum alloy material after different stress relaxation aging states at the corresponding temperature; S3, based on the test data obtained in S1 and S2, analyzing and calculating the creep aging properties of the aluminum alloy material at different temperatures, different stress levels or strain levels, and different stress relaxation times. The deformation activation energy Q of the material; S4, establishing an activation energy change model considering temperature, deformation and time, and an aluminum alloy creep deformation model with activation energy as a variable; S5, establishing an aluminum alloy precipitation phase evolution model and an aluminum alloy strengthening prediction model considering temperature based on the precipitation kinetics of aluminum alloy and the influence of stress coupling; S6, conducting creep aging and stress relaxation tests at different temperatures, different stress levels or strain levels on a uniaxial tensile testing machine with a heating furnace, and obtaining creep curves, stress relaxation curves and performance evolution curve data of aluminum alloy materials at corresponding temperatures and deformation levels; S7, based on the test data obtained in S6, performing parameter fitting calibration and verification on the aluminum alloy strengthening prediction model in S5; S8, predicting the creep aging properties of aluminum alloy by means of the aluminum alloy creep deformation model and strengthening prediction model.

[0008] The present invention provides a collaborative prediction method for creep-aging properties of aluminum alloys taking into account the temperature history. This method considers the influence of temperature on the change of material activation energy, thereby achieving the understanding and prediction of creep or stress relaxation deformation mechanism.

[0009] At the same time, the influence of temperature on the evolution of the precipitated phase is taken into account, and the prediction of the material microstructure and its strengthening characteristics under the influence of temperature is simultaneously realized.

[0010] The developed prediction model can realize the coordinated prediction of the full-process CAF process of heating-insulation-cooling and the new temperature-variable CAF process, which has important guiding significance for the accurate prediction of the full-process CAF process and its improved high-efficiency CAF process and the numerical simulation of component manufacturing.

[0011] Specifically, the selected temperature range is 20°C-200°C;

[0012] Specifically, in step S2, after reaching the specified stress relaxation time (according to the overall time t required by the process, three time points of 0.3t, 0.5t, and t are selected);

[0013] Specifically, in step S7, the parameters of the above equations are fitted, calibrated, and verified based on experimental data. Based on creep or stress relaxation and yield strength evolution data at three different deformation levels, a particle swarm optimization algorithm is used to fit the creep and yield strength equations, calibrating the non-temperature-dependent material constants.

[0014] Furthermore, based on creep or stress relaxation and yield strength evolution data at three different temperatures and deformation levels, the temperature-dependent material constants of the aforementioned materials were fitted and calibrated to obtain all the required material constants. The obtained material constants were verified using data at a fourth temperature and stress level to verify the validity of the relevant material constants and equations.

[0015] In addition, the technical solution provided by the embodiment of the present invention may also have the following additional technical features:

[0016] In any of the above technical solutions, the steps of the uniaxial tension-continuous short-time stress relaxation test specifically include: S101, using a quasi-static method, stretching the material to a specified deformation level through a uniaxial tensile testing machine, stopping the stretching, maintaining a short time of stress relaxation, and recording data on the stress reduction over time; S102, using a rapid loading method, stretching the material through a uniaxial tensile testing machine to the stress level recorded by the uniaxial tensile machine when the stretching was stopped in S101, stopping the stretching, maintaining a short time of stress relaxation, and recording data on the stress change over time; S103, repeating steps S101-S102 twice; S104, selecting the next level of deformation level, repeating steps S101-S103 until the stretching reaches the highest strain level, and ending the experiment; wherein the deformation level includes stress level and strain level, the stress level in the elastic region is set to at least three levels from low to high, and the strain level in the plastic region is set to at least three levels from low to high, and the short time is 10s-30s.

[0017] In this technical solution, the quasi-static state is the strain rate of 10 -3 ~10 -4 / s, uniaxial stretching to a specified stress level (elastic region) or strain level (plastic region);

[0018] Rapid loading is a strain rate of 10 -1 ~10 -2 / s. During the experiment, at least three stress levels are specified for the elastic area, and at least three strain levels are specified for the plastic area. The tensile testing machine increases the stress level of the material from low to high step by step. The stress level or plastic area is set and adjusted according to actual needs. Multiple multi-stage stretching can ensure that the material as a whole in the experiment will not suffer from large fractures or internal and surface cracks, which will cause the termination of the later experiment.

[0019] Specifically, in step S102, when the stretching target is the strain level, the material is stretched by the uniaxial tensile testing machine to the stress level recorded by the uniaxial tensile machine when the stretching is stopped in S101, where the stress level is the stress level corresponding to the corresponding strain level, and the stress level uniaxial tensile machine will record it during the experiment.

[0020] In any of the above technical solutions, the step S3 specifically includes: S301, based on the experimental data in S2, fitting is performed in the following manner to obtain the internal stress component σ under this material state, this temperature and stress level i : Where t is the stress relaxation time, σ0 is the initial stress level, σ is the stress data that changes with time, a and b are constants to be fitted, and σ i is the internal stress component and is related to the effective stress σ e Together they form the overall stress, expressed as σ = σ i +σ e v302, extract the strain rate at the end of the first stress relaxation in S101 and the strain rate at the beginning of the second stress relaxation in S102, and use the following equation to fit the activation volume data V of the material in this state: Where T is the temperature, is the strain rate at the beginning of the second stress relaxation, is the strain rate at the end of the first stress relaxation, Δσ is the overall stress change during the first stress relaxation process, and c is the constant to be fitted; S303, based on the internal stress component σ in S302 i , effective internal stress component σ e and activation volume V, the activation energy Q of the material under different stress relaxation time, temperature and stress state is calculated by the following formula: Q=(ΔG0-Vσ e ); where ΔG0 is the Gibbs free energy of the material at absolute zero, V is the activation volume, and σ e is the effective stress.

[0021] In this technical solution, based on the first stress relaxation curve data of the three short-time stress relaxation curves obtained under different material states, different temperatures, and different deformation levels, the equation Fitting is performed to obtain the internal stress component σ under this material state, this temperature and deformation level i ;

[0022] Since the stress relaxation time is short (10-30s) and the reloading rate after relaxation is high, the material is still in elastic deformation during the loading process. It can be approximately considered that the internal state of the material (such as dislocation, etc.) has not changed significantly during this process. Therefore, based on the three short-term stress relaxation curves obtained under different material states, different temperatures, and different deformation levels, the strain rate at the end of the first stress relaxation and the strain rate at the beginning of the second stress relaxation after subsequent rapid loading are extracted. The equation Fitting to obtain the activation volume data V of the material in this state;

[0023] Based on the internal stress components σ of the material under different states obtained above i , effective internal stress component σ e , and the activation volume V, through its equation Q = (ΔG0-Vσ * ), the activation energy Q of aluminum alloy under different stress relaxation time, temperature and deformation state is calculated.

[0024] In any of the above technical solutions, the step S4 specifically includes: S401, establishing a model of activation energy changes with temperature and deformation state during stress relaxation; S402, establishing an aluminum alloy creep deformation material model that includes variables of activation energy changes, and through the changes in activation energy with time, temperature and deformation level in step S401, the aluminum alloy creep deformation model can be used to simulate and predict various temperature and deformation change processes.

[0025] In this technical solution, since the activation energy of the material is affected by different temperatures and deformation states, a model is established to analyze the activation energy under temperature and deformation state during stress relaxation. This overcomes the limitation of existing methods that can only predict the creep aging of aluminum alloys at a single constant temperature.

[0026] Then, an aluminum alloy creep deformation material model considering the change of activation energy is established. With the help of the influence of activation energy on time, temperature and deformation level, the creep deformation equation is used to simulate and predict the change history of various temperatures and deformation.

[0027] In any of the above technical solutions, the step S401 specifically includes: a. The activation energy changes with temperature Q0(T) model is: Q0(T) = (1-k1T / T m )Q0; where T m is the melting temperature of the material, T is the creep temperature, Q0 is the reference activation energy, and k1 is the material constant; b. Activation energy changes with deformation The model is: Where k2 is a constant, is the plastic strain rate.

[0028] In this technical solution, Q0 and k1 are obtained by fitting the data of activation energy Q obtained at different stress relaxation temperatures in step S303. By adding the temperature variable to the activation energy variation with temperature Q0(T) model, the overall model can fully consider the influence of temperature on the activation energy of the material.

[0029] k1 can be obtained by fitting the data of activation energy Q obtained at different stress relaxation temperatures in step S303, and by The model adds the variable of deformation state, so that the overall model can fully consider the influence of deformation state on the activation energy of the material.

[0030] In any of the above technical solutions, the step S402 specifically includes: a. The creep deformation model is: in, is the dislocation density, R is the air constant, k3, k4 are material constants, is the creep strain rate; b. The material constant k3 is set to change with temperature, specifically the following formula: Among them, k 30 is the material constant, Q A is the corresponding activation energy; c. The evolution equation of dislocation density is as follows: Among them, k5, k6 and m1 are the material constants to be fitted.

[0031] In this technical solution, a creep deformation model of the material is established to simulate and predict the temperature and stress change process of various types of materials, and the material constant k3 and dislocation density that change with temperature in the model are calculated. Use the formula to solve.

[0032] In any of the above technical solutions, the step S5 specifically includes: S501, simplifying the precipitation phase evolution model of the creep aging forming process, only considering the nucleation and growth stages of the precipitation phase inside the material during the creep aging process; S502, establishing the material yield strength equation under the influence of the microstructure evolution during the aging process.

[0033] In this technical solution, since the aluminum alloy CAF process generally requires ensuring that the material properties are in a strengthened state after forming, the aging treatment typically results in peak or near-peak conditions. During this process, the precipitates within the material are primarily in the nucleation and growth stages, with relatively little coarsening. Therefore, a simplified model of the precipitation evolution during the CAF process is employed. Only the nucleation and growth stages of the precipitates within the material during creep aging are considered.

[0034] In any of the above technical solutions, the step S501 specifically includes: a. the evolution equation of the number N of precipitations in the precipitation phase nucleation process, specifically the following formula: Where z is the lattice constant of aluminum, γ is the precipitation specific energy, v is the molar volume of aluminum, k is the Boltzmann constant, c(t) is the solid solubility of the material that changes with aging time, and c e is the solid solubility at equilibrium at the corresponding temperature, ΔG is the Gibbs free energy, and D eff is the diffusion constant; b. The equivalent size evolution equation of the precipitate phase during the nucleation and growth process of the precipitate phase, which is represented by the equivalent average size r, is as follows: Among them, c i (r) is the solid solubility of the material that changes with the equivalent average size r of the precipitated phase; c. Based on the number and size of the precipitated phases obtained above, the precipitation volume fraction is calculated using the following formula: Where f is the precipitation volume fraction, N is the total number of precipitated phases per unit volume; d. The solid solubility within the material is calculated using the following formula: Where c0 is the initial solid solubility of the solid solution atoms in the aluminum alloy.

[0035] In this technical solution, the diffusion constant D eff Corrected to the comprehensive value of the material's self-diffusion energy and the diffusion energy along the dislocation line;

[0036] By adding the solid solubility c(t) inside the material into the evolution equation of the precipitation number N during the precipitation phase nucleation process, the equation can simultaneously consider the effect of temperature on the solid solution evolution.

[0037] In any of the above technical solutions, the step S502 specifically includes: a. Precipitation phase strengthening σ p It is mainly determined by the size of the precipitated phase, and the specific equation is: Where M is the Taylor factor, which is 2, b is the Burgers vector, which is 0.28 nm, L is the average spacing between precipitates, and F(r) is the average strength of the second phase of the precipitate. b. Solid solution strengthening is determined by the solubility, and the equation for solid solution strengthening is: ss =C ss c(t) 2 / 3 Among them, C ss is the material constant, σ ss is the degree of solid solution strengthening; c. dislocation strengthening is determined by the dislocation density, and the equation for dislocation strengthening is: Among them, α is the material constant, σ dis is the dislocation strengthening component, G is the shear modulus, and ρ is the dislocation density; d. The calculation equation for the overall yield strength of the material is:

[0038] In this technical solution, the average spacing L of the precipitated phases can be expressed by the formula: Where, f is the volume fraction of the precipitated phase;

[0039] The strengthening mechanism and expression of F(r) are different according to the size of the precipitate phase. When the precipitate phase is small, it manifests as shear strengthening, and when the precipitate phase is large, it manifests as bypassing the strengthening mechanism. The mechanism transition passes through the critical radius r. c To express it, the calculation equation of F(r) is:

[0040]

[0041] Among them, k and β are constants to be determined, and their relationship with the critical radius is:

[0042] In any of the above technical solutions, D eff It is defined as the following formula: Among them, Q diff is the material self-diffusion energy, Q pipe is the diffusion energy along the dislocation, D0 and D p0 are diffusion coefficients, which are obtained by referring to corresponding material manuals and literature.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The existing models describing creep aging behavior only consider the deformation and performance evolution prediction of the CAF process during the insulation process at a single temperature, and cannot effectively predict the deformation and performance of materials in new processes such as the heating and cooling stages and variable temperature CAF.

[0045] The main reason is that the existing models do not accurately understand the material deformation and strengthening mechanism caused by temperature.

[0046] The present invention considers the influence of temperature on the change of material activation energy, thereby realizing the understanding and prediction of creep or stress relaxation deformation mechanism; at the same time, considering the influence of temperature on the evolution of precipitation phase, solid solution, etc., synchronously realizing the prediction of material microstructure and its strengthening characteristics under the influence of temperature.

[0047] The developed unified model can realize the coordinated prediction of the full-process CAF process of heating-insulating-cooling and the shape and properties of the new variable-temperature CAF process, which has important guiding significance for the accurate prediction of the full-process CAF process and its improved high-efficiency CAF process and the numerical simulation of its component manufacturing.

[0048] Additional aspects and advantages of embodiments according to the present invention will become apparent in the following description or may be learned through practice of embodiments according to the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings are only for purposes of illustrating particular embodiments and are not to be considered limiting of the invention.

[0050] Figure 1 Schematic diagram of the stretching-continuous short-time stress relaxation test method of the present invention;

[0051] Figure 2 Schematic diagram of the stress relaxation + stretching-continuous short-time stress relaxation experimental method of the present invention;

[0052] Figure 3 Schematic diagram of calculation of effective stress components in aluminum-zinc-magnesium alloy at different temperatures and result curves thereof;

[0053] Figure 4 Schematic diagram of the activation energy evolution experiment and prediction data curve in the aluminum-zinc-magnesium alloy at different temperatures of the present invention;

[0054] Figure 5 Schematic diagram of stress relaxation curves of aluminum-zinc-magnesium alloys obtained by experiment and prediction at different temperatures and different stress levels according to the present invention;

[0055] Figure 6 Schematic diagram of the experimental and predicted strength curves of the aluminum-zinc-magnesium alloy at different temperatures and different stress levels of the present invention;

[0056] Figure 7 Schematic diagram of the temperature and strength change prediction curve of aluminum-zinc-magnesium alloy under the specific temperature-varying creep aging path of the present invention;

[0057] Figure 8 It is a schematic diagram of the temperature and creep strain change prediction curve of aluminum-zinc-magnesium alloy under the specific temperature-varying creep aging path of the present invention. DETAILED DESCRIPTION

[0058] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0060] Example 1

[0061] The embodiment of the first aspect of the present invention is as follows Figure 1-8As shown, a method for collaboratively predicting the creep-aging properties of aluminum alloys based on temperature history is provided. The sample used in this example is a commercial 7B04 aluminum-zinc-magnesium alloy, wherein the method includes:

[0062] Step 1: Conduct continuous short-term stress relaxation experiments on the uniaxial tensile testing machine with a heating furnace at different temperatures (115°C, 140°C, 165°C) to different stress levels, such as Figure 1 As shown, the process is as follows:

[0063] 1) At a strain rate of 5x10 -4 After uniaxial stretching at a rate of 10 / s to a specified stress level (elastic region: 200MPa, 300MPa, 350MPa) or strain level (plastic region: 1%, 2%, 6%), the crossbeam of the testing machine stops moving and remains for 30s for stress relaxation. The testing machine records the stress reduction data over time. Then, the crossbeam is stretched at a rate of 10 / s to a specified stress level (elastic region: 200MPa, 300MPa, 350MPa) or strain level (plastic region: 1%, 2%, 6%). -2 / s quickly load to the original stress level, then the crossbeam of the testing machine stops moving, and a short-term stress relaxation of 30s is performed again, and the stress change data over time is recorded; the short-term stress relaxation test is repeated three times in sequence;

[0064] 2) The material is then stretched in a quasi-static manner until it reaches the next specified stress level or strain level (200 MPa, 300 MPa, 350 MPa, 1%, 2%, 6%), and the short-term stress relaxation test is repeated three times;

[0065] 3) Repeat the above process until the stretching reaches the maximum specified plastic strain level (6%), and then end the experiment;

[0066] Step 2: Conduct stress relaxation tests on materials at different temperatures (115°C, 140°C, 165°C) on a uniaxial tensile testing machine with a heating furnace. After reaching the specified stress relaxation time (0.5min, 10min, 30min, 2h, 4h), unload the force. Then, refer to the specific plan in step 1 to conduct uniaxial tensile + continuous short-time stress relaxation tests on the material under this state, such as Figure 2 As shown in Figure 3, continuous short-time stress relaxation experimental data of the material after different stress relaxation aging states are obtained.

[0067] Step 3: Analyze and calculate the deformation activation energy Q of the aluminum alloy material at different temperatures and different stress or strain levels. The process is as follows:

[0068] 1) Based on the first stress relaxation curve data of the three short-time stress relaxation curves obtained under different material states, different temperatures, and different deformation levels, the following equation is used for fitting to obtain the internal stress component σ under this material state, this temperature, and deformation level: i :

[0069]

[0070] Where t is the stress relaxation time, σ0 is the initial stress level, σ is the stress data that changes with time, a and b are constants to be fitted; σ i is the internal stress component, which is related to the effective stress σ e Together they form the overall stress, expressed as σ = σ i +σ e ,like Figure 3 As shown;

[0071] 2) Based on the three short-term stress relaxation curves obtained under different material states, different temperatures, and different deformation levels, the strain rate at the end of the first stress relaxation and the strain rate at the beginning of the second stress relaxation after subsequent rapid loading were extracted. The activation volume data V of the material in this state was obtained by fitting using the following equation:

[0072]

[0073] Where T is the temperature, is the strain rate at the beginning of the second stress relaxation, is the strain rate at the end of the first stress relaxation, Δσ is the overall stress change during the first stress relaxation process, and c is the constant to be fitted.

[0074] 3) Based on the internal stress components σ of the material under different states obtained above i , effective internal stress component σ e , and the activation volume V, through Q = (ΔG0-Vσ e ) to obtain the activation energy Q of aluminum alloy under different stress relaxation time, temperature and deformation state, where ΔG0 is the Gibbs free energy of the material at absolute zero, V is the activation volume, σ e is the effective internal stress component; Figure 4 shown.

[0075] Step 4: Establish an activation energy change model considering the influence of different temperatures and deformation levels, as well as an aluminum alloy creep deformation model considering the activation energy change. The process is as follows:

[0076] 1) The activation energy of the material is affected by different temperatures and deformation states. A model of activation energy changing with temperature and deformation state during stress relaxation is established. The activation energy changing with temperature Q0(T) model is:

[0077] Q0(T)=(1-k1T / T m )Q0

[0078] Where T mis the melting temperature of the material, Q0 is the reference activation energy, k1 is the material constant, and is obtained by fitting the activation energy data obtained at different stress relaxation temperatures, as shown in Table 1. The prediction results are as follows: Figure 4 As shown. In addition, the activation energy changes with deformation The model is:

[0079]

[0080] Wherein, k2 is a constant, which is obtained by fitting the activation energy data obtained under different stress relaxation times, see Table 1.

[0081] 2) Establish an aluminum alloy creep deformation material model that takes into account the change in activation energy. By considering the change in activation energy with time, temperature, and deformation level, the creep deformation equation can be used to simulate and predict the change history of various temperatures and stresses. The process is as follows:

[0082] a) Creep deformation model is:

[0083]

[0084] in is the dislocation density, R is the air constant, k3 and k4 are material constants.

[0085] b) In the above creep model, the material constant k0 is set to vary with temperature and is expressed as:

[0086]

[0087] Among them, k 30 is the material constant, Q A is the corresponding activation energy;

[0088] c) The equation for the evolution of dislocation density during creep aging is:

[0089]

[0090] Where k5, k6, and m1 are constants to be fitted.

[0091] Step 5: Establish a prediction model for the precipitation phase evolution and strengthening of aluminum alloys based on the precipitation kinetics of aluminum alloys and the influence of stress coupling, taking into account the influence of temperature. The process is as follows:

[0092] 1) Establish a model related to the nucleation and growth stages of the precipitation phase inside the material during creep aging, including:

[0093] a) Evolution equation of the number of precipitations N during the nucleation process of precipitation phase:

[0094]

[0095]

[0096] Where z is the lattice constant of aluminum, γ is the precipitation specific energy, v is the molar volume of aluminum, k is the Boltzmann constant, c(t) is the solid solubility of the material that changes with aging time, and c e is the solid solubility at equilibrium at the corresponding temperature, which can be obtained from Calculated, where c e0 , Q c Determined by specific material, see Table 2. D eff is the diffusion constant, considering the coupling effect of stress and temperature during creep aging, D eff Corrected to the comprehensive value of the material's self-diffusion energy and the diffusion energy along dislocations, it is defined as:

[0097]

[0098] where Q diff is the material self-diffusion energy, Q pipe is the diffusion energy along the dislocation, D0 and D p0 are diffusion coefficients, which are obtained by referring to the corresponding material manuals and literature, see Table 2.

[0099] b) Equation of equivalent size evolution of precipitate phase during nucleation and growth process, where the equivalent average size r represents the size of precipitate phase inside the material:

[0100]

[0101]

[0102] c) Based on the amount and size of the precipitated phases obtained above, the precipitation volume fraction is calculated as:

[0103]

[0104] d) Further calculate the internal solid solubility of the material:

[0105]

[0106] 2) Establish the material yield strength equation under the influence of the microstructural evolution during the aging process, including:

[0107] a) Precipitation phase strengthening σ p It is mainly determined by the size of the precipitated phase, and its equation is:

[0108]

[0109] Where M is the Taylor factor, which is 2; b is the Burgers vector, which is 0.28 nm; L is the average spacing of the precipitated phase, which is expressed as F(r) is the average strength of the second phase of the precipitated phase, and the calculation equation is:

[0110]

[0111] Where k and β are constants to be determined, and their relationship with the critical radius is: r c For specific aluminum alloys, the relevant literature was consulted and the results are shown in Table 2.

[0112] b) The solid solution strengthening equation is, σ ss =C ss c(t) 2 / 3 , where C ss is the material constant, σ ss is the degree of solid solution strengthening;

[0113] c) The dislocation strengthening equation is, Where α is the material constant, σ dis is the dislocation strengthening component, G is the material shear modulus, and ρ is the dislocation density;

[0114] d) The calculation equation for the overall yield strength of the material is:

[0115] Step 6: Carry out stress relaxation experiments at different temperatures (115℃, 140℃, 155℃, 165℃, 175℃) and different stress levels (200MPa, 250MPa, 300MPa, 370MPa, 390MPa, 410MPa) on a uniaxial tensile testing machine with a heating furnace to obtain the stress relaxation curves and performance evolution curves of the materials at the corresponding temperatures and stress levels, see Figure 5 and Figure 6 .

[0116] Step 7: Based on the experimental data, carry out the fitting calibration and verification of the parameters of the above equations. Based on the stress relaxation and yield strength evolution data at three different stress levels (250MP, 300MPa, 370MPa), the particle swarm optimization algorithm is used to fit the creep and yield strength equations mentioned above, and the non-temperature-related material constants are calibrated to obtain them, as shown in Table 1; further based on the stress relaxation and yield strength evolution data at three different temperatures (115℃, 140℃, 165℃) with the same stress level (250MPa), the temperature-related material constants of the above materials are fitted and calibrated to obtain all the required material constants. The obtained material constants are verified using other temperature and stress level data to verify the validity of the relevant material constants and equations. The prediction results are as follows: Figure 5 and Figure 6 shown.

[0117] By using the above-mentioned fitted and verified material model and material constants, the creep deformation / stress relaxation and aging strengthening evolution of the material under different temperature histories can be simulated and predicted, such as Figure 7 and 8 shown.

[0118] Table 1: Material constants obtained by fitting

[0119] <![CDATA[k1(-)]]> <![CDATA[k2(-)]]> <![CDATA[k 30 (s -1 )]]> <![CDATA[k4(MPa -1 )]]> <![CDATA[k5(s -1 )]]> <![CDATA[k6(s -1 )]]> <![CDATA[Q A (kJ / mol)]]> <![CDATA[m1(-)]]> 0.71 0.91 0.04 0.028 220 0.02 120.70 1.02 <![CDATA[T m (K)]]> <![CDATA[Q0(kJ / mol)]]> <![CDATA[γ(J / m 2 )]]> <![CDATA[D0(m 2 / s)]]> <![CDATA[D p0 (m 2 / s)]]> <![CDATA[C ss (MPs)]]> β(-) 930 234.70 0.15 <![CDATA[0.6*10 -8 ]]> <![CDATA[1*10 -8 ]]> 840 0.36

[0120] Table 2: Material constants obtained from the data and references based on the properties of 7B04 aluminum-zinc-magnesium material

[0121]

[0122] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A method for collaborative prediction of creep-aging properties of aluminum alloys considering temperature history, characterized in that: The steps include: S1, on a uniaxial tensile testing machine with a heating furnace, uniaxial tension-continuous short-time stress relaxation tests are performed on the aluminum alloy material at different temperatures to obtain short-time stress relaxation curve data of the material in the initial state at the corresponding temperature; S2, on a uniaxial tensile testing machine with a heating furnace, at different temperatures, a stress relaxation test and a uniaxial tensile-continuous short-time stress relaxation test are performed on the aluminum alloy material to obtain continuous short-time stress relaxation experimental data of the aluminum alloy material after different stress relaxation aging states at the corresponding temperature; S3, based on the test data obtained in S1 and S2, analyze and calculate the deformation activation energy Q of the aluminum alloy material at different temperatures, different stress levels or strain levels, and different stress relaxation times; S4, establish an activation energy variation model considering temperature, stress and time, as well as an aluminum alloy creep deformation model with activation energy as a variable; S5. Establish an aluminum alloy precipitation phase evolution model and aluminum alloy strengthening prediction model based on aluminum alloy precipitation kinetics and stress coupling effects taking into account temperature effects; S6, creep aging and stress relaxation tests are carried out at different temperatures and stress levels on a uniaxial tensile testing machine with a heating furnace to obtain creep curves, stress relaxation curves, and performance evolution curves of the aluminum alloy material at corresponding temperatures and stress levels; S7, based on the experimental data obtained in S6, perform parameter fitting calibration and verification on the aluminum alloy strengthening prediction model in S5; S8, predicting the creep aging properties of the aluminum alloy using the aluminum alloy creep deformation model and strengthening prediction model; The steps of the uniaxial stretching-continuous short-time stress relaxation test specifically include: S101, using a quasi-static method, stretches the material to a specified deformation level using a uniaxial tensile testing machine, stops stretching, maintains stress relaxation for a short period of time, and records the stress reduction data over time; S102, using a rapid loading method, stretching the material using a uniaxial tensile testing machine to the stress level recorded by the uniaxial tensile testing machine when the stretching was stopped in S101, stopping the stretching, maintaining stress relaxation for a short period of time, and recording the stress change data over time; S103, repeat steps S101-S102 twice; S104, selecting the next deformation level, repeating steps S101-S103 until the stretching reaches the highest strain level, and ending the experiment; The deformation level includes stress level and strain level. The stress level in the elastic region is set to at least three levels from low to high, and the strain level in the plastic region is set to at least three levels from low to high. The short time is 10s-30s. The steps of S3 specifically include: S301, based on the experimental data in S2, use the following method to fit to obtain the internal stress component σ under this material state, this temperature and deformation level i : Where t is the stress relaxation time, σ0 is the initial stress level, σ is the stress data that changes with time, a and b are constants to be fitted, and σ i is the internal stress component and is related to the effective stress σ e Together they form the overall stress, expressed as σ = σ i +σ e ; S302, extracting the strain rate at the end of the first stress relaxation in S101 and the strain rate at the beginning of the second stress relaxation in S102, and fitting the activation volume data V of the material in this state using the following equation: Where T is the temperature, is the strain rate at the beginning of the second stress relaxation, is the strain rate at the end of the first stress relaxation, Δσ is the overall stress change during the first stress relaxation process, and c is the constant to be fitted; S303, based on the internal stress component σ in S302 i , effective internal stress component σ e and activation volume V, the activation energy Q of the material under different stress relaxation times, temperatures and deformation states is calculated by the following formula: Q=(ΔG0-Vσ e ); Where ΔG0 is the Gibbs free energy of the material at absolute zero, V is the activation volume, and σ e is the effective internal stress component; The step S4 specifically includes: S401, establish a model of activation energy changes with temperature and deformation state during stress relaxation; S402, establishing an aluminum alloy creep deformation material model including variables of activation energy change, and realizing simulation prediction of various temperature and deformation change processes of the aluminum alloy creep deformation model by changing the activation energy with time, temperature and deformation level in step S401.

2. The method for collaborative prediction of creep-aging properties of aluminum alloys considering temperature history according to claim 1, characterized in that: The step S401 specifically includes: a. The activation energy changes with temperature Q0(T) model is: Q0(T)=(1-k1T / T m )Q0; Among them, T m is the melting temperature of the material, T is the creep aging temperature, Q0 is the reference activation energy, and k1 is the material constant; b. Activation energy changes with deformation The model is: Where k2 is a constant, is the plastic strain rate.

3. The method for collaboratively predicting creep-aging properties of aluminum alloys considering temperature history according to claim 1, characterized in that: The step S402 specifically includes: a. Creep deformation model is: in, is the dislocation density, R is the air constant, k3, k4 are material constants, is the creep strain rate; b. The material constant k3 is set to vary with temperature, specifically according to the following formula: Among them, k 30 is the material constant, Q A is the corresponding activation energy; c. The evolution equation of dislocation density is as follows: Among them, k5, k6 and m1 are the material constants to be fitted.

4. The method for collaboratively predicting creep-aging properties of aluminum alloys considering temperature history according to claim 1, characterized in that: The step S5 specifically includes: S501, simplifying the precipitation phase evolution model during creep aging forming, and only considering the nucleation and growth stages of the precipitation phase inside the material during creep aging; S502, establishing a material yield strength equation under the influence of the microstructure evolution during the aging process.

5. The method for collaboratively predicting creep-aging properties of aluminum alloys considering temperature history according to claim 4, characterized in that: The step S501 specifically includes: a. The evolution equation of the number of precipitations N during the precipitation phase nucleation process is specifically the following formula: Where z is the lattice constant of aluminum, γ is the precipitation specific energy, v is the molar volume of aluminum, k is the Boltzmann constant, c(t) is the solid solubility of the material that changes with aging time, and c e is the solid solubility at equilibrium temperature, ΔG is the Gibbs free energy, and D eff is the diffusion constant; b. The equivalent size evolution equation of the precipitate phase during the nucleation and growth process of the precipitate phase is represented by the equivalent average size r, which is specifically the following formula: Among them, c i (r) is the solid solubility of the material that changes with the equivalent average size r of the precipitated phase; c. Based on the number and size of the precipitated phases obtained above, the precipitation volume fraction is calculated using the following formula: Where f is the precipitation volume fraction, N is the total number of precipitated phases per unit volume; d. The solid solubility inside the material is calculated using the following formula: Where c0 is the initial solid solubility of the solid solution atoms in the aluminum alloy.

6. The method for collaboratively predicting creep-aging and shape-properties of aluminum alloys considering temperature history according to claim 4, characterized in that: The step S502 specifically includes: a. Precipitation phase strengthening σ p It is mainly determined by the size of the precipitated phase, and the specific equation is: Where M is the Taylor factor, which is 2, b is the Burgers vector, which is 0.28 nm, L is the average spacing between precipitates, and F(r) is the average intensity of the second phase of the precipitates. b. Solid solution strengthening is determined by solid solubility. The equation for solid solution strengthening is: σ ss =C ss c(t) 2 / 3 ; Among them, C ss is the material constant, σ ss is the degree of solid solution strengthening; c. Dislocation strengthening is determined by dislocation density. The equation for dislocation strengthening is: Among them, α is the material constant, σ dis is the dislocation strengthening component, G is the material shear modulus, and ρ is the dislocation density; d. The calculation equation for the overall yield strength of the material is:

7. The method for collaboratively predicting creep-aging and shape-properties of aluminum alloys considering temperature history according to claim 5, characterized in that: D eff It is defined as the following formula: Among them, Q diff is the material self-diffusion energy, Q pipe is the diffusion energy along the dislocation, D0 and D p0 are diffusion coefficients, which are obtained by referring to corresponding material manuals and literature.

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