Method for predicting deformation behavior of alloy treated by different quenching processes

Abstract

The invention belongs to the field of numerical simulation of heat treatment of aluminum alloy large forgings, and discloses a method for predicting the thermal deformation behavior of an alloy treated by different quenching processes. According to the method, an Arrhenius model commonly used for deformation behavior prediction in quenching simulation of aluminum alloy forgings is improved, and a modified Arrhenius constitutive model considering the quenching cooling process and deformation condition influence is provided. According to the corrected model, the thermal deformation critical activation energy is assumed to be a function influenced by the quenching cooling transformation degree, the deformation temperature and the rate according to the real situation. A quenching factor is introduced as an intermediate quantity to establish an activation energy evolution function, description of evolution characteristics of critical activation energy required by deformation of the alloy after different quenching cooling processes is realized, and finally accurate prediction of thermal deformation behaviors of the aluminum alloy under different quenching cooling processes and different deformation conditions is realized. Therefore, the prediction error of the forging heat treatment residual stress is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of alloy forging, and in particular to a method for predicting the deformation behavior of an alloy after being treated with different quenching processes. Background Art

[0002] Aluminum alloy forgings are widely used in aerospace, automobile manufacturing and other fields due to their good comprehensive mechanical properties. In order to further improve their mechanical properties, aluminum alloy forgings usually need to undergo solid solution quenching treatment after forging. Solid solution quenching can not only improve the hardness and strength of the material, but also improve its corrosion resistance. However, during the quenching process, due to the existence of non-uniform plastic deformation, complex residual stress distribution often occurs inside the aluminum alloy forgings. If these residual stresses are not properly controlled, they may lead to problems such as unstable part dimensions and shortened fatigue life. Therefore, how to accurately predict and control residual stress has become one of the key technologies to improve the quality of aluminum alloy forgings.

[0003] Finite element simulation is a commonly used residual stress prediction method in production today. A high-precision constitutive model of aluminum alloy is the prerequisite for accurate simulation of residual stress. However, most of the current constitutive models of aluminum alloys are established and calibrated based on a fixed heat treatment system. Although this method can reflect the basic properties of the material to a certain extent, it has many shortcomings in practical applications: First, due to the obvious temperature gradient in the quenching process after forging, different parts of the material will experience different cooling paths, which will lead to changes in the microstructure of the material. This change directly affects the mechanical behavior of the material, making it impossible for the constitutive model established under a single heat treatment system to accurately reflect the actual working conditions. For example, when the cooling rate is high, a dispersed second phase will precipitate in the aluminum alloy crystal, which will achieve good pinning of the dislocation movement and enhance the strength of the material; on the contrary, a slower cooling rate may cause a coarse second phase to precipitate at the grain boundary, weakening the strengthening effect. Secondly, the existing constitutive models usually ignore the effect of cooling rate on the deformation behavior of the material. This means that even under the same loading conditions, due to the difference in cooling rate during the actual quenching process, the actual deformation behavior of the alloy will be different, resulting in the distribution of residual stress inconsistent with the results predicted by the model.

[0004] In summary, the existing constitutive model of aluminum alloy has great limitations and cannot accurately simulate the plastic deformation behavior of different parts after cooling treatment. When the existing model is used to numerically simulate the heat treatment process of forgings, there is a large error between the residual stress simulation results and the true value, which makes it difficult to guide actual production. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the purpose of the present application is to provide a method for predicting the deformation behavior of alloys after different quenching processes, so as to solve the problem that the constitutive model of aluminum alloys in the prior art is difficult to accurately predict the residual stress conditions of alloy forgings after heat treatment.

[0006] In order to solve the above technical problems, this application adopts the following technical solutions:

[0007] A method for predicting the deformation behavior of an alloy after being treated with different quenching processes comprises the following steps:

[0008] Step 1: Calibrate the TTP curve of the alloy material through isothermal quenching experiment, and calculate the critical transformation time required for the alloy to complete the precipitation transformation at any cooling rate and any temperature;

[0009] Step 2: The equivalent transformation time at each moment is calculated by using the microelement method for different quenching processes, and the degree of desolvation transformation of different quenching processes is obtained by cumulative superposition calculation;

[0010] Step 3: subjecting the alloy to solid solution quenching treatment and isothermal compression treatment to obtain true stress-strain curves under different cooling rates and different deformation conditions;

[0011] Step 4: For the same strain condition, perform multiple linear fittings on the true stress-strain curves at different cooling rates and strain rates to obtain the constitutive model parameter values ​​under specific cooling rates and deformation conditions;

[0012] Step 5: Perform interactive polynomial fitting on the parameter values ​​under different quenching cooling rate-deformation conditions to obtain the interactive relationship between quenching transformation degree, deformation temperature and deformation rate and critical activation energy of thermal deformation, and obtain the mathematical relationship corresponding to different quenching cooling process-transformation degree-deformation conditions-constitutive model parameter values ​​under specific strain conditions;

[0013] Step 6: Repeat steps 4-5 for different strain conditions to determine the mathematical relationship between different quenching cooling processes, transformation degree, deformation conditions and constitutive model parameter values ​​under different strain conditions, and use interactive polynomials to compensate for the fluctuation of model parameter values ​​in the full strain range;

[0014] Step 7: Establish the Arrhenius model based on the mathematical relationship obtained in step 6 to calculate the flow stress under any quenching cooling condition.

[0015] Preferably, in step 1, the critical transition time is calculated by the following steps:

[0016]

[0017] Wherein, C(T) is the shortest time required for a supersaturated solid solution to complete the precipitation reaction at a specific temperature, k1 is the natural logarithm of the content without precipitation reaction, k2 is a constant related to the nucleation density, k3 is a constant related to the unit nucleation energy, k4 is related to the second phase precipitation temperature and vacancy concentration, k5 is related to the diffusion activation energy, R is the gas constant, and the value of R is 8.314 J / (mol·K), and T is the thermodynamic temperature; k1, k2, k3, k4, and k5 are obtained by measuring the hardness evolution of the alloy after the graded isothermal quenching experiment.

[0018] Preferably, in step 2, the degree of desolventization transformation is obtained by the following steps:

[0019]

[0020] In the formula, ε represents the residual supersaturated solid solution content without precipitation reaction, τ represents the equivalent reaction time of the whole quenching process, Δt represents a small time interval in the quenching process, and t c (T) represents the reaction time required for the complete desolvation reaction to occur at this temperature, calculated by the method of step 1 at this small time interval.

[0021] Preferably, in step 4, based on the true stress-strain curve, Linear fitting, the parameter M in the flow stress model under various quenching and deformation conditions C3 Carry out parameter calibration, and then carry out the calibration of the parameters 1000 / T-ln[sinh(M C3 σ)] and ln[sinh(M C3 The parameter values ​​at different cooling rates and strain rates were obtained by linear fitting of the [σ)]-LnZ.

[0022] Preferably, the specific calculation steps of the parameter values ​​at different cooling rates and different strain rates are as follows:

[0023] S1: Based on the true stress-strain curve, calculate and Lnσ, and draw the corresponding scatter plot; perform linear regression on the scatter plot, and the slope of the scatter plot regression equation under the same conditions is the parameter M C3 Numeric value;

[0024] S2: M under each condition C3 The value is calculated according to the steps of S1 to obtain n under different conditions s value; specifically: M under each condition C3 Substitute the values ​​into the formula to calculate the different conditions ln[sinh(M C3σ)] and plot the corresponding and ln[sinh(M C3 σ)], using the and ln[sinh(M C3 The slope obtained by linear regression of the scatter plot of [σ)] is the n under different conditions. s value;

[0025] S3: 1000 / T-ln[sinh(M C3 The scatter plot of [σ)] is drawn and linear regression is performed. The slope of the straight line is the S value under each condition, and according to the calculation formula:

[0026] Q=1000R gas n s S, where R gas represents the gas constant, which is 8.314 J / (mol·K);

[0027] Calculate the thermal activation energy values ​​in the constitutive model under various conditions;

[0028] S4: Plot ln[sinh(M C3 The intercept of the linear regression is set as F. The lnA value under different conditions is obtained by the following calculation formula, and the parameter A value in the constitutive model is determined by exponential function calculation:

[0029]

[0030] Preferably, in step 5:

[0031] (1) Draw a scatter plot to describe the temperature T, the degree of desolvation D calculated in step 2, and the M under different conditions calculated in step 4. C3 The correlation between the values ​​is calculated by using the Levenberg-Marquardt algorithm. C (T,D)=a1+b1T+c1D+d1T 2 +e1D 2 +f1TD is fitted, and the parameter a1-f1 is calibrated to obtain the M in the constitutive model. C The correlation between deformation temperature and quenching process;

[0032] (2) The temperature T, desolvation degree D and different n s The relationship between values ​​is expressed in n s =a2+b2T+c2D+d2T 2 +e2D 2+f2TD relationship is fitted, and the parameter a2-f2 is calibrated to obtain the n in the constitutive model. s Correlation between deformation conditions and quenching process;

[0033] (3) The strain rate The relationship between the degree of desolvation D and the values ​​of different intermediate functions S is shown in The relationship is fitted, the parameters a3-f3 are calibrated, the correlation between the S value and the deformation conditions and the quenching process in the constitutive model is obtained, and the thermal activation energy evolves with the quenching and deformation conditions according to the above-mentioned;

[0034] (4) The strain rate The relationship between the degree of desolvation D and the values ​​of different intermediate functions F is shown in The relationship is fitted and the parameters a4-f4 are calibrated to obtain the correlation between the F value in the constitutive model and the deformation conditions and quenching process, and the evolution law of the constitutive model parameter A value is calculated according to step 4.

[0035] Preferably, in step 6, by repeating the constitutive model parameters obtained in steps 4 to 5 under the same quenching process but different strains, the constitutive model parameter values ​​and strains are fitted according to the following polynomial to obtain the evolution law of the parameters along the deformation process:

[0036]

[0037] Where: B0~B5, C0~C5, D0~D5 and E0~E5 are the parameters Q, n under the conditions of the same quenching transformation degree, deformation rate and temperature. s , lnA and M C3 The coefficients of the fifth-order polynomial fitting with the dependent variable ε.

[0038] Preferably, in step 7, an improved Arrhenius model as shown in the following formula is obtained, and the quenching process and deformation temperature, strain and strain rate are substituted into the modified model to calculate the flow stress value of the material under any quenching and deformation conditions:

[0039]

[0040] Where σ is the calculated flow stress value, in MPa.

[0041] Compared with the prior art, this application has the following beneficial effects:

[0042] 1. This application calibrates the TTP (Time-Temperature-Precipitation) curve of the alloy material through an isothermal quenching experiment, so that the critical transformation time required for the alloy material to complete the precipitation transformation at any cooling rate and any temperature can be accurately calculated. This step lays the foundation for the subsequent microstructure evolution analysis. The establishment of the TTP curve not only provides the phase change kinetics data of the alloy material under different cooling conditions, but also provides a theoretical basis for understanding the microstructure changes of the alloy material during the quenching process. Through these data, the phase change behavior of the alloy material at different cooling rates can be predicted and controlled, thereby optimizing the quenching process.

[0043] 2. This application uses the microelement method to calculate the equivalent transformation time at each moment for different quenching processes, and obtains the degree of precipitation transformation of different quenching processes by cumulative superposition calculation. This step can accurately describe the microstructural evolution of the alloy material during the quenching process, especially when the cooling rate and temperature change greatly. Through the cumulative superposition of equivalent transformation time, the influence of different quenching processes on the microstructure of the alloy material can be quantified, providing reliable microstructural information for subsequent mechanical property analysis.

[0044] 3. The present application can obtain true stress-strain curves under different cooling rates and different deformation conditions by subjecting the alloy material to solution quenching treatment and isothermal compression treatment. These curves not only reflect the mechanical properties of the alloy material under different quenching conditions, but also reveal the influence of microstructural changes on the deformation behavior of the alloy material. Through multiple linear fits of these curves, the constitutive model parameter values ​​under specific cooling rates and deformation conditions can be extracted, and these parameter values ​​are the basis for establishing the constitutive model of the alloy material.

[0045] 4. This application also establishes the interactive relationship between the quenching transformation degree, deformation temperature and deformation rate and the critical activation energy of thermal deformation by performing interactive polynomial fitting on the parameter values ​​under different quenching cooling rate-deformation conditions. The establishment of this relationship can not only describe the microstructural evolution of the alloy material during the quenching process, but also predict the influence of different quenching conditions on the deformation behavior of the alloy material. In this way, the degree of plastic deformation of the alloy during the quenching process can be simulated more accurately, thereby providing a scientific basis for optimizing the heat treatment process.

[0046] 5. This application calculates the flow stress under any quenching and cooling conditions by establishing the Arrhenius model. This model not only takes into account the effect of the quenching and cooling process on the microstructure of the alloy, but also combines the constitutive relationship of the material, so that it can more accurately predict the mechanical properties of the alloy under different quenching conditions. Applying this model to finite element simulation software can correct the effect of the previous quenching process on the critical activation energy required for material deformation, and more accurately simulate the degree of plastic deformation of each region during the quenching process, thereby greatly improving the prediction accuracy of residual stress during the heat treatment process of forgings.

[0047] 6. The method described in this application comprehensively considers the influence of quenching and cooling process on the microstructure and mechanical properties of the alloy through systematic experiments and mathematical modeling, and provides a more accurate prediction and control method for the heat treatment process of large forgings, which not only improves the prediction accuracy of material properties, but also provides a scientific basis for optimizing the heat treatment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The present invention is a flowchart of the method described in the present application.

[0049] Figure 2 It is a comparison chart of the predicted effect of the embodiment of the present invention versus the comparative example; wherein, a is the flow stress comparison result of the alloy obtained by inverse calculation of the embodiment and the comparative example respectively and actually obtained by thermal simulation experiment after solutionizing at a quenching cooling rate of 5°C / s and deformation process with a strain rate of 0.001; b is the flow stress comparison result of the alloy obtained by inverse calculation of the two models respectively and actually obtained by thermal simulation experiment after solutionizing at a quenching cooling rate of 15°C / s and deformation process with a strain rate of 0.001; c is the flow stress comparison result of the alloy obtained by inverse calculation of the two models respectively and actually obtained by thermal simulation experiment after solutionizing at a quenching cooling rate of 5°C / s and deformation process with a strain rate of 0.1; d is the flow stress comparison result of the alloy obtained by inverse calculation of the two models respectively and actually obtained by thermal simulation experiment after solutionizing at a quenching cooling rate of 5°C / s and deformation process with a strain rate of 0.1. DETAILED DESCRIPTION

[0050] This application will combine the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on this application belong to the scope of protection of this application.

[0051] Unless otherwise indicated in specific cases in this application, the numerical ranges listed herein include the upper and lower limits, as well as all integers and fractions within the range, and are not limited to the specific values ​​listed when defining the range.

[0052] 1. A method to predict the deformation behavior of alloys after different quenching processes

[0053] Step 1: Calibrate the TTP curve of the alloy material through isothermal quenching experiment, and calculate the critical transformation time required for the alloy to complete the precipitation transformation at any cooling rate and any temperature;

[0054] Step 2: The equivalent transformation time at each moment is calculated by using the microelement method for different quenching processes, and the degree of desolvation transformation of different quenching processes is obtained by cumulative superposition calculation;

[0055] Step 3: subjecting the alloy to solid solution quenching treatment and isothermal compression treatment to obtain true stress-strain curves under different cooling rates and different deformation conditions;

[0056] Step 4: For the same strain condition, perform multiple linear fittings on the true stress-strain curves at different cooling rates and strain rates to obtain the constitutive model parameter values ​​under specific cooling rates and deformation conditions;

[0057] Step 5: Perform interactive polynomial fitting on the parameter values ​​under different quenching cooling rate-deformation conditions to obtain the interactive relationship between quenching transformation degree, deformation temperature and deformation rate and critical activation energy of thermal deformation, and obtain the mathematical relationship corresponding to different quenching cooling process-transformation degree-deformation conditions-constitutive model parameter values ​​under specific strain conditions;

[0058] Step 6: Repeat steps 4-5 for different strain conditions to determine the mathematical relationship between different quenching cooling processes, transformation degree, deformation conditions and constitutive model parameter values ​​under different strain conditions, and use interactive polynomials to compensate for the fluctuation of model parameter values ​​in the full strain range;

[0059] Step 7: Establish the Arrhenius model based on the mathematical relationship obtained in step 6 to calculate the flow stress under any quenching cooling condition.

[0060] When studying the traditional model, the present application found that the traditional Arrhenius model believes that the parameters in the model are only affected by strain. Therefore, the solution is to sequentially perform the stress-strain curve obtained by the hot compression test under a specific condition. 1000 / T-ln[sinh(M C3 σ)] and ln[sinh(M C3σ)]-LnZ linear fitting is performed, and the fitting results are averaged to determine the parameter values. However, in practical applications, different quenching cooling rates will lead to significant changes in the microstructure of the alloy, such as the size and distribution of the precipitated phase, grain size, etc. These changes in microstructure directly affect the plastic deformation behavior of the material, and thus affect the values ​​of the model parameters. Therefore, a single parameter value cannot accurately describe the material behavior under different quenching cooling rates. At the same time, different deformation conditions (such as deformation temperature and strain rate) will also significantly affect the plastic deformation behavior of the alloy. For example, higher deformation temperatures and lower strain rates usually lead to softening of the material, while lower deformation temperatures and higher strain rates may lead to hardening of the material. These changes will also affect the values ​​of the model parameters. Moreover, in the actual deformation process, the model parameters are not constant, but are dynamically adjusted with changes in deformation conditions and microstructure. The traditional linear fitting method cannot capture these dynamic changes, which in turn causes fluctuations in the parameters in the model, and ultimately reduces the prediction accuracy of the model. To this end, the present application conceives to improve the model, using the microelement method to calculate the equivalent transformation time at each moment for different quenching processes, and obtains the degree of precipitation transformation of different quenching processes by cumulative superposition calculation. In this way, the microstructure evolution process of the alloy during the quenching process can be accurately described, especially when the cooling rate and temperature change greatly. Through the cumulative superposition of the equivalent transformation time, the influence of different quenching processes on the microstructure of the alloy is quantified, and reliable microstructure information is provided for the subsequent mechanical property analysis. Then, the true stress-strain curves under different cooling rates and different deformation conditions are obtained by solution quenching and isothermal compression treatment of the alloy. These curves not only reflect the mechanical properties of the alloy under different quenching conditions, but also reveal the influence of microstructure changes on the deformation behavior of the material. Through multiple linear fittings of these curves, the constitutive model parameter values ​​under specific cooling rates and deformation conditions are extracted. These parameter values ​​are the basis for establishing the material constitutive model, and through the integration of multi-condition experimental data, the mechanical behavior of the alloy under different conditions can be more comprehensively described. Subsequently, by performing interactive polynomial fitting on the parameter values ​​under different quenching cooling rate-deformation conditions, the interactive relationship between the quenching transformation degree, deformation temperature and deformation rate and the critical activation energy of thermal deformation was established. The establishment of this relationship can not only describe the microstructural evolution of the alloy during the quenching process, but also predict the influence of different quenching conditions on the deformation behavior of the material. In this way, the degree of plastic deformation of the alloy during the quenching process can be simulated more accurately, thus providing a scientific basis for optimizing the heat treatment process. Finally, by establishing the Arrhenius model, the flow stress under any quenching cooling condition is calculated. This model not only takes into account the influence of the quenching cooling process on the microstructure of the alloy, but also combines the constitutive relationship of the material, so that the mechanical properties of the alloy under different quenching conditions can be more accurately predicted.Applying this model to finite element simulation software can correct the influence of the previous quenching process on the critical activation energy required for material deformation, and more accurately simulate the degree of plastic deformation of each region during the quenching process, thereby greatly improving the prediction accuracy of residual stress in the heat treatment process of forgings.

[0061] In some embodiments of the present application, in step 1, the TTP (Time-Temperature-Precipitation) curve of the alloy material is calibrated by an isothermal quenching experiment, so that the critical transformation time required for the alloy material to complete the precipitation transformation at any cooling rate and any temperature can be accurately calculated. This step lays the foundation for the subsequent microstructure evolution analysis. The TTP curve of the alloy material is calibrated by an isothermal quenching experiment, and the critical transformation time required for the alloy to complete the precipitation transformation at any cooling rate and any temperature is calculated, as shown in the following formula:

[0062]

[0063] In formula (1), C(T) is the shortest time required for a supersaturated solid solution to complete the precipitation reaction at a specific temperature, k1 is the natural logarithm of the content without precipitation reaction, k2 is a constant related to the nucleation density, k3 is a constant related to the unit nucleation energy, k4 is related to the second phase precipitation temperature and vacancy concentration, k5 is related to the diffusion activation energy, R is a gas constant, which is 8.314 J / (mol·K), and T is the thermodynamic temperature corresponding to the quenching transformation temperature (in Kelvin, K). Among the above parameters, k1, k2, k3, k4, and k5 are obtained by measuring the hardness evolution of the alloy after the graded isothermal quenching experiment. The implementation process is as follows: First, the alloy to be tested is cut into small samples of a certain specification, the sample is placed in a heating furnace for a certain period of solid solution treatment, and then quickly transferred to a salt bath furnace with different lower temperatures for isothermal insulation for a period of time, and finally the sample is placed in clean water to cool to room temperature. The cooled sample is polished and Vickers hardness tested and a curve is drawn. The transformation time at different temperatures can be determined by interpolating the hardness change curves with different degrees of decline. The values ​​of parameters k1-k5 in the model can be determined by taking the insulation temperature and time corresponding to the different degrees of decline in peak hardness. Among them, the calculation relationship between the degree of hardness decline and the degree of precipitation transformation during the quenching process can be calculated by the following formula:

[0064]

[0065] In the formula, ψ represents the degree of transformation of the quenching precipitation process, H Max Indicates the hardness obtained after direct water quenching from the solution temperature, H measuredRepresents the hardness measured after different graded quenching experiments. According to ψ, the value of K1 can be determined first, that is, K1=1=ln(1-ψ). At the same time, according to formula (2), the corresponding hardness of different degrees of transformation is calculated, and the time corresponding to 5% transformation at each temperature is statistically calculated in combination with real experiments. Substitute the corresponding relationship between transformation time-transformation degree and isothermal transformation temperature into formula (1), use 1stopt software to perform nonlinear fitting on the obtained data, obtain the values ​​of k2-k5, and complete the construction of TTP model.

[0066] In some embodiments of the present application, in step 2, the equivalent transformation time at each moment in the quenching process is calculated by the microelement method, and the degree of desolvation transformation of different quenching processes is obtained by cumulative superposition calculation. The calculation method is shown in the following formula:

[0067]

[0068] In formula (3), ε represents the residual supersaturated solid solution content without precipitation reaction, the degree of precipitation D = 1-ε, τ represents the equivalent reaction time of the whole quenching process, Δt represents a small time interval of the quenching process, t c (T) represents the reaction time required for the complete desolventizing reaction to occur at this temperature calculated by formula (1) at this tiny time interval. Δt1 represents the first infinitesimal time interval at the beginning of quenching, Δt2 represents the second time interval immediately following the quenching process, and Δt f-1 represents the last infinitesimal time interval near the completion of quenching, t c1 The alloy calculated by formula (1) is the reaction time required for the alloy to completely dissolve and precipitate from the supersaturated solid solution at the corresponding temperature at the time interval Δt1, t c2 represents the reaction time required for complete precipitation of the supersaturated solid solution at the corresponding temperature at the time interval Δt2 calculated by formula (1), t cf-1 It means that the alloy at Δt calculated by formula (1) f-1 The time corresponding to the temperature corresponding to the time interval is the time corresponding to the complete precipitation reaction of the supersaturated solid solution. Λ represents the ratio of each infinitesimal time interval in the quenching process to the critical reaction time required at the temperature corresponding to the interval. The actual quenching process is divided into micro-elements, and each time interval after the micro-element division and the time required for the corresponding alloy temperature at the infinitesimal interval calculated by formula (1) are input into calculation formula (3) to calculate the equivalent quenching reaction time of each quenching process, and further calculate the degree of precipitation reaction D of each quenching process, which is used for parameter calibration in the subsequent calculation formula (4).

[0069] In some embodiments of the present application, in step 3, the alloy is subjected to solution quenching and isothermal compression treatment on the Gleeble-3500 platform to obtain true stress-strain curves under different cooling rates and different deformation conditions. The heating rate, holding time, cooling rate, subsequent compression rate, and deformation amount are set on the experimental operation platform, and the experimental platform system will automatically complete the solution, quenching, and hot compression experiments according to the preset process parameter route. During the hot compression process, the Gleeble-3500 platform will automatically record the stress-strain curve of the alloy during the deformation process for the calibration of the constitutive model parameters in the next step.

[0070] In some embodiments of the present application, in step 4, the present application calibrates the parameters under each quenching and deformation condition separately, and first performs the true stress-strain curve obtained based on step 3. Linear fitting, the parameter M in the flow stress model under various quenching and deformation conditions C3 Carry out parameter calibration, and then carry out the calibration of the parameters 1000 / T-ln[sinh(M C3 σ)] and ln[sinh(M C3 σ)]-LnZ linear fitting is performed to obtain parameter values ​​at different cooling speeds and different strain rates, thereby improving the prediction accuracy of the model. The specific calculation steps of the parameter values ​​at different cooling speeds and different strain rates are as follows:

[0071] S1: Based on the true stress-strain curve, calculate The values ​​of Lnσ are pasted into the same worksheet of Origin software and the corresponding scatter plot is drawn; the scatter plot is linearly regressed, and the slope of the scatter plot regression equation under the same conditions is the parameter M. C3 Numeric value;

[0072] S2: M under each condition C3 Substitute the value into the calculation formula: ln[sinh(M C3 σ)], the calculation formula ln[sinh(M C3 σ)] and plot the corresponding and ln[sinh(M C3 σ)] scatter plot, using software to and ln[sinh(M C3 σ)] scatter plot is linearly regressed, and the slope obtained is n under different conditions s value;

[0073] S3: 1000 / T-ln[sinh(M C3The scatter plot of [σ)] is drawn and linear regression is performed. The slope of the straight line is the S value under each condition, and according to the calculation formula:

[0074] Q=1000R gas n s S, (4)

[0075] In the formula, R gas represents the gas constant, which is 8.314 J / (mol·K);

[0076] Calculate the thermal activation energy value Q in the constitutive model under various conditions;

[0077] S4: Plot ln[sinh(M C3 The intercept of the linear regression is set as F. The lnA value under different conditions is obtained by the following calculation formula, and the parameter A value in the constitutive model is determined by exponential function calculation:

[0078]

[0079] In some embodiments of the present application, in step 5:

[0080] (1) Draw a scatter plot to describe the temperature T, the degree of desolvation D calculated in step 2, and the M under different conditions calculated in step 4. C3 The correlation between the values ​​is calculated by using the Levenberg-Marquardt algorithm, and the temperature T, the degree of desolvation D calculated in step 2, and the M under different conditions calculated in step 4 are calculated. c3 Substitute the following calculation formula:

[0081] M C (T,D)=a1+b1T+c1D+d1T 2 +e1D 2 +f1TD is fitted to obtain T, D and M c3 The relationship between these three variables is used to calibrate the parameters a1-f1 to obtain the M in the constitutive model. C The correlation between deformation temperature and quenching process;

[0082] (2) The temperature T, desolvation degree D and different n s Substitute the value into the following calculation formula:

[0083] n s =a2+b2T+c2D+d2T 2 +e2D 2 +f2TD is fitted, and the parameter a2-f2 is calibrated to obtain the n in the constitutive model. s Correlation between deformation conditions and quenching process;

[0084] (3) The strain rate The desolvation degree D and different intermediate function S values ​​are substituted into the following calculation formula:

[0085] The fitting is performed in the constitutive model, and the parameters a3-f3 are calibrated to obtain the correlation between the S value and the deformation conditions and the quenching process in the constitutive model. The thermal activation energy calculated according to formula (3) evolves with the quenching and deformation conditions.

[0086] (4) The strain rate The desolvation degree D and different intermediate function F values ​​are substituted into the following calculation formula:

[0087] The fitting is performed in step 4, and the parameters a4-f4 are calibrated to obtain the correlation between the F value in the constitutive model and the deformation conditions and quenching process. The evolution law of the constitutive model parameter A value is calculated according to step 4.

[0088] In some embodiments of the present application, in step 6, the constitutive model parameters under the same quenching process but different strains obtained by repeating steps 4 to 5 are fitted to the constitutive model parameter values ​​and strains in the Origin software according to the following polynomial to obtain the evolution law of the parameters along the deformation process:

[0089]

[0090] In formula (6), B0~B5, C0~C5, D0~D5 and E0~E5 are respectively the parameters Q, n under the condition of the same quenching transformation degree, deformation rate and temperature. s , lnA and M C3 The coefficients of the quintic polynomial fitting are used with the strain variable ε. Substitute the quintic fitting coefficients obtained in the previous step into formula (6) for parameter inversion to clarify the same temperature T, quenching precipitation reaction degree D, strain rate The parameters Q and n in the constitutive model under the condition of strain ε s , lnA and M C3 The corresponding numerical value of is used for calculating the numerical value of flow stress in subsequent steps.

[0091] In some embodiments of the present application, in step 7, an improved Arrhenius model as shown in the following formula is obtained, and the quenching process and deformation temperature, strain and strain rate are substituted into the modified model to calculate the flow stress value of the material under any quenching and deformation conditions:

[0092]

[0093] In formula (7), σ is the numerical value of the calculated flow stress, in MPa. Here, the constitutive model parameters calibrated in steps 1 to 6 and the actual strain, strain rate and temperature of the alloy at the corresponding time of the quenching process are substituted into formula (7) for calculation to obtain the numerical value of the flow stress of the alloy under the quenching process and deformation conditions.

[0094] 2. Examples and Comparative Examples

[0095] This application calibrates the parameters through real experiments, substitutes the quenching process and deformation temperature, strain and strain rate into the model constructed according to the method described in this application, and calculates the numerical value of the flow stress of the material under any quenching and deformation conditions.

[0096] Taking Al-Zn-Mg-Cu (Al-5.7Zn-2.3Mg-2.2Cu) alloy as the object, a model was constructed according to the above method of this application to predict the flow stress of the quenched alloy after different cooling processes. The specific steps of constructing the modified model are as follows:

[0097] Step 1: Calibrate the TTP curve of the alloy material through isothermal quenching experiment, and calculate the critical transformation time required for the alloy to complete the precipitation transformation at any cooling rate and any temperature. First, cut the Al-5.7Zn-2.3Mg-2.2Cu alloy to be tested into small samples of size 15×15×5 (unit: mm), place the sample in a resistance furnace for solution treatment at 477℃ / 1h, and then transfer it to a salt bath furnace with different temperatures in the range of 200℃-440℃ with a gradient increase of 20℃ (a total of 12 temperatures) within a time interval of less than 3s, and keep it isothermally for 5s, 10s, 30s, 60s, 300s, 600s, 1200s and 1800s, respectively. Finally, place the sample in clean water and cool it to room temperature. The cooled sample is polished and tested for Vickers hardness, and the curve is drawn. Combined with the real experiment, the time corresponding to 5% transformation at each temperature is calculated. The transition time at different temperatures can be determined by interpolating the hardness change curves with different degrees of decline. The values ​​of parameters k1-k5 in the TTP (Time-Temperature-Precipitation) curve model of the alloy can be determined by taking the insulation temperature and time corresponding to different degrees of reduction in peak hardness. In the actual operation of this embodiment, the values ​​of parameters k1-k5 in the model can be determined by taking the insulation temperature and time corresponding to 95% of the peak hardness (145Hv) of the quenched sample. For this alloy, the TTP model parameter values ​​of Al-5.7Zn-2.3Mg-2.2Cu alloy obtained by isothermal quenching experiment are: k2=1.53×10 -11 , k3=2.760, k4=869, k5=1.22×10 5 .

[0098] Step 2: The alloy is subjected to solution quenching and isothermal compression treatment to obtain the true stress-strain curves of the alloy under different cooling rates and different deformation conditions. The aluminum alloy is prepared into cylindrical specimens with a diameter of 8 mm and a height of 12 mm. The cylindrical specimens are subjected to solution quenching and isothermal compression treatment on the Gleeble-3500 platform to obtain the true stress-strain curves under different cooling rates and different deformation conditions. The heating rate of the alloy is set to 10℃ / s, the solution temperature is 477℃, and the solution holding time is 10min on the experimental operation platform. After solution, the samples are cooled to different temperatures at three cooling rates of 5℃ / s, 10℃ / s, and 15℃ / s, and are immediately compressed with a deformation of 25%. The strain rates of the compression process are 0.001, 0.01, and 0.1 / s. The experimental platform system will automatically complete the solution, quenching, and hot compression experiments according to the preset process parameter route. During the hot compression process, the stress-strain curve of the alloy in each deformation process will be automatically recorded on the Gleeble-3500 platform for the calibration of constitutive model parameters in the next step.

[0099] Step 3: The equivalent transformation time at each moment is calculated by the microelement method for different quenching processes, and the degree of dissolution transformation of different quenching processes is obtained by cumulative superposition calculation. The TTP curve model of the Al-5.7Zn-2.3Mg-2.2Cu alloy calibrated in step 1 is used to calculate the degree of quenching transformation of the samples in each thermal simulation experiment process in step 2.

[0100] Step 4: For the same strain condition, the true stress-strain curve obtained in step 3 is firstly analyzed at different cooling rates and strain rates. Linear fitting, the parameter M in the flow stress model under various quenching and deformation conditions C3 Carry out parameter calibration, and then carry out the calibration of the parameters 1000 / T-ln[sinh(M C3 σ)] and ln[sinh(M C3 The parameter values ​​under different cooling rates and strain rates were obtained by linear fitting of [σ)]-LnZ to improve the prediction accuracy of the model.

[0101] Step 5: Perform interactive polynomial fitting on the three cooling rates of 5℃ / s, 10℃ / s and 15℃ / s obtained in step 3 to the quenching transformation degree of the sample at different temperatures and the parameter values ​​in the constitutive model obtained by inverse analysis based on the true stress-strain curve in step 4, and obtain the interactive relationship between the quenching transformation degree, deformation temperature and deformation rate and the critical activation energy of thermal deformation, and obtain the mathematical relationship corresponding to different quenching cooling processes-transformation degree-deformation conditions-constitutive model parameter values ​​under specific strain conditions. For Al-5.7Zn-2.3Mg-2.2Cu alloy, the relationship between the four key parameters of the constitutive model and the cooling rate at a strain degree of 0.1 is:

[0102] M C (T,D)=2.792-0.091T-7.509D+7.466*10 -6 T 2 +0.066*D 2 +0.0137TD,

[0103] n s (T,D)=-247.568+0.926*10 -4 T-29.681D-8.116T 2 +1951.282D 2 -0.502TD,

[0104]

[0105] F(T,D)=-251.275+0.780T+705.421D-6.279*10 -4 T 2 +34.380D 2 -1.243TD.

[0106] Step 6: For strains in the range of 0.05-0.30, repeat steps 4-5 at intervals of 0.05 increments to determine the mathematical relationship between different quenching cooling processes, transformation degree, deformation conditions and constitutive model parameter values ​​under various strain conditions, and use interactive polynomials according to formula (6) to compensate for the fluctuations of the model parameter values ​​in the full strain range.

[0107] Step 7: Substitute the mathematical relationship obtained in step 6 into equation (7) to establish a modified Arrhenius model, and calculate the flow stress of the Al-5.7Zn-2.3Mg-2.2Cu alloy in the range of 0.05-0.30 under quenching cooling conditions of 5°C / s, 10°C / s and 15°C / s.

[0108] The modified Arrhenius model considering the quenching cooling process was used to inversely calculate the flow stress evolution of the alloy from the solution temperature of 477°C to 300°C, 350°C and 400°C at quenching cooling rates of 5°C / s and 15°C / s respectively, and then immediately compressed as Example 1. Comparative Example 1 is the result of the traditional Arrhenius model predicting the flow stress evolution of the above process.

[0109] The prediction results of the two models were compared with the actual values ​​recorded in the experiment to evaluate the prediction accuracy. Figure 2 (a) The flow stress comparison results of the alloy obtained by inverse calculation of the two models and the actual thermal simulation experiment after solution treatment at a quenching cooling rate of 5°C / s and deformation process with a strain rate of 0.001 are shown; Figure 2 (b) The flow stress comparison results of the alloy obtained by inverse calculation of the two models and the actual thermal simulation experiment after solution treatment at a quenching cooling rate of 15°C / s and deformation process with a strain rate of 0.001 are shown; Figure 2 (c) The flow stress comparison results of the alloy obtained by inverse calculation of the two models and the actual thermal simulation experiment after solution treatment at a quenching cooling rate of 5°C / s and deformation process with a strain rate of 0.1. Figure 2 (d) is the flow stress comparison result of the alloy obtained by inverse calculation of the two models and the actual result obtained by thermal simulation experiment after solution treatment, when the alloy was cooled to different temperatures at a quenching cooling rate of 5℃ / s and deformed at a strain rate of 0.1. The results show that the traditional Arrhenius model used in the comparative example is based on the heat treatment system of the intermediate quenching cooling rate (10℃ / s), and the error of inverse calculation of the deformation process under the other cooling conditions can reach 35%. This is because the traditional model ignores the influence of the cooling rate on the quenched structure and the corresponding critical deformation activation energy of each structure, resulting in errors in the simulation. This further shows that the traditional Arrhenius model can only predict the flow stress curve under one heat treatment system, and the prediction accuracy of the plastic deformation behavior of the alloy after quenching with a changed cooling rate is poor. The method described in this application is used to predict the flow stress under different quenching and cooling processes, with an error of less than 15%. This shows that the Arrhenius model considering the quenching and cooling process established in this application can correct the influence of the previous quenching process on the critical activation energy required for material deformation, and more accurately simulate the degree of plastic deformation of each region during the quenching process, thereby greatly improving the prediction accuracy of residual stress in the heat treatment process of forgings.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application rather than to limit the technical solution. Ordinary technicians in this field should understand that those modifications or equivalent substitutions of the technical solution of the present application that do not depart from the purpose and scope of the technical solution of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for predicting the deformation behavior of alloys after different quenching processes, characterized in that: The specific steps include: Step 1: Calibrate the TTP curve of the alloy material through isothermal quenching experiment, and calculate the critical transformation time required for the alloy to complete the precipitation transformation at any cooling rate and any temperature; Step 2: The equivalent transformation time at each moment is calculated by using the microelement method for different quenching processes, and the degree of desolvation transformation of different quenching processes is obtained by cumulative superposition calculation; Step 3: subjecting the alloy to solid solution quenching treatment and isothermal compression treatment to obtain true stress-strain curves under different cooling rates and different deformation conditions; Step 4: For the same strain condition, perform multiple linear fittings on the true stress-strain curves at different cooling rates and strain rates to obtain the constitutive model parameter values ​​under specific cooling rates and deformation conditions; Step 5: Perform interactive polynomial fitting on the parameter values ​​under different quenching cooling rate-deformation conditions to obtain the interactive relationship between quenching transformation degree, deformation temperature and deformation rate and critical activation energy of thermal deformation, and obtain the mathematical relationship corresponding to different quenching cooling process-transformation degree-deformation conditions-constitutive model parameter values ​​under specific strain conditions; Step 6: Repeat steps 4-5 for different strain conditions to determine the mathematical relationship between different quenching cooling processes, transformation degree, deformation conditions and constitutive model parameter values ​​under different strain conditions, and use interactive polynomials to compensate for the fluctuation of model parameter values ​​in the full strain range; Step 7: Establish the Arrhenius model based on the mathematical relationship obtained in step 6 to calculate the flow stress under any quenching cooling condition.

2. The method according to claim 1, characterized in that: In step 1, the critical transition time is calculated as follows: Wherein, C(T) is the shortest time required for a supersaturated solid solution to complete the precipitation reaction at a specific temperature, k1 is the natural logarithm of the content without precipitation reaction, k2 is a constant related to the nucleation density, k3 is a constant related to the unit nucleation energy, k4 is related to the second phase precipitation temperature and vacancy concentration, k5 is related to the diffusion activation energy, R is the gas constant, and the value of R is 8.314 J / (mol·K), and T is the thermodynamic temperature; k1, k2, k3, k4, and k5 are obtained by measuring the hardness evolution of the alloy after isothermal quenching experiment.

3. The method according to claim 1, characterized in that: In step 2, the degree of desolventization conversion is obtained by the following steps: In the formula, ε represents the residual supersaturated solid solution content without precipitation reaction, τ represents the equivalent reaction time of the whole quenching process, Δt represents a small time interval in the quenching process, and t c (T) represents the reaction time required for the complete desolvation reaction to occur at this temperature, calculated by step 1 at this small time interval.

4. The method according to claim 1, characterized in that: In step 4, based on the true stress-strain curve, Linear fitting, the parameter M in the flow stress model under various quenching and deformation conditions C3 Carry out parameter calibration, and then carry out the calibration of the parameters 1000 / T-ln[sinh(M C3 σ)] and ln[sinh(M C3 The parameter values ​​at different cooling rates and strain rates were obtained by linear fitting of the [σ)]-LnZ.

5. The method according to claim 4, characterized in that: The specific calculation steps of the parameter values ​​under different cooling speeds and different strain rates are as follows: S1: Based on the true stress-strain curve, calculate and Lnσ, and draw the corresponding scatter plot; perform linear regression on the scatter plot, and the slope of the scatter plot regression equation under the same conditions is the parameter M C3 Numeric value; S2: M under each condition C3 The value is calculated according to the steps of S1 to obtain n under different conditions s value; specifically: M under each condition C3 Substitute the values ​​into the formula to calculate the different conditions ln[sinh(M C3 σ)] and plot the corresponding and ln[sinh(M C3 σ)], using the and ln[sinh(M C3 The slope obtained by linear regression of the scatter plot of [σ)] is the n under different conditions. s value; S3: 1000 / T-ln[sinh(M C3 The scatter plot of [σ)] is drawn and linear regression is performed. The slope of the straight line is the S value under each condition, and according to the calculation formula: Q=1000R gas n s S, where R gas represents the gas constant, which is 8.314 J / (mol·K); Calculate the thermal activation energy values ​​in the constitutive model under various conditions; S4: Plot ln[sinh(M C3 The intercept of the linear regression is set as F. The lnA value under different conditions is obtained by the following calculation formula, and the parameter A value in the constitutive model is determined by exponential function calculation:

6. The method according to claim 5, characterized in that: In step 5: (1) Draw a scatter plot to describe the temperature T, the degree of desolvation D calculated in step 2, and the M under different conditions calculated in step 4. C3 The correlation between the values ​​is calculated by using the Levenberg-Marquardt algorithm. C (T,D)=a1+b1T+c1D+d1T 2 +e1D 2 +f1TD is fitted, and the parameter a1-f1 is calibrated to obtain the M C The correlation between deformation temperature and quenching process; (2) Temperature T, desolvation degree D and different n s The relationship between values ​​is expressed in n s =a2+b2T+c2D+d2T 2 +e2D 2 +f2TD relationship is fitted, and the parameter a2-f2 is calibrated to obtain the n in the constitutive model. s Correlation between deformation conditions and quenching process; (3) The strain rate The relationship between the degree of desolvation D and the values ​​of different intermediate functions S is shown in The relationship is fitted, the parameters a3-f3 are calibrated, the correlation between the S value and the deformation conditions and the quenching process in the constitutive model is obtained, and the thermal activation energy evolves with the quenching and deformation conditions according to the above-mentioned; (4) The strain rate The relationship between the degree of desolvation D and the values ​​of different intermediate functions F is shown in The relationship is fitted and the parameters a4-f4 are calibrated to obtain the correlation between the F value in the constitutive model and the deformation conditions and quenching process, and the evolution law of the constitutive model parameter A value is calculated according to step 4.

7. The method according to claim 1, characterized in that: In step 6, by repeating the constitutive model parameters obtained in steps 4 to 5 under the same quenching process but different strains, the constitutive model parameter values ​​and strains are fitted according to the following polynomial to obtain the evolution law of the parameters along the deformation process: Where: B0~B5, C0~C5, D0~D5 and E0~E5 are the parameters Q, n under the conditions of the same quenching transformation degree, deformation rate and temperature. s , lnA and M C3 The coefficients of the fifth-order polynomial fitting with the dependent variable ε.

8. The method according to claim 1, characterized in that: In step 7, the improved Arrhenius model is obtained as shown in the following formula. The quenching process and deformation temperature, strain and strain rate are substituted into the modified model to calculate the flow stress value of the material under any quenching and deformation conditions: Where σ is the calculated flow stress value, unit: MPa.

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