Constitutive Model Establishment Method and Apparatus for Simulation Application of High-Temperature Alloy Plastic Hot Forming

Abstract

This disclosure relates to a constitutive model establishment method and apparatus for simulation applications of high-temperature alloy plastic hot forming, comprising: obtaining multiple stress-strain curves of experimental samples at multiple sample temperatures through compression tests; determining multiple peak data corresponding to the multiple stress-strain curves, wherein the peak data includes peak stress, strain data, and sample temperature; establishing a constitutive equation containing parameters to be determined based on multiple peak stresses and empirical formulas, wherein the empirical formulas are used to describe the relationship between strain data and temperature; establishing a mapping relationship between multiple peak data and parameters to be determined; and establishing a constitutive model based on the mapping relationship and constitutive equations. Based on this, a constitutive model containing multiple factors can be established, thereby reflecting the coupling influence of multiple factors, thus accurately describing the hot deformation behavior and deformation characteristics of experimental samples under different temperature conditions, providing model and data support for completing high-confidence modeling of the material forging process.

Description

Technical Field

[0001] This disclosure relates to the field of high-temperature alloy hot forming, and in particular to a constitutive model establishment method and apparatus for simulation applications of high-temperature alloy plastic hot forming. Background Technology

[0002] In existing technologies, the constitutive relations provided by simulation software for metal forging and other plastic forming mainly consist of one or more sets of flow stress variation curves within a certain temperature, strain, and strain rate range. The flow stress of the material under other temperature, strain, and strain rate conditions is estimated by interpolation. However, since the data points for determining the flow stress variation curves are often insufficient, the resulting flow stress curves differ significantly from reality. At the same time, the flow stress of most high-temperature alloy materials is greatly affected by multiple factors such as temperature, strain, and strain rate, resulting in large errors in the established high-temperature alloy flow stress curves and inaccurate material constitutive relations. This further leads to a large discrepancy between the forging simulation results and experimental results, seriously affecting the simulation accuracy and resulting in poor confidence. Summary of the Invention

[0003] In view of this, the present disclosure provides a method and apparatus for establishing a constitutive model for simulation applications of high-temperature alloy plastic thermoforming, so as to solve the problem of inaccurate material constitutive relations in related technologies.

[0004] A first aspect of this disclosure provides a method and apparatus for establishing a constitutive model for simulation applications of high-temperature alloy plastic hot forming. The method includes: obtaining multiple stress-strain curves of experimental samples at multiple sample temperatures through compression tests; determining multiple peak data corresponding to the multiple stress-strain curves, wherein the peak data includes peak stress, strain data, and sample temperature; establishing a constitutive equation containing parameters to be determined based on the multiple peak stresses and empirical formulas, wherein the empirical formulas are used to describe the relationship between strain data and temperature; establishing a mapping relationship between the multiple peak data and the parameters to be determined; and establishing a constitutive model based on the mapping relationship and the constitutive equation.

[0005] A second aspect of this disclosure provides a constitutive model building apparatus for simulation applications of high-temperature alloy plastic hot forming, applied to the constitutive model building method for high-temperature alloys as described in the first aspect. The apparatus includes: an acquisition module configured to acquire multiple stress-strain curves of an experimental sample at multiple sample temperatures through compression tests; a determination module configured to determine multiple peak data corresponding to the multiple stress-strain curves, wherein the peak data includes peak stress, strain data, and sample temperature; a first building module configured to build a constitutive equation containing parameters to be determined based on the multiple peak stresses and an empirical formula, wherein the empirical formula describes the relationship between strain data and temperature; a second building module configured to establish a mapping relationship between the multiple peak data and the parameters to be determined; and a construction module configured to build a constitutive model based on the mapping relationship and the constitutive equation.

[0006] A third aspect of this disclosure provides an electronic device including at least one processor; a memory for storing at least one processor-executable instruction; wherein the at least one processor is used to execute the instruction to implement the steps of the control method of the signal transceiver device described above.

[0007] The at least one technical solution adopted in this disclosure can achieve the following beneficial effects: Multiple stress-strain curves of the experimental sample at multiple sample temperatures are obtained through compression tests, and the peak data corresponding to the multiple curves are determined. Then, constitutive equations containing parameters to be determined are established based on the multiple peak stresses and empirical formulas, and a mapping relationship is established between the multiple curve peak data and the parameters to be determined. Finally, a constitutive model is established based on the mapping relationship and the constitutive equations. Based on this, a constitutive model containing multiple factors can be established, thereby reflecting the coupling influence of multiple factors, accurately describing the thermal deformation behavior and deformation characteristics of the experimental sample under different temperature conditions, and providing model and data support for completing high-confidence modeling of the material forging process. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This illustration shows a flowchart of a constitutive model establishment method and apparatus for a high-temperature alloy plastic thermoforming simulation application provided in this disclosure.

[0010] Figure 2A The stress-strain curves provided in this embodiment of the present disclosure at a reaction rate of 0.01 are shown.

[0011] Figure 2B The stress-strain curves provided in this embodiment of the present disclosure at a reaction rate of 0.1 are shown.

[0012] Figure 2C The stress-strain curves for a reaction rate of 1 provided in this embodiment of the present disclosure are shown.

[0013] Figure 3A The example of this formula shows that n2 is... Relationship curve diagram;

[0014] Figure 3B The example of this formula shows that n2 is... Relationship curve diagram;

[0015] Figure 4 The embodiments provided in this disclosure are shown. A schematic diagram of the relationship curve;

[0016] Figure 5 A schematic diagram of the relationship curve of ln[sinh(ασ)]-1 / T provided in the embodiments of this disclosure is shown;

[0017] Figure 6 A schematic diagram of the relationship curve between lnZ and ln[sinh(ασ)] provided in an embodiment of this disclosure is shown.

[0018] Figure 7A A schematic diagram of the relationship curve between the structure factor A of the experimental sample provided in this disclosure and strain is shown.

[0019] Figure 7B A schematic diagram of the relationship curve between the stress exponent n and strain of the test sample provided in the embodiments of this disclosure is shown.

[0020] Figure 7C A schematic diagram of the polynomial fitting curve of the deformation activation energy Q of the test sample provided in the embodiments of this disclosure with respect to strain is shown.

[0021] Figure 7D The diagram shows a polynomial fitting curve of the stress level parameter α as a function of strain when the experimental sample is subjected to force, according to an embodiment of this disclosure.

[0022] Figure 8A A comparative graph of experimental results provided in the embodiments of this disclosure at a reaction rate of 0.01 is shown;

[0023] Figure 8B A comparative graph showing experimental results at a reaction rate of 0.1, provided in the embodiments of this disclosure, is shown.

[0024] Figure 8C A comparative graph of experimental results at a reaction rate of 1 is shown, according to an embodiment of this disclosure.

[0025] Figure 9 A schematic diagram of a constitutive model building apparatus for a high-temperature alloy provided in an embodiment of this disclosure is shown;

[0026] Figure 10 A schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure is shown.

[0027] Figure 11 A schematic diagram of the structure of a computer system provided in an embodiment of this disclosure is shown. Detailed Implementation

[0028] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0029] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0030] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc., used in this disclosure are only used to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0031] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0032] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0033] In existing technologies, high-temperature alloys are high-temperature metallic materials that operate for extended periods at temperatures above 760–1500℃ and under certain stress conditions. They possess excellent high-temperature strength, good resistance to oxidation and hot corrosion, and good fatigue performance and fracture toughness, making them irreplaceable key materials for hot-end components of military and civilian gas turbine engines, and widely used in the aerospace field. Forgings play a core role in the aerospace field. The hot forging and plastic forming process of aerospace forgings is a complex process, and the forging process of high-temperature alloy materials presents challenges such as difficulty in performance control and high deformation loads. Compared with general metals, they have higher requirements in terms of manufacturing processes. The accurate analysis of the process and the optimization of process parameters are difficult to achieve the desired results using traditional experience-based trial-and-error design methods. With the continuous development and improvement of computer technology and plasticity finite element theory, numerical simulation methods, represented by the finite element method, have been widely used in solving and analyzing the hot forming problems of high-temperature alloy forgings. The simulation technology for the forging process has typical nonlinear and large deformation characteristics.

[0034] In practical applications, simulation technology for forging processes is primarily based on material constitutive relations. These relations describe the stress-strain relationship of materials, reflecting the influence of parameters such as strain data, strain, and deformation temperature on flow stress during plastic deformation. They are a crucial basis for the design and control of hot forming processes. Therefore, constitutive models established based on material constitutive relations are indispensable fundamental theoretical models in the plastic deformation process, and the prediction accuracy of these models directly affects the confidence level of plastic forming simulation results. Currently, the material constitutive relations provided by metal forging and other plastic forming simulation software mainly consist of one or more sets of flow stress variation curves within a certain temperature, strain, and strain rate range. Interpolation methods are used to estimate the flow stress under other temperature, strain, and strain rate conditions. However, because the data points for determining the flow stress variation curves are often insufficient, the resulting flow stress curves differ significantly from reality. Furthermore, the flow stress of most high-temperature alloys is greatly affected by multiple factors such as temperature, strain, and strain rate, leading to large errors in the established high-temperature alloy flow stress curves and inaccurate material constitutive relations. This further results in significant discrepancies between forging simulation results and experimental results, severely impacting simulation accuracy and confidence level.

[0035] To address the aforementioned issues, this disclosure provides a constitutive model establishment method and apparatus for simulation applications of high-temperature alloy plastic hot forming. This method fully considers the influence of multiple factors, such as temperature, deformation rate, and deformation degree, on the flow stress of high-temperature alloy materials during high-temperature deformation. It establishes a constitutive model of the high-temperature alloy material, accurately reflecting the coupled influence of these multiple factors, thereby precisely describing the hot deformation behavior and characteristics under high-temperature conditions. This provides model and data support for completing high-confidence modeling of the forging process of high-temperature alloy materials, serving as the foundation and prerequisite for achieving high-precision simulation of high-temperature alloy material forging and other plastic forming processes. It is of great significance for further analysis of material deformation characteristics and optimization of forming processes.

[0036] The constitutive model establishment method for a high-temperature alloy plastic thermoforming simulation application according to an embodiment of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0037] Figure 1 A schematic flowchart illustrating a constitutive model establishment method for a high-temperature alloy plastic thermoforming simulation application provided in this disclosure is shown. Figure 1 As shown, the constitutive model establishment method for this high-temperature alloy plastic hot forming simulation application includes:

[0038] S101: Obtain multiple stress-strain curves of the experimental sample at multiple sample temperatures through compression tests.

[0039] S102: Determine the peak data of multiple stress-strain curves, where the peak data includes peak stress, strain data and specimen temperature.

[0040] S103: Constitutive equations containing parameters to be determined are established based on multiple peak stresses and empirical formulas, where empirical formulas are used to describe the relationship between strain data and temperature.

[0041] S104: Establish the mapping relationship between multiple curve peak data and the parameters to be determined;

[0042] S105: Establish a constitutive model based on the mapping relationship and constitutive equation.

[0043] An exemplary embodiment of this disclosure allows for the acquisition of multiple stress-strain curves of experimental samples at multiple sample temperatures through compression tests. The peak values ​​of these curves are then determined, and constitutive equations containing parameters to be determined are established using the multiple peak stresses and empirical formulas. A mapping relationship is established between the peak values ​​of the multiple curves and the parameters to be determined. Finally, a constitutive model is established based on the mapping relationship and the constitutive equations. Based on this, a constitutive model containing multiple factors can be established, thereby reflecting the coupling influence of multiple factors and accurately describing the thermal deformation behavior and deformation characteristics of experimental samples under different temperature conditions. This provides model and data support for completing high-confidence modeling of the material forging process.

[0044] In some instances, this disclosure allows setting test parameters, including multiple specimen temperatures and multiple strain data; performing compression tests on the material according to the test parameters to obtain compression test data of the experimental specimen, wherein the compression test data includes stress data and strain data; and plotting stress-strain curves based on the stress data and strain. It should be understood that the strain data in this disclosure can be strain rate.

[0045] Specifically, the first step is to prepare test samples, which can be made of high-temperature alloy materials. In practical applications, wire cutting can be used to process high-temperature alloy rods into cylindrical specimens with dimensions of Φ8×12mm. The surface roughness of the processed cylindrical specimens should not exceed 0.8μm at the end face and 1.6μm at the side face. This helps to reduce friction between the high-temperature alloy rod and the indenter, ensuring that the high-temperature alloy rod deforms as uniformly as possible and guaranteeing accurate and reliable test results. Furthermore, to eliminate structural defects and remove residual stress, the processed cylindrical specimens undergo homogenization annealing treatment to obtain the test samples.

[0046] In practical applications, isothermal and constant strain rate compression tests can be performed on the test specimens. During the compression process, graphite sheets are used for lubrication to reduce friction between the specimen and the indenter, ensuring that the specimen deforms as uniformly as possible. The selected test conditions are: specimen temperature can be 940℃, 980℃, 1020℃, 1060℃, 1100℃, etc.; strain rate is selected as 0.01s. -1 0.1s -1 1s -1 The final deformation amount was selected as 60%, and the experimental scheme was designed based on the selected experimental conditions. It should be understood that, based on the deformation characteristics of the material under actual working conditions, selecting a deformation amount of 60% can cover the research needs of the material's thermal deformation behavior under working conditions.

[0047] In one alternative approach, the prepared Φ8×12mm test specimen can be heated to the deformation temperature on a testing machine at a heating rate of 10℃ / s, held at that temperature for 2 minutes to homogenize the temperature of the test specimen, and then compressed to different deformation amounts under different strain rate parameters. After deformation, it is rapidly water-cooled. After the test, stress and strain data under different conditions are obtained, and then multiple stress-strain curves are plotted based on the multiple stress and strain data.

[0048] In some instances, the fundamental equations of the constitutive equations employ empirical formulas to describe the relationship between strain rate, flow stress, and temperature at different temperatures. These empirical formulas can be the Arrhenius formula, expressed as:

[0049]

[0050] in, It is the strain rate, measured in seconds (s). -1 α is the stress level parameter, with units of MPa. -1 A is the structure factor, with units of s. -1 σ is the instantaneous stress during deformation, in MPa; Q is the deformation activation energy, in kJ / mol; R is the gas constant; n is the stress exponent; T is the deformation temperature, in K; where A, α, n, and Q are the parameters to be determined.

[0051] In practical applications, empirical formulas mainly describe the relationship between strain rate, stress, and temperature under a certain strain. However, empirical formulas do not involve variable strain. Therefore, this disclosure extends and modifies the empirical formulas. For each strain, a set of parameters to be determined by empirical formulas can be identified. These parameters include the structural factor A of the experimental sample, the stress level parameter α of the experimental sample under stress, the stress exponent n of the experimental sample, and the deformation activation energy Q of the experimental sample. The parameter values ​​A, α, n, and Q are considered as the strain function f(ε) = A, α, n, Q.

[0052] The above empirical formula has the following exponential function form:

[0053]

[0054] Taking the logarithm of both sides of the exponential function form of the empirical formula, we get:

[0055]

[0056] The power function form of the empirical formula is:

[0057]

[0058] Taking the logarithm of both sides of the power function form of the empirical formula, we get:

[0059]

[0060] In the formula, the stress level parameter α of the experimental sample under stress is determined by n1 and n2, and:

[0061]

[0062] From equations (3) and (5), it can be seen that when the temperature is constant, n1 is a straight line. The slope of the line, n2 is the slope of the line. The slope of , where A1 is the structure factor of the empirical formula exponential function, and A2 represents the structure factor of the empirical formula power function form.

[0063] In some instances, embodiments of this disclosure can process the peak curve data based on constitutive equations to obtain a first scatter plot at multiple temperatures; perform linear regression processing on the first scatter plot to obtain multiple relationship curves of peak stress data and strain data at different temperatures; and determine the solution formula for the parameters to be determined based on the multiple relationship curves.

[0064] Specifically, the peak data of multiple curves on the stress-strain curves obtained from the compression test can be processed to plot the first scatter plot at different temperatures. Then, linear regression using the least squares method can be performed to obtain the values ​​at different temperatures. and The relationship curve. Where n2 is... The slope of the relationship curve; n1 is The slope of the relationship curve, n1, n2, can be taken as the average of the slopes of multiple relationship curves, so that the stress level parameter α of the experimental sample under stress can be calculated by formula (6).

[0065] Taking the logarithm of both sides of formula (1) yields:

[0066]

[0067] Then, the stress level parameter α is substituted into formula (7), and the peak value data and strain data on the stress-strain curve of the compression test are processed. Scatter plots are drawn at different temperatures, and linear regression is performed using the least squares method to obtain the results. The relationship curve, the stress index n of the test sample is The slope of the relationship curve can be obtained by taking the average of the slopes of the curves.

[0068] By performing partial differential processing on formula (7) and rearranging it, we can obtain the solution formula for the deformation activation energy Q of the experimental sample:

[0069]

[0070] Multiple peak values ​​and corresponding temperature data from the stress-strain curves of the compression test are processed to create scatter plots for different strain data. Linear regression is performed using the least squares method to obtain the relationship curve of ln[sinh(ασ)]⁻¹ / T. Let k be the slope of the ln[sinh(ασ)]⁻¹ / T relationship curve, and its value is obtained by taking the average of the curve slopes. Based on this, formula (8) can be rewritten as Q=nRk.

[0071] Equation (1) can be transformed to obtain equation (10), that is, the relationship between the variables of high-temperature alloy materials during hot deformation can also be represented by parameter Z, which includes two variables: strain data and deformation temperature during hot deformation.

[0072]

[0073] Transforming formula (10) yields:

[0074]

[0075] Based on the definition of the hyperbolic sine function and its inverse form, we can obtain

[0076]

[0077] Taking the logarithm of both sides of equation (9) yields

[0078] lnZ=lnA+n ln[sinh(ασ)] (12)

[0079] Substituting the strain rate and deformation activation energy Q at different deformation temperatures into formula (9) yields the Z value under different deformation conditions. Data such as peak stress on the stress-strain curve of the thermal simulation compression test are processed, and scatter plots at different strain rates are drawn. Linear regression is performed using the least squares method to obtain the relationship curve of lnZ-ln[sinh(ασ)]. The parameter A can be further determined from the fitting result of the relationship curve.

[0080] In summary, the constitutive equation for high-temperature alloy materials under peak stress is:

[0081]

[0082] in, It is the strain rate, measured in seconds (s). -1 α represents the stress level parameter when the experimental sample is subjected to force, and the unit is MPa. -1 A represents the structure factor of the experimental sample, in units of s. -1 σ is the instantaneous stress during deformation, in MPa; Q represents the deformation activation energy of the test sample, in kJ / mol; R is the gas constant; n represents the stress exponent n of the test sample; T is the deformation temperature, in K; Z represents the relationship between various variables of the test sample during hot deformation. A, α, n, and Q are the values ​​of the parameters to be determined.

[0083] Based on this, a constitutive equation can be constructed to describe the peak stress of the experimental sample under different deformation conditions and temperatures.

[0084] In some instances, embodiments of this disclosure can solve for the parameter values ​​corresponding to the parameters to be solved in the constitutive equation based on the solution formula; plot a second scatter plot of the parameter values ​​under different external forces; and perform fitting processing on the second scatter plot based on the polynomial fitting method to obtain the fitting result.

[0085] In practical applications, when the parameters to be determined include A, n, Q, and α, the specific values ​​of A, n, Q, and α under different stresses ε can be calculated based on the solution formula. The parameters to be determined, A, n, Q, and α, and strain ε can be plotted as a second scatter plot. Then, multiple polynomial fitting processes can be performed to establish the function relationship curve.

[0086] In some instances, embodiments of this disclosure can establish a mapping relationship between multiple peak curve data and the parameters to be determined, provided that the fitting results meet preset conditions.

[0087] In practical applications, the goodness of fit R of each functional relationship curve can be evaluated based on the fitting results. 2 (COD) is used to judge the quality of the fit and determine the degree of the fitted polynomial, thus determining the goodness of fit Ri. 2 If (COD) > 0.95, it indicates that the fitting results meet the requirements for establishing a constitutive model. The mapping relationship function between parameters A, n, Q, α, and strain is then established.

[0088]

[0089] Where f1(ε) represents the strain function of α, m represents the total degree of the fitting polynomial, i represents the degree of the fitting polynomial, and B 1i Let ε represent the fitting coefficient of f1(ε). i Let f2(ε) represent the terms of the fitted polynomial, and let B represent the strain function of n. 2i f2(ε) represents the fitting coefficient, f3(ε) represents the strain function of Q, and B represents the strain coefficient of Q. 3i f3(ε) represents the fitting coefficient; f4(ε) represents the strain function of ln(A), B 4i f4(ε) represents the strain function of Q.

[0090] Based on this, a constitutive model can be established according to the mapping relationship and constitutive equation:

[0091]

[0092] The following section uses a high-temperature alloy, GH500, as an example to illustrate the use of constitutive models.

[0093] In some instances, a thermally simulated compression test is required first for the GH500 material. It should be understood that the compression test can be a thermally simulated compression test.

[0094] Preparation of hot compression specimens: GH500 bars were machined into cylindrical specimens of Φ8×12mm using wire cutting. The end face roughness of the specimens did not exceed 0.8μm, and the side face roughness did not exceed 1.6μm. To eliminate structural defects and remove residual stress, the hot compression specimens underwent homogenization annealing treatment.

[0095] Experimental Procedure: The experimental specimens were subjected to isothermal and constant strain rate compression tests. During compression, graphite sheets were used for lubrication to reduce friction between the specimen and the indenter, ensuring uniform deformation. The selected experimental parameters were: deformation temperatures of 940℃, 980℃, 1020℃, 1060℃, and 1100℃; strain rates of 0.01 s⁻¹, 0.1 s⁻¹, and 1 s⁻¹; and a final deformation of 60%. The specific experimental procedure is shown in the table below. It should be understood that, based on the deformation characteristics of the material under actual working conditions, selecting 60% covers the research needs of the material's thermal deformation behavior under operating conditions.

[0096] Experimental Procedure: A prepared Φ8×12 GH500 cylindrical specimen was heated to its deformation temperature at a heating rate of 10℃ / s on a thermal simulation testing machine. The temperature was held for 2 minutes to homogenize the specimen. Then, it was compressed to different deformation amounts under different deformation process parameters. After deformation, the specimen was rapidly water-cooled. Stress-strain curves under different conditions were obtained after the experiment. Based on the results of the thermal simulation compression test, stress-strain curves were plotted using the data in Table 1.

[0097] Table 1 GH500 Hot Compression Test Procedure

[0098]

[0099] Figure 2A The stress-strain curves provided in this embodiment of the present disclosure at a reaction rate of 0.01 are shown. Figure 2B The stress-strain curves provided in this embodiment of the present disclosure at a reaction rate of 0.1 are shown. Figure 2C The stress-strain curves for a reaction rate of 1, as provided in the embodiments of this disclosure, are shown. Figures 2A to 2C As shown in the figure, tests were conducted at temperatures of 940℃, 980℃, 1020℃, 1060℃, and 1100℃, respectively. Table 1 was created by selecting the peak data of each curve based on the test results. Then, stress-strain curves at different reaction rates can be plotted based on the data in Table 1.

[0100] In some instances, after completing the thermal simulation compression test of GH500 material, it is necessary to establish the constitutive equation of the high-temperature alloy material GH500 under the peak stress.

[0101] Specifically, the constitutive model of the high-temperature alloy material GH500 is represented by the Arrheninus equation:

[0102]

[0103] In the formula, It is the strain rate, measured in seconds (s). -1α is the stress level parameter, with units of MPa. -1 A is the structure factor, with units of s. -1 σ is the instantaneous stress during deformation, in MPa; Q is the deformation activation energy, in kJ / mol; R is the gas constant; n is the stress exponent; T is the deformation temperature, in K; where A, α, n, and Q are the parameters to be determined.

[0104] The Arrheninus equation primarily describes the strain rate at a given strain. The relationship between true stress σ and temperature T is not addressed in the equations for variable strain ε. This is an extension of the Arrheninus equations. For each strain ε, a set of undetermined parameter values ​​A, α, n, and Q from the Arrheninus equations can be determined. These undetermined parameter values ​​A, α, n, and Q can be considered as functions of strain ε, f(ε) = A, α, n, Q.

[0105] The exponential function form of the Arrheninus equation is:

[0106]

[0107] The power function form of the Arrheninus equation is:

[0108]

[0109] In the formula, the stress level parameters are determined by n1 and n2, and:

[0110]

[0111] Taking the logarithm of both sides of equations (17) and (18) yields:

[0112]

[0113]

[0114] From equations (20) and (21), it can be seen that when the deformation temperature is constant, n1 and n2 are respectively linear... and The slope of A1 represents the structure factor of the empirical formula exponential function, and A2 represents the structure factor of the empirical formula power function form.

[0115] The peak stresses on the stress-strain curves of the thermal simulation compression test are shown in Table 2. The data in Table 2 were processed to create scatter plots at different temperatures. Linear regression using the least squares method was then performed to obtain the values ​​at different temperatures. and The relationship curve.

[0116] Table 2 Peak stress under different conditions

[0117]

[0118] Relationship curve and The results of the relationship curve fitting are shown in Tables 3 and 4 below:

[0119] Table 3 Curve fitting results

[0120]

[0121] Table 4 Curve fitting results

[0122]

[0123] Figure 3A The example of this formula shows that n2 is... Relationship curve diagram Figure 3B The example of this formula shows that n2 is... Relationship curve graph. For example... Figure 3A and 3B As shown, by taking the average value from Table 3, we can obtain n2 = 0.030442; n1 is... The slope of the relationship curve, obtained by averaging the values ​​in Table 4, is n1 = 9.593914;

[0124] Therefore, the stress level parameter α is:

[0125]

[0126] Taking the logarithm of both sides of equation (16) yields:

[0127]

[0128] Figure 4 The embodiments provided in this disclosure are shown. A schematic diagram of the relationship curve. For example... Figure 4 As shown, the stress level parameter α is substituted into equation (22), and the peak stress and strain rate data on the stress-strain curve of the thermal simulation compression test are processed to draw scatter plots at different temperatures. Linear regression is then performed using the least squares method to obtain... The relationship curve.

[0129] The results of the curve fitting are shown in the table below.

[0130] Table 5 Curve fitting results

[0131]

[0132] Where n is The slope of the relationship curve, obtained by averaging the values ​​in the table above, is n = 6.61368.

[0133] By performing partial differential operations and rearranging equation (22), we obtain:

[0134]

[0135] Figure 5 A schematic diagram of the relationship curve of ln[sinh(ασ)]⁻¹ / T provided in an embodiment of this disclosure is shown. Figure 5 As shown, the peak stress and temperature data on the stress-strain curve of the thermal simulation compression test were processed, and scatter plots were drawn under different strain rates. The least squares method was used for linear regression to obtain the relationship curve of ln[sinh(ασ)]-1 / T. The fitting results of the ln[sinh(ασ)]-1 / T relationship curve are shown in Table 6 below.

[0136] Table 6. Fitting results of the ln[sinh(ασ)]⁻¹ / T curve

[0137]

[0138] Let k be the slope of the ln[sinh(ασ)]⁻¹ / T relationship curve. Taking the average value from the table above, we get k = 22384.90364. According to equation (9), the average deformation activation energy of the high-temperature alloy GH500 is Q = nRk = 1230.859 kJ·mol⁻¹. -1 .

[0139] The relationship between the GH500 hot deformation activation energy, hot deformation conditions, and peak stress can also be expressed by the temperature-compensated strain rate sensitivity factor Zener-Hollomon parameter. Equation (16) can be transformed to obtain:

[0140]

[0141] Transforming equation (24) yields:

[0142]

[0143] Based on the definition of the hyperbolic sine function and its inverse form, we can obtain

[0144]

[0145] Taking the logarithm of both sides of equation (24) yields

[0146] ln Z=ln A+nln[sinh(ασ)] (27)

[0147] Figure 6 A schematic diagram of the relationship curve between lnZ and ln[sinh(ασ)] provided in an embodiment of this disclosure is shown. Figure 6 As shown, the strain rate and deformation activation energy Q at different deformation temperatures can be substituted into equation (24) to obtain the Z value under different deformation conditions. Data such as peak stress on the stress-strain curve of the thermal simulation compression test are processed, and scatter plots at different strain rates are drawn. Linear regression is performed using the least squares method to obtain the relationship curve of lnZ-ln[sinh(ασ)]. The fitting results of the lnZ-ln[sinh(ασ)] relationship curve are shown in the table below. The parameter A = 1.226 × 10⁻⁶ is determined from the relationship curve. 48 s -1 .

[0148] Table 7 Fitting results of the ln z-ln[sinh(ασ)] curve

[0149]

[0150] Therefore, the constitutive equation for the high-temperature alloy GH500 under peak stress is as follows:

[0151]

[0152] It can describe the peak stress of high-temperature alloy GH500 under different deformation conditions and temperatures.

[0153] In some instances, based on the peak stress constitutive equation and the computational solution steps, the values ​​of parameters A, n, Q, and α in the Arrhenius equation corresponding to different strains ε can be calculated, and the calculation results are shown in Table 8. Scatter plots of the parameters A, n, Q, and α against strain ε are then plotted, and a 7th-order polynomial fitting is performed to establish the functional relationship curves. The fitting coefficient results are shown in Table 9.

[0154] Figure 7A The diagram shows a polynomial fitting curve of the structure factor A of the experimental sample provided in this embodiment of the present disclosure with respect to strain. Figure 7B The diagram shows a schematic representation of the polynomial fitting curve of the stress exponent n with respect to strain for the test sample provided in this embodiment of the present disclosure. Figure 7C The diagram shows a schematic representation of the polynomial fitting curve of the deformation activation energy Q of the test sample provided in this embodiment of the present disclosure with respect to strain. Figure 7D This diagram illustrates a polynomial fitting curve of the stress level parameter α versus strain for an experimental sample subjected to stress according to an embodiment of this disclosure. Figures 7A to 7DAs shown, the goodness of fit R of each functional relationship curve is evaluated. 2 (COD) is used to judge the quality of the fit and determine the goodness of fit R. 2 If (COD) > 0.95, it indicates that the fitting results meet the requirements for constructing a constitutive model. The correlation fitting coefficients of the 7th-order polynomials for parameters A, n, Q, and α are shown in the table below. It can be seen that the goodness of fit of the functional relationship model for each parameter (A, n, Q, α) is greater than 0.95, indicating a good fit and meeting the requirements.

[0155] Table 8. Parameters in the Arrhenius equation under different strains.

[0156]

[0157] Table 9 shows the fitting coefficients for f(ε).

[0158]

[0159] The mapping function between parameters A, n, Q, α, etc., and strain is as follows:

[0160]

[0161] Based on the above calculation results, the constitutive model of the high-temperature alloy material GH500 under high-temperature conditions can be obtained as follows:

[0162]

[0163] This constitutive model is suitable for describing the plastic deformation of the high-temperature alloy GH500 with a strain rate of 0.01 s⁻¹. -1 ~1s -1 The rheological stress behavior with a deformation temperature of 940℃~1100℃. Among them, ε 1 ε represents the term in the fitting polynomial corresponding to the first fitting result. 2 ε represents the term in the fitting polynomial corresponding to the second fitting result. 3 ε represents the term in the fitting polynomial corresponding to the third fitting result. 4 ε represents the term in the fitting polynomial corresponding to the fourth fitting result. 5 ε represents the term in the fitting polynomial corresponding to the fifth fitting result. 6 ε represents the term in the fitting polynomial corresponding to the sixth fitting result. 7 This represents the term in the fitted polynomial corresponding to the seventh fitting result.

[0164] Figure 8A The accompanying diagram shows a comparison of experimental results provided in this embodiment of the present disclosure at a reaction rate of 0.01. Figure 8B The accompanying diagram shows a comparison of experimental results at a reaction rate of 0.1, as provided in the embodiments of this disclosure. Figure 8CA comparative graph showing experimental results at a reaction rate of 1, provided in an embodiment of this disclosure, is illustrated. For example... Figures 8A to 8C As shown, stress values ​​under different strain, strain rate, and temperature conditions are calculated based on the constitutive model and compared with thermal simulation compression test data.

[0165] like Figures 8A-8C As shown, curve 1A represents the predicted value at 940℃, curve 1B represents the experimental value at 940℃, and the two curves at 940℃ are compared. Curve 2A represents the predicted value at 980℃, curve 2B represents the experimental value at 930℃, and the two curves at 930℃ are compared. Curve 3A represents the predicted value at 1020℃, curve 3B represents the experimental value at 1020℃, and the two curves at 1020℃ are compared. Curve 4A represents the predicted value at 1060℃, curve 4B represents the experimental value at 1060℃, and the two curves at 1060℃ are compared. Curve 5A represents the predicted value at 1100℃, curve 5B represents the experimental value at 1100℃, and the two curves at 1100℃ are compared. Based on this, the average relative error between the predicted value and the reference value of each curve under different deformation conditions is shown in Table 10. It can be seen that the relative error is basically controlled within 10%, and the established constitutive model under high temperature conditions is in good agreement with the experimental data.

[0166] Table 10 shows the average relative error between predicted and reference values.

[0167]

[0168] Based on this, the method provided in this disclosure can quickly establish a constitutive model for simulating the plastic forming of high-temperature alloy materials under high-temperature conditions. It comprehensively and accurately reflects the coupled influence of multiple factors such as temperature, deformation rate, and deformation degree on the flow stress of high-temperature alloy materials during high-temperature deformation, precisely describing the thermal deformation behavior and characteristics under high-temperature conditions. Simultaneously, it provides model and data support for completing high-confidence modeling of plastic forming processes such as forging of high-temperature alloy materials. This is the foundation and prerequisite for achieving high-precision simulation of plastic forming processes such as forging of high-temperature alloy materials, and is of great significance for further analysis of material deformation characteristics and optimization of forming processes. Furthermore, the method provided in this disclosure is simple and easy to implement, has high prediction accuracy, is efficient, saves experimental costs and time, and has a wide range of applications, making it widely applicable to various types of high-temperature alloy materials.

[0169] Figure 9 A schematic diagram of a constitutive model building apparatus for a high-temperature alloy provided in an embodiment of this disclosure is shown, as follows: Figure 9 As shown, the constitutive model building device 900 for the high-temperature alloy includes:

[0170] The acquisition module 901 is configured to acquire multiple stress-strain curves of the experimental sample at multiple sample temperatures through compression tests;

[0171] The determination module 902 is configured to determine multiple peak data corresponding to multiple stress-strain curves, wherein the peak data includes peak stress, strain data and sample temperature;

[0172] The first establishment module 903 is configured to establish a constitutive equation containing parameters to be determined based on multiple peak stresses and empirical formulas, wherein the empirical formulas are used to describe the relationship between strain data and temperature.

[0173] The second establishment module 904 is configured to establish a mapping relationship between multiple curve peak data and the parameters to be determined.

[0174] Construction module 905 is configured to build a constitutive model based on the mapping relationship and the constitutive equation.

[0175] In one possible implementation, obtaining multiple stress-strain curves of the experimental sample at different temperatures through compression testing includes: setting test parameters, the test parameters including multiple sample temperatures and multiple strain data; performing a compression test on the material according to the test parameters to obtain compression test data of the experimental sample, wherein the compression test data includes stress data and strain data; and plotting stress-strain curves based on the stress data and the strain data.

[0176] In one possible implementation, the peak data of the curve is processed based on the constitutive equation to obtain a first scatter plot at multiple temperatures; the first scatter plot is subjected to linear regression processing to obtain multiple relationship curves between the peak stress data and the strain data at different temperatures; and the solution formula for the parameter to be determined is determined based on the multiple relationship curves.

[0177] In one possible implementation, the parameter values ​​corresponding to the parameters to be solved in the constitutive equation are obtained based on the solution formula; a second scatter plot of the parameter values ​​under different external forces is plotted; and the second scatter plot is fitted using a polynomial fitting method to obtain the fitting result.

[0178] In one possible implementation, if the fitting result meets preset conditions, a mapping relationship is established between multiple curve peak data and the parameters to be determined.

[0179] In one possible implementation, the parameters to be determined include at least one of the following: the structural factor A of the experimental sample, the stress level parameter α of the experimental sample under stress, the stress index n of the experimental sample, and the deformation activation energy Q of the experimental sample.

[0180] In one possible implementation, the constitutive equation is expressed as:

[0181]

[0182] in, It is the strain rate, measured in seconds (s). -1 α represents the stress level parameter when the experimental sample is subjected to force, and the unit is MPa. -1 A represents the structure factor of the experimental sample, in units of s. -1 σ is the instantaneous stress during deformation, in MPa; Q represents the deformation activation energy of the test sample, in kJ / mol; R is the gas constant; n represents the stress exponent n of the test sample; T is the deformation temperature, in K.

[0183] In one possible implementation, the constitutive model is expressed as:

[0184]

[0185] Where f1(ε) represents the strain function of α, m represents the total degree of the fitting polynomial, i represents the degree of the fitting polynomial, and V 1i Let ε represent the fitting coefficient of f1(ε). i Let f2(ε) represent the terms of the fitted polynomial, and let B represent the strain function of n. 2i f2(ε) represents the fitting coefficient, f3(ε) represents the strain function of Q, and B represents the strain coefficient of Q. 3i f3(ε) represents the fitting coefficient; f4(ε) represents the strain function of ln(A), B 4i f4(ε) represents the strain function of Q.

[0186] This disclosure also provides an electronic device, including: at least one processor; a memory for storing at least one processor-executable instruction; wherein the at least one processor is used to execute the instruction to implement the steps of the method disclosed in this disclosure.

[0187] Figure 10 A schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure is shown. For example... Figure 10 As shown, the electronic device 1000 includes at least one processor 1001 and a memory 1002 coupled to the processor 1001. The processor 1001 can perform the corresponding steps in the methods disclosed in the embodiments of this disclosure.

[0188] The processor 1001 described above can also be referred to as a Central Processing Unit (CPU), which can be an integrated circuit chip with signal processing capabilities. Each step in the method disclosed in this embodiment can be implemented by the integrated logic circuitry in the hardware of the processor 1001 or by instructions in software form. The processor 1001 can be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this embodiment can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can be located in the memory 1002, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor 1001 reads information from the memory 1002 and, in conjunction with its hardware, completes the steps of the method described above.

[0189] Furthermore, various operations / processes according to this disclosure, implemented via software and / or firmware, can be transmitted from a storage medium or network to a computer system with a dedicated hardware architecture, for example, Figure 11 The computer system 1100 shown is equipped with the programs that constitute the software. When various programs are installed, the computer system is able to perform various functions, including functions such as those described above. Figure 11 A schematic diagram of the structure of a computer system provided in an embodiment of this disclosure is shown.

[0190] Computer system 1100 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0191] like Figure 11As shown, the computer system 1100 includes a computing unit 1101, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 1102 or a computer program loaded from a storage unit 1108 into a random access memory (RAM) 1103. The RAM 1103 may also store various programs and data required for the operation of the computer system 1100. The computing unit 1101, ROM 1102, and RAM 1103 are interconnected via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0192] Multiple components in computer system 1100 are connected to I / O interface 1105, including: input unit 1106, output unit 1107, storage unit 1108, and communication unit 1109. Input unit 1106 can be any type of device capable of inputting information into computer system 1100. Input unit 1106 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 1107 can be any type of device capable of presenting information and may include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 1108 may include, but is not limited to, hard disks and optical disks. Communication unit 1109 allows computer system 1100 to exchange information / data with other devices via a network such as the Internet, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0193] The computing unit 1101 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1101 performs the various methods and processes described above. For example, in some embodiments, the methods disclosed in this disclosure can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 1108. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 1100 via ROM 1102 and / or communication unit 1109. In some embodiments, the computing unit 1101 can be configured to perform the methods disclosed in this disclosure by any other suitable means (e.g., by means of firmware).

[0194] This disclosure also provides a computer-readable storage medium, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is able to perform the methods disclosed in this disclosure.

[0195] The computer-readable storage medium in this disclosure can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. The aforementioned computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specifically, the aforementioned computer-readable storage medium may include electrical connections based on one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0196] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0197] This disclosure also provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, it implements the methods disclosed in the embodiments of this disclosure.

[0198] In embodiments of this disclosure, computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or it can be connected to an external computer.

[0199] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0200] The modules, components, or units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the modules, components, or units do not necessarily constitute a limitation on the module, component, or unit itself.

[0201] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary hardware logic components that can be used include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0202] The above description is merely an embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0203] While specific embodiments of this disclosure have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of this disclosure. The scope of this disclosure is defined by the appended claims.

Claims

1. A method for establishing a constitutive model for simulation applications of high-temperature alloy plastic hot forming, characterized in that, include: Multiple stress-strain curves of the experimental sample at multiple sample temperatures were obtained through compression tests; Determine multiple peak data points corresponding to multiple stress-strain curves, wherein the peak data points include peak stress, strain data, and sample temperature; Constitutive equations containing parameters to be determined are established based on multiple peak stresses and empirical formulas, wherein the empirical formulas are used to describe the relationship between strain data and temperature; Establish a mapping relationship between multiple peak data points of the curves and the parameters to be determined; Establish a constitutive model based on the mapping relationship and the constitutive equation; The expression for the constitutive model is: in, express α The strain function, m This represents the total degree of the fitted polynomial. i This indicates the degree of the fitted polynomial. express The fitting coefficient, Denotes the terms of the fitted polynomial. express n The strain function, express The fitting coefficient, express Q The strain function, express The fitting coefficient; The strain function representing ln(A) express express Q The strain function.

2. The method according to claim 1, characterized in that, The process of obtaining multiple stress-strain curves of the experimental sample at different temperatures through compression testing includes: Set test parameters, which include multiple sample temperatures and multiple strain data; A compression test is performed on the material according to the test parameters to obtain the compression test data of the test sample, wherein the compression test data includes stress data and strain data; Stress-strain curves are plotted based on the stress and strain data.

3. The method according to claim 1, characterized in that, The method further includes: The peak data of the curve are processed based on the constitutive equation to obtain a first scatter plot at multiple temperatures; Perform linear regression processing on the first scatter plot to obtain multiple relationship curves between the peak stress data and the strain data at different temperatures; The solution formula for the parameter to be determined is determined based on multiple relation curves.

4. The method according to claim 3, characterized in that, The method further includes: Based on the solution formula, solve for the parameter values ​​corresponding to the parameters to be solved in the constitutive equation; Plot a second scatter plot of the parameter values ​​under different external forces; The second scatter plot is fitted using a polynomial fitting method to obtain the fitting result.

5. The method according to claim 4, characterized in that, The method further includes: If the fitting results meet the preset conditions, a mapping relationship is established between multiple curve peak data and the parameters to be determined.

6. The method according to claim 1, characterized in that, The parameters to be determined include the structure factor of the experimental sample. A The stress level parameters of the experimental sample under stress α The stress index of the test sample n and the deformation activation energy of the test sample Q At least one of them.

7. The method according to claim 1, characterized in that, The expression for the constitutive equation is: in, It is the strain rate, measured in seconds (s). -1 ; α This parameter represents the stress level of the experimental sample under stress, and its unit is MPa. -1 ; A The structure factor of the experimental sample is expressed in s. -1 ; σ It is the instantaneous stress during the deformation process, and its unit is MPa; Q The deformation activation energy of the test sample is expressed in kJ / mol. R It is the gas constant; n Indicates the stress index of the test sample n ; T It is the deformation temperature, and the unit is K.

8. A constitutive model establishment device for simulation application of high-temperature alloy plastic thermoforming, characterized in that, include: The acquisition module is configured to acquire multiple stress-strain curves of the experimental sample at multiple sample temperatures through compression tests; The determination module is configured to determine multiple peak data corresponding to multiple stress-strain curves, wherein the peak data includes peak stress, strain data and sample temperature; The first establishment module is configured to establish a constitutive equation containing parameters to be determined based on multiple peak stresses and empirical formulas, wherein the empirical formulas are used to describe the relationship between strain data and temperature. The second module is configured to establish a mapping relationship between multiple curve peak data and the parameters to be determined. The building module is configured to build a constitutive model based on the mapping relationship and the constitutive equation; The expression for the constitutive model is: in, express α The strain function, m This represents the total degree of the fitted polynomial. i This indicates the degree of the fitted polynomial. express The fitting coefficient, Denotes the terms of the fitted polynomial. express n The strain function, express The fitting coefficient, express Q The strain function, express The fitting coefficient; The strain function representing ln(A) express express Q The strain function.

9. An electronic device, characterized in that, include: At least one processor; Memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the steps of the method as described in any one of claims 1 to 7.

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