Method and device for predicting low-cycle fatigue life of metal-based composite material

Abstract

The invention provides a method and a device for predicting the low-cycle fatigue life of a metal-based composite material, relates to the technical field of engines, and aims to improve the prediction efficiency of the low-cycle fatigue life and reduce the cost. The method comprises the following steps: acquiring mechanical parameters of the metal-based composite material, a stress value of the metal-based composite material and a low-cycle fatigue parameter of a metal matrix; determining a mapping relation between the stress value of the metal-based composite material and the stress value or the strain value of each component material by using a unit cell method; fitting the initial low-cycle fatigue life prediction model of the metal matrix based on the low-cycle fatigue parameters of the metal matrix to obtain a precise low-cycle fatigue life model of the metal matrix; and based on the parameters, the mapping relation and the model, determining the low-cycle fatigue life of the metal-based composite material corresponding to the stress value of the metal-based composite material.

Description

Technical Field

[0001] The present invention relates to the field of engine technology, and in particular to a method and device for predicting the low cycle fatigue life of a metal matrix composite material. Background Art

[0002] Metal matrix composites have a series of advantages such as high specific strength, specific stiffness, specific modulus, excellent creep and fatigue performance, and good high temperature performance. They are particularly suitable for components that are mainly subjected to unidirectional tension and compression loads. In addition, metal matrix composites can adjust the microscopic structural parameters such as component material properties, volume fraction, fiber arrangement mode, etc. according to structural needs, thereby changing the macroscopic mechanical properties of metal matrix composites.

[0003] However, the currently commonly used macroscopic phenomenological method to establish a constitutive model for low-cycle fatigue life analysis requires a large number of experiments, which is inefficient and has high research costs. Summary of the invention

[0004] According to one aspect of the present disclosure, a method for predicting low cycle fatigue life of a metal matrix composite material is provided, the method comprising:

[0005] Obtaining mechanical parameters of multiple component materials of the metal matrix composite material, stress values ​​of the metal matrix composite material, and low cycle fatigue parameters of the metal matrix;

[0006] Based on the constitutive model of each of the component materials and the mechanical parameters of the plurality of the component materials, a mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each of the component materials is determined by using a unit cell method;

[0007] Fitting an initial low cycle fatigue life prediction model of the metal matrix based on the low cycle fatigue parameters of the metal matrix to obtain an accurate low cycle fatigue life model of the metal matrix;

[0008] Based on the mechanical parameters of multiple component materials of the metal matrix composite material, the low-cycle fatigue parameters of the metal matrix, the stress value of the metal matrix composite material, the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material, and the accurate low-cycle fatigue life model of the metal matrix, the low-cycle fatigue life of the metal matrix composite material corresponding to the stress value of the metal matrix composite material is determined.

[0009] According to another aspect of the present disclosure, a device for predicting low cycle fatigue life of a metal matrix composite material is provided, the device comprising:

[0010] An acquisition module, used to acquire mechanical parameters of multiple component materials of the metal matrix composite material, stress values ​​of the metal matrix composite material and low cycle fatigue parameters of the metal matrix;

[0011] A determination module, configured to determine a mapping relationship between a stress value of the metal matrix composite material and a stress value or a strain value of each of the component materials using a unit cell method based on a constitutive model of each of the component materials and mechanical parameters of a plurality of the component materials;

[0012] The determination module is also used to fit the initial low-cycle fatigue life prediction model of the metal matrix based on the low-cycle fatigue parameters of the metal matrix to obtain an accurate low-cycle fatigue life model of the metal matrix;

[0013] The determination module is also used to determine the low-cycle fatigue life of the metal matrix composite material corresponding to the stress value of the metal matrix composite material based on the mechanical parameters of multiple component materials of the metal matrix composite material, the low-cycle fatigue parameters of the metal matrix, the stress value of the metal matrix composite material, the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material, and the accurate low-cycle fatigue life model of the metal matrix.

[0014] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0015] processor;

[0016] and a memory for storing programs;

[0017] The program includes instructions, which, when executed by the processor, cause the processor to perform any one of the methods provided in the present disclosure.

[0018] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions are used to enable the computer to execute any one of the methods provided in the present disclosure.

[0019] In one or more technical solutions provided in the embodiments of the present application, the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material is determined by using the unit cell method, and the initial low-cycle fatigue life prediction model of the metal matrix is ​​fitted with the low-cycle fatigue parameters of the metal matrix included in the component materials. By combining the fitted accurate low-cycle fatigue life prediction model of the metal matrix and the stress state of the metal matrix corresponding to the stress value of the metal matrix composite material, the low-cycle fatigue life of the metal matrix composite material under a certain fatigue stress can be calculated, that is, the low-cycle fatigue life of the metal matrix composite material is analyzed based on the mesoscopic parameters of the metal matrix composite material, which can reflect the influence of the mesoscopic parameters of the metal matrix composite material, so as to facilitate the low-cycle fatigue life prediction of metal matrix composite materials with different volume fractions, reduce the number of experiments, and reduce the cost of experimental research. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0021] Figure 1 A flowchart of a method for predicting low cycle fatigue life of a metal matrix composite material provided according to an exemplary embodiment of the present disclosure is shown;

[0022] Figure 2 A flowchart of a method for determining a mapping relationship between a stress value of a metal matrix composite material and a stress value or a strain value of each component material provided according to an exemplary embodiment of the present disclosure is shown;

[0023] Figure 3 A flowchart of a method for determining a mapping relationship between a strain value of a metal matrix composite material and a strain value of each component material provided according to an exemplary embodiment of the present disclosure is shown;

[0024] Figure 4A The structure of the unit cell model provided according to the exemplary embodiment of the present disclosure is shown. Figure 1 ;

[0025] Figure 4B The structure of the unit cell model provided according to the exemplary embodiment of the present disclosure is shown. Figure 2 ;

[0026] Figure 5 A flowchart of a method for determining a mapping relationship between a stress value of a metal matrix composite material and a stress value or a strain value of each component material provided according to an exemplary embodiment of the present disclosure is shown;

[0027] Figure 6 A flowchart of an iterative method for calculating a stress value of a metal matrix composite material according to an exemplary embodiment of the present disclosure is shown;

[0028] Figure 7 A flowchart of a method for determining an accurate low-cycle fatigue life prediction model for a metal matrix provided according to an exemplary embodiment of the present disclosure is shown;

[0029] Figure 8 A schematic block diagram showing functional modules of a device for predicting low cycle fatigue life of a metal matrix composite material according to an exemplary embodiment of the present disclosure is shown;

[0030] Fig. 9 A schematic block diagram of a chip according to an exemplary embodiment of the present disclosure is shown;

[0031] Fig.10 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0032] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0033] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0034] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0035] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0036] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0037] Before introducing the embodiments of the present disclosure, the following definitions are given for the terms involved in the embodiments of the present disclosure:

[0038] Modulus refers to the ratio of stress to strain of a material under load.

[0039] The Manson-Coffin formula is a method based on statistical analysis of material fatigue test data. It is based on the following assumption: the total fatigue life of the material is determined by the relationship between the strain amplitude (or stress amplitude) and the cyclic strain (or cyclic stress) fatigue test data of the material. The stress state refers to the fact that when an object is subjected to force, the magnitude and direction of its internal stress not only varies with the orientation of the cross section, but is also not necessarily the same at each point on the same cross section. The stress state theory is the basis of strength calculation.

[0040] Metal Matrix Composites (MMCs) have a series of advantages such as high specific strength, specific stiffness, specific modulus, excellent creep and fatigue properties, and good high temperature performance. In particular, unidirectional fiber-reinforced metal matrix composites are particularly suitable for components that are mainly subjected to unidirectional tensile and compressive loads. For example, the integral blade ring of SiC fiber-reinforced titanium alloy matrix composite (SiC / Ti composite) of aircraft engine compressor. The high modulus and high strength of SiC fiber can significantly improve the tensile and compressive strength in the fiber direction and improve the load-bearing capacity of the overall blade ring.

[0041] Metal matrix composites are designable, with materials and structures designed as an integrated whole. The microstructural parameters such as component material properties, volume fraction, and fiber arrangement can be adjusted according to structural requirements. The macroscopic mechanical properties of the material change with the microscopic design parameters. Since a large number of experiments are required to establish a constitutive model using a macroscopic phenomenological method, researchers have been committed to studying the damage and failure mechanisms of materials and their structures, and establishing constitutive models based on microscopic structural parameters to reduce the number of experiments.

[0042] An exemplary embodiment of the present disclosure provides a method for predicting the low-cycle fatigue life of a metal-based composite material. A general unit cell method is used to establish a mapping relationship between macroscopic stress / strain and component stress / strain, and mechanical properties such as the modulus under the current macroscopic stress are determined based on the fiber volume fraction, the mechanical property parameters of the component materials, and their stress-strain state. The prediction of the material cyclic stress-strain relationship based on the microscopic parameters of the silicon carbide titanium alloy composite material is achieved through the continuous iteration of the stress increment.

[0043] On this basis, based on the low-cycle fatigue damage mechanism of silicon carbide fiber reinforced titanium alloy-based composites, the fatigue test data of titanium alloy was used to fit the modified Manson-Coffin formula (Morrow, SWT), and the low-cycle fatigue life analysis method of silicon carbide titanium alloy composites and integral blade rings was established in combination with the corresponding titanium alloy matrix stress / strain state under a certain macroscopic fatigue stress, thereby reducing the number of tests and reducing research costs.

[0044] Figure 1A flowchart of a method for predicting the low cycle fatigue life of a metal matrix composite material provided according to an exemplary embodiment of the present disclosure is shown. Figure 1 As shown, the method for predicting the low cycle fatigue life of a metal matrix composite material provided by an exemplary embodiment of the present disclosure includes:

[0045] Step 110: obtaining mechanical parameters of multiple component materials of the metal matrix composite material, stress values ​​of the metal matrix composite material, and low cycle fatigue parameters of the metal matrix.

[0046] Exemplarily, the mechanical parameters of the multiple component materials of the metal matrix composite material include the volume fraction of each component material and the stress-strain performance parameters of each component material. The stress value of the metal matrix composite material can be the stress value of a certain location of the product prepared by the metal matrix composite material. For example, when the metal matrix composite material is used in an engine compressor in the aerospace field, the stress value of the metal matrix composite material can be a pressure parameter at a certain location of the engine compressor. The low-cycle fatigue parameters of the metal matrix included in the component materials can be the low-cycle fatigue test data of the titanium alloy, and the low-cycle fatigue test data of the titanium alloy can be obtained through a standard fatigue test. The following is an example of silicon carbide / titanium alloy composite material.

[0047] Step 120: Based on the constitutive model of each component material and the mechanical parameters of the multiple component materials, the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material is determined using the unit cell method. Since the constitutive model of each component material and the mechanical parameters of the multiple component materials are known, the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material can be determined based on the unit cell method in combination with the constitutive model of each component material and the mechanical parameters of the multiple component materials.

[0048] It should be noted that in the process of solving the constitutive model, the silicon carbide fiber data uses the fiber data in foreign literature, and the relevant data of the titanium alloy matrix uses the domestic TC17 material data, and the data comes from the "Aerospace Engine Material Data Manual".

[0049] Step 130: Fitting the initial low-cycle fatigue life prediction model of the metal matrix based on the low-cycle fatigue parameters of the metal matrix to obtain an accurate low-cycle fatigue life prediction model of the metal matrix. At this time, the low-cycle fatigue experimental data of the metal matrix can be used for fitting to obtain the low-cycle fatigue life prediction model of the metal matrix. It should be understood that the low-cycle fatigue experimental data of the metal matrix here can be obtained from the material data manual.

[0050] In practical applications, the cyclic stress-strain curve of the titanium alloy matrix can be obtained based on the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material. The amplitude / mean value of the stress / strain under cyclic load and other parameters can be calculated and substituted into the accurate low-cycle fatigue life prediction model of the corresponding metal matrix. The life of the metal matrix composite material can be predicted according to the strain life (ε-N) curve of the material.

[0051] Among them, the initial low-cycle fatigue life prediction model of the metal matrix can adopt the total strain equation based on the Manson-Coffin formula, which is expressed as follows:

[0052]

[0053] In the formula, ε a is the total strain amplitude, ε ea is the elastic strain amplitude, ε pa Plastic strain amplitude, E is the elastic modulus, σ f is the fatigue strength coefficient, N is the number of fatigue times, b is the fatigue strength index, ε f is the fatigue ductility coefficient, and c is the fatigue ductility index.

[0054] Based on the strain-cycle curves of the TC17 titanium alloy matrix at different temperatures in the material data manual, the initial low-cycle fatigue life prediction model of the metal matrix is ​​fitted to obtain the accurate low-cycle fatigue life prediction model parameters of the metal matrix. Then, according to the accurate low-cycle fatigue life prediction model parameters of the metal matrix, the accurate low-cycle fatigue life prediction model of the metal matrix can be determined. The specific parameters are shown in Table 1.

[0055] Table 1 Parameters of strain-cycle curves of TC17 titanium alloy matrix at different temperatures

[0056] Temperature / ℃ <![CDATA[σ f / MPa]]> E / MPa b <![CDATA[ε f ]]> c 20 1866 111500 -0.091 0.3239 -0.701 200 1464 110000 -0.082 0.4317 -0.741 400 1406 103500 -0.087 08810 -0.826

[0057] Step 140: Determine the low cycle fatigue life of the metal matrix composite material corresponding to the stress value of the metal matrix composite material based on the mechanical parameters of the multiple component materials of the metal matrix composite material, the low cycle fatigue parameters of the metal matrix, the stress value of the metal matrix composite material, the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material, and the accurate low cycle fatigue life model of the metal matrix.

[0058] It can be seen that the present disclosure adopts the unit cell method to determine the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material, and uses the low-cycle fatigue parameters of the metal matrix included in the component materials to fit the initial low-cycle fatigue life prediction model of the metal matrix. By combining the fitted accurate low-cycle fatigue life prediction model of the metal matrix and the stress state of the metal matrix corresponding to the stress value of the metal matrix composite material, the low-cycle fatigue life of the metal matrix composite material under a certain fatigue stress can be calculated, that is, the low-cycle fatigue life of the metal matrix composite material is analyzed based on the mesoscopic parameters of the metal matrix composite material, which can reflect the influence of the mesoscopic parameters of the metal matrix composite material, so as to facilitate the low-cycle fatigue life prediction of metal matrix composite materials with different volume fractions, reduce the number of experiments, and reduce the cost of experimental research.

[0059] As a possible implementation, Figure 2 A flowchart of a method for determining a mapping relationship between a stress value of a metal matrix composite material and a stress value or a strain value of each component material provided according to an exemplary embodiment of the present disclosure is shown. Figure 2 As shown, the above constitutive model based on each component material uses the unit cell method to determine the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material, including:

[0060] Step 210: Determine the mapping relationship between the strain value of the metal matrix composite material and the strain value of each component material using the unit cell method, wherein the strain value of the metal matrix composite material is the average value of the strain values ​​of the multiple component materials.

[0061] Step 220: Determine a mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material based on the constitutive model of each component material and the mapping relationship between the strain value of the metal matrix composite material and the strain value of each component material.

[0062] In some alternatives, Figure 3 A flowchart of a method for determining a mapping relationship between a strain value of a metal matrix composite material and a strain value of each component material provided according to an exemplary embodiment of the present disclosure is shown. Figure 3 As shown, the mapping relationship between the strain value of the metal matrix composite material and the strain value of each component material is determined by the unit cell method, including:

[0063] Step 310: Determine the strain value of each component material based on the unit cell model of the metal matrix composite material.

[0064] For example, Figure 4A The structure of the unit cell model provided according to the exemplary embodiment of the present disclosure is shown. Figure 1 , Figure 4BThe structure of the unit cell model provided according to the exemplary embodiment of the present disclosure is shown. Figure 2 .like Figure 4A and Figure 4B As shown, a three-dimensional fiber reinforced composite material model can be used as a cell model. Due to its periodicity, its basic cell structure is a 2×2 subcell structure, in which one subcell is a fiber (gray part) and the other three subcells are metal matrices (white parts). The x1 direction is the fiber direction, that is, the main direction of the material; the x2 and x3 directions are transverse, and the cell is periodic in the transverse x2 and x3 directions. The fiber area is h1×l1, and the spacing in the two directions is h2 and l2, respectively. Each circular fiber is simplified into a square cross-section with the same volume fraction. This equivalent stress-strain field distribution is different from that of the real fiber cross-section, but its macroscopic equivalent stress-strain is consistent. Each subcell is marked with (β,γ=1,2), and there is a local coordinate system x1 in each subcell. The origin of the local coordinate system is located at the center of each subcell. Since the present disclosure mainly focuses on the average effect of stress and strain of multiple component materials of the metal matrix composite material and its influence on the macroscopic properties of the metal matrix composite material, the subcell can use a linear expression to describe the displacement equation, and the displacement function can be expressed as:

[0065]

[0066] In the formula, represents the displacement of the daughter cell, represents the displacement of the daughter cell center, and They are the subcell displacement functions about and The coefficient of Figure 4A The direction in which β and γ represent Figure 4B The number combination of the daughter cells in .

[0067] The strain value in each component material (subcell) can be obtained through the strain-displacement relationship. The specific formula is as follows:

[0068]

[0069] In the formula, represents the strain within each daughter cell.

[0070] Step 320: Determine the strain value of the metal matrix composite material based on the strain value of each component material.

[0071] In practical applications, since the displacement between sub-cells of the cell model must meet the continuity condition and the periodic boundary condition between adjacent cells, the displacement of sub-cells within the cell needs to be equal on the sub-cell boundary, that is:

[0072]

[0073] In the formula, h1 and h2 represent the height of the subcell, l1 and l2 represent the width of the subcell, In the equation, u represents displacement, superscript 1γ represents that the β value of βγ is 1, and γ is a variable; similarly, superscript β1 represents that the γ value of βγ is 1, and β is a variable. Indicates Figure 4A The related symbol rules in the following text are the same and will not be repeated.

[0074] In the single cell model, the displacement continuity condition of the subcell boundary is relaxed to the displacement integral (equivalent average displacement) of the boundary is equal. The integral at the boundary is subtracted to zero to indicate that the equivalent average displacement is equal. The calculation formula is as follows:

[0075]

[0076] Substituting the subcell displacement function (1) into equations (4-a) and (4-b), we can obtain:

[0077]

[0078] At the same time, on the cell boundary, the periodic displacement boundary condition with the adjacent cells needs to be satisfied, that is, the displacement between cell a and the adjacent a-1 and a+1 cells also satisfies the weakened continuity condition. For example, in the x2 direction:

[0079]

[0080] In the formula, the subscript below indicates the cell boundary below this cell, and above indicates the cell boundary above this cell. Through similar derivation, we can get:

[0081]

[0082] Subtracting (7-b) from (7-a) and (7-a) from (5-a) yields:

[0083]

[0084] Similarly, in the x3 direction we can get:

[0085]

[0086] In the x1 direction (i.e., the fiber direction), the equal strain condition must be met. Therefore, the partial derivative of the above displacement function with respect to the direction variable x1 is taken to obtain the corresponding strain, and its expression is as follows:

[0087]

[0088] From equations (8), (10) and (12), we can get:

[0089]

[0090] From formula (13), we can know that the strain in the subcell is a constant, that is, it satisfies the equal strain condition. At the same time, the elastic strain, thermal strain, inelastic strain and stress in the subcell are all constants, which are equal to the average value of the subcell. Then the strain component expression in the subcell is:

[0091]

[0092] The average strain of the cell, that is, the strain value (macroscopic strain) of the metal matrix composite material, is expressed as:

[0093]

[0094] Step 330: Based on the strain value of each component material and the strain value of the metal matrix composite material, determine a mapping relationship between the strain value of the metal matrix composite material and the strain value of each component material.

[0095] In practical applications, the above equations (9), (11) and (12) are expressed in terms of daughter cell and cell strain as follows:

[0096]

[0097] In the formula, represents the macroscopic average strain, the subscript 22 represents the strain in the x2 direction, the subscript 12 represents the shear strain in the x1 and x2 directions, γ=1,2 represents the sum of the ε values ​​with superscripts γ=1 and γ=2 in the formula, and β=1,2 represents the sum of the ε values ​​with superscripts β=1 and β=2 in the formula.

[0098] The 13 equations in formula (16) are written in matrix form as follows:

[0099]

[0100] in is the macroscopic strain, A G It is used to reflect the mapping relationship between macroscopic strain and microscopic strain, that is, the mapping relationship between the strain value of the metal matrix composite material and the strain value of each component material. Its components are:

[0101] And ε s is the strain component of the subcell, and its expression is: s =(ε (11) , ε (12) , ε (21) , ε (22) ).

[0102] In some alternatives, Figure 5 A flowchart of a method for determining a mapping relationship between a stress value of a metal matrix composite material and a stress value or a strain value of each component material provided according to an exemplary embodiment of the present disclosure is shown. Figure 5 As shown, based on the constitutive model of each component material and the mapping relationship between the strain value of the metal matrix composite material and the strain value of each component material, the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material is determined, including:

[0103] Step 510: Determine the corresponding relationship between the strain value of the metal matrix composite material and the stress value of each component material based on the constitutive model of each component material and the mapping relationship between the strain value of the metal matrix composite material and the strain value of each component material.

[0104] In practical applications, since the load continuity condition needs to be met on the subcell boundary and the stress inside the subcell is equal, the expression of the stress boundary condition on the subcell and cell boundary is:

[0105]

[0106] In the formula, σ represents stress. The rules of superscripts and subscripts can be referred to the relevant description in the previous text and will not be repeated here.

[0107]

[0108] In the formula, is the subcell material stiffness matrix, is the total strain of daughter cells, is the inelastic strain (such as plastic strain), is the thermal strain. Substituting the constitutive equation (19) into the stress boundary condition equation (18), we get the stress boundary condition equation in strain form:

[0109]

[0110] By deriving the displacement and load continuity of the sub-cell boundary within the cell and the periodic boundary between cells, the cell mechanical equation with the sub-cell strain as the basic unknown quantity is obtained. Combining equations (17) and (20), we can obtain:

[0111]

[0112] in,

[0113] Solving equation (21) yields:

[0114] Matrices A and D are the strain concentration matrices (Strain Concentration Tensor) in the homogenization theory. Among them: Matrix A is a 24×6 matrix, and D is a 24-order square matrix. Matrices A and D can be written in the form of subcell combinations:

[0115] The strain of each daughter cell can be written as:

[0116]

[0117] Substituting formula (23) into the subcell constitutive equation (19), the subcell stress expressed by the cell strain is obtained, that is, the mapping relationship between the strain value of the metal matrix composite material and the stress value of each component material, which is expressed as follows:

[0118]

[0119] Step 520: Determine the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material based on the mapping relationship between the stress value of each component material, the strain value of the metal matrix composite material and the stress value of each component material, and the strain value of the metal matrix composite material and the strain value of each component material.

[0120] In practical applications, since the cell stress is the volume average of the stress of each sub-cell (the stress of the sub-cell is multiplied by its area and the overall average is taken), that is, the mapping relationship between the stress value of the metal matrix composite material and the stress value of each component material, its expression formula is as follows:

[0121]

[0122] In some optional embodiments, the method provided by the present disclosure also includes: determining the constitutive relationship between the strain value of the metal matrix composite material and the stress value of the metal matrix composite material based on the stress value of each component material and the corresponding relationship between the strain value of the metal matrix composite material and the stress value of each component material.

[0123] In practical applications, the explicit relationship between cell stress and cell strain can be further derived based on the previous formula, that is, the constitutive relationship between the strain value of the metal matrix composite material and the stress value of the metal matrix composite material is expressed as:

[0124]

[0125] In the formula, C * is the macroscopic equivalent stiffness matrix of the composite material, and its expression is:

[0126]

[0127] is the macroscopic inelastic strain of the composite material, and its expression is:

[0128]

[0129] is the macroscopic thermal strain of the composite material, and its expression is:

[0130]

[0131] The expression of the macroscopic thermal expansion coefficient is:

[0132]

[0133] At this point, the mapping relationship between the stress value of the metal matrix composite material and the strain value of each component material can be deduced through the constitutive relationship between the strain value of the metal matrix composite material and the stress value of the metal matrix composite material, the mapping relationship between the stress value of the metal matrix composite material and the stress value of each component material, and the mapping relationship between the strain value of the metal matrix composite material and the strain value of each component material.

[0134] As a possible implementation, Figure 6 FIG. 1 is a flowchart of an iterative method for calculating the stress value of a metal matrix composite material according to an exemplary embodiment of the present disclosure. Figure 6 As shown, before determining the low cycle fatigue life of the metal matrix composite material corresponding to the stress value of the metal matrix composite material, the method provided by the present disclosure further includes:

[0135] Step 610: Determine the plastic strain value of the metal matrix based on the mechanical parameters of the multiple component materials of the metal matrix composite material, the stress value of the metal matrix composite material, and the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material.

[0136] For example, it can be derived from the micromechanical equation of the above-mentioned universal unit cell that when the macroscopic strain (increment) is known In the case of , the strain (increment) ε of each subcell in the cell is obtained (βγ) (Δε (βγ) ) (can be directly obtained by mapping formula (17)), and then the constitutive model of the component material is used on the subcell to update the current subcell stress Finally, the macro stress

[0137] Step 620: When the plastic strain value of the metal matrix satisfies the plastic yield iteration condition, the strain value of each component material after yielding is determined based on the plastic constitutive model of the metal matrix. It should be understood that the plastic yield iteration condition here refers to the inelastic strain ε of the right-hand subcell of equation (23) after the metal matrix enters the inelastic stage, that is, after plasticity. I(βγ) is greater than 0 and equal to the total strain ε (βγ) At this time, the plastic strain value of the metal matrix meets the plastic yield iteration condition and needs to be solved iteratively.

[0138] For example, when the titanium alloy exceeds the yield point due to excessive load, the elastic constitutive model needs to be developed into a corresponding inelastic constitutive program to solve the nonlinear cyclic stress-strain relationship problem of low-cycle fatigue life. Among them, since the titanium alloy matrix is ​​isotropic, the isotropic hardening model can be used for iterative solution. Since the isotropic hardening model is relatively mature and is introduced in many textbooks, it will not be elaborated in detail here.

[0139] The key to the plastic constitutive model is the yield function, flow law and hardening law. The yield function expression is: f = J2-σ Y -R(p); where J2 is Mises stress, σ Y is the yield stress under uniaxial loading, R(p) is the isotropic hardening term, and p is the accumulated plastic strain.

[0140] The expression of the flow law is:

[0141] The hardening law is the relationship between post-yield stress and plastic strain, and can be expressed in linear, piecewise linear, or nonlinear terms. Here, the nonlinear plastic hardening equation commonly used in cyclic plasticity is used, and its expression is: dR = b*(QR)dp, where b and Q are material parameters for isotropic hardening.

[0142] Step 630: When the strain value after yielding of each component material satisfies the iteration termination condition, the strain value and the stress value of the metal matrix composite material are determined based on the strain value after yielding of each component material. It should be understood that the iteration termination condition here can be that the yield function f corresponding to the strain value after yielding of each component material is less than the residual.

[0143] In practical applications, the calculation process of the above plastic strain is (n cycles):

[0144] (a) Initially assuming that all strain increments are elastic strains, calculate the stress increment Δσ trial =CΔε, and obtain the corresponding stress deviator and Mises stress;

[0145] (b) Substitute this Mises stress into the yield criterion. If f<0, no new yield occurs, the strain increment is all elastic strain, and the calculation ends; if f>=0, yield occurs, and proceed to the next step (k cycle);

[0146] (c) According to the current hardening modulus H, the expression is Calculate the cumulative plastic strain increment Δp, the expression is: Update the hardening term R(p);

[0147] (d) Obtaining the plastic strain increment according to the flow law;

[0148] (e) Update stress and Jacobi matrix;

[0149] (f) Check that the yield function f is smaller than the residual. If not, return to c. If it is satisfied, the plastic calculation ends.

[0150] In some alternatives, Figure 7 A flowchart of a method for determining an accurate low-cycle fatigue life prediction model for a metal matrix according to an exemplary embodiment of the present disclosure is shown. Figure 7 As shown, before determining the low cycle fatigue life of the metal matrix composite material corresponding to the stress value of the metal matrix composite material, the method provided by the present disclosure further includes:

[0151] Step 710: Obtain low cycle fatigue parameters of the metal matrix based on fatigue test data, that is, low cycle fatigue data of the titanium alloy matrix in the silicon carbide titanium alloy composite material.

[0152] Step 720: Based on the low cycle fatigue parameters of the metal matrix, the initial low cycle fatigue life prediction model of the metal matrix is ​​modified to obtain an accurate low cycle fatigue life prediction model of the metal matrix.

[0153] In practical applications, since the fatigue performance data of titanium alloys are obtained through standard fatigue tests, they will be different from the performance of titanium alloys of metal matrix composites, and the influence of average stress on fatigue life needs to be considered in actual use. It is necessary to correct the strain-cycle curve, that is, the initial low-cycle fatigue life prediction model of the metal matrix. The correction methods used in this disclosure are Morrow correction and SWT correction to obtain an accurate low-cycle fatigue life prediction model for the metal matrix.

[0154] The strain-life equation modified by Morrow is:

[0155]

[0156] SWT (Smith-Watson-Topper) correction:

[0157]

[0158] The cyclic stress and strain of the titanium alloy matrix are substituted into the above two formulas respectively, and the smaller value is selected as the accurate low-cycle fatigue life prediction model, and the fatigue life of the metal matrix composite material is calculated based on the accurate low-cycle fatigue life prediction model.

[0159] The above mainly introduces the solution provided by the embodiment of the present disclosure from the perspective of the server. It is understandable that, in order to implement the above functions, the server includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0160] The embodiments of the present disclosure may divide the server into functional units according to the above method examples. For example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiments of the present disclosure is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0161] In the case of dividing each functional module according to each function, an exemplary embodiment of the present disclosure provides a device for predicting the low cycle fatigue life of a metal matrix composite material, and the device for predicting the low cycle fatigue life of a metal matrix composite material can be a server or a chip applied to a server. Figure 8 FIG. 1 is a schematic block diagram showing the functional modules of a device for predicting the low cycle fatigue life of a metal matrix composite material according to an exemplary embodiment of the present disclosure. Figure 8 As shown, the prediction device 800 for the low cycle fatigue life of the metal matrix composite material comprises:

[0162] An acquisition module 801 is used to acquire mechanical parameters of multiple component materials of the metal matrix composite material, stress values ​​of the metal matrix composite material, and low cycle fatigue parameters of the metal matrix;

[0163] A determination module 802 is used to determine the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material by using a unit cell method based on the constitutive model of each component material and the mechanical parameters of the plurality of component materials;

[0164] The determination module 802 is further used to fit the initial low cycle fatigue life prediction model of the metal matrix based on the low cycle fatigue parameters of the metal matrix to obtain an accurate low cycle fatigue life model of the metal matrix;

[0165] The determination module 802 is also used to determine the low-cycle fatigue life of the metal matrix composite material corresponding to the stress value of the metal matrix composite material based on the mechanical parameters of multiple component materials of the metal matrix composite material, the low-cycle fatigue parameters of the metal matrix, the stress value of the metal matrix composite material, the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material, and the accurate low-cycle fatigue life model of the metal matrix.

[0166] As a possible implementation manner, the parameters of the multiple component materials of the metal matrix composite material include the volume fraction of each component material and the stress-strain performance parameter of each component material.

[0167] As a possible implementation method, the above-mentioned determination module 802 is also used to determine the mapping relationship between the strain value of the metal matrix composite material and the strain value of each component material using the unit cell method; based on the constitutive model of each component material and the mapping relationship between the strain value of the metal matrix composite material and the strain value of each component material, determine the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material.

[0168] In some optional embodiments, the above-mentioned determination module 802 is also used to determine the strain value of each component material based on the unit cell model of the metal-based composite material; determine the strain value of the metal-based composite material based on the strain value of each component material; determine the mapping relationship between the strain value of the metal-based composite material and the strain value of each component material based on the strain value of each component material and the strain value of the metal-based composite material.

[0169] In some optional embodiments, the strain value of the metal matrix composite material is an average of the strain values ​​of multiple component materials.

[0170] In some optional embodiments, the above-mentioned determination module 802 is also used to determine the mapping relationship between the strain value of the metal matrix composite material and the stress value of each component material based on the constitutive model of each component material and the mapping relationship between the strain value of the metal matrix composite material and the strain value of each component material; based on the stress value of each component material, the mapping relationship between the strain value of the metal matrix composite material and the stress value of each component material, and the mapping relationship between the strain value of the metal matrix composite material and the strain value of each component material, determine the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material.

[0171] In some optional embodiments, the above-mentioned determination module 802 is also used to determine the constitutive relationship between the strain value of the metal matrix composite material and the stress value of the metal matrix composite material based on the stress value of each component material and the corresponding relationship between the strain value of the metal matrix composite material and the stress value of each component material.

[0172] As a possible implementation method, the above-mentioned determination module 802 is also used to determine the plastic strain value of the metal matrix based on the mechanical parameters of multiple component materials of the metal matrix composite material, the stress value of the metal matrix composite material, and the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material; when the plastic strain value of the metal matrix satisfies the plastic yield iteration condition, the strain value of each component material after yielding is determined based on the plastic constitutive model of the metal matrix; when the strain value of each component material after yielding satisfies the iteration termination condition, the strain value of the metal matrix composite material and the stress value of the metal matrix composite material are determined based on the strain value of each component material after yielding.

[0173] In some optional embodiments, the acquisition module 801 is further used to acquire low cycle fatigue parameters of the metal matrix based on fatigue test data;

[0174] The determination module 802 is also used to modify the initial low cycle fatigue life prediction model of the metal matrix composite material based on the low cycle fatigue parameters of the metal matrix, so as to obtain an accurate low cycle fatigue life prediction model of the metal matrix.

[0175] Fig. 9 Schematic block diagram of a chip according to an exemplary embodiment of the present disclosure is shown. Fig. 9 As shown, the chip 900 includes one or more (including two) processors 901 and a communication interface 902. The communication interface 902 can support the server to perform the data sending and receiving steps in the above image processing method, and the processor 901 can support the server to perform the data processing steps in the above image processing method.

[0176] Optional, such as Fig. 9 As shown, the chip 900 also includes a memory 903, which may include a read-only memory and a random access memory, and provides operation instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory (NVRAM).

[0177] In some embodiments, Fig. 9As shown, the processor 901 performs corresponding operations by calling the operation instructions stored in the memory (the operation instructions may be stored in the operating system). The processor 901 controls the processing operations of any one of the terminal devices, and the processor may also be called a central processing unit (CPU). The memory 903 may include a read-only memory and a random access memory, and provides instructions and data to the processor 901. A portion of the memory 903 may also include NVRAM. For example, in an application, the memory, the communication interface, and the memory are coupled together through a bus system, wherein the bus system may include a power bus, a control bus, and a status signal bus in addition to a data bus. However, for the sake of clarity, in Fig. 9 Various buses are labeled as bus system 904.

[0178] The method disclosed in the above-mentioned embodiment of the present disclosure can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above-mentioned processor may be a general-purpose processor, a digital signal processor (digital signal processing, DSP), an ASIC, a field-programmable gate array (field-programmable gate array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiment of the present disclosure can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware.

[0179] The exemplary embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program that can be executed by the at least one processor, and the computer program is used to cause the electronic device to perform the method according to the embodiment of the present disclosure when executed by the at least one processor.

[0180] The exemplary embodiments of the present disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform the method according to the embodiments of the present disclosure.

[0181] The exemplary embodiments of the present disclosure further provide a computer program product, including a computer program, wherein when the computer program is executed by a processor of a computer, the computer is used to enable the computer to perform the method according to the embodiments of the present disclosure.

[0182] refer to Fig.10 , a block diagram of an electronic device 1000 that can be used as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0183] like Fig.10 As shown, the electronic device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the electronic device 1000 can also be stored. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0184] A plurality of components in the electronic device 1000 are connected to the I / O interface 1005, including: an input unit 1006, an output unit 1007, a storage unit 1008, and a communication unit 1009. The input unit 1006 may be any type of device capable of inputting information to the electronic device 1000, and the input unit 1006 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 1007 may be any type of device capable of presenting information, and may include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1008 may include but is not limited to a disk, an optical disk. The communication unit 1009 allows the electronic device 1000 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0185] The computing unit 1001 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1001 performs the various methods and processes described above. For example, in some embodiments, the aforementioned method may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1008. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1000 via the ROM 1002 and / or the communication unit 1009. In some embodiments, the computing unit 1001 may be configured to perform the method provided by the exemplary embodiments of the present disclosure in any other appropriate manner (e.g., by means of firmware).

[0186] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0187] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0188] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0189] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0190] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0191] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0192] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instruction is loaded and executed on a computer, the process or function described in the embodiment of the present disclosure is executed in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user device or other programmable device. The computer program or instruction may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program or instruction may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server, data center, etc. that integrates one or more available media. The available medium may be a magnetic medium, for example, a floppy disk, a hard disk, a tape; it may also be an optical medium, for example, a digital video disc (DVD); it may also be a semiconductor medium, for example, a solid state drive (SSD).

[0193] Although the present disclosure has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present disclosure. Accordingly, this specification and the drawings are merely exemplary illustrations of the present disclosure as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present disclosure. Obviously, those skilled in the art may make various modifications and variations to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is also intended to include these modifications and variations.

Claims

1. A method for predicting the low cycle fatigue life of a metal matrix composite material, characterized in that: The method comprises: Obtaining mechanical parameters of multiple component materials of the metal matrix composite material, stress values ​​of the metal matrix composite material, and low cycle fatigue parameters of the metal matrix; Based on the constitutive model of each of the component materials and the mechanical parameters of the plurality of the component materials, a mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each of the component materials is determined by using a unit cell method; Fitting an initial low cycle fatigue life prediction model of the metal matrix based on the low cycle fatigue parameters of the metal matrix to obtain an accurate low cycle fatigue life model of the metal matrix; Based on the mechanical parameters of multiple component materials of the metal matrix composite material, the low-cycle fatigue parameters of the metal matrix, the stress value of the metal matrix composite material, the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material, and the accurate low-cycle fatigue life model of the metal matrix, the low-cycle fatigue life of the metal matrix composite material corresponding to the stress value of the metal matrix composite material is determined.

2. The method according to claim 1, characterized in that The mechanical parameters of the multiple component materials of the metal matrix composite material include the volume fraction of each component material and the stress-strain performance parameter of each component material.

3. The method according to claim 1, characterized in that The method of determining the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material by using a unit cell method based on the constitutive model of each component material comprises: Determine the mapping relationship between the strain value of the metal matrix composite material and the strain value of each of the component materials by using a unit cell method; Based on the constitutive model of each of the component materials and the mapping relationship between the strain value of the metal matrix composite material and the strain value of each of the component materials, the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each of the component materials is determined.

4. The method according to claim 3, characterized in that The method of using the unit cell method to determine the mapping relationship between the strain value of the metal matrix composite material and the strain value of each of the component materials includes: Determining a strain value of each of the component materials based on a unit cell model of the metal matrix composite material; determining a strain value of the metal matrix composite material based on a strain value of each of the component materials; Determining a mapping relationship between the strain value of the metal matrix composite material and the strain value of each of the component materials based on the strain value of each of the component materials and the strain value of the metal matrix composite material; and / or, The strain value of the metal matrix composite material is an average of the strain values ​​of the plurality of component materials.

5. The method according to claim 3, characterized in that: The determining of the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each of the component materials based on the constitutive model of each of the component materials and the mapping relationship between the strain value of the metal matrix composite material and the strain value of each of the component materials comprises: Determine a mapping relationship between the strain value of the metal matrix composite material and the stress value of each of the component materials based on a constitutive model of each of the component materials and a mapping relationship between the strain value of the metal matrix composite material and the strain value of each of the component materials; Based on the stress value of each component material, the mapping relationship between the strain value of the metal-based composite material and the stress value of each component material, and the mapping relationship between the strain value of the metal-based composite material and the strain value of each component material, the mapping relationship between the stress value of the metal-based composite material and the stress value or strain value of each component material is determined.

6. The method according to claim 5, characterized in that The method further includes: determining a constitutive relationship between the strain value of the metal matrix composite material and the stress value of the metal matrix composite material based on the stress value of each of the component materials and the corresponding relationship between the strain value of the metal matrix composite material and the stress value of each of the component materials.

7. The method according to claim 1, characterized in that Before determining the low cycle fatigue life of the metal matrix composite material corresponding to the stress value of the metal matrix composite material, the method further includes: Determining the plastic strain value of the metal matrix based on the mechanical parameters of the multiple component materials of the metal matrix composite material, the stress value of the metal matrix composite material, and the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each of the component materials; When the plastic strain value of the metal matrix satisfies the plastic yield iteration condition, determining the strain value of each component material after yielding based on the plastic constitutive model of the metal matrix; When the strain value after yielding of each component material meets the iteration termination condition, the strain value of the metal matrix composite material and the stress value of the metal matrix composite material are determined based on the strain value after yielding of each component material.

8. The method according to claim 7, characterized in that Before determining the low cycle fatigue life of the metal matrix composite material corresponding to the stress value of the metal matrix composite material, the method further includes: Based on fatigue test data, obtaining low cycle fatigue parameters of the metal matrix; The initial low cycle fatigue life prediction model of the metal matrix composite material is corrected based on the low cycle fatigue parameters of the metal matrix to obtain an accurate low cycle fatigue life prediction model of the metal matrix.

9. A device for predicting low cycle fatigue life of metal matrix composite materials, characterized in that: The device comprises: An acquisition module, used to acquire mechanical parameters of multiple component materials of the metal matrix composite material, stress values ​​of the metal matrix composite material and low cycle fatigue parameters of the metal matrix; A determination module, configured to determine a mapping relationship between a stress value of the metal matrix composite material and a stress value or a strain value of each of the component materials using a unit cell method based on a constitutive model of each of the component materials and mechanical parameters of a plurality of the component materials; The determination module is also used to fit the initial low-cycle fatigue life prediction model of the metal matrix based on the low-cycle fatigue parameters of the metal matrix to obtain an accurate low-cycle fatigue life model of the metal matrix; The determination module is also used to determine the low-cycle fatigue life of the metal matrix composite material corresponding to the stress value of the metal matrix composite material based on the mechanical parameters of multiple component materials of the metal matrix composite material, the low-cycle fatigue parameters of the metal matrix, the stress value of the metal matrix composite material, the mapping relationship between the stress value of the metal matrix composite material and the stress value or strain value of each component material, and the accurate low-cycle fatigue life model of the metal matrix.

10. An electronic device, characterized in that: include: processor; and a memory for storing programs; The program includes instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions for causing the computer to execute the method according to any one of claims 1-8.

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