A Prediction Method for Additive Fatigue Life and Residual Strength Based on Random Defects

Abstract

The present invention relates to a method for predicting the fatigue life and remaining strength of additive manufacturing based on random defects. By obtaining the specific characteristics and random distribution forms of defects, and focusing on calculating three types of defects, namely diagonal cracks, surface cracks, and embedded cracks, which are likely to cause structural failure under actual working conditions, the obtained fatigue life is more in line with the dispersion of the actual fatigue life experimental results compared with the results obtained by traditional continuum mechanics calculation methods. Compared with the commonly used equivalent area projection method, this method generates defects in the panel where the critical failure plane is located during life calculation, has higher design accuracy and reliability, simplifies the defect model and calculation process, is more convenient to operate when the accuracy is similar, and is especially faster in calculation when the calculation target is a large structural component.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical digital data processing, and more particularly, to a method for predicting the additive fatigue life and remaining strength based on random defects. Background Art

[0002] Aviation fatigue and structural integrity assessment are important guarantees for the safe service of aircraft. With the wide application of advanced processes such as additive manufacturing, the problems of fatigue crack propagation life and remaining strength analysis of additives have become increasingly prominent. During the additive manufacturing process, due to factors such as inherent pores in metal powders, unstable laser power, incomplete overlap of scanning paths, severe heat exchange, and repeated phase changes of liquefaction and solidification, it is difficult to avoid internal defects in the additive structure.

[0003] Regarding the technical problems of fatigue crack propagation life and remaining strength analysis of additives, the existing calculation methods for fatigue crack propagation life ignore the defect characteristics in the additive manufacturing process and the fatigue damage they may cause, limiting the accurate prediction of the fatigue crack propagation life of additive manufacturing aerospace structures. Specifically, first, the fatigue performance and integrity assessment of traditional aircraft structures are mostly based on assumptions such as continuum mechanics and no defects inside the material. When designing additive structures and assessing fatigue life, a large safety margin is often given to cover the inhomogeneity inside the material. Second, when considering the influence of defect factors on fatigue performance, the general method is to use the stress concentration factor to correct the stress field at the crack tip according to the defect type and size, and the commonly used models are mostly based on the equivalent projected area method, which fails to fully reflect the three-dimensional spatial geometric characteristics of internal defects in the material, and the fatigue performance calculation margin is insufficient under severe working conditions where multiple defects are in the same plane.

[0004] In response to this, the existing technology further proposes a more comprehensive calculation method for fatigue crack propagation life to analyze the fatigue crack propagation life and remaining strength of additives, but this method has the technical defect of a cumbersome calculation process, especially when the calculation target is a large structural component, it cannot quickly predict the structural life. Summary of the Invention

[0005] The technical problem to be solved by the present invention is how to overcome the technical defects of low accuracy and cumbersome calculation process in the technical solutions for the fatigue crack propagation life and remaining strength analysis of additives. To overcome the above defects of the prior art, the present invention provides a method for predicting the additive fatigue life and remaining strength based on random defects.

[0006] A method for predicting the additive fatigue life and remaining strength based on random defects provided by the present invention includes the following steps:

[0007] S1: Obtain the defect information image of the additive printed part of the wall material to be measured using an X-ray microtomography device, and use a three-dimensional visualization module to obtain defect features by means of feature extraction based on gray-scale transformation;

[0008] S2: Obtain the random distribution information of the defect size using the defect features through parameter estimation;

[0009] S3: Obtain the panel where the critical failure plane is located in the additive printed part, and use an engineering simulation device to generate multiple defects including corner cracks, surface cracks, and buried cracks on the panel according to the random distribution information;

[0010] S4: Based on the multiple defects generated in step S3, given the material properties and the structural stress concentration factor, obtain the number of cycles under the specified working conditions using the engineering simulation device, and use this number of cycles as the initial number of cycles;

[0011] S5: Apply a load to the panel once, and obtain the crack increment according to the stress intensity factor range during the loading process of the panel, and obtain the current size of each defect on the panel using the crack increment;

[0012] S6: Calculate the distance between adjacent defects based on the current size of each defect, and determine whether there is crack fusion between adjacent defects according to the distance between adjacent defects;

[0013] If so, fuse all adjacent defects with crack fusion into a new defect according to the defect connectivity criterion for defect update, and then execute the next step;

[0014] If not, execute the next step;

[0015] S7: Obtain the maximum stress intensity factor at the crack tip based on the current defects of the panel, and determine whether the maximum stress intensity factor is greater than the fracture toughness;

[0016] If so, use the number of cycles at this time as the fatigue crack growth life of the wall material to be measured;

[0017] If not, obtain the remaining strength of the wall material to be measured using the remaining strength calculation formula, update the number of cycles, and then loop back to execute step S5.

[0018] The method for predicting the additive fatigue life and remaining strength based on random defects disclosed by the present invention, in view of the technical problems of the present invention, obtains the specific characteristics and random distribution forms of defects by performing steps S1 - S3, and focuses on calculating three types of defects (i.e., corner cracks, surface cracks, and embedded cracks) that are likely to cause structural failure in actual working conditions. The obtained fatigue life is more in line with the dispersion of the actual fatigue life test results compared with the results obtained by the traditional continuous medium mechanics calculation method. Moreover, compared with the commonly used equivalent area projection method, since the method in this application generates defects in the panel where the critical failure plane is located during life calculation, it has higher design accuracy and reliability; simplifies the defect model and calculation process, and is more convenient to operate when the accuracy is similar, especially when the calculation target is a large structural component, the calculation is also faster.

[0019] In a possible implementation manner, the defect characteristics include the volume, aspect ratio, angle, and spatial distribution information of the defects; this solution can ensure more accurate extraction of defect characteristics and contribute to further analysis of the defects.

[0020] In a possible implementation manner, the step S1 includes the following steps:

[0021] S11: Use the X-ray microtomography equipment to extract the size and distribution characteristic information of the defects in the additive printed part, and obtain the defect information image;

[0022] S12: Use the three-dimensional visualization module to obtain the defect characteristics from the defect information image by means of feature extraction based on gray-scale transformation.

[0023] In a possible implementation manner, the step S2 includes the following steps:

[0024] S21: Consider the shape of the defect as the shape of an ellipsoid to obtain the maximum axis size of each defect, and use the log-normal distribution as the fitting random distribution of the maximum axis size;

[0025] S22: Use the spatial distribution information in the defect characteristics to solve all the fitting parameters of the fitting random distribution by means of parameter estimation to obtain the probability distribution function of the maximum axis size;

[0026] This solution simplifies the defect shape to the shape of an ellipsoid, and fits the fitting random distribution of the maximum axis size according to the spatial distribution information in the defect characteristics, further ensuring the random regularity of generating defects on the panel where the critical failure plane is located.

[0027] In a possible implementation manner, the arithmetic formula of the probability distribution function of the fitting random distribution is as follows:

[0028] ,

[0029] In the formula,

[0030] represents the offset;

[0031] represents the area;

[0032] represents the logarithmic standard deviation;

[0033] represents the central abscissa;

[0034] represents the maximum axis size;

[0035] represents the proportion of the maximum axis size being ;

[0036] This solution can calculate the range of the aspect ratio of the defect, and further ensure that defects are generated on the panel where the critical failure plane is located.

[0037] In a possible implementation manner, in step S5, the formula for obtaining the crack increment according to the stress intensity factor range during the loading of the panel is as follows;

[0038] ,

[0039] In the formula,

[0040] represents the crack increment;

[0041] , and represent the material constants of the measured wall material;

[0042] represents the stress intensity factor range during the loading of the panel;

[0043] represents the stress ratio;

[0044] This solution can ensure the feasibility of obtaining the current sizes of the defects on the panel through the crack increment, contribute to the realization of defect update, not only ensure the accuracy of the calculation results, but also reduce the calculation process to a certain extent.

[0045] In a possible implementation manner, the expression of the remaining strength calculation formula in step S7 is as follows:

[0046] ,

[0047] In the formula,

[0048] represents the remaining strength of the wall material to be measured;

[0049] represents the pore diameter of the defect;

[0050] represents the width of the panel;

[0051] represents the thickness of the panel;

[0052] represents the area of the corner crack;

[0053] represents the area of the surface crack;

[0054] represents the area of the embedded crack;

[0055] represents the yield stress of the wall material to be measured;

[0056] Using the above calculation formula, the remaining strength can be calculated more accurately, and it helps to judge whether there is a situation where the remaining strength is insufficient.

[0057] In a possible implementation manner, in the step S7, if it is determined that the maximum stress intensity factor is not greater than the fracture toughness, after obtaining the remaining strength of the wall material to be measured, the following steps need to be executed before updating the number of cycles:

[0058] Judge whether the remaining strength is less than the allowable value of the remaining strength;

[0059] If so, take the number of cycles at this time as the fatigue crack growth life of the wall material to be measured;

[0060] If not, directly update the number of cycles. Description of the Drawings

[0061] Figure 1 It is a flowchart of a method for predicting the additive manufacturing fatigue life and remaining strength based on random defects disclosed in an embodiment of the present application;

[0062] Figure 2 It is an external view of an additive manufacturing part disclosed in an embodiment of the present application;

[0063] Figure 3 It is a curve graph of the defect size distribution function disclosed in an embodiment of the present application;

[0064] Figure 4Schematic diagram of a randomly generated single-hole plate with defects disclosed in the embodiments of the present application;

[0065] Figure 5 Schematic diagram of the meshing of the model and the crack front disclosed in the embodiments of the present application;

[0066] Figure 6 Schematic diagram of the connection mode of adjacent defects disclosed in the embodiments of the present application;

[0067] Figure 7 Schematic diagram of the fatigue life prediction result and life distribution fitting curve of the single-hole plate structure disclosed in the embodiments of the present application. Detailed implementation manners

[0068] First of all, those skilled in the art should understand that these implementation manners are only used to explain the technical principles of the embodiments of the present application, and are not intended to limit the protection scope of the embodiments of the present application. Those skilled in the art can adjust it as needed to adapt to specific application scenarios.

[0069] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0070] See Figures 1 to 5 , the embodiments of the present application disclose a method for predicting the fatigue life and remaining strength of additive manufacturing based on random defects, Figure 1 which is a flowchart of the method, and the method includes the following steps:

[0071] S1: Use an X-ray microtomography device to obtain an image of defect information of an additive printed part of a wall material to be measured, and use a three-dimensional visualization module to obtain defect features by means of feature extraction based on gray-scale transformation. In this embodiment, the defect features include the volume, aspect ratio, angle, and spatial distribution information of the defects.

[0072] Specifically, step S1 includes the following steps: S11: Use an X-ray microtomography device to extract the size and distribution feature information of defects in the additive printed part to obtain an image of defect information. S12: Use a three-dimensional visualization module to obtain defect features from the defect information image by means of feature extraction based on gray-scale transformation, and in this embodiment, the feature extraction method based on gray-scale transformation is a feature extraction method using binary processing.

[0073] In this embodiment, the image of defect information obtained by using an X-ray microtomography (abbreviated as CT scan) device is as Figure 2 shown, Figure 2Shows the external shape characteristics and loading direction of the additively manufactured titanium alloy single-hole plate to be analyzed. Among the size information of the titanium alloy single-hole plate, the length is 80 mm, the width is 24 mm, and the thickness is 4 mm. Then it is processed using a three-dimensional visualization module (the Avizo processing module in this embodiment). Through this processing module, defect characteristics can be obtained from the defect information image, and data and spatial distribution information such as defect volume, length-to-width ratio, and angle can be collected.

[0074] S2: Obtain the random distribution information of defect sizes by using defect characteristics through parameter estimation.

[0075] See Figure 3 , specifically in this embodiment, step S2 includes the following steps:

[0076] S21: Consider the shape of the defect as the shape of an ellipsoid to obtain the maximum axis size of each defect, and use the lognormal distribution as the fitting random distribution of the maximum axis size. The arithmetic formula of the probability distribution function of the fitting random distribution is as follows:

[0077] ,

[0078] In the formula,

[0079] represents the offset, which is a fitting parameter, and is written as y0 in Figure 3 ;

[0080] represents the area, which is a fitting parameter;

[0081] represents the log standard deviation, which is a fitting parameter;

[0082] represents the central abscissa, which is a fitting parameter, and is written as xc in Figure 3 ;

[0083] represents the maximum axis size;

[0084] represents the proportion of the maximum axis size being .

[0085] S22: Use the spatial distribution information in the defect characteristics to solve all the fitting parameters of the fitting random distribution through parameter estimation to obtain the probability distribution function of the maximum axis size.

[0086] The fitting function of the defect size distribution is obtained by fitting according to the Figure 3 statistical defect size distribution information, Figure 3The CT scan results herein refer to the spatial distribution information in the defect characteristics obtained by an X-ray microtomography device. The fitting parameters after heat treatment are selected for modeling, regarding the proportion of ellipsoidal defects , which is statistically uniformly distributed in [1, 4]. For convenience of calculation, is taken as 0, and after parameter estimation, is 9.99437, is 0.67627, is 62.2079.

[0087] S3: Obtain the panel where the critical failure plane is located in the additive printed part, and generate multiple defects including corner cracks, surface cracks, and embedded cracks on the panel according to the random distribution information by an engineering simulation device.

[0088] See Figure 2 , in this embodiment, in order to obtain the panel where the critical failure plane is located, this step adopts the most severe working condition, that is, a total load P of 15 KV is set on the titanium alloy single-hole plate, and round holes are set on the titanium alloy single-hole plate to apply nail load. The diameter of the round hole is 6 mm, the center of the hole is 12 mm away from the left side and the upper and lower sides, and the nail load P1 is 9 KV. Under this most severe working condition, the critical failure plane of the titanium alloy single-hole plate is Figure 2 the plane perpendicular to the total load P and passing through the center of the hole shown in ( Figure 2 the dashed box in represents the critical failure plane).

[0089] On the panel containing the critical failure plane, generate a plurality of randomly distributed ellipsoidal defects according to the law of the random distribution information obtained in step S22. According to the typical characteristics of the aviation structure, the defects in this example include corner cracks, surface cracks, and embedded cracks. As Figure 4 shown, the crack size range generated in the critical failure plane is 0.3 mm - 1 mm, and the number of generated cracks is 4, which includes three types of defects: corner cracks, surface cracks, and embedded cracks.

[0090] S4: Based on the multiple defects generated in step S3, given the material properties and the structural stress concentration coefficient, obtain the number of cycles under the specified working condition by an engineering simulation device, and use this number of cycles as the initial number of cycles.

[0091] Specifically, in this embodiment, when the material properties and the structural stress concentration coefficient are given, the Young's modulus of the given titanium alloy single-hole plate is 1150000 MPa, the Poisson's ratio is 0.33, the yield strength is 899 MPa, and the fatigue limit of the structure is 512.5 MPa. Subsequently, the number of cycles under the specified working condition, that is, the initial number of cycles, is automatically modeled and calculated in Abaqus, and the initial number of cycles is 10000.

[0092] S5: Load the panel once, obtain the crack increment based on the stress intensity factor range during the loading process of the panel, and obtain the current sizes of each defect on the panel based on the crack increment.

[0093] Specifically, refer to Figure 5 , and automatically calculate the crack increment through a python program after loading. During the process of obtaining the stress intensity factor range during the loading process of the panel, for the mesh division method of the titanium alloy single-hole plate (refer to Figure 5 ) is: the mesh size is set to 1 mm, the mesh size of the wedge-shaped element at the crack tip center is 0.02 mm, the mesh size of the elements outside the crack tip increases from 0.02 mm to 0.05 mm, and the total number of model meshes is approximately 300,000.

[0094] In step S5, the formula for obtaining the crack increment based on the stress intensity factor range during the loading process of the panel is as follows;

[0095] ,

[0096] In the formula,

[0097] represents the crack increment, that is, the increase in crack length per unit cycle;

[0098] , and represent the material constants of the measured wall material, which need to be determined through experiments. The material constants given in this example are 5.217E-9, is 0.64, is 3.29;

[0099] represents the stress intensity factor range during the loading process of the panel, indicating the stress intensity change at the crack tip;

[0100] represents the stress ratio, defined as the ratio of the minimum stress to the maximum stress, and is set to 0.1 in this example.

[0101] S6: Calculate the distance between adjacent defects based on the current sizes of each defect, and determine whether there is crack fusion between adjacent defects according to the distance between adjacent defects; if so, fuse all adjacent defects with crack fusion into a new defect according to the defect connection criterion for defect update, and then execute the next step; if not, execute the next step.

[0102] Specifically, it is determined whether crack fusion occurs according to the distance between adjacent defects. If fusion occurs, the information of the fused defects is deleted according to the given defect connectivity criterion and new defects are generated. Subsequently, mesh division can be re-performed to obtain the range of stress intensity factors of the panel during the loading process in the next cycle. If no fusion occurs, the mesh division is directly updated.

[0103] The schematic diagram of the defect connectivity criterion used in this example is as Figure 6 shown. If it is case (1), that is, a defect that has not developed into a through crack, new defects are generated according to the envelope method; if it is case (2), that is, a defect that has developed into a through crack fuses with other defects, the equivalent area method is adopted. If it is case (3), that is, the corner crack propagates, when the propagation area of the corner crack reaches 90% of the plane it is in, it develops into a through crack, and at this time it fuses in the same way as case (2), otherwise it fuses in the same way as case (1). If no defect fusion occurs, that is, case (4), the crack normally propagates incrementally.

[0104] S7: Obtain the maximum stress intensity factor at the crack tip based on the current defects of the panel, and determine whether the maximum stress intensity factor is greater than the fracture toughness;

[0105] If so, take the current number of cycles as the fatigue crack propagation life of the wall material under test;

[0106] If not, obtain the remaining strength of the wall material under test using the remaining strength calculation formula, and then determine whether the remaining strength is less than the allowable value of the remaining strength;

[0107] If so, it is considered that the remaining strength is insufficient, and take the current number of cycles as the fatigue crack propagation life of the wall material under test;

[0108] If not, update the number of cycles (add 1 to the original number of cycles to get the new number of cycles), and then go back to execute step S5.

[0109] Specifically, the given fracture toughness in this example is 107.93 , and in step S7, the expression of the remaining strength calculation formula is as follows:

[0110] ,

[0111] In the formula,

[0112] represents the remaining strength of the wall material under test;

[0113] represents the hole diameter of the defect;

[0114] represents the width of the panel;

[0115] represents the thickness of the representative panel;

[0116] represents the area of the corner crack;

[0117] represents the area of the surface crack;

[0118] represents the area of the embedded crack;

[0119] represents the yield stress of the wall material to be measured.

[0120] In this example, after running the calculation 20 times, all the results are statistically analyzed. The statistical results are as Figure 7 shown. Subsequently, the life distribution function of the wall material to be measured is obtained by fitting, which conforms to the dispersion and estimated value of the fatigue life in the actual experiment. It can be seen from the figure that the fitting result still follows the lognormal distribution.

[0121] It should be noted that one traversal of steps S5 - S7 is considered as one cycle. If in step S7, the maximum stress intensity factor is greater than the fracture toughness, then the cycle number at this time is taken as the fatigue crack growth life of the wall material to be measured. For example, if the cycle number at this time is the initial cycle number 10000, then 10000 is the fatigue crack growth life of the wall material to be measured. If the maximum stress intensity factor is not greater than the fracture toughness and it is necessary to enter the next cycle, that is, to return to execute step S5, then the cycle number at this time is the previous cycle number plus 1. That is, when updating the cycle number, if the cycle number at this time is the initial cycle number 10000, then the updated result is 10001.

[0122] The method for predicting the additive fatigue life and remaining strength based on random defects disclosed in this embodiment obtains the specific characteristics and random distribution forms of the defects by executing steps S1 - S3, and focuses on calculating three types of defects, namely corner cracks, surface cracks, and embedded cracks, which are likely to cause structural failure in actual working conditions. The obtained fatigue life is more in line with the dispersion of the actual fatigue life experimental results compared with the results obtained by the traditional continuous medium mechanics calculation method. Moreover, compared with the commonly used equivalent area projection method, since the method in this embodiment generates defects in the panel where the critical failure plane is located during life calculation, it has higher design accuracy and reliability; simplifies the defect model and calculation process, and is more convenient to operate when the accuracy is close, especially when the calculation target is a large structural component, the calculation is also faster.

[0123] In the description of the embodiments of the present application, it should be noted that in the description of the present application, the terms "inner", "outer", and other terms indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or component must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present application.

[0124] In the description of the present application, the description with reference to terms such as "one embodiment", "some embodiments", "in this embodiment", "specific example", or "some examples" means that the specific features, mechanisms, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, mechanisms, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0125] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for predicting the fatigue life and remaining strength of additive manufacturing based on random defects, characterized in that It includes the following steps: S1: Use an X-ray microtomography device to obtain a defect information image of the additive printed part of the wall material to be measured, and use a 3D visualization module to obtain defect features by means of feature extraction based on gray-scale transformation; S2: Obtain the random distribution information of the defect size by using the defect features through parameter estimation; S3: Obtain the panel where the critical failure plane is located in the additive printed part, and use an engineering simulation device to generate multiple defects including corner cracks, surface cracks and embedded cracks on the panel according to the random distribution information; S4: Based on the multiple defects generated in step S3, given the material properties and the structural stress concentration factor, obtain the number of cycles under the specified working conditions through the engineering simulation device, and use this number of cycles as the initial number of cycles; S5: Apply a load to the panel once, and obtain the crack increment according to the stress intensity factor range during the loading process of the panel, and obtain the current size of each defect on the panel with the crack increment; S6: Calculate the distance between adjacent defects based on the current size of each defect, and judge whether there is crack fusion between adjacent defects according to the distance between adjacent defects; If so, fuse all adjacent defects with crack fusion into a new defect according to the defect connectivity criterion for defect update, and then execute the next step; If not, execute the next step; S7: Obtain the maximum stress intensity factor at the crack tip based on the current defects of the panel, and judge whether the maximum stress intensity factor is greater than the fracture toughness; If so, use the number of cycles at this time as the fatigue crack growth life of the wall material to be measured; If not, obtain the remaining strength of the wall material to be measured by using the remaining strength calculation formula, update the number of cycles, and then return to execute step S5.

2. The method for predicting the additive fatigue life and remaining strength based on random defects according to claim 1, wherein The defect features include the volume, aspect ratio, angle and spatial distribution information of the defects.

3. The additive fatigue life and remaining strength prediction method based on random defects according to claim 2, wherein Step S1 includes the following steps: S11: Use the X-ray microtomography device to extract the size and distribution characteristic information of the defects in the additive printed part to obtain the defect information image; S12: Use the 3D visualization module to obtain the defect features from the defect information image by means of feature extraction based on gray-scale transformation.

4. The method for predicting the additive fatigue life and remaining strength based on random defects according to claim 2 or 3, characterized in that, Step S2 includes the following steps: S21: Consider the shape of the defect as an ellipsoidal shape to obtain the maximum axis size of each defect, and use the lognormal distribution as the fitting random distribution of the maximum axis size; S22: Use the spatial distribution information in the defect features to solve all fitting parameters of the fitting random distribution through parameter estimation to obtain the probability distribution function of the maximum axis size.

5. The method for predicting the additive fatigue life and remaining strength based on random defects according to claim 4, wherein The arithmetic formula of the probability distribution function of the fitting random distribution is as follows: ， In the formula, Represents an offset; represents an area; Represents the logarithmic standard deviation; represents the central abscissa; representing the maximum shaft size; representing that the maximum shaft size is proportion of.

6. The method for predicting the additive fatigue life and remaining strength based on random defects according to claim 5, characterized in that In step S5, the arithmetic formula for obtaining the crack increment according to the stress intensity factor range during the loading process of the panel is as follows; ， In the formula, representing the crack increment; , and represent the material constants of the wall material under test; represents the stress intensity factor range of the panel during the loading process; Represents the stress ratio.

7. The method for predicting the additive fatigue life and remaining strength based on random defects according to claim 5 or 6, characterized in that The expression of the remaining strength calculation formula in step S7 is as follows: ， In the formula, represent the remaining strength of the wall material to be measured; The hole diameter representing the defect; represent the width of the panel; represents the thickness of the panel; The area representing the corner crack; Represent the area of the surface crack; Represents the area of the embedded crack; represents the yield stress of the wall material to be measured.

8. The method for predicting the additive fatigue life and remaining strength based on random defects according to claim 7, wherein In the step S7, if it is determined that the maximum stress intensity factor is not greater than the fracture toughness, after obtaining the remaining strength of the wall material to be measured, the following steps need to be executed before updating the number of cycles: Determine whether the remaining strength is less than the allowable value of the remaining strength; If so, use the number of cycles at this time as the fatigue crack growth life of the wall material to be measured; If not, directly update the number of cycles.

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