A method, apparatus, medium, and product for predicting the fatigue residual strength of a fiber-reinforced composite
By constructing a binary mapping model of stiffness and strength and a damage accumulation function, and combining finite element analysis and experimental data, the accurate prediction of the residual fatigue strength of fiber-reinforced composite materials is achieved, solving the problems of insufficient accuracy and high cost in traditional methods. This method is applicable to the evaluation of composite material structures in the aerospace and automotive fields.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
- Filing Date
- 2026-03-09
- Publication Date
- 2026-06-05
Smart Images
Figure CN122154322A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of fiber-reinforced composite materials technology, and in particular to a method, equipment, medium, and product for predicting the fatigue residual strength of fiber-reinforced composite materials. Background Technology
[0002] In practical applications, fiber-reinforced composite components inevitably face fatigue damage due to long-term alternating loads. Unlike traditional metallic materials, the fatigue damage mechanism of composite materials is more complex, involving the coupled effects of multiple damage forms such as fiber fracture, matrix cracking, fiber-matrix interface debonding, and interlaminar delamination. These damages accumulate with the increase of cyclic loading, ultimately leading to a significant degradation of the material's strength, stiffness, and other mechanical properties. This not only affects the normal working performance of the component but may also trigger sudden structural failures, posing a serious threat to engineering safety.
[0003] Currently, researchers mainly use residual strength degradation models and finite element simulations to predict fatigue residual strength. However, these methods have significant drawbacks: on the one hand, macroscopic phenomenological residual strength degradation models fail to fully reflect the microscopic fatigue damage mechanism and cannot reveal the induction and coupling mechanisms between different damage modes. Furthermore, fatigue testing is costly and time-consuming. On the other hand, traditional finite element simulation methods focus on studying the strength of finite element structures under a single damage mode, resulting in insufficient prediction accuracy and the inability to obtain the degradation curve between simulated strength and the number of fatigue cycles. Summary of the Invention
[0004] The purpose of this application is to provide a method, device, medium, and product for predicting the residual fatigue strength of fiber-reinforced composite materials, which can predict the degradation curve of residual strength versus cycle number when the test stiffness and cycle number are known, thereby improving the accuracy of residual strength prediction.
[0005] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for predicting the residual fatigue strength of fiber-reinforced composite materials, including: Construct a stiffness binary mapping model and a strength binary mapping model; The stiffness univariate mapping model and the strength univariate mapping model are obtained by using the damage accumulation function based on the stiffness univariate mapping model and the strength univariate mapping model, respectively. Obtain the test stiffness and number of cycles of the fiber-reinforced composite material; The parameters of the damage accumulation function are obtained by fitting the stiffness univariate mapping model based on the experimental stiffness and cycle number of the fiber-reinforced composite material. Based on the parameters of the damage accumulation function and the intensity univariate mapping model, the degradation curve of the remaining intensity versus the number of cycles is obtained; based on the degradation curve, the remaining intensity prediction result is obtained.
[0006] In one embodiment, constructing a stiffness binary mapping model and a strength binary mapping model includes: Construct a finite element model of fiber-reinforced composite laminates with multiple damage modes; Longitudinal tensile simulation was performed on the finite element model of the fiber-reinforced composite laminate to obtain the simulation stiffness, simulation strength, matrix damage variables, and fiber damage variables. The simulated stiffness, the matrix damage variable, and the fiber damage variable are fitted to obtain the stiffness binary mapping model; The simulated intensity, the matrix damage variable, and the fiber damage variable are fitted to obtain the intensity binary mapping model.
[0007] In one implementation, based on the formula Determine the matrix damage variable; where, Indicates matrix damage variable, Indicates the number of cracks in the laminate matrix. Indicates the length of the laminate.
[0008] In one implementation, based on the formula Determine the fiber damage variable; where, Indicates fiber damage variable, Indicates the number of fiber fracture surfaces. Indicates the length of the laminate. Indicates the width of the laminate. This indicates the size of the fiber fracture surface.
[0009] In one embodiment, after applying a concentrated force load at the geometric center of the fiber-reinforced composite laminate finite element model at one end of the fixed fiber-reinforced composite laminate finite element model and at the other end of the fiber-reinforced composite laminate finite element model, a longitudinal tensile simulation is performed on the fiber-reinforced composite laminate finite element model.
[0010] In one embodiment, the process of obtaining the simulation intensity includes: During longitudinal tensile simulation, the initial fiber tensile damage coefficient at the unit integration point in the volume average region is obtained. The simulated strength is determined based on the initial damage coefficient of the fiber under tension.
[0011] In one embodiment, the damage accumulation function is expressed as: ; In the formula, Indicates the number of loops. Indicates the first matrix damage variable at the number of iterations. Indicates the first Fiber damage variable at the number of cycles , , , and These are the parameters of the damage accumulation function.
[0012] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the fiber-reinforced composite material fatigue residual strength prediction method described in any one of the above applications.
[0013] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for predicting the residual fatigue strength of fiber-reinforced composite materials as described above.
[0014] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method for predicting the residual fatigue strength of fiber-reinforced composite materials as described above.
[0015] According to the specific embodiments provided in this application, this application has the following technical effects: On the other hand, traditional finite element simulation methods focus on studying the strength of finite element structures under a single damage mode, resulting in insufficient prediction accuracy and an inability to obtain the relationship between simulation strength and fatigue cycle count.
[0016] This application provides a method, device, medium, and product for predicting the residual fatigue strength of fiber-reinforced composite materials. It employs a damage accumulation function to derive a stiffness univariate mapping model and a strength univariate mapping model based on both a stiffness binary mapping model and a strength binary mapping model, respectively, thus achieving the conversion between the two models. Furthermore, it obtains the parameters of the damage accumulation function by fitting the stiffness univariate mapping model with fiber-reinforced composite material test data (including test stiffness and cycle count). Then, based on the parameters of the damage accumulation function and the strength univariate mapping model, it obtains the degradation curve of residual strength versus cycle count. This method predicts the degradation curve of residual strength versus cycle count when the test stiffness and cycle count are known, thereby improving the accuracy of residual strength prediction. Moreover, it achieves residual strength prediction without requiring a large number of fatigue tests, significantly reducing fatigue testing costs and shortening the prediction cycle. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a flowchart of a method for predicting the fatigue residual strength of fiber-reinforced composite materials in one embodiment of this application; Figure 2 A schematic diagram of matrix cracking damage modeling provided in an embodiment of this application; Figure 3 This is a schematic diagram of fiber fracture damage modeling provided in an embodiment of this application; Figure 4 This is a schematic diagram of interlayer delamination damage modeling provided in an embodiment of this application; Figure 5 A schematic diagram of the average volume region provided in an embodiment of this application; Figure 6 This is a schematic diagram of a simulated stiffness degradation surface provided in an embodiment of this application; Figure 7 This is a schematic diagram of the simulated intensity degradation surface provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application.
[0019] Figure reference numerals: 1-fiber direction, 2-perpendicular to fiber direction, 3-laminate thickness direction, 4-0° layer, 5-fiber fracture surface, 6-interlaminar delamination damage, 7-matrix crack, 8-bulk average region, 9-simulated stiffness degradation surface, 10-simulated strength degradation surface. Detailed Implementation
[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0021] Based on the aforementioned background technology, this application develops a method for predicting the residual strength of composite materials that considers the coupling of multiple damage modes and is based on the microscopic fatigue damage mechanism. This method predicts the residual strength-cycle degradation curve given the known experimental stiffness-cycle count. The accurate prediction of this degradation curve has significant theoretical and engineering application value for ensuring the safe service of engineering structures and reducing life-cycle costs.
[0022] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0023] In one exemplary embodiment, such as Figure 1 As shown, a method for predicting the residual fatigue strength of fiber-reinforced composite materials is provided, including: S1, construct the stiffness binary mapping model and the strength binary mapping model.
[0024] S2, using the damage accumulation function, obtains the stiffness univariate mapping model and the strength univariate mapping model based on the stiffness binary mapping model and the strength binary mapping model respectively.
[0025] S3, obtain the test stiffness and number of cycles of the fiber-reinforced composite material.
[0026] S4. Based on the experimental stiffness and cycle number of fiber-reinforced composite materials, the parameters of the damage accumulation function are obtained by fitting a univariate stiffness mapping model.
[0027] S5. Based on the parameter and intensity univariate mapping model of the damage accumulation function, the degradation curve of residual strength versus cycle number is obtained. The prediction result of residual strength is obtained based on the degradation curve.
[0028] In one embodiment, to address the problem that traditional finite element simulation methods often focus on studying the strength of finite element structures under a single damage mode, resulting in insufficient prediction accuracy, the implementation process of step S1 in this application may include: S11, construct a finite element model of fiber-reinforced composite laminate with multiple damage modes.
[0029] S12, longitudinal tensile simulation is performed on the finite element model of fiber-reinforced composite laminate to obtain simulation stiffness, simulation strength, matrix damage variables and fiber damage variables.
[0030] S13, fit the simulation stiffness, matrix damage variables and fiber damage variables to obtain a stiffness binary mapping model.
[0031] S14, the simulated intensity and the corresponding matrix damage variables and fiber damage variables are fitted to obtain a binary strength mapping model. Specifically, the process of obtaining the simulated intensity includes: during the longitudinal tensile simulation, obtaining the initial fiber tensile damage coefficient at the element integration point in the volume-averaged region. The simulated intensity is determined based on the initial fiber tensile damage coefficient.
[0032] In this process, after applying a concentrated force load at the geometric center of one end of the fiber-reinforced composite laminate finite element model and the other end of the fiber-reinforced composite laminate finite element model, a longitudinal tensile simulation is performed on the fiber-reinforced composite laminate finite element model.
[0033] Based on formula Determine the matrix damage variable. Where, Indicates matrix damage variable, Indicates the number of cracks in the laminate matrix. Indicates the length of the laminate. Based on the formula. Determine the fiber damage variable. Where, Indicates fiber damage variable, Indicates the number of fiber fracture surfaces. Indicates the length of the laminate. Indicates the width of the laminate. This indicates the fiber fracture surface size. In practical applications, the laminate size, the number of matrix cracks, the number of fiber fracture surfaces, the fiber fracture surface size, and the delamination size at the crack tip can all be set in the Python script.
[0034] In one embodiment, to reveal the induction and coupling mechanisms between different damage modes, this application can establish a high-fidelity finite element model of composite laminate (i.e., a fiber-reinforced composite laminate finite element model) based on the ABAQUS finite element numerical analysis platform and Python secondary development, which can reflect the coupled damage of matrix cracking, interlaminar delamination, and fiber fracture. The degree of damage to the fiber-reinforced composite laminate structure can be controlled by matrix damage variables and fiber damage variables.
[0035] Modeling of matrix cracking damage (matrix cracks) as follows Figure 2 As shown: First, several rectangular and trapezoidal sub-blocks are created in the ABAQUS finite element numerical analysis platform to assemble the 90° layers in the composite laminate (e.g., ...). Figure 2 (as shown in part a) and 45° layer (as shown in part a) Figure 2 (as shown in part b); during the assembly process, only the relative positions of the sub-blocks are changed to splice the sub-blocks into a complete single layer. The Interaction module is not used to edit the spliced single layer. Therefore, a break effect can be produced at the seam of the spliced sub-blocks (i.e., matrix crack 7, to simulate matrix cracking).
[0036] Fiber fracture damage modeling, such as Figure 3 As shown: Figure 3 Part a is a side view of the laminate. Figure 3Part b is a top view of the laminate. Several cohesive units are inserted in a uniform array along the fiber direction 2 in the 0° layer 4, and then the cohesive units are deleted to achieve the fiber breakage effect (i.e., to generate the fiber breakage surface 5).
[0037] Interlayer delamination damage modeling, such as Figure 4 As shown, the 0° layer and the 90° layer (such as...) Figure 4 (as shown in part a) and 45° layer (as shown in part a) Figure 4 As shown in part b), the layers are stacked together in the specified layup order, and the upper and lower surfaces of all adjacent layers are constrained by the tie constraint in the Interaction module. Then, the tie constraint near the matrix crack is released to achieve local delamination (simulating interlayer delamination damage 6).
[0038] In one embodiment, a longitudinal tensile simulation of the fiber-reinforced composite laminate finite element model is performed based on the dynamic visualization analysis step of a finite element analysis platform (ABAQUS finite element numerical analysis platform is used in this embodiment). Specifically, one end of the fiber-reinforced composite laminate finite element model is constrained and fixed, while a concentrated force load is applied at the geometric center of the other end of the model. This is achieved through formulas... Determine the simulation stiffness; where, Indicates the simulated stiffness; The value represents the magnitude of the load (i.e., the magnitude of the concentrated force load), which can be manually entered in the Load module of the ABAQUS finite element numerical analysis platform. The displacement in the tensile direction of the finite element model of the fiber-reinforced composite laminate is obtained by extracting the displacement history variables of the force application point after simulation. The cross-sectional area of the laminate can be determined using parameters from the finite element model of the fiber-reinforced composite laminate in a Python script.
[0039] To determine the simulation intensity, this embodiment proposes the volume average coefficient method: First, a volume average region 8 that can representatively characterize the structural damage is determined, for example, such as... Figure 5The volume-average region 8 is shown as a reasonably defined region. Different failure criteria (such as the maximum tensile stress criterion, Hashin criterion, LaRC05 criterion, etc.) are written into the VUMAT subroutine using Fortran language to output the initial tensile damage coefficients of the fibers. Longitudinal tensile simulation of the fiber-reinforced composite laminate finite element model is performed in the ABAQUS finite element numerical analysis platform using the ABAQUS dynamic display analysis step. One end of the fiber-reinforced composite laminate finite element model is constrained and fixed, while a concentrated force load is applied at the geometric center of the other end. The initial tensile damage coefficients of the fibers at the element integration points within the volume-average region 8 are extracted, and their average value is calculated. When the average value reaches 1, the corresponding load is the structural failure strength (i.e., the simulated strength). The expression for the structural strength criterion using the volume-average coefficient method is: In the formula, Represents the average region of the body. This represents the initial damage coefficient of the fiber under tension. This represents the volume of the average region of the volume.
[0040] Furthermore, by constructing a Single-Hidden-Layer Feedforward Neural Network (SFLN) (a three-layer fully connected topology of "input layer-hidden layer-output layer"), the simulated stiffness, matrix damage variables, and fiber damage variables, as well as the simulated strength, matrix damage variables, and fiber damage variables, are fitted to obtain a stiffness binary mapping model and a strength binary mapping model, forming the simulated stiffness degradation surface 9 and the simulated strength degradation surface 10, as shown below. Figure 6 and Figure 7 As shown, where, and These represent the maximum matrix damage variable and the maximum fiber damage variable that can be obtained from the simulation data, respectively. In this embodiment, the number of neurons in the hidden layer of the single hidden layer feedforward neural network is 12; the tansig function is used as the activation function of the hidden layer to adapt to the magnitude of the input features after normalization; the purelin linear activation function is used as the output layer to ensure that the output value can cover the full range of mechanical properties; and the mean-square error (MSE) is used as the loss function.
[0041] In one embodiment, based on the laws governing fatigue damage evolution, a damage accumulation function is proposed to describe the functional relationship between damage variables (including matrix damage variables and fiber damage variables) and the number of cycles. The damage accumulation function is expressed as: In the formula, Indicates the number of loops. Indicates the first matrix damage variable at the number of iterations. Indicates the first Fiber damage variable at the number of cycles , , , and These are the parameters of the damage accumulation function.
[0042] Substituting the damage accumulation function into the stiffness binary mapping model and the strength binary mapping model respectively yields the stiffness univariate mapping model and the strength univariate mapping model. Further fitting the stiffness univariate mapping model using fiber-reinforced composite experimental data yields the parameters of the damage accumulation function. Substituting the fitted parameters of the damage accumulation function into the strength univariate mapping model provides the degradation curve of remaining strength versus cycle number.
[0043] Specifically, test data (including test stiffness and number of cycles) of fiber-reinforced composite materials within a certain time period are obtained and fitted with a univariate stiffness mapping model to finally obtain the degradation curve of residual strength versus number of cycles within this time period. Then, based on the degradation curve, the predicted residual strength corresponding to any number of cycles within this time period is determined.
[0044] Based on the above embodiments, compared with the prior art, this application has at least the following beneficial effects: 1. By explicitly creating a finite element model of fiber-reinforced composite laminates with multi-damage coupling of matrix cracking, delamination, and fiber fracture, we have achieved accurate simulation of the interaction mechanism of multiple damage modes during the fatigue damage evolution of fiber-reinforced composites. This overcomes the limitations of traditional methods that only consider a single damage mode and improves the prediction accuracy of residual strength. Furthermore, based on the ABAQUS finite element numerical analysis platform and Python secondary development, we have achieved quantitative control of damage variables, improving the flexibility and versatility of the residual strength prediction method.
[0045] 2. The proposed volume average coefficient method effectively solves the problems of complex programming and high computational cost of traditional numerical methods such as stiffness reduction method and progressive damage method.
[0046] 3. The proposed conversion between the stiffness / strength binary mapping model and the stiffness / strength univariate mapping model is achieved by fitting a single hidden layer feedforward neural network and optimizing the damage accumulation function. This enables the prediction of residual strength without the need for a large number of fatigue tests, significantly reducing test costs and shortening the prediction cycle. It provides a scientific basis for the optimized design, life assessment and health monitoring of composite material components.
[0047] 4. The fatigue residual strength prediction method for fiber-reinforced composite materials provided in this application is applicable to composite laminates with different layup forms and can be widely used in aerospace, automotive and other fields. It can effectively ensure the safety and reliability of composite material structures during service, reduce the total life cycle cost, and has significant engineering application value.
[0048] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 8 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores data related to a method for predicting the residual fatigue strength of fiber-reinforced composite materials. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements a method for predicting the residual fatigue strength of fiber-reinforced composite materials.
[0049] Those skilled in the art will understand that Figure 8 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.
[0050] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0051] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0052] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0053] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0054] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.
[0055] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0056] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for predicting the residual fatigue strength of fiber-reinforced composite materials, characterized in that, include: Construct a stiffness binary mapping model and a strength binary mapping model; The stiffness univariate mapping model and the strength univariate mapping model are obtained by using the damage accumulation function based on the stiffness univariate mapping model and the strength univariate mapping model, respectively. Obtain the test stiffness and number of cycles of the fiber-reinforced composite material; The parameters of the damage accumulation function are obtained by fitting the stiffness univariate mapping model based on the experimental stiffness and cycle number of the fiber-reinforced composite material. Based on the parameters of the damage accumulation function and the intensity univariate mapping model, the degradation curve of the remaining intensity versus the number of cycles is obtained; based on the degradation curve, the remaining intensity prediction result is obtained.
2. The method for predicting the residual fatigue strength of fiber-reinforced composite materials according to claim 1, characterized in that, Constructing a stiffness-binary mapping model and a strength-binary mapping model, including: Construct a finite element model of fiber-reinforced composite laminates with multiple damage modes; Longitudinal tensile simulation was performed on the finite element model of the fiber-reinforced composite laminate to obtain the simulation stiffness, simulation strength, matrix damage variables, and fiber damage variables. The simulated stiffness, the matrix damage variable, and the fiber damage variable are fitted to obtain the stiffness binary mapping model; The simulated intensity, the matrix damage variable, and the fiber damage variable are fitted to obtain the intensity binary mapping model.
3. The method for predicting the residual fatigue strength of fiber-reinforced composite materials according to claim 2, characterized in that, Based on formula Determine the matrix damage variable; where, Indicates matrix damage variable, Indicates the number of cracks in the laminate matrix. Indicates the length of the laminate.
4. The method for predicting the residual fatigue strength of fiber-reinforced composite materials according to claim 2, characterized in that, Based on formula Determine the fiber damage variable; where, Indicates fiber damage variable, Indicates the number of fiber fracture surfaces. Indicates the length of the laminate. Indicates the width of the laminate. This indicates the size of the fiber fracture surface.
5. The method for predicting the residual fatigue strength of fiber-reinforced composite materials according to claim 2, characterized in that, After applying a concentrated force load at the geometric center of the fiber-reinforced composite laminate finite element model at one end of the fixed fiber-reinforced composite laminate finite element model and at the other end of the fiber-reinforced composite laminate finite element model, a longitudinal tensile simulation is performed on the fiber-reinforced composite laminate finite element model.
6. The method for predicting the residual fatigue strength of fiber-reinforced composite materials according to claim 2, characterized in that, The process of obtaining the simulation intensity includes: During longitudinal tensile simulation, the initial fiber tensile damage coefficient at the unit integration point in the volume average region is obtained. The simulated strength is determined based on the initial damage coefficient of the fiber under tension.
7. The method for predicting the residual fatigue strength of fiber-reinforced composite materials according to claim 1, characterized in that, The damage accumulation function is expressed as: ； In the formula, Indicates the number of loops. Indicates the first matrix damage variable at the number of iterations. Indicates the first Fiber damage variable at the number of cycles , , , and These are the parameters of the damage accumulation function.
8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for predicting the residual fatigue strength of fiber-reinforced composite materials according to any one of claims 1-7.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for predicting the residual fatigue strength of fiber-reinforced composite materials according to any one of claims 1-7.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for predicting the residual fatigue strength of fiber-reinforced composite materials according to any one of claims 1-7.