A method and device for constructing a fatigue crack propagation parameter library of a complex integral structure

Abstract

The application provides a fatigue crack propagation parameter library construction method and device for a complex overall structure, and belongs to the field of crack data processing. The method comprises the following steps: constructing a finite element model of a complex overall structure, performing fatigue crack propagation simulation analysis on the finite element model to obtain crack propagation data; simplifying a stress intensity factor calculation formula and constructing a surrogate model; constructing a neural network model, training the neural network model based on the crack propagation data, and obtaining a trained neural network model; obtaining new crack propagation data of a complex overall structure, predicting the crack propagation data of the new complex overall structure under different working conditions through the trained neural network model, and obtaining multiple new crack propagation prediction data of the complex overall structure, wherein the crack propagation prediction data comprises a crack length, a load cycle number, a stress intensity factor and a surrogate coefficient in the surrogate model, so that a parameter library of the whole process of crack propagation of a new overall structure is constructed.

Description

Technical Field

[0001] The present application belongs to the technical field of fatigue data processing, and in particular relates to a method and device for constructing a fatigue crack propagation parameter library for a complex integral structure. Background Art

[0002] In materials science and engineering applications, fatigue crack growth behavior is a key factor affecting the lifespan and safety of structural components. The stress intensity factor (SIF) of a crack is a crucial parameter for evaluating crack growth rate and predicting remaining life. It is crucial in fatigue and crack growth analysis of engineering structures and can effectively assess material performance degradation over long-term use.

[0003] When obtaining the stress intensity factor, the traditional method generally refers to consulting the manual, selecting a specific type of structural member, obtaining the corresponding comprehensive correction factor Y, and then calculating the stress intensity factor Y. The stress intensity factor is calculated using the formula, where Y is the geometric correction factor and σ is the stress difference. This method suffers from poor representation and applicability when applied to complex, integrated structures in engineering. Furthermore, empirical data is highly dispersed and relies heavily on experimental results. Calculations are performed using an engineering combination method that interpolates and multiplies elements. Errors gradually increase with iteration, and the final result often deviates significantly from reality. Therefore, using traditional methods is time-consuming, costly, and subject to significant errors.

[0004] When faced with structural parts with continuous structures and complex types in actual engineering problems, how to use existing crack growth data to analyze, calculate, predict and build a database of stress intensity factors, so as to facilitate the use of them to achieve rapid and accurate assessment and prediction of fatigue crack growth life, is an urgent problem that needs to be solved. Summary of the Invention

[0005] The purpose of this application is to provide a method and device for constructing a fatigue crack growth parameter library for a complex integral structure, so as to solve or alleviate at least one problem in the background technology.

[0006] The technical solution of this application is: a method for constructing a fatigue crack growth parameter library for complex integral structures, comprising:

[0007] Constructing a finite element model of a complex overall structure, performing fatigue crack propagation simulation analysis on the finite element model of the complex overall structure, and obtaining crack propagation data, wherein the crack propagation data includes crack length a, number of load cycles n, and stress intensity factor K;

[0008] Simplify the stress intensity factor calculation formula and build a proxy model Where ΔK is the proxy stress intensity factor, a is the crack length, and β is the proxy coefficient;

[0009] Constructing a neural network model, and training the neural network model based on the crack extension data to obtain a trained neural network model;

[0010] Crack propagation data of a new complex integral structure is obtained, and the crack propagation data of the new complex integral structure under different working conditions is predicted using a trained neural network model to obtain crack propagation prediction data of multiple new complex integral structures, wherein the crack propagation prediction data includes crack length a, number of load cycles n, stress intensity factor K and its corresponding proxy coefficient β in a proxy model. The crack propagation data of the new complex integral structure and the crack propagation prediction data are combined to construct a parameter library of the entire crack propagation process of a new integral structure.

[0011] Preferably, the finite element model of the complex overall structure is constructed based on finite element software, and the finite element software includes Abaqus and Ansys.

[0012] Preferably, fatigue crack propagation simulation analysis is performed on the complex integral structure finite element model by crack propagation simulation software, and the crack propagation simulation software includes Zencrack.

[0013] Preferably, during the fatigue crack propagation simulation analysis of the complex integral structure finite element model, a slit of a predetermined width is introduced to simulate the extension of the crack to match the crack propagation position.

[0014] Preferably, the width of the slit is determined by comparing the values ​​of the stress intensity factors before and after the simulation analysis, and using a regression analysis method to ensure that the error meets the requirements.

[0015] Preferably, the neural network model adopts a sequential model, which is composed of multiple layers stacked in sequence, and data is passed from front to back between the multiple layers.

[0016] Preferably, the first layer of the sequential model is a fully connected layer, comprising 128 neurons, an input dimension of 2, and using ReLU as an activation function;

[0017] The second layer of the sequential model is a fully connected layer with 64 neurons, using ReLU as the activation function, and the first and second layers introduce Dropout layers;

[0018] The third layer of the sequential model is a fully connected layer, which contains 64 neurons and uses ReLU as the activation function;

[0019] The fourth layer of the sequential model is a fully connected layer, which contains 32 neurons and uses ReLU as the activation function;

[0020] The output layer of the sequential model is a fully connected layer that contains 1 neuron and uses a linear activation function to output real values.

[0021] Preferably, the crack growth prediction data of the new complex integral structure is stored in an Excel file.

[0022] On the other hand, the present application provides a device for constructing a fatigue crack growth parameter library for a complex integral structure, comprising:

[0023] a crack growth data acquisition module, configured to construct a finite element model of a complex overall structure, perform fatigue crack growth simulation analysis on the finite element model of the complex overall structure, and obtain crack growth data, wherein the crack growth data includes crack length a, number of load cycles n, and stress intensity factor K;

[0024] The proxy model construction module is used to simplify the stress intensity factor calculation formula and build a proxy model Where ΔK is the proxy stress intensity factor, a is the crack length, and β is the proxy coefficient;

[0025] A neural network model building module is used to build a neural network model and train the neural network model based on the crack extension data to obtain a trained neural network model;

[0026] The data generation module is used to obtain crack propagation data of a new complex integral structure, and predict the crack propagation data of the new complex integral structure under different working conditions through a trained neural network model to obtain crack propagation prediction data of multiple new complex integral structures. The crack propagation prediction data includes crack length a, number of load cycles n, stress intensity factor K and its corresponding proxy coefficient β in the proxy model. The crack propagation data of the new complex integral structure and the crack propagation prediction data are combined to construct a parameter library of the entire crack propagation process of the new integral structure.

[0027] In a third aspect, the present application provides an electronic device, comprising:

[0028] one or more processors;

[0029] Memory;

[0030] One or more applications, the one or more applications are stored in the memory and are configured to be executed by the one or more processors, the one or more applications are configured to implement the method for constructing a fatigue crack growth parameter library for a complex integral structure as described in any one of the above.

[0031] In the final aspect, the present application provides a computer storage medium, which stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the method for constructing a fatigue crack propagation parameter library for a complex integral structure as described in any of the above items.

[0032] The method for constructing a fatigue crack propagation parameter library for complex integral structures in this application solves the shortcomings of the existing technology in fatigue crack propagation parameter prediction. By using numerical simulation to expand the data set, and then using proxy models and neural network technology to calculate and predict stress intensity factors, and based on this, a parameter library for the entire fatigue crack propagation process is constructed to achieve rapid and accurate crack propagation assessment, improve the accuracy and efficiency of fatigue crack propagation prediction, and provide strong technical support for structural health monitoring and safety assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions provided by this application, the following is a brief introduction to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of this application.

[0034] Figure 1 This is a flow chart of the method for constructing a fatigue crack propagation parameter library for complex integral structures in this application.

[0035] Figure 2 Schematic diagram of crack length and its prediction results in one embodiment of the present application.

[0036] Figure 3 This is a schematic diagram of a new overall structural crack propagation parameter library in one embodiment of the present application.

[0037] Figure 4 This is a curve of crack length and load cycle number based on the parameter library in one embodiment of the present application.

[0038] Figure 5 This is a stress intensity factor and load cycle number curve based on the parameter library in one embodiment of the present application.

[0039] Figure 6 This is a curve of proxy coefficient and load cycle number based on the parameter library in one embodiment of the present application.

[0040] Figure 7 Schematic diagram of the device for constructing the fatigue crack propagation parameter library of complex integral structures in this application.

[0041] Figure 8 This is a schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the implementation of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below in conjunction with the drawings in the embodiments of this application.

[0043] like Figure 1 As shown, the present application provides a method for constructing a fatigue crack growth parameter library for a complex integral structure, comprising:

[0044] S10, constructing a finite element model of the complex overall structure, and performing fatigue crack propagation simulation analysis on the finite element model of the complex overall structure, thereby obtaining crack propagation data, including crack length a, number of load cycles n, and stress intensity factor K.

[0045] In some embodiments of the present application, finite element software such as Abaqus or Ansys may be used to model a finite element model of a complex overall structure.

[0046] In this application, Zencrack crack propagation simulation software can be used to perform fatigue crack propagation simulation analysis on the finite element model of the complex overall structure. After the finite element model of the complex overall structure is constructed in finite element software such as Abaqus, it can be imported into the Zencrack crack propagation analysis software. By introducing the initial crack and setting the fatigue analysis type, material parameters and stress ratio, simulation analysis is carried out to obtain crack propagation data.

[0047] In this application, due to the presence of multiple structures such as hole edges, R zones, and long stringers in the crack propagation area, the simulation analysis process encountered issues such as cross-regional cracking, increased crack count, and local singularities. During the simulation calculation, the finite element model was adjusted to introduce a slit of predetermined width to simulate crack extension to match the crack propagation position of the previous step, thereby completing the crack propagation calculation on a continuous and complex structure. The slit width can be determined by comparing the stress intensity factor K values ​​before and after the simulation analysis, using regression analysis to ensure that the error meets the requirements. This ensures that the data meets the continuity and scientific requirements before and after the simulation calculation of cross-regional cracking and increased crack count.

[0048] In a preferred embodiment of the present application, crack propagation test data can be obtained by conducting fatigue crack propagation tests on complex integral structures of different materials under different working conditions. By comparing the crack propagation test data with the crack propagation data obtained by simulation, the effectiveness and scientificity of the simulation structure can be verified.

[0049] In the cross-region fatigue crack propagation simulation of complex integral structures, multiple sets of crack propagation data can be obtained. Through regression analysis, the data that meet the requirements of accuracy and continuity are spliced, and the data are cleaned and standardized, and invalid data are eliminated, so as to obtain a large amount of high-precision simulated crack propagation data for subsequent training use.

[0050] S20, the calculation formula of stress intensity factor K is simplified and a proxy model is established, namely:

[0051]

[0052] Where ΔK is the proxy stress intensity factor;

[0053] a is the crack length;

[0054] β is the proxy coefficient.

[0055] S30, constructing a neural network model, and training the neural network model with the crack extension data in step S10 to obtain a trained neural network model.

[0056] In this application, the neural network model adopts a sequential model, which consists of multiple layers stacked in sequence, with data transferred from front to back between these layers. The input of the neural network model is crack growth data including crack length a, number of load cycles n, and stress intensity factor K. The output of the neural network model is the proxy coefficient β in the proxy model and the predicted crack length a, number of load cycles n, and stress intensity factor K.

[0057] The first layer is a fully connected layer (Dense Layer), which contains 128 neurons. The input dimension is 2, that is, the input layer has two features. Here, we assume that the input data has two features and use ReLU (Rectified Linear Unit) as the activation function. It can introduce nonlinearity and enable the model to learn more complex patterns.

[0058] The second layer is a fully connected layer with 64 neurons, using ReLU as the activation function. The first two layers introduce a Dropout layer and regularization techniques to prevent overfitting of the model. During each training session, 20% (0.2) of the neurons are randomly disabled. Increasing the depth of the hidden layer and using Dropout techniques improve the model's ability to capture complex patterns in the data and reduce the risk of overfitting, making it suitable for more complex data prediction tasks. The final output layer is used to generate the final prediction value.

[0059] The third layer is a fully connected layer containing 64 neurons and using ReLU as the activation function.

[0060] The fourth layer is still another fully connected layer, containing 32 neurons and using ReLU as the activation function.

[0061] Finally, the output layer selects a fully connected layer, contains 1 neuron, and uses a linear activation function. Because this is a regression task, the linear activation function can directly output real values.

[0062] In the compilation of the neural network model, Adam is used to adaptively adjust the learning rate, so that the neural network model can converge faster during training. At the same time, it is less sensitive to the initial learning rate and is suitable for most tasks. MAPE (mean absolute error) and R are also introduced. 2 (coefficient of determination) to control the error.

[0063] During the neural network model training process of this application, the simulated crack growth data is divided into a training set and a validation set. The neural network model is trained using the training set, and tested and evaluated using the validation set. The neural network model traverses the entire training set n times, processing m samples before each weight update. During the training process, a progress bar and loss information for each epoch are displayed. A custom callback function using metrics is used to calculate and record the calculation accuracy and loss value during the training process. This facilitates post-training analysis and adjustment of the neural network model structure and parameters to obtain the best prediction effect of the stress intensity factor K.

[0064] S40, obtaining crack propagation data of the new complex overall structure, predicting the crack propagation data of the new complex overall structure under different working conditions through the trained neural network model, obtaining a large amount of crack propagation prediction data of the new complex overall structure, including crack length a, number of load cycles n, stress intensity factor K and its corresponding proxy coefficient β in the proxy model, merging the crack propagation data of the new complex overall structure with the crack propagation prediction data to construct a parameter library of the entire crack propagation process of the new overall structure.

[0065] like Figure 2 The left figure shows the embodiment of this application. The original crack extension data and its predicted data of the input layer characteristics are as follows: Figure 2 The figure on the right shows the original crack extension data and its predicted data of the crack length a in this embodiment of the present application. It can be seen from the curve in the figure that the prediction result is good.

[0066] In some embodiments of the present application, the crack growth prediction data of the new complex integral structure may be stored in an Excel file.

[0067] like Figure 3 Shown is a new parameter library for the entire process of overall structural crack propagation constructed in this embodiment of the present application.

[0068] like Figures 4 to 6 The following are the examples based on the present invention: Figure 3 The parameter library shown here shows the curves of crack length a and load cycle number n, stress intensity factor K and load cycle number n, and proxy coefficient β and load cycle number n.

[0069] The method for constructing a fatigue crack propagation parameter library for complex integral structures in this application solves the shortcomings of the existing technology in fatigue crack propagation parameter prediction. By using numerical simulation to expand the data set, and then using proxy models and neural network technology to calculate and predict stress intensity factors, and based on this, a parameter library for the entire fatigue crack propagation process is constructed to achieve rapid and accurate crack propagation assessment, improve the accuracy and efficiency of fatigue crack propagation prediction, and provide strong technical support for structural health monitoring and safety assessment.

[0070] like Figure 7 As shown, based on the above content, the present application also provides a device for constructing a fatigue crack growth parameter library for a complex integral structure, the device 100 comprising:

[0071] A crack growth data acquisition module 101 is used to construct a finite element model of a complex overall structure, perform fatigue crack growth simulation analysis on the finite element model of the complex overall structure, and obtain crack growth data, wherein the crack growth data includes crack length a, number of load cycles n, and stress intensity factor K;

[0072] The proxy model construction module 102 is used to simplify the stress intensity factor calculation formula and construct a proxy model Where ΔK is the proxy stress intensity factor, a is the crack length, and β is the proxy coefficient;

[0073] A neural network model building module 103 is used to build a neural network model and train the neural network model based on the crack extension data to obtain a trained neural network model;

[0074] The data generation module 104 is used to obtain crack propagation data of a new complex integral structure, and predict the crack propagation data of the new complex integral structure under different working conditions through the trained neural network model to obtain crack propagation prediction data of multiple new complex integral structures, wherein the crack propagation prediction data includes crack length a, number of load cycles n, stress intensity factor K and its corresponding proxy coefficient β in the proxy model, and the crack propagation data of the new complex integral structure and the crack propagation prediction data are combined to construct a parameter library of the entire crack propagation process of the new integral structure.

[0075] The specific processing process of the apparatus 100 for constructing a fatigue crack growth parameter library for a complex integral structure of the present application may refer to the contents or steps in the method for constructing a fatigue crack growth parameter library for a complex integral structure, and will not be described in detail here.

[0076] like Figure 8 As shown, an embodiment of the present application further provides an electronic device 200, which includes a processing device 201, which can perform various appropriate actions and processes according to a program stored in a ROM (read-only memory) 202 or a program loaded from a storage device 208 into a RAM (random access memory) 203. The processing device 201 may, for example, include a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processing device 201 may also include an onboard memory for caching purposes. The processing device 201 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0077] Various programs and data required for the operation of the electronic device 200 are stored in the RAM 203. The processing device 201, the ROM 202, and the RAM 203 are connected to each other via a bus 204. The processing device 201 performs various operations of the method flow according to the embodiment of the present application by executing the programs in the ROM 202 and / or the RAM 203. It should be noted that the programs may also be stored in one or more memories other than the ROM 202 and the RAM 203. The processing device 201 may also perform various operations of the method or flow according to the embodiment of the application by executing the programs stored in the one or more memories.

[0078] According to an embodiment of the present application, the electronic device 200 may further include an I / O interface 205 (input / output interface), which is also connected to the bus 204. The electronic device 200 may further include one or more of the following components connected to the I / O interface 205: an input device 206 including a keyboard, a mouse, etc.; an output device 207 including a cathode ray tube (CRT), a liquid crystal display (LCD), a speaker, etc.; a storage device 208 including a hard disk, etc.; and a communication device 209 including a network interface card such as a LAN card, a modem, etc. The communication device 209 performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface 205 as needed. Removable media, such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed in the drive as needed so that computer programs read therefrom can be installed into the storage device 208 as needed.

[0079] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the various operations of the above methods or processes according to the embodiments of this application are implemented.

[0080] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for constructing a fatigue crack growth parameter library for a complex integral structure, characterized in that: include: Constructing a finite element model of a complex integral structure, performing fatigue crack propagation simulation analysis on the finite element model of the complex integral structure, simulating crack extension by introducing a slit of predetermined width to match the crack propagation position, the slit width being determined by comparing the values ​​of the stress intensity factor before and after the simulation analysis, and using a regression analysis method to ensure that the error meets the requirements, thereby obtaining crack propagation data, the crack propagation data including the crack length a, the number of load cycles n, and the stress intensity factor K; Simplify the stress intensity factor calculation formula and build a proxy model Where ΔK is the proxy stress intensity factor, a is the crack length, and β is the proxy coefficient; Constructing a neural network model, wherein the input of the neural network model is crack growth data including crack length a, number of load cycles n, and stress intensity factor K, the output of the neural network model is a proxy coefficient β in a proxy model and the predicted crack length a, number of load cycles n, and stress intensity factor K, and the neural network model is trained based on the crack growth data to obtain a trained neural network model; Crack propagation data of a new complex integral structure is obtained, and the crack propagation data of the new complex integral structure under different working conditions is predicted using a trained neural network model to obtain crack propagation prediction data of multiple new complex integral structures, wherein the crack propagation prediction data includes crack length a, number of load cycles n, stress intensity factor K and its corresponding proxy coefficient β in a proxy model. The crack propagation data of the new complex integral structure and the crack propagation prediction data are combined to construct a parameter library of the entire crack propagation process of a new integral structure.

2. The method for constructing a fatigue crack growth parameter library for a complex integral structure according to claim 1, wherein: The finite element model of the complex overall structure is constructed based on finite element software, and the finite element software includes Abaqus and Ansys.

3. The method for constructing a fatigue crack growth parameter library for a complex integral structure according to claim 2, wherein: Fatigue crack propagation simulation analysis is performed on the finite element model of the complex integral structure using crack propagation simulation software, wherein the crack propagation simulation software includes Zencrack.

4. The method for constructing a fatigue crack growth parameter library for a complex integral structure according to any one of claims 1 to 3, characterized in that: The neural network model adopts a sequential model, which is composed of multiple layers stacked in sequence, and data is transmitted from front to back between the multiple layers.

5. The method for constructing a fatigue crack growth parameter library for a complex integral structure according to claim 4, wherein: The first layer of the sequential model is a fully connected layer containing 128 neurons, with an input dimension of 2 and using ReLU as the activation function; The second layer of the sequential model is a fully connected layer with 64 neurons, using ReLU as the activation function, and the first and second layers introduce Dropout layers; The third layer of the sequential model is a fully connected layer, which contains 64 neurons and uses ReLU as the activation function; The fourth layer of the sequential model is a fully connected layer, which contains 32 neurons and uses ReLU as the activation function; The output layer of the sequential model is a fully connected layer that contains 1 neuron and uses a linear activation function to output real values.

6. The method for constructing a fatigue crack growth parameter library for a complex integral structure according to claim 1, wherein: The crack growth prediction data of the new complex integral structure is stored in an Excel file.

7. A device for constructing a fatigue crack growth parameter library for a complex integral structure, characterized in that: include: a crack growth data acquisition module, configured to construct a finite element model of a complex integral structure and perform fatigue crack growth simulation analysis on the finite element model. A slit of predetermined width is introduced to simulate crack extension to match the crack growth position. The slit width is determined by comparing the stress intensity factor values ​​before and after the simulation analysis, and a regression analysis method is used to ensure that the error meets the requirements, thereby obtaining crack growth data. The crack growth data includes crack length a, number of load cycles n, and stress intensity factor K. The proxy model construction module is used to simplify the stress intensity factor calculation formula and build a proxy model Where ΔK is the proxy stress intensity factor, a is the crack length, and β is the proxy coefficient; A neural network model building module is used to build a neural network model, wherein the input of the neural network model is crack growth data including crack length a, number of load cycles n, and stress intensity factor K, and the output of the neural network model is the proxy coefficient β in the proxy model and the predicted crack length a, number of load cycles n, and stress intensity factor K. The neural network model is trained based on the crack growth data to obtain a trained neural network model; The data generation module is used to obtain crack propagation data of a new complex integral structure, and predict the crack propagation data of the new complex integral structure under different working conditions through a trained neural network model to obtain crack propagation prediction data of multiple new complex integral structures. The crack propagation prediction data includes crack length a, number of load cycles n, stress intensity factor K and its corresponding proxy coefficient β in the proxy model. The crack propagation data of the new complex integral structure and the crack propagation prediction data are combined to construct a parameter library of the entire crack propagation process of the new integral structure.

8. An electronic device, characterized in that: include: one or more processors; Memory; One or more applications, the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications are configured to implement the method for constructing a fatigue crack growth parameter library for a complex integral structure according to any one of claims 1 to 6.

9. A computer storage medium, characterized in that The computer storage medium stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the method for constructing a fatigue crack growth parameter library for a complex integral structure according to any one of claims 1 to 6.

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