Multi-scale multi-deep neural network driven metal material fatigue life prediction method

Abstract

The invention discloses a multi-scale multi-deep neural network driven metal material fatigue life prediction method. The method comprises the following steps: constructing a metal material fatigue test data set; adding the maximum normal stress of the critical plane into a data set by considering the additional hardening effect of the metal material under a non-proportional load, and preprocessing the data set; establishing a multi-scale multi-depth neural network framework; considering physical constraint to guide neural network training so as to increase prediction precision, and establishing a physical loss function with soft regulation capability; building a metal material fatigue life prediction model driven by the multi-scale multi-depth neural network based on the multi-scale multi-depth neural network framework and the physical loss function; continuously iteratively optimizing the model to obtain an optimal prediction model; and predicting the fatigue life of the metal material by using the optimal prediction model. The method not only has the advantage of high prediction precision, but also can enhance the stability and generalization ability of the prediction precision.

Description

Technical Field

[0001] The present invention belongs to the field of material fatigue life prediction methods, and specifically provides a metal material fatigue life prediction method driven by a multi-scale and multi-depth neural network. Background Art

[0002] Fatigue damage of engineering materials is a common cause of failure of engineering components. In actual engineering, many mechanical components are subjected to cyclic loads, which is particularly common in transportation vehicles and structures such as airplanes, high-speed trains, cars, bridges and ships. Therefore, it is particularly important to conduct in-depth research on the fatigue problem of engineering materials. So far, traditional fatigue life prediction methods for metal materials can be mainly divided into energy-based methods, equivalent strain-based methods and critical plane-based methods. However, these prediction methods often add additional parameters as the complexity of the material loading path increases, which reduces the robustness and generalization ability of the model while improving the prediction accuracy.

[0003] In recent years, neural networks have been widely used in the fatigue life prediction of metal materials due to their excellent learning and generalization capabilities. However, due to the lack of physical constraints, these models often have large fluctuations in prediction accuracy. In addition, in previous studies, most of the methods for predicting the fatigue life of metal materials with the help of neural networks only extracted and processed sample data at a single scale or single depth, and were unable to fully capture the deep and shallow characteristics of the data, which resulted in poor stability in the prediction accuracy of the model. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a metal material fatigue life prediction method driven by a multi-scale and multi-depth neural network, which can effectively solve the problems of large fluctuations in prediction accuracy, low robustness, and incomplete feature extraction and processing in existing metal material fatigue life prediction methods.

[0005] The technical solution adopted by the present invention is: a metal material fatigue life prediction method driven by a multi-scale and multi-depth neural network, which specifically includes the following steps:

[0006] S1: Construction of metal material fatigue test dataset;

[0007] Collect fatigue test data sets of metal materials under different stress levels and multiple loading paths; including material performance parameters, dimensions, load parameters, loading paths, and fatigue life parameters;

[0008] S2: calculation of maximum normal stress on critical plane;

[0009] Based on the performance parameters and dimensions of metal materials, a finite element model is established; cyclic loads at different stress levels are applied to the specimens, and the maximum normal stress σ of the critical plane of the specimen under different loading paths is obtained according to the critical plane theory. n,max ; This stress reflects the additional hardening effect of the material under non-proportional load;

[0010] S3: The maximum normal stress σ on the critical plane n,max , the load parameters obtained in step S1 are used as input data, and the fatigue life N e As output data; input data and output data together constitute the data set of the model;

[0011] S4: Dataset preprocessing for the model;

[0012] S41: Use the maximum and minimum normalization method to normalize the data set of the model to eliminate the impact of the dimension difference of the data set on the model prediction results;

[0013] S42: Use K-fold cross validation method to process the data set of the model to obtain a training set and a test set;

[0014] S5: Construct a fatigue life prediction model for metal materials driven by multi-scale and multi-depth neural networks;

[0015] S51: Construct a feature extraction module to extract features at multiple scales (channel scale, feature dependency scale);

[0016] S52: Construct feature processing module to process features at multiple scales (global scale, local scale, feature enhancement scale) and multiple depths (deep features, shallow features);

[0017] S53: Incorporate physical constraints into the loss function and construct a physical loss function with soft adjustment function;

[0018] S6: Iterative optimization model, using the optimal multi-scale and multi-depth neural network model to predict the fatigue life of metal materials;

[0019] Preferably, in step S1, the fatigue test data set includes material performance parameters: elastic modulus (E), Poisson's ratio (ν); load parameters: strain amplitude (ε a ,γ a ), stress amplitude (σ a ,τ a ), mean stress σ m ; Fatigue life parameter: Fatigue life N e .

[0020] Further preferably, in step S2, the normal stress of any plane is as shown in formula (1):

[0021]

[0022] Where: θ is the normal stress of the plane; σ x is the normal stress; θ is the angle between any material plane and the specimen axis; τ xy is the shear stress;

[0023] For different loading paths, plane positions from 0° to 180° are defined with a step size of 0.1° to accurately capture the stress state on each plane; for each specific plane position, the corresponding stress component is calculated; based on the critical plane theory, the maximum normal stress on the critical plane is obtained.

[0024] Further preferably, in step S41, the data is scaled proportionally by using the maximum and minimum normalization method to eliminate the influence of the dimension difference between the data on the model prediction results; the maximum and minimum normalization formula is shown in formula (2):

[0025]

[0026] Where: X N is the normalized input parameter; X is the initial input parameter; X min and X max are the minimum and maximum values ​​of the input parameters, respectively.

[0027] Further preferably, in step S42, the K-fold cross validation method is used to process the data set, divide the training set and the test set, and assist the model in parameter fitting and hyperparameter optimization, so as to obtain the optimal metal material fatigue life prediction model based on neural network; the specific steps are as follows:

[0028] a. First, the data is randomly divided into k equal-sized and non-overlapping subsets;

[0029] b. Then, k-1 of these subsets are selected as training data to train the model, and the remaining one is used as validation data to evaluate the performance of the model;

[0030] c. This process is repeated k times, each time using a different subset as the validation set;

[0031] d. Calculate the model error on different validation sets and accumulate k error values;

[0032] e. Finally, find the average of these error values ​​to get the average error of cross validation;

[0033] f. Optimize model hyperparameters based on the average error.

[0034] Further preferably, in step S51, in the feature extraction module, an innovative multi-head attention mechanism is adopted, which integrates the new channel attention and evolutionary attention to efficiently extract multi-scale features, that is, extracting features from the channel scale of the feature map and the dependency scale between features; then, these features are integrated through a fully connected neural network, which consists of a Dropout function, a linear layer and a Relu activation function to enhance the expressiveness and generalization ability of the features.

[0035] In the channel attention module, the input data is first average-pooled through two pooling windows of different sizes to achieve feature fusion of the input data channel dimension; then, the pooled data is further processed by a 1×1 two-dimensional convolution operator to achieve multi-scale analysis of the feature channel; the processed feature maps are merged and weighted summed with the original data. This process aims to generate more accurate spatial features through feature processing at different spatial scales to improve the efficiency and accuracy of the model in completing regression tasks; in order to overcome the degradation problem in deep network training, a residual network structure is introduced to achieve self-repair of the model, ensuring that the model performance increases instead of decreases as the number of network layers increases, thereby effectively avoiding network degradation.

[0036] The evolutionary attention mechanism consists of two sub-modules: sequence attention and IP attention, which aims to obtain the logical evolution relationship between the input sequence and fatigue life. First, the input data is preprocessed by upsampling (bilinear interpolation) to obtain a smoother information transition. In the sequence attention module, the data is first processed by the dilated convolution operator to extract the long-term characteristic trends of the data. These features are then processed nonlinearly through the normalization layer, linear layer and Relu activation function to enhance the model's ability to recognize features. Finally, the processed features are weighted summed by the Sigmoid function. This step is crucial to strengthening effective features because it allows the model to dynamically adjust its weights according to the importance of the features, thereby further improving the model's sensitivity to key features and prediction accuracy.

[0037] In IP attention, for each input sample, the one-dimensional data sequence is first randomly rearranged to construct a two-dimensional feature map in order to capture the interdependence of data features in the spatial dimension; then, after upsampling, pooling operations are performed in the two orthogonal directions of the X-axis and the Y-axis, thereby simplifying the two-dimensional feature map into two one-dimensional sequences; after the dimension conversion, the two sequences will interact with each other in a single dimension, and further enhance their mapping capabilities through nonlinear transformation; the features after nonlinear transformation are then reorganized and restored to the form before the sequence dimension conversion; finally, these features are weighted summed with the original input data through the Sigmoid function to achieve further feature enhancement; this process is designed to enable the neural network to capture the feature dependencies between different positions on the feature map, so as to deeply explore the logical deduction relationship between strain amplitude, stress amplitude, and maximum stress on the critical surface and fatigue life in the spatial dimension.

[0038] Further preferably, in step S52, in the feature processing module, after being processed by a two-dimensional convolution operator with a 1×1 convolution kernel, the features are respectively sent to three feature processing mechanisms of different scales; these mechanisms are responsible for extracting local features, global features, and enhanced features, respectively, to comprehensively capture the multi-scale and multi-depth features of the data, that is, to capture deep and shallow features from the global scale, local scale, and feature enhancement scale; then, these features are fused in the channel dimension to integrate feature information of different scales and depths; the fused features are further processed by a feature attention mechanism composed of a linear layer and a Relu activation function, which purifies the features to enhance the model's ability to recognize key information; finally, through the global average pooling mechanism, the model outputs the fatigue life prediction value N of the metal material p , ensuring the stability and reliability of the prediction results.

[0039] Further preferably, in step S53, the physical loss function expression of the soft adjustment function is as shown in formula (3):

[0040] L=λ1L h +λ2L p (3)

[0041] Where: L is the physical loss function; λ1 and λ2 are the weights of the two loss functions; L h is the Huber loss function; L p is the physical constraint function; L h and L p The expressions of are shown in equations (4) and (5):

[0042]

[0043] Where: N eis the fatigue life verified in the experiment; N p is the fatigue life predicted by the model after training; δ and d are adjustment parameters, which can adjust the degree of penalty for model training by changing their numerical values.

[0044] The physical loss function controls the participation of physical constraints in neural network training by adjusting the values ​​of λ1 and λ2 to achieve soft adjustment function.

[0045] Further preferably, in step S6, the fatigue test data obtained in step S1 includes the strain amplitude (ε a ,γ a ), stress amplitude (σ a ,τ a ), mean stress σ m , and the maximum normal stress value σ of the critical surface calculated in step S2 n,max , these data are used as input to the model; fatigue life N p is used as the output of the model; then, the input data is preprocessed according to the data processing method described in step S4; then, the model constructed in step S5 is trained and iteratively optimized to obtain the optimal multi-scale and multi-depth neural network driven metal material fatigue life prediction model; finally, the optimal multi-scale and multi-depth neural network driven metal material fatigue life prediction model is used to predict the fatigue life of the metal material under cyclic load; the prediction results are visualized through a scatter plot for easy analysis and understanding.

[0046] The beneficial effects of the present invention are as follows: by integrating a new neural network architecture and a physical loss function, a novel multi-scale and multi-depth neural network driven fatigue life prediction model for metal materials is constructed; the model uses stress amplitude, strain amplitude, average stress and maximum normal stress on the critical plane as input parameters, and the fatigue life of the metal material as the output result, thereby achieving a fatigue life prediction of the metal material with higher prediction accuracy and stronger prediction stability; compared with traditional models, the present invention effectively solves the problems of insufficient prediction accuracy, poor prediction stability and limited robustness; therefore, the method of the present invention is used to predict the fatigue life of metal materials under cyclic loads, and a more accurate and stable mapping relationship between fatigue parameters and fatigue life can be established. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of an implementation scheme of the present invention;

[0048] Figure 2 It is the overall framework diagram of the prediction model of the present invention;

[0049] Figure 3 It is a spatial attention framework diagram of the present invention;

[0050] Figure 4 This is a diagram of the evolutionary attention framework of the present invention;

[0051] Figure 5 It is a comparison chart of the predicted life and the test life under cyclic load in the example of the present invention; DETAILED DESCRIPTION

[0052] In order to allow professionals in the field to more clearly understand the implementation details of the present invention, the specific contents of the present invention will be described in detail below with reference to relevant illustrations.

[0053] The present invention proposes a method for predicting fatigue life of metal materials driven by a multi-scale and multi-depth neural network. The implementation steps are detailed in Figure 1 , the specific steps are outlined as follows.

[0054] S1: Construction of metal material fatigue test dataset;

[0055] The fatigue data of AZ61A magnesium alloy under four different loading paths (completely reverse tension and compression, completely reverse cyclic torsion, proportional loading, and non-proportional loading) provided in the literature published abroad [1] Yu Q, Zhang J, Jiang Y, Li Q.'Multiaxial fatigue of extruded AZ61A magnesium alloy, International Journal of Fatigue, 2011; 33: 437-47. were collected, including the dimensions, elastic modulus (E), Poisson's ratio (ν), strain amplitude (ε a ,γ a ), stress amplitude (σ a ,τ a ), mean stress σ m , and fatigue life N e .

[0056] S2: calculation of maximum normal stress on critical plane;

[0057] An accurate finite element model was constructed based on the dimensions, elastic modulus (E) and Poisson's ratio (ν) of the metal material. By simulating experimental conditions, cyclic loads including tension, torsion and tension-torsion combined directions were applied to the specimen to obtain the maximum normal stress σ on the critical plane of the specimen under different loading paths. n,max ; This stress parameter reflects the additional hardening effect of the material under non-proportional load, providing a more comprehensive description of the stress state for fatigue analysis of the material; the normal stress of any plane is shown in formula (1):

[0058]

[0059] Where: θ is the normal stress of the plane; σ x is the normal stress; θ is the angle between any material plane and the specimen axis; τ xy is the shear stress.

[0060] For different loading paths, plane positions from 0° to 180° are defined with a step size of 0.1° to accurately capture the stress state on each plane; for each specific plane position, the corresponding stress component is calculated; based on the critical plane theory, the maximum normal stress on the critical plane is obtained.

[0061] This process ensures that the fatigue behavior of materials under various complex loading conditions can be accurately evaluated, providing key stress parameters for fatigue life prediction.

[0062] S3: The maximum normal stress σ on the critical plane n,max , strain amplitude (ε a ,γ a ), stress amplitude (σ a ,τ a ), mean stress σ m As input data, fatigue life N e As output data; input data and output data together constitute the data set of the model;

[0063] S4: Dataset preprocessing for the model;

[0064] S41: Use the maximum and minimum normalization method to normalize the data set of the model. This method effectively eliminates the dimension differences between the data by scaling them proportionally, providing a unified data scale for the model, thereby improving the generalization ability and prediction accuracy of the model. The normalization formula is shown in formula (2):

[0065]

[0066] Where: X N is the normalized input parameter; X is the initial input parameter; X min and X max are the minimum and maximum values ​​of the input parameters, respectively.

[0067] S42: Use K-fold cross validation method to process the data set of the model to obtain a training set and a test set;

[0068] The K-fold cross-validation method is used to divide the data set into a training set and a validation set to assist the model in parameter fitting and hyperparameter optimization, thereby obtaining the optimal neural network-based metal material fatigue life prediction model; the specific steps are as follows:

[0069] a. First, the data is randomly divided into k equal-sized and non-overlapping subsets;

[0070] b. Then, k-1 of these subsets are selected as training data to train the model, and the remaining one is used as validation data to evaluate the performance of the model;

[0071] c. This process is repeated k times, each time using a different subset as the validation set;

[0072] d. Calculate the model error on different validation sets and accumulate k error values;

[0073] e. Finally, find the average of these error values ​​to get the average error of cross validation;

[0074] f. Optimize model hyperparameters based on the average error.

[0075] S5: Construct a fatigue life prediction model for metal materials driven by multi-scale and multi-depth neural networks;

[0076] Feature extraction module and feature processing module are used to capture and process deep and shallow features from the channel scale, feature dependency scale, global scale and local scale to build a metal material fatigue life prediction model driven by a multi-scale and multi-depth neural network.

[0077] S51: Construct feature extraction module, multi-scale (channel scale, feature dependency scale)

[0078] Extract features;

[0079] In the feature extraction module, an innovative multi-head attention mechanism is adopted, which combines the new channel attention and evolutionary attention to efficiently extract multi-scale features, that is, extracting features from the channel scale of the feature map and the dependency scale between features; then, these features are integrated through a fully connected neural network composed of a Dropout function, a linear layer, and a Relu activation function to enhance the expressiveness and generalization ability of the features.

[0080] In the channel attention module, the input data is first average-pooled through two pooling windows of different sizes to achieve feature fusion of the input data channel dimension; then, the pooled data is further processed by a 1×1 two-dimensional convolution operator to achieve multi-scale analysis of the feature channel; the processed feature maps are merged and weighted summed with the original data. This process aims to generate more accurate spatial features through feature processing at different spatial scales to improve the efficiency and accuracy of the model in completing regression tasks; in order to overcome the degradation problem in deep network training, a residual network structure is introduced to achieve self-repair of the model, ensuring that the model performance increases instead of decreases as the number of network layers increases, thereby effectively avoiding network degradation.

[0081] The evolutionary attention mechanism consists of two sub-modules: sequence attention and IP attention, which aims to obtain the logical evolution relationship between the input sequence and fatigue life. First, the input data is preprocessed by upsampling (bilinear interpolation) to obtain a smoother information transition. In the sequence attention module, the data is first processed by the dilated convolution operator to extract the long-term characteristic trends of the data. These features are then processed nonlinearly through the normalization layer, linear layer and Relu activation function to enhance the model's ability to recognize features. Finally, the processed features are weighted summed by the Sigmoid function. This step is crucial to strengthening effective features because it allows the model to dynamically adjust its weights according to the importance of the features, thereby further improving the model's sensitivity to key features and prediction accuracy.

[0082] In IP attention, for each input sample, the one-dimensional data sequence is first randomly rearranged to construct a two-dimensional feature map in order to capture the interdependence of data features in the spatial dimension; then, after upsampling, pooling operations are performed in the two orthogonal directions of the X-axis and the Y-axis, thereby simplifying the two-dimensional feature map into two one-dimensional sequences; after the dimension conversion, the two sequences will interact with each other in a single dimension, and further enhance their mapping capabilities through nonlinear transformation; the features after nonlinear transformation are then reorganized and restored to the form before the sequence dimension conversion; finally, these features are weighted summed with the original input data through the Sigmoid function to achieve further feature enhancement; this process is designed to enable the neural network to capture the feature dependencies between different positions on the feature map, so as to deeply explore the logical deduction relationship between strain amplitude, stress amplitude, and maximum stress on the critical surface and fatigue life in the spatial dimension.

[0083] S52: Construct feature processing module to process features at multiple scales (global scale, local scale, feature enhancement scale) and multiple depths (deep features, shallow features);

[0084] In the feature processing module, after being processed by a two-dimensional convolution operator with a 1×1 convolution kernel, the features are sent to three feature processing mechanisms of different scales respectively; these mechanisms are responsible for extracting local features, global features, and enhanced features respectively to fully capture the multi-scale and multi-depth features of the data, that is, to capture deep and shallow features from the global scale, local scale, and feature enhancement scale; subsequently, these features are fused in the channel dimension to integrate feature information of different scales and depths; the fused features are further processed by a feature attention mechanism composed of a linear layer and a Relu activation function, which purifies the features to enhance the model's ability to recognize key information; finally, through the global average pooling mechanism, the model outputs the fatigue life prediction value N of the metal material p , ensuring the stability and reliability of the prediction results.

[0085] S53: Incorporate physical constraints into the loss function and construct a physical loss function with soft adjustment function;

[0086] In order to increase the optimization process of the model, the physical constraints are integrated into the loss function, and a physical loss function with soft adjustment function is constructed; the physical loss function expression of the soft adjustment function is shown in formula (3):

[0087] L=λ1L h +λ2L p (3)

[0088] Where: L is the physical loss function; λ1 and λ2 are the weights of the two loss functions; L h is the Huber loss function; L p is the physical constraint function; L h and L p The expressions of are shown in equations (4) and (5):

[0089]

[0090] Where: N e is the fatigue life verified in the experiment; N p is the fatigue life predicted by the model after training; δ and d are adjustment parameters, which can adjust the degree of penalty for model training by changing their numerical values.

[0091] The physical loss function controls the participation of physical constraints in neural network training by adjusting the values ​​of λ1 and λ2 to achieve soft adjustment function.

[0092] S6: Iterative optimization model, using the optimal multi-scale and multi-depth neural network model to predict the fatigue life of metal materials;

[0093] The fatigue test data obtained in step S1 include strain amplitude (εa ,γ a ), stress amplitude (σ a ,τ a ), mean stress σ m , and the maximum normal stress value σ of the critical surface calculated in step S2 n,max , these data are used as input to the model; fatigue life N p is used as the output of the model; then, the input data is preprocessed according to the data processing method described in step S4; then, the model constructed in step S5 is trained and iteratively optimized to obtain the optimal multi-scale and multi-depth neural network driven metal material fatigue life prediction model, whose hyper parameters are shown in Tables 1, 2, and 3;

[0094] Table 1

[0095]

[0096] Table 2

[0097]

[0098] Table 3

[0099]

[0100] Next, the optimal neural network fatigue life prediction model is used to predict the fatigue life of metal materials. In order to intuitively display the prediction results, a scatter plot is used to visualize the prediction results. Figure 5 .

[0101] according to Figure 5 The results show that the model proposed in the present invention exhibits high prediction accuracy in four different loading paths; the probability that the prediction result is within 1.5 times the scatter band is 87%, and the probability that it is within 1.5 scatter band is 93%; the prediction accuracy of the test set is lower than that of the training set, which is due to the generalization error inherent in the structural characteristics of the neural network; in summary, in the test set under the four loading paths, the prediction results of the model proposed in the present invention are basically within 1.5 times the scatter band, and the prediction accuracy is relatively stable.

[0102] Professionals in this field should understand that the above embodiments are only used to illustrate the basic principles of the present invention, and do not mean that the scope of protection of the present invention is limited to this specific implementation mode; for those skilled in the art, the implementation of the present invention may have many changes and adjustments; any modification, adjustment and equivalent substitution within the framework of the basic principles of the present invention should be deemed to be included in the scope of protection of the claims of the present invention.

Claims

1. A metal material fatigue life prediction method driven by a multi-scale and multi-depth neural network, specifically comprising the following steps: S1: Construction of metal material fatigue test dataset; Collect fatigue test data sets of metal materials under different stress levels and multiple loading paths; including material performance parameters, dimensions, load parameters, loading paths, and fatigue life parameters; S2: calculation of maximum normal stress on critical plane; Based on the performance parameters and dimensions of metal materials, a finite element model is established; cyclic loads at different stress levels are applied to the specimens, and the maximum normal stress σ of the critical plane of the specimen under different loading paths is obtained according to the critical plane theory. n,max ; This stress reflects the additional hardening effect of the material under non-proportional load; S3: The maximum normal stress σ on the critical plane n,max , the load parameters obtained in step S1 are used as input data, and the fatigue life N e As output data; input data and output data together constitute the data set of the model; S4: Dataset preprocessing for the model; S41: Use the maximum and minimum normalization method to normalize the data set of the model to eliminate the impact of the dimension difference of the data set on the model prediction results; S42: Use K-fold cross validation method to process the data set of the model to obtain a training set and a test set; S5: Construct a fatigue life prediction model for metal materials driven by multi-scale and multi-depth neural networks; S51: Construct a feature extraction module to extract features at multiple scales (channel scale, feature dependency scale); S52: Construct feature processing module to process features at multiple scales (global scale, local scale, feature enhancement scale) and multiple depths (deep features, shallow features); S53: Incorporate physical constraints into the loss function and construct a physical loss function with soft adjustment function; S6: Iterative optimization model, using the optimal multi-scale and multi-depth neural network model to predict the fatigue life of metal materials.

2. The method for predicting fatigue life of metal materials driven by a multi-scale and multi-depth neural network according to claim 1 is characterized in that: In step S1, the fatigue test data set includes material performance parameters: elastic modulus (E), Poisson's ratio (ν); load parameters: strain amplitude (ε a ,γ a ), stress amplitude (σ a ,τ a ), mean stress σ m ; Fatigue life parameter: Fatigue life N e .

3. The method for predicting fatigue life of metal materials driven by a multi-scale and multi-depth neural network according to claim 1 is characterized in that: In step S2, the normal stress of any plane is as shown in formula (1): Where: θ is the normal stress of the plane; σ x is the normal stress; θ is the angle between any material plane and the specimen axis; τ xy is the shear stress; For different loading paths, plane positions from 0° to 180° are defined with a step size of 0.1° to accurately capture the stress state on each plane; for each specific plane position, the corresponding stress component is calculated; Based on the critical plane theory, the maximum normal stress on the critical plane is obtained.

4. The method for predicting fatigue life of metal materials driven by a multi-scale and multi-depth neural network according to claim 1 is characterized in that: In step S41, the data is scaled proportionally by using the maximum and minimum normalization method to eliminate the influence of the dimension difference between the data on the model prediction results; the maximum and minimum normalization formula is shown in formula (2): Where: X N is the normalized input parameter; X is the initial input parameter; X min and X max are the minimum and maximum values ​​of the input parameters, respectively.

5. The method for predicting fatigue life of metal materials driven by a multi-scale and multi-depth neural network according to claim 1, characterized in that: In step S42, the K-fold cross validation method is used to process the data set, divide the training set and the test set, and assist the model in parameter fitting and hyperparameter optimization, so as to obtain the optimal metal material fatigue life prediction model based on neural network; the specific steps are as follows: a. First, the data is randomly divided into k equal-sized and non-overlapping subsets; b. Then, k-1 of these subsets are selected as training data to train the model, and the remaining one is used as validation data to evaluate the performance of the model; c. This process is repeated k times, each time using a different subset as the validation set; d. Calculate the model error on different validation sets and accumulate k error values; e. Finally, find the average of these error values ​​to get the average error of cross validation; f. Optimize model hyperparameters based on the average error.

6. The method for predicting fatigue life of metal materials driven by a multi-scale and multi-depth neural network according to claim 1 is characterized in that: In step S51, in the feature extraction module, an innovative multi-head attention mechanism is adopted, which combines the new channel attention and evolutionary attention to efficiently extract multi-scale features, that is, extract features from the channel scale of the feature map and the dependency scale between features; Then, These features are integrated through a fully connected neural network, which consists of a Dropout function, a linear layer, and a Relu activation function to enhance the expressiveness and generalization ability of the features; In the channel attention module, the input data is first average-pooled through two pooling windows of different sizes to achieve feature fusion of the input data channel dimension; then, the pooled data is further processed by a 1×1 two-dimensional convolution operator to achieve multi-scale analysis of the feature channel; the processed feature maps are merged and weighted summed with the original data. This process aims to generate more accurate spatial features through feature processing at different spatial scales to improve the efficiency and accuracy of the model in completing regression tasks; in order to overcome the degradation problem in deep network training, a residual network structure is introduced to achieve self-repair of the model, ensuring that the model performance increases instead of decreases as the number of network layers increases, thereby effectively avoiding network degradation; The evolutionary attention mechanism consists of two submodules: sequence attention and IP attention, which aims to obtain the logical evolution relationship between the input sequence and fatigue life. First, the input data is preprocessed by upsampling (bilinear interpolation) to obtain a smoother information transition. In the sequence attention module, the data is first processed by the dilated convolution operator to extract the long-term characteristic trend of the data. These features are then processed nonlinearly through normalization layers, linear layers, and Relu activation functions to enhance the model's ability to recognize features. Finally, the processed features are weighted summed through the Sigmoid function. This step is crucial for strengthening effective features because it allows the model to dynamically adjust its weights based on the importance of the features, thereby further improving the model's sensitivity to key features and prediction accuracy. In IP attention, for each input sample, the one-dimensional data sequence is first randomly rearranged to construct a two-dimensional feature map in order to capture the interdependence of data features in the spatial dimension; Subsequently, after upsampling, pooling operations are performed in two orthogonal directions, the X-axis and the Y-axis, thereby simplifying the two-dimensional feature map into two one-dimensional sequences; After the dimensionality transformation, the two sequences will interact with each other in a single dimension, and further enhance their mapping ability through nonlinear transformation; the features after nonlinear transformation are then reorganized and restored to the form before the sequence dimension transformation; Finally, these features are weighted and summed with the original input data through the Sigmoid function to achieve further feature enhancement; this process is designed to enable the neural network to capture the feature dependencies between different positions on the feature map, so as to deeply explore the logical deduction relationship between strain amplitude, stress amplitude, maximum stress on the critical surface and fatigue life in the spatial dimension.

7. The method for predicting fatigue life of metal materials driven by a multi-scale and multi-depth neural network according to claim 1 is characterized in that: In step S52, in the feature processing module, after being processed by a two-dimensional convolution operator with a 1×1 convolution kernel, the features are respectively sent to three feature processing mechanisms of different scales; these mechanisms are responsible for extracting local features, global features, and enhanced features, respectively, to comprehensively capture the multi-scale and multi-depth features of the data, that is, to capture deep and shallow features from the global scale, local scale, and feature enhancement scale; subsequently, these features are fused in the channel dimension to integrate feature information of different scales and depths; the fused features are further processed by a feature attention mechanism composed of a linear layer and a Relu activation function, which purifies the features to enhance the model's ability to recognize key information; finally, through the global average pooling mechanism, the model outputs the fatigue life prediction value N of the metal material p , ensuring the stability and reliability of the prediction results.

8. The method for predicting fatigue life of metal materials driven by a multi-scale and multi-depth neural network according to claim 1, characterized in that: In step S53, the physical loss function expression of the soft adjustment function is shown in formula (3): L=λ1L h +λ2L p (3) Where: L is the physical loss function; λ1 and λ2 are the weights of the two loss functions; L h is the Huber loss function; L p is the physical constraint function; L h and L p The expressions of are shown in equations (4) and (5): Where: N e is the fatigue life verified in the experiment; N p is the fatigue life predicted by the model after training; δ and d are adjustment parameters, which can adjust the degree of penalty for model training by changing the value; The physical loss function controls the participation of physical constraints in neural network training by adjusting the values ​​of λ1 and λ2 to achieve soft adjustment function.

9. The method for predicting fatigue life of metal materials driven by a multi-scale and multi-depth neural network according to claim 1, characterized in that: In step S6, the fatigue test data obtained in step S1, including the strain amplitude (ε a ,γ a ), stress amplitude (σ a ,τ a ), mean stress σ m , and the maximum normal stress value σ of the critical surface calculated in step S2 n,max , these data are used as input to the model; fatigue life N p is used as the output of the model; then, the input data is preprocessed according to the data processing method described in step S4; then, the model constructed in step S5 is trained and iteratively optimized to obtain the optimal multi-scale and multi-depth neural network driven metal material fatigue life prediction model; finally, the optimal multi-scale and multi-depth neural network driven metal material fatigue life prediction model is used to predict the fatigue life of the metal material under cyclic load; the prediction results are visualized through a scatter plot for easy analysis and understanding.

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