Bolt connection model construction method and device fusing physical information and deep learning

Abstract

The application discloses a bolt connection model construction method and device fusing physical information and deep learning, and the method comprises the following steps: obtaining a hysteresis response data set of a bolt connection interface according to an Ivan model through a bolt connection excitation; performing marking processing on the hysteresis response data set to obtain a target data set; constructing a target loss function according to the hysteresis response data set; constructing an initial training model based on a convolutional neural network and a long short-term memory network; and training the initial training model according to the target loss function and the target data set to obtain a bolt hysteresis system prediction model. The application can improve the efficiency and accuracy of identifying the bolt connection model and can be widely applied to the technical field of computer assistance.

Description

Technical Field

[0001] The present invention relates to the field of computer-aided technology, and in particular to a method and device for constructing a bolt connection model by integrating physical information and deep learning. Background Art

[0002] Bolted connections are one of the most commonly used connection methods in mechanical structures and are widely used in industries such as construction, automotive, and aerospace. Bolted connections create a strong connection between two or more components by tightening bolts and nuts, providing the necessary structural stability. Current bolted connection modeling methods can be categorized as constitutive models and phenomenological models. Constitutive models begin by applying the laws of physics to the microscopic physical behavior of the connection interface and gradually establish a mechanical model of the connection interface. However, these models rely on some simplistic assumptions and cannot fully characterize the surface roughness of the bolted connection. Furthermore, the variance of the statistical parameters is heavily dependent on the resolution of the measuring instrument, making it impossible to uniquely describe and analyze the rough surface. Phenomenological models utilize the macroscopic mechanical response of the connection structure and employ system identification theory and methods to determine the mechanical model of the connection interface. However, phenomenological models are subject to model errors and have difficulty representing viscous and sliding friction states. Furthermore, phenomenological models struggle to represent complex connection interfaces and the nonlinear hysteretic response of bolted connections under load. This results in low efficiency and high computational cost in bolted connection model identification, which reduces identification accuracy. Summary of the Invention

[0003] In view of this, the main purpose of the embodiments of the present invention is to provide a method and device for constructing a bolt connection model that integrates physical information and deep learning, in order to solve at least one of the problems of the existing technology. The present invention can improve the efficiency and accuracy of identifying bolt connection models.

[0004] To achieve the above objectives, an embodiment of the present invention provides a method for constructing a bolt connection model by integrating physical information and deep learning. The method includes:

[0005] Through the excitation of the bolt connection, the hysteretic response data set of the bolt connection interface is obtained according to the Ivan model;

[0006] performing labeling processing on the hysteresis response data set to obtain a target data set;

[0007] constructing a target loss function according to the hysteresis response data set;

[0008] Build an initial training model based on convolutional neural networks and long short-term memory networks;

[0009] The initial training model is trained according to the target loss function and the target data set to obtain a bolt hysteresis system prediction model.

[0010] In some embodiments, a method for constructing a bolt connection model integrating physical information and deep learning further includes:

[0011] The bolt hysteresis system prediction model is verified according to the verification data set in the target data set.

[0012] In some embodiments, the step of obtaining a hysteretic response dataset of a bolt connection interface according to an Ivan model through bolt connection excitation includes the following steps:

[0013] According to the Ivan model, a bolt connection structure is simulated and modeled to obtain a bolt connection simulation model;

[0014] The bolt connection excitation is applied to the bolt connection simulation model to generate a hysteretic response data set of the bolt connection interface.

[0015] In some embodiments, the labeling process of the hysteresis response dataset to obtain a target dataset includes the following steps:

[0016] Randomly selecting a number of the hysteresis response data sets as known data sets, and dividing the known data sets according to a preset ratio to obtain a training data set and a validation data set;

[0017] Using the remaining hysteresis response data set as a test data set;

[0018] The target data set includes the training data set, the verification data set, and the test data set.

[0019] In some embodiments, constructing a target loss function based on the hysteresis response dataset comprises the following steps:

[0020] obtaining a normalized hysteresis force in the hysteresis response data set, and obtaining a displacement of the bolt connection structure in the hysteresis response data set;

[0021] constructing a first loss function according to the normalized hysteresis force;

[0022] constructing a second loss function according to the smoothness between the displacement and the normalized hysteresis force;

[0023] The target loss function is obtained according to the first loss function and the second loss function.

[0024] In some embodiments, training the initial training model according to the target loss function and the target data set to obtain a bolt hysteresis system prediction model includes the following steps:

[0025] Classifying the training data set in the target data set according to the speed condition to obtain a first data set and a second data set;

[0026] The first data set and the second data set are input into the first channel and the second channel of the initial training model respectively, and the initial training model is trained in combination with the constraints of the target loss function to obtain the bolt hysteresis system prediction model.

[0027] To achieve the above objectives, another aspect of an embodiment of the present invention provides a device for constructing a bolt connection model by integrating physical information and deep learning, the device comprising:

[0028] The first module is used to obtain the hysteretic response data set of the bolt connection interface according to the Ivan model through bolt connection excitation;

[0029] The second module is used to label the hysteresis response data set to obtain a target data set;

[0030] A third module is used to construct a target loss function according to the hysteresis response data set;

[0031] The fourth module is used to build an initial training model based on convolutional neural networks and long short-term memory networks;

[0032] The fifth module is used to train the initial training model according to the target loss function and the target data set to obtain a bolt hysteresis system prediction model.

[0033] To achieve the above-mentioned purpose, another aspect of an embodiment of the present invention provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the aforementioned method for constructing a bolt connection model that integrates physical information and deep learning.

[0034] To achieve the above-mentioned purpose, another aspect of an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the aforementioned method for constructing a bolt connection model that integrates physical information and deep learning.

[0035] To achieve the above objectives, another aspect of an embodiment of the present invention provides a computer program product or computer program, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method for constructing a bolt connection model that integrates physical information and deep learning.

[0036] The embodiment of the present application at least has the following beneficial effects: the present application provides a bolt connection model construction method fusing physical information and deep learning, the scheme obtains a hysteresis response data set of a bolt connection interface according to an Ivan model through bolt connection excitation; the hysteresis response data set is marked to obtain a target data set; a target loss function is constructed according to the hysteresis response data set; an initial training model is constructed based on a convolutional neural network and a long short-term memory network; the initial training model is trained according to the target loss function and the target data set to obtain a bolt hysteresis system prediction model, which can improve the efficiency and accuracy of identifying the bolt connection model. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0038] Figure 1 is a flowchart of a bolt connection model construction method fusing physical information and deep learning provided by the embodiment of the present application;

[0039] Figure 2 is a schematic diagram of a bolt connection structure provided by the embodiment of the present application;

[0040] Figure 3a-Figure 3b is a schematic diagram of a bolt connection simulation model provided by the embodiment of the present application;

[0041] Figure 4 is a schematic diagram of an LSTM unit structure provided by the embodiment of the present application;

[0042] Figure 5 is a deep learning framework schematic diagram of CNN-LSTM provided by the embodiment of the present application;

[0043] Figure 6 is a schematic diagram of a bolt connection element subjected to tension force in a simulation experiment provided by the embodiment of the present application;

[0044] Figure 7a-7d is a recognition result schematic diagram of a deep learning model provided by the embodiment of the present application;

[0045] Figure 8 is a schematic diagram of a bolt connection element subjected to torque in a simulation experiment provided by the embodiment of the present application;

[0046] Figure 9is a schematic diagram of the response result under the torsional hysteresis loop provided by an embodiment of the present invention;

[0047] Figure 10 It is a schematic diagram of the hardware structure of the electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present invention. They are merely examples of devices and methods consistent with some aspects of the embodiments of the present invention as detailed in the appended claims.

[0049] It should be noted that although the functional modules are divided in the system schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first / S100" and "second / S200" in the specification and claims and the above-mentioned figures may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to a determination".

[0050] The terms "at least one", "plurality", "each", "any", etc. used in the present invention include at least one, two or more, multiple, two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention pertains. The terms used herein are for the purpose of describing embodiments of the present invention only and are not intended to limit the present invention.

[0052] Before describing the embodiments of the present invention in detail, some nouns and terms involved in the embodiments of the present invention are first described. The nouns and terms involved in the embodiments of the present invention are subject to the following explanations.

[0053] Bolted connection: Bolted connection is a common mechanical connection method that uses bolts to connect two or more parts together to form a fixed structure.

[0054] Deep learning: Originated from the study of artificial neural networks. It discovers distributed feature representations of data by combining lower-level features to form more abstract higher-level representations of attribute categories or features.

[0055] Physical information: When constructing a physical model (such as a mechanical model, a thermodynamic model, an electromagnetic model, etc.), physical information includes various physical quantities related to the model and their relationships.

[0056] Hysteresis system: When the input and output of a system exhibit a nonlinear characteristic, this characteristic is called hysteresis.

[0057] Bolted connections, as one of the most commonly used connection methods in mechanical structures, are widely used in industries such as construction, automobiles, and aerospace. Bolted connections form a strong connection between two or more components by tightening bolts and nuts, providing the necessary structural stability. With the development of industrial technology, the design and application of bolted connections have gradually become intelligent and automated. Especially in scenarios with high-strength and high-precision connections, the reliability and predictive performance of bolted connections are becoming increasingly important. The reliability of bolted connections not only depends on the material, size, and accuracy of the bolt holes, but is also affected by many factors such as the connection preload, load changes, temperature fluctuations, and environmental factors. The modeling and identification of bolted connection structures remain a research hotspot and frontier.

[0058] Currently, bolt connection modeling methods can be categorized into constitutive models and phenomenological models. Constitutive models begin by analyzing the microscopic physical behavior of the connection interface and gradually establish a mechanical model of the connection interface using physical laws. However, these models rely on simplistic assumptions and cannot fully characterize the actual surface roughness of bolted connections. Furthermore, the variance of statistical parameters is heavily dependent on the resolution of the measuring instrument, making it impossible to uniquely describe and analyze the rough surface. When large-scale structures are subjected to dynamic loads, complex nonlinear hysteretic behavior can occur. Existing models incur heavy computational costs when addressing this problem. Phenomenological models, on the other hand, utilize the macroscopic mechanical response of the connection structure and employ system identification theory and methods to determine the mechanical model of the connection interface. However, phenomenological models suffer from issues with zero-speed detection and difficulty switching between the viscous and sliding friction equations of state. Furthermore, phenomenological models struggle to characterize complex connection interfaces and the nonlinear hysteretic response of bolted connections under load. Consequently, existing bolt connection modeling methods suffer from low efficiency, high computational costs, and low accuracy in identifying bolted connection models.

[0059] In view of this, if Figure 1As shown, an embodiment of the present invention provides a method for constructing a bolt connection model by integrating physical information and deep learning. The method may include but is not limited to steps S100 to S500:

[0060] Step S100, obtaining a hysteretic response data set of the bolt connection interface according to the Ivan model through bolt connection excitation;

[0061] Step S200, marking the hysteresis response data set to obtain a target data set;

[0062] Step S300, constructing a target loss function according to the hysteresis response data set;

[0063] Step S400, constructing an initial training model based on a convolutional neural network and a long short-term memory network;

[0064] Step S500: training the initial training model according to the target loss function and the target data set to obtain a bolt hysteresis system prediction model.

[0065] In some embodiments, step S100 may include but is not limited to steps S110 to S120:

[0066] Step S110, performing simulation modeling on the bolt connection structure according to the Ivan model to obtain a bolt connection simulation model;

[0067] Step S120 : applying the bolt connection excitation to the bolt connection simulation model to generate a hysteresis response data set of the bolt connection interface.

[0068] In steps S110 to S120 of some embodiments, a bolt connection structure can be established using simulation modeling software to obtain a bolt connection simulation model. In establishing the bolt connection simulation model, a simulation model is constructed based on the Iwan model, which can include geometric parameters, material properties, preload, etc. of the bolt connection structure. Parameters of the Iwan model, such as spring stiffness, friction coefficient, etc., can also be pre-set in the simulation model. These parameters will affect the nonlinear response of the model. According to the actual application scenario, corresponding external excitations, such as loads, temperature changes, vibrations, etc., are input into the simulation model. When running the simulation model, the behavior of the bolt connection structure under the action of bolt connection excitations of different amplitudes will be simulated to generate a hysteresis response data set of the bolt connection interface.

[0069] refer to Figure 2 , Figure 2A physical structure of an actual bolted connection is shown, consisting of a bolt, a nut, connected parts, etc., for fixing two or more components together. Such structures are very common in engineering and construction, used to withstand different types of loads such as tension, shear, etc. Based on the physical properties and behavior of the bolted connection structure, a bolted connection simulation model can be built using computer software or programming languages, which can simulate the performance of the bolted connection structure under different conditions, such as the response when subjected to external conditions such as load, temperature change, vibration, etc. Through the simulation model, engineers can predict and analyze the performance of the bolted connection structure without actually manufacturing and testing the physical structure, evaluate the impact of different design parameters on the performance of the structure, optimize the design, reduce costs, and shorten the development cycle.

[0070] As shown in the bolted connection simulation model, wherein, Figure 3a-Figure 3b Figure 3a is a Jenkins unit, which is one of the basic units that make up the Iwan model. Each Jenkins unit is composed of a linear spring and a Coulomb friction element (slider) to simulate the nonlinear behavior in the bolted connection, especially the stick-slip characteristics. This stick-slip characteristic is manifested as the bolted connection interface first experiences a stick state (i.e., relative static) during a certain loading process, and then slips under the action of external force. The Jenkins unit can capture this nonlinear dynamic behavior from micro-slip to macro-slip.

[0071] In some embodiments, in order to simulate the nonlinear behavior of the bolted connection structure, a mathematical model such as the Iwan model can be used. As shown in the Iwan model, Figure 3b

[0072]

[0073] where F I represents the hysteresis force of the Iwan model; φ represents the yield displacement of the Jenkins unit; ρ(φ) represents the distribution function of the Jenkins unit with yield displacement; U represents the displacement of the bolted connection structure; represents the velocity of the bolted connection structure; represents the function of the hysteresis force of the Iwan model; represents the function of the velocity of the Coulomb slider; p represents the parameters specified for each Iwan model; z(t,φ) represents the displacement of the Coulomb slider; represents the velocity of the Coulomb slider. Hereinafter, the symbol z(t,φ) is simplified as z, and the symbol is simplified as ​​

[0074] At the same time, considering the relationship between energy dissipation and the amplitude of the external load, as well as the discontinuity of stiffness at the beginning of large sliding, the distribution function of the four-parameter Iwan model can be expressed as:

[0075] ρ(φ)=Rφ -X [H(φ)-H(φ-φ max )]+Sδ(φ-φ max ) (2)

[0076] Where R represents the coefficient of the power law part; χ represents the positive power law value of the distribution; H(·) represents the unit step function; δ(·) represents the Dirac trigonometric function; φ represents the yield displacement of the Jenkins unit; φ max represents the maximum yield displacement of all Jenkins units; S represents the slope of the force-displacement curve before the large slip, taking into account that the stiffness may be discontinuous at the beginning of the large slip. At this time, the hysteresis force of the four-parameter Ivan model can be expressed as:

[0077]

[0078] Where, PI = [R,χ,S,φ max ] is the parameter of Ivan model; u represents the displacement of the bolt connection structure; Indicates the speed of the bolted connection structure; represents the hysteresis force of the four-parameter Ivan model; z and z(t,φ) both represent the displacement of the Coulomb slider; z(t,φ max ) represents the maximum yield displacement of the Coulomb slider; Function representing the Coulomb slider velocity of the four-parameter Ivan model; represents the function of the maximum yield displacement of the Coulomb slider velocity in the four-parameter Ivan model; R represents the coefficient of the power law part; χ represents the positive power law value of the distribution; S represents the slope of the force-displacement curve before the large sliding; φ represents the yield displacement of the Jenkins unit; φ max Represents the maximum yield displacement of all Jenkins elements.

[0079] For example, the equation in Equation (1) is solved while generating a synthetic database of 100 samples (e.g., independent bolt response sequences) for a single-degree-of-freedom (SDOF) nonlinear system under an external excitation of u = 0.8 sin(πt) in a numerical simulation using bolt excitations of varying amplitudes. Each simulation is performed at a sampling frequency of 50 Hz for 160 seconds, with each record containing 9001 data points.

[0080] In some embodiments, step S200 may include but is not limited to steps S210 to S220:

[0081] Step S210, randomly select several of the hysteresis response data sets as known data sets, divide the known data sets according to a preset proportion to obtain a training data set and a validation data set;

[0082] Step S220, take the remaining hysteresis response data sets as test data sets;

[0083] The target data set includes the training data set, the validation data set, and the test data set.

[0084] In steps S210 to S220 of some embodiments, the hysteresis response data set Artificial marking is performed using data simulation software. In the generated hysteresis response data set, 10 data sets containing Gaussian white noise (BLWN) input and corresponding structural displacement and velocity response are randomly selected, and the 10 randomly selected data sets are used as "known" data sets for training and validation. The "known" data sets are divided into a training data set and a validation data set according to a preset proportion. Optionally, the "known" data sets are divided into a training data set and a validation data set according to a 0.8 / 0.2 proportion. The remaining hysteresis response data sets are used as "unknown" data sets (i.e., test data sets) for testing the prediction effect of the trained meta-model. In addition, 50 configuration samples under different amplitudes (i.e., different amplitudes of bolt excitation) can be additionally provided to assist the subsequent model training process under physical constraints.

[0085] In some embodiments, step S300 can include but is not limited to steps S310 to S320:

[0086] Step S310, obtaining the normalized hysteresis force in the hysteresis response data set, and obtaining the displacement of the bolted structure in the hysteresis response data set;

[0087] Step S320, constructing a first loss function according to the normalized hysteresis force;

[0088] Step S330, constructing a second loss function according to the smoothness between the displacement and the normalized hysteresis force;

[0089] Step S340, obtaining the target loss function according to the first loss function and the second loss function.

[0090] In steps S310 to S340 of some embodiments, the Jenkins unit is composed of a spring with a stiffness k and a Coulomb slider with a critical yield displacement φ. The normalized hysteresis force F in the Jenkins unit can be expressed as and the displacement z of the spring element relative to the Coulomb slider, the following expression can be obtained:

[0091]

[0092] Where, P J =[k,φ] are the Jenkins unit parameters; k is the stiffness of the Jenkins unit spring; represents the hysteresis force of the Jenkins unit; u represents the displacement of the bolted connection structure; represents the velocity of the bolted connection structure; z represents the displacement of the Coulomb slider; represents the velocity of the Coulomb slider; H(·) represents the unit step function; φ represents the yield displacement of the Jenkins unit.

[0093] When z = ±φ, the deformation velocity of the spring in the Jenkins unit is zero. Function Indicates that the extension of the spring element connected to the sliding element is limited to -φ <z<φ的区间内,与滑移元件相连的弹簧元件的伸长量在-φ<z<φ时,滑移单元的相对速度必须为零。

[0094] Since multiple Jenkins units can be connected in parallel to form an Iwan model, the hysteresis force of the Jenkins unit is incorporated into the subsequent construction of the Iwan model. Deep learning models of physical information.

[0095] Since Jenkins has typical stick-slip characteristics and is a significant non-smooth unit, a target loss function that integrates physical information is designed based on the characteristics of the Jenkins unit. This target loss function can be integrated into deep learning training. The target loss function also ensures the smoothness of the prediction results by constraining the relationship between hysteresis force and displacement. For example, by obtaining a hysteresis response dataset The normalized hysteresis force F in the Jenkins unit is expressed as J ), multiple Jenkins units are connected in parallel to form the Ivan model. By introducing the hysteresis physical information of the Jenkins unit, the hysteresis F of the Ivan model can be obtained. I Hysteresis force according to the Ivan model and hysteresis force predicted by deep learning Construct a first loss function; construct a second loss function based on the displacement u of the bolt connection structure in the hysteresis response data set and the hysteresis force of the Ivan model; based on the first loss function and the second loss function, the target loss function can be obtained. Optionally, the target loss function can be defined as:

[0096] L=L1+L2 (6)

[0097] Where,

[0098]

[0099] Where L represents the target loss function; represents the hysteresis force predicted by deep learning; L1 represents the loss function composed of the hysteresis force of the nonlinear response Ivan model calculated by the equation in formula (1) and the hysteresis force predicted by deep learning, that is, the first loss function; L2 represents the fusion of displacement u and hysteresis force F I The physical information loss function of the smoothness between them is the second loss function; λ, μ, ξ represent the parameters optimized by the deep learning model; λ, μ, ξ can be optimized by the Adam optimizer.

[0100] Based on the macroscopic viscosity-slip characteristics of the bolted connection structure, the Jenkins unit physical information is introduced, and a physical information loss function that integrates the macroscopic viscosity-slip characteristics, displacement state and hysteresis smoothness is constructed to evaluate the gap between the predicted output of the deep learning model and the actual target (label).

[0101] In step S400 of some embodiments, an initial training model is constructed based on a convolutional neural network and a long short-term memory network (CNN-LSTM). Among them, a convolutional neural network (CNN) is a type of feedforward neural network (Feedforward Neural Networks) that includes convolution calculations and has a deep structure. It is one of the representative algorithms of deep learning. Their design is inspired by the visual system in biology and aims to simulate the way human vision is processed. Recurrent Neural Network (RNN) is a neural network model widely used in sequence data processing. Long Short-Term Memory Network (LSTM) is an important variant of RNN (such as Figure 4 By introducing a gating mechanism, the gradient vanishing and gradient exploding problems in the traditional RNN model are effectively solved.

[0102] In some embodiments, as Figure 4 As shown in Figure 1, the LSTM model consists of an input layer, a hidden layer, and an output layer. There are h hidden units, a batch size of n, and the number of inputs is d. The hidden state at the previous time step is The input gate is The forget gate is The output gate is Candidate memory units memory unit Then there is the following mathematical expression of LSTM:

[0103] I t =σ(X t W xi +H t-1 W hi +b i ) (9)

[0104] F t =σ(X t W xf +H t-1 W hf +b f ) (10)

[0105] O t =σ(X t W xo +H t-1 W ho +b o ) (11)

[0106]

[0107] H t =O t ⊙tanh(C t ) (14)

[0108] Where, and is the weight matrix; is the bias parameter matrix; σ(·) represents the sigmoid activation function; tanh(·) represents the hyperbolic tangent function; ⊙ represents the Hadamard integral (element-wise product).

[0109] In some embodiments, as Figure 5 The deep learning network architecture of CNN-LSTM is shown in the figure. In the CNN-LSTM network architecture, displacement, velocity, and hysteresis force at time t-1 are used as inputs, and hysteresis force at time t is used as output. and The data is divided into loading and unloading paths. A CNN is used to extract data features. A dual-channel CNN-LSTM is then trained on each type of data. Subsequently, an LSTM layer with 32 hidden units is used. More hidden units yield more accurate predictions. To output a sequence with the same number of channels as the input, a fully connected layer with an output size equal to the number of input channels is used. Finally, a regression layer is used to obtain the predicted response.

[0110] In some embodiments, step S500 may include but is not limited to steps S510 to S520:

[0111] Step S510, classifying the training data set in the target data set according to the speed condition to obtain a first data set and a second data set;

[0112] Step S520: input the first data set and the second data set into the first channel and the second channel of the initial training model respectively, and train the initial training model in combination with the constraints of the target loss function to obtain the bolt hysteresis system prediction model.

[0113] In steps S510 to S520 of some embodiments, as Figure 5 As shown, the training data sets under different amplitude states are input into the network, and the speed and As the classification criteria, they are respectively brought into the two channels of the initial training model network structure to classify the input training data set. The first data set obtained by classification is input into the first channel of the initial training model, which will meet the speed condition The classified second dataset is fed into the second channel of the initial training model to train the network. In the initial training model network, a convolutional neural network (CNN) is used to extract features. 1×1 convolution kernels are inserted between 3×3 convolution kernels to compress the features. Batch normalization is used to accelerate network convergence, combined with a Leaky ReLU activation function to extract complex features. This data is then fed into an LSTM network. Each LSTM network consists of two LSTM layers and one fully connected (FC) layer. Constraints are applied to the initial training model network during training, in conjunction with the objective loss function. The model is pre-trained using the Adam optimizer, with a learning rate of 0.001 for the first 5000 epochs and then reduced to 0.0001 for the next 5000 epochs. The pre-trained model is then further optimized using the L-BFGS optimizer until the default convergence criteria are met.

[0114] In some embodiments, a pre-processed hysteresis response dataset of a bolt connection interface is input into a network for training to form a bolt hysteresis system prediction model. By using the target dataset and the supplementary dataset, the model is further optimized so that it can learn the time series characteristics of the bolt connection and has the ability to model the bolt connection. The displacement, velocity, and force data in the supplementary dataset (optionally, the supplementary dataset can be an additional 50 configuration samples provided to assist the model training process under physical constraints) provide the model with diverse features under different amplitude scenarios, thereby improving the prediction accuracy and adaptability of the model in different working conditions.

[0115] In some embodiments, a method for constructing a bolt connection model that integrates physical information and deep learning may also include, but is not limited to, step S600: verifying the bolt hysteresis system prediction model based on a verification data set in the target data set. Optionally, a verification data set is constructed through a bolt hysteresis response data set constructed through a tensile simulation experiment and a torsional simulation experiment, and the bolt hysteresis system prediction model is verified using the verification data set. The effectiveness and applicability of the proposed bolt connection model that integrates physical information and deep learning in nonlinear dynamic systems are evaluated. The system considered is a bolt hysteresis system, including a four-parameter Iwan model and a torsional hysteresis experiment. The recovery effects of different models are verified through numerical examples, and the sensitivity of the bolt connection model construction method that integrates physical information and deep learning of the present invention to multiple factors is analyzed, including data usage, sampling frequency, simulation time step, selection of basis function library, degree of damage, noise type, and model accuracy. For example, the standard deviation of Gaussian noise is set to Randomly assigned to the manually labeled hysteresis response dataset. The specific noise formula is as follows:

[0116]

[0117] Where F is the normalized hysteresis force vector of the hysteresis response data set; F Noise is the force vector after adding noise; ζ is a scaling factor used to adjust the noise intensity; R is a random noise vector generated from a standard normal distribution, whose elements are independent and identically distributed random variables; is a diagonal matrix whose elements are the standard deviations of the measured forces Used to scale noise.

[0118] In some embodiments, numerical examples are used to explore how to use deep learning models to predict the response of bolted connection structures when they are subjected to quasi-static loads and are affected by a specific dynamic load u=Asin(πt). In this process, the performance of deep learning models in motion mechanisms in nonlinear dynamic systems will be focused on. That is, numerical simulations are used to analyze and predict the response of bolted connection structures when subjected to periodic dynamic loads, with special attention paid to the effectiveness of deep learning models in dealing with nonlinear dynamic problems. Optionally, a finite element simulation model is established using three-dimensional modeling software, as shown in the following example. Figure 6 The bolt connection structure shown in Figure 1 is used. Under the dynamic load u=Asin(πt), a deep learning model is constructed based on formulas (4)-(6). The convolution layer of the convolutional neural network is used to remove redundant parameters caused by noise, prevent the deep learning model from being too complex, remove interference information such as noise, ensure the robustness of the model, and make the model retain the main physical properties. Figure 7a-7d The recognition results of the deep learning model. Figure 7a The response prediction of the four-parameter Iwan model of the simulation model when A=0.3 is shown. Figure 7b The hysteresis loop of the four-parameter Iwan model of the simulation model when A=0.3 is shown. Figure 7c The response prediction of the four-parameter Iwan model of the simulation model when A=0.7 is shown. Figure 7d The hysteresis loop of the four-parameter Iwan model of the simulation model when A=0.7 is shown. Figure 7a-7d The results and data show that the constructed deep learning model has a good recognition effect. Furthermore, through simulation research, a bolt connection experiment with torsional load excitation is used to evaluate the performance of the method of the present invention in an actual model. Optionally, due to the excellent generalization and approximation capabilities of the four-parameter Iwan model, the four-parameter Iwan model is used to model the hysteresis curve obtained under the bolt preload, and the following can be obtained: Figure 8 The simulation experiment shows a torque bolt connection structure. By using the method of the present invention to predict the hysteresis force and hysteresis loop of the torsion test data, the following can be obtained: Figure 9 The response results under the torsional hysteresis loop are shown. In order to visualize the recognition performance, Figure 9 The hysteresis curves predicted by the bolt hysteresis system prediction model are presented and compared with the experimentally measured hysteresis curves. It can be seen that the proposed method provides excellent identification capabilities for bolt connection models in terms of good fit between the predicted and experimental hysteresis curves.

[0119] The bolt connection model that integrates physical information and deep learning was simulated and the accuracy of the method was verified by numerical simulation. Compared with traditional constitutive models and phenomenological models, the neural network that integrates physical information shows strong robustness in processing noisy data, especially when the observation data is limited, which can effectively reduce the overfitting of the model. In the numerical example, the physical information neural network has a good prediction effect on non-smoothness (such as Figure 7b This is because the method of the present invention integrates the physical information of the Jenkins unit, and has better performance when dealing with non-smoothness. In order to further verify the advantages of the neural network integrated with the physical information of the Jenkins unit for hysteresis response prediction, in the simulation experiment, the response data generated by the bolt connection excited by the torsional load has a certain smoothness. Figure 9 It can be seen that good prediction results can be achieved by using the neural network model of the method of the present invention for prediction.

[0120] An embodiment of the present invention further provides a device for constructing a bolt connection model by integrating physical information and deep learning, which can implement the above-mentioned method for constructing a bolt connection model by integrating physical information and deep learning. The device includes:

[0121] The first module is used to obtain the hysteretic response data set of the bolt connection interface according to the Ivan model through bolt connection excitation;

[0122] The second module is used to label the hysteresis response data set to obtain a target data set;

[0123] A third module is used to construct a target loss function according to the hysteresis response dataset;

[0124] The fourth module is used to build an initial training model based on convolutional neural networks and long short-term memory networks;

[0125] The fifth module is used to train the initial training model according to the target loss function and the target data set to obtain a bolt hysteresis system prediction model.

[0126] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0127] An embodiment of the present invention further provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program. When the processor executes the computer program, it implements the aforementioned method for constructing a bolt connection model that integrates physical information and deep learning. The electronic device can be any intelligent terminal, including a tablet computer and an in-vehicle computer.

[0128] It can be understood that the contents in the above method embodiments are all applicable to the present device embodiments, the present device embodiments specifically implement the functions same as those of the above method embodiments, and achieve the same beneficial effects as those of the above method embodiments.

[0129] Reference Figure 10 , Figure 10 The hardware structure of the electronic device of another embodiment is illustrated, and the electronic device comprises:

[0130] The processor 701 can be implemented in a manner of a general CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., and is used to execute related programs to implement the technical solutions provided by the embodiments of the present application;

[0131] The memory 702 can be implemented in a form of a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory) etc. The memory 702 can store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are saved in the memory 702 and are called and executed by the processor 701 to implement a bolt connection model construction method fusing physical information and deep learning;

[0132] The input / output interface 703 is used to realize information input and output;

[0133] The communication interface 704 is used to realize the communication interaction between the present device and other devices, and can realize the communication through a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.);

[0134] The bus 705 is used to transmit information between various components (for example, the processor 701, the memory 702, the input / output interface 703, and the communication interface 704) of the device.

[0135] The processor 701, the memory 702, the input / output interface 703, and the communication interface 704 are connected to each other through the bus 705 to realize the communication connection between them inside the device.

[0136] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for constructing a bolt connection model that integrates physical information and deep learning.

[0137] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiment, the functions specifically implemented by the present storage medium embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0138] An embodiment of the present invention further provides a computer program product or computer program, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method for constructing a bolt connection model that integrates physical information and deep learning.

[0139] In summary, the method and device for constructing a bolt connection model by integrating physical information and deep learning according to the embodiments of the present invention have the following advantages:

[0140] 1. The embodiment of the present invention generates a synthetic data set through simulation modeling software, which ensures the validity and scalability of the data set on the one hand, and avoids the difficulties and labor and time consumption caused by collecting bolt node data sets on the other hand.

[0141] 2. The embodiment of the present invention constructs a physical information loss function that integrates the smoothness of the displacement state and the hysteresis force, which can model the bolt connection model and exhibits good convergence.

[0142] 3. The embodiments of the present invention can efficiently and accurately identify bolt connection models, including complex working conditions such as tension and bending, and can also be applied to identify bolt connection structures under loads such as time-varying normal stiffness and high temperature.

[0143] 4. The embodiment of the present invention only uses displacement, velocity, and force data, and data acquisition is convenient and efficient. In addition, the bolt hysteresis system prediction model of the embodiment of the present invention has high robustness, good recognition effect, and strong model versatility.

[0144] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0145] Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise indicated, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It will also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the ordinary skill of an engineer. Therefore, a person skilled in the art using ordinary skill will be able to implement the present invention set forth in the claims without undue experimentation. It will also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0146] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0147] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0148] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0149] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0150] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0151] While the embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary and are not to be construed as limiting the scope of the application. The scope of the application is defined by the appended claims and their equivalents.

[0152] The above is a specific description of the preferred embodiment of the present application, but the present application is not limited to the described embodiment, and those skilled in the art can make various equivalent modifications or replacements without departing from the spirit of the present application, and these equivalent modifications or replacements are all included in the scope defined by the claims of the present application.

Claims

1. A method for constructing a bolt connection model by integrating physical information and deep learning, characterized in that: Through the excitation of the bolt connection, the hysteretic response data set of the bolt connection interface is obtained according to the Ivan model; performing labeling processing on the hysteresis response data set to obtain a target data set; constructing a target loss function according to the hysteresis response data set; Build an initial training model based on convolutional neural networks and long short-term memory networks; Training the initial training model according to the target loss function and the target data set to obtain a bolt hysteresis system prediction model; The step of obtaining a hysteretic response data set of the bolt connection interface according to the Ivan model through bolt connection excitation includes the following steps: According to the Ivan model, a bolt connection structure is simulated and modeled to obtain a bolt connection simulation model; Applying the bolt connection excitation to the bolt connection simulation model to generate a hysteretic response data set of the bolt connection interface; The method of constructing a target loss function according to the hysteresis response data set comprises the following steps: obtaining a normalized hysteresis force in the hysteresis response data set, and obtaining a displacement of the bolt connection structure in the hysteresis response data set; constructing a first loss function according to the normalized hysteresis force; constructing a second loss function according to the smoothness between the displacement and the normalized hysteresis force; Obtaining the target loss function according to the first loss function and the second loss function; The initial training model is trained according to the target loss function and the target data set to obtain a bolt hysteresis system prediction model, comprising the following steps: Classifying the training data set in the target data set according to the speed condition to obtain a first data set and a second data set; The first data set and the second data set are input into the first channel and the second channel of the initial training model respectively, and the initial training model is trained in combination with the constraints of the target loss function to obtain the bolt hysteresis system prediction model.

2. The method for constructing a bolt connection model integrating physical information and deep learning according to claim 1, characterized in that: The following steps are also included: The bolt hysteresis system prediction model is verified according to the verification data set in the target data set.

3. The method for constructing a bolt connection model integrating physical information and deep learning according to claim 1, characterized in that: The step of labeling the hysteresis response dataset to obtain a target dataset comprises the following steps: Randomly selecting a number of the hysteresis response data sets as known data sets, and dividing the known data sets according to a preset ratio to obtain a training data set and a validation data set; Using the remaining hysteresis response data set as a test data set; The target data set includes the training data set, the verification data set, and the test data set.

4. A bolt connection model construction device integrating physical information and deep learning, characterized in that: include: The first module is used to obtain the hysteretic response data set of the bolt connection interface according to the Ivan model through bolt connection excitation; The second module is used to label the hysteresis response data set to obtain a target data set; A third module is used to construct a target loss function according to the hysteresis response dataset; The fourth module is used to build an initial training model based on convolutional neural networks and long short-term memory networks; A fifth module is configured to train the initial training model according to the target loss function and the target data set to obtain a bolt hysteresis system prediction model; Wherein, the first module is specifically used for: According to the Ivan model, a bolt connection structure is simulated and modeled to obtain a bolt connection simulation model; Applying the bolt connection excitation to the bolt connection simulation model to generate a hysteretic response data set of the bolt connection interface; The third module is specifically used for: obtaining a normalized hysteresis force in the hysteresis response data set, and obtaining a displacement of the bolt connection structure in the hysteresis response data set; constructing a first loss function according to the normalized hysteresis force; constructing a second loss function according to the smoothness between the displacement and the normalized hysteresis force; Obtaining the target loss function according to the first loss function and the second loss function; The fifth module is specifically used for: Classifying the training data set in the target data set according to the speed condition to obtain a first data set and a second data set; The first data set and the second data set are input into the first channel and the second channel of the initial training model respectively, and the initial training model is trained in combination with the constraints of the target loss function to obtain the bolt hysteresis system prediction model.

5. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 3.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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