A method and device for analyzing strength parameters of a steel plate in a bullet penetration steel plate scenario

Abstract

The application discloses a kind of bullet penetration steel plate scene under the analysis method, device and equipment of steel plate strength parameter and computer readable storage medium, comprising: extracting the influence parameter of steel plate strength in the simulation result of bullet penetration steel plate finite element model, according to influence parameter, construct a data sample, utilize neural network to fit out steel plate maximum stress and the functional relationship of maximum stress and influence parameter, then establish maximum stress reliability limit state function, obtain the reliability sensitivity of each influence parameter, establish steel plate reliability, according to steel plate reliability and the reliability sensitivity of each parameter, determine maximum influence parameter, optimization is carried out using suitable optimization method, improve the strength of steel plate;The application uses the result of simulation experiment, extracts multiple parameters that influence the strength of steel plate, calculates the most influential parameter in multiple influence parameters, optimizes it, improves the strength of steel plate, reduces research cost, improves prediction accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of limit calculation of metal steel plate structure, and particularly relates to a method and device for analyzing strength parameters of a steel plate in a scenario of bullet penetration of the steel plate, an equipment and a computer readable storage medium. BACKGROUND

[0002] Currently, there are three methods for studying the problem of steel plate penetration, namely, experiment, theoretical analysis and numerical simulation. (1) The result of the experiment method is reliable, and can provide a reference standard for theoretical analysis and numerical simulation. However, the experiment method has a high cost, a long time for preparing experimental materials, and can only obtain a small amount of data points. (2) The theoretical analysis method can deeply understand the penetration principle, and can quickly establish the relationship between multiple variables through theoretical formulas. However, the theoretical analysis needs idealized assumptions and a large number of simplifications of the model. For the problem of bullet penetration, the material failure process of the steel plate is relatively complex, and it is difficult to achieve theoretical analysis. (3) The numerical simulation method can completely reproduce the entire penetration process through numerical solution, and can obtain data that is difficult to obtain in experiments. With the development of computers, the numerical simulation method has become one of the important means for studying the problem of bullet penetration of steel plates.

[0003] Currently, the research on the reliability of bullet penetration of steel plates is based on real experiments or theoretical analysis methods. The real experiment scene has a high cost and a long cycle. As for the theoretical analysis method, multiple ideal assumptions are needed, and the result has a relatively large error. Moreover, in the case of a large amount of data, the calculation period is relatively long.

[0004] In summary, it can be seen that how to combine simulation and theoretical analysis, optimize the reliability parameters of the steel plate, and improve the defense performance of the steel plate is a problem to be solved at present. SUMMARY

[0005] The purpose of the present application is to provide a method and device for analyzing strength parameters of a steel plate in a scenario of bullet penetration of the steel plate, which solves the problem that in the prior art, the steel plate strength needs to be optimized by a high-cost and long-cycle experiment method.

[0006] To solve the above technical problems, the present application provides a method for analyzing strength parameters of a steel plate in a scenario of bullet penetration of the steel plate, comprising:

[0007] extracting multiple influence parameters affecting the strength of the steel plate and influence parameter data in simulation results of a finite element model of bullet penetration of the steel plate, and expanding the data according to the mean value and standard deviation of each influence parameter data to construct multiple influence parameter data samples;

[0008] inputting the multiple influence parameter data samples into a neural network in sequence to obtain a maximum stress and a functional relationship between the maximum stress and all influence parameters;

[0009] establishing a limit state function of the steel plate failure according to a function relationship of the maximum stress and all influence parameters;

[0010] calculating the steel plate reliability by using a first order second moment method, and differentiating each influence parameter by using the limit state function to obtain a reliability sensitivity of each influence parameter;

[0011] judging the steel plate reliability and the reliability sensitivity of each influence parameter, selecting a parameter with the maximum steel plate reliability and the reliability sensitivity value as an optimal influence parameter, and optimizing the optimal influence parameter by using a suitable optimization method.

[0012] The extracting of the multiple influence parameters affecting the strength of the steel plate from the simulation result of the finite element model of the bullet penetrating the steel plate and the influence parameter data includes:

[0013] constructing the finite element model of the bullet penetrating the steel plate by using a Johnson-Cook material model according to structure information and material properties of the bullet and the steel plate;

[0014] Preferably, the steel plate is hexahedrally meshed, the bullet is tetrahedrally meshed, and a simulation experiment is performed to obtain the simulation result of the finite element model of the bullet penetrating the steel plate.

[0015] Preferably, the expanding of the data according to the mean value and the standard deviation of each influence parameter data and the constructing of the multiple influence parameter data samples include:

[0016] extracting the influence parameter data affecting the strength of the steel plate from the simulation result;

[0017] determining the standard deviation and the mean value of each influence parameter data, taking the sum of the mean value and the standard deviation of each influence parameter data as an upper limit, taking the difference between the mean value and the standard deviation of each influence parameter data as a lower limit as a random value range, and randomly generating each influence parameter data set, and the calculation formula is: σ x =z·μ x , wherein σ x is the standard deviation of the influence parameter, μ x is the mean value of the influence parameter, and z is a variation coefficient;

[0018] collecting all the influence parameter data sets to construct the multiple influence parameter data samples.

[0019] Preferably, the inputting of the multiple influence parameter data samples into the neural network in sequence to obtain the maximum stress and the function relationship of the maximum stress and all influence parameters includes:

[0020] randomly dividing the multiple influence parameter data samples into the training samples and the test samples;

[0021] Training in the BP neural network with the training sample, obtaining the trained BP neural network and fitting function;

[0022] The test sample is input into the trained BP neural network, the error value of the maximum stress prediction value and the maximum stress true value is calculated, and the parameters in the BP neural network are corrected according to the error value;

[0023] Obtaining the maximum stress value and the functional relationship of the maximum stress and all influencing parameters.

[0024] Preferably, the establishment of the limit state function of the steel plate failure according to the functional relationship of the maximum stress and all influencing parameters comprises:

[0025] According to the functional relationship of the maximum stress and all influencing parameters and the strength-stress model, the limit state function is constructed, and its expression is:

[0026] g(X)=g(x1,x2,x3,...x i ,...x n )

[0027] Wherein, x1, x2, x3,... x i ,... x n are independent influencing parameter data;

[0028] Using the limit state function to judge the state of the steel plate structure;

[0029] When g(X) > 0, the steel plate structure is reliable;

[0030] When g(X) < 0, the steel plate structure is unreliable;

[0031] When g(X) = 0, the steel plate structure is in a limit state.

[0032] Preferably, the calculation of the steel plate reliability using the first order second moment method comprises:

[0033] The improved first order second moment method is used to calculate the mean value and standard value of all influencing parameters under the limit state function:

[0034]

[0035]

[0036] Based on the mean value μ g(X) and the standard value σ g(X) of all influencing parameters, the steel plate reliability β is calculated, and its expression is:

[0037]

[0038] According to the reliability index β of the steel plate, the reliability R is calculated by the formula R = 1 - P f = φ (β) ;

[0039] Wherein, P f is the failure probability, φ (β) is the standard normal distribution function, x i is the i th influence parameter, σ i and μ i are the standard deviation and mean value of the i th influence parameter respectively, P * is the design point, x i is the i th influence parameter at the P i point. *

[0040] Preferably, the step of differentiating each influence parameter by using the limit state function to obtain the reliability sensitivity of each influence parameter comprises:

[0041] The mean value and the standard deviation of each influence parameter are differentiated under the limit state function to obtain the mean value sensitivity and the standard deviation sensitivity of each influence parameter, and the expressions are as follows:

[0042]

[0043]

[0044] Wherein, P f is the failure probability of the steel plate, β is the reliability index, σ i is the mean difference of the i th influence parameter, σ i is the standard deviation of the i th influence parameter, μ g(X) is the mean difference of all influence parameters, σ g(X) is the standard deviation of all influence parameters, x i is the i th influence parameter.

[0045] The application also provides a device for optimizing the strength parameters of a steel plate in a scenario of a bullet penetrating the steel plate, comprising:

[0046] A data sample construction module is configured to extract a plurality of influence parameters affecting the strength of the steel plate and influence parameter data from the simulation results of a finite element model of a bullet penetrating the steel plate, and to expand the data according to the mean value and the standard deviation of each influence parameter data to construct a plurality of influence parameter data samples;

[0047] A maximum stress and function relationship calculation module is configured to input the plurality of influence parameter data samples into a neural network in sequence to obtain the maximum stress and the function relationship between the maximum stress and all influence parameters;

[0048] ​The limit state function module is configured to establish a limit state function of the steel plate failure according to the function relationship between the maximum stress and all the influence parameters;

[0049] The reliability and reliability sensitivity model is configured to calculate the steel plate reliability by using the first order second moment method, and to obtain the reliability sensitivity of each influence parameter by differentiating the limit state function with respect to the influence parameter.

[0050] The optimal influence parameter module is configured to determine the steel plate reliability and reliability sensitivity of each influence parameter, select the parameter with the maximum steel plate reliability and reliability sensitivity value as the optimal influence parameter, and optimize the optimal influence parameter by using a suitable optimization method.

[0051] The application further provides a device for optimizing the strength parameter of a steel plate in a bullet penetration steel plate scenario, which comprises:

[0052] The memory is configured to store a computer program, and the processor is configured to implement the steps of the analysis method for optimizing the strength parameter of a steel plate in a bullet penetration steel plate scenario when executing the computer program.

[0053] The application further provides a computer readable storage medium, which stores a computer program, and the computer program is configured to implement the steps of the analysis method for optimizing the strength parameter of a steel plate in a bullet penetration steel plate scenario when executed by a processor.

[0054] The analysis method for optimizing the strength parameter of a steel plate in a bullet penetration steel plate scenario provided by the application firstly extracts the influence parameters of the strength of the steel plate in the simulation results of the finite element model of the bullet penetration dry plate, constructs a data sample according to the influence parameters, fits the maximum stress of the steel plate and the function relationship between the maximum stress and the influence parameters by using a neural network, then establishes a limit state function of the steel plate, calculates the reliability of the steel plate, determines the maximum influence parameter according to the reliability of the steel plate and the reliability sensitivity of each parameter, and optimizes the parameter by using a corresponding optimization method to improve the strength of the steel plate. The application extracts multiple influence parameters of the strength of the steel plate by using the results of the simulation experiment, obtains the maximum influence parameter among the multiple influence parameters, and optimizes the parameter to improve the strength of the steel plate. The application greatly simplifies the analysis process, reduces the difficulty, and avoids the problem of large theoretical analysis error. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and those skilled in the art can also obtain other drawings according to these drawings without any creative effort.

[0056] Figure 1 A flowchart of a first specific embodiment of the method for analyzing the strength parameters of a steel plate in a bullet-penetration scenario provided by the present invention;

[0057] Figure 2 A flowchart illustrating a second specific embodiment of the method for analyzing the strength parameters of a steel plate in a bullet-penetration scenario provided by the present invention;

[0058] Figure 3 Model for a standard steel-core bullet penetrating a 4mm steel plate;

[0059] Figure 4 This is a structural diagram of the BP neural network provided by the present invention;

[0060] Figure 5 This is a graph showing the error variation during the neural network training process.

[0061] Figure 6 A comparison chart of expected and actual values ​​during the testing phase;

[0062] Figure 7 This is a structural block diagram of a device for optimizing the strength parameters of a steel plate in a bullet-penetration scenario, as provided in an embodiment of the present invention. Detailed Implementation

[0063] The core of this invention is to provide an analysis method for the strength parameters of steel plates under the scenario of bullet penetration. Based on finite element simulation analysis, multiple influencing parameters affecting the strength of the steel plate are obtained, and data samples are constructed according to these parameters. Then, a neural network is used to construct the relationship between the maximum stress of the steel plate and the influencing parameters, construct the limit state function of the failure point of the steel plate, analyze the reliability and sensitivity of multiple influencing parameters, determine the maximum influencing parameter, and then optimize the maximum influencing parameter, which greatly reduces the calculation time and lowers the cost.

[0064] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] Please refer to Figure 1 , Figure 1 The flowchart illustrates a first specific embodiment of the method for analyzing the strength parameters of a steel plate under bullet penetration scenarios provided by this invention; the specific operation steps are as follows:

[0066] Step S101: extract a plurality of influence parameters and influence parameter data affecting the strength of the steel plate from the simulation results of the finite element model of the bullet penetrating the steel plate, and expand the data according to the mean and standard deviation of each influence parameter data to construct a plurality of influence parameter data samples;

[0067] The most widely used Johnson-Cook material model can well describe the mechanical behavior of the material under high strain conditions; in order to improve the calculation stability and solving accuracy, reduce the sand hour energy, divide the steel plate into hexahedral mesh and the bullet into tetrahedral mesh, and the mesh division quality reaches 0.91; finally, the simulation experiment is carried out, and the parameters affecting the strength of the steel plate are extracted.

[0068] A number of parameters significantly affecting the strength of the steel plate are selected as influence parameters through ANSYS dynamic simulation, and Latin hypercube sampling is performed in Python. In this paper, each random variable parameter obeys normal distribution.

[0069] Step S102: input the plurality of influence parameter data samples into the neural network in turn to obtain the maximum stress and the functional relationship between the maximum stress and all influence parameters;

[0070] Randomly divide the randomly extracted data into training samples and test samples (training sample quantity / test sample quantity=5:1). First, train the neural network on the training sample data to fit the function relationship between the maximum stress and the random variable; then calculate the results of the test set according to the training model, and the error between the expected value and the actual value can be obtained. In this application, the Sigmoid function is used in the hidden layer. The advantage of using this function is that the neural network can approximate the required function with arbitrary precision, so that the result is closer to the true value.

[0071] Step S103: establishing a limit state function of the steel plate failure according to the functional relationship between the maximum stress and all influence parameters;

[0072] Step S104: calculating the reliability of the steel plate by using the first order second moment method, and differentiating each influence parameter by using the limit state function to obtain the reliability sensitivity of each influence parameter;

[0073] The first order second moment method is used in MATLAB to calculate the reliability of the steel plate. Then, the limit state function is used to differentiate the basic variables to obtain the reliability sensitivity of each parameter. The idea of Monte-Carlo method for calculating the reliability sensitivity of the structure is adopted: the joint density distribution function of the random variable is established; a large number of data samples are extracted; the function function value is calculated respectively; the mean value and standard deviation of each parameter are calculated. The calculation method in BP neural network is well proved by this method.

[0074] Step S105: Determine the steel plate reliability and reliability sensitivity of each influencing parameter, select the parameter with the maximum steel plate reliability and reliability sensitivity value as the optimal influencing parameter, and optimize the optimal influencing parameter using a suitable optimization method.

[0075] In this embodiment, a finite element model of a bullet penetrating a steel plate is established using ANSYS finite element analysis software, and penetration simulation is performed in the display dynamics module. Important material parameters are initially selected as design variables. Sample data is extracted using the Latin hypercube sampling method, and a backpropagation neural network is used to fit the functional relationship between the design variables and the maximum stress of the steel plate during penetration. The failure limit state equation of the steel plate is established, and the reliability and reliability sensitivity of the steel plate are analyzed using a modified first-order second-moment method in MATLAB to obtain the parameters with the greatest influence.

[0076] Based on the above embodiments, this embodiment provides a detailed description of the method of the present invention for studying the penetration of ordinary steel-core bullets into a 60mm x 40mm x 4mm steel plate. Please refer to [link / reference]. Figure 2 , Figure 2 The flowchart shows a second specific embodiment of the method for analyzing the strength parameters of a steel plate under a bullet penetration scenario provided by the present invention; the specific operation steps are as follows:

[0077] Step S201: Based on the structural information and material properties of the bullet and the steel plate, construct a finite element model of the bullet penetrating the steel plate using the Johnson-Cook material model;

[0078] Bullet penetration of steel plates involves large deformations, high strain rates, and material fracture, thus requiring the selection of an appropriate material constitutive model. The Johnson-Cook material model is the most widely used in impact penetration problems, effectively describing the mechanical behavior of metallic materials under high strain conditions.

[0079] The material properties of bullets and steel plates are shown in Tables 1 and 2 below:

[0080] Table 1. Parameters of the Johnson-Cook constitutive model for ordinary steel-core bullets.

[0081]

[0082] Table 2 Parameters of the Johnson-Cook Constitutive Model for Steel Plates

[0083]

[0084] Where: ρ is the material density, c is the specific heat capacity, E is the elastic modulus, μ is Poisson's ratio, A is the initial yield stress, B is the strain hardening modulus, n is the hardening index, C is the strain rate strengthening parameter, and m is the thermal softening index.

[0085] Step S202: Hexahedral meshing is performed on the steel plate, tetrahedral meshing is performed on the bullet, penetration simulation experiment is performed, and simulation results of the bullet penetrating the steel plate are obtained;

[0086] The steel plate is hexahedrally meshed, and the grid cell size is 2 mm; the bullet is tetrahedrally meshed, and the final overall grid division quality reaches 0.91. In the solving process, the bullet is set as a rigid body, and a finite element model is constructed, Figure 3 The ordinary steel core bullet shoots the 4mm steel plate model.

[0087] In this model, due to the high strength of the steel plate, it is widely used in armored vehicles, and the bullet penetration is a process of short time and large force, so only a small deformation will occur in a small range around the bullet hole. In order to achieve high calculation efficiency and also meet the actual situation, the size of the steel plate is 40mmx60mmx4mm, and the ordinary steel core bullet of a pistol is selected (the speed is about 450m / s).

[0088] Step S203: Extract the influence parameters in the simulation structure, including the penetration speed, the elastic modulus, the density and the Poisson's ratio of the steel plate;

[0089] Step S204: According to the standard deviation and mean value of the influence parameters, data is extracted by using Latin hypercube sampling method to constitute the data sample;

[0090] In order to analyze the reliability of the steel plate in the bullet penetration steel plate model, the relationship expression between the maximum stress value and the influence parameters needs to be obtained, and then the mathematical model for calculating the reliability is derived. In this paper, each random variable parameter obeys normal distribution, and the mean value and standard deviation of the random variable have the following relationship:

[0091] σ x = z μ x

[0092] In the formula: σ x is the standard deviation, μ x is the mean value, and z is the variation coefficient.

[0093] For material parameters, z is 0.05, and μ x ± σ x is the upper and lower limit of the value of each random variable, and 480 groups of samples are extracted by using Latin hypercube sampling method. Table 3 shows the main random variable properties of the steel plate strength.

[0094] Table 3 Random variable property table

[0095]

[0096] Step S205: Fit the function of maximum stress and influence parameters using a BP neural network;

[0097] S51 randomly divides the data samples into the training samples and the test samples;

[0098] S52 uses the training samples to train the BP neural network, and obtains the trained BP neural network and the fitting function;

[0099] S53 inputs the test sample into the trained BP neural network, calculates the error between the predicted maximum stress value and the actual maximum stress value, and corrects the parameters in the BP neural network based on the error;

[0100] S54 obtains the maximum stress and the functional relationship between the maximum stress and the influence parameter.

[0101] The input layer of the BP neural network consists of the four variables mentioned above, while the output layer represents the maximum stress on the steel plate during penetration. In general scenarios, if the hidden layers of the neural network have a sufficient number of nodes and use the sigmoid function, a single hidden layer can enable the neural network to approximate the desired function with arbitrary precision. Therefore, this paper uses only one hidden layer.

[0102] like Figure 4 As shown in the diagram, the structure of the BP neural network is obtained by inputting all the influencing parameters into the BP neural network, resulting in the functional relationship between the maximum stress F and the random variable X:

[0103]

[0104] Where, ω ij Indicates the network connection weights from the input layer to the hidden layer; υ j Indicates the network connection weights from the hidden layer to the output layer; b j The threshold of the hidden layer; c θ Indicates the threshold of the output layer; ψ(·) represents the transfer function of the hidden layer; in this paper, the Sigmoid function is chosen.

[0105] The 480 datasets extracted using Latin hypercube sampling were randomly divided into 400 training samples and 80 test samples. Training was performed using the 400 training samples, followed by function fitting. The error curve during the neural network training process is shown below. Figure 5 As shown; then, test samples were used for testing and error analysis was performed. The trained values ​​were very close to the expected output values, with relatively small errors. Figure 6 This reflects the relative error of the test data.

[0106] Step S206: establishing the limit state function of the steel plate;

[0107] The limit state function of the steel plate, i.e. the performance function, is

[0108] g(X) = g(x1, x2, x3,... x i ,... x n )

[0109] In the above formula, X = (x1, x2, x3,... x i ,... x n ) are mutually independent basic random variables. When g(X) > 0, it indicates that the structure is reliable; when g(X) < 0, it indicates that the structure is unreliable; when g(X) = 0, the structure is in the limit state, and g(X) at this time is called the limit state equation. The performance function can be defined as the difference between the corresponding quantity r(X) and the threshold value r * .

[0110] Step S207: calculating the steel plate reliability and reliability sensitivity of the steel plate;

[0111] The improved first-order second-moment method is used to expand the nonlinear performance function g(X) at the design point by Taylor formula to obtain:

[0112]

[0113]

[0114] wherein μ g(X) and σ g(X) are the mean value and standard deviation of the performance function, respectively.

[0115] This function is a nonlinear function, and the reliability index β can be expressed as:

[0116]

[0117] Since the random variables are subject to normal distribution, the reliability R can be expressed as

[0118] R = 1 - P f = φ(β)

[0119] P f = φ(-β)

[0120] In the formula, P f is the failure probability, φ(β) is the standard normal distribution function, x i is the i th influence parameter, is the mean value and standard deviation of the i th influence parameter, respectively, is the i th influence parameter xi In P * The value of the point.

[0121] According to the definition of reliability sensitivity and the derivative rule of composite function, the reliability sensitivity of the failure probability to the distribution parameters of the basic random variables under the condition that the basic variables are independent is obtained as follows:

[0122]

[0123]

[0124] Because the units of each random parameter are different, causing the reliability sensitivity between them to be incomparable, so the reliability sensitivity results need to be dimensionless

[0125]

[0126]

[0127] Step S208: According to the reliability of the steel plate and the reliability sensitivity of each parameter, determine the best influence parameter.

[0128] Because the units of each random parameter are different, causing the reliability sensitivity between them to be incomparable, so the reliability sensitivity results need to be dimensionless, and the calculation formula is:

[0129]

[0130]

[0131] Compare the dimensionless influence parameter data, select the largest influence parameter data as the best influence parameter, and select the optimization method corresponding to the best influence parameter to optimize the parameters.

[0132] In the embodiment, a finite element model of bullet penetration into a steel plate is constructed for simulation, parameters affecting the strength of the steel plate in the simulation result are extracted, a data sample set is constructed by expanding the parameters, a function relationship between the maximum stress and the parameters is determined by using a BP network, a maximum stress reliability limit state equation is established, the reliability and reliability sensitivity of the steel plate are analyzed by using an improved first-order second-moment method in MATLAB, and the parameter with the maximum influence is obtained according to the reliability and sensitivity. Then, the parameter with the maximum influence is optimized to improve the strength of the steel plate. The simulation experiment is used to reduce the cost of building a real experiment, the influence parameters in the experimental result are extracted, the data set is enriched, the function relationship between the maximum stress and each parameter is calculated, the reliability sensitivity and the reliability value of the steel plate are obtained by analysis, the parameter with the best influence on the strength of the steel plate is determined, and the parameter is optimized. The simulation experiment and theoretical data analysis are combined to analyze the reliability and reliability sensitivity of the strength of the steel plate, the parameter with the maximum influence is determined, and then the parameter is optimized, thereby reducing the research cost, improving the calculation efficiency and accuracy.

[0133] Please refer to Figure 7 , Figure 7 A structural block diagram of a steel plate strength parameter optimization device in a bullet penetration into a steel plate scenario is provided for the embodiment of the application. The specific device can include:

[0134] The data sample construction module 100 is used to extract a plurality of influence parameters affecting the strength of the steel plate in the simulation result of the finite element model of bullet penetration into the steel plate and influence parameter data, and to construct a plurality of influence parameter data samples according to the mean value and standard deviation of each influence parameter data.

[0135] The maximum stress and function relationship calculation module 200 is used to input the plurality of influence parameter data samples into a neural network in sequence to obtain the maximum stress and the function relationship between the maximum stress and all influence parameters.

[0136] The limit state function construction module 300 is used to establish a limit state function of the failure of the steel plate according to the function relationship between the maximum stress and all influence parameters.

[0137] The reliability and reliability sensitivity calculation module 400 is used to calculate the reliability of the steel plate by using the first-order second-moment method, and to obtain the reliability sensitivity of each influence parameter by differentiating each influence parameter by using the limit state function.

[0138] The maximum influence parameter determination module 500 is used to determine the reliability and reliability sensitivity of each influence parameter of the steel plate, to select the parameter with the maximum reliability and reliability sensitivity value as the best influence parameter, and to optimize the best influence parameter by using a suitable optimization method.

[0139] The optimization of the strength parameters of the steel plate in the bullet penetration steel plate scenario of the embodiment is used to implement the analysis method of the strength parameters of the steel plate in the bullet penetration steel plate scenario, and therefore the specific embodiments of the optimization device of the strength parameters of the steel plate in the bullet penetration steel plate scenario can be seen from the embodiment part of the analysis method of the strength parameters of the steel plate in the bullet penetration steel plate scenario, for example, the data sample construction module 100, the maximum stress calculation and function relationship module 200, the limit state function construction module 300, the reliability and reliability sensitivity model calculation module 400, and the maximum influence parameter determination module 500 are respectively used to implement the steps S101, S102, S103, S104 and S104 in the analysis method of the strength parameters of the steel plate in the bullet penetration steel plate scenario, and therefore the specific embodiments can refer to the description of the respective embodiment parts, and will not be described here.

[0140] The embodiment of the application further provides an analysis device of the strength parameters of the steel plate in the bullet penetration steel plate scenario, which comprises a memory for storing a computer program and a processor for executing the computer program to implement the steps of the analysis method of the strength parameters of the steel plate in the bullet penetration steel plate scenario.

[0141] The embodiment of the application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the analysis method of the strength parameters of the steel plate in the bullet penetration steel plate scenario.

[0142] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same or similar parts of each embodiment can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the related parts can be referred to the method part.

[0143] The professional person can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present application can be realized by electronic hardware, computer software or combination of the two, and in order to clearly show the interchangeability of hardware and software, the composition and steps of each example have been described in the above description. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. The professional person can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.

[0144] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in random access memory (RAM), flash memory, read-only memory (ROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0145] The above describes in detail the bullet penetration steel plate scene under the strength parameter analysis method, device and equipment provided by the application and computer readable storage medium. The principle and implementation of the application are described by specific examples in this paper. The above example is only used to help understand the method and core idea of the application. It should be pointed out that for ordinary skilled in the art, without departing from the principle of the application, the application can be improved and modified. These improvements and modifications also fall within the protection scope of the claims of the application.

Claims

1. A method for analyzing the strength parameters of a steel plate under a bullet penetration scenario, characterized in that, include: Multiple influencing parameters and their data affecting the strength of the steel plate were extracted from the simulation results of the finite element model of bullet penetration into the steel plate. The data were then expanded based on the mean and standard deviation of each influencing parameter to construct multiple influencing parameter data samples. The influencing parameters include penetration velocity, elastic modulus, density, and Poisson's ratio of the steel plate. The data samples of the multiple influencing parameters are sequentially input into a BP neural network to obtain the maximum stress and the functional relationship between the maximum stress and all influencing parameters; the input layer of the BP neural network is the above-mentioned influencing parameters, the output layer is the maximum stress of the steel plate during the penetration process, and the hidden layer uses the Sigmoid function; The limit state function for steel plate failure is established based on the functional relationship between the maximum stress and all influencing parameters, including: constructing the limit state function based on the functional relationship between the maximum stress and all influencing parameters and the strength-stress model, the expression of which is: ； in, These are mutually independent influence parameter data; The state of the steel plate structure is determined using the limit state function. when If so, the steel plate structure is reliable; when If this is the case, then the steel plate structure is unreliable; when When this happens, the steel plate structure is in a limit state; The reliability of steel plates is calculated using the first-order second-moment method, including: calculating the mean and standard values ​​of all influencing parameters under the limit state function using an improved first-order second-moment method. ； ； Based on the mean of all the aforementioned influencing parameters and standard value Calculate the reliability of the steel plate. Its expression is: ； According to the reliability index of the steel plate From the formula Calculate reliability ; in, This represents the probability of failure. It is the standard normal distribution function. For the first One influencing parameter, , The first The standard deviation and mean of each influencing parameter As a design point, For the first One influencing parameter exist The value of a point; The reliability sensitivity of each influence parameter is obtained by differentiating it with respect to the limiting state function. Determine the steel plate reliability and reliability sensitivity for each influencing parameter, select the parameter with the maximum steel plate reliability and reliability sensitivity value as the optimal influencing parameter, and optimize the optimal influencing parameter using a suitable optimization method.

2. The method for analyzing the strength parameters of steel plates as described in claim 1, characterized in that, The simulation results of the finite element model of bullet penetration into the steel plate include multiple parameters affecting the strength of the steel plate, and the data of these parameters include: Based on the structural information and material properties of the bullet and the steel plate, the Johnson-Cook material model is used to construct a finite element model of the bullet penetrating the steel plate; The steel plate was meshed with a hexahedron, and the bullet was meshed with a tetrahedron. A simulation experiment was conducted to obtain the simulation results of the finite element model of the bullet penetrating the steel plate.

3. The method for analyzing the strength parameters of steel plates as described in claim 1, characterized in that, The process of expanding the data based on the mean and standard deviation of each influencing parameter data to construct multiple influencing parameter data samples includes: Extract the data of parameters affecting the strength of the steel plate from the simulation results; Based on the standard deviation and mean of each influencing parameter data, and using the sum of the mean and standard deviation of each influencing parameter data as the upper limit and the difference between the mean and standard deviation of each influencing parameter data as the lower limit, a random value range is generated for each influencing parameter. The calculation formula is as follows: ,in, To influence the standard deviation of the parameter, To influence the mean of the parameters, The coefficient of variation; Collect all the datasets of influencing parameters to construct the multiple influencing parameter data samples.

4. The method for analyzing the strength parameters of steel plates as described in claim 1, characterized in that, The step of sequentially inputting the multiple influence parameter data samples into the neural network to obtain the maximum stress and the functional relationship between the maximum stress and all influence parameters includes: The data samples of the multiple influencing parameters are randomly divided into training samples and validation samples; The BP neural network is trained using the training samples to obtain the trained BP neural network and the fitting function; The verification sample is input into the trained BP neural network, the error between the predicted maximum stress value and the actual maximum stress value is calculated, and the parameters in the BP neural network are corrected based on the error value. The maximum stress value and the functional relationship between the maximum stress and all influencing parameters are obtained.

5. The method for analyzing the strength parameters of steel plates as described in claim 1, characterized in that, The step of differentiating each influence parameter using the limiting state function to obtain the reliability sensitivity of each influence parameter includes: Differentiating the mean and standard deviation of each influencing parameter under the limiting state function yields the mean sensitivity and standard deviation sensitivity of each influencing parameter, expressed as follows: ； ； in, This represents the probability of steel plate failure. As a reliability indicator, For the first The mean difference of each influencing parameter For the first The standard deviation of each influencing parameter Let be the mean difference of all influencing parameters. The standard deviation of all influencing parameters, For the first One influencing parameter.

6. A device for analyzing the strength parameters of a steel plate under a bullet penetration scenario, characterized in that, include: A data sample module is constructed to extract multiple influencing parameters and data affecting the strength of steel plates from the simulation results of the finite element model of bullet penetration into steel plates. The data is expanded based on the mean and standard deviation of each influencing parameter data to construct multiple influencing parameter data samples. The influencing parameters include penetration velocity, elastic modulus, density, and Poisson's ratio of the steel plate. The module for calculating the maximum stress and functional relationship is used to sequentially input the data samples of the multiple influencing parameters into the BP neural network to obtain the maximum stress and the functional relationship between the maximum stress and all influencing parameters; the input layer of the BP neural network is the above-mentioned influencing parameters, the output layer is the maximum stress of the steel plate during the penetration process, and the hidden layer uses the Sigmoid function; A limit state function construction module is used to establish the limit state function for steel plate failure based on the functional relationship between the maximum stress and all influencing parameters. This includes: constructing the limit state function based on the functional relationship between the maximum stress and all influencing parameters, and the strength-stress model. The expression of this limit state function is: ； in, These are mutually independent influence parameter data; The state of the steel plate structure is determined using the limit state function. when If so, the steel plate structure is reliable; when If this is the case, then the steel plate structure is unreliable; when When this happens, the steel plate structure is in a limit state; A reliability and reliability sensitivity model is calculated to determine the reliability of steel plates using the first-order second-moment method. This includes calculating the mean and standard values ​​of all influencing parameters under the limit state function using an improved first-order second-moment method. ； ； Based on the mean of all the aforementioned influencing parameters and standard value Calculate the reliability of the steel plate. Its expression is: ； According to the reliability index of the steel plate From the formula Calculate reliability ; in, This represents the probability of failure. It is the standard normal distribution function. For the first One influencing parameter, , The first The standard deviation and mean of each influencing parameter As a design point, For the first One influencing parameter exist The value of the point is determined; and the differential of each influence parameter is obtained by using the limit state function to obtain the reliability sensitivity of each influence parameter; The module for determining the optimal influence parameter is used to judge the steel plate reliability and reliability sensitivity of each influence parameter, select the parameter with the maximum steel plate reliability and reliability sensitivity value as the optimal influence parameter, and optimize the optimal influence parameter using an appropriate optimization method.

7. A device for optimizing the strength parameters of a steel plate under the scenario of bullet penetration, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the method for analyzing the strength parameters of a steel plate in a bullet-penetrating steel plate scenario as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for analyzing the strength parameters of a steel plate in a bullet-penetrating steel plate scenario as described in any one of claims 1 to 5.

Images