A parameter calibration method, device, apparatus and storage medium

Abstract

This application provides a parameter calibration method, apparatus, device, and storage medium, relating to the fields of damage mechanics and materials engineering, to address the problem of low parameter calibration accuracy. The parameter calibration method includes: obtaining the fracture strain of the target specimen in a tensile test, and the average stress state parameters of the target specimen during plastic deformation; the average stress state parameters include: average stress triaxiality and average Lode angle parameters; constructing a damage accumulation model using the average stress state parameters and fracture strain as inputs and the damage accumulation of the target specimen as output; the damage accumulation model includes the damage parameters to be calibrated; constructing a fitness function with the damage parameters to be calibrated as variables, constrained by the damage accumulation equaling a preset threshold; the fitness function is used to quantify the deviation between the damage accumulation and the preset threshold; and globally optimizing the fitness function based on a genetic algorithm to obtain the parameter values ​​of the damage parameters to be calibrated.

Description

Technical Field

[0001] This application relates to the fields of damage mechanics and materials engineering, and in particular to a parameter calibration method, apparatus, device and storage medium. Background Technology

[0002] In the prediction of fracture behavior in metallic materials, the Modified Mohr-Coulomb (MMC) fracture criterion is widely used to describe the fracture characteristics of materials under complex stress states. This criterion includes multiple damage parameters, the accuracy of which directly affects the accuracy of fracture prediction.

[0003] Currently, the calibration of damage parameters in MMC models typically employs gradient-based numerical optimization methods, such as the least squares method or Newton's method. These methods construct a deviation function between experimental results and model predictions, and then use gradient information to iteratively solve for the parameter combination that minimizes the deviation.

[0004] However, due to the strong nonlinearity of the MMC model, its objective function typically has multiple local extrema. Traditional gradient optimization methods perform local searches starting from a given initial point, and their convergence results are highly dependent on the choice of initial parameter values. When the initial values ​​are not chosen properly, it is easy to get trapped in local optima and fail to obtain the globally optimal parameters. Furthermore, the iterative process may oscillate or diverge, making it difficult to guarantee the stability and reliability of the calibration results. This problem limits the accurate application of the MMC fracture criterion in engineering practice.

[0005] Therefore, how to accurately calibrate the damage parameters in the MMC model is a technical problem that urgently needs to be solved. Summary of the Invention

[0006] This disclosure provides a parameter calibration method, apparatus, device, and storage medium to solve the problem of low parameter calibration accuracy.

[0007] To achieve the above objectives, this application adopts the following technical solution: Firstly, a parameter calibration method is provided, comprising: obtaining the fracture strain of the target specimen in a tensile test and the average stress state parameters of the target specimen during plastic deformation; the average stress state parameters include: average stress triaxiality and average Lode angle parameters; constructing a damage accumulation model with the average stress state parameters and fracture strain as inputs and the damage accumulation amount of the target specimen as output; the damage accumulation model includes the damage parameters to be calibrated; constructing a fitness function with the damage parameters to be calibrated as variables, constrained by the damage accumulation amount equal to a preset threshold; the fitness function is used to quantify the deviation between the damage accumulation amount and the preset threshold; and globally optimizing the fitness function based on a genetic algorithm to obtain the parameter values ​​of the damage parameters to be calibrated.

[0008] As can be seen from the above, the embodiments of this application use a genetic algorithm to globally optimize the fitness function. The genetic algorithm searches in parallel throughout the parameter space through a population evolution mechanism, without relying on a single initial point. Therefore, it can effectively escape local extreme points and find the globally optimal or near-global optimal parameter combination, overcoming the defect of traditional gradient optimization methods that get stuck in local optima due to improper selection of initial points.

[0009] Secondly, since genetic algorithms do not require users to provide initial parameter values ​​and the population initialization is randomly generated, their convergence results do not depend on any manually set initial point. Therefore, they avoid the disadvantage of traditional methods being highly sensitive to initial values ​​and reduce the dependence of the calibration process on the operator's experience.

[0010] Furthermore, genetic algorithms, based on a probabilistic search mechanism, do not require the objective function to be continuously differentiable, and are inherently robust to the discreteness of experimental data and measurement noise. Even with some data errors, the algorithm can still converge stably, avoiding the oscillation or divergence problems caused by the non-smoothness of the objective function in traditional gradient methods.

[0011] Furthermore, in this embodiment, the fitness function is constructed using the cumulative damage amount equal to a preset threshold (usually 1) as a physical constraint, thus ensuring a high degree of consistency between the optimization objective and the physical mechanism of material fracture. Simultaneously, by introducing mean stress triaxiality and mean Lode angle parameters to describe complex stress states, the calibrated damage parameters can more accurately predict the fracture behavior of materials under different stress states.

[0012] Furthermore, the embodiments of this application have good versatility, can be applied to the calibration of MMC model parameters for various metallic materials, and can be seamlessly integrated with existing finite element analysis processes, making it easy to promote and apply in engineering practice.

[0013] Secondly, a parameter calibration device is provided, comprising: a communication unit and a processing unit; the communication unit is used to acquire the fracture strain of the target specimen in a tensile test, and the average stress state parameters of the target specimen during plastic deformation; the average stress state parameters include: average stress triaxiality and average Lode angle parameters; the processing unit is used to construct a damage accumulation model with the average stress state parameters and fracture strain as inputs and the damage accumulation of the target specimen as output; the damage accumulation model contains damage parameters to be calibrated; the processing unit is also used to construct a fitness function with the damage parameters to be calibrated as variables, constrained by the damage accumulation equal to a preset threshold; the fitness function is used to quantify the deviation between the damage accumulation and the preset threshold; the processing unit is also used to perform global optimization of the fitness function based on a genetic algorithm to obtain the parameter values ​​of the damage parameters to be calibrated.

[0014] Thirdly, a parameter calibration device is provided, including a memory and a processor; the memory is used to store computer-executed instructions, and the processor is connected to the memory via a bus; when the parameter calibration device is running, the processor executes the computer-executed instructions stored in the memory, so that the parameter calibration device performs the parameter calibration method of the first aspect.

[0015] The parameter calibration device can be an electronic device or a component of an electronic device, such as a chip system within the electronic device. This chip system supports the electronic device in implementing the functions involved in the first aspect and any possible implementation thereof, such as acquiring and determining the data and / or information involved in the aforementioned parameter calibration method. The chip system includes a chip, but may also include other discrete devices or circuit structures.

[0016] Fourthly, a computer-readable storage medium is provided, comprising computer-executable instructions that, when executed on a computer, cause the computer to perform the parameter calibration method described in the first aspect.

[0017] Fifthly, a computer program product is provided, comprising a computer program or instructions that, when executed on a parameter calibration device, cause the parameter calibration device to perform the parameter calibration method as described in the first aspect above.

[0018] It should be noted that the aforementioned computer instructions may be stored, in whole or in part, on a computer-readable storage medium. This computer-readable storage medium may be packaged together with the processor of the parameter calibration device, or it may be packaged separately from the processor of the parameter calibration device; this application does not limit this.

[0019] The descriptions of the second, third, fourth, and fifth aspects of this application can be referenced to the detailed description of the first aspect.

[0020] In the embodiments of this application, the names of the parameter calibration devices do not limit the devices or functional modules themselves. In actual implementation, these devices or functional modules may appear under other names. For example, the receiving unit may also be called a receiving module, receiver, etc. As long as the functions of each device or functional module are similar to those of this application, they fall within the scope of the claims of this application and their equivalents. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the structure of a parameter calibration system provided in an embodiment of this application; Figure 2 A flowchart illustrating a parameter calibration method provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a target sample provided in an embodiment of this application; Figure 4 A flowchart illustrating another parameter calibration method provided in this application embodiment; Figure 5 A schematic diagram showing a comparison between prediction results and experimental results provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a parameter calibration device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the hardware structure of a parameter calibration device provided in an embodiment of this application. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0024] To facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish the same or similar items with essentially the same function and effect. Those skilled in the art can understand that the terms "first" and "second" are not intended to limit the quantity or execution order.

[0025] As described in the background section, fracture failure is one of the main failure modes in the safety analysis of engineering structures. Traditional fracture criteria are mostly based on uniaxial tensile test results, considering only the strength characteristics of the material, and are difficult to accurately describe the fracture behavior of the material under complex three-dimensional stress states. In order to more accurately predict the fracture behavior of metallic materials under complex stress states, researchers have introduced fracture criteria based on the theory of continuum damage mechanics. These criteria, by introducing stress state parameters, correlate the fracture behavior of the material with the stress state it is subjected to, and can more comprehensively describe the fracture mechanism of the material.

[0026] The MMC fracture criterion is one of the most widely used advanced fracture criteria. Based on the Mohr-Coulomb strength theory, this criterion establishes a quantitative relationship between material fracture strain and stress state by introducing two key stress state parameters: stress triaxiality η and the Lode angle parameter θ. The mathematical expression of the MMC criterion is: ; in, The fracture strain predicted by the MMC criterion. For stress triaxiality, For Lode angle parameters, and n are the hardening model parameters of the target sample. These are the material fracture parameters that need to be determined through experimental calibration, i.e., the damage parameters to be calibrated.

[0027] The accurate application of the MMC criterion hinges on its three material fracture parameters. Precise calibration is required. Existing parameter calibration methods mainly employ gradient-based numerical optimization methods, such as the least squares method and Newton's method. The basic idea of ​​these methods is to construct an optimization function with the objective of minimizing the deviation between the experimental results and the model prediction results through multiple sets of fracture tests under different stress states, and then use gradient information to iteratively solve the problem.

[0028] However, traditional gradient-based parameter calibration methods have the following technical problems: First, gradient optimization algorithms start searching from a given initial point and can only find local optima near that point. Due to the complex nonlinear characteristics of the MMC model, the objective function often has multiple local extrema. Different initial parameter values ​​may lead to convergence to different local optima, making it difficult to guarantee finding the globally optimal parameter combination.

[0029] Secondly, the convergence and final result of gradient optimization algorithms are highly dependent on the choice of initial parameter values. Inappropriate initial values ​​may lead to slow convergence, getting stuck in local optima, or even divergence. The selection of initial values ​​often lacks theoretical guidance and relies mainly on experience or trial and error.

[0030] Furthermore, gradient optimization methods require the objective function to be continuously differentiable. However, in practical applications, due to the discreteness of experimental data and the influence of noise, the objective function may not meet the smoothness requirement, leading to inaccurate gradient calculations and causing oscillations or divergences during the iteration process.

[0031] Furthermore, when there are measurement errors in the experimental data or when the experimental conditions change, the stability of traditional optimization methods is significantly reduced, and the reliability of the calibration results is difficult to guarantee.

[0032] These problems severely limit the application of the MMC fracture criterion in practical engineering and affect its accuracy in predicting material fracture behavior under complex stress states. Therefore, a method for calibrating damage parameters in the MMC model that can overcome the above-mentioned shortcomings is needed.

[0033] To address the aforementioned issues, this application provides a parameter calibration method that can obtain the fracture strain of a target specimen in a tensile test and the average stress state parameters of the target specimen during plastic deformation. These average stress state parameters include average stress triaxiality and average Lode angle parameters. Next, using the average stress state parameters and fracture strain as inputs and the cumulative damage of the target specimen as output, a damage accumulation model can be constructed, which includes the damage parameters to be calibrated. Then, with the cumulative damage equal to a preset threshold as a constraint, a fitness function can be constructed, using the damage parameters to be calibrated as variables. This fitness function quantifies the deviation between the cumulative damage and the preset threshold. Subsequently, a genetic algorithm can be used to globally optimize the fitness function to obtain the parameter values ​​of the damage parameters to be calibrated.

[0034] As can be seen from the above, the embodiments of this application use a genetic algorithm to globally optimize the fitness function. The genetic algorithm searches in parallel throughout the parameter space through a population evolution mechanism, without relying on a single initial point. Therefore, it can effectively escape local extreme points and find the globally optimal or near-global optimal parameter combination, overcoming the defect of traditional gradient optimization methods that get stuck in local optima due to improper selection of initial points.

[0035] Secondly, since genetic algorithms do not require users to provide initial parameter values ​​and the population initialization is randomly generated, their convergence results do not depend on any manually set initial point. Therefore, they avoid the disadvantage of traditional methods being highly sensitive to initial values ​​and reduce the dependence of the calibration process on the operator's experience.

[0036] Furthermore, genetic algorithms, based on a probabilistic search mechanism, do not require the objective function to be continuously differentiable, and are inherently robust to the discreteness of experimental data and measurement noise. Even with some data errors, the algorithm can still converge stably, avoiding the oscillation or divergence problems caused by the non-smoothness of the objective function in traditional gradient methods.

[0037] Furthermore, in this embodiment, the fitness function is constructed using the cumulative damage amount equal to a preset threshold (usually 1) as a physical constraint, thus ensuring a high degree of consistency between the optimization objective and the physical mechanism of material fracture. Simultaneously, by introducing mean stress triaxiality and mean Lode angle parameters to describe complex stress states, the calibrated damage parameters can more accurately predict the fracture behavior of materials under different stress states.

[0038] Furthermore, the embodiments of this application have good versatility, can be applied to the calibration of MMC model parameters for various metallic materials, and can be seamlessly integrated with existing finite element analysis processes, making it easy to promote and apply in engineering practice.

[0039] The implementation environment for the above parameter calibration method can be the parameter calibration system provided in the embodiments of this application.

[0040] Figure 1 This is a schematic diagram of a parameter calibration system provided in an embodiment of this application. Figure 1 As shown, the parameter calibration system includes a parameter calibration device 101 and a data acquisition device 102.

[0041] The parameter calibration device 101 and the data acquisition device 102 are connected by communication.

[0042] In this embodiment, the data acquisition device 102 provides the parameter calibration device 101 with data required for parameter calibration, such as the fracture strain of the target specimen in a tensile test and the average stress state parameters of the target specimen during plastic deformation. The parameter calibration device 101 is used to evaluate pipeline defects in the target pipeline area based on the data provided by the data acquisition device 102.

[0043] In practical applications, the parameter calibration device 101 can be connected to any number of data acquisition devices 102. For ease of understanding, Figure 1 The following explanation uses a parameter calibration device 101 connected to a data acquisition device 102 as an example.

[0044] Optionally, the physical device of the parameter calibration device 101 can be a server, a terminal, or other types of electronic devices, and this application embodiment does not limit it.

[0045] Optionally, the aforementioned terminal may be a device that provides voice and / or data connectivity to a user, a handheld device with wireless connectivity, or other processing device connected to a wireless modem. The wireless terminal may communicate with one or more core networks via a radio access network (RAN). The wireless terminal may be a mobile terminal, such as a mobile phone (or "cellular" phone) and a computer with a mobile terminal, or a portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile device that exchanges voice and / or data with the radio access network, such as a mobile phone, tablet computer, laptop computer, netbook, or personal digital assistant (PDA).

[0046] Optionally, the server mentioned above can be one of the servers in a server cluster (composed of multiple servers), a chip in the server, a system-on-a-chip in the server, or a virtual machine (VM) deployed on a physical machine. This application embodiment does not limit this.

[0047] Optionally, the parameter calibration device 101 and the data acquisition device 102 can be two independently configured devices, or they can be integrated into the same device. When the parameter calibration device 101 and the data acquisition device 102 are integrated into the same device, the data acquisition device 102 can be the data collector of the parameter calibration device 101.

[0048] It is easy to understand that when the parameter calibration device 101 and the data acquisition device 102 are integrated into the same device, the communication method between the parameter calibration device 101 and the data acquisition device 102 is the same as the communication between internal modules of the device. In this case, the communication process between the two is the same as when the parameter calibration device 101 and the data acquisition device 102 are independent of each other.

[0049] It should be noted that the system architecture and application scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems. For ease of understanding, this application uses the example of parameter calibration device 101 and data acquisition device 102 being independent of each other for illustration.

[0050] The parameter calibration method provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0051] The parameter calibration method provided in this application embodiment is applied to Figure 1 The parameter calibration device 101 in the parameter calibration system shown is, for example Figure 2 As shown in the embodiments of this application, a parameter calibration method includes: S201. Obtain the fracture strain of the target specimen in the tensile test, and the average stress state parameters of the target specimen during the plastic deformation process.

[0052] The mean stress state parameters include: mean stress triaxiality and mean Lode angle parameters.

[0053] In some embodiments, the number of target specimens is multiple, and the multiple target specimens include at least one of: a standard smooth round bar specimen, a standard smooth plate specimen, a round bar specimen with a circumferential notch having a different notch radius, and a plate specimen with a planar notch having a different notch radius.

[0054] Specifically, the parameter calibration equipment can acquire tensile test data for various types of target specimens according to tensile testing standards for metallic materials (such as GB / T228.1-2021). Target specimens include standard smooth round bar specimens, standard smooth plate specimens, round bar specimens with circumferential notches and different notch radii, and plate specimens with planar notches and different notch radii. Combinations of various target specimen types cover different ranges of stress triaxiality and Lode angle parameters.

[0055] In some embodiments, the method for obtaining the fracture strain of the target specimen in a tensile test and the average stress state parameters of the target specimen during plastic deformation specifically includes: Obtain the load-displacement curve and engineering stress-strain curve of the target specimen in the tensile test, and convert the engineering stress-strain curve into the true stress-strain curve. Establish a finite element model corresponding to the geometry of the target specimen, and perform elastoplastic finite element analysis using the true stress-strain relationship to obtain the element with the largest equivalent plastic strain. Extract the equivalent plastic strain evolution curve, stress triaxiality evolution curve, and Lode angle parameter evolution curve of the element with the largest equivalent plastic strain from the start of plastic deformation to the fracture time. Using the fracture displacement measured in the experiment as a reference, determine the fracture time in the finite element analysis, and read the equivalent plastic strain at the fracture time as the fracture strain according to the equivalent plastic strain evolution curve. Perform integral averaging on the stress triaxiality evolution curve and the Lode angle parameter evolution curve to obtain the average stress triaxiality and the average Lode angle parameter.

[0056] Specifically, the parameter calibration equipment can obtain the load-displacement curve and engineering stress-strain curve obtained by performing quasi-static tensile tests on each target specimen using a universal testing machine at room temperature.

[0057] Next, the parameter calibration equipment extracts the basic mechanical property parameters of the target specimen from the engineering stress-strain curve, including elastic modulus, yield strength, tensile strength and elongation.

[0058] Next, the parameter calibration equipment transforms the engineering stress-strain curve into the real stress-strain curve. The transformation is based on the assumption of constant volume and uses the formulas σtrue=σeng(1+εeng)σ and εtrue=ln(1+εeng) for the transformation.

[0059] Where σtrue is the true stress, εtrue is the true strain, σeng is the engineering stress, and εeng is the engineering strain. The parameter calibration equipment also fits the hardening model parameters K and n of the target specimen from the true stress-strain curve for subsequent MMC criterion calculations.

[0060] Next, the parameter calibration equipment establishes a corresponding finite element model in ABAQUS for each target specimen's geometry and dimensions. The equipment applies the same boundary and load conditions as the tensile test to the finite element model and uses the actual stress-strain relationship as the elastoplastic constitutive model of the target specimen for elastoplastic finite element analysis.

[0061] Next, the parameter calibration equipment identifies the element with the largest equivalent plastic strain in the elastoplastic finite element analysis results. The element with the largest equivalent plastic strain corresponds to the location where the plastic deformation of the target specimen is most concentrated during the tensile process, and is also the location where the target specimen first fractures.

[0062] Next, the parameter calibration equipment extracts the equivalent plastic strain evolution curve, stress triaxiality evolution curve, and Lode angle parameter evolution curve of the element with the largest equivalent plastic strain from the start of plastic deformation to the fracture moment. The equivalent plastic strain evolution curve records the change of equivalent plastic strain with the deformation process, the stress triaxiality evolution curve records the change of instantaneous stress triaxiality with equivalent plastic strain, and the Lode angle parameter evolution curve records the change of instantaneous Lode angle parameter with equivalent plastic strain.

[0063] Next, the parameter calibration equipment uses the fracture displacement measured in the experiment as a reference to determine the fracture time in the finite element analysis.

[0064] Specifically, the parameter calibration equipment uses the displacement corresponding to the point of sudden load drop on the load-displacement curve as the fracture displacement. Then, it searches for the time corresponding to the same displacement in the finite element analysis results and takes that time as the fracture time. Based on the equivalent plastic strain evolution curve, the parameter calibration equipment reads the equivalent plastic strain value corresponding to the fracture time and takes this value as the fracture strain of the target specimen, denoted as . .

[0065] Next, the calibration equipment performs integral averaging on the extracted stress triaxiality evolution curve and Lode angle parameter evolution curve to obtain the average stress triaxiality and average Lode angle parameter.

[0066] In some embodiments, the mean stress triaxiality and the mean Lode angle parameter satisfy the following formula: .

[0067] .

[0068] in, For mean stress triaxiality, The average Lode angle parameter, For fracture strain, The stress triaxiality evolution curve is shown. The evolution curve of the Lode angle parameter. Let be the integral variable, representing the equivalent plastic strain.

[0069] Through the above steps, the parameter calibration equipment obtains the fracture strain, mean stress triaxiality, and mean Lode angle parameters for each target specimen. When there are multiple target specimens (e.g., target specimens with different geometries or different notch radii), the parameter calibration equipment performs the above process independently for each target specimen to obtain a set of data corresponding to each target specimen.

[0070] S202. Using the average stress state parameters and fracture strain as inputs and the damage accumulation of the target specimen as output, construct a damage accumulation model.

[0071] The damage accumulation model includes damage parameters to be calibrated.

[0072] The parameter calibration equipment can construct a damage accumulation model based on the theory of continuous damage mechanics and using the MMC fracture criterion. This model is used to describe the behavior of the target specimen in gradually accumulating damage until fracture during plastic deformation.

[0073] Specifically, the parameter calibration equipment uses the average stress triaxiality, average Lode angle parameter, and fracture strain of each target specimen as inputs to the model. The model output is the damage accumulation D, which physically represents the sum of damage generated at each step of plastic deformation from the start of plastic deformation to the current moment. When the damage accumulation D reaches a preset threshold (usually 1), it indicates that the material has fractured.

[0074] In some embodiments, the damage accumulation model is constructed based on the MMC criterion and satisfies the following formula: .

[0075] .

[0076] in, This represents the cumulative amount of damage. For equivalent plastic strain, The fracture strain predicted by the MMC criterion. For stress triaxiality, For Lode angle parameters, and n are the hardening model parameters of the target specimen, obtained by fitting the actual stress-strain curve in S201. The damage parameters are to be calibrated.

[0077] In the integral formula of the above damage accumulation model, the denominator It is the fracture strain prediction value given by the MMC criterion, which depends on the current stress state (determined by stress triaxiality and Lode angle parameter).

[0078] The parameter calibration device embeds the aforementioned MMC expression into the damage accumulation integral formula, thereby establishing a mathematical relationship between the input parameters (mean stress triaxiality, mean Lode angle parameter, fracture strain) and the output (damage accumulation). In this model, These are unknown variables and will be calibrated using a genetic algorithm later. The core idea of ​​the model is that, under ideal parameter values, the calculated cumulative damage D for each target sample should be equal to 1 (i.e., a preset threshold).

[0079] For multiple target specimens, the parameter calibration device independently applies the aforementioned damage accumulation model to each specimen, calculating the damage accumulation Di for each specimen. These calculation results will be used in the next step of constructing the fitness function.

[0080] S203. With the cumulative damage amount equal to a preset threshold as a constraint, construct a fitness function with the damage parameters to be calibrated as variables.

[0081] The fitness function is used to quantify the deviation between the accumulated damage and a preset threshold.

[0082] The parameter calibration device, based on the damage accumulation model constructed in S202, can calculate the damage parameters (i.e., ...) of each target specimen at the current damage parameters. The cumulative damage amount under the given value.

[0083] Ideally, when a material fractures, the accumulated damage should be exactly equal to a preset threshold. According to the theory of continuous damage mechanics, this preset threshold is usually set to 1, meaning that the accumulated damage increases from 0 to 1 as the material undergoes plastic deformation until fracture.

[0084] The parameter calibration equipment is set so that the cumulative damage equals a preset threshold (i.e., D). i =1) As a physical constraint, a function is constructed to measure the overall deviation between the calculated damage accumulation of all target samples and a preset threshold. This function is called the fitness function. The smaller the fitness function value, the closer the damage accumulation of each target sample is to the preset threshold under the current damage parameter value, that is, the more accurate the parameter calibration result.

[0085] In some embodiments, the fitness function satisfies the following formula: .

[0086] Where n is the total number of samples, i.e., the number of all target samples participating in the calibration, and D i Let be the cumulative damage amount of the i-th target sample. Let be the average stress triaxiality of the i-th target specimen. Let be the average Lode angle parameter of the i-th target sample. Let be the fracture strain of the i-th target specimen.

[0087] In this fitness function, for each target specimen, its cumulative damage D is calculated. i The difference between the value and the preset threshold 1 is squared to obtain the deviation term for that sample. Then, the deviation terms of all target samples are summed to obtain the total fitness function value. In this way, the fitness function will calibrate the damage parameters to be calibrated. This is correlated with the overall deviation of all target samples. The goal of the parameter calibration device is to find a set of damage parameters that minimizes the fitness function value, i.e., the cumulative damage of all target samples is as close as possible to a preset threshold of 1.

[0088] In this way, the parameter calibration device transforms the calibration problem of damage parameters in the MMC model into a nonlinear optimization problem with the constraint that the cumulative damage amount equals 1, and clarifies the objective to be optimized (fitness function). This fitness function will serve as the direct basis for subsequent global optimization by the genetic algorithm.

[0089] S204. The fitness function is globally optimized based on the genetic algorithm to obtain the parameter values ​​of the damage parameters to be calibrated.

[0090] The parameter calibration device can use a genetic algorithm (GA) to globally optimize the fitness function constructed by S203.

[0091] Genetic algorithms are intelligent optimization algorithms that simulate the biological evolution process in nature. They search in parallel within the parameter space through operations such as selection, crossover, and mutation. They have global convergence capabilities, do not depend on the selection of initial values, and are suitable for solving complex optimization problems with nonlinearity and multiple extrema.

[0092] The specific process of the parameter calibration device executing the genetic algorithm is as follows: First, the parameter calibration equipment will calibrate the three damage parameters to be calibrated. Encoding is done on chromosomes. The encoding method can use binary encoding or real number encoding, with each chromosome representing a complete set of damage parameters. ).

[0093] Next, the parameter calibration device randomly generates a certain number of chromosomes within a preset parameter value range to form an initial population. The population size can be set according to actual needs, for example, 100 individuals. Each individual in the initial population is a candidate solution for the damage parameters.

[0094] In some embodiments, the parameters of the genetic algorithm include: a population size of 50 to 200, a number of generations of genetic inheritance of 100 to 500, a crossover probability of 0.6 to 0.8, and a mutation probability of 0.001 to 0.01.

[0095] In other words, the parameter calibration device sets the control parameters required for the genetic algorithm to run, including: Population size: 50-200 individuals (e.g., set to 100); Genetic generations: 100-500 (e.g., set to 300 generations); Crossover probability: 0.6 to 0.8 (e.g., set to 0.7); Mutation probability: 0.001 to 0.01 (e.g., set to 0.0013).

[0096] The values ​​of these parameters affect the convergence speed and solution accuracy of the algorithm. The above ranges are the optimal values ​​obtained through experimental verification.

[0097] Next, for each chromosome in the current population (i.e., each set) (Values ​​are taken), and the parameter calibration equipment substitutes them into the damage accumulation model of S202 to calculate the damage accumulation D for each target sample. iThen, the fitness function value of the chromosome is calculated according to the fitness function formula of S203. The smaller the fitness function value, the better the set of damage parameters.

[0098] Next, the parameter calibration device selects individuals from the current population based on their fitness function values. Individuals with lower fitness function values ​​(i.e., better combinations of damage parameters) are more likely to be selected, thus having the opportunity to pass on their genetic information to the next generation. Common selection methods include roulette wheel selection and tournament selection.

[0099] Next, the parameter calibration device randomly selects a pair of chromosomes from the chosen individuals with a set crossover probability (e.g., 0.7), exchanging a portion of their gene segments to produce two new offspring individuals. This crossover operation simulates gene recombination during biological reproduction, contributing to the generation of better gene combinations.

[0100] Next, the parameter calibration device randomly perturbs gene loci on certain chromosomes with a set mutation probability (e.g., 0.0013), such as changing a bit in the binary code or adding a small random amount to the real number code. The mutation operation introduces new gene patterns, which helps maintain population diversity and prevents the algorithm from getting trapped in local optima too early.

[0101] Next, the parameter calibration device repeatedly performs selection, crossover, and mutation operations to generate a new generation of population, and then recalculates the fitness function value for the new population. This process is repeated iteratively, and with each generation, the average fitness function value of the population typically decreases gradually.

[0102] The parameter calibration device can determine convergence by: reaching a preset maximum number of generations (e.g., 300 generations), or the fitness function value of the best individual no longer significantly decreasing over multiple consecutive generations (e.g., 50 generations). When the convergence condition is met, the algorithm stops.

[0103] Next, the parameter calibration device selects the chromosome with the smallest fitness function value from the final population, decodes the chromosome, and obtains... The optimal parameter values ​​are then determined. These parameter values ​​represent the final calibration results of the damage parameters in the MMC model.

[0104] Through the above-mentioned genetic algorithm optimization process, the parameter calibration device can globally search for the optimal combination of damage parameters without relying on manual initial values, effectively avoiding getting trapped in local optima and ensuring the accuracy and reliability of the calibration results.

[0105] Subsequently, the parameter calibration equipment will optimize the obtained damage parameters. Substituting the values ​​into the finite element model, the fracture behavior of the target specimen is predicted, and the predicted results are compared and verified with the experimental results. If the verification is successful, it indicates that the calibrated damage parameters can be used to predict the fracture behavior of the metallic material under different stress states.

[0106] In summary, the embodiments of this application employ a genetic algorithm to globally optimize the fitness function. The genetic algorithm searches in parallel throughout the parameter space through a population evolution mechanism, is insensitive to the continuity and differentiability of the fitness function, overcomes the drawback of traditional gradient methods' high dependence on initial values, and can more easily find globally optimal or near-global optimal combinations of damage parameters.

[0107] Secondly, the embodiments of this application, through reasonable design of the population size and number of generations of the genetic algorithm, enable the calibrated fracture parameters to be more accurate. The MMC model can achieve good fracture strain prediction accuracy under a wide range of stress states (covering different stress triaxiality and Lode angle parameters), thereby improving the accuracy of predicting the fracture behavior of metallic materials.

[0108] Furthermore, genetic algorithms, based on a probabilistic search mechanism, do not require the objective function to be continuously differentiable; only that the objective function's value is computable. Therefore, the embodiments of this application are inherently robust to the discreteness of experimental data and measurement noise, avoiding the oscillation or divergence problems caused by the non-smoothness of the objective function in traditional gradient methods.

[0109] Furthermore, the parameter calibration method provided in this application embodiment does not depend on a specific material type and is applicable to the parameter calibration of MMC models for various metallic materials. It performs excellently for complex nonlinear optimization problems and has good engineering promotion value.

[0110] The implementation process of the embodiments of this application will be described in detail below with reference to specific examples: The parameter calibration equipment is designed and prepares various types of target specimens according to the tensile testing standards for metallic materials (e.g., GB / T 228.1-2021 "Metallic materials, tensile testing—Part 1: Test at room temperature"). Figure 3 As shown, the target sample includes: Standard smooth plate-shaped specimens (FB for short) are prepared according to standard dimensions; Standard smooth round bar specimens (FS) are prepared according to standard dimensions; Circular notch specimens with different notch radii: Specimens with notch radii R20, R10, and R5 were prepared respectively, where L is the length, b is the width, and R is the notch radius.

[0111] At room temperature, the parameter calibration equipment performs quasi-static tensile tests on each target specimen using a universal testing machine. During the test, load and displacement data are recorded in real time, obtaining the load-displacement curve and engineering stress-strain curve for each target specimen. The parameter calibration equipment then processes the data to obtain the basic mechanical property parameters of the target specimen, including elastic modulus, yield strength, tensile strength, and elongation. Finally, the equipment converts the engineering stress-strain curve into a true stress-strain curve, providing fundamental data for subsequent finite element analysis.

[0112] The parameter calibration equipment establishes a finite element model in ABAQUS finite element software corresponding to the geometry and dimensions of each target specimen. The parameter calibration equipment applies the same boundary conditions and load conditions as the tensile test to each finite element model, and uses the actual stress-strain relationship obtained from the test as the elastoplastic constitutive model of the material to perform elastoplastic finite element analysis.

[0113] In the analysis results, the parameter calibration equipment identified the element with the largest equivalent plastic strain in each target specimen, which corresponds to the location where the target specimen first fractured. The parameter calibration equipment extracted the equivalent plastic strain evolution curve, stress triaxiality evolution curve, and Lode angle parameter evolution curve of this key element from the onset of plastic deformation to the moment of fracture.

[0114] The parameter calibration equipment uses the fracture displacement measured in the experiment as a benchmark, and determines the corresponding fracture time in the finite element simulation by comparing the load-displacement curves of the experiment and the simulation.

[0115] Next, the parameter calibration equipment calculates the average stress triaxiality and average Lode angle parameters of each target specimen based on the evolution curve obtained in the previous step using the integral method. The calculation process can be referred to above and will not be repeated here.

[0116] The parameter calibration equipment uses numerical integration to calculate the average stress state parameters for each target specimen. Due to differences in geometry and size, different target specimen types have different average stress triaxiality and average Lode angle parameters, forming parameter combinations covering different stress states, as shown in Table 1.

[0117] Table 1

[0118] Next, the parameter calibration device constructs a damage accumulation amount D based on the MMC model theory to characterize the damage evolution during the plastic deformation process.

[0119] The parameter calibration equipment calculates the corresponding cumulative damage for each target specimen. Theoretically, when a material fractures, the cumulative damage... It should be equal to 1.

[0120] Next, the parameter calibration device calibrates the parameters based on the accumulated damage obtained in the previous step. A fitness function is constructed with the goal of minimizing the deviation between the cumulative damage of different target samples and the preset threshold (1).

[0121] Then, the parameter calibration device uses a genetic algorithm for global optimization, such as... Figure 4 As shown. The specific process is as follows: The parameter calibration device first creates an initial population, and then randomly generates multiple sets of damage parameters within the parameter value range. The chromosomes are encoded, with each chromosome representing a candidate solution.

[0122] Next, the parameter calibration device calculates the optimization target value for each individual, that is, according to the fitness function F( Calculate the fitness function value corresponding to each candidate solution.

[0123] Then, the parameter calibration device performs a selection operation, selecting the better individuals to enter the next generation based on the fitness function value (the smaller the value, the better).

[0124] Afterwards, the parameter calibration device performs mutation (variation) operations on the selected individuals, randomly changing certain gene loci on the chromosome with a set mutation probability, and introducing new gene patterns.

[0125] At the same time, the parameter calibration device also performs a crossover operation, exchanging partial gene segments of two chromosomes at a set crossover probability.

[0126] After selection, crossover, and mutation, the parameter calibration device completes one evolutionary cycle, generating a new generation of the population. The parameter calibration device determines whether the algorithm has converged. The convergence condition can be reaching a preset maximum number of generations, or the fitness function value of the best individual no longer significantly decreases over multiple consecutive generations.

[0127] If convergence is not achieved, the optimization objective value for each individual in the new population is recalculated, and the iteration continues; if convergence is achieved, the evolution is stopped.

[0128] Subsequently, the parameter calibration device outputs the chromosome with the smallest fitness function value from the final population, and decodes it to obtain the optimal breakage parameters. The optimization process ends here.

[0129] The calibration equipment ultimately yielded the optimal damage parameters for the MMC model, as shown in Table 2.

[0130] Table 2

[0131] The parameter calibration equipment inputs the optimized damage parameters of the MMC model into the finite element model to predict the fracture behavior of the target specimen. The predicted results are then compared with the experimental results, such as... Figure 5 As shown in the figure, the horizontal axis represents displacement (unit: mm), and the vertical axis represents load (unit: kN). This figure presents a comparison of the load-displacement curves of the R20 specimen, used to verify the accuracy of the model. The comparison results show that the predicted curve and the experimental curve agree well.

[0132] This demonstrates that the parameter calibration method provided in this application can accurately calibrate the damage parameters of the MMC model, and can be used to predict the fracture behavior of metallic materials under different stress states, providing a reliable theoretical basis for the safety design and failure analysis of engineering structures.

[0133] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, it includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0134] This application embodiment can divide the parameter calibration device into functional modules according to the above method example. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0135] Figure 6 This is a schematic diagram of a parameter calibration device provided in an embodiment of this application. Figure 6 As shown, the parameter calibration device includes: a communication unit 601 and a processing unit 602; The communication unit 601 is used to acquire the fracture strain of the target specimen in the tensile test, as well as the average stress state parameters of the target specimen during the plastic deformation process; the average stress state parameters include: average stress triaxiality and average Lode angle parameters. The processing unit 602 is used to construct a damage accumulation model with the average stress state parameters and fracture strain as inputs and the damage accumulation of the target specimen as output; the damage accumulation model contains the damage parameters to be calibrated. The processing unit 602 is also used to construct a fitness function with the damage parameter to be calibrated as a variable, with the damage accumulation amount equal to a preset threshold as a constraint; the fitness function is used to quantify the deviation between the damage accumulation amount and the preset threshold. The processing unit 602 is also used to perform global optimization of the fitness function based on a genetic algorithm to obtain the parameter values ​​of the damage parameters to be calibrated.

[0136] Optionally, the processing unit 602 is specifically used for: Obtain the load-displacement curve and engineering stress-strain curve of the target specimen in the tensile test, and convert the engineering stress-strain curve into the true stress-strain curve; A finite element model corresponding to the geometry of the target specimen is established, and an elastic-plastic finite element analysis is performed using the actual stress-strain relationship to obtain the element with the largest equivalent plastic strain. Extract the equivalent plastic strain evolution curve, stress triaxiality evolution curve, and Lode angle parameter evolution curve of the element with the largest equivalent plastic strain from the start of plastic deformation to the fracture time; Using the fracture displacement measured by the experiment as a benchmark, the fracture time in the finite element analysis is determined, and the equivalent plastic strain at the fracture time is read as the fracture strain according to the equivalent plastic strain evolution curve. The average stress triaxiality and average Lode angle parameter are obtained by integral averaging of the stress triaxiality evolution curve and the Lode angle parameter evolution curve.

[0137] Optionally, the mean stress triaxiality and mean Lode angle parameters satisfy the following formula: ; ; in, For mean stress triaxiality, The average Lode angle parameter, For fracture strain, The stress triaxiality evolution curve is shown. The evolution curve of the Lode angle parameter. It is the integral variable.

[0138] Optionally, the damage accumulation model is constructed based on the modified Mohr-Coulomb fracture MMC criterion, satisfying the following formula: ; ; in, This represents the cumulative amount of damage. For equivalent plastic strain, The fracture strain predicted by the MMC criterion. For stress triaxiality, For Lode angle parameters, and n are the hardening model parameters of the target sample. The damage parameters are to be calibrated.

[0139] Optionally, the fitness function satisfies the following formula: ; Where n is the total number of samples, D i Let be the cumulative damage amount of the i-th target sample. Let be the average stress triaxiality of the i-th target specimen. Let be the average Lode angle parameter of the i-th target sample. Let be the fracture strain of the i-th target specimen.

[0140] Optionally, the number of target specimens may be multiple, and the multiple target specimens may include at least one of the following: a standard smooth round bar specimen, a standard smooth plate specimen, a round bar specimen with a circumferential notch having a different notch radius, and a plate specimen with a planar notch having a different notch radius.

[0141] Optionally, the parameters of the genetic algorithm can be set as follows: population size of 50 to 200, number of generations of genetic inheritance of 100 to 500, crossover probability of 0.6 to 0.8, and mutation probability of 0.001 to 0.01.

[0142] The parameter calibration equipment in the parameter calibration system includes, for example: Figure 7 The components included. The following are examples. Figure 7 Taking the parameter calibration device shown as an example, the hardware structure of the parameter calibration device is introduced.

[0143] Figure 7 This is a schematic diagram of the hardware structure of a parameter calibration device provided in an embodiment of this application. Figure 7 As shown, the parameter calibration device includes: a processor 701, a memory 702, a communication interface 703, and a bus 704. The processor 701, the memory 702, and the communication interface 703 can be connected via the bus 704.

[0144] Processor 701 is the control center of the parameter calibration device. It can be a single processor or a collective term for multiple processing elements. For example, processor 701 can be a general-purpose central processing unit (CPU) or other general-purpose processors. The general-purpose processor can be a microprocessor or any conventional processor.

[0145] As one embodiment, processor 701 may include one or more CPUs, for example Figure 7 CPU0 and CPU1 are shown in the diagram.

[0146] The memory 702 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0147] In one possible implementation, the memory 702 can exist independently of the processor 701. The memory 702 can be connected to the processor 701 via a bus 704 and is used to store instructions or program code. When the processor 701 calls and executes the instructions or program code stored in the memory 702, it can implement the parameter calibration method provided in the following embodiments of this application.

[0148] In this embodiment, the software programs stored in memory 702 differ for the parameter calibration devices, resulting in different functions implemented by the parameter calibration devices. The functions performed by each device will be described in conjunction with the following flowchart.

[0149] In another possible implementation, the memory 702 can also be integrated with the processor 701.

[0150] The communication interface 703 is used for connecting the parameter calibration device to other devices via a communication network, such as Ethernet, wireless access network, or wireless local area network (WLAN). The communication interface 703 may include a receiving unit for receiving data and a transmitting unit for sending data.

[0151] Bus 704 can be an industry standard architecture (ISA) bus, a peripheral component interconnect (PCI) bus, or an extended industry standard architecture (EISA) bus. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0152] It should be pointed out that, Figure 7 The structure shown does not constitute a limitation on the parameter calibration device, except Figure 7 In addition to the components shown, the parameter calibration device may include more or fewer components than those shown, or combine certain components, or have different component arrangements.

[0153] This application also provides a computer-readable storage medium, which includes computer-executable instructions. When the computer-executable instructions are run on a computer, the computer performs the parameter calibration method provided in the above embodiments.

[0154] This application also provides a computer program that can be directly loaded into a memory and contains software code. After being loaded and executed by a computer, the computer program can implement the parameter calibration method provided in the above embodiments.

[0155] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this application can be implemented using hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer-readable storage media and communication media, wherein communication media include any medium that facilitates the transmission of a computer program from one place to another. Storage media can be any available medium accessible to a general-purpose or special-purpose computer.

[0156] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0157] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and other division methods may exist in actual implementation. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate; components shown as units may be one physical unit or multiple physical units, i.e., they may be located in one place or distributed in multiple different places. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0158] Furthermore, the functional units in the various embodiments of this application can be integrated into a single defect detection unit, or each unit can exist physically separately, or two or more units can be integrated into a single unit. The integrated unit can be implemented in hardware or as a software functional unit. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, in essence, or the part that contributes to general technology, or all or part of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0159] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A parameter calibration method, characterized in that, include: Obtain the fracture strain of the target specimen in the tensile test, and the average stress state parameters of the target specimen during the plastic deformation process; The average stress state parameters include: average stress triaxiality and average Lode angle parameters; Using the average stress state parameters and the fracture strain as inputs, and the damage accumulation of the target specimen as output, a damage accumulation model is constructed; the damage accumulation model includes damage parameters to be calibrated. With the cumulative damage amount equal to a preset threshold as a constraint, a fitness function is constructed with the damage parameter to be calibrated as a variable; the fitness function is used to quantify the deviation between the cumulative damage amount and the preset threshold. The fitness function is globally optimized using a genetic algorithm to obtain the parameter values ​​of the damage parameters to be calibrated.

2. The method according to claim 1, characterized in that, The acquisition of the fracture strain of the target specimen in the tensile test and the average stress state parameters of the target specimen during plastic deformation includes: Obtain the load-displacement curve and engineering stress-strain curve of the target specimen in the tensile test, and convert the engineering stress-strain curve into a true stress-strain curve; A finite element model corresponding to the geometry of the target specimen is established, and an elastoplastic finite element analysis is performed using the actual stress-strain relationship to obtain the element with the largest equivalent plastic strain. Extract the equivalent plastic strain evolution curve, stress triaxiality evolution curve, and Lode angle parameter evolution curve of the element with the largest equivalent plastic strain from the start of plastic deformation to the fracture time; Using the fracture displacement measured by the experiment as a benchmark, the fracture time in the finite element analysis is determined, and the equivalent plastic strain at the fracture time is read as the fracture strain according to the equivalent plastic strain evolution curve. The average stress triaxiality and the average Lode angle parameter are obtained by integrating and averaging the stress triaxiality evolution curve and the Lode angle parameter evolution curve.

3. The method according to claim 2, characterized in that, The mean stress triaxiality and mean Lode angle parameters satisfy the following formula: ； ； in, For mean stress triaxiality, The average Lode angle parameter, For fracture strain, The stress triaxiality evolution curve is shown. The evolution curve of the Lode angle parameter. It is the integral variable.

4. The method according to claim 1, characterized in that, The damage accumulation model is constructed based on the modified Mohr-Coulomb fracture MMC criterion and satisfies the following formula: ； ； in, This represents the cumulative amount of damage. For equivalent plastic strain, The fracture strain predicted by the MMC criterion. For stress triaxiality, For Lode angle parameters, and n are the hardening model parameters of the target sample. The damage parameters are to be calibrated.

5. The method according to claim 1, characterized in that, The fitness function satisfies the following formula: ； Where n is the total number of samples, D i Let be the cumulative damage amount of the i-th target sample. Let be the average stress triaxiality of the i-th target specimen. Let be the average Lode angle parameter of the i-th target sample. Let be the fracture strain of the i-th target specimen.

6. The method according to claim 1, characterized in that, The number of target specimens is multiple, and the multiple target specimens include at least one of the following: a standard smooth round bar specimen, a standard smooth plate specimen, a round bar specimen with a circumferential notch having a different notch radius, and a plate specimen with a planar notch having a different notch radius.

7. The method according to claim 1, characterized in that, The parameters of the genetic algorithm include: population size of 50-200, number of generations of genetic inheritance of 100-500, crossover probability of 0.6-0.8, and mutation probability of 0.001-0.

01.

8. A parameter calibration device, characterized in that, include: Communication unit and processing unit; The communication unit is used to acquire the fracture strain of the target specimen in the tensile test, and the average stress state parameters of the target specimen during the plastic deformation process. The average stress state parameters include: average stress triaxiality and average Lode angle parameters; The processing unit is used to construct a damage accumulation model by taking the average stress state parameters and the fracture strain as inputs and the damage accumulation of the target specimen as output; the damage accumulation model includes damage parameters to be calibrated. The processing unit is further configured to construct a fitness function with the damage parameter to be calibrated as a variable, using the cumulative damage amount equal to a preset threshold as a constraint; the fitness function is used to quantify the deviation between the cumulative damage amount and the preset threshold. The processing unit is further configured to perform global optimization of the fitness function based on a genetic algorithm to obtain the parameter values ​​of the damage parameters to be calibrated.

9. An electronic device, characterized in that, The electronic device includes: processor; A memory configured to store processor-executable instructions; The processor is configured to execute the instructions to implement the method as described in any one of claims 1-7.

10. A non-volatile storage medium, characterized in that, The storage medium stores a computer program, which, when read and executed, implements the method as described in any one of claims 1-7.

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