A method, apparatus, and electronic device for predicting fracture strain in metallic materials.

Abstract

This application discloses a method, apparatus, and electronic device for predicting fracture strain in metallic materials. The method includes first determining the stress triaxiality and Lode angle of the metallic material under test based on the stress state at the test point; then constructing a stress fracture prediction model based on the correlation function between fracture strain and stress triaxiality and Lode angle; next, coupling this model with the Johnson-Cook fracture model to form a thermo-mechanical-strain rate coupled fracture prediction model; and finally, determining the target fracture strain based on this model. This approach not only overcomes the limitation of insufficient consideration of the Lode angle in existing fracture models but also achieves efficient model construction with very few parameters to be calibrated. The model is applicable to fracture prediction under complex stress, high temperature, and high strain rate multi-field coupling, significantly improving prediction accuracy while enhancing the model's engineering applicability and robustness, providing a reliable fracture assessment tool for cutting-edge engineering scenarios such as advanced manufacturing and extreme environment equipment design.

Description

Technical Field

[0001] This application relates to the field of materials strain technology, and in particular to a method for predicting fracture strain in metallic materials. Background Technology

[0002] With the acceleration of urbanization in my country, modular steel structure buildings are widely used in engineering construction. However, steel structure collapse accidents caused by fires are increasingly attracting the attention of researchers. In actual disaster situations, explosions or impacts inside buildings are often accompanied by secondary disasters such as fires. The combined effect of such explosions (impacts) and the high temperatures of fires often causes more severe damage to the engineering structure. Steel structure connection nodes and components often have to withstand the individual or combined effects of multiple extreme disasters such as explosions, impacts, and fires. Such coupled disaster conditions place nodes and components in a harsh service environment with high temperature, high stress triaxiality, and high strain rate, subjecting them to the coupling of multiple physical fields of "thermal-mechanical-rate," which easily leads to fracture and loss of the load-bearing capacity of the structure or components.

[0003] Currently, most fracture models for metallic materials are based on ambient temperature and quasi-static conditions, or only consider the influence of single factors such as temperature and strain rate. They fail to comprehensively consider the coupling effects between high temperature, stress triaxiality, Lode angle parameters, and high strain rate throughout the entire fire process, thus making it difficult to accurately describe the mechanical behavior and failure mechanisms of metallic materials under multi-field coupling. In engineering and academia, the Johnson-Cook fracture model is widely used to simulate damage, fracture, and failure analysis of metallic materials. This model is simple in form, and its parameters can be obtained through macroscopic experiments, making it applicable to various high-temperature and impact dynamics problems. However, as a purely empirical macroscopic phenomenological model, the Johnson-Cook fracture model does not incorporate microscopic physical mechanisms controlling ductile fracture, such as pore evolution, and therefore lacks a clear physical foundation. Furthermore, the model contains many undetermined parameters, requiring extensive material property experiments for calibration, which increases its calibration cost and complexity in engineering applications, reducing its applicability. Furthermore, the model does not consider the influence of the Lode angle parameter on fracture behavior, so its accuracy in predicting the fracture of ductile metals under the combined effects of complex stress, high temperature and high strain rate still has considerable room for improvement. Summary of the Invention

[0004] This application provides a method, apparatus, and electronic device for predicting fracture strain in metallic materials, the technical solution of which is as follows:

[0005] In a first aspect, embodiments of this application provide a method for predicting the fracture strain of metallic materials, the method comprising:

[0006] For any test point in the metal material under test, the stress triaxiality and Lode angle of the metal material under test are determined according to the stress state of the test point.

[0007] A stress fracture prediction model is constructed based on the relationship functions between fracture strain performance and the stress triaxiality and the Lode angle, respectively.

[0008] The stress fracture prediction model and the Johnson-Cook fracture prediction model are coupled to obtain the thermo-mechanical-strain rate coupled fracture prediction model for the metal material under test, and the target predicted fracture strain of the metal material under test is determined based on the thermo-mechanical-strain rate coupled fracture prediction model.

[0009] Secondly, a fracture strain prediction device for metallic materials is provided, the device comprising:

[0010] The determination module is used to determine the stress triaxiality and Lode angle of the metal material under test based on the stress state of the test point for any test point in the metal material under test.

[0011] The construction module is used to construct a stress fracture prediction model based on the relationship function between fracture strain performance and the stress triaxiality and the Lode angle, respectively.

[0012] The prediction module is used to couple the stress fracture prediction model and the Johnson-Cook fracture prediction model to obtain the thermo-mechanical-strain rate coupled fracture prediction model corresponding to the metal material under test, and to determine the target predicted fracture strain of the metal material under test based on the thermo-mechanical-strain rate coupled fracture prediction model.

[0013] Thirdly, an electronic device is provided, including a device processor and a memory;

[0014] The device processor is connected to the memory;

[0015] The memory is used to store executable program code;

[0016] The device processor runs a program corresponding to the executable program code stored in the memory to perform the steps of the method provided as in the first aspect or any possible implementation thereof.

[0017] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, the computer-readable storage medium storing instructions that, when executed on a computer or device processor, cause the computer or device processor to perform the method provided as in the first aspect or any possible implementation thereof.

[0018] The beneficial effects of the technical solutions provided in some embodiments of this application include at least the following:

[0019] In one or more embodiments of this application, the stress triaxiality and Lode angle of the tested metallic material are determined based on the stress state at the test point. Then, a stress fracture prediction model is constructed based on the relationship functions between fracture strain performance and stress triaxiality and Lode angle. Finally, the stress fracture prediction model and the Johnson-Cook fracture prediction model are coupled to obtain a thermo-mechanical-strain rate coupled fracture prediction model for the tested metallic material. The target predicted fracture strain of the tested metallic material is then determined based on the thermo-mechanical-strain rate coupled fracture prediction model. Through these prediction steps, not only is the deficiency of existing fracture models in insufficient consideration of the Lode angle parameter compensated for, but the number of parameters to be calibrated in the constructed prediction model is also minimal. This makes it applicable to fracture prediction scenarios under complex stress, high temperature, and impact loads, improving both the model's applicability and the accuracy of fracture strain prediction results, thereby increasing user satisfaction. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a method for predicting fracture strain in metallic materials, provided in an embodiment of this application;

[0022] Figure 2 A schematic diagram of the structure of a fracture strain prediction device for metallic materials provided in an embodiment of this application;

[0023] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0024] The key terms involved are explained below:

[0025] Strain rate: describes the rate at which material deformation (strain) occurs; it is a physical quantity that measures the rate of deformation.

[0026] Stress triaxiality: a core parameter describing stress state, defined as the ratio of hydrostatic stress to equivalent stress;

[0027] Lode angle parameter: It is an angle defined on the deviatoric stress plane, which quantitatively describes the influence of the intermediate principal stress relative to the maximum and minimum principal stress, and completely determines the type of three-dimensional stress state;

[0028] Fracture trajectory: The fracture strain model of metallic materials can be represented as a surface in a three-dimensional space of "stress triaxiality - Lode angle parameter - fracture strain". These surfaces are usually referred to as the fracture trajectory of the material in the field of ductile fracture mechanics. They can be understood as the boundary between whether the material fractures or not. When the equivalent plastic strain borne by the material exceeds this boundary, the material is considered to have fractured. Detailed Implementation

[0029] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0030] The terms "first," "second," "third," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0031] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of this application. Various processes or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0032] Please see Figure 1 , Figure 1 The diagram shows an overall flowchart of a method for predicting fracture strain of metallic materials provided in an embodiment of this application.

[0033] like Figure 1 As shown, the method for predicting the fracture strain of this metallic material may include at least the following steps:

[0034] Step 101: For any test point in the metal material to be tested, determine the stress triaxiality and Lode angle of the metal material to be tested based on the stress state of the test point.

[0035] In this embodiment of the application, in order to accurately predict the fracture strain of the metal material under test under the combined action of complex stress, high temperature and impact load, it is necessary to obtain the stress state corresponding to any test point in the metal material under test after determining the target metal material under test. Because the traditional Johnson-Cook fracture model does not consider the influence of the Lode angle parameter on fracture behavior, its fracture strain prediction under complex stress, high temperature, and high strain rate conditions cannot meet the high accuracy requirements. Therefore, to facilitate the subsequent construction of a prediction model that includes the Lode angle parameter, it is necessary to calculate the stress triaxiality and Lode angle of the tested metal material based on the stress state at the test points. Specifically, the output can be directly calculated using the established calculation model, or the hydrostatic stress and equivalent stress of the tested metal material can be calculated first based on the stress state, and then the stress triaxiality and Lode angle can be calculated based on the hydrostatic stress and equivalent stress.

[0036] In one possible implementation, determining the stress triaxiality and Lode angle of the metal material under test based on the stress state at the test point includes:

[0037] Calculate the hydrostatic stress and equivalent stress of the metal material under test based on the stress state at the test points;

[0038] The stress triaxiality and Lode angle of the tested metallic material are calculated based on the hydrostatic stress and equivalent stress.

[0039] In this embodiment of the application, when determining the stress triaxiality and Lode angle of the metal material under test based on the stress state at the test point, the stress state at the test point can be determined first. Calculate the hydrostatic stress of the metal material to be tested. and equivalent stress The specific calculation formula is as follows:

[0040]

[0041]

[0042] Where p is the hydrostatic pressure. , These are hydrostatic stress and equivalent stress, respectively.

[0043] Next, the stress triaxiality of the tested metallic material is calculated based on hydrostatic stress and equivalent stress. The specific calculation formula is as follows:

[0044]

[0045]

[0046] Furthermore, the Lode angle of the tested metallic material is calculated based on hydrostatic stress and equivalent stress. The specific calculation formula is as follows:

[0047]

[0048]

[0049]

[0050] in, Let r be the third invariant of the deviatoric stress tensor. The function, It is the third invariant of the orthogonally normalized deviatoric stress tensor.

[0051] In one possible implementation, after calculating the stress triaxiality and Lode angle of the tested metallic material based on the hydrostatic stress and equivalent stress, the method further includes:

[0052] The Lode angle is orthogonalized to obtain the Lode angle parameters;

[0053] The stress fracture prediction model is constructed based on the relationship functions between fracture strain performance and the stress triaxiality and the Lode angle, including:

[0054] A stress fracture prediction model is constructed based on the relationship functions between fracture strain performance and the stress triaxiality and the Lode angle parameter.

[0055] In this embodiment, after calculating the stress triaxiality and Lode angle based on hydrostatic stress and equivalent stress, the calculated Lode angle needs to be standardized for subsequent calculations. Orthogonalization is performed to obtain the Lode angle parameters. The specific calculation formula is as follows:

[0056]

[0057] When determining the stress fracture prediction model, it can be constructed directly based on the relationship function between fracture strain performance and stress triaxiality and Lode angle parameters.

[0058] Step 103: Construct a stress fracture prediction model based on the relationship functions between fracture strain performance and the stress triaxiality and the Lode angle, respectively.

[0059] In this embodiment, since both stress triaxiality and Lode angle belong to the stress state, in order to simultaneously consider the influence of stress triaxiality and Lode angle on stress fracture, it is necessary to first determine the relationship functions between fracture strain performance and stress triaxiality and Lode angle, respectively. Then, a stress fracture prediction model is constructed based on each relationship function so that subsequent model fusion can be performed to obtain a fracture strain prediction model that fully considers the thermo-mechanical-strain rate coupling effect and comprehensively integrates the influence of four factors—stress triaxiality, Lode angle parameter, temperature, and strain rate—on the material fracture behavior, thus making up for the insufficient consideration of Lode angle parameter.

[0060] In one possible implementation, constructing a stress fracture prediction model based on the relationship functions between fracture strain performance and the stress triaxiality and the Lode angle includes:

[0061] Determine the first relationship function between the stress triaxiality and the fracture strain performance, and the second relationship function between the Lode angle and the fracture strain performance;

[0062] By integrating the first relational function and the second relational function, a stress fracture prediction model is constructed.

[0063] In this embodiment of the application, when constructing the stress fracture prediction model, the first relationship function between stress triaxiality and fracture strain performance, and the second relationship function between the Lode angle and fracture strain performance can be determined first. Specifically, the first relationship function between stress triaxiality and fracture strain performance is shown in the following formula:

[0064]

[0065] in, For stress triaxiality, For the equivalent plastic strain increment, denoted as σf, where σc represents the fracture strain of the metal material under test, and c represents the metal material parameter.

[0066] Optionally, when the triaxiality of the stress on the metallic material is constant, the above formula can be transformed into the following formula:

[0067]

[0068] Next, to consider the influence of deviatoric stress state on the fracture properties of metallic materials, since metallic materials exhibit significantly different fracture properties under axisymmetric tensile and plane strain states, the influence of stress triaxiality on fracture strain under these two stress states should be expressed using two different functional relationships:

[0069]

[0070]

[0071] in, The time indicates that the tested metallic material is in an axisymmetric stress state. The time characterizes the plane strain state of the tested metallic material. The fracture strain is under axisymmetric tensile conditions. The fracture strain under plane strain conditions. and These are the material parameters under two different stress states.

[0072] The second relationship function between the Lode angle and fracture strain properties is shown in the following formula:

[0073]

[0074]

[0075] Where n is the hardening index of the material. The third invariant of the orthogonally normalized deviatoric stress tensor. For Lode angle parameters.

[0076] Furthermore, by integrating the first and second relational functions, a stress-fracture prediction model is constructed, which is the fracture strain of the metal material under test. This can be expressed by the following formula:

[0077]

[0078] in, and These are the material parameters under two different stress states.

[0079] Step 105: Couple the stress fracture prediction model and the Johnson-Cook fracture prediction model to obtain the thermo-mechanical-strain rate coupled fracture prediction model corresponding to the metal material under test, and determine the target predicted fracture strain of the metal material under test based on the thermo-mechanical-strain rate coupled fracture prediction model.

[0080] In this embodiment, after constructing a stress fracture prediction model that compensates for the insufficient consideration of the Lode angle parameter, this stress fracture prediction model can be named the Xue-Wierzbicki fracture model. Since the Xue-Wierzbicki fracture model only considers the influence of stress triaxiality and the Lode angle parameter, while the traditional Johnson-Cook fracture prediction model considers the influence of strain rate and temperature, the stress fracture prediction model and the Johnson-Cook fracture prediction model can be coupled to obtain a thermo-mechanical-strain rate coupled fracture prediction model that comprehensively considers the influence of four factors—stress triaxiality, Lode angle parameter, temperature, and strain rate—on the fracture performance of the tested metallic material. Then, based on the determined thermo-mechanical-strain rate coupled fracture prediction model, the stress triaxiality and Lode angle calculated based on the stress state are substituted into it to obtain the target predicted fracture strain of the tested metallic material.

[0081] In one possible implementation, coupling the stress fracture prediction model and the Johnson-Cook fracture prediction model to obtain a thermo-mechanical-strain rate coupled fracture prediction model for the metal material under test includes:

[0082] The stress fracture prediction model and the Johnson-Cook fracture prediction model are coupled to obtain the initial coupled fracture prediction model corresponding to the metal material under test.

[0083] The parameters of the initial coupled fracture prediction model are optimized to obtain the thermo-mechanical-strain rate coupled fracture prediction model.

[0084] In this embodiment of the application, in order to obtain the thermo-mechanical-strain rate coupled fracture prediction model, the stress fracture prediction model and the Johnson-Cook fracture prediction model can be coupled first to obtain the initial coupled fracture prediction model corresponding to the metal material to be tested, which can be expressed by the following formula:

[0085]

[0086] Where n is the hardening index of the material, which can be obtained from standard material property tests, and four other parameters are involved. , , and ,parameter and The value of needs to be obtained from tensile tests on at least two different notched specimens. (Parameter) and The values ​​of each parameter need to be obtained through at least one set of tensile tests at different strain rates and temperatures. Therefore, in order to minimize the number of experiments to obtain the parameters, it is necessary to optimize the initial coupled fracture prediction model by using the parameter relationship between the fracture strain in the plane strain state and the tensile state, so as to obtain a thermo-mechanical-strain rate coupled fracture prediction model with as few parameters as possible.

[0087] In one possible implementation, optimizing the parameters of the initial coupled fracture prediction model to obtain a thermo-mechanical-strain rate coupled fracture prediction model includes:

[0088] Based on the Tresca failure criterion and the Swift hardening criterion, the corresponding parametric relationship between the fracture strain in the plane strain state and the axisymmetric tensile state is determined.

[0089] The initial coupled fracture prediction model is optimized based on the parameter relationships to obtain a thermo-mechanical-strain rate coupled fracture prediction model.

[0090] In this embodiment of the application, when optimizing the parameters of the initial coupled fracture prediction model, the parameter relationship between the fracture strain in the plane strain state and the axisymmetric tensile state can be determined first based on the Tresca failure criterion and the Swift hardening criterion. The parameter relationship can be expressed by the following formula:

[0091]

[0092] in, The fracture strain is under axisymmetric tensile conditions. The fracture strain under plane strain conditions. and These are the material parameters under two different stress states.

[0093] Next, the initial coupled fracture prediction model was optimized based on the parameter relationships. During the optimization process, the material property parameters were... The fracture strain can be determined by axial tensile testing of a smooth circular bar specimen to obtain stress-strain curve information. The average stress triaxiality of the smooth circular bar specimen during loading is approximately 1 / 3, and the fracture strain at the moment of fracture can be defined as... And the following formula can be obtained:

[0094]

[0095] After optimization, the resulting thermo-mechanical-strain rate coupled fracture prediction model can be expressed by the following formula:

[0096]

[0097] In one possible implementation, determining the target predicted fracture strain of the metal material under test based on the thermo-mechanical-strain rate coupled fracture prediction model includes:

[0098] The test tensile data of the metal material under test were obtained based on temperature-strain tensile simulation.

[0099] The target parameters in the thermo-mechanical-strain rate coupled fracture prediction model are calibrated based on the test tensile data to obtain the calibration parameters.

[0100] The target predicted fracture strain is determined based on the calibration parameters and the thermo-mechanical-strain rate coupled fracture prediction model.

[0101] In this embodiment, the thermo-mechanical-strain rate coupled fracture prediction model formula has four undetermined parameters, namely the material hardening exponent n, the fracture strain of the smooth specimen, and the fracture strain of the smooth specimen. ,parameter and The hardening index n is related to the fracture strain of the smooth specimen. All of these can be obtained from the existing stress-strain curves of a large number of metallic materials, without the need to conduct tensile tests on the material properties again. Therefore, it is necessary to adjust the parameters. and Calibration is performed. Specifically, test tensile data of the metal material under test can be obtained based on temperature-strain tensile simulation, and then the target parameters in the thermo-mechanical-strain rate coupled fracture prediction model, i.e., the parameters, can be calibrated based on the test tensile data. and After calibration, the calibrated thermo-mechanical-strain rate coupled fracture prediction model can be obtained. Finally, the stress triaxiality is calculated. And Lode's corner Enter the following formula:

[0102]

[0103] The target predicted fracture strain can then be obtained. .

[0104] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0105] Please refer to the following. Figure 2 , Figure 2 A schematic diagram of a fracture strain prediction device for metallic materials provided in an embodiment of this application is shown. It should be noted that... Figure 2 The fracture strain prediction device for metallic materials shown is used to perform the functions described in this application. Figure 1 The methods shown in the embodiments are for illustrative purposes only, illustrating the parts relevant to the embodiments of this application. For specific technical details not disclosed, please refer to this application. Figure 1 The example shown.

[0106] like Figure 2 As shown, the fracture strain prediction device for this metallic material may include at least:

[0107] The determination module 201 is used to determine the stress triaxiality and Lode angle of the metal material under test based on the stress state of the test point for any test point in the metal material under test.

[0108] Construction module 202 is used to construct a stress fracture prediction model based on the relationship function between fracture strain performance and the stress triaxiality and the Lode angle, respectively;

[0109] The prediction module 203 is used to couple the stress fracture prediction model and the Johnson-Cook fracture prediction model to obtain the thermo-mechanical-strain rate coupled fracture prediction model corresponding to the metal material under test, and to determine the target predicted fracture strain of the metal material under test based on the thermo-mechanical-strain rate coupled fracture prediction model.

[0110] In one possible implementation, the determining module 201 is specifically used for:

[0111] Calculate the hydrostatic stress and equivalent stress of the metal material under test based on the stress state at the test points;

[0112] The stress triaxiality and Lode angle of the tested metallic material are calculated based on the hydrostatic stress and equivalent stress.

[0113] In one possible implementation, the determining module 201 is further configured to:

[0114] The Lode angle is orthogonalized to obtain the Lode angle parameters;

[0115] The stress fracture prediction model is constructed based on the relationship functions between fracture strain performance and the stress triaxiality and the Lode angle, including:

[0116] A stress fracture prediction model is constructed based on the relationship functions between fracture strain performance and the stress triaxiality and the Lode angle parameter.

[0117] In one possible implementation, the construction module 202 is specifically used for:

[0118] Determine the first relationship function between the stress triaxiality and the fracture strain performance, and the second relationship function between the Lode angle and the fracture strain performance;

[0119] By integrating the first relational function and the second relational function, a stress fracture prediction model is constructed.

[0120] In one possible implementation, the prediction module 203 is specifically used for:

[0121] The stress fracture prediction model and the Johnson-Cook fracture prediction model are coupled to obtain the initial coupled fracture prediction model corresponding to the metal material under test.

[0122] The parameters of the initial coupled fracture prediction model are optimized to obtain the thermo-mechanical-strain rate coupled fracture prediction model.

[0123] In one possible implementation, the prediction module 203 is further configured to:

[0124] Based on the Tresca failure criterion and the Swift hardening criterion, the corresponding parametric relationship between the fracture strain in the plane strain state and the axisymmetric tensile state is determined.

[0125] The initial coupled fracture prediction model is optimized based on the parameter relationships to obtain a thermo-mechanical-strain rate coupled fracture prediction model.

[0126] In one possible implementation, the prediction module 203 is further configured to:

[0127] The test tensile data of the metal material under test were obtained based on temperature-strain tensile simulation.

[0128] The target parameters in the thermo-mechanical-strain rate coupled fracture prediction model are calibrated based on the test tensile data to obtain the calibration parameters.

[0129] The target predicted fracture strain is determined based on the calibration parameters and the thermo-mechanical-strain rate coupled fracture prediction model.

[0130] Those skilled in the art will clearly understand that the technical solutions of the embodiments of this application can be implemented by means of software and / or hardware. In this application, "unit" and "module" refer to software and / or hardware that can independently complete or cooperate with other components to complete a specific function, wherein the hardware may be, for example, a field-programmable gate array (FPGA), an integrated circuit (IC), etc.

[0131] Each processing unit and / or module in the embodiments of this application can be implemented by an analog circuit that implements the functions described in the embodiments of this application, or by software that executes the functions described in the embodiments of this application.

[0132] Please refer to the following. Figure 3 , Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.

[0133] like Figure 3 As shown, the electronic device 300 may include at least one device processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0134] The communication bus 302 can be used to realize the connection and communication of the above components.

[0135] The user interface 303 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0136] The network interface 304 may include, but is not limited to, Bluetooth modules, NFC modules, Wi-Fi modules, etc.

[0137] The device processor 301 may include one or more processing cores. The device processor 301 connects to various parts within the electronic device 300 using various interfaces and lines. It executes various functions and processes data of the electronic device 300 by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the device processor 301 may be implemented using at least one hardware form of DSP, FPGA, or PLA. The device processor 301 may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the device processor 301 and may be implemented as a separate chip.

[0138] The memory 305 may include RAM or ROM. Optionally, the memory 305 may include a non-transitory computer-readable medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned device processor 301. Figure 3 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.

[0139] Specifically, the device processor 301 can be used to call the fracture strain prediction application for metallic materials stored in the memory 305, and specifically perform the following operations:

[0140] For any test point in the metal material under test, the stress triaxiality and Lode angle of the metal material under test are determined according to the stress state of the test point.

[0141] A stress fracture prediction model is constructed based on the relationship functions between fracture strain performance and the stress triaxiality and the Lode angle, respectively.

[0142] The stress fracture prediction model and the Johnson-Cook fracture prediction model are coupled to obtain the thermo-mechanical-strain rate coupled fracture prediction model for the metal material under test, and the target predicted fracture strain of the metal material under test is determined based on the thermo-mechanical-strain rate coupled fracture prediction model.

[0143] As an optional embodiment of this application, determining the stress triaxiality and Lode angle of the metal material under test based on the stress state at the test point includes:

[0144] Calculate the hydrostatic stress and equivalent stress of the metal material under test based on the stress state at the test points;

[0145] The stress triaxiality and Lode angle of the tested metallic material are calculated based on the hydrostatic stress and equivalent stress.

[0146] As an optional embodiment of this application, after calculating the stress triaxiality and Lode angle of the tested metallic material based on the hydrostatic stress and equivalent stress, the method further includes:

[0147] The Lode angle is orthogonalized to obtain the Lode angle parameters;

[0148] The stress fracture prediction model is constructed based on the relationship functions between fracture strain performance and the stress triaxiality and the Lode angle, including:

[0149] A stress fracture prediction model is constructed based on the relationship functions between fracture strain performance and the stress triaxiality and the Lode angle parameter.

[0150] As an optional embodiment of this application, the step of constructing a stress fracture prediction model based on the relationship functions between fracture strain performance and the stress triaxiality and the Lode angle includes:

[0151] Determine the first relationship function between the stress triaxiality and the fracture strain performance, and the second relationship function between the Lode angle and the fracture strain performance;

[0152] By integrating the first relational function and the second relational function, a stress fracture prediction model is constructed.

[0153] As an optional embodiment of this application, the coupling of the stress fracture prediction model and the Johnson-Cook fracture prediction model to obtain the thermo-mechanical-strain rate coupled fracture prediction model corresponding to the metal material under test includes:

[0154] The stress fracture prediction model and the Johnson-Cook fracture prediction model are coupled to obtain the initial coupled fracture prediction model corresponding to the metal material under test.

[0155] The parameters of the initial coupled fracture prediction model are optimized to obtain the thermo-mechanical-strain rate coupled fracture prediction model.

[0156] As an optional embodiment of this application, the step of optimizing the parameters of the initial coupled fracture prediction model to obtain a thermo-mechanical-strain rate coupled fracture prediction model includes:

[0157] Based on the Tresca failure criterion and the Swift hardening criterion, the corresponding parametric relationship between the fracture strain in the plane strain state and the axisymmetric tensile state is determined.

[0158] The initial coupled fracture prediction model is optimized based on the parameter relationships to obtain a thermo-mechanical-strain rate coupled fracture prediction model.

[0159] As an optional embodiment of this application, determining the target predicted fracture strain of the metal material under test based on the thermo-mechanical-strain rate coupled fracture prediction model includes:

[0160] The test tensile data of the metal material under test were obtained based on temperature-strain tensile simulation.

[0161] The target parameters in the thermo-mechanical-strain rate coupled fracture prediction model are calibrated based on the test tensile data to obtain the calibration parameters.

[0162] The target predicted fracture strain is determined based on the calibration parameters and the thermo-mechanical-strain rate coupled fracture prediction model.

[0163] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0164] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0165] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0166] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0167] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0168] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0169] 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 computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0170] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0171] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for predicting fracture strain in metallic materials, characterized in that, The method includes: For any test point in the metal material under test, the stress triaxiality and Lode angle of the metal material under test are determined according to the stress state of the test point. A stress fracture prediction model is constructed based on the relationship functions between fracture strain performance and the stress triaxiality and the Lode angle, respectively. The stress fracture prediction model and the Johnson-Cook fracture prediction model are coupled to obtain the thermo-mechanical-strain rate coupled fracture prediction model for the metal material under test, and the target predicted fracture strain of the metal material under test is determined based on the thermo-mechanical-strain rate coupled fracture prediction model.

2. The method according to claim 1, characterized in that, The determination of the stress triaxiality and Lode angle of the metal material under test based on the stress state at the test point includes: Calculate the hydrostatic stress and equivalent stress of the metal material under test based on the stress state at the test points; The stress triaxiality and Lode angle of the tested metallic material are calculated based on the hydrostatic stress and equivalent stress.

3. The method according to claim 2, characterized in that, After calculating the stress triaxiality and Lode angle of the tested metallic material based on the hydrostatic stress and equivalent stress, the method further includes: The Lode angle is orthogonalized to obtain the Lode angle parameters; The stress fracture prediction model is constructed based on the relationship functions between fracture strain performance and the stress triaxiality and the Lode angle, including: A stress fracture prediction model is constructed based on the relationship functions between fracture strain performance and the stress triaxiality and the Lode angle parameter.

4. The method according to claim 1, characterized in that, The stress fracture prediction model is constructed based on the relationship functions between fracture strain performance and the stress triaxiality and the Lode angle, including: Determine the first relationship function between the stress triaxiality and the fracture strain performance, and the second relationship function between the Lode angle and the fracture strain performance; By integrating the first relational function and the second relational function, a stress fracture prediction model is constructed.

5. The method according to claim 1, characterized in that, The coupling of the stress fracture prediction model and the Johnson-Cook fracture prediction model to obtain the thermo-mechanical-strain rate coupled fracture prediction model for the metal material under test includes: The stress fracture prediction model and the Johnson-Cook fracture prediction model are coupled to obtain the initial coupled fracture prediction model corresponding to the metal material under test. The parameters of the initial coupled fracture prediction model are optimized to obtain the thermo-mechanical-strain rate coupled fracture prediction model.

6. The method according to claim 5, characterized in that, The parameter optimization of the initial coupled fracture prediction model to obtain the thermo-mechanical-strain rate coupled fracture prediction model includes: Based on the Tresca failure criterion and the Swift hardening criterion, the corresponding parametric relationship between the fracture strain in the plane strain state and the axisymmetric tensile state is determined. The initial coupled fracture prediction model is optimized based on the parameter relationships to obtain a thermo-mechanical-strain rate coupled fracture prediction model.

7. The method according to claim 1, characterized in that, The step of determining the target predicted fracture strain of the metal material under test based on the thermo-mechanical-strain rate coupled fracture prediction model includes: The test tensile data of the metal material under test were obtained based on temperature-strain tensile simulation. The target parameters in the thermo-mechanical-strain rate coupled fracture prediction model are calibrated based on the test tensile data to obtain the calibration parameters. The target predicted fracture strain is determined based on the calibration parameters and the thermo-mechanical-strain rate coupled fracture prediction model.

8. A device for predicting fracture strain of metallic materials, characterized in that, The device includes: The determination module is used to determine the stress triaxiality and Lode angle of the metal material under test based on the stress state of the test point for any test point in the metal material under test. The construction module is used to construct a stress fracture prediction model based on the relationship function between fracture strain performance and the stress triaxiality and the Lode angle, respectively. The prediction module is used to couple the stress fracture prediction model and the Johnson-Cook fracture prediction model to obtain the thermo-mechanical-strain rate coupled fracture prediction model corresponding to the metal material under test, and to determine the target predicted fracture strain of the metal material under test based on the thermo-mechanical-strain rate coupled fracture prediction model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer or processor, cause the computer or processor to perform the steps of the method as described in any one of claims 1-7.

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