A model generation method, apparatus, storage medium, and computer device

Abstract

This application provides a model generation method, apparatus, storage medium, and computer device. The method includes: acquiring material parameter information of a target material; the material parameter information includes at least various material components of the target material, phase parameters of various phases constituting each material component, and grain parameters of each grain constituting each phase; generating a model parameter file for at least one representative volumetric element (RVE) model corresponding to each material component based on the material parameter information; and generating individual RVE models corresponding to each material component using the model parameter file. The entire RVE model generation process in this application embodiment is automated, requiring no manual intervention. This improves the efficiency and accuracy of model construction and avoids the problem of inconsistent RVE models generated for the same target material, thus improving the consistency of model generation.

Description

Technical Field

[0001] This application relates to the field of micromechanics technology, and more specifically, to a model generation method, apparatus, storage medium, and computer device. Background Technology

[0002] The Crystal Plasticity Finite Element Method (CPFEM) is an important method for analyzing the microstructure and mechanical properties of materials. The quality of its analysis results is related to the representative volume element (RVE) model constructed for the material. However, existing RVE model construction methods suffer from low construction efficiency and poor accuracy, which are obvious drawbacks. Summary of the Invention

[0003] In view of this, this application provides a model generation method, apparatus, storage medium, and computer device to improve the efficiency of RVE model construction, as well as the accuracy and consistency of model construction.

[0004] Specifically, this application is implemented through the following technical solution:

[0005] In a first aspect, embodiments of this disclosure provide a model generation method, including:

[0006] Obtain material parameter information of the target material; the material parameter information includes at least the various material components of the target material, the phase parameters of the various phases that make up each material component, and the grain parameters of each grain that makes up each phase;

[0007] Based on the material parameter information, generate model parameter files for at least one representative volume element RVE model corresponding to each of the material components;

[0008] Using the model parameter file, generate individual RVE models for each of the material components.

[0009] In one possible implementation, after generating individual RVE models for each of the material components using the model parameter file, the method further includes:

[0010] Determine the grain orientation parameters for each of the grains;

[0011] Using the RVE models and grain orientation parameters, the target material is analyzed by the crystal plasticity finite element method (CPFEM) to obtain the mechanical property analysis results of the target material.

[0012] In one possible implementation, determining the grain orientation parameter for each of the grains includes:

[0013] For any of the aforementioned grains, extract multiple orientation Euler angles corresponding to the grain;

[0014] Based on the multiple orientation Euler angles, the orientation quaternion corresponding to the grain is determined, and the orientation quaternion is used as the grain orientation parameter of the grain.

[0015] In one possible implementation, generating a model parameter file for at least one representative volumetric element RVE model corresponding to each of the material components based on the material parameter information includes:

[0016] For any of the aforementioned material compositions, determine the number of RVE models required to be generated for that material composition;

[0017] Obtain the model attribute information corresponding to each required RVE model; the model attribute information includes at least model size, number of meshes, and resolution;

[0018] Based on the quantity and the model attribute information, and according to the phase parameters of various phases in the material composition and the grain parameters of each grain that makes up each phase, a model parameter file corresponding to the material composition is generated.

[0019] In one possible implementation, generating model parameter files corresponding to the material composition according to the quantity and the model attribute information, based on the phase parameters of various phases in the material composition and the grain parameters of each grain constituting each phase, includes:

[0020] Using a preset visual algorithm and a graphical user interface algorithm, based on various model attribute information, phase parameters of various phases in the material composition, and grain parameters of each grain that makes up each phase, various model parameter files corresponding to the material composition are generated.

[0021] In one possible implementation, obtaining the material parameter information of the target material includes:

[0022] Obtain the material file of the target material;

[0023] Determine whether experimental data corresponding to the material file exists;

[0024] If so, the material parameter information is obtained based on the experimental data and / or the preset parameter file; wherein, the preset parameter file includes a preset phase parameter file and a preset grain parameter file.

[0025] In one possible implementation, obtaining the material parameter information based on the experimental data and / or a preset parameter file includes:

[0026] From the experimental data, obtain the phase parameters of each first phase of each material component of the target material, and the grain parameters of each first grain that makes up each first phase;

[0027] From the preset phase parameter file, obtain the phase parameters of various second phases for each material component of the target material;

[0028] From the preset grain parameter file, obtain the grain parameters of each second grain that constitutes each third phase; the third phase includes the first phase and the second phase;

[0029] The material parameter information is determined based on the phase parameters of each first phase and the grain parameters of each first grain, and / or the phase parameters of each second phase and the grain parameters of each second grain.

[0030] Secondly, embodiments of this disclosure also provide a model generation apparatus, comprising:

[0031] The acquisition module is used to acquire material parameter information in the target material; the material parameter information includes at least the various material components of the target material, the phase parameters of the various phases that make up each material component, and the grain parameters of each grain that makes up each phase;

[0032] The first generation module is used to generate model parameter files for at least one representative volume element RVE model corresponding to each material composition based on the material parameter information.

[0033] The second generation module is used to generate RVE models corresponding to each of the material components using the model parameter file.

[0034] In one possible implementation, the apparatus further includes an analysis module, which, after generating individual RVE models for each of the material components using the model parameter file, is used to:

[0035] Determine the grain orientation parameters for each of the grains;

[0036] Using the RVE models and grain orientation parameters, the target material is analyzed by the crystal plasticity finite element method (CPFEM) to obtain the mechanical property analysis results of the target material.

[0037] In one possible implementation, the analysis module, when determining the grain orientation parameter of each of the grains, is used to:

[0038] For any of the aforementioned grains, extract multiple orientation Euler angles corresponding to the grain;

[0039] Based on the multiple orientation Euler angles, the orientation quaternion corresponding to the grain is determined, and the orientation quaternion is used as the grain orientation parameter of the grain.

[0040] In one possible implementation, the first generation module, when generating model parameter files for at least one representative volumetric element RVE model corresponding to each of the material components based on the material parameter information, is configured to:

[0041] For any of the aforementioned material compositions, determine the number of RVE models required to be generated for that material composition;

[0042] Obtain the model attribute information corresponding to each required RVE model; the model attribute information includes at least model size, number of meshes, and resolution;

[0043] Based on the quantity and the model attribute information, and according to the phase parameters of various phases in the material composition and the grain parameters of each grain that makes up each phase, a model parameter file corresponding to the material composition is generated.

[0044] In one possible implementation, the first generation module, when generating model parameter files corresponding to the material composition according to the quantity and the model attribute information, based on the phase parameters of various phases in the material composition and the grain parameters of each grain constituting each phase, is configured to:

[0045] Using a preset visual algorithm and a graphical user interface algorithm, based on various model attribute information, phase parameters of various phases in the material composition, and grain parameters of each grain that makes up each phase, various model parameter files corresponding to the material composition are generated.

[0046] In one possible implementation, the acquisition module, when acquiring material parameter information in the target material, is used to:

[0047] Obtain the material file of the target material;

[0048] Determine whether experimental data corresponding to the material file exists;

[0049] If so, the material parameter information is obtained based on the experimental data and / or the preset parameter file; wherein, the preset parameter file includes a preset phase parameter file and a preset grain parameter file.

[0050] In one possible implementation, the acquisition module, when acquiring the material parameter information based on the experimental data and / or a preset parameter file, is used to:

[0051] From the experimental data, obtain the phase parameters of each first phase of each material component of the target material, and the grain parameters of each first grain that makes up each first phase;

[0052] From the preset phase parameter file, obtain the phase parameters of various second phases for each material component of the target material;

[0053] From the preset grain parameter file, obtain the grain parameters of each second grain that constitutes each third phase; the third phase includes the first phase and the second phase;

[0054] The material parameter information is determined based on the phase parameters of each first phase and the grain parameters of each first grain, and / or the phase parameters of each second phase and the grain parameters of each second grain.

[0055] Thirdly, an optional implementation of this disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when run, implements the steps of the first aspect above, or any possible implementation of the first aspect.

[0056] Thirdly, an optional implementation of this disclosure also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program and performs the steps of the first aspect above, or any possible implementation of the first aspect.

[0057] The model generation method, apparatus, storage medium, and computer equipment provided in this disclosure can automatically convert the acquired material parameter information of the target material into model parameter files for RVE models corresponding to various material compositions, and then automatically generate various RVE models using these model parameter files. The entire RVE model generation process is automated, requiring no manual intervention, which improves the efficiency and accuracy of model construction, avoids the problem of inconsistent RVE models generated for the same target material, and enhances the consistency of model generation.

[0058] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating a model generation method in an exemplary embodiment of this application;

[0060] Figure 2 This is a schematic diagram illustrating the specific process of a model generation method according to an exemplary embodiment of this application;

[0061] Figure 3 This is a hardware structure diagram of a computer device containing a model generation apparatus 400, as illustrated in an exemplary embodiment of this application.

[0062] Figure 4 This is a schematic diagram of a model generation apparatus shown in an exemplary embodiment of this application. Detailed Implementation

[0063] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0064] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0065] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0066] Research has shown that CPFEM, as an advanced numerical simulation technique, is a method for studying the plastic behavior of crystals based on crystal plasticity theory coupled with the finite element method. It can quantify the microscopic deformation of crystals into plastic strain in traditional plasticity theory, thereby establishing a correlation between microscopic deformation mechanisms and macroscopic mechanical behavior. Therefore, CPFEM plays a crucial role in analyzing the microstructure and mechanical properties of materials. Establishing a material RVE model can transform the complex microstructure of a material into a representative, simplified model suitable for mechanical analysis, which is a vital step in CPFEM analysis. Its modeling efficiency and accuracy are crucial for predicting the macroscopic mechanical properties of materials using CPFEM. However, traditional RVE model construction methods are manual. Due to the complexity of grain interactions and geometric relationships within materials, it is difficult to efficiently generate high-quality RVE models manually, affecting the efficiency and accuracy of model construction. Furthermore, manual generation is easily influenced by the subjective factors of the builder, leading to inconsistencies in models obtained from different constructions of the same material, affecting the consistency of model construction.

[0067] Based on the above research, this disclosure provides a model generation method, apparatus, storage medium, and computer device. Utilizing the acquired material parameter information of the target material, model parameter files for RVE models corresponding to various material compositions can be automatically converted. Furthermore, each RVE model can be automatically generated using these model parameter files. The entire RVE model generation process is automated, requiring no manual intervention. This improves the efficiency and accuracy of model construction and avoids the problem of inconsistent RVE models generated for the same target material, thus enhancing the consistency of model generation.

[0068] The shortcomings of the above solutions are the result of the inventor's practical experience and careful research. Therefore, the discovery process of the above problems and the solutions proposed in this disclosure below are all contributions made by the inventor to this disclosure.

[0069] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0070] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0071] To facilitate understanding of this embodiment, a model generation method disclosed in this disclosure will first be described in detail. The execution subject of the model generation method provided in this disclosure is generally a terminal device or other processing device with certain computing power. The terminal device can be a user equipment (UE), a mobile device, a terminal, a personal digital assistant (PDA), a handheld device, a computer device, etc. In some possible implementations, the model generation method can be implemented by the processor calling computer-readable instructions stored in the memory.

[0072] The model generation method provided in this disclosure embodiment will be described below using a computer device as an example.

[0073] like Figure 1 The flowchart shown is a model generation method provided in an embodiment of this disclosure, which may include the following steps:

[0074] S101: Obtain material parameter information of the target material; the material parameter information includes at least the various material components of the target material, the phase parameters of the various phases that make up each material component, and the grain parameters of each grain that makes up each phase.

[0075] Here, the target material can be any multiphase material. For each multiphase material, it can include at least one material component, and each material component can include multiple phases. Different phases can have different phase volumes, and each phase can include a large number of grains, with different grains in different phases. For example, the target material can be steel, and the material components in this material can include iron, cobalt, molybdenum, etc. Each component can be various phases with different phase volumes; for example, the phases in steel can include martensite, austenite, ferrite, etc.

[0076] Phase parameters reflect the compositional properties of a phase. For example, a phase parameter can be the volume fraction of the phase, which indicates the proportion of the phase in the material composition. Grain parameters reflect the properties of grains. For example, grain parameters can include the crystal lattice structure, grain size, etc.

[0077] Material parameter information is used to indicate the material composition, phase, and grain information of the target material. Specifically, the material parameter information may include various material components that make up the target material, the phase composition and phase volume of each material component, and the grain parameters of each grain of each phase.

[0078] In practice, for any target material for which an RVE model needs to be generated, the target file of that material can be obtained. The target file can include various parameter information of the target material. The target file is then automatically parsed to automatically extract the material composition information of the target material from the material file.

[0079] Optionally, this application supports batch acquisition of target files for multiple target materials, followed by simultaneous parameter extraction from these target files to obtain material composition information for each target material. Furthermore, using steps S102 and S103 described later, RVE models for various target materials can be generated in batches.

[0080] In one embodiment, S101 can be implemented according to the following steps:

[0081] S101-1: Obtain the material file for the target material.

[0082] Here, the material file can be user-inputted material information, which may include detailed compositional information of the target material, such as various material components, phase composition of each material component, phase volume fraction, individual grains in each phase, and grain size and lattice structure of each grain. Alternatively, the material file may only include basic compositional information of the target material, such as various material components.

[0083] S101-2: Determine if experimental data corresponding to the material file exists.

[0084] Here, the experimental data includes data obtained from preliminary experimental analysis of the target material. This data may include detailed compositional information of the target material, or it may only include partial compositional information of the target material.

[0085] In practice, after obtaining the material file of the target material, the material file can be parsed to determine if detailed composition information of the target material exists in the file. If so, the detailed composition information can be extracted from the material file and used as the material parameter information of the target material. If not, the various material components of the target material can be obtained from the material file. Then, it is determined whether experimental data matching the various material components in the material file exists (or whether experimental data related to the target material exists). If so, S102-3 below can be executed. If not, the material parameter information of the target material can be determined using a preset parameter file.

[0086] The reason why there may be a situation where experimental data is not available is because the experiments are difficult and few in number, but the demand for RVE models is large. Therefore, it may be necessary to use the preset parameter file mentioned later to determine the material parameter information.

[0087] S101-3: If so, obtain material parameter information based on experimental data and / or preset parameter files; wherein, preset parameter files include preset phase parameter files and preset grain parameter files.

[0088] Here, the preset phase parameter file includes preset phase parameters for various preset phases in various material compositions, and the preset grain parameter file may include preset grain parameters, which may include preset grain size and preset lattice structure.

[0089] In practice, if experimental data exists, it can be determined whether the experimental data includes detailed composition information of the target material. If so, the detailed composition information in the experimental data can be used as the material parameter information of the target material. If not, partial material parameter information can be obtained from the experimental data, and another part of the material parameter information can be obtained from a preset parameter file. Both parts of the material parameter information can then be used as the material parameter information of the target material.

[0090] If no experimental data is available, the preset phase parameters of various preset phases and the preset grain parameters of each grain in the preset phase can be obtained directly from the preset parameter file. These preset phase parameters and preset grain parameters are then used as the material parameter information of the target material.

[0091] Alternatively, if no experimental data is available, but the current experimental conditions allow for experimental processing, the target material can be processed to obtain its material parameter information.

[0092] In one embodiment, the steps in S101-3 above can be implemented as follows:

[0093] S101-3-1: Obtain the phase parameters of each first phase of each material component of the target material, and the grain parameters of each first grain that makes up each first phase, from the experimental data.

[0094] Here, the first phase refers to the constituent phase of each material component included in the experimental data, and the phase parameter of the first phase is the phase volume fraction of the first phase indicated in the experimental data. The first grain refers to the grain parameter of each first grain of the first phase included in the experimental data. It can be understood that the first phase of each material component included in the experimental data can be a partial phase in the target material, the first grain can be a partial grain of the first phase, and the grain parameter of the first grain can be a partial parameter of the first grain.

[0095] Optionally, the phase parameters of the first phase and the grain parameters of the first grain in the experimental data can be left blank. If they are left blank, the phase parameters of the second phase, the phase parameters of the third phase, and the grain parameters of the third grain in the following text will be used directly as the material parameter information of the target material.

[0096] For example, when experimental data is available, data extraction can be performed on the experimental data to obtain the phase parameters of each first phase of each material component of the target material, as well as the grain parameters of each first grain that makes up each first phase.

[0097] S101-3-2: Obtain the phase parameters of various second phases for each material component of the target material from the preset phase parameter file.

[0098] In practice, the various material components in the target material, as well as each second phase and its phase parameters, can be obtained from a preset phase parameter file. For example, the preset phase parameter file may include material components, phase composition, and phase volume fraction. Each phase under the phase composition in the preset phase parameter file is taken as the second phase, and the phase volume fraction of each phase is taken as the phase parameter of the second phase.

[0099] S101-3-3: Obtain the grain parameters of each second grain that makes up each third phase from the preset grain parameter file; the third phase includes the first phase and the second phase.

[0100] Here, if the first phase is extracted from the experimental data, the third phase can include each of the first and second phases. If the first phase is not extracted from the experimental data, then the third phase can only include the second phase. If each second phase has the same first phase, and there are other remaining first phases, then the third phase can include the first phase.

[0101] In practice, the grain parameters of each second grain constituting each third phase can be obtained from a preset grain parameter file. For example, the grain size and lattice structure of each second grain constituting each third phase can be automatically set using the preset grain file.

[0102] S101-3-4: Determine material parameter information based on the phase parameters of each first phase and the grain parameters of each first grain, and / or the phase parameters of each second phase and the grain parameters of each second grain.

[0103] Here, if the first phase is empty, the material parameter information can be determined based on the phase parameters of each second phase and the grain parameters of each second grain of each second phase. If the first phase is not empty, all the phase parameters of the first phase and the phase parameters of the second phase can be used as the phase parameters in the material parameter information. Furthermore, the grain parameters of each second grain in the first phase and the grain parameters of each first grain in the first phase are deduplicated and completed to obtain the complete grain parameters of each grain of the first phase. Then, based on the grain parameters of each second grain in the second phase and the complete grain parameters of each grain of the first phase, the grain parameter information in the material parameter information is determined. For example, the deduplication and completion process can include, for instance, if a first grain and a second grain are duplicated, only the grain parameters of the first grain or the second grain are included; if there are other grains besides the first grain in the second grain, these other grains can also be included as grains in the target material.

[0104] For example, if the phase parameters of each first phase and the grain parameters of each first grain are empty, the material parameter information can be directly determined based on the phase parameters of each second phase and the grain parameters of each second grain. If the phase parameters of each first phase and the grain parameters of each first grain are not empty, the phase parameters of the first phase and the phase parameters of the second phase (excluding the first phase) can be used as the phase parameters in the material parameter information; and the grain parameters of each first grain and the grain parameters of the second grain (excluding the first grain) can be used as the grain parameters in the material parameter information. If the phase parameters of each first phase and the grain parameters of each first grain are not empty, the overlap between the first and second phases and the overlap between the first and second grains can also be determined. If the average of the two overlaps is greater than a preset overlap, the phase parameters of the first phase and the grain parameters of each first grain can be directly used as the material parameter information of the target material. When the average of the two overlaps is not greater than the preset overlap, the phase parameters of the overlapping first phase, the grain parameters of the overlapping first grain, the phase parameters of the non-overlapping second phase, and the grain parameters of the non-overlapping second grain can be used as the material parameter information of the target material.

[0105] If experimental data for the target material is unavailable, the material composition, phase composition, phase volume fraction, lattice structure, and grain size can be automatically extracted directly from the preset phase parameter file and preset grain parameter file. The extracted material parameters can then be used to determine the target material's material parameters. Alternatively, if the phase parameters of the first phase and / or the grain parameters of the first grain in the experimental data are incomplete (e.g., a first phase is absent in a certain material composition, a phase volume fraction is missing in a certain first phase, a grain parameter is missing in a certain first grain, or the grain parameters lack grain size or lattice structure), the preset phase parameter file and preset grain parameter file can be used directly to determine the target material's material parameters. Alternatively, the preset phase parameter file and preset grain parameter file can be used to complete the incomplete information in the experimental data to obtain the target material's material parameters.

[0106] S102: Based on the material parameter information, generate the model parameter file for at least one representative volumetric element RVE model corresponding to each material composition.

[0107] Here, the model parameter file is used to indicate various geometric parameters of the RVE model. The corresponding RVE model can be modeled using the RVE model parameter file. One model parameter file is used to generate one RVE model.

[0108] A material composition can correspond to at least one Relational Virtual Equipment (RVE) model. Different RVE models for the same material composition have different model properties, which may include, for example, model size, level of detail, and model decomposability. The RVE model corresponding to each material composition is related to the phase parameters and grain parameters of that material composition.

[0109] In practice, for each material composition in the target material, the phase parameters and grain parameters of each phase under that material composition can be converted into model parameter files for at least one RVE model corresponding to that material composition. By converting the extracted material parameter information into model parameter files, it is convenient to directly and quickly construct the RVE model corresponding to the material composition.

[0110] In one embodiment, S102 described above can be implemented according to the following steps:

[0111] S102-1: For any given material composition, determine the number of RVE models required to generate for that material composition.

[0112] In practice, for any given material composition, the number of RVE models required can be determined based on the composition's type and the pre-defined relationship between type and model quantity. Alternatively, the number of models specified by the user can be used as the required number of RVE models for each material composition. Furthermore, the number of RVE models required can be determined based on the number of phases, crystal structure, and grain size within the material composition. For example, a material composition with more phases, a more complex crystal structure, and smaller grain size requires more RVE models. Alternatively, the complexity of the material composition can be determined based on various pre-defined complexities, and the number of RVE models required can be determined accordingly.

[0113] S102-2: Obtain the model attribute information corresponding to each required RVE model; the model attribute information includes at least the model size, number of meshes, and resolution.

[0114] Here, the model attribute information can be determined based on the phase parameters of various phases that make up the material composition, as well as the grain parameters of each grain that makes up each phase, or it can be preset attribute information. Model size is used to indicate the size of the RVE model to be generated; mesh number is used to indicate the number of mesh elements in the RVE model to be generated; resolution is used to indicate the resolution of the RVE model to be generated.

[0115] In practical implementation, for each material composition, when determining the number of RVE models required for that material composition, model attribute information matching the number of RVE models can be generated based on the phase parameter complexity of each phase and the grain parameter complexity of each grain in that phase. Alternatively, model attribute information pre-specified for that material composition can be obtained, and the number of model attribute information corresponding to that material composition can be used as the number of RVE models required for that material composition. Alternatively, the user can pre-set only one type of model attribute information for a material composition. After obtaining this model attribute information, one or more pieces of information in this model attribute information can be transformed according to the number of RVE models determined in S102-1 to obtain various model attribute information matching the number of RVE models. For example, transformation operations on the user-preset model attribute information may include proportionally enlarging / reducing the model size, increasing or decreasing the number of meshes, or increasing or decreasing the resolution.

[0116] S102-3: Based on the quantity and model attribute information, generate the model parameter files corresponding to the material composition according to the phase parameters of various phases in the material composition and the grain parameters of each grain that makes up each phase.

[0117] In practice, each model attribute information, matched with the number of models, can be used to generate a model parameter file corresponding to each model attribute information, based on the phase parameters of various phases in the material composition and the grain parameters of each grain that makes up each phase. The number of model parameter files is consistent with the number of RVE models required to be generated for the material composition, and also with the number of model attribute information.

[0118] For example, the required number of RVE models for material composition 1 is 3, and the obtained model attribute information will also be 3 (assuming model attribute information 1~3). Model parameter conversion can be performed on model attribute information 1, the phase parameters of various phases in the material composition, and the grain parameters of each grain constituting each phase to obtain model parameter file 1 corresponding to model attribute information 1. Simultaneously, model parameter conversion can be performed on model attribute information 2, the phase parameters of various phases in the material composition, and the grain parameters of each grain constituting each phase to obtain model parameter file 2 corresponding to model attribute information 2. Model parameter conversion can be performed on model attribute information 3, the phase parameters of various phases in the material composition, and the grain parameters of each grain constituting each phase to obtain model parameter file 3 corresponding to model attribute information 3.

[0119] Optionally, the model attribute information may also include processing parameters for adjusting the phase parameters of various phases and the grain parameters of each grain in the material composition. For example, processing parameters may include adjustment parameters for adjusting phase parameters, filtering parameters for filtering grain sizes, and enhancement parameters for enhancing the crystal structure. When generating the model parameter file, the processing parameters in the model attribute information can be used first to process the phase parameters of various phases and the grain parameters of each grain in the material composition, resulting in processed phase parameters and processed grain parameters. Then, the model size, mesh count, resolution, processed phase parameters, and processed grain parameters in the model attribute information are converted to obtain the model parameter file.

[0120] In one embodiment, S102-3 described above can be implemented according to the following steps:

[0121] Using preset visual algorithms and graphical user interface algorithms, various model parameter files corresponding to the material composition are generated based on various model attribute information, phase parameters of various phases in the material composition, and grain parameters of each grain that makes up each phase.

[0122] Here, the preset vision algorithm can be a preset computer vision algorithm, such as the YOLO11 algorithm. The Graphical User Interface Algorithm (GUI) is used to create, render, and manage graphical user interfaces.

[0123] In practice, preset visual algorithms and graphical user interface algorithms can be invoked to process and transform various model attribute information, phase parameters of various phases in the material composition, and grain parameters of each grain that makes up each phase, to obtain model parameter files under various model attribute information. For example, computer vision algorithms and GUI algorithms can be used to convert the phase parameters of various phases and the grain parameters of each grain in the material composition into phase model files and grain files, and then, based on the model attribute information, phase model files, and grain files, model parameter files corresponding to the model attribute information can be generated.

[0124] S103: Using the model parameter file, generate each RVE model corresponding to each material composition.

[0125] In practice, for each model parameter file corresponding to each material composition, modeling algorithms can be used to extract various parameters from the model parameter file, and these parameters can be calculated and generated in high throughput to obtain each RVE model corresponding to that material composition. Specifically, high throughput calculation can include large-scale processing, calculation, and transformation operations on the model parameter file.

[0126] Understandably, the model generation method of this application can be used to generate batch RVE models for batch target materials, which greatly improves the efficiency of model building.

[0127] Optionally, after generating the model parameter file corresponding to each material component using S102, the model parameter file can be stored in a folder named after the material component. If there are multiple model parameter files corresponding to a material component, multiple folders related to the material component can be generated according to the name of the material component, and then the multiple models can be stored in different folders. For example, if there are three model parameter files corresponding to material component A (such as model parameter files 1 to 3), the three folders corresponding to material component A can be used (i.e., folder 1 named material component A-1, folder 2 named material component A-2, and folder 4 named material component A-3). Then, model parameter file 1 can be stored in folder 1, model parameter file 3 in folder 2, and model parameter file 4 in folder 3. Further, when executing S103, for each material component, the model parameter file can be obtained from the folder corresponding to the material component, and then the corresponding RVE model can be generated using the model parameter file, and the RVE model can be stored in the folder corresponding to the material component.

[0128] In one embodiment, while generating the RVE model, steps 1 and 2 can also be performed to conduct CPFEM analysis:

[0129] Step 1: Determine the grain orientation parameters for each grain.

[0130] Here, grain orientation parameters are a series of physical quantities used to quantitatively describe the grain orientation distribution characteristics in polycrystalline materials. These parameters can clarify whether preferred orientation (i.e., "texture") exists in the material, the type, strength, and distribution pattern of the texture, etc. Each grain in a phase has a corresponding grain orientation parameter.

[0131] In practice, the grain orientation parameters of each grain in each material composition within the target material can be obtained through experimental analysis, or the grain orientation parameters can be automatically and randomly generated. When randomly generating grain orientation parameters, the parameters can be generated based on a user-specified preferred orientation, such as setting a preferred orientation in the x-direction. Since many grains exist within a single material composition, a folder corresponding to one material composition can contain numerous grain orientation parameters.

[0132] Optionally, after determining the grain orientation parameters of each grain, these parameters can be compiled into a grain parameter file and stored in the folder corresponding to the material composition of that grain. If there are multiple model parameter files for a material composition, the grain parameter file composed of the grain orientation parameters of each grain in that material composition can be used as the grain parameter file for each model parameter file. That is, the grain orientation parameters related to each model parameter file can be set to be the same. Therefore, after obtaining the grain orientation parameters of each grain in the material composition and compiling them into a grain parameter file, the grain parameter file can be stored in the respective folders corresponding to that material composition. Alternatively, different grain orientation parameters can be generated for different model parameter files, resulting in different grain parameter files for different model parameter files. For each model parameter file, the model parameter file and its corresponding grain parameter file can be stored in the corresponding folder. For example, model parameter file 1 corresponds to grain parameter file 1 composed of each grain orientation parameter 1, and model parameter file 2 corresponds to grain parameter file 2 composed of each grain orientation parameter 2. Model parameter file 1 and grain parameter file 1 can be stored in the same folder; model parameter file 2 and grain parameter file 2 can be stored in the same folder.

[0133] In one embodiment, the step of determining the grain orientation parameters in step 1 can be determined according to the following steps:

[0134] Step 1-1: For any given grain, extract the multiple orientation Euler angles corresponding to the grain.

[0135] Here, Euler angles for orientation are important parameters describing the orientation of grains in space. They quantitatively characterize the rotational relationship of one coordinate system relative to another reference coordinate system through three ordered rotation angles.

[0136] In practice, if experimental data exists for a target material, the multiple orientation Euler angles of each grain of the target material can be directly obtained from the experimental data. If experimental data does not exist for a target material, the multiple orientation Euler angles of the grains can be automatically set using preset parameter setting rules.

[0137] Step 1-2: Based on multiple orientation Euler angles, determine the orientation quaternion corresponding to the grain, and use the orientation quaternion as the grain orientation parameter of the grain.

[0138] In practice, for each grain, multiple orientation Euler angles of the grain can be mathematically calculated to convert them into orientation quaternions, thereby obtaining the orientation quaternion corresponding to the grain, and using the orientation quaternion as the grain orientation parameter of the grain.

[0139] Step 2: Using each RVE model and each grain orientation parameter, perform crystal plasticity finite element method (CPFEM) analysis on the target material to obtain the mechanical property analysis results of the target material.

[0140] In practice, for each RVE model, the CPFEM method can be used to analyze the mechanical properties of the RVE model and its corresponding grain orientation parameters, obtaining the mechanical property analysis results for that RVE model. Then, based on the mechanical property analysis results of the RVE models corresponding to various material compositions in the target material, the mechanical property analysis results for the target material can be determined.

[0141] For example, assuming the target material contains components 1 and 2, component 1 corresponds to RVE model 1, component 2 corresponds to RVE models 2 and 3, and RVE models 1-3 correspond to the same grain parameter file, then the CPFEM method can be used to obtain the mechanical property analysis result 1 corresponding to RVE model 1 for each grain orientation parameter in RVE model 1 and the grain parameter file; the CPFEM method can be used to obtain the mechanical property analysis result 2 corresponding to RVE model 2 for each grain orientation parameter in RVE model 2 and the grain parameter file; and the CPFEM method can be used to obtain the mechanical property analysis result 3 corresponding to RVE model 3 for each grain orientation parameter in RVE model 3 and the grain parameter file. Then, the mechanical property analysis results 1-3 can be directly used as the mechanical property analysis results of the target material; alternatively, the mechanical property analysis results 1-3 can be analyzed and integrated to obtain the mechanical property analysis results of the target material.

[0142] like Figure 2 The diagram shown is a schematic representation of a model generation method provided in an embodiment of this application, which may include the following steps:

[0143] S201: Determine if experimental data corresponding to the target material exists.

[0144] If yes, then execute S202 below; if no, then execute S203 below.

[0145] S202: Determine the material parameters of the target material based on the experimental data.

[0146] S203: Based on preset phase parameter files and preset grain parameter files, automatically acquire material composition, phase composition, phase volume fraction, crystal structure and grain size to obtain material parameter information of the target material.

[0147] For example, based on a preset phase parameter file, various material components, phase composition, and phase volume fraction of the target material can be obtained.

[0148] Based on a preset grain parameter file, the lattice structure and grain size of each grain are automatically set.

[0149] S204: Using a preset visual algorithm and a graphical user interface algorithm, generate model parameter files for at least one representative volumetric element RVE model corresponding to each material composition based on material parameter information.

[0150] S205: Perform high-throughput calculations and generate model parameter files to obtain various RVE models corresponding to each material composition.

[0151] S206: Based on the multiple orientation Euler angles of each grain, determine the orientation quaternion corresponding to the grain, and use the orientation quaternion as the grain orientation parameter of the grain.

[0152] The execution order of S205 and S206 is not specifically limited in this application.

[0153] S207: Using each RVE model and each grain orientation parameter, the target material is analyzed by the crystal plasticity finite element method (CPFEM) to obtain the mechanical property analysis results of the target material.

[0154] For the specific implementation process of S201 to S207 above, please refer to the description of each embodiment above, which will not be repeated here.

[0155] Based on the same technical concept, embodiments of this application also provide a computer device. (Refer to...) Figure 3 The diagram shown is a structural schematic of a computer device provided in an embodiment of this application, comprising:

[0156] The system includes a processor 301, a memory 302, and a bus 303. The memory 302 stores machine-readable instructions executable by the processor 301. The processor 301 executes these machine-readable instructions, and when executed, performs the following steps: S101: Obtain material parameter information from the target material; the material parameter information includes at least the various material components of the target material, the phase parameters of the various phases constituting each material component, and the grain parameters of each grain constituting each phase; S102: Based on the material parameter information, generate a model parameter file for at least one representative volumetric element (RVE) model corresponding to each material component; S103: Using the model parameter file, generate individual RVE models corresponding to each material component.

[0157] The aforementioned memory 302 includes a main memory 3021 and an external memory 3022. The main memory 3021, also known as internal memory, is used to temporarily store the computational data in the processor 301, as well as the data exchanged with external memory such as a hard disk. The processor 301 exchanges data with the external memory 3022 through the main memory 3021. When the computer device is running, the processor 301 and the memory 302 communicate through the bus 303, so that the processor 301 executes the execution instructions mentioned in the above method embodiments.

[0158] This disclosure also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the model generation method described in the above-described method embodiments. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0159] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the software update method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0160] The computer program product can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0161] Please refer to Figure 4 The diagram below illustrates a model generation apparatus provided in an embodiment of this application, comprising:

[0162] The acquisition module 401 is used to acquire material parameter information in the target material; the material parameter information includes at least the various material components of the target material, the phase parameters of the various phases that make up each material component, and the grain parameters of each grain that makes up each phase;

[0163] The first generation module 402 is used to generate a model parameter file for at least one representative volume element RVE model corresponding to each material composition based on the material parameter information.

[0164] The second generation module 403 is used to generate each RVE model corresponding to each of the material components using the model parameter file.

[0165] In one possible implementation, the apparatus further includes an analysis module 404, which, after generating individual RVE models for each of the material components using the model parameter file, is used to:

[0166] Determine the grain orientation parameters for each of the grains;

[0167] Using the RVE models and grain orientation parameters, the target material is analyzed by the crystal plasticity finite element method (CPFEM) to obtain the mechanical property analysis results of the target material.

[0168] In one possible implementation, the analysis module 404, when determining the grain orientation parameter of each of the grains, is used to:

[0169] For any of the aforementioned grains, extract multiple orientation Euler angles corresponding to the grain;

[0170] Based on the multiple orientation Euler angles, the orientation quaternion corresponding to the grain is determined, and the orientation quaternion is used as the grain orientation parameter of the grain.

[0171] In one possible implementation, the first generation module 402, when generating model parameter files for at least one representative volume element RVE model corresponding to each material composition based on the material parameter information, is configured to:

[0172] For any of the aforementioned material compositions, determine the number of RVE models required to be generated for that material composition;

[0173] Obtain the model attribute information corresponding to each required RVE model; the model attribute information includes at least model size, number of meshes, and resolution;

[0174] Based on the quantity and the model attribute information, and according to the phase parameters of various phases in the material composition and the grain parameters of each grain that makes up each phase, a model parameter file corresponding to the material composition is generated.

[0175] In one possible implementation, the first generation module 402, when generating the model parameter files corresponding to the material composition according to the quantity and the model attribute information, based on the phase parameters of various phases in the material composition and the grain parameters of each grain constituting each phase, is used to:

[0176] Using a preset visual algorithm and a graphical user interface algorithm, based on various model attribute information, phase parameters of various phases in the material composition, and grain parameters of each grain that makes up each phase, various model parameter files corresponding to the material composition are generated.

[0177] In one possible implementation, the acquisition module 401, when acquiring material parameter information in the target material, is used to:

[0178] Obtain the material file of the target material;

[0179] Determine whether experimental data corresponding to the material file exists;

[0180] If so, the material parameter information is obtained based on the experimental data and / or the preset parameter file; wherein, the preset parameter file includes a preset phase parameter file and a preset grain parameter file.

[0181] In one possible implementation, the acquisition module 401, when acquiring the material parameter information based on the experimental data and / or a preset parameter file, is used to:

[0182] From the experimental data, obtain the phase parameters of each first phase of each material component of the target material, and the grain parameters of each first grain that makes up each first phase;

[0183] From the preset phase parameter file, obtain the phase parameters of various second phases for each material component of the target material;

[0184] From the preset grain parameter file, obtain the grain parameters of each second grain that constitutes each third phase; the third phase includes the first phase and the second phase;

[0185] The material parameter information is determined based on the phase parameters of each first phase and the grain parameters of each first grain, and / or the phase parameters of each second phase and the grain parameters of each second grain.

[0186] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0187] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and 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 modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0188] The embodiments of the subject matter and functional operation described in this specification can be implemented in the following ways: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or combinations thereof. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing apparatus or for controlling the operation of a data processing apparatus. Alternatively or additionally, the program instructions may be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information and transmit it to a suitable receiving device for execution by the data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or combinations thereof.

[0189] The processing and logic flow described in this specification can be executed by one or more programmable computers that execute one or more computer programs to perform corresponding functions by operating on input data and generating output. The processing and logic flow can also be executed by dedicated logic circuitry—such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits), and the device can also be implemented as dedicated logic circuitry.

[0190] Suitable computers for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit receives instructions and data from read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as disks, magneto-optical disks, or optical disks, or the computer will be operatively coupled to such mass storage devices to receive data from or transfer data to them, or both. However, a computer is not required to have such devices. Furthermore, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0191] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. Processors and memory may be supplemented by or incorporated into dedicated logic circuitry.

[0192] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather are primarily intended to describe features of specific embodiments of a particular invention. Certain features described in the various embodiments herein may also be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment may also be implemented separately in various embodiments or in any suitable sub-combination. Furthermore, while features may function in certain combinations as described above and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and a claimed combination may refer to a sub-combination or a variation thereof.

[0193] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order shown or sequentially, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0194] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings are not necessarily shown in a specific order or sequence to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0195] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A model generation method, characterized in that, The method includes: Obtain material parameter information of the target material; the material parameter information includes at least the various material components of the target material, the phase parameters of the various phases that make up each material component, and the grain parameters of each grain that makes up each phase; the grain parameters include at least the crystal lattice structure and the grain size; Based on the material parameter information, a model parameter file is generated for at least one representative volumetric element RVE model corresponding to each material composition; for any given material composition, the number of RVE models required to be generated for that material composition is determined; wherein, the model attribute information of different RVE models for the same material composition is different; the model attribute information includes at least model size, number of meshes, and resolution; the number of RVE models corresponding to any given material composition is related to the number of phases in the material composition, the crystal structure, and the grain size; Using the model parameter file, generate individual RVE models for each of the material components.

2. The method according to claim 1, characterized in that, After generating each RVE model corresponding to each of the material components using the model parameter file, the method further includes: Determine the grain orientation parameters for each of the grains; Using the RVE models and grain orientation parameters, the target material is analyzed by the crystal plasticity finite element method (CPFEM) to obtain the mechanical property analysis results of the target material.

3. The method according to claim 2, characterized in that, Determining the grain orientation parameters for each of the grains includes: For any of the aforementioned grains, extract multiple orientation Euler angles corresponding to the grain; Based on the multiple orientation Euler angles, the orientation quaternion corresponding to the grain is determined, and the orientation quaternion is used as the grain orientation parameter of the grain.

4. The method according to claim 1, characterized in that, The step of generating a model parameter file for at least one representative volumetric element RVE model corresponding to each of the material components based on the material parameter information includes: For any of the aforementioned material compositions, determine the number of RVE models required to be generated for that material composition; Obtain the model attribute information corresponding to each required RVE model; Based on the quantity and the model attribute information, and according to the phase parameters of various phases in the material composition and the grain parameters of each grain that makes up each phase, a model parameter file corresponding to the material composition is generated.

5. The method according to claim 4, characterized in that, The step of generating various model parameter files corresponding to the material composition according to the quantity and the model attribute information, based on the phase parameters of various phases in the material composition and the grain parameters of each grain constituting each phase, includes: Using a preset visual algorithm and a graphical user interface algorithm, based on various model attribute information, phase parameters of various phases in the material composition, and grain parameters of each grain that makes up each phase, various model parameter files corresponding to the material composition are generated.

6. The method according to claim 1, characterized in that, The acquisition of material parameter information in the target material includes: Obtain the material file of the target material; Determine whether experimental data corresponding to the material file exists; If so, the material parameter information is obtained based on the experimental data and / or the preset parameter file; wherein, the preset parameter file includes a preset phase parameter file and a preset grain parameter file.

7. The method according to claim 6, characterized in that, Based on the experimental data and / or preset parameter files, obtain the material parameter information, including: From the experimental data, obtain the phase parameters of each first phase of each material component of the target material, and the grain parameters of each first grain that makes up each first phase; From the preset phase parameter file, obtain the phase parameters of various second phases for each material component of the target material; From the preset grain parameter file, obtain the grain parameters of each second grain that constitutes each third phase; the third phase includes the first phase and the second phase; The material parameter information is determined based on the phase parameters of each first phase and the grain parameters of each first grain, and / or the phase parameters of each second phase and the grain parameters of each second grain.

8. A model generation apparatus, characterized in that, The device includes: The acquisition module is used to acquire material parameter information of the target material; the material parameter information includes at least the various material components of the target material, the phase parameters of the various phases that make up each material component, and the grain parameters of each grain that makes up each phase; the grain parameters include at least the crystal structure and the grain size. The first generation module is used to generate model parameter files for at least one representative volumetric element RVE model corresponding to each material composition based on the material parameter information; and to determine the number of RVE models to be generated for any given material composition; wherein, the model attribute information of different RVE models for the same material composition is different; the model attribute information includes at least model size, number of meshes, and resolution; the number of RVE models corresponding to any given material composition is related to the number of phases, the crystal structure, and the grain size in the material composition. The second generation module is used to generate RVE models corresponding to each of the material components using the model parameter file.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer 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 program, it implements the steps of the method as described in any one of claims 1 to 7.

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