Full-automatic modeling method and system for macro-micro multi-scale model based on real microstructure

Abstract

The invention belongs to the field of material science and engineering, and discloses a full-automatic modeling method and system for a macro-micro multi-scale model based on a real microstructure, which adopts a fine EBSD data processing technology, not only can accurately obtain the orientation, grain boundary, size and other information of grains, but also can accurately obtain the size of the grains by effectively filtering the extremely small grains, thereby improving the modeling efficiency of the macro-micro multi-scale model based on the real microstructure. The overall quality of the data is improved; according to the method, through accurate data input, it is ensured that the microstructure can be fully reflected in the macroscopic model; according to the method, the EBSD data and the ABAQUS finite element simulation are effectively integrated, and the close association between the macro scale and the micro scale is successfully established. On this basis, the change of the microstructure can be tracked in real time and transmitted to the macroscopic stress-strain response model, and it is ensured that stress distribution and plastic behaviors of the material under different load conditions are described more accurately.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the technical field of materials science and engineering, and in particular relates to a fully automatic modeling method and system for a macro-micro multi-scale model based on real microstructures. Background Art

[0002] In the field of materials science and engineering, grain structure has a crucial influence on the macroscopic mechanical properties of materials. Especially in metal materials, the size, morphology and orientation of grains directly affect the strength, ductility, hardness and fatigue resistance of materials. Therefore, studying the evolution of grains under different conditions, especially the orientation changes under complex stress fields and their influence on the macroscopic properties of materials, is an important research topic in materials science.

[0003] At present, electron backscatter diffraction (EBSD) technology has been widely used in the characterization of the microstructure of materials. EBSD can provide detailed microstructural information such as grain orientation, grain boundaries, phase information, etc., so that researchers can deeply understand the relationship between the microstructure and macroscopic mechanical properties of materials. However, EBSD technology usually only provides static grain information and cannot directly capture the evolution of grains during dynamic loading. To make up for this deficiency, the finite element method (FEM) is widely used to simulate and predict the deformation behavior of materials during loading. By combining EBSD experimental data with finite element simulation, researchers can more accurately describe the grain rotation and deformation behavior of materials under complex stress states.

[0004] Although the method of combining EBSD data with finite element simulation has made some progress in material research, the existing technology still has certain limitations. In macro-micro multi-scale analysis, it is difficult for existing technology to effectively establish an accurate correlation between the two. Traditional finite element methods often focus on the material response at the macro scale and ignore the evolution of the micro grain structure under stress. Although EBSD technology can characterize the microstructure in detail, it lacks the ability to dynamically update in real time when associated with macro properties. This leads to the failure to fully consider the influence of microstructure on the macro mechanical properties of materials under complex loading conditions, limiting the accuracy and applicability of the simulation results.

[0005] In summary, establishing accurate multi-scale models is of great significance in the field of materials science and engineering. By combining EBSD data with finite element simulation, it is not only possible to conduct in-depth analysis of structural features such as grain orientation and grain boundary evolution at the microscopic level, but also to transfer these microscopic information to the macroscopic scale to more accurately predict the overall mechanical properties of the material. Therefore, the development of dynamic models that can effectively combine macroscopic and microscopic scales is crucial to improving the accuracy and reliability of material behavior predictions. This not only helps to promote the development of materials science, but also provides a solid theoretical basis for the optimization of material properties in engineering applications. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention provides a fully automatic modeling method and system for a macro-micro multi-scale model based on real microstructures.

[0007] The present invention is implemented as follows: a fully automatic modeling method of a macro-micro multi-scale model based on real microstructure, the method comprising:

[0008] S1: Call basic controls such as uifigure and uilabel through MATLAB, use SelectionChangedFcn and SizeChangedFcn to realize responsive interaction of multi-scale modeling system, and build the user interface of macro-micro multi-scale modeling system;

[0009] S2: Select the corresponding macro model and its corresponding boundary conditions in the interface of S1, and use MATLAB to write the py file to create the ABAQUS initial model entity; at the same time, complete the creation of the equipment body, establishment of analysis steps, creation of reference points, establishment of reference point coupling constraints, addition of fixed constraints, addition of load constraints, etc. and the setting of corresponding boundary conditions;

[0010] S3: Read the data of the real microstructure obtained by EBSD through MATLAB, automatically obtain the length, width and scanning step of the EBSD experimental scanning area, and divide the area where the micro model needs to be added in the initial macro model into a surface area with the same length and width as the EBSD scanning area, and record the initial vertex coordinates of the surface area; then, perform coarse mesh division on the initial macro model, mesh the micro model area according to the size of the EBSD experimental scanning step, and generate an INP file;

[0011] S4: Use MATLAB to call the METX toolbox to import EBSD data and create an ebsd data set in the MATLAB workspace;

[0012] S5: Use methods such as grain segmentation, confidence index filtering, and small grain filtering to establish grain sets for EBSD data, delete extremely small grains, reduce noise in EBSD data, and improve the quality of EBSD data;

[0013] S6: extract the denoised EBSD data, draw a denoised grain distribution map, and compare it with the grain distribution map of the EBSD data;

[0014] S7: Read each node data and unit data of the initial model through MATLAB; generate a data file of the node and unit set of each grain in ABAQUS according to the position (X, Y coordinates) of each grain and the pixel value occupied by each grain; then, align the generated data file with the initial vertex coordinates of the face domain recorded in S3, write it into the INP file of the initial model, and generate an INP file of a multi-scale crystal plasticity finite element model containing both a microscopic grain structure model and a macroscopic model;

[0015] S8: Convert the grain Euler angle into radian value, and use MATLAB to output a PYTHON file that assigns material properties to each grain and material properties of the macro model;

[0016] S9: Call ABAQUS through MATLAB to execute the PYTHON file of material property assignment, assign material properties to each grain and macro model at the same time, and generate the final macro-micro multi-scale crystal plasticity finite element model INP file;

[0017] S10: Call ABAQUS through MATLAB to run the final generated INP file, call the subroutine of crystal plasticity finite element simulation, output the orientation information of each grain in the final generated crystal plasticity finite element model, call METX to draw the pole figure of each grain in the final output; at the same time, call the METX toolbox through MATLAB to draw the pole figures of the original EBSD data and the pole figures of the grain set after denoising, and compare the consistency of the pole figures finally output by the model with the initial pole figures and the pole figures after denoising;

[0018] S11: Write a PYTHON file to extract the stress-strain curve of crystal plasticity finite element simulation through MATLAB, call ABAQUS to execute the PYTHON file through MATLAB, obtain the stress-strain curve of macro-micro multi-scale crystal plasticity finite element simulation and compare it with the experimental results to verify the accuracy of the model.

[0019] Furthermore, the initial model entities in S2 include macroscopic standard models commonly used in experimental tests, such as tensile models, crack extension models, and impact toughness models; the data set in S4 contains important information such as grain type, grain ID, grain orientation, phase sequence, and grain rotation.

[0020] Furthermore, the formulas for grain segmentation, confidence index filtering, and small grain filtering in S5 are:

[0021]

[0022] Among them, the grain rotation is represented by R 1 and R 2 , Δθ is the orientation angle difference between the two points. If Δθ>angle, the two points are considered to belong to different grains;

[0023]

[0024] For each measurement point i, its confidence index is CI i ;

[0025]

[0026] Among them, g is each grain, and size(g) is the size of each grain.

[0027] Furthermore, the EBSD data in S6 includes data such as the grain number, equivalent diameter, grain orientation Euler angle, grain pixel coordinates, and grain set of each grain.

[0028] Furthermore, the macro model material property assignment code in S8 includes four parts: automatically reading the radian value of the grain Euler angle, converting the radian value into the Miller index, assigning the grain size effect, and converting the macro model engineering stress and strain into real stress and strain.

[0029] Furthermore, the radian value is converted into the Miller index, the grain size effect, and the engineering stress-strain is converted into the true stress-strain formula respectively:

[0030]

[0031] τ i =τ 0 +Kd -0.5 (i=1,2,3…)

[0032] σ t =σ e (1+ε e )

[0033] ε t =ln(1+ε e )

[0034] Among them, θ is the nutation angle, ψ is the precession angle, φ is the rotation angle; u, v, w are the crystal direction indices; h, k, l are the crystal plane indices; i is the grain number, τ 0is the initial yield strength of the grain, K is the influence coefficient of the reaction grain boundary on the deformation, d is the equivalent diameter of the grain, σ e is the engineering stress, σ t is the true stress, ε e is the engineering strain, ε t For true resilience.

[0035] Another object of the present invention is to provide a fully automatic modeling system of a macro-micro multi-scale model based on a real microstructure based on the fully automatic modeling method of the macro-micro multi-scale model based on the real microstructure, the system specifically comprising:

[0036] The interface construction module calls basic controls such as uifigure and uilabel through MATLAB, uses SelectionChangedFcn and SizeChangedFcn to achieve responsive interaction of the multi-scale modeling system, and builds the user interface of the macro-micro multi-scale modeling system;

[0037] The boundary condition setting module is connected to the interface building module. In the user interface, the corresponding macro model and its corresponding boundary conditions are selected, and MATLAB is used to write the py file to create the ABAQUS initial model entity; at the same time, the creation of the equipment body, establishment of the analysis step, creation of the reference point, establishment of the reference point coupling constraint, addition of the fixed constraint, addition of the load constraint and the setting of the corresponding boundary conditions are completed;

[0038] The INP file generation module is connected to the boundary condition setting module, and reads the real microstructure data obtained by EBSD through MATLAB, automatically obtains the length, width and scanning step of the EBSD experimental scanning area, and divides the area where the micro model needs to be added in the initial macro model into a surface area with the same length and width as the EBSD scanning area, and records the initial vertex coordinates of the surface area; then, the initial macro model is coarsely meshed, the micro model area is meshed according to the size of the EBSD experimental scanning step, and an INP file is generated;

[0039] The data set creation module is connected with the INP file generation module, and uses MATLAB to call the METX toolbox to import EBSD data and create an ebsd data set in the MATLAB workspace;

[0040] The grain establishment module is connected to the data set creation module. It uses methods such as grain segmentation, confidence index filtering, and small grain filtering to establish grain sets for EBSD data, delete extremely small grains, reduce noise for EBSD data, and improve the quality of EBSD data.

[0041] The distribution map drawing module is connected with the grain establishment module, extracts the denoised EBSD data, draws the denoised grain distribution map, and compares it with the grain distribution map of the EBSD data;

[0042] The INP file writing module is connected with the distribution map drawing module and the INP file generation module, and each node data and unit data of the initial model are read through MATLAB; according to the X, Y coordinates of the position of each grain and the pixel value occupied by each grain, a data file of the node and unit set of each grain in ABAQUS is generated; then, the generated data file is aligned with the initial vertex coordinates of the face domain recorded by the INP file generation module, and written into the INP file of the initial model, so as to generate an INP file of a multi-scale crystal plasticity finite element model containing both a microscopic grain structure model and a macroscopic model;

[0043] The property assignment module converts the grain Euler angle into radian value, and uses MATLAB to output the PYTHON file that assigns the material properties of each grain and the material properties of the macro model;

[0044] The final INP file generation module is connected with the INP file writing module and the attribute assignment module. It calls ABAQUS through MATLAB to execute the PYTHON file of material attribute assignment, assigns material attributes to each grain and macro model at the same time, and generates the final macro-micro multi-scale crystal plasticity finite element model INP file;

[0045] The pole figure drawing module is connected to the final INP file generation module. MATLAB calls ABAQUS to run the final generated INP file, and calls the subroutine of the crystal plasticity finite element simulation to output the orientation information of each grain in the final generated crystal plasticity finite element model, and calls METX to draw the pole figure of each grain in the final output; at the same time, MATLAB calls the METX toolbox to draw the pole figures of the original EBSD data and the pole figures of the grain set after denoising, and compares the consistency of the pole figures finally output by the model with the initial pole figures and the pole figures after denoising;

[0046] The verification module is connected to the pole figure drawing module, and the PYTHON file for extracting the stress-strain curve of the crystal plasticity finite element simulation is written through MATLAB. ABAQUS is called through MATLAB to execute the PYTHON file, and the stress-strain curve of the macro-micro multi-scale crystal plasticity finite element simulation is obtained and compared with the experimental results to verify the accuracy of the model.

[0047] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the fully automatic modeling method of the macro-micro multi-scale model based on the real microstructure.

[0048] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the fully automatic modeling method of the macro-micro multi-scale model based on real microstructures.

[0049] Another object of the present invention is to provide an information data processing terminal, which is used to implement the fully automatic modeling system of the macro-micro multi-scale model based on real microstructures.

[0050] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0051] First, the present invention aims at the deficiencies of the prior art in modeling the multi-scale grain plastic behavior, and proposes an improved multi-scale modeling method by combining EBSD experimental data with finite element simulation technology. First, the present invention adopts sophisticated EBSD data processing technology, which can not only accurately obtain information such as grain orientation, grain boundary and size, but also improve the overall quality of data by effectively filtering extremely small grains. When dealing with these details, traditional methods often ignore the complexity of microscopic grains, resulting in insufficient modeling accuracy. The present invention ensures that the microstructure can be fully reflected in the macro model through precise data input.

[0052] Secondly, the present invention successfully establishes a close relationship between macro and micro scales by effectively integrating EBSD data with ABAQUS finite element simulation. On this basis, the changes in microstructure can be tracked in real time and transferred to the macro stress-strain response model to ensure that the stress distribution and plastic behavior of the material under different load conditions are more accurately described. This process does not require cumbersome post-processing steps, greatly improves the efficiency and reliability of the simulation, and reduces manpower and time costs. In addition, the implementation of this method relies on common software platforms, such as MATLAB and ABAQUS, and the system structure is simple and easy to operate, with wide applicability and promotion value.

[0053] In summary, the present invention improves the existing EBSD data processing and finite element simulation methods, not only improving the multi-scale analysis accuracy of grain dynamic behavior, but also effectively capturing the microstructure evolution under dynamic stress conditions, thereby improving the accuracy and applicability of macroscopic simulation results. This technology has the advantages of simple structure, excellent performance, easy implementation and low cost, and has broad application prospects in materials science research and industrial applications.

[0054] Second, as auxiliary evidence of the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0055] (1) The expected benefits and commercial value of the technical solution of the present invention after transformation are:

[0056] The present invention can greatly improve the research efficiency in the field of materials science by combining EBSD experimental data and finite element simulation through automated modeling technology. This automated, multi-scale modeling system has important application value in material performance prediction, structural optimization and failure analysis. It can provide more accurate simulation and prediction for the design and manufacturing process of materials, especially in the fields of aerospace, automobile manufacturing, shipbuilding and high-end equipment manufacturing, and has significant market demand and commercial value. By optimizing the mechanical properties of materials, the system can shorten the R&D cycle, reduce experimental costs, improve product quality, and thus create considerable economic benefits for enterprises.

[0057] (2) The technical solution of the present invention fills the technical gap in the industry at home and abroad:

[0058] Currently, the industry lacks an automated modeling system that can effectively associate microstructure information with macroscopic mechanical properties. The present invention fills this gap and realizes the fully automatic construction of a macro-micro multiscale model based on real microstructures for the first time. By integrating EBSD technology and finite element simulation, the present invention can dynamically capture the evolution of grain structure under complex stress states, thereby more accurately describing the impact of microstructure on the macroscopic properties of materials. This innovation breaks through the limitations of existing technologies and brings a new research method to the field of materials science.

[0059] (3) The technical solution of the present invention solves the technical problems that people have been eager to solve but have never been able to solve successfully:

[0060] The grain rotation and deformation behavior of materials under complex stress fields has always been an important research problem in materials science. Traditional experimental methods cannot capture the dynamic evolution of grains during loading in real time, and existing simulation methods also have difficulty effectively linking micro and macro structures. The present invention successfully solves this technical problem by developing a multi-scale automatic modeling system based on real microstructures, realizes the simulation of the dynamic evolution of grain structure under complex stress states, and accurately transmits these microscopic information to the macro scale. This technological breakthrough provides a reliable tool for studying the deformation behavior of materials under complex stress fields.

[0061] (4) The technical solution of the present invention overcomes technical prejudice:.

[0062] Existing technologies usually focus on predicting material properties at the macroscopic scale, ignoring the impact of the evolution of microstructures under stress on overall performance. The present invention overcomes this bias in methodology, combines microscopic grain structure with macroscopic material response, and realizes the dynamic correlation between microstructure and macroscopic performance through a multi-scale automatic modeling system. This innovative perspective not only expands the application scope of existing material simulation technology, but also provides a more comprehensive and accurate theoretical basis and practical support for the prediction of material behavior.

[0063] Third, the technical solution of the present invention solves many problems in the prior art in industrial applications and has achieved significant technical progress, which is specifically reflected in the following aspects:

[0064] 1. Existing technical problems solved

[0065] (1) Manual modeling is inefficient and difficult to handle complex multi-scale problems

[0066] In the existing technology, the construction process of macro-micro multi-scale models usually relies on manual operation, especially when processing real microstructure data, which has high manual participation, low efficiency and easy to produce errors. Especially in complex materials, the processing of microstructure and the integration of macro models put forward extremely high requirements for manual intervention, resulting in a lengthy and cumbersome modeling process, which affects the efficiency in industrial applications.

[0067] (2) Microscopic data processing is complex, and data noise reduction and grain feature extraction are difficult

[0068] In the existing technology, the processing of real microstructure data of materials (such as EBSD data) often requires highly specialized software and complex operation procedures. The steps of noise reduction and grain feature extraction are cumbersome and easily affected by noise and small grains, resulting in inaccurate extraction of microscopic features, which in turn affects the final simulation and analysis results.

[0069] (3) Inaccurate assignment of material properties in multi-scale models

[0070] In the traditional modeling process, there is a lack of effective automated solutions for assigning material properties in multi-scale models, especially in the process of combining micro-grain structure with macro-model, the assignment of material properties is relatively complicated. In the existing technology, manual operation or imperfect automated processes often lead to insufficient accuracy in assigning material properties, affecting the reliability of subsequent simulation analysis.

[0071] 2. Significant technical advancement of the present invention

[0072] (1) Fully automatic modeling to improve modeling efficiency

[0073] The present invention solves the problem of low efficiency of existing manual operation by introducing an automated modeling system. The system uses MATLAB to call multiple modules, integrates the entire process of data acquisition, processing and model generation, and realizes fully automated operations from interface design, boundary condition setting to INP file generation. It greatly reduces manual operations, improves the efficiency and accuracy of model construction, and is particularly suitable for complex materials and multi-scale modeling needs in industry.

[0074] (2) Integrated EBSD data processing to improve microstructure processing accuracy

[0075] The technical solution of the present invention integrates MATLAB with the METX toolbox to automatically process EBSD data, including operations such as data set creation, grain segmentation, confidence index filtering and noise reduction. By introducing grain noise reduction and filtering processing, the interference of small-sized grains and noise on model construction is eliminated, the feature extraction accuracy of the microstructure is greatly improved, and the accuracy of the microscopic part in the multi-scale model is ensured.

[0076] (3) Automatically assign material properties to ensure the consistency of model material properties

[0077] The present invention assigns material properties through an automated process, uses a Python script generated by MATLAB to call ABAQUS, and assigns material properties at different scales of model construction at the same time. Both the material properties of microscopic grains and the material properties of macroscopic models can be accurately assigned in an automated manner. This not only reduces the errors generated when manually assigning material properties, but also improves the accuracy of model simulation calculations.

[0078] (4) Multi-module collaboration to achieve data verification and model optimization

[0079] The technical solution of the present invention realizes the collaborative work of multiple modules in each stage of modeling, such as through INP file generation, pole figure drawing and stress-strain curve analysis, to ensure the accuracy of the model. In particular, the pole figure drawing module verifies the consistency of the noise reduction data and the simulation results by generating and comparing the grain pole figures. The verification module further ensures the reliability of the simulation results by comparing with the experimental data, which is suitable for comprehensive performance evaluation in engineering applications.

[0080] 3. Significant progress in industrial applications

[0081] The technical solution of the present invention realizes a fully automated process from data collection to model generation in industrial applications, which not only improves the efficiency of model construction, but also significantly improves the modeling accuracy. The system can be widely used in many fields such as material research and development, industrial manufacturing, aerospace, etc., and is particularly suitable for multi-scale model construction for processing complex materials, providing an efficient and reliable solution. In addition, the system integrates data verification and material performance analysis modules, which can help enterprises to carry out product development and structural optimization more quickly, reduce development costs and improve work efficiency.

[0082] Therefore, the present invention solves many pain points in the prior art through multi-module integration and a fully automated modeling system, and demonstrates significant technological progress in industrial applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a flow chart of a fully automatic modeling method of a macro-micro multi-scale model based on real microstructures provided by an embodiment of the present invention;

[0084] Figure 2 This is an interface diagram of a multi-scale model building system provided by an embodiment of the present invention;

[0085] Figure 3 is an initial model and its boundary condition diagram provided by an embodiment of the present invention;

[0086] Figure 4 is an initial model grid division diagram provided by an embodiment of the present invention;

[0087] Figure 5 is a comparison diagram of the original EBSD grain distribution and the grain distribution after noise reduction provided by an embodiment of the present invention;

[0088] Figure 6 is a macro-micro multi-scale crystal plasticity model diagram provided by an embodiment of the present invention;

[0089] Figure 7 is a polar figure comparison diagram provided by an embodiment of the present invention;

[0090] Figure 8It is a comparison diagram between simulation and experiment provided by an embodiment of the present invention;

[0091] Fig. 9 is a graph showing the variation of dislocation density during a multi-scale model fatigue simulation process according to an embodiment of the present invention;

[0092] Fig.10 is a life prediction curve diagram based on a multi-scale model fatigue simulation according to an embodiment of the present invention;

[0093] Fig.11 It is a structural diagram of a fully automatic modeling system of a macro-micro multi-scale model based on real microstructures provided by an embodiment of the present invention;

[0094] In the figure: 1. Interface construction module; 2. Boundary condition setting module; 3. INP file generation module; 4. Data set creation module; 5. Grain establishment module; 6. Distribution map drawing module; 7. INP file writing module; 8. Attribute assignment module; 9. Final INP file generation module; 10. Pole figure drawing module; 11. Verification module. DETAILED DESCRIPTION

[0095] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0096] like Figure 1 As shown, an embodiment of the present invention provides a fully automatic modeling method of a macro-micro multi-scale model based on a real microstructure, the method comprising:

[0097] S1: Call basic controls such as uifigure and uilabel through MATLAB, use SelectionChangedFcn and SizeChangedFcn to realize responsive interaction of multi-scale modeling system, and build the user interface of macro-micro multi-scale modeling system, such as Figure 2 shown.

[0098] S2: Select the corresponding macro model and its corresponding boundary conditions in the interface of step 1, and use MATLAB to write the py file to create the ABAQUS initial model entity. The initial model entity includes macro standard models such as tensile model, crack extension model, impact toughness model, etc. commonly used in experimental tests. At the same time, complete the creation of equipment body, establishment of analysis steps, creation of reference points, establishment of reference point coupling constraints, addition of fixed constraints, addition of load constraints, etc., and the setting of the boundary conditions corresponding to step 1, such as Figure 3 shown.

[0099] S3: Read the real microstructure data obtained by EBSD through MATLAB, automatically obtain the length, width and scanning step of the EBSD experimental scanning area, and divide the area where the micro model needs to be added in the initial macro model into a surface area with the same length and width as the EBSD scanning area, and record the initial vertex coordinates of the surface area. Then, the initial macro model is coarsely meshed, and the micro model area is meshed according to the size of the EBSD experimental scanning step, and an INP file is generated, such as Figure 4 shown.

[0100] S4: Use MATLAB to call the METX toolbox to import EBSD data and create an ebsd data set in the MATLAB workspace. The data set contains important information such as grain type, grain ID, grain orientation, phase sequence, and grain rotation.

[0101] S5: Use methods such as grain segmentation, confidence index filtering, and small grain filtering to establish a grain set for EBSD data, delete extremely small grains, reduce noise for EBSD data, and improve the quality of EBSD data. The formulas for grain segmentation, confidence index filtering, and small grain filtering are:

[0102]

[0103] Among them, the grain rotation is represented by R 1 and R 2 , Δθ is the orientation angle difference between the two points. If Δθ>angle, the two points are considered to belong to different grains.

[0104]

[0105] For each measurement point i, its confidence index is CI i .

[0106]

[0107] Among them, g is each grain, and size(g) is the size of each grain.

[0108] S6: Extract the EBSD data after noise reduction, including the grain number, equivalent diameter, grain orientation Euler angle, grain pixel coordinates, grain set and other data of each grain, draw the grain distribution map after noise reduction, and compare it with the grain distribution map of EBSD data, such as Figure 5 shown.

[0109] S7: Read each node data and unit data of the initial model through MATLAB. According to the position of each grain (X, Y coordinates) and the pixel value occupied by each grain, generate a data file of the node and unit set of each grain in ABAQUS. Then, align the generated data file with the initial vertex coordinates of the face domain recorded in step 3, write it into the INP file of the initial model, and generate an INP file of the multi-scale crystal plasticity finite element model containing both the microscopic grain structure model and the macroscopic model, such as Figure 6 shown.

[0110] S8: Convert the grain Euler angle to radian value, use MATLAB to output the PYTHON file that assigns the material properties of each grain and the material properties of the macro model. The material property assignment code includes four parts: automatically reading the grain Euler angle radian value, converting the radian value to Miller index, assigning grain size effect, and converting the macro model engineering stress and strain to real stress and strain. The formulas for converting radian value to Miller index, grain size effect, and engineering stress and strain to real stress and strain are:

[0111]

[0112] τ i =τ 0 +Kd -0.5 (i=1,2,3…)

[0113] σ t =σ e (1+ε e )

[0114] ε t =ln(1+ε e )

[0115] Where θ is the nutation angle, ψ is the precession angle, and φ is the rotation angle. u, v, w are the crystal direction indices. h, k, l are the crystal plane indices. i is the number of the grain, τ 0 is the initial yield strength of the grain, K is the influence coefficient of the reaction grain boundary on the deformation, d is the equivalent diameter of the grain, σ e is the engineering stress, σ t is the true stress, ε e is the engineering strain, ε t For true resilience.

[0116] S9: Call ABAQUS through MATLAB to execute the PYTHON file of material property assignment, assign material properties to each grain and macro model at the same time, and generate the final macro-micro multi-scale crystal plasticity finite element model INP file.

[0117] S10: MATLAB calls ABAQUS to run the final generated INP file, and calls the subroutine of the crystal plasticity finite element simulation to output the orientation information of each grain in the final generated crystal plasticity finite element model, and calls METX to draw the pole figure of each grain in the final output. At the same time, MATLAB calls the METX toolbox to draw the pole figures of the original EBSD data and the pole figures of the grain set after denoising, and compares the consistency of the pole figures finally output by the model with the initial pole figures and the pole figures after denoising, such as Figure 7 shown.

[0118] S11: Write a PYTHON file to extract the stress-strain curve of crystal plasticity finite element simulation through MATLAB, call ABAQUS to execute the PYTHON file through MATLAB, obtain the stress-strain curve of macro-micro multi-scale crystal plasticity finite element simulation and compare it with the experimental results to verify the accuracy of the model, such as Figure 8 At the same time, after assigning the fatigue crack growth boundary conditions, the multi-scale model can also output the dislocation density change after crack growth, fatigue life prediction and other results, as shown in Fig. 9 and Fig.10 shown.

[0119] In summary, MATLAB calls ABAQUS and PYTHON for joint simulation programming, and proposes a fully automatic modeling method and system for macro-micro multi-scale models based on the real microstructure of the material, which can realize the fully automatic establishment from EBSD microstructure data to macro-micro multi-scale models. The fully automatic multi-scale modeling method and system proposed by the present invention are universal and can be used to establish macro-micro multi-scale crystal plasticity finite element models of materials such as aluminum alloys, titanium alloys, magnesium alloys, and stainless steel.

[0120] like Fig.11 As shown, an embodiment of the present invention provides a fully automatic modeling system of a macro-micro multi-scale model based on a real microstructure based on the fully automatic modeling method of a macro-micro multi-scale model based on a real microstructure, and the system specifically includes:

[0121] Interface construction module 1, through MATLAB calling uifigure, uilabel and other basic controls, using SelectionChangedFcn and SizeChangedFcn to achieve responsive interaction of multi-scale modeling system, and build the user interface of macro-micro multi-scale modeling system;

[0122] Boundary condition setting module 2 is connected to interface building module 1. In the user interface, the corresponding macro model and its corresponding boundary conditions are selected, and MATLAB is used to write the py file for creating the ABAQUS initial model entity; at the same time, the creation of the equipment body, establishment of the analysis step, creation of the reference point, establishment of the reference point coupling constraint, addition of the fixed constraint, addition of the load constraint and the setting of the corresponding boundary conditions are completed;

[0123] The INP file generation module 3 is connected to the boundary condition setting module 2, and reads the data of the real microstructure obtained by EBSD through MATLAB, automatically obtains the length, width and scanning step of the EBSD experimental scanning area, and divides the area where the micro model needs to be added in the initial macro model into a surface area with the same length and width as the EBSD scanning area, and records the initial vertex coordinates of the surface area; then, the initial macro model is coarsely meshed, the micro model area is meshed according to the size of the EBSD experimental scanning step, and an INP file is generated;

[0124] The data set creation module 4 is connected with the INP file generation module 3, uses MATLAB to call the METX toolbox to import the EBSD data, and creates an ebsd data set in the MATLAB workspace;

[0125] The grain establishment module 5 is connected to the data set creation module 4, and uses methods such as grain segmentation, confidence index filtering, and small grain filtering to establish a grain set for the EBSD data, delete extremely small grains, reduce noise for the EBSD data, and improve the quality of the EBSD data;

[0126] The distribution map drawing module 6 is connected to the grain establishment module 5, extracts the EBSD data after noise reduction, draws the grain distribution map after noise reduction, and compares it with the grain distribution map of the EBSD data;

[0127] The INP file writing module 7 is connected with the distribution map drawing module 6 and the INP file generating module 3, and reads each node data and unit data of the initial model through MATLAB; according to the X, Y coordinates of the position of each grain and the pixel value occupied by each grain, a data file of the node and unit set of each grain in ABAQUS is generated; then, the generated data file is aligned with the initial vertex coordinates of the face domain recorded by the INP file generating module, and written into the INP file of the initial model, so as to generate an INP file of a multi-scale crystal plasticity finite element model containing both a microscopic grain structure model and a macroscopic model;

[0128] The property assignment module 8 converts the Euler angle of the grain into a radian value, and uses MATLAB to output a PYTHON file that assigns material properties to each grain and material properties of the macro model;

[0129] The final INP file generation module 9 is connected with the INP file writing module 7 and the attribute assignment module 8, and calls ABAQUS through MATLAB to execute the PYTHON file of material attribute assignment, assigns material attributes to each grain and macro model at the same time, and generates the final macro-micro multi-scale crystal plasticity finite element model INP file;

[0130] The pole figure drawing module 10 is connected to the final INP file generation module 9. MATLAB calls ABAQUS to run the final generated INP file, and calls the subroutine of the crystal plasticity finite element simulation to output the orientation information of each grain in the final generated crystal plasticity finite element model, and calls METX to draw the pole figure of each grain output in the end; at the same time, MATLAB calls the METX toolbox to draw the pole figures of the original EBSD data and the pole figures of the grain set after denoising, and compares the consistency of the pole figures finally output by the model with the initial pole figures and the pole figures after denoising;

[0131] The verification module 11 is connected to the pole figure drawing module 10, writes a PYTHON file for extracting the stress-strain curve of the crystal plasticity finite element simulation through MATLAB, calls ABAQUS through MATLAB to execute the PYTHON file, obtains the stress-strain curve of the macro-micro multi-scale crystal plasticity finite element simulation and compares it with the experimental results to verify the accuracy of the model.

[0132] An embodiment of the present invention provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the fully automatic modeling method of the macro-micro multi-scale model based on the real microstructure.

[0133] An embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method for fully automatic modeling of a macro-micro multi-scale model based on real microstructures.

[0134] An embodiment of the present invention provides an information data processing terminal, which is used to implement the fully automatic modeling system of the macro-micro multi-scale model based on real microstructures.

[0135] The fully automatic modeling system of macro-micro multi-scale model based on real microstructure of the present invention aims to realize multi-scale modeling of material microstructure and macro model through automated process. The system integrates MATLAB and ABAQUS, and realizes model generation, analysis and verification through the collaborative work of various modules. Its working principle is specifically divided into the following six steps:

[0136] First, the interface construction module 1 provides the user with an operation interface, using MATLAB's uifigure, uilabel and other controls, and realizing the responsive interaction of the multi-scale modeling system through the SelectionChangedFcn and SizeChangedFcn functions. The user selects the macro model through the interface and sets the corresponding boundary conditions. After the boundary conditions are set, the system generates a py file for ABAQUS through MATLAB to ensure the initial creation of the model and the correct setting of the boundary conditions. This module also includes necessary operations such as creating equipment bodies, reference points, coupling constraints, fixed constraints, and load constraints.

[0137] Next, the boundary condition setting module 2 allows the user to define the corresponding macro model and its corresponding boundary conditions in the interface. This module is integrated with ABAQUS through MATLAB, automatically generates py files related to boundary conditions, and completes the creation and analysis of the initial model entity. This module handles the initial definition of the model, including creating equipment bodies, reference points, establishing coupling constraints, adding fixed constraints and load constraints. The generated files will be used for the preliminary settings of the ABAQUS model.

[0138] Then, the INP file generation module 3 reads the EBSD experimental data through MATLAB, automatically obtains the scanning information of the real microstructure (such as the length, width and step length of the scanning area), and generates a model of the microstructure with the same size in a specific area of ​​the macro model. This module first performs coarse meshing on the macro model, then performs finer meshing according to the step size of the microstructure, and generates the INP file used by ABAQUS to establish the basis for the multi-scale model.

[0139] On this basis, the data set creation module 4 is responsible for importing EBSD data, using MATLAB to call the METX toolbox, and generating the corresponding data set in the MATLAB workspace. By processing the EBSD data, the system can create a collection of microscopic grains to ensure that the microstructure of the material can be accurately modeled. In addition, this module supports operations such as data denoising and grain segmentation to further improve data quality and modeling accuracy.

[0140] Afterwards, the grain establishment module 5 establishes a grain set for the EBSD data and deletes extremely small grains by grain segmentation, confidence index filtering, small grain filtering, etc. The core function of this module is noise reduction processing to improve the quality of data and the accuracy of modeling. By extracting and optimizing the grain set, this module ensures that the grain data contained in the microscopic model can truly reflect the microstructural characteristics of the material.

[0141] Finally, the pole figure drawing module 10 is responsible for verifying the multi-scale crystal plasticity finite element model finally generated. By calling ABAQUS through MATLAB to simulate and draw the pole figures of the grains, the system compares the original EBSD data with the denoised data. The accuracy and consistency of the system are verified by drawing and comparing the pole figures generated by the model multiple times. At the same time, the module also verifies the mechanical properties of the model by extracting the stress-strain curve and compares it with the experimental results to ensure that the final modeling results are accurate and reliable.

[0142] In summary, the present invention automatically completes the entire process from data acquisition, model generation to simulation analysis through the efficient collaboration of various modules, greatly improving the efficiency and accuracy of multi-scale modeling based on real microstructures.

[0143] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0144] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A fully automatic modeling method of macro-micro multi-scale model based on real microstructure, characterized in that: The method includes: S1: Call basic controls such as uifigure and uilabel through MATLAB, use SelectionChangedFcn and SizeChangedFcn to realize responsive interaction of multi-scale modeling system, and build the user interface of macro-micro multi-scale modeling system; S2: Select the corresponding macro model and its corresponding boundary conditions in the interface of S1, and use MATLAB to write the py file to create the ABAQUS initial model entity; at the same time, complete the creation of the equipment body, establishment of analysis steps, creation of reference points, establishment of reference point coupling constraints, addition of fixed constraints, addition of load constraints, etc. and the setting of corresponding boundary conditions; S3: Read the data of the real microstructure obtained by EBSD through MATLAB, automatically obtain the length, width and scanning step of the EBSD experimental scanning area, and divide the area where the micro model needs to be added in the initial macro model into a surface area with the same length and width as the EBSD scanning area, and record the initial vertex coordinates of the surface area; then, perform coarse mesh division on the initial macro model, mesh the micro model area according to the size of the EBSD experimental scanning step, and generate an INP file; S4: Use MATLAB to call the METX toolbox to import EBSD data and create an ebsd data set in the MATLAB workspace; S5: Use methods such as grain segmentation, confidence index filtering, and small grain filtering to establish grain sets for EBSD data, delete extremely small grains, reduce noise in EBSD data, and improve the quality of EBSD data; S6: extract the denoised EBSD data, draw a denoised grain distribution map, and compare it with the grain distribution map of the EBSD data; S7: Read each node data and unit data of the initial model through MATLAB; generate a data file of the node and unit set of each grain in ABAQUS according to the position (X, Y coordinates) of each grain and the pixel value occupied by each grain; then, align the generated data file with the initial vertex coordinates of the face domain recorded in S3, write it into the INP file of the initial model, and generate an INP file of a multi-scale crystal plasticity finite element model containing both a microscopic grain structure model and a macroscopic model; S8: Convert the grain Euler angle into radian value, and use MATLAB to output a PYTHON file that assigns material properties to each grain and material properties of the macro model; S9: Call ABAQUS through MATLAB to execute the PYTHON file of material property assignment, assign material properties to each grain and macro model at the same time, and generate the final macro-micro multi-scale crystal plasticity finite element model INP file; S10: Call ABAQUS through MATLAB to run the final generated INP file, call the subroutine of crystal plasticity finite element simulation, output the orientation information of each grain in the final generated crystal plasticity finite element model, call METX to draw the pole figure of each grain in the final output; at the same time, call the METX toolbox through MATLAB to draw the pole figures of the original EBSD data and the pole figures of the grain set after denoising, and compare the consistency of the pole figures finally output by the model with the initial pole figures and the pole figures after denoising; S11: Write a PYTHON file to extract the stress-strain curve of crystal plasticity finite element simulation through MATLAB, call ABAQUS to execute the PYTHON file through MATLAB, obtain the stress-strain curve of macro-micro multi-scale crystal plasticity finite element simulation and compare it with the experimental results to verify the accuracy of the model.

2. The fully automatic modeling method of the macro-micro multi-scale model based on real microstructure according to claim 1 is characterized in that: The initial model entities in S2 include macroscopic standard models commonly used in experimental tests, such as tensile models, crack extension models, and impact toughness models; the data set in S4 includes important information such as grain type, grain ID, grain orientation, phase sequence, and grain rotation.

3. The fully automatic modeling method of the macro-micro multi-scale model based on real microstructure as claimed in claim 1, characterized in that: The formulas for grain segmentation, confidence index filtering, and small grain filtering in S5 are: Where the grain rotation is represented by R1 and R2, and Δθ is the orientation angle difference between the two points. If Δθ>angle, the two points are considered to belong to different grains; For each measurement point i, its confidence index is CI i ; Among them, g is each grain, and size(g) is the size of each grain.

4. The fully automatic modeling method of the macro-micro multi-scale model based on real microstructure as claimed in claim 1, characterized in that: The EBSD data in S6 include data such as the grain number, equivalent diameter, grain orientation Euler angle, grain pixel coordinates, and grain set of each grain.

5. The fully automatic modeling method of the macro-micro multi-scale model based on real microstructure as claimed in claim 1, characterized in that: The macro model material property assignment code in S8 includes four parts: automatically reading the radian value of the grain Euler angle, converting the radian value into the Miller index, assigning the grain size effect, and converting the macro model engineering stress and strain into real stress and strain.

6. The fully automatic modeling method of the macro-micro multi-scale model based on real microstructure as claimed in claim 5, characterized in that: The formulas for converting the radian value into the Miller index, the grain size effect, and the engineering stress-strain into the true stress-strain are: t i =τ0+Kd -0.5 (i=1,2,3…) s t =s e (1+e e ) e t =ln(1+ε e ) Among them, θ is the nutation angle, ψ is the precession angle, φ is the rotation angle; u, v, w are the crystal direction indices; h, k, l are the crystal plane indices; i is the grain number, τ0 is the initial yield strength of the grain, K is the influence coefficient of the reaction grain boundary on the deformation, d is the equivalent diameter of the grain, σ e is the engineering stress, σ t is the true stress, ε e is the engineering strain, ε t For true resilience.

7. A fully automatic modeling system for macro-micro multi-scale models based on real microstructures based on the fully automatic modeling method for macro-micro multi-scale models based on real microstructures as claimed in claims 1-6, characterized in that: The system specifically includes: The interface construction module calls basic controls such as uifigure and uilabel through MATLAB, uses SelectionChangedFcn and SizeChangedFcn to achieve responsive interaction of the multi-scale modeling system, and builds the user interface of the macro-micro multi-scale modeling system; The boundary condition setting module is connected to the interface building module. In the user interface, the corresponding macro model and its corresponding boundary conditions are selected, and MATLAB is used to write the py file to create the ABAQUS initial model entity; at the same time, the creation of the equipment body, establishment of the analysis step, creation of the reference point, establishment of the reference point coupling constraint, addition of the fixed constraint, addition of the load constraint and the setting of the corresponding boundary conditions are completed; The INP file generation module is connected to the boundary condition setting module, and reads the real microstructure data obtained by EBSD through MATLAB, automatically obtains the length, width and scanning step of the EBSD experimental scanning area, and divides the area where the micro model needs to be added in the initial macro model into a surface area with the same length and width as the EBSD scanning area, and records the initial vertex coordinates of the surface area; then, the initial macro model is coarsely meshed, the micro model area is meshed according to the size of the EBSD experimental scanning step, and an INP file is generated; The data set creation module is connected with the INP file generation module, and uses MATLAB to call the METX toolbox to import EBSD data and create an ebsd data set in the MATLAB workspace; The grain establishment module is connected to the data set creation module. It uses methods such as grain segmentation, confidence index filtering, and small grain filtering to establish grain sets for EBSD data, delete extremely small grains, reduce noise for EBSD data, and improve the quality of EBSD data. The distribution map drawing module is connected with the grain establishment module, extracts the denoised EBSD data, draws the denoised grain distribution map, and compares it with the grain distribution map of the EBSD data; The INP file writing module is connected with the distribution map drawing module and the INP file generation module, and each node data and unit data of the initial model are read through MATLAB; according to the X, Y coordinates of the position of each grain and the pixel value occupied by each grain, a data file of the node and unit set of each grain in ABAQUS is generated; then, the generated data file is aligned with the initial vertex coordinates of the face domain recorded by the INP file generation module, and written into the INP file of the initial model, so as to generate an INP file of a multi-scale crystal plasticity finite element model containing both a microscopic grain structure model and a macroscopic model; The property assignment module converts the grain Euler angle into radian value, and uses MATLAB to output the PYTHON file that assigns the material properties of each grain and the material properties of the macro model; The final INP file generation module is connected with the INP file writing module and the attribute assignment module. It calls ABAQUS through MATLAB to execute the PYTHON file of material attribute assignment, assigns material attributes to each grain and macro model at the same time, and generates the final macro-micro multi-scale crystal plasticity finite element model INP file; The pole figure drawing module is connected to the final INP file generation module. MATLAB calls ABAQUS to run the final generated INP file, and calls the subroutine of the crystal plasticity finite element simulation to output the orientation information of each grain in the final generated crystal plasticity finite element model, and calls METX to draw the pole figure of each grain in the final output; at the same time, MATLAB calls the METX toolbox to draw the pole figures of the original EBSD data and the pole figures of the grain set after denoising, and compares the consistency of the pole figures finally output by the model with the initial pole figures and the pole figures after denoising; The verification module is connected to the pole figure drawing module, and the PYTHON file for extracting the stress-strain curve of the crystal plasticity finite element simulation is written through MATLAB. ABAQUS is called through MATLAB to execute the PYTHON file, and the stress-strain curve of the macro-micro multi-scale crystal plasticity finite element simulation is obtained and compared with the experimental results to verify the accuracy of the model.

8. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the fully automatic modeling method of the macro-micro multi-scale model based on real microstructure as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the fully automatic modeling method of a macro-micro multi-scale model based on real microstructures as described in any one of claims 1 to 6.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the fully automatic modeling system of the macro-micro multi-scale model based on real microstructure as described in claim 7.

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