Dislocation Evolution Prediction and Process Optimization Method for Spin Forming Based on Multi-Scale Modeling

Abstract

The present application discloses a method for predicting dislocation evolution and optimizing process in spin forming based on multi-scale modeling. By combining three computational methods at different scales and levels, namely macroscopic finite element simulation, mesoscopic crystal plasticity simulation, and microscopic dislocation dynamics simulation, it can provide a complete perspective from macroscopic mechanical behavior to mesoscopic scale mechanical response and dislocation density evolution, and then to the motion and interaction mechanism of individual dislocations at the microscopic scale, so as to more comprehensively understand the plastic forming process of metallic materials. It not only improves the accuracy and reliability of prediction, but also enhances the understanding depth of material deformation behavior and its influencing factors. By integrating information at different scales, key features such as macroscopic stress, strain distribution, grain deformation response, dislocation density evolution, and the motion and interaction of individual dislocations during the plastic forming process of materials can be predicted more accurately. At the same time, combined with multi-scale simulation, process parameters can be optimized to obtain better forming quality and performance.

Description

Technical Field

[0001] This application relates to the technical field of part forming manufacturing, and particularly relates to a method for predicting dislocation evolution and process optimization in spin forming based on multi-scale modeling. Background Art

[0002] In the field of metal processing, spin forming, as an important plastic forming process, is widely used in multiple industries such as aerospace, automotive, and medical devices. This process enables the blank to gradually deform under the action of the die through the relative movement between the rotating die and the blank, ultimately obtaining the desired shape and size. However, the evolution of the dislocation structure of materials during the spin forming process is a complex and unpredictable process, which is affected by various factors, including the mechanical properties of materials, spin forming process parameters, die shape, and temperature, etc.

[0003] In the existing technologies, the prediction of the evolution of the dislocation structure of materials during the spin forming process mainly relies on traditional experimental methods and empirical formulas. Although these methods can reflect the deformation behavior of materials to a certain extent, they have the following main problems:

[0004] Limited prediction accuracy: Traditional experimental methods usually can only obtain mechanical property parameters at the macro scale, such as tensile strength, yield strength, etc., and cannot directly observe the microstructural changes of materials during the spin forming process. Therefore, there is a large uncertainty in these parameters when predicting the evolution of the dislocation structure of materials, resulting in limited prediction accuracy.

[0005] High experimental cost: To obtain accurate material property parameters, a large number of experimental tests are required, which is not only time-consuming and laborious but also costly. Especially for new materials or spin-formed parts with complex shapes, the difficulty and cost of experimental tests increase significantly.

[0006] Difficult real-time monitoring: During the spin forming process, real-time monitoring of the microstructural changes of materials is an extremely challenging task. Most of the existing monitoring technologies can only provide deformation information at the macro scale and cannot directly observe the evolution process of the micro dislocation structure.

[0007] To solve the above problems, the existing technologies have proposed a prediction method based on finite element simulation. This method can simulate the stress, strain distribution, and dislocation structure evolution of materials during the spin forming process by establishing a constitutive model of the material and combining finite element analysis technology. However, most of the existing finite element simulation methods stay at the macro scale and cannot accurately reflect the stress and strain evolution of materials at the micro and meso scales. In addition, these methods often ignore complex mechanisms such as micro defects and dislocation evolution inside the material during the simulation process, resulting in a large deviation between the prediction results and the actual situation. Summary of the Invention

[0008] In an embodiment of the present application, by providing a method for predicting dislocation evolution and process optimization in spin forming based on multi-scale modeling, the problem of poor accuracy and efficiency in predicting the evolution of the material dislocation structure during the spin forming process in the prior art is solved.

[0009] To achieve the above object, the technical solution of the embodiment of the present invention is as follows:

[0010] In a first aspect, an embodiment of the present invention provides a method for predicting dislocation evolution and process optimization in spin forming based on multi-scale modeling, including: obtaining mechanical property parameters, crystallographic information, spin die parameters, and spin process parameters of a target blank; constructing a macroscopic finite element model of the spin forming process according to the mechanical property parameters, the geometric shape of the target blank, the spin die parameters, and the spin process parameters; the macroscopic finite element model is used to simulate and analyze the evolution of macroscopic stress and strain of the target blank during the spin forming process; determining a first target area from the macroscopic finite element model, constructing a crystal plasticity finite element model of the first target area based on the crystallographic information, and extracting stress, strain, or displacement information of the first target area from the macroscopic finite element model as a first boundary condition to be applied to the crystal plasticity finite element model to simulate the deformation response at the grain scale during the spin forming process and predict the evolution of stress, strain, and dislocation density at the mesoscopic grain scale; determining a second target area from the crystal plasticity finite element model, constructing a dislocation dynamics model of the second target area based on the crystallographic information, and extracting stress, strain, or displacement information of the second target area at the grain scale from the crystal plasticity finite element model as a second boundary condition to be applied to the dislocation dynamics model to simulate and analyze the evolution of microstructure, stress, strain, and dislocations at the microscopic dislocation scale during the spin forming process; combining the analysis results of the macroscopic finite element model, the crystal plasticity finite element model, and the dislocation dynamics model to comprehensively predict the evolution law of the microstructure during the spin forming process, and optimizing the process parameters of the spin forming based on the prediction results.

[0011] In some possible implementation manners, the mechanical property parameters are obtained by performing a uniaxial tensile experiment on the target blank, and the mechanical property parameters include, but are not limited to, elastic modulus, yield strength, and tensile strength; the crystallographic information is obtained by performing EBSD characterization on the target blank, and the crystallographic information includes, but is not limited to, grain size, grain morphology, and crystal orientation information.

[0012] In some possible implementation manners, the first target area is the area prone to cracking during the spin forming process of the target blank.

[0013] In some possible implementation manners, the second target area is the local stress concentration area during the microscopic dislocation evolution in the first target area.

[0014] In some possible implementation manners, the macro finite element model, the crystal plasticity finite element model, and the dislocation dynamics model are integrated for multi-scale simulation through data sharing and boundary condition transfer, so as to comprehensively predict the stress and strain evolution during the spinning forming process.

[0015] In some possible implementation manners, the process parameters for optimizing the spinning forming include but are not limited to adjusting the core mold rotation speed, the feed ratio, the roller fillet radius, and the roller trajectory to reduce or eliminate the easily cracked areas.

[0016] In some possible implementation manners, the method further includes: establishing a bidirectional cross-scale feedback channel, wherein the crystal plasticity constitutive equation in the crystal plasticity finite element model is corrected backward through the microscopic dislocation density data, and the activation state of the mesoscopic slip system is fed back to the macro finite element model to update the hardening criterion.

[0017] In some possible implementation manners, after the process parameters for the spinning forming are optimized, the method further includes: performing spinning forming simulation with the optimized process parameters for the spinning forming; evaluating the forming quality of the spun formed part obtained by the spinning forming simulation to determine whether the spun formed part meets the preset mechanical property requirements; when the spun formed part meets the preset forming quality requirements, outputting the optimized process parameters for the spinning forming; and when the spun formed part does not meet the preset forming quality requirements, iteratively optimizing the process parameters for the spinning forming.

[0018] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0019] In the embodiments of the present invention, the macroscopic stress and strain evolution of the target blank during the spinning forming process are simulated and analyzed through a macroscopic finite element model; a first target area is determined from the macroscopic finite element model, and a crystal plasticity finite element model of the first target area is constructed based on crystallographic information to simulate the deformation response at the grain scale during the spinning forming process and predict the stress, strain, and dislocation density evolution at the mesoscopic grain scale; a second target area is determined from the crystal plasticity finite element model, and a dislocation dynamics model of the second target area is constructed based on crystallographic information to simulate and analyze the stress, strain, and dislocation evolution at the microscopic dislocation scale during the spinning forming process; combining the analysis results of the macroscopic finite element model, the crystal plasticity finite element model, and the dislocation dynamics model, the evolution law of the microstructure during the spinning forming process is comprehensively predicted, and the process parameters of the spinning forming are optimized based on the prediction results. In this way, by combining the three computational methods at different scales and levels of macroscopic finite element simulation, mesoscopic crystal plasticity simulation, and microscopic dislocation dynamics simulation, a complete perspective can be provided from macroscopic mechanical behavior to mesoscopic scale mechanical response and dislocation density evolution, and then to the motion and interaction mechanism of individual dislocations at the microscopic scale, so as to more comprehensively understand the plastic forming process of metal materials. It not only improves the accuracy and reliability of prediction, but also enhances the understanding depth of the material deformation behavior and its influencing factors. By integrating information at different scales, the key characteristics such as macroscopic stress, strain distribution, grain deformation response, dislocation density evolution, and the motion and interaction of individual dislocations during the plastic forming process of the material can be predicted more accurately. At the same time, by combining multi-scale simulation, the process parameters can be optimized to obtain better forming quality and performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention, the drawings required for use in the embodiments of the present invention will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0021] Figure 1 It is a schematic flowchart of an embodiment of a method for predicting dislocation evolution and process optimization of spinning forming based on multi-scale modeling provided for the embodiments of the present invention;

[0022] Figure 2 It is a schematic structural diagram of a macroscopic finite element model constructed in the embodiments of the present invention;

[0023] Figure 3 It is a schematic structural diagram for predicting and analyzing the dislocation structure evolution of spinning forming by realizing different scale modeling in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0025] In the relevant descriptions of this embodiment, terms such as "including", "containing", "having", etc. are all open terms, and are generally preferably understood as including but not limited to; the term "at least one" is generally preferably understood as one or more, where "multiple" means two or more; the term "at least one (item)" or its similar expression refers to any combination of these items, including any combination of single item (item) or plural items (items). For example, "at least one (item) of a, b or c", or, "at least one (item) of a, b and c" can all represent: a, b, c, a - b (that is, a and b), a - c, b - c, or a - b - c, where a, b, c can be single or multiple respectively; the symbol "A / B" is used to describe the selection relationship of associated objects, and generally represents an "or" relationship before and after.

[0026] In the following description of this embodiment, the terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms of "a" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0027] Those skilled in the art should understand that in the following description of the embodiments of the present application, the sequence numbers do not mean the order of execution, and some or all of the steps can be executed in parallel or successively. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0028] Those skilled in the art should understand that the numerical range in the embodiments of the present application should be understood as specifically disclosing each intermediate value between the upper and lower limits of the range. The intermediate value within any stated value or stated range, as well as each smaller range between any other stated value or intermediate value within the range, is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded from the range.

[0029] Unless otherwise specified, the technical / scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this application pertains. Although this application only describes preferred methods and materials, any methods and materials similar or equivalent to those described herein can also be used in the implementation or testing of this application. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials related to the documents. In case of conflict with any incorporated document, the content of this specification shall prevail.

[0030] In order to illustrate the technical solution of the present invention, specific embodiments will be described below.

[0031] In the field of metal processing, spin forming, as an important plastic forming process, is widely used in multiple industries such as aerospace, automotive, and medical devices. This process enables the blank to gradually deform under the action of the die through the relative movement between the rotating die and the blank, ultimately obtaining the desired shape and size. However, the evolution of the dislocation structure of the material during the spin forming process is a complex and unpredictable process, which is affected by various factors, including the mechanical properties of the material, spin forming process parameters, die shape, and temperature, etc.

[0032] In the existing technology, the prediction of the evolution of the dislocation structure of the material during the spin forming process mainly relies on traditional experimental methods and empirical formulas. Although these methods can reflect the deformation behavior of the material to a certain extent, the following main problems exist:

[0033] Limited prediction accuracy: Traditional experimental methods usually can only obtain mechanical property parameters at the macroscopic scale, such as tensile strength, yield strength, etc., and cannot directly observe the changes in the microstructure of the material during the spin forming process. Therefore, there is a large uncertainty in using these parameters to predict the evolution of the dislocation structure of the material, resulting in limited prediction accuracy.

[0034] High experimental cost: In order to obtain accurate material property parameters, a large number of experimental tests are required, which not only takes time and effort but also incurs high costs. Especially for new materials or spin-formed parts with complex shapes, the difficulty and cost of experimental tests increase significantly.

[0035] Difficult real-time monitoring: During the spin forming process, real-time monitoring of the changes in the microstructure of the material is an extremely challenging task. Most of the existing monitoring technologies can only provide deformation information at the macroscopic scale and cannot directly observe the evolution process of the microstructure.

[0036] To solve the above problems, some researchers have proposed a prediction method based on finite element simulation. By establishing a constitutive model of the material and combining finite element analysis techniques, this method can simulate the stress, strain distribution, and dislocation structure evolution of the material during the spinning process. However, most of the existing finite element simulation methods stay at the macroscopic scale and cannot accurately reflect the stress and strain evolution of the material at the micro and meso scales. In addition, these methods often ignore complex mechanisms such as micro defects and dislocation evolution inside the material during the simulation process, resulting in a large deviation between the prediction results and the actual situation.

[0037] An embodiment of the present invention provides a prediction and process optimization method for dislocation evolution in spinning forming based on multi-scale modeling, which solves the problems of poor accuracy and efficiency in predicting the dislocation structure evolution of materials during the spinning forming process in the prior art.

[0038] Figure 1 It is a schematic flowchart of an embodiment of a prediction and process optimization method for dislocation evolution in spinning forming based on multi-scale modeling provided for an embodiment of the present invention. Refer to Figure 1 As shown, the above-mentioned prediction method for dislocation structure evolution and process parameter optimization in spinning forming based on multi-scale modeling may include:

[0039] S101, obtaining the mechanical property parameters, crystallographic information, spinning die parameters, and spinning process parameters of the target blank;

[0040] In some embodiments, the mechanical property parameters are obtained by conducting a uniaxial tensile test on the target blank, and the mechanical property parameters include but are not limited to elastic modulus, yield strength, tensile strength, elongation, and hardness;

[0041] It should be noted that the uniaxial tensile test is a commonly used method for testing the mechanical properties of materials, which is used to evaluate the mechanical behavior of materials under unidirectional tensile loads. In this experiment, a constant tensile speed or force can be applied to gradually elongate the specimen in the axial direction until it breaks, so as to measure and record the stress-strain relationship throughout the process.

[0042] Specifically, the process of conducting mechanical property tests through uniaxial tensile tests may include:

[0043] According to standards or experimental requirements, specimens of a certain size and shape are intercepted from the material to be tested, and necessary processing and surface treatment are carried out; the specimens are installed in the fixtures of the material testing machine to ensure close contact between the specimens and the fixtures to avoid slippage or fracture during the tensile process; according to experimental requirements, parameters such as tensile speed and data acquisition frequency are set; the material testing machine is started to apply tensile loads and experimental data are recorded; during the experiment, the deformation of the specimens is observed, and the fracture position and morphology are recorded. After the experiment, the experimental data are analyzed to calculate the mechanical property parameters.

[0044] In the embodiments of the present invention, through a uniaxial tensile test, the tensile stress state that the blank may be subjected to during the spinning process can be simulated, so as to measure the mechanical property parameters as described above.

[0045] Among them, the elastic modulus is used to reflect the proportional relationship between stress and strain during the elastic deformation stage of the material, and is an important index for evaluating the stiffness of the material. The yield strength represents the stress value when the material begins to undergo plastic deformation, and is a key parameter for judging the ability of the material to resist plastic deformation. The tensile strength is used to reflect the maximum stress value that the material can withstand during the tensile process, reflecting the ultimate bearing capacity of the material. The elongation is used to represent the measure of the plastic deformation experienced by the material before fracture, reflecting the plasticity and ductility of the material. Hardness can reflect the ability of the material to resist local pressure deformation, and is an important index for evaluating the wear resistance and scratch resistance of the material.

[0046] In some embodiments, the crystallographic information is obtained by characterizing the target blank with Electron Back Scatter Diffraction (EBSD). The crystallographic information includes, but is not limited to, grain size, grain morphology, and crystal orientation information.

[0047] It should be noted that EBSD characterization is an advanced material microstructure analysis technology. It is based on the scanning electron microscope (SEM) platform. By analyzing the diffraction pattern of the backscattered electrons generated after the interaction between the incident electron beam and the sample, it provides important information about the crystal structure, grain orientation, grain boundary distribution, phase, and crystal defects of the sample. When the incident electron beam enters the sample, some electrons escape from the sample surface due to large scattering angles. These escaping electrons are called backscattered electrons. Among these escaping electrons, the electrons that satisfy the Bragg diffraction condition will undergo diffraction, forming a series of Kikuchi patterns. EBSD uses the diffraction patterns of these electrons. Through comparative analysis, crystallographic parameters such as interplanar spacing and interplanar angle can be obtained, and then the crystal structure and orientation of the sample can be determined.

[0048] Among them, the grain size can indicate the size distribution of grains, directly affecting the mechanical properties and processing properties of the material. Fine grains usually can improve the strength and toughness of the material. The grain morphology, including the shape and arrangement of grains, also affects the properties of the material, such as anisotropy, toughness, etc. The crystal orientation information can be used to predict its deformation behavior under a specific stress state, such as slip, twinning, etc.

[0049] In some embodiments, the parameters of the spinning die may include the specific material, hardness, geometry (such as the curvature radius of the core die, etc.) of the die, and the spinning process parameters may include the external process condition parameters during the spinning process such as temperature control and lubrication conditions, and may also include the actual spinning process parameters of the die such as the spinning trajectory and feed ratio. Through reasonable die design and high-precision process parameters, the forming quality and production efficiency of the spun parts can be significantly improved.

[0050] S102. Construct a macroscopic finite element model of the spinning forming process according to the mechanical property parameters, the geometry of the target blank, the parameters of the spinning die, and the spinning process parameters; the macroscopic finite element model is used to simulate and analyze the evolution of the macroscopic stress and strain of the target blank during the spinning forming process.

[0051] It should be noted that macroscopic finite element analysis is a numerical analysis method. It divides the continuum into a series of discrete elements (i.e., finite elements) and establishes equilibrium equations on these elements to solve the mechanical behavior of the entire structure. Macroscopic finite element analysis mainly focuses on the mechanical behavior of the overall structure and can simulate the deformation process of metal materials. During the metal plastic forming process, macroscopic finite element analysis can predict parameters such as the overall stress, strain, and displacement of metal materials, and simulate phenomena such as large-scale deformation and stress concentration regions.

[0052] In the above step S102, after obtaining the above mechanical property parameters, the parameters of the spinning die, and the die process parameters, the three-dimensional geometry of the target blank can be accurately described and imported into the finite element software through CAD software or other digital means to construct a finite element model corresponding to the actual blank. After inputting the mechanical property parameters, geometry, and process parameters, the construction of the macroscopic finite element model can specifically include:

[0053] Geometric modeling: Construct a corresponding geometric model in the finite element software according to the geometry of the target blank, the parameters of the spinning die, and the spinning process parameters.

[0054] Mesh generation: Divide the geometric model into a series of discrete elements (i.e., finite elements). The density and shape of the mesh generation can be determined according to the simulation accuracy and calculation efficiency.

[0055] Material model selection: Select a suitable material model for simulation according to the mechanical property parameters of the target material. For example, commonly used material models include elastic-plastic models, rigid-plastic models, etc.

[0056] Boundary condition setting: Set the boundary conditions of the model according to the actual situation of the spinning forming, such as fixed constraints, load application, etc. These boundary conditions will directly affect the accuracy of the simulation results.

[0057] Contact and friction treatment: During the spinning process, contact and friction between the die and the blank are inevitable. Therefore, when constructing a finite element model, it is also necessary to reasonably handle contact and friction problems to ensure the reliability of the simulation results.

[0058] Exemplarily, Figure 2 The structural schematic diagram of a macroscopic finite element model constructed for the embodiments of the present invention. Refer to Figure 2 As shown, this macroscopic finite element model shows the key components and their interactions during the spinning process. The model includes a spinning wheel 21, a core die 22, and a target blank 23. The spinning wheel 21 is responsible for providing the spinning force. By driving the target blank 23 to rotate through the core die 22 and contacting the spinning wheel 21, plastic deformation of the blank occurs. The core die 22 is located at the center of the target blank 23, and the target blank 23 is fixed on the core die 22 by the tailstock. During the spinning process, the spinning wheel 21 moves along a predetermined path, repeatedly contacting the target blank 23 and applying the spinning force to ensure that the target blank 23 can be uniformly and stably subjected to the spinning force of the spinning wheel 21 until the target blank 23 is shaped into the expected shape. The target blank 23 is the raw material to be processed, which can be metal, plastic, or other plastic materials. During the spinning process, the target blank 23 is subjected to the spinning force of the spinning wheel 21 and the supporting action of the core die 22.

[0059] By controlling parameters such as the rotation speed of the core die 22, the feed rate of the spinning wheel 21, and the contact pressure between the spinning wheel 21 and the target blank 23, the deformation process of the target blank 23 can be precisely controlled. At the same time, the macroscopic finite element model can also consider factors such as the mechanical properties of the material, temperature distribution, and friction conditions to more accurately predict the results of the spinning process.

[0060] It can be understood that the macroscopic finite element model is a simplified mathematical model. Based on continuum mechanics and the finite element method, it simulates physical phenomena during the spinning process through numerical calculations. Therefore, when constructing and using the macroscopic finite element model, it is necessary to fully consider factors such as the nonlinear behavior of the material, contact and friction conditions, and boundary conditions to ensure the accuracy and reliability of the model.

[0061] After constructing the macroscopic finite element model, by running finite element software, the entire spinning process of the target blank can be simulated, and the overall stress and strain evolution during the spinning process can be obtained synchronously during the simulation. In addition, the simulation results can be processed, such as extracting stress and strain contour maps, displacement curves, etc., to more intuitively understand the deformation process of the metal material.

[0062] S103. Determine the first target region from the macroscopic finite element model, construct a crystal plasticity finite element model of the first target region based on crystallographic information, and extract the stress, strain, or displacement information of the first target region from the macroscopic finite element model as the first boundary condition to be applied to the crystal plasticity finite element model, simulate the deformation response at the grain scale during the spinning forming process, and predict the evolution of stress, strain, and dislocation density at the mesoscopic grain scale.

[0063] It can be understood that step S103 is a step for further refined analysis based on the macroscopic finite element model constructed in step S102. It focuses on delving from the macroscopic scale to the grain scale to reveal the evolution law of the microstructure of the material during the spinning forming process.

[0064] In step S102, a macroscopic finite element model of the spinning forming process was constructed, and the stress and strain evolution at the macroscopic scale of the target blank during the spinning process was simulated. However, although the macroscopic model can capture the mechanical behavior of the overall structure, it cannot reveal the distribution laws of stress, strain, dislocations, etc. inside the material grains. Therefore, in step S103, first determine one or more regions of interest from the macroscopic model, that is, the first target region. The first target region can be a key position where large stress, strain, or displacement gradients occur, or the material deformation behavior is complex, etc.

[0065] Exemplarily, the determination of the first target region can focus on the specific behaviors and potential problems of the target blank during the spinning forming process. For example, the first target region can be the easy-to-crack region of the target blank during the spinning forming process. These easy-to-crack regions are usually parts that are prone to damage or failure due to factors such as complex deformation, high stress concentration, large temperature change gradient, or strong friction between the die and the blank during the spinning process.

[0066] After determining the first target region, a crystal plasticity finite element model of this region can be constructed based on crystallographic information. The crystal plasticity finite element model is a numerical method that can simulate the mechanical behavior of materials at the mesoscopic scale. It takes into account crystallographic information such as the crystal structure, grain orientation, and slip system of the material, as well as the microscopic mechanisms (such as slip, twinning, etc.) during the plastic deformation process of the material.

[0067] When constructing the crystal plasticity finite element model, it is necessary to first obtain the crystallographic information of the first target region, such as grain shape, size, orientation distribution, etc. The crystallographic information can be obtained through the above experimental method EBSD characterization. Then, based on this information, a crystal plasticity finite element model corresponding to the first target region can be constructed in the finite element software.

[0068] After constructing the crystal plasticity finite element model, the stress, strain, or displacement information of the first target region can be extracted from the macroscopic finite element model and used as the first boundary condition to be applied to the crystal plasticity finite element model. Through this step, the macroscale and microscale can be connected to ensure that the crystal plasticity finite element model can inherit the mechanical behavior of the macroscopic model during the simulation process.

[0069] It should be noted that when extracting the first boundary condition, attention should be paid to ensuring the accuracy and consistency of the information. Due to the differences in spatial scales between the macroscopic model and the crystal plasticity model (the macroscopic model usually contains more elements and nodes), interpolation or mapping methods can be used to accurately transfer the boundary conditions of the macroscopic model to the crystal plasticity model.

[0070] In addition, it should also be noted that during the plastic deformation process, stress, strain, and displacement are interrelated. According to the basic principles of material mechanics, stress is the internal force per unit area, strain is the change in the shape or size of an object, and displacement is the change in the position of a point on an object relative to a reference point. In plastic deformation, these physical quantities together describe the deformation behavior of the material. Therefore, when constructing the model, choosing one of these physical quantities as the input parameter can indirectly reflect the changes in other physical quantities. And in practical applications, to simplify the model and improve the calculation efficiency, usually one of the most representative physical quantities is chosen as the input parameter. For example, in the crystal plasticity model, the stress state has an important influence on the deformation and rotation of grains, so stress can be chosen as the input parameter. And in the subsequent dislocation dynamics model, the evolution of dislocations is closely related to the strain state, so strain can also be chosen as the input parameter.

[0071] After applying the first boundary condition, the crystal plasticity finite element model can be run to simulate the evolution of the dislocation structure at the grain scale during the spinning forming process. During the simulation, the model takes into account the crystallographic information and micro-mechanisms of the material, and calculates the stress, strain evolution, and possible micro-mechanical responses and dislocation density evolution of each grain during the deformation process. By simulating the evolution of the dislocation density at the microscale grain level, the stress and strain evolution at the grain scale can be further predicted. These prediction results can be used to understand the micro-mechanical behavior of the material, evaluate the forming performance of the material, and optimize the spinning process parameters. For example, by comparing the stress and strain evolution at the grain scale under different process parameters, the optimal combination of process parameters can be found to improve the forming quality and production efficiency of the material.

[0072] S104. Determine the second target region from the crystal plasticity finite element model. Based on crystallographic information, construct a dislocation dynamics model for the second target region, and extract the stress, strain, or displacement information of the second target region at the grain scale from the crystal plasticity finite element model as the second boundary condition to be applied to the dislocation dynamics model, and simulate and analyze the evolution of the microstructure, stress, strain, and dislocations at the micro-dislocation scale during the spin forming process;

[0073] Specifically, in step S104, it is first necessary to determine the second target region from the previously constructed crystal plasticity finite element model. This step is carried out based on an in-depth analysis of the first target region (such as the vulnerable region). Specifically, the second target region is selected as the region in the first target region where the dislocation evolution is the most active or the most critical. These regions usually undergo severe plastic deformation, which easily leads to significant dislocation multiplication, recombination, and interaction, and has an important impact on the macroscopic properties and microstructure of the material. That is, the first target region is at the mesoscopic grain scale, and the second target region is at the microscopic single crystal scale.

[0074] In some embodiments, the second target region may be the region in the first target region that has undergone the largest plastic deformation. For example, the second target region is the local stress concentration region during the micro-dislocation evolution in the first target region. These regions usually have the highest dislocation density and the lowest toughness, so they are the key to optimizing the spin forming process and material design. By constructing a dislocation dynamics model and simulating the dislocation evolution process in these regions, the macroscopic properties and microstructure changes of the material can be predicted more accurately, thus providing strong support for process optimization and material design.

[0075] After determining the second target region, a dislocation dynamics model can be constructed based on the crystallographic information of this region. The dislocation dynamics model is a mathematical model that can simulate and analyze the dislocation behavior in materials. It takes into account processes such as dislocation multiplication, movement, annihilation, and their interactions. When constructing the model, factors such as the material properties, deformation history, and external environmental conditions of the second target region need to be considered.

[0076] In some embodiments, in order to apply the simulation results of the crystal plasticity finite element model to the dislocation dynamics model, it is necessary to extract the stress, strain, or displacement information of the second target region at the grain scale from the crystal plasticity model. These information are applied as the second boundary condition to the dislocation dynamics model to ensure that the model can accurately capture the micro-dislocation mechanical behavior of the second target region during the spin forming process.

[0077] After applying the second boundary condition, the dislocation dynamics model can be run to simulate and analyze the evolution of the dislocation-scale microstructure, stress, and strain during the spin forming process. Through this step, the dynamic behavior of dislocations in the material can be deeply understood, as well as how they affect the microstructure and macroscopic properties of the material. From the simulation results, processes such as dislocation multiplication, recombination, and interaction can be observed, as well as their effects on properties such as the hardness, toughness, and plasticity of the material.

[0078] Exemplarily, simulating the dynamic evolution process of dislocations during the plastic deformation of the second target region includes:

[0079] Determine the initial dislocation configuration of the second target region and apply a preset resolved shear stress to the dislocation dynamics model ;

[0080] When the resolved shear stress exceeds the preset time of the dislocation source strength, an edge dislocation with a Burgers vector of nucleates from the dislocation source and continues to slide along the corresponding slip plane. The dislocation nucleation time is:

[0081] ;

[0082] where is a constant related to the dislocation drag coefficient, is the length of the dislocation source. When the attraction between dislocations is in equilibrium with the applied resolved shear stress nuc the diameter of the initial dislocation loop is:

[0083] ;

[0084] where G is the shear modulus, v is the Poisson's ratio, nuc is the dislocation source strength, and its value can be obtained from specific experimental data. The sliding speed of the dislocation between pinning obstacles is:

[0085] ;

[0086] where, is the Boltzmann constant, is the representative distance of cooperative dislocation slip, is the atomic vacancy volume, is the temperature and is the vacancy diffusion coefficient.

[0087] The following uses a specific embodiment to specifically illustrate the processes of the above steps S102 to S104.

[0088] Exemplarily, Figure 3 It is a schematic structural diagram of the prediction and analysis of the evolution of the dislocation structure in spin forming for realizing different-scale modeling in the embodiment of the present invention. Refer to Figure 3 As shown, after constructing the macroscopic finite element model, the overall stress and strain evolution of the target blank during the spin forming process can be simulated and analyzed through the macroscopic finite element model. Then, a first target area can be determined in the macroscopic finite element model, a crystal plasticity finite element model can be constructed, and the stress, strain or displacement information of the first target area can be applied to the crystal plasticity finite element model as the first boundary condition to simulate the evolution of the dislocation structure at the mesoscopic grain scale during the spin forming process and predict the stress and strain evolution at the mesoscopic grain scale. Then, a second target area is determined from the crystal plasticity finite element model, and the dislocation dynamics model is used to extract the stress, strain or displacement information of the second target area at the grain scale as the second boundary condition and apply it to the dislocation dynamics model to simulate and analyze the stress, strain and dislocation evolution at the microscopic scale of a single dislocation during the spin forming process. Thus, the correlation of multi-scale information is realized.

[0089] In some embodiments, after obtaining the prediction results of the stress, strain, and dislocation density evolution at the mesoscopic grain scale and the stress, strain, and dislocation evolution at the microscopic scale through the above steps S102 to S104, the above method may further include:

[0090] Establish a two-way cross-scale feedback channel, wherein the crystal plasticity constitutive equation in the crystal plasticity finite element model is corrected backward through the microscopic dislocation density data, and the hardening criterion is updated by feeding back the mesoscopic slip system activation state to the macroscopic finite element model.

[0091] In some embodiments, for the feedback from the microscopic to the mesoscopic, a feedback channel from the microscopic to the mesoscopic can be established. The function of this channel is to use the dislocation density data calculated at the microscopic scale to correct the crystal plasticity constitutive equation in the crystal plasticity finite element model backward. The dislocation density is an important parameter describing the change of the internal microscopic structure of the material, which directly affects the mechanical properties and plastic deformation behavior of the material. The microscopic dislocation density data obtained through precise measurement or calculation can more accurately reflect the deformation mechanism of the material at the microscopic level, thereby correcting and optimizing the relevant parameters in the crystal plasticity constitutive equation. This correction helps to improve the accuracy and reliability of the model in predicting the mesoscopic behavior of the material.

[0092] Exemplarily, the feedback from the microscopic to the mesoscopic may include:

[0093] (1) Data fusion and transformation: Extract discrete dislocation density distribution data from the dislocation dynamics model, and use the spatial averaging method to generate a continuous dislocation density field. Eliminate the local fluctuation noise at the microscale through Gaussian filtering, and retain the macroscopic distribution characteristics matching the grain size.

[0094] (2) Crystal plasticity constitutive modification: Dynamically embed the average dislocation density value into the hardening law of the crystal plasticity model, including:

[0095] Strengthening effect: Automatically increase the hardening coefficient of the slip system when the dislocation density increases;

[0096] Softening effect: Trigger the dynamic recovery mechanism when the dislocation density exceeds the critical value.

[0097] Perform parameter update once every fixed number of mesoscopic calculation steps to ensure the timeliness response of dislocation evolution.

[0098] In some embodiments, for the mesoscopic to macroscopic feedback, a feedback channel from mesoscopic to macroscopic can also be established. The role of this channel is to feedback the activation state information of the slip system at the mesoscopic scale to the macroscopic finite element model for updating the hardening criterion. The activation state of the slip system reflects the plastic deformation mode of the material at the mesoscopic scale, and this information is crucial for understanding the macroscopic mechanical properties of the material. By incorporating the activation state of the mesoscopic slip system into the consideration of the macroscopic model, the hardening behavior of the material at different deformation stages can be more comprehensively described, thereby further improving the accuracy of the macroscopic finite element model in predicting the overall properties of the material.

[0099] Exemplarily, the mesoscopic to macroscopic feedback can include:

[0100] (1) Cross-scale feature extraction: Statistically calculate the activation ratio of the slip system for all grains, and calculate the anisotropy tensor of the slip direction distribution. Extract the orientation distribution function (ODF) of the dominant slip system to quantify the texture evolution characteristics.

[0101] (2) Macroscopic constitutive model upgrade: Convert the mesoscopic slip characteristics into macroscopic anisotropic hardening parameters, including:

[0102] Modify the equivalent plastic flow equation based on the activation intensity of the slip system;

[0103] Dynamically adjust the shape of the anisotropic yield surface according to the texture evolution.

[0104] Automatically trigger the parameter update module when the local strain increment exceeds the threshold (e.g., 1%).

[0105] In the embodiments of the present invention, by introducing a two-way cross-scale feedback mechanism, not only can the information interaction and synergy between different scales of the model be enhanced, but also the accuracy and practicality of the prediction results can be improved. Through continuous iteration and optimization, the true behavior of the material can be gradually approximated, providing more accurate predictions and guidance for the field of materials science and engineering.

[0106] S105, combining the analysis results of the macroscopic finite element model, the crystal plasticity finite element model, and the dislocation dynamics model, comprehensively predict the stress and strain evolution laws during the spinning forming process, and optimize the process parameters of the spinning forming based on the prediction results.

[0107] Specifically, first, integrate the analysis results of the macroscopic finite element model, the mesoscopic crystal plasticity finite element model, and the microscopic dislocation dynamics model. These models respectively provide information at different scales from macroscopic mechanical behavior to mesoscopic scale dislocation density evolution, and then to microscopic scale single dislocation motion and interaction mechanisms, including overall deformation, stress and strain distribution at the grain scale, and microstructure changes at the dislocation scale. Through a multi-scale analysis method, correlate these different scale information with each other to reveal the evolution law of the microstructure during the spinning forming process. This includes analyzing the change of grain orientation, generation and annihilation of dislocations, migration of grain boundaries, and possible phase transformations.

[0108] In some embodiments, the integration of multi-scale simulations is achieved through data sharing and boundary condition transfer between the macroscopic finite element model, the crystal plasticity finite element model, and the dislocation dynamics model, realizing a comprehensive prediction of the stress and strain evolution during the spinning forming process.

[0109] Data sharing is the core of multi-scale simulation integration. Under this framework, key data such as stress, strain, temperature, and microscopic structure parameters can be exchanged in real time between the macroscopic finite element model, the crystal plasticity finite element model, and the dislocation dynamics model. These data not only provide necessary input conditions for each model, but also make the interaction and feedback between models possible. Through data sharing, we can more accurately capture the macroscopic deformation behavior and microscopic structure changes of the material during the spinning forming process, thus achieving a comprehensive understanding of the entire forming process.

[0110] The accurate transfer of boundary conditions is another key to realizing the integration of multi-scale simulations. During the spin forming process, the macroscopic finite element model is mainly responsible for simulating the overall deformation behavior of the material, while the crystal plasticity finite element model and the dislocation dynamics model focus on the crystal structure and dislocation evolution of the material respectively. To ensure the collaborative work among these models, the macroscopic deformation field calculated by the macroscopic finite element model can be used as the boundary conditions for the crystal plasticity finite element model and the dislocation dynamics model. At the same time, the microstructural changes calculated by the crystal plasticity finite element model and the dislocation dynamics model will also be fed back to the macroscopic finite element model in a certain way to update the input parameters of the macroscopic model. This transfer of boundary conditions ensures the consistency and accuracy of multi-scale simulations.

[0111] The integration of multi-scale simulations achieved through data sharing and boundary condition transfer can comprehensively predict the evolution of dislocation structures during the spin forming process. This includes predicting the grain morphology, dislocation density, phase transformation, and possible defects of the material. These prediction results not only help to better understand the physical mechanisms during the spin forming process, but also provide valuable guidance for process optimization and material design. For example, the process parameters such as spin speed and feed rate can be adjusted according to the prediction results to optimize the microstructure and properties of the material. At the same time, we can also use these prediction results for material selection and optimization of heat treatment processes to further improve the quality and reliability of spin formed parts.

[0112] In the embodiments of the present invention, based on the integrated data and multi-scale analysis results, a comprehensive model capable of predicting the evolution of dislocation structures during the spin forming process can be constructed. The comprehensive model takes into account the influence of various process parameters (such as spin speed, feed rate, die shape, temperature, etc.) on the evolution of dislocation structures.

[0113] For example, in step S105 of the spin forming process, by comprehensively analyzing the results of the macroscopic finite element model, the crystal plasticity finite element model, and the dislocation dynamics model, the stress and strain evolution laws during the spin forming process can be deeply predicted, and based on this, the possible errors during the spin forming process can be determined, which may affect the forming quality, and further analyze the root causes of these errors.

[0114] Specifically, first, the model results at the macro, meso, and micro scales of crystal plasticity and micro dislocation dynamics are integrated to form a comprehensive understanding of the dislocation structure evolution during the spin forming process. This includes grain rotation and refinement, dislocation multiplication and annihilation, and possible accompanying phase transformations. Then, in the predicted laws of dislocation structure evolution, deviations from the ideal state or expected goals are identified, and these deviations are potential error sources. For example, these errors can be dimensional deviations of the formed part, shape distortion, reduction in material properties (such as decreased strength and toughness), or abnormalities in the meso-structure, such as uneven grain distribution, or abnormalities in the micro-structure, such as excessive dislocation density.

[0115] After determining the possible errors through prediction, the causes of the errors can be further analyzed.

[0116] Exemplarily, the causes of the errors can be process parameter effects. In this case, the effects of process parameters (such as spin speed, feed rate, die shape, temperature control, etc.) on dislocation structure evolution and error generation can be analyzed. For example, too high a spin speed may cause excessive heating of the material, leading to grain growth and a decrease in dislocation density, affecting the mechanical properties of the material. Or the cause of the error may also be the influence of material properties. In this case, the inherent properties of the material, such as chemical composition, micro-structure, heat treatment state, etc., on the dislocation structure evolution and errors during the spin forming process can be considered. Or the error may also be caused by die design. The design parameters of the die, such as the hardness of the die, lubrication conditions, contact area between the die and the blank, etc., unreasonable die design may cause uneven stress on the blank during spin forming, resulting in shape distortion.

[0117] After determining the possible errors in spin forming and the causes of the errors, the spin forming process parameters can be further optimized. Specifically, the following steps can be included:

[0118] First, using the constructed comprehensive prediction model, a sensitivity analysis of the process parameters is carried out. This includes evaluating the influence degree of different process parameters on dislocation structure evolution, material properties, and forming quality. Then, based on the results of the sensitivity analysis, determine which process parameters have a significant impact on forming quality and optimize these parameters accordingly. The optimization goal is to improve the forming performance of the material, reduce the generation of defects, and improve the final quality of the product. Finally, in order to verify the effectiveness of the optimized process parameters, experimental verification is required. This includes carrying out spin forming experiments under the optimized process parameters and observing and analyzing the evolution of the microstructure during the forming process and the properties and quality of the final product.

[0119] In some embodiments, optimizing the process parameters of spin forming includes, but is not limited to, adjusting the mandrel rotation speed, feed ratio, roller fillet radius, and roller trajectory to reduce or eliminate the crack-prone area.

[0120] In some embodiments, after optimizing the process parameters of spin forming, the above method may further include:

[0121] Performing spin forming simulation with the optimized spin forming process parameters;

[0122] Evaluating the forming quality of the spin formed part in the spin forming simulation to determine whether the spin formed part meets the preset mechanical property requirements;

[0123] When the spin formed part meets the preset forming quality requirements, outputting the optimized spin forming process parameters;

[0124] When the spin formed part does not meet the preset forming quality requirements, iteratively optimizing the spin forming process parameters.

[0125] It can be understood that after initially optimizing the process parameters of spin forming, these optimized parameters can be used for spin forming simulation. Through simulation, key information such as the flow behavior of the material during spin forming, temperature distribution, stress-strain state, and the evolution of the microstructure can be observed. After the simulation is completed, it is necessary to evaluate the forming quality of the spin formed part obtained from the spin forming simulation. This includes, but is not limited to, checking the dimensional accuracy, surface finish, wall thickness uniformity, and defects such as cracks or folds. The evaluation process is usually based on the material microstructure information and macroscopic deformation data obtained in the simulation, combined with the constitutive relationship of the material and the forming quality database.

[0126] According to the results of the forming quality evaluation, determine whether the spin formed part meets the preset forming quality requirements. These requirements may be specified by the customer or determined according to industry standards or internal specifications. If the forming quality evaluation results of the spin formed part show that it meets or exceeds the preset requirements, it is considered that the current optimization is effective, and the optimized spin forming process parameters are prepared for output. These parameters will be used as a guide for subsequent production practice to ensure that the spin formed parts produced have stable forming quality.

[0127] If the forming quality evaluation results of the spin formed part show that it does not meet the preset requirements, then the spin forming process parameters need to be iteratively optimized. This step includes readjusting the process parameters (such as spin speed, feed rate, die shape, die temperature, and lubrication conditions), and performing spin forming simulation and forming quality evaluation again. This process can be iterated multiple times until the best combination of process parameters that meets all mechanical property requirements is found.

[0128] It is understandable that macroscopic finite element analysis mainly focuses on the mechanical behavior of the overall structure and can simulate the deformation process of metal materials. During the metal plastic forming process, macroscopic finite element analysis can predict parameters such as the overall stress, strain, and displacement of metal materials, as well as simulate phenomena such as deformation and stress concentration areas at a larger scale. It is applicable to analyzing mechanical properties and deformation characteristics with lower precision, but it cannot directly describe the changes in the microstructure of metal materials. Mesoscopic crystal plasticity simulation focuses on studying the internal deformation behavior of metal materials. By considering characteristics at the mesoscopic level such as grain morphology and crystal orientation, it can better understand the crystal deformation mechanism and the evolution of dislocation density in metal materials, and predict the deformation behavior of materials more accurately. Microscopic dislocation dynamics simulation aims to study the generation, movement, and interaction of individual dislocations in metal materials, and explain the details of dislocation evolution in deformation behavior and plastic processing.

[0129] In summary, macroscopic finite element, crystal plasticity, and dislocation dynamics are computational methods at different scales and levels, applicable to studying different aspects of the metal material plastic forming process. Macroscopic finite element analysis focuses on macroscopic mechanical behavior, crystal plasticity simulation focuses on deformation behavior at the grain scale, and dislocation dynamics simulation focuses on the movement and interaction of dislocations. In the present exemplary embodiment, by combining these three methods, the plastic forming process of metal materials can be more comprehensively understood, and then the process parameters can be optimized to obtain better forming quality and performance.

[0130] The various embodiments in this specification are described in a progressive manner. For the same or similar parts between the various embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments.

[0131] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting the present application; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the present application.

Claims

1. A method for predicting dislocation evolution and optimizing process of spinning based on multi-scale modeling, characterized in that: include: Obtain the mechanical properties parameters, crystallographic information, spinning die parameters and spinning process parameters of the target blank; According to the mechanical property parameters, the geometric shape of the target blank, the spinning die parameters and the spinning process parameters, a macroscopic finite element model of the spinning forming process is constructed; the macroscopic finite element model is used to simulate and analyze the evolution of macroscopic stress and strain of the target blank during the spinning forming process; Determine a first target region from the macroscopic finite element model, construct a crystal plasticity finite element model of the first target region based on the crystallographic information, extract stress, strain or displacement information of the first target region from the macroscopic finite element model as a first boundary condition and apply it to the crystal plasticity finite element model, simulate the deformation response of the grain scale during the spinning process, and predict the evolution of stress, strain and dislocation density at the microscopic grain scale; Determine a second target region from the crystal plasticity finite element model, construct a dislocation dynamics model of the second target region based on the crystallographic information, extract stress, strain or displacement information of the second target region at the grain scale from the crystal plasticity finite element model as a second boundary condition and apply it to the dislocation dynamics model to simulate and analyze the evolution of stress, strain and dislocation at the microscopic scale during the spinning process; By combining the analysis results of the macroscopic finite element model, the crystal plasticity finite element model and the dislocation dynamics model, the stress and strain evolution laws during the spinning process are comprehensively predicted, and the process parameters of the spinning process are optimized based on the prediction results.

2. The method according to claim 1, characterized in that The mechanical property parameters are obtained by performing a uniaxial tensile test on the target blank, and the mechanical property parameters include but are not limited to elastic modulus, yield strength, and tensile strength; The crystallographic information is obtained by performing EBSD characterization on the target blank, and the crystallographic information includes but is not limited to grain size, grain morphology and crystal orientation information.

3. The method according to claim 2, characterized in that The first target area is an area of ​​the target blank that is prone to cracking during the spin forming process.

4. The method according to claim 3, characterized in that The second target region is a local stress concentration region during the evolution of micro dislocations in the first target region.

5. The method according to claim 4, characterized in that The macroscopic finite element model, the crystal plasticity finite element model and the dislocation dynamics model are integrated with multi-scale simulation through data sharing and boundary condition transfer, thereby achieving a comprehensive prediction of the evolution of stress and strain during the spinning process.

6. The method according to claim 5, characterized in that The process parameters for optimizing the spinning forming include, but are not limited to, adjusting the core mold rotation speed, feed ratio, roller corner radius, and roller trajectory to reduce or eliminate the crack-prone area.

7. The method according to claim 6, characterized in that The method also includes: establishing a bidirectional cross-scale feedback channel, wherein the crystal plasticity constitutive equation in the crystal plasticity finite element model is reversely corrected through microscopic dislocation density data, and the activation state of the mesoscopic slip system is fed back to the macroscopic finite element model to update the hardening criterion.

8. The method according to any one of claims 1 to 7, characterized in that: After optimizing the process parameters of the spin forming, the method further includes: Perform spin forming simulation using optimized spin forming process parameters; Performing a forming quality assessment on the spin-formed part of the spin-formed simulation to determine whether the spin-formed part meets a preset forming quality requirement; When the spin-formed part meets the preset forming quality requirements, outputting optimized spin-forming process parameters; When the spin-formed part does not meet the preset forming quality requirements, the spin-forming process parameters are iteratively optimized.

Images