Spinning forming dislocation evolution prediction and process optimization method based on multi-scale modeling

Abstract

The invention discloses a spinning forming dislocation evolution prediction and process optimization method based on multi-scale modeling, which combines three calculation methods with different scales and levels, namely macroscopic finite element simulation, microcosmic crystal plasticity simulation and microcosmic dislocation dynamics simulation. A complete perspective from macroscopic mechanical behaviors to microscale mechanical response and dislocation density evolution and then to microscale single dislocation motion and interaction mechanism can be provided, so that the plastic forming process of the metal material can be understood more comprehensively. Not only are the prediction precision and reliability improved, but also the understanding depth of the material deformation behavior and the influence factors thereof is enhanced. By integrating information of different scales, key characteristics such as macroscopic stress, strain distribution, grain deformation response, dislocation density evolution and movement and interaction of a single dislocation of the material in the plastic forming process can be predicted more accurately. Meanwhile, in combination with multi-scale simulation, process parameters can be optimized to obtain better forming quality and performance.

Description

Technical Field

[0001] The present application relates to the technical field of parts forming and manufacturing, and in particular to a method for predicting dislocation evolution and optimizing process of spinning forming based on multi-scale modeling. Background Art

[0002] In the field of metal processing, spin forming is an important plastic forming process widely used in aerospace, automobile, medical equipment and other industries. This process uses the relative movement between the rotating die and the blank to gradually deform the blank under the action of the die, and finally obtain 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 many factors, including the mechanical properties of the material, the spinning process parameters, the die shape and temperature, etc.

[0003] In the existing technology, the prediction of the evolution of material dislocation structure during spin forming 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 can usually only obtain macroscopic mechanical properties parameters, such as tensile strength, yield strength, etc., but cannot directly observe the microstructural changes of the material during the spinning process. Therefore, these parameters have great uncertainty in predicting the evolution of the material dislocation structure, resulting in limited prediction accuracy.

[0005] High experimental cost: In order to obtain accurate material performance parameters, a large number of experimental tests are required, which is not only time-consuming and labor-intensive, but also costly. Especially for new materials or spun parts with complex shapes, the difficulty and cost of experimental testing are significantly increased.

[0006] Real-time monitoring is difficult: During the spinning process, real-time monitoring of the microstructural changes of the material is a very challenging task. Most existing monitoring technologies can only provide macroscopic deformation information, but cannot directly observe the evolution of the microscopic dislocation structure.

[0007] In order to solve the above problems, the prior art has proposed a prediction method based on finite element simulation. This method can simulate the stress, strain distribution and dislocation structure evolution of the material during the spinning process by establishing a constitutive model of the material and combining it with finite element analysis technology. However, most of the existing finite element simulation methods remain at the macroscopic scale and cannot accurately reflect the stress and strain evolution of the material at the microscopic and mesoscopic scales. In addition, these methods often ignore complex mechanisms such as microscopic 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] The embodiments of the present application solve the problem of poor accuracy and efficiency in predicting the evolution of material dislocation structure during spin forming in the prior art by providing a method for predicting the dislocation evolution and optimizing the process of spin forming based on multi-scale modeling.

[0009] In order to achieve the above object, the technical solution of the embodiment of the present invention is:

[0010] In the first aspect, an embodiment of the present invention provides a method for predicting dislocation evolution and optimizing process of spinning based on multi-scale modeling, including: obtaining mechanical property parameters, crystallographic information, spinning mold parameters and spinning process parameters of a target blank; constructing a macroscopic finite element model of the spinning process according to the mechanical property parameters, the geometric shape of the target blank, the spinning mold parameters and the spinning 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 spinning 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 It is applied to the crystal plasticity finite element model to simulate the deformation response at the grain scale during the spinning process and predict the evolution of stress, strain and dislocation density at the micro-grain scale. The second target area is determined from the crystal plasticity finite element model, and the dislocation dynamics model of the second target area is constructed based on the crystallographic information. The stress, strain or displacement information of the second target area at the grain scale is extracted from the crystal plasticity finite element model as the second boundary condition and applied to the dislocation dynamics model to simulate and analyze the evolution of the organization, stress, strain and dislocation at the micro-dislocation scale during the spinning process. Combined with the analysis results of the macro finite element model, the crystal plasticity finite element model and the dislocation dynamics model, the evolution law of the organization during the spinning process is comprehensively predicted, and the process parameters of the spinning process are optimized based on the prediction results.

[0011] In some possible implementations, mechanical property parameters are obtained by performing uniaxial tensile tests on the target blank, and the mechanical property parameters include but are not limited to elastic modulus, yield strength, and tensile strength; 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 implementations, the first target area is an area of ​​the target blank that is prone to cracking during the spin forming process.

[0013] In some possible implementations, the second target region is a local stress concentration region during the evolution of micro dislocations in the first target region.

[0014] In some possible implementations, multi-scale simulation integration is achieved through data sharing and boundary condition transfer among the macroscopic finite element model, crystal plasticity finite element model and dislocation dynamics model, thus realizing a comprehensive prediction of the stress and strain evolution during the spinning process.

[0015] In some possible implementations, optimizing the process parameters of the spin forming process includes, but is 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.

[0016] In some possible implementations, 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 reversely corrected by microscopic dislocation density data, and the hardening criterion is updated by feeding back the activation state of the mesoscopic slip system to the macroscopic finite element model.

[0017] In some possible implementations, after optimizing the process parameters of the spinning, the method further includes: performing a spinning simulation using the optimized spinning process parameters; performing a forming quality assessment on the spinning parts of the spinning simulation to determine whether the spinning parts meet the preset mechanical property requirements; when the spinning parts meet the preset forming quality requirements, outputting the optimized spinning process parameters; when the spinning parts do not meet the preset forming quality requirements, iteratively optimizing the spinning process parameters.

[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 an embodiment of the present invention, a macroscopic finite element model is used to simulate and analyze the evolution of macroscopic stress and strain of a target blank during a spinning process; a first target region is determined from the macroscopic finite element model, and a crystal plasticity finite element model of the first target region is constructed based on crystallographic information to 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; a second target region is determined from the crystal plasticity finite element model, and a dislocation dynamics model of the second target region is constructed based on crystallographic information to simulate and analyze the evolution of stress, strain, and dislocation at the microscopic dislocation scale during the spinning process; the analysis results of the macroscopic finite element model, the crystal plasticity finite element model, and the dislocation dynamics model are combined to comprehensively predict the evolution law of the organization during the spinning process, and the process parameters of the spinning process are optimized based on the prediction results. In this way, the combination of three computational methods of different scales and levels, namely macroscopic finite element simulation, microscopic crystal plasticity simulation and microscopic dislocation dynamics simulation, can provide a complete perspective from macroscopic mechanical behavior to microscopic mechanical response and dislocation density evolution, and then to the motion and interaction mechanism of single dislocations at the microscopic scale, so as to have a more comprehensive understanding of the plastic forming process of metal materials. It not only improves the accuracy and reliability of prediction, but also enhances the depth of understanding of the deformation behavior of materials 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 motion and interaction of single dislocations in the plastic forming process can be more accurately predicted. At the same time, combined with multi-scale simulation, 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 accompanying drawings required for use in the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0021] Figure 1 A schematic flow chart of an embodiment of a method for predicting dislocation evolution and optimizing process of spinning forming based on multi-scale modeling provided for the implementation of the present invention;

[0022] Figure 2 A schematic diagram of the structure of a macroscopic finite element model constructed for an embodiment of the present invention;

[0023] Figure 3 It is a structural schematic diagram of the prediction and analysis of the dislocation structure evolution in spinning forming by implementing modeling at different scales in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] In the relevant description of this embodiment, the terms "including, containing, having" and the like are open terms and are generally understood to include but not be limited to; the term "at least one" is generally understood to mean one or more, where "plurality" refers to two or more; the term "at least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items, for example, "at least one of a, b or c", or "at least one of a, b and c", can all represent: a, b, c, ab (i.e., a and b), ac, bc, or abc, where a, b, c can be single or multiple, respectively; the symbol "A / B" is used to describe the selection relationship of associated objects, generally indicating an "or" relationship before and after.

[0026] In the following description of the present 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 "a" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

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

[0028] Those skilled in the art will appreciate that the numerical ranges in the embodiments of the present application are to be construed as also specifically disclosing each intermediate value between the upper and lower limits of the scope. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the range is also included in the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded in the scope.

[0029] Unless otherwise specified, the technical / scientific terms used herein have the same meanings as those generally understood by those skilled in the art to which this application belongs. Although this application only describes preferred methods and materials, any methods and materials similar or equivalent to these may 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 methods and / or materials related to the documents. In the event of a 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 are provided below for illustration.

[0031] In the field of metal processing, spin forming is an important plastic forming process widely used in aerospace, automobile, medical equipment and other industries. This process uses the relative movement between the rotating die and the blank to gradually deform the blank under the action of the die, and finally obtain 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 many factors, including the mechanical properties of the material, the spinning process parameters, the die shape and temperature, etc.

[0032] In the existing technology, the prediction of the evolution of material dislocation structure during spin forming 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:

[0033] Limited prediction accuracy: Traditional experimental methods can usually only obtain macroscopic mechanical properties parameters, such as tensile strength, yield strength, etc., but cannot directly observe the microstructural changes of the material during the spinning process. Therefore, these parameters have great uncertainty in predicting the evolution of the material dislocation structure, resulting in limited prediction accuracy.

[0034] High experimental cost: In order to obtain accurate material performance parameters, a large number of experimental tests are required, which is not only time-consuming and labor-intensive, but also costly. Especially for new materials or spun parts with complex shapes, the difficulty and cost of experimental testing are significantly increased.

[0035] Real-time monitoring is difficult: During the spinning process, real-time monitoring of the microstructure changes of the material is a very challenging task. Most existing monitoring technologies can only provide macroscopic deformation information, but cannot directly observe the evolution of the microstructure.

[0036] In order to solve the above problems, some researchers have proposed a prediction method based on finite element simulation. This method can simulate the stress, strain distribution and dislocation structure evolution of the material during the spinning process by establishing a constitutive model of the material and combining it with finite element analysis technology. However, most of the existing finite element simulation methods remain at the macroscopic scale and cannot accurately reflect the stress and strain evolution of the material at the microscopic and mesoscopic scales. In addition, these methods often ignore complex mechanisms such as microscopic 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] The embodiment of the present invention solves the problem of poor accuracy and efficiency in predicting the evolution of material dislocation structure during spin forming in the prior art by providing a method for predicting the dislocation evolution during spin forming and optimizing the process based on multi-scale modeling.

[0038] Figure 1 A schematic diagram of an embodiment of a method for predicting dislocation evolution and optimizing process of spinning forming based on multi-scale modeling provided for the implementation of the present invention, see Figure 1 As shown, the above-mentioned method for predicting the evolution of dislocation structure and optimizing process parameters of spinning forming based on multi-scale modeling may include:

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

[0040] In some embodiments, 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, tensile strength, elongation and hardness;

[0041] It should be noted that the uniaxial tensile test is a commonly used material mechanical properties test method used to evaluate the mechanical behavior of materials under uniaxial tensile loads. This experiment can gradually stretch the specimen in the axial direction until it breaks by applying a constant tensile speed or force, thereby measuring and recording the stress-strain relationship throughout the process.

[0042] Specifically, the process of testing mechanical properties through uniaxial tensile testing may include:

[0043] According to the standard or experimental requirements, cut a sample of a certain size and shape from the material to be tested, and perform necessary processing and surface treatment; install the sample in the fixture of the material testing machine to ensure close contact between the sample and the fixture to avoid slippage or fracture during the stretching process; set the stretching speed, data acquisition frequency and other parameters according to the experimental requirements; start the material testing machine, start applying the tensile load, and record the experimental data; during the experiment, observe the deformation of the sample, record the fracture location and morphology. After the experiment, analyze the experimental data and calculate the mechanical performance parameters.

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

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

[0046] In some embodiments, the crystallographic information is obtained by performing electron backscatter diffraction (EBSD) characterization on the target blank, and 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 a scanning electron microscope (SEM) platform and provides important information about the sample's crystal structure, grain orientation, grain boundary distribution, physical phase, and crystal defects by analyzing the diffraction pattern of backscattered electrons generated after the incident electron beam interacts with the sample. When the incident electron beam enters the sample, some electrons escape from the sample surface due to the large scattering angle. These escaped electrons are called backscattered electrons. Among these escaped electrons, electrons that meet the Bragg diffraction conditions will diffract to form a series of Kikuchi patterns. EBSD uses the diffraction patterns of these electrons, and through comparative analysis, it can derive crystallographic parameters such as interplanar spacing and interplanar angle, and then determine the crystal structure and orientation of the sample.

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

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

[0050] S102, constructing a macroscopic finite element model of the spinning process according to the mechanical property parameters, the geometric shape of the target blank, the spinning die parameters and the spinning 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 spinning process;

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

[0052] In the above step S102, after obtaining the above mechanical performance parameters, spinning mold parameters and mold process parameters, the three-dimensional geometric shape 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 performance parameters, geometric shape and process parameters, constructing the macro finite element model can specifically include:

[0053] Geometric modeling: According to the geometric shape of the target blank, the spinning die parameters and the spinning process parameters, the corresponding geometric model is constructed in the finite element software.

[0054] Meshing: Divide the geometric model into a series of discrete units (i.e. finite elements). The density and shape of the mesh can be determined based on the accuracy and computational efficiency of the simulation.

[0055] Material model selection: According to the mechanical properties of the target material, select the appropriate material model for simulation. For example, commonly used material models include elastic-plastic model, rigid-plastic model, etc.

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

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

[0058] For example, Figure 2 A schematic diagram of the structure of a macroscopic finite element model constructed in accordance with an embodiment of the present invention. Figure 2 As shown, the macroscopic finite element model shows the key components and their interactions in the spinning process. The model includes a spinning wheel 21, a core mold 22, and a target blank 23. The spinning wheel 21 is responsible for providing the spinning force, and drives the target blank 23 to rotate and contact the spinning wheel 21 through the core mold 22, so that the blank undergoes plastic deformation. The core mold 22 is located at the center of the target blank 23, and the target blank 23 is fixed on the core mold 22 through the tail top. During the spinning process, the spinning wheel 21 moves along a predetermined path, repeatedly contacts the target blank 23 and applies 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 desired shape. The target blank 23 is the raw material to be processed, which can be metal, plastic or other plastic material. During the spinning process, the target blank 23 is subjected to the spinning force of the spinning wheel 21 and the support of the core mold 22.

[0059] By controlling the rotation speed of the core mold 22, the feed amount of the roller 21, and the contact pressure between the roller 21 and the target blank 23, the deformation process of the target blank 23 can be accurately 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 forming.

[0060] It can be understood that the macroscopic finite element model is a simplified mathematical model, which is based on continuum mechanics and the finite element method to simulate the physical phenomena in the spinning process through numerical calculation. 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, the entire spinning process of the target blank can be simulated by running the finite element software, and the overall stress and strain evolution during the spinning process can be obtained simultaneously during the simulation. In addition, the simulation results can also be processed, such as extracting stress, strain cloud diagrams, displacement curves, etc., in order to more intuitively understand the deformation process of metal materials.

[0062] S103, determining a first target region from the macroscopic finite element model, constructing a crystal plasticity finite element model of the first target region based on crystallographic information, and extracting stress, strain or displacement information of the first target region from the macroscopic finite element model, applying the information to the crystal plasticity finite element model as a first boundary condition, simulating the deformation response of the grain scale during the spinning process, and predicting the evolution of stress, strain and dislocation density at the microscopic grain scale;

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

[0064] In step S102, a macroscopic finite element model of the spinning process is constructed, and the macroscopic-scale stress and strain evolution of the target blank during the spinning process is simulated. However, although the macroscopic model can capture the mechanical behavior of the overall structure, it cannot reveal the distribution law of stress, strain, dislocation, etc. inside the material grains. Therefore, in step S103, one or more regions of interest, i.e., the first target region, are first determined from the macroscopic model. The first target region may be a key location where stress, strain, or displacement gradients are large, or material deformation behavior is complex.

[0065] Exemplarily, the determination of the first target area can focus on specific behaviors and potential problems of the target blank during the spinning process. For example, the first target area can be a crack-prone area of ​​the target blank during the spinning process. These crack-prone areas are usually areas that are prone to damage or failure due to factors such as complex deformation, high stress concentration, large temperature gradient, or strong friction between the mold and the blank experienced by the material during the spinning process.

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

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

[0068] After the crystal plasticity finite element model is constructed, the stress, strain or displacement information of the first target area can be extracted from the macroscopic finite element model and applied to the crystal plasticity finite element model as the first boundary condition. This step can connect the macroscopic and microscopic scales 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, it is necessary to ensure the accuracy and consistency of the information. Since the macro model and the crystal plasticity model differ in spatial scale (the macro model usually contains more units and nodes), an interpolation or mapping method can be used to accurately transfer the boundary conditions of the macro model to the crystal plasticity model.

[0070] In addition, it should be noted that in the process of plastic deformation, 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 the object relative to a reference point. In plastic deformation, these physical quantities jointly describe the deformation behavior of the material. Therefore, when constructing a model, selecting one of the physical quantities as an input parameter can indirectly reflect the changes in other physical quantities. And in practical applications, in order to simplify the model and improve computational efficiency, one of the most representative physical quantities is usually selected as an input parameter. For example, in the crystal plasticity model, the stress state has an important influence on the deformation and rotation of the grains, so stress can be selected as an input parameter. In the subsequent dislocation dynamics model, the evolution of dislocations is closely related to the strain state, so strain can also be selected as an input parameter.

[0071] After applying the first boundary condition, the crystal plasticity finite element model can be run to simulate the dislocation structure evolution at the grain scale during the spinning process. During the simulation, the model takes into account the crystallographic information and microscopic mechanism of the material, and calculates the stress, strain evolution, possible mesomechanical response and dislocation density evolution of each grain during the deformation process. By simulating the dislocation density evolution at the mesograin scale, the stress and strain evolution at the grain scale can be further predicted. These prediction results can be used to understand the mesomechanical 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, determining a second target region from the crystal plasticity finite element model, constructing a dislocation dynamics model of the second target region based on crystallographic information, extracting stress, strain or displacement information of the second target region at a grain scale from the crystal plasticity finite element model as a second boundary condition and applying it to the dislocation dynamics model, simulating and analyzing the microstructure, stress, strain and dislocation evolution at a microscopic dislocation scale during the spinning process;

[0073] Specifically, in step S104, it is first necessary to determine the second target area from the previously constructed crystal plasticity finite element model. This step is performed based on an in-depth analysis of the first target area (e.g., the vulnerable area). Specifically, the second target area is selected as the area in the first target area where dislocation evolution is most active or most critical. These areas usually undergo severe plastic deformation, which can easily lead to significant dislocation proliferation, recombination, and interaction, which have an important impact on the macroscopic properties and microstructure of the material. That is, the first target area is the mesoscopic grain scale, and the second target area is the microscopic single crystal scale.

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

[0075] After the second target region is determined, a dislocation dynamics model can be constructed based on the crystallographic information of the region. The dislocation dynamics model is a mathematical model that can simulate and analyze the behavior of dislocations in materials. It takes into account processes such as the proliferation, movement, annihilation of dislocations, 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. This information is applied to the dislocation dynamics model as the second boundary condition to ensure that the model can accurately capture the microscopic dislocation mechanical behavior of the second target region during the spinning process.

[0077] After applying the second boundary condition, the dislocation dynamics model can be run to simulate and analyze the evolution of dislocation-scale organization, stress, and strain during the spinning process. Through this step, we can deeply understand the dynamic behavior of dislocations in the material and how they affect the microstructure and macroscopic properties of the material. Through the simulation results, we can observe the processes of dislocation proliferation, reorganization, and interaction, as well as their effects on the hardness, toughness, plasticity and other properties of the material.

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

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

[0080] When the shear stress After the dislocation source intensity exceeds the preset time, the Burgers vector is The edge dislocation nucleates from the dislocation source and continues to slide along the corresponding slip plane. The dislocation nucleation time for:

[0081] ;

[0082] in is a constant related to the dislocation drag coefficient, is the length of the dislocation source. When the attraction between dislocations is equal to the applied shear stress nuc At equilibrium, the diameter of the initial dislocation ring is for:

[0083] ;

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

[0085] ;

[0086] in, is the Boltzmann constant, is the representative distance of cooperative dislocation slip, is the atomic vacancy volume, It is the temperature The vacancy equilibrium concentration under is the vacancy diffusion coefficient.

[0087] The process of steps S102 to S104 is described in detail below using a specific embodiment.

[0088] For example, Figure 3 This is a schematic diagram of the structure of the prediction and analysis of the dislocation structure evolution of the spinning forming by implementing different scale modeling in the embodiment of the present invention. Figure 3 As shown, after constructing the macroscopic finite element model, the macroscopic finite element model can be used to simulate and analyze the evolution of the overall stress and strain of the target blank during the spinning process. Afterwards, the first target area can be determined in the macroscopic finite element model, and a crystal plasticity finite element model can be constructed. 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 dislocation structure evolution at the microscopic grain scale during the spinning process, and predict the stress and strain evolution at the microscopic grain scale. Afterwards, the second target area is determined from the crystal plasticity finite element model, and the dislocation dynamics model extracts the stress, strain or displacement information of the second target area at the grain scale from the crystal plasticity finite element model as the second boundary condition and applies it to the dislocation dynamics model to simulate and analyze the stress, strain and dislocation evolution of a single dislocation scale at the microscopic scale during the spinning process. Thereby, the association of multi-scale information is achieved.

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

[0090] A bidirectional cross-scale feedback channel is established, in which the crystal plasticity constitutive equation in the crystal plasticity finite element model is back-corrected by the microscopic dislocation density data, and the hardening criterion is updated by feeding back the activation state of the mesoscopic slip system to the macroscopic finite element model.

[0091] In some embodiments, for feedback from micro to meso, a feedback channel from micro to meso can be established. The function of this channel is to use the dislocation density data calculated at the micro scale to reversely correct the crystal plasticity constitutive equation in the crystal plasticity finite element model. Dislocation density is an important parameter to describe the changes in the internal microstructure of the material, which directly affects the mechanical properties and plastic deformation behavior of the material. The microscopic dislocation density data obtained by 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] Exemplary feedback from micro to fine level may include:

[0093] (1) Data fusion and conversion: The discrete dislocation density distribution data is extracted from the dislocation dynamics model, and the continuous dislocation density field is generated by spatial averaging. The local fluctuation noise at the microscopic scale is eliminated by Gaussian filtering, and the macroscopic distribution characteristics matching the grain size are retained.

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

[0095] Strengthening effect: When the dislocation density increases, the slip system hardening coefficient will automatically increase;

[0096] Softening effect: Dynamic recovery mechanism is triggered when the dislocation density exceeds a critical value.

[0097] The parameter update is performed every fixed number of mesoscopic calculation steps to ensure the timely response of dislocation evolution.

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

[0099] Exemplary feedback from micro to macro may include:

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

[0101] (2) Upgrading the macroscopic constitutive model: Converting the microscopic slip characteristics into macroscopic anisotropic hardening parameters, including:

[0102] Modification of the equivalent plastic flow equation based on the activation strength of the slip system;

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

[0104] The parameter update module is automatically triggered when the local strain increment exceeds a threshold (e.g. 1%).

[0105] In the embodiment of the present invention, by introducing a bidirectional 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 real behavior of the material can be gradually approached, 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 predicting the stress and strain evolution law during the spinning process, and optimizing the spinning process parameters based on the prediction results.

[0107] Specifically, first, the analysis results of the macroscopic finite element model, the microscopic crystal plasticity finite element model and the microscopic dislocation dynamics model are integrated. These models provide information on different scales, from macroscopic mechanical behavior to the evolution of dislocation density at the microscopic scale, and then to the motion and interaction mechanism of individual dislocations at the microscopic scale, including overall deformation, stress and strain distribution at the grain scale, and organizational changes at the dislocation scale. Through multi-scale analysis methods, these different scales of information are correlated to reveal the evolution of the organization during the spinning process. This includes analyzing the orientation changes of grains, the generation and annihilation of dislocations, the migration of grain boundaries, and possible phase changes.

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

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

[0110] Accurate transmission of boundary conditions is another key to achieving multi-scale simulation integration. 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. In order to ensure the synergy between these models, the macroscopic deformation field calculated by the macroscopic finite element model can be used as the boundary condition of 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 transmission of boundary conditions ensures the consistency and accuracy of multi-scale simulations.

[0111] Multi-scale simulation integration achieved through data sharing and boundary condition transfer can comprehensively predict the evolution of dislocation structure 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 mechanism of the spin forming process, but also provide valuable guidance for process optimization and material design. For example, process parameters such as spinning 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 to select materials and optimize the heat treatment process to further improve the quality and reliability of spin-formed parts.

[0112] In the embodiment of the present invention, based on the integrated data and multi-scale analysis results, a comprehensive model capable of predicting the evolution of dislocation structure during the spinning process can be constructed. The comprehensive model considers the influence of various process parameters (such as spinning speed, feed rate, mold shape, temperature, etc.) on the evolution of dislocation structure.

[0113] For example, in the S105 step 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 the possible errors in the spin forming process can be determined accordingly, which may affect the forming quality, and the root causes of these errors can be further analyzed.

[0114] Specifically, first, the model results of the three scales of macroscopic, microscopic crystal plasticity and microscopic dislocation dynamics are integrated to form a comprehensive understanding of the evolution of dislocation structure during spin forming. This includes the rotation and refinement of grains, the proliferation and annihilation of dislocations, and possible accompanying phase changes. Then, in the predicted law of dislocation structure evolution, deviations that do not conform to the ideal state or expected goals are identified. These deviations are potential sources of error. For example, these errors can be dimensional deviations, shape distortions, reductions in material properties (such as decreased strength and toughness) of the formed part, or abnormalities in the mesostructure, such as uneven grain distribution, or abnormalities in the microstructure, 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 cause of the error may be the influence of process parameters. At this time, the influence of process parameters (such as spinning speed, feed rate, mold shape, temperature control, etc.) on the evolution of dislocation structure and the generation of errors can be analyzed. For example, too high a spinning speed may cause the material to be overheated, which in turn causes 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. At this time, the inherent properties of the material, such as chemical composition, microstructure, heat treatment state, etc., can be considered to affect the evolution of dislocation structure and errors during spinning. Or the error may also be caused by mold design, such as mold design parameters such as mold hardness, lubrication conditions, and the contact area between the mold and the blank. Unreasonable mold design may cause uneven force on the blank during spinning, resulting in shape distortion.

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

[0118] First, a sensitivity analysis of the process parameters is performed using the constructed comprehensive prediction model. This includes evaluating the degree of influence of different process parameters on the evolution of dislocation structure, material properties and forming quality. Then, based on the results of the sensitivity analysis, it is determined which process parameters have a significant impact on the forming quality, and these parameters are optimized accordingly. The goal of the optimization 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 performing spin forming experiments under the optimized process parameters, and observing and analyzing the evolution of the organization during the forming process and the performance and quality of the final product.

[0119] In some embodiments, optimizing the process parameters of the spin forming process includes but is 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.

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

[0121] Perform spin forming simulation using optimized spin forming process parameters;

[0122] Perform forming quality evaluation on the spin-formed parts in the spin-forming simulation to determine whether the spin-formed parts meet the preset mechanical property requirements;

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

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

[0125] It can be understood that after the initial optimization of the process parameters of the spinning, these optimized parameters can be used to simulate the spinning. Through simulation, key information such as the flow behavior, temperature distribution, stress-strain state, and evolution of the microstructure of the material during the spinning process can be observed. After the simulation is completed, it is necessary to evaluate the forming quality of the spinning parts obtained by the spinning simulation. This includes but is not limited to inspections of dimensional accuracy, surface finish, wall thickness uniformity, and the presence or absence of 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 material constitutive relationship and the forming quality database.

[0126] Based on the results of the forming quality assessment, 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 assessment results of the spin-formed part show that it meets or exceeds the preset requirements, the current optimization is considered to be effective and the optimized spin-forming process parameters are prepared to be output. These parameters will serve as a guide for subsequent production practices to ensure that the produced spin-formed parts 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, it is necessary to iteratively optimize the spinning process parameters. This step includes re-adjusting the process parameters (such as spinning speed, feed rate, mold shape, mold temperature and lubrication conditions, etc.), and re-simulating the spinning and evaluating the forming quality. This process can be iterated multiple times until the optimal combination of process parameters that meets all mechanical property requirements is found.

[0128] Understandably, macroscopic finite element analysis focuses on the mechanical behavior of the overall structure and can simulate the deformation process of metal materials. In the process of metal plastic forming, macroscopic finite element analysis can predict the overall stress, strain, displacement and other parameters of the metal material, as well as simulate larger-scale deformation, stress concentration areas and other phenomena. It is suitable for analyzing mechanical properties and deformation characteristics with lower precision, but cannot directly describe the microstructural changes of metal materials. Mesoscopic crystal plasticity simulation focuses on studying the internal deformation behavior of metal materials. By considering the characteristics of the mesoscopic level such as grain morphology and crystal orientation, the crystal deformation mechanism and dislocation density evolution phenomenon of metal materials can be better understood, and the deformation behavior of materials can be predicted more accurately. The microscopic dislocation dynamics simulation aims to study the generation, movement and interaction of single dislocations in metal materials, and explain the details of deformation behavior and dislocation evolution in plastic processing.

[0129] In summary, macroscopic finite element, crystal plasticity and dislocation dynamics are computational methods of different scales and levels, which are suitable for studying different aspects of the plastic forming process of metal materials. Macroscopic finite element analysis focuses on macroscopic mechanical behavior, crystal plasticity simulation focuses on the deformation behavior at the grain scale, and dislocation dynamics simulation focuses on the movement and interaction of dislocations. In this embodiment, the combination of these three methods can provide a more comprehensive understanding of the plastic forming process of metal materials, thereby optimizing process parameters to obtain better forming quality and performance.

[0130] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced 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 to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to 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.

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