Complex structure wheel forging rolling whole process digital design and performance prediction method

Abstract

The invention discloses a digital design and performance prediction method for the whole forging and rolling process of a wheel with a complex structure, and belongs to the technical field of metal plastic forming and digital simulation. In order to overcome the defects of an existing method, an integrated forming module is designed; the invention provides a parametric modeling method, based on Visual Basic and SolidWorks secondary development, wheel parameters in an Access database are accessed, and three-dimensional modeling and assembling of all molds and blanks are automatically completed; a structure performance prediction method is provided, a parameterized model is automatically imported into finite element software, a microstructure model and a cellular automaton are integrated through secondary development, macro-micro coupling calculation is carried out, and microstructure distribution after the wheel is formed is predicted. According to the method, full-process digitization and automation from wheel design parameter input to final structure performance prediction are achieved, the research and development efficiency and the forming quality prediction precision of the complex-structure wheel are remarkably improved, and a core technical support is provided for optimal manufacturing of rail transit key components.

Description

Technical Field

[0001] This invention belongs to the field of metal plastic forming and digital simulation technology, specifically involving a digital design and performance prediction method for the entire forging and rolling process of complex structure wheels. Background Technology

[0002] Complex-structured wheels (such as high-speed train wheels) are key load-bearing components of rail transit equipment, and their internal structure and mechanical properties directly affect operational safety and service life. Currently, these wheels generally adopt an integrated forming process of "forging + rolling". However, this process is complex, involving multiple steps, large deformation, and high-temperature unsteady-state processes. There is a highly nonlinear coupling relationship between its process parameters (such as die shape, reduction, rolling speed, friction conditions, etc.) and the microstructure (such as grain size, phase composition) and macroscopic properties (such as strength, toughness, fatigue life) of the final workpiece.

[0003] The existing technology has the following shortcomings:

[0004] Low modeling efficiency: Traditional finite element modeling methods rely on manual operation. For different specifications of wheel blanks or process adjustments, it is necessary to rebuild the geometric model, divide the mesh, and set boundary conditions. The process is cumbersome and time-consuming, making it difficult to achieve rapid process analysis and optimization.

[0005] The disconnect between process and performance: Existing simulation analyses mostly focus on stress, strain, and temperature field analysis during the forming process, lacking a direct and accurate correlation with microstructure evolution (such as dynamic / static recrystallization and grain growth). Process designers cannot directly predict the final performance indicators of wheels based on simulation results and still need to rely on expensive experimental prototyping and destructive testing.

[0006] Lack of systematic integration: The simulation of each step, such as forging and rolling, is often isolated and fails to form a seamless digital twin model that connects the entire process from billet to finished product. It cannot accurately reflect the genetic influence of the previous step on the next step (such as the microstructure inhomogeneity caused by preforming).

[0007] Therefore, designing a method that can quickly model, accurately simulate, and achieve integrated prediction of organizational performance is of great significance for improving the manufacturing level of complex structure wheels, shortening the R&D cycle, and ensuring product quality. Summary of the Invention

[0008] To address the problems of low modeling efficiency, disconnect between process and performance, and lack of systematic integration in existing methods, this invention provides a digital design and performance prediction method for the entire forging and rolling process of complex structure wheels. It achieves rapid model construction through parameterization and realizes accurate prediction of the entire forming history and final performance of wheels by integrating multiphysics simulation and microstructure evolution model.

[0009] To achieve the above objectives, the present invention employs the following technical solutions:

[0010] A digital design and performance prediction method for the entire forging and rolling process of complex structure wheels, the method comprising the following steps:

[0011] A multi-scale digital simulation platform for wheel forging-rolling integrated forming is constructed, which includes a forming system development module, a parametric modeling module, and a microstructure and performance prediction module. The characteristic dimension design parameters of the complex structure wheel are input into the forming module and parametric modeling module in the multi-scale digital simulation platform for wheel forging-rolling integrated forming to obtain the mold required for the macro-micro coupling model of forging-rolling forming.

[0012] Stress-strain data and microstructure state variables of materials at different temperatures, strain rates and deformation amounts are obtained through hot compression experiments. These data are then input into the microstructure performance prediction module of the multi-scale digital simulation platform for wheel forging-rolling integrated forming, enabling the prediction of the microstructure evolution process at different local locations during the pre-forging, final forging and rolling forming processes of wheels.

[0013] The specific steps of the forming system development module are as follows: Develop a parameterized design interface that integrates the forging sub-module and the rolling sub-module based on the Visual Basic platform; develop the main control program framework, adopt a GUI interface design, and realize the page switching and parallel operation of the forging and rolling modules; create a unified parameter input panel, set up process stage selection pages including pre-forging, final forging and rolling, and realize the dynamic loading and display of process-related parameters;

[0014] The forging submodule includes pre-forging upper / lower dies, final forging upper / lower dies, and corresponding billets; the rolling submodule includes main rolls, pressing rolls, spoke rolls, and corresponding billets.

[0015] The specific steps of the parametric modeling module are as follows: develop a parametric design interface for the integrated forging and rolling subsystem based on the Visual Basic platform; establish an Access database to store wheel design parameters; and use Solidworks software for secondary development, call API interface functions, and realize the automatic 3D modeling and process assembly of all molds and billets based on the database parameters.

[0016] The wheel design parameters include wheel blank diameter, rim height, spoke thickness, hub bore diameter, tread profile curve, and roller mold profile.

[0017] The SolidWorks secondary development based on Visual Basic is implemented using COM technology. By calling the API interfaces exposed by SolidWorks, it achieves seamless integration of process design and 3D modeling. Partial code is shown below:

[0018] Dim swApp As Object

[0019] Dim Part As Object

[0020] Dim boolstatus As Boolean

[0021] Dim longstatus As Long, longwarnings As Long

[0022] Sub defport()

[0023] Set swApp = CreateObject("sldworks.application")

[0024] Set Part = swApp.ActiveDoc

[0025] End Sub

[0026] Private Sub Command1_Click()

[0027] Set swApp = CreateObject("solidworks.application")

[0028] swApp.Visible = True

[0029] Call defport 'Define interface'

[0030] '2019

[0031] 'call caculation2 ...

[0033] End Sub

[0034] This document describes how to write an Access database interface code using Visual Basic to achieve seamless connection and automated operation between the application and the backend database. The core functionality of this interface utilizes ADO (ActiveX Data Objects) components to locate and open the locally stored Access database file (.mdb or .accdb format) using a specific connection string. A portion of the code is shown below:

[0035] Option Explicit

[0036] Dim sql As String

[0037] Dim conn As New ADODB.Connection

[0038] Dim rs As ADODB.Recordset

[0039] 'Set grid control column width'

[0040] MSHFlexGrid1.ColWidth(0)=1

[0041] MSHFlexGrid1.ColWidth(1) = 1500

[0042] Access database connected to VB ...

[0044] End Sub

[0045] The specific steps of the microstructure performance prediction module are as follows: The model obtained from 3D modeling is automatically imported into finite element software to construct a forging-rolling finite element model. Secondary development of the finite element software is used to complete model assembly, mesh generation, material definition, and the setting of process parameters and boundary conditions. Secondary development is performed in the finite element software, integrating the microstructure evolution model through user subroutines and combining it with a cellular automata model to perform coupled calculations of macroscopic deformation and microstructure evolution, calculating and updating the microstructure state variables of the wheel blank in real time. The forging-rolling finite element model, microstructure evolution model, and cellular automata model are integrated to establish a macro-micro coupling model for forging-rolling. Based on the obtained macroscopic stress-strain data and the coupled calculation results, the microstructure distribution after wheel forming is predicted.

[0046] The forging-rolling forming finite element model includes: a pre-forging finite element model, a final forging finite element model, and a rolling finite element model.

[0047] The microstructure evolution model includes a dislocation density evolution model, a nucleation rate model, a grain growth model, and a dynamic recrystallization model, and is coupled and calculated through a user-defined subroutine interface of the finite element software.

[0048] The dislocation density evolution model is as follows:

[0049]

[0050] In the formula, For stress, It is a constant. For the average dislocation density, Shear modulus It is the Burgers vector;

[0051]

[0052] In the formula, For strain, k1 is the work hardening coefficient, and k2 is the dynamic softening coefficient. The average dislocation density of the cell;

[0053] The nucleation rate model is as follows:

[0054]

[0055] In the formula, Where C is the nucleation rate, and C and m are material parameters, both of which are constants;

[0056] The grain growth model is as follows:

[0057]

[0058] In the formula, and Grains Growth rate and driving force For mobility;

[0059] The dynamic recrystallization model is as follows:

[0060]

[0061] In the formula Indicates the orientation difference of grain i. For grain boundary energy, To account for the large-angle grain boundary orientation difference, we take 15°.

[0062]

[0063] In the formula, Poisson's ratio;

[0064]

[0065] In the formula, It is a constant;

[0066]

[0067] In the formula For grain boundary mobility, For grain boundary thickness, The self-diffusion coefficient of the grain boundary. Boltzmann's constant, This is the activation energy for grain boundary diffusion.

[0068] The microstructure state variables include average grain size and recrystallization volume fraction.

[0069] The macro-micro coupling model for forging and rolling is implemented using the finite element simulation software DEFORM and the ABAQUS coupled cellular automata. The input parameters for the macro-micro coupling model for forging and rolling are stress, strain, strain rate, temperature, grain size, and recrystallization percentage, which are developed using the Fortran language.

[0070] Compared with the prior art, the present invention has the following advantages:

[0071] High efficiency and automation: Through parametric modeling and script-driven approaches, the entire process from geometric modeling to simulation setup is automated, reducing the traditional modeling and analysis time of several days or even weeks to several hours, greatly improving the efficiency of process development.

[0072] High prediction accuracy: By closely coupling the microstructure evolution model with the macro-forming process, it can more realistically reflect the evolution law of materials under thermo-mechanical coupling, thereby realizing quantitative and spatial distribution prediction of the final grain structure and mechanical properties of wheels.

[0073] End-to-end integration: This approach connects the entire chain from "process parameters → forming process → microstructure → macroscopic properties," creating a complete digital twin. Process engineers can systematically evaluate the impact of different process schemes on the final product quality in a virtual space, providing a scientific basis for process optimization and reducing trial-and-error costs.

[0074] Versatility and scalability: The parametric model and core method described herein can be adapted to the forming process analysis and optimization of complex structural wheel parts (such as gear blanks, flanges, etc.) of different specifications and materials by adjusting key parameters. Furthermore, this method framework is easily integrated with more advanced material models or artificial intelligence algorithms, exhibiting excellent scalability. Attached Figure Description

[0075] Figure 1 This is a diagram showing the wheel structure and dimensions of the present invention.

[0076] Figure 2 This is the parametric modeling mind map of the present invention.

[0077] Figure 3 This is a diagram of the wheel forging-rolling integrated forming system of the present invention.

[0078] Figure 4 This is a parametric modeling diagram of the wheel forging module of the present invention.

[0079] Figure 5 This is a parametric modeling diagram of the wheel rolling module of the present invention.

[0080] Figure 6 This is a drawing of the wheel forging-rolling integrated forming mold of the present invention.

[0081] Figure 7 This is a finite element model diagram of the forging-rolling forming of the present invention.

[0082] Figure 8 This is a macroscopic finite element simulation (stress and strain distribution) diagram of the wheel forging-rolling integrated system of the present invention.

[0083] Figure 9 This is a microscopic finite element simulation (grain size) diagram of the wheel forging-rolling integrated system of the present invention.

[0084] Figure 10 This is a two-dimensional cellular automaton simulation diagram of the grain evolution at different positions of the wheel according to the present invention.

[0085] Figure 11 This is a two-dimensional cellular automaton simulation diagram of the grain evolution at the wheel feature position according to the present invention. Detailed Implementation

[0086] To gain a deeper understanding of this invention, we will provide a comprehensive and detailed description. However, this invention has various implementations and is not limited to the specific examples listed herein. These examples are presented to enhance a full understanding of the disclosure of this invention.

[0087] A digital design and performance prediction method for the entire forging and rolling process of complex structure wheels, the method comprising the following steps:

[0088] A multi-scale digital simulation platform for wheel forging-rolling integrated forming is constructed, which includes a forming system development module, a parametric modeling module, and a microstructure and performance prediction module. The characteristic dimension design parameters of the complex structure wheel are input into the forming module and parametric modeling module in the multi-scale digital simulation platform for wheel forging-rolling integrated forming to obtain the mold required for the macro-micro coupling model of forging-rolling forming.

[0089] Stress-strain data and microstructure state variables of materials at different temperatures, strain rates and deformation amounts are obtained through hot compression experiments. These data are then input into the microstructure performance prediction module of the multi-scale digital simulation platform for wheel forging-rolling integrated forming, enabling the prediction of the microstructure evolution process at different local locations during the pre-forging, final forging and rolling forming processes of wheels.

[0090] The specific steps of the forming system development module are as follows: Develop a parameterized design interface that integrates the forging sub-module and the rolling sub-module based on the Visual Basic platform; develop the main control program framework, adopt a GUI interface design, and realize the page switching and parallel operation of the forging and rolling modules; create a unified parameter input panel, set up process stage selection pages including pre-forging, final forging and rolling, and realize the dynamic loading and display of process-related parameters;

[0091] The forging submodule includes pre-forging upper / lower dies, final forging upper / lower dies, and corresponding billets; the rolling submodule includes main rolls, pressing rolls, spoke rolls, and corresponding billets.

[0092] The specific steps of the parametric modeling module are as follows: develop a parametric design interface for the integrated forging and rolling subsystem based on the Visual Basic platform; establish an Access database to store wheel design parameters; and use Solidworks software for secondary development, call API interface functions, and realize the automatic 3D modeling and process assembly of all molds and billets based on the database parameters.

[0093] The wheel design parameters include wheel blank diameter, rim height, spoke thickness, hub bore diameter, tread profile curve, and roller mold profile.

[0094] The SolidWorks secondary development based on Visual Basic is implemented using COM technology. By calling the API interfaces exposed by SolidWorks, it achieves seamless integration of process design and 3D modeling. Partial code is shown below:

[0095] Dim swApp As Object

[0096] Dim Part As Object

[0097] Dim boolstatus As Boolean

[0098] Dim longstatus As Long, longwarnings As Long

[0099] Sub defport()

[0100] Set swApp = CreateObject("sldworks.application")

[0101] Set Part = swApp.ActiveDoc

[0102] End Sub

[0103] Private Sub Command1_Click()

[0104] Set swApp = CreateObject("solidworks.application")

[0105] swApp.Visible = True

[0106] Call defport 'Define interface'

[0107] '2019

[0108] 'call caculation2 ...

[0110] End Sub

[0111] This document describes how to write an Access database interface code using Visual Basic to achieve seamless connection and automated operation between the application and the backend database. The core functionality of this interface utilizes ADO (ActiveX Data Objects) components to locate and open the locally stored Access database file (.mdb or .accdb format) using a specific connection string. A portion of the code is shown below:

[0112] Option Explicit

[0113] Dim sql As String

[0114] Dim conn As New ADODB.Connection

[0115] Dim rs As ADODB.Recordset

[0116] 'Set grid control column width'

[0117] MSHFlexGrid1.ColWidth(0)=1

[0118] MSHFlexGrid1.ColWidth(1) = 1500

[0119] Access database connected to VB ...

[0121] End Sub

[0122] The specific steps of the microstructure performance prediction module are as follows: The model obtained from 3D modeling is automatically imported into finite element software to construct a forging-rolling finite element model. Secondary development of the finite element software is used to complete model assembly, mesh generation, material definition, and the setting of process parameters and boundary conditions. Secondary development is performed in the finite element software, integrating the microstructure evolution model through user subroutines and combining it with a cellular automata model to perform coupled calculations of macroscopic deformation and microstructure evolution, calculating and updating the microstructure state variables of the wheel blank in real time. The forging-rolling finite element model, microstructure evolution model, and cellular automata model are integrated to establish a macro-micro coupling model for forging-rolling. Based on the obtained macroscopic stress-strain data and the coupled calculation results, the microstructure distribution after wheel forming is predicted.

[0123] The forging-rolling forming finite element model includes: a pre-forging finite element model, a final forging finite element model, and a rolling finite element model.

[0124] The microstructure evolution model includes a dislocation density evolution model, a nucleation rate model, a grain growth model, and a dynamic recrystallization model, and is coupled and calculated through a user-defined subroutine interface of the finite element software.

[0125] The dislocation density evolution model is as follows:

[0126]

[0127] In the formula, For stress, It is a constant. For the average dislocation density, Shear modulus It is the Burgers vector;

[0128]

[0129] In the formula, For strain, k1 is the work hardening coefficient, and k2 is the dynamic softening coefficient. The average dislocation density of the cell;

[0130] The nucleation rate model is as follows:

[0131]

[0132] In the formula, Where C is the nucleation rate, and C and m are material parameters, both of which are constants;

[0133] The grain growth model is as follows:

[0134]

[0135] In the formula, and Grains Growth rate and driving force For mobility;

[0136] The dynamic recrystallization model is as follows:

[0137]

[0138] In the formula Indicates the orientation difference of grain i. For grain boundary energy, To account for the large-angle grain boundary orientation difference, we take 15°.

[0139]

[0140] In the formula, Poisson's ratio;

[0141]

[0142] In the formula, It is a constant;

[0143]

[0144] In the formula For grain boundary mobility, For grain boundary thickness, The self-diffusion coefficient of the grain boundary. Boltzmann's constant, This is the activation energy for grain boundary diffusion.

[0145] The microstructure state variables include average grain size and recrystallization volume fraction.

[0146] The macro-micro coupling model for forging and rolling is implemented using the finite element simulation software DEFORM and the ABAQUS coupled cellular automata. The input parameters for the macro-micro coupling model for forging and rolling are stress, strain, strain rate, temperature, grain size, and recrystallization percentage, which are developed using the Fortran language.

[0147] Example: The forging-rolling forming process of a certain type of high-speed rail wheel is taken as an example.

[0148] S1: The operator inputs key parameters of the target wheel into the integrated system interface: wheel blank outer diameter is φ1250mm, rim height is 135mm, spoke inclination angle is 10°, etc. The system calls the background parametric script to automatically generate the corresponding 3D models of the wheel blank, forging die, and roll in the Solidworks environment.

[0149] S2: The system automatically imports the generated geometric model into the DEFORM-3D software. Through a preset Python script, it automatically completes the mesh generation (the wheel blank is divided into approximately 500,000 tetrahedral elements, with densification at the rim), material assignment (selecting AAR-C grade steel from the material library), and sets the process parameters for the forging steps (pre-forging and final forging) and rolling steps (initial forging temperature 1150°C, final rolling temperature not lower than 850°C, roll speed 2.5 rad / s).

[0150] S3: Activate the user-defined microstructure evolution module in DEFORM-3D software. This module includes a dynamic recrystallization model, a static recrystallization model, and a grain growth model for this steel grade. During the simulation calculation, the software not only outputs the stress and strain fields but also calculates and records the grain size evolution history of each unit cell in real time.

[0151] S4: After the simulation, the grain size distribution field data of the wheel after it has completely cooled is extracted. Using the system's built-in Hall-Petch formula (σ_0 and k for this steel grade have been experimentally calibrated), the grain size field is converted into a yield strength field and a hardness field. The prediction results show that the grains are the finest (~10μm) and the predicted hardness is the highest (~280HB) at the rim tread due to the large deformation and sufficient recrystallization; while the deformation in the center area of ​​the wheel hub is smaller, the grains are coarser (~30μm), and the predicted hardness is lower (~220HB).

[0152] S5: The system generates a complete analysis report, including key process curves, microstructure and property distribution cloud maps, and data. Based on this prediction, the operator determines whether the current process scheme meets the design requirements (e.g., the rim hardness must be greater than 260HB). If not, the system returns to S1 to modify process parameters (e.g., lowering the final rolling temperature to increase recrystallization driving force) and quickly conducts a new round of simulation verification until the optimal process scheme is obtained.

[0153] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

[0154] like Figure 1As shown in the figure, the wheel structure corresponding to the digital design and performance prediction method for the entire forging and rolling process of a complex structure wheel provided in this invention is illustrated in the two figures above. The two figures show that the Access database storing the key design parameters of the wheel has achieved a precise definition of "structural partitioning + parameter quantification": the former clarifies the functional structural division of the wheel, covering the hub with hub holes, spokes, and rims including inner / outer rim surfaces, treads, and flanges, clearly presenting the spatial relationship of each structure; the latter marks key geometric parameters such as hub diameter (e.g., φ150, φ815), spoke thickness (e.g., 29, 12), and rim size (e.g., 218, 630) in the form of parametric cross-sectional dimension diagrams, transforming the wheel structure into quantitative data that can be directly connected to the forging-rolling process module, providing a structured design basis for subsequent mold design and forming parameter matching;

[0155] like Figure 2 As shown, this invention constructs a collaborative work chain mindset of "data-driven - modeling - multi-module application": preset parameters are retrieved from the Access database through the Visual Basic development interface and imported into Solidworks to complete the 3D modeling of the wheel and mold; after modeling, the model can be synchronously connected to functional modules such as motion simulation, mold design, and finite element analysis, realizing an integrated process from parameter calling, model building to process simulation and analysis, providing efficient toolchain support for the forging-rolling process design of complex structure wheels;

[0156] like Figure 3 As shown in the figure, this invention provides a digital design and performance prediction method for the entire forging and rolling process of complex structure wheels, and develops a self-developed integrated wheel forging and rolling forming software system. The software system interface clearly displays multiple process schemes for wheel forming (including die forging, final forging, and rolling forming) and the corresponding 3D models of the finished wheel products. Specific operation steps are listed for each process module. Through this software system interface, users can not only intuitively view the process logic of different forming processes, but also easily switch between functions: clicking the "Enter XX Interface" button for the corresponding process module allows access to the specialized operation interface for die forging and rolling. Simultaneously, the software supports data exchange with finite element software (such as Deform and Abaqus), allowing modeling parameters to be directly transferred to the simulation platform, realizing numerical simulation of the forming process and correlation prediction of microstructure and properties.

[0157] like Figure 4As shown, in this invention, the software system interface focuses on the refined design of the die forging process, clearly displaying the complete flow of the die forging process. It also presents 3D models of each stage of die forging and a three-dimensional view of the finished wheel, intuitively showing the morphological evolution of the workpiece from the blank state to the completed die forging. The interface also displays detailed cross-sectional contour diagrams of different die forging states (pre-forging state, final forging state, etc.), and each contour diagram is associated with a corresponding key dimension parameter table (labeled with dimensions such as L1, L2, and their specific values). The interface is also equipped with function buttons such as "Query Parameters" and "Save Specifications" for quick retrieval and verification of current process parameters.

[0158] like Figure 5 As shown, in this invention, the software system interface focuses on the parametric modeling of locomotive wheel molds and the design of the rolling process. It clearly demonstrates the matching rules between the mold and the wheel blank (covering core constraints such as "the main roll is tangent to the wheel blank", "the inner spoke roll is tangent to the wheel blank", "the outer spoke roll is tangent to the wheel blank", and "the pressing roll and the wheel blank rim are fitted together"). At the same time, it presents a three-dimensional schematic diagram of wheel rolling, intuitively restoring the synergistic relationship between the main roll, inner / outer spoke roll, pressing roll and blank.

[0159] The interface further details the part drawings of each rolling die (including the outline structure of the main roll, outer spoke roll, inner spoke roll, and pressing roll). Each part drawing is associated with a corresponding key dimension parameter table (marked with dimensions such as L1 and L2 and their specific values). The interface is also equipped with function buttons such as "Generate Main Roll Part" and "Save Rolling Die as a Whole", which can realize the quick retrieval of die parameters, one-click generation of part models, and storage and export of the whole rolling die.

[0160] like Figure 6 As shown, the aforementioned calling of 3D modeling software refers to secondary development of SolidWorks software through API interface functions to achieve automated and parametric modeling of wheel forging-rolling dies and billets, specifically including: billet, pre-forging upper and lower dies, final forging upper and lower dies, pressing roll, spoke roll and main roll;

[0161] like Figure 7 As shown, the macroscopic finite element numerical simulation of wheel forging-rolling integration is carried out using finite element software, that is, the finite element simulation of the wheel forging-rolling process is performed to obtain the simulated finished wheel model, and the variation law of equivalent stress and equivalent strain at different positions during the forging-rolling process is analyzed.

[0162] like Figure 8As shown, a finite element model of forging-rolling is constructed for numerical simulation, which shows the grain size distribution cloud map of the wheel after each process of pre-forging, final forging and rolling. It can intuitively present the grain evolution state of key areas of the wheel (such as spokes and rims) at different forming stages. At the same time, the interface synchronously displays the grain size distribution histogram of the corresponding process, quantitatively presenting the grain ratio of each size range, and realizing the qualitative and quantitative analysis of microstructure and properties.

[0163] like Figure 9 , Figure 10 As shown, by establishing a macro-micro coupled model and combining the secondary development technology of the finite element model with the cellular automata (CA) method, the evolution law of local grain size during the plastic deformation process of wheel forging and rolling can be obtained through simulation. This allows for the acquisition of the grain size evolution at different characteristic positions of the wheel and the real-time evolution of grains at the same position at different times during the forging and rolling process.

[0164] The foregoing has shown and described the main features and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

[0165] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0166] Contents not described in detail in this specification are prior art known to those skilled in the art. Although illustrative specific embodiments of the invention have been described above to facilitate understanding by those skilled in the art, it should be understood that the invention is not limited to the scope of the specific embodiments. Various modifications are readily apparent to those skilled in the art as long as they fall within the spirit and scope of the invention as defined and determined by the appended claims, and all inventions utilizing the concept of this invention are protected.

Claims

1. A method for digital design and performance prediction of the entire forging and rolling process of complex structure wheels, characterized in that, The method includes the following steps: A multi-scale digital simulation platform for wheel forging-rolling integrated forming is constructed, which includes a forming system development module, a parametric modeling module, and a microstructure and performance prediction module. The design parameters of complex structure wheels are input into the forming module and parametric modeling module of the multi-scale digital simulation platform for wheel forging-rolling integrated forming to obtain the mold required for the macro-micro coupling model of forging-rolling forming. Stress-strain data and microstructure state variables of materials at different temperatures, strain rates and deformation amounts are obtained through hot compression experiments. These data are then input into the microstructure performance prediction module of the multi-scale digital simulation platform for wheel forging-rolling integrated forming, enabling the prediction of the microstructure evolution process at different local locations during the pre-forging, final forging and rolling forming processes of wheels.

2. The method for digital design and performance prediction of the entire forging and rolling process of complex structure wheels according to claim 1, characterized in that, The specific steps of the forming system development module are as follows: Develop a parameterized design interface that integrates the forging sub-module and the rolling sub-module based on the Visual Basic platform; develop the main control program framework, adopt a GUI interface design, and realize the page switching and parallel operation of the forging and rolling modules; create a unified parameter input panel, set up process stage selection pages including pre-forging, final forging and rolling, and realize the dynamic loading and display of process-related parameters; The specific steps of the parametric modeling module are as follows: develop a parametric design interface for the integrated forging and rolling subsystem based on the Visual Basic platform; establish an Access database to store wheel design parameters; and use Solidworks software for secondary development, call API interface functions, and realize the automatic 3D modeling and process assembly of all molds and billets based on the database parameters. The specific steps of the microstructure performance prediction module are as follows: The model obtained from 3D modeling is automatically imported into finite element software to construct a forging-rolling finite element model. Secondary development of the finite element software is used to complete model assembly, mesh generation, material definition, and the setting of process parameters and boundary conditions. Secondary development is performed in the finite element software, integrating the microstructure evolution model through user subroutines and combining it with a cellular automata model to perform coupled calculations of macroscopic deformation and microstructure evolution, calculating and updating the microstructure state variables of the wheel blank in real time. The forging-rolling finite element model, microstructure evolution model, and cellular automata model are integrated to establish a macro-micro coupling model for forging-rolling. Based on the obtained macroscopic stress-strain data and the coupled calculation results, the microstructure distribution after wheel forming is predicted.

3. The method for digital design and performance prediction of the entire forging and rolling process of complex structure wheels according to claim 2, characterized in that, The forging module includes pre-forging upper / lower dies, final forging upper / lower dies, and corresponding billets; the rolling module includes main rolls, pressing rolls, spoke rolls, and corresponding billets.

4. The method for digital design and performance prediction of the entire forging and rolling process of complex structure wheels according to claim 3, characterized in that, The wheel design parameters include wheel blank diameter, rim height, spoke thickness, hub bore diameter, tread profile curve, and roller mold profile.

5. The method for digital design and performance prediction of the entire forging and rolling process of complex structure wheels according to claim 4, characterized in that, The forging-rolling forming finite element model includes: a pre-forging finite element model, a final forging finite element model, and a rolling finite element model.

6. The method for digital design and performance prediction of the entire forging and rolling process of complex structure wheels according to claim 5, characterized in that, The microstructure evolution model includes a dislocation density evolution model, a nucleation rate model, a grain growth model, and a dynamic recrystallization model, and is coupled and calculated through a user-defined subroutine interface of the finite element software.

7. The method for full-process digital design and performance prediction of complex structure wheel forging and rolling according to claim 6, characterized in that, The microstructure state variables include average grain size and recrystallization volume fraction.

8. The method for full-process digital design and performance prediction of complex structure wheel forging and rolling according to claim 7, characterized in that, The macro-micro coupling model for forging and rolling is implemented using the finite element simulation software DEFORM and the ABAQUS coupled cellular automata. The input parameters for the macro-micro coupling model for forging and rolling are stress, strain, strain rate, temperature, grain size, and recrystallization percentage, which are developed using the Fortran language.

9. The method for digital design and performance prediction of the entire forging and rolling process of complex structure wheels according to claim 6, characterized in that, The dislocation density evolution model is as follows: In the formula, For stress, It is a constant. For the average dislocation density, Shear modulus It is the Burgers vector; In the formula, For strain, k1 is the work hardening coefficient, and k2 is the dynamic softening coefficient. The average dislocation density of the cell; The nucleation rate model is as follows: In the formula, Where C is the nucleation rate, and C and m are material parameters, both of which are constants; The grain growth model is as follows: In the formula, and Grains Growth rate and driving force For mobility; The dynamic recrystallization model is as follows: In the formula Indicates the orientation difference of grain i. For grain boundary energy, To account for the large-angle grain boundary orientation difference, we take 15°. In the formula, Poisson's ratio; In the formula, It is a constant; In the formula For grain boundary mobility, For grain boundary thickness, The self-diffusion coefficient of the grain boundary. Boltzmann's constant, This is the activation energy for grain boundary diffusion.

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