A method and device for creating a high-precision radial CAE approximate model of a steel wheel

Abstract

The present invention provides a method and device for creating a high-precision radial CAE approximate model for a steel wheel, wherein the method comprises: equating the radial pressure distribution of the steel wheel to an elliptical function pressure distribution; wherein the steel wheel comprises a rim and spokes, and a bead seat is provided on the rim; loading the elliptical function pressure distribution onto the bead seat; parametrically modeling the outer surface of the rim using CATIA software; importing the model into ABAQUS software for radial CAE model construction and analysis; utilizing ISIGHT software to integrate CATIA software and ABAQUS software to construct a design of element (DOE) design process; wherein the DOE design process includes input factors and output factors; fitting the input factors and output factors to obtain a high-precision approximate model. The present invention improves the optimization efficiency of radial CAE for steel wheels, shortens calculation time, and reduces product development cycles. Compared with traditional radial CAE simulation models, the approximate model accuracy of the present invention reaches over 96%. At the same time, the present invention can also improve the radial fatigue life and lightweight level of steel wheels.

Description

Technical Field

[0001] The present invention relates to the technical field of automobiles and mechanical engineering, and in particular to a method and device for creating a radial CAE high-precision approximate model of a steel wheel. Background Art

[0002] CAE (Computer Aided Engineering) is an approximate numerical analysis method that uses computers to assist in solving problems such as the analysis and calculation of mechanical properties such as strength, stiffness, buckling stability, dynamic response, heat conduction, three-dimensional multi-body contact, and elastic-plasticity of complex engineering and product structures, as well as the optimization design of structural performance. It has now become an indispensable numerical calculation tool in engineering and product structure analysis (such as aviation, aerospace, machinery, civil structures and other fields).

[0003] Steel wheels are important automotive components and play an important role in the safety performance of the entire vehicle during driving. Steel wheels are welded together from spokes and rims. When optimizing the rim design of steel wheels, the traditional method is mainly to find the optimal structural curve of the rim through integrated optimization of the radial CAE simulation model of the steel wheel. However, the radial CAE simulation model of the steel wheel contains multiple components such as spokes, rims, tires, bolts, nuts, bolt fixing plates, drums, tire pressure, radial loads, etc. The contact relationship, nonlinear problems, and air pressure fluctuations between the various parts are very complex. The entire radial CAE simulation model of the steel wheel is very large and the creation process is very complicated. It is time-consuming to simulate the radial loading analysis of the steel wheel in detail, resulting in a long time to calculate each solution. Hundreds of solutions are generally calculated when optimizing the rim design, which consumes a lot of time and seriously affects the product development cycle.

[0004] In view of this, overcoming the defects of the prior art is an urgent problem to be solved in this technical field. Summary of the Invention

[0005] The present invention provides a solution to the technical problem that the traditional steel wheel radial CAE simulation model consumes a lot of time during the optimization process and affects the product development cycle.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for creating a high-precision radial CAE approximate model of a steel wheel, comprising:

[0008] The radial pressure distribution of the steel wheel is equivalent to the pressure distribution of the elliptical line function; wherein the steel wheel includes a rim and a spoke, and a bead seat is provided on the rim;

[0009] Loading the bead seat with an elliptical line function pressure distribution;

[0010] Parametric modeling of the rim outer surface is performed using CATIA software;

[0011] Import the model into ABAQUS software for radial CAE model construction and analysis;

[0012] ISIGHT software is used to integrate CATIA software and ABAQUS software to build a DOE design process; the DOE design process includes input factors and output factors;

[0013] The input factors and output factors are fitted to obtain a high-precision approximate model.

[0014] Preferably, the step of equating the radial pressure distribution of the steel wheel to an elliptical function pressure distribution includes:

[0015] Establish a rectangular coordinate system with the center of the rim as the center O;

[0016] Define the circle equation x of the bead seat in rectangular coordinates 2 +y12=a2; where a is the bead seat radius;

[0017] With a as the minor axis and b as the maximum pressure on the rim at 90° as the major axis, establish the ellipse equation x 2 / a2+y22 / b2=1, so that the pressure load acts in the closed area between the circle and the ellipse.

[0018] Preferably, the step of loading the elliptical line function pressure distribution onto the bead seat comprises:

[0019] According to the closed area pattern, model A and model B are respectively established on the bead seats on both sides of the rim;

[0020] A radial force pressure F is applied to the top of model A and model B through a rigid plate C, and the radial force pressure F is transmitted to the bead seat through model A and model B in the form of an elliptical line function pressure distribution.

[0021] Preferably, the parametric modeling of the outer surface of the rim by using CATIA software includes:

[0022] Determine the key parameters of the rim outer surface and parameterize the key parameters;

[0023] Export the design parameter table design.txt, write a CATIA macro file, and use the macro program to automatically modify the key parameters of the rim outer surface and update the model.

[0024] Preferably, after performing parametric modeling on the outer surface of the rim by using CATIA software, and before importing the model into ABAQUS software for radial CAE model construction and analysis, the method further includes:

[0025] Secondary development of the original CAE rpy program ensures that the air pressure is correctly applied to the area where the rim is located.

[0026] Preferably, the secondary development of the original CAE rpy program includes:

[0027] Take points P1 and P2 at the center of the bead seats on both sides of the rim, and take points Q1 and Q2 on model A and model B respectively;

[0028] Use the FindAt function to find the surface passing through points P1, P2, Q1, and Q2 so that the contact relationship between model A and model B and the bead seats on both sides of the rim remains correct;

[0029] Use the getByBoundingBox function to lock the pressure application range.

[0030] Preferably, after the secondary development of the original CAE rpy program to ensure that the air pressure is correctly applied to the area where the rim is located, the process further includes:

[0031] The secondary developed program replaces the original CAE rpy program, converts the rpy file into a py program, debugs and runs it, and uses the bat command to automate the entire radial CAE analysis process.

[0032] Preferably, the DOE design process includes:

[0033] The optimized Latin square is used to sample N key parameters, and the number of samples is (N+1)×(N+2), where N is a natural number;

[0034] The calculation results in (N+1)×(N+2) groups of sample data with N key parameters as input factors and rim weight M, stress S1 at the fillet of the rim groove bottom, stress S2 at the fillet of the bead seat, and overall rim displacement U as output factors.

[0035] Evaluate and analyze the result files, and remove or recalculate abnormal data.

[0036] Preferably, fitting the input factors and the output factors comprises:

[0037] Using the principles of neural networks, a fitting method is performed with N key parameters as input factors and rim weight M, stress S1 at the rim well bottom corner, stress S2 at the bead seat corner, and overall rim displacement U as output factors. During the fitting process, ≥ 2 / 3 of the sample data is introduced, and the fitting accuracy of each of these parameters, rim weight M, stress S1 at the rim well bottom corner, stress S2 at the bead seat corner, and overall rim displacement U, is ensured to be ≥ 96%.

[0038] By the coefficient of determination R 2 The fitting error is analyzed using two indicators: accuracy and reproducibility AV.

[0039] In a second aspect, the present invention provides a device for creating a high-precision CAE approximate model of a steel wheel radial direction, comprising:

[0040] at least one processor; and,

[0041] A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the method for creating a high-precision approximate model of a radial CAE of a steel wheel as described in the first aspect.

[0042] In view of the deficiencies in the prior art, the present invention can achieve the following beneficial effects:

[0043] The present invention provides a method and device for creating a high-precision radial CAE approximate model of a steel wheel, which solves the problem of extremely time-consuming optimization of traditional radial CAE simulation models, shortens calculation time, improves the optimization efficiency of radial CAE of steel wheels, and simultaneously improves the radial fatigue life and lightweight level of steel wheels. Compared with traditional radial CAE simulation models, the accuracy of the approximate model of the present invention reaches over 96%.

[0044] Application results show that the present invention can shorten the product development cycle from 9-10 days to 1-2 days. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0046] Figure 1 It is a flow chart of a method for creating a high-precision approximate radial CAE model of a steel wheel;

[0047] Figure 2-Figure 7 yes Figure 1 Schematic diagram of the process corresponding to the steps in the method;

[0048] Figure 8 It is a schematic diagram of the process of establishing the equation of a circle and an ellipse in a rectangular coordinate system;

[0049] Figure 9 It is a schematic diagram of the closed area where radial pressure load acts;

[0050] Figure 10 This is a front view of the steel wheel modeling process;

[0051] Figure 11 This is an axial view of the steel wheel modeling process;

[0052] Figure 12 It is a partial cross-sectional view of a steel wheel;

[0053] Figure 13 This is a schematic diagram of the process of taking point Q1 in model A;

[0054] Figure 14 This is a comparison chart of steel wheel accuracy based on fatigue life tests;

[0055] Figure 15 This is a comparison chart of the time taken for steel wheels based on 1 million fatigue life tests;

[0056] Figure 16 The present invention is a structural diagram of a device for creating a high-precision radial CAE approximate model of a steel wheel. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, the technical features in the various embodiments or single embodiments provided by the present invention can be arbitrarily combined with each other to form a feasible technical solution. This combination is not restricted by the sequence of steps and / or structural composition mode, but must be based on the ability of ordinary technicians in this field to implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0058] Example 1:

[0059] In order to solve the technical problem that the traditional steel wheel radial CAE simulation model consumes a lot of time in the optimization process and affects the product development cycle, this embodiment 1 provides a method for creating a high-precision approximate steel wheel radial CAE model, such as Figure 1 Shown, including:

[0060] S10, equating the radial pressure distribution of the steel wheel to an elliptical line function pressure distribution; wherein the steel wheel includes a rim and spokes, and a bead seat is provided on the rim.

[0061] The bead seat, also known as the mounting surface, is the part of the rim that contacts the tire and supports and maintains the tire in the radial direction.

[0062] Since tires are nonlinear materials, detailed simulation of tire stress conditions is time-consuming. The radial pressure is transmitted to the bead seats on the left and right sides of the rim through the tire. In this step, the radial pressure distribution of the steel wheel is equivalent to the elliptical function pressure distribution. This is mainly to replace the complex pattern of tire loading with a simplified model, and the elliptical function pressure distribution is also more in line with actual engineering conditions.

[0063] In specific implementation, the radial pressure distribution of the steel wheel is equivalent to the pressure distribution of the elliptical line function, such as Figure 2 Shown, including:

[0064] S11, establishing a rectangular coordinate system with the center of the rim as the center O.

[0065] S12, define the circle equation x of the bead seat in the rectangular coordinate system 2 +y12=a2; where a is the bead seat radius.

[0066] S13, with a as the minor axis and b as the maximum pressure on the rim at 90° as the major axis, establish the ellipse equation x 2 / a2+y22 / b2=1, so that the pressure load acts in the closed area between the circle and the ellipse.

[0067] like Figure 8-Figure 9 As shown in the figure, the circle equation and the ellipse equation both take the center O as the circle center. After establishing the circle equation and the ellipse equation, there is a closed area between the circle and the ellipse (the shaded part in the figure). Combined with the actual engineering situation, it can be seen that the radial pressure load mainly acts in the closed area between the circle and the ellipse.

[0068] S20, loading the elliptical line function pressure distribution onto the bead seat.

[0069] In this step, in order to improve loading efficiency, the traditional bolt force loading method is eliminated in the loading process. At the same time, the rim and spokes both adopt a sheet-like structure of equal thickness, thereby improving loading efficiency.

[0070] Specifically, the elliptical line function pressure distribution is loaded onto the bead seat, as shown in FIG. Figure 3 Shown, including:

[0071] S21, establishing a model A and a model B on the bead seats on both sides of the rim according to the closed area pattern.

[0072] Model A and Model B are made using 3D drawing software, preferably CATIA software. During the material property configuration process, in order to ensure high material hardness and good force transmission effect, Model A and Model B are preferably made of polyurethane.

[0073] S22, applying radial force pressure F to the top of model A and model B through the rigid plate C, and transmitting the radial force pressure F to the bead seat through model A and model B in the form of elliptical line function pressure distribution.

[0074] like Figure 10-11 Shown is a schematic diagram of the steel wheel modeling process.

[0075] Through the above equations and model establishment process, the pressure distribution law expression can be calculated as:

[0076]

[0077] Similarly, the combined force of the pressure on the bead seats on both sides of the rim bears the external radial pressure F, so:

[0078]

[0079] Through the above formula, we can get:

[0080]

[0081] Therefore, the regular expression of the pressure loading function on the rim bead seat is:

[0082]

[0083] S30, parametric modeling of the rim outer surface is performed using CATIA software.

[0084] The rim outer surface is parametrically modeled using CATIA software, as shown in FIG. Figure 4 Shown, including:

[0085] S31, determining key parameters of the outer surface of the rim and parameterizing the key parameters.

[0086] like Figure 12 As shown, it is a partial cross-sectional view of a steel wheel (wherein the spokes are only partially cut out), and the key parameters of the outer surface of the rim include the maximum width D1 of the bead seat, the minimum width D2 of the bead seat, the rim groove bottom width D3 and the relevant chamfer arc radius and other key parameters.

[0087] S32, export the design parameter table design.txt, write a CATIA macro file, and use the macro program to realize the automatic modification of key parameters of the rim outer surface and model update.

[0088] When the key parameters of the rim outer surface mentioned above change, the key parameters of the rim outer surface and the corresponding model will be automatically adjusted and updated through the macro program.

[0089] Due to the update of the rim model during the optimization process, the node numbers and positions of the rim elements will also change randomly. The original CAE rpy program records the node numbers or positions captured by manual modeling. If the original rpy program is directly used in the subsequent optimization process, it will lead to errors in the constraint relationship, load relationship, etc. during the optimization process, which will affect the application of air pressure and make the optimization design unable to proceed smoothly.

[0090] In one implementation, in order to solve the above problem, after the rim outer surface is parametrically modeled by CATIA software, and before the model is imported into ABAQUS software for radial CAE model construction and analysis, as shown in FIG. Figure 1 As shown, it also includes:

[0091] S40, secondary development of the original CAE rpy program ensures that the air pressure is correctly applied to the area where the rim is located.

[0092] In the specific implementation, the original CAE rpy program is secondary developed, such as Figure 5 Shown, including:

[0093] S41, respectively select points P1 and P2 at the center of the bead seats on both sides of the rim, and respectively select points Q1 and Q2 on model A and model B.

[0094] S42, using the FindAt function to find a surface passing through points P1, P2, Q1, and Q2, so that the contact relationship between model A and model B and the bead seats on both sides of the rim remains correct.

[0095] like Figure 12 As shown, points P1 and P2 are located at the center of the bead seats on both sides of the rim. Since points P1 and P2 are fixed, it can be ensured that the surface passing through points P1 and P2 does not change, thereby ensuring that the positions of the bead seats on both sides of the rim do not change. Figure 13 As shown, points Q1 and Q2 are located on model A and model B respectively (wherein model B and point Q2 are not marked). Points Q1 and Q2 can be located at any position on model A and model B respectively, as long as the relative position relationship of the selected points Q1 and Q2 is synchronized and corresponding, and points Q1 and Q2 can contact the bead seats on both sides of the rim respectively.

[0096] Since the coordinates of points Q1, Q2, P1, and P2 are certain, the surface passing through these four points is also certain, thereby ensuring that there is no error in the contact relationship between models A and B and the bead seats on both sides of the rim.

[0097] S43, locking the air pressure application range through the getByBoundingBox function.

[0098] In specific implementation, through getByBoundingBox(x min =m1,x max =m2,y min =n1,y max =n2,z min =v1,z max =v2) function, and secondary development of the rim air pressure loading surface selection program to determine the coordinate range in three-dimensional space. Through this secondary development program, the air pressure application range is locked. No matter how the rim model is updated, it can always ensure that the air pressure is correctly applied to the area where the rim is located.

[0099] In one implementation, in order to reduce manual participation and improve work efficiency, the original CAE rpy program is secondary developed to ensure that the air pressure is correctly applied to the area where the rim is located. Figure 1 As shown, it also includes:

[0100] S50, replace the original CAE rpy program with the secondary developed program, convert the rpy file into a py program, debug and run it, and automate the entire radial CAE analysis process through the bat command.

[0101] S60, import the model into ABAQUS software for radial CAE model construction and analysis.

[0102] In the specific implementation, the parametric model of the rim outer surface, the inner surface of the spoke, model A and model B are imported into the ABAQUS software for radial CAE model construction and analysis. The parts import, property setting, assembly, analysis step setting, contact relationship setting, load and boundary conditions, meshing, calculation and result output are completed in the ABAQUS software.

[0103] S70, use ISIGHT software to integrate CATIA software and ABAQUS software to build a DOE design process; the DOE design process includes input factors and output factors.

[0104] ISIGHT software is a simulation analysis process automation and multi-disciplinary multi-objective optimization tool. It provides a visual and flexible simulation process building platform, and provides dedicated interfaces with a variety of mainstream CAE analysis tools. Users can quickly establish complex simulation analysis processes, set and modify design variables and design goals, and automatically perform multiple analysis cycles. It also provides a complete optimization software package including experimental design, optimization methods, approximate models and Six Sigma design.

[0105] In specific implementation, the DOE design process is as follows: Figure 6 Shown, including:

[0106] S71, use optimized Latin square to sample N key parameters, the number of samples is (N+1)×(N+2), where N is a natural number.

[0107] S72, calculate and obtain (N+1)×(N+2) groups of sample data with N key parameters as input factors and rim weight M, stress S1 at the fillet of the rim groove bottom, stress S2 at the fillet of the bead seat, and overall rim displacement U as output factors.

[0108] S73, evaluating and analyzing the result file, and removing or recalculating abnormal data.

[0109] S80, fitting the input factors and output factors to obtain a high-precision approximate model.

[0110] In specific implementation, the input factors and output factors are fitted, such as Figure 7 Shown, including:

[0111] S81, using the principle of neural network, with N key parameters as input factors, and rim weight M, stress S1 at the fillet of the rim well bottom, stress S2 at the fillet of the bead seat, and overall rim displacement U as output factors for fitting; wherein, ≥ 2 / 3 of the sample data are introduced in the fitting process, and the fitting accuracy of each of the rim weight M, stress S1 at the fillet of the rim well bottom, stress S2 at the fillet of the bead seat, and overall rim displacement U is ensured to be ≥ 96%.

[0112] S82, through the coefficient of determination R 2 The fitting error is analyzed using two indicators: accuracy and reproducibility AV.

[0113] By analyzing the fitting error, the stability and reliability of the fitting results can be improved.

[0114] This embodiment 1 provides a method for creating a high-precision radial CAE approximate model of a steel wheel. By designing a simplified, high-precision approximate model to replace the complex tire loading pattern in the traditional steel wheel radial CAE simulation model analysis process, the calculation time of the model can be greatly reduced, thereby improving product development efficiency. In addition, after verifying and finding the optimal structural curve of the rim, the steel wheel structure can be enhanced, and the steel wheel material thickness can be reduced, thereby achieving the purpose of improving fatigue life and lightweight design.

[0115] like Figure 14As shown in the figure, it is a comparison chart of steel wheel accuracy based on fatigue life test. A certain number of physical samples of 15-inch, 16-inch and 17-inch steel wheels are selected respectively. For example, 3 samples are selected for each specification, and radial fatigue test is carried out under the load conditions required by the standard. The experimental data of the samples are used to conduct Weibull reliability analysis on steel wheels of different specifications, and the fatigue life is calculated at 95% reliability (corresponding to 5% failure rate, i.e. F(t) = 5%). Then, the fatigue life is compared with the fatigue life calculated by the simulation model and the approximate model to obtain the accuracy of the simulation model and the approximate model.

[0116] Taking 16-inch steel wheels as an example, at a failure rate of 5%, the fatigue lives of the three samples of 4#, 5#, and 6# were measured to be 1.106 million times, 1.083 million times, and 1.129 million times respectively. Based on the experimental data of the samples, the fatigue life was calculated to be 1.049 million times through Weibull reliability analysis. At the same time, the fatigue life was calculated to be 1.065 million times through the simulation model, and 1.076 million times through the myopia model. Further calculations show that the fatigue life is relatively In the actual experiment, the accuracy of the simulation model was 104.9 / 106.5 = 98.5%, and the accuracy of the near-vision model was 104.9 / 107.6 = 97.5%. Compared with the simulation model, the accuracy of the near-vision model was 106.5 / 107.6 = 99.0%. Similarly, for 15-inch and 17-inch steel wheels, the accuracy of the near-vision model was 93.7 / 94.3 = 99.4% and 141.3 / 144.6 = 97.7%, respectively, compared with their respective simulation models. Through comparative analysis, it can be seen that the accuracy of the near-vision model provided in this embodiment 1 can reach above 96% compared with the simulation model.

[0117] like Figure 15 The figure shows the time comparison of steel wheels based on 1 million fatigue life tests.

[0118] Using a 16-inch steel wheel as an example, the average time spent on physical testing of the steel wheel was approximately 30 hours, the average time spent on simulation modeling was approximately 10 hours, and the average time spent on the near-vision modeling was approximately 1 hour. Comparative analysis shows that compared to the simulation modeling, the near-vision modeling provided in Example 1 significantly reduces model calculation time, accelerating product development cycles and improving product development efficiency.

[0119] Example 2:

[0120] Based on the same general technical solution as Example 1, Figure 16The figure shows a schematic structural diagram of a device for creating a high-precision CAE approximate model of a radial steel wheel provided in this embodiment 2, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the method for creating a high-precision CAE approximate model of a radial steel wheel as described in embodiment 1.

[0121] In summary, the present invention provides a method and device for creating a high-precision approximate model of the radial CAE of a steel wheel, which improves the optimization efficiency of the radial CAE of the steel wheel, shortens the calculation time, and reduces the product development cycle. Compared with the traditional radial CAE simulation model, the approximate model accuracy of the present invention reaches more than 96%. At the same time, the present invention can also improve the radial fatigue life and lightweight level of the steel wheel.

[0122] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0123] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, electronic devices, or computer software program products. Thus, the present invention may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] The present invention is described with reference to flowcharts and / or block diagrams of methods, systems, electronic devices, or computer software program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0125] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system that is implemented in the process. Figure 1a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0127] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

Claims

1. A method for creating a high-precision radial CAE approximate model of a steel wheel, characterized by: include: Equivalent the radial pressure distribution of the steel wheel to the pressure distribution of the elliptical line function; wherein, the steel wheel includes a rim and a spoke, and a bead seat is provided on the rim; the elliptical line function pressure distribution is loaded onto the bead seat; the outer surface of the rim is parametrically modeled by CATIA software; the model is imported into ABAQUS software for radial CAE model construction and analysis; ISIGHT software is used to integrate CATIA software and ABAQUS software to build a DOE design process; wherein, the DOE design process includes input factors and output factors; the input factors and the output factors are fitted to obtain a high-precision approximate model; the equivalent of the radial pressure distribution of the steel wheel to the pressure distribution of the elliptical line function includes: establishing a rectangular coordinate system with the center O of the rim as the center; defining the circle equation x of the bead seat in the rectangular coordinate system 2 +y1 2 =a 2 ; Where a is the bead seat radius; With a as the minor axis and b, the maximum pressure on the rim at 90°, as the major axis, establish the ellipse equation x 2 / a 2 +y2 2 / b 2 =1, so that the pressure load acts on the closed area between the circle and the ellipse; the loading of the elliptical line function pressure distribution onto the bead seat includes: establishing models A and B on the bead seats on both sides of the rim according to the closed area pattern; applying radial force pressure F through a rigid plate C on the top of models A and B, and transmitting the radial force pressure F to the bead seat through models A and B in the form of an elliptical line function pressure distribution.

2. The method for creating a high-precision radial CAE approximate model of a steel wheel according to claim 1, characterized in that: The parametric modeling of the rim outer surface using CATIA software includes: determining key parameters of the rim outer surface and parameterizing the key parameters; exporting a design parameter table design.txt, writing a CATIA macro file, and realizing automatic modification of the key parameters of the rim outer surface and model update through the macro program.

3. The method for creating a high-precision radial CAE approximate model of a steel wheel according to claim 1, characterized in that: After the rim outer surface is parametrically modeled by CATIA software, and before the model is imported into ABAQUS software for radial CAE model construction and analysis, it also includes: secondary development of the original CAE rpy program to ensure that the air pressure is correctly applied to the area where the rim is located.

4. The method for creating a high-precision radial CAE approximate model of a steel wheel according to claim 3, characterized in that: The secondary development of the original CAE rpy program includes: taking points P1 and P2 at the center of the bead seats on both sides of the rim, and taking points Q1 and Q2 on model A and model B respectively; finding the surface passing through points P1, P2, Q1, and Q2 through the FindAt function, so that the contact relationship between model A and model B and the bead seats on both sides of the rim remains correct; and locking the air pressure application range through the getByBoundingBox function.

5. The method for creating a high-precision radial CAE approximate model of a steel wheel according to claim 4, characterized in that: After the secondary development of the original CAE rpy program to ensure that the air pressure is correctly applied to the area where the rim is located, it also includes: replacing the original CAE rpy program with the secondary development program, converting the rpy file into a py program, debugging and running it, and realizing the automation of the entire radial CAE analysis process through the bat command.

6. The method for creating a high-precision radial CAE approximate model of a steel wheel according to claim 5, characterized in that: The DOE design process includes: using optimized Latin square to sample N key parameters, with the number of samples being (N+1)×(N+2), where N is a natural number; calculating (N+1)×(N+2) groups of sample data with the N key parameters as input factors and the rim weight M, the stress S1 at the fillet of the rim groove bottom, the stress S2 at the fillet of the bead seat, and the overall rim displacement U as output factors; evaluating and analyzing the result file, and eliminating or recalculating abnormal data.

7. The method for creating a high-precision radial CAE approximate model of a steel wheel according to claim 6, characterized in that: The input factors and output factors are fitted, including: using the principle of neural network, taking N key parameters as input factors, and fitting the rim weight M, the stress S1 at the fillet of the rim groove bottom, the stress S2 at the fillet of the bead seat, and the overall displacement U of the rim as output factors; wherein, ≥ 2 / 3 of the sample data are introduced in the fitting process, and the fitting accuracy of each of the rim weight M, the stress S1 at the fillet of the rim groove bottom, the stress S2 at the fillet of the bead seat, and the overall displacement U of the rim is ensured to be ≥ 96%; through the determination coefficient R 2 The fitting error is analyzed using two indicators: accuracy and reproducibility AV.

8. A device for creating a high-precision CAE approximate model of a steel wheel radial direction, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the steel wheel radial CAE high-precision approximate model creation method as described in any one of claims 1-7.

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