Tire life prediction method, device and equipment and storage medium

Abstract

The invention discloses a tire life prediction method and device, equipment and a storage medium, and relates to the technical field of tire detection, and the method comprises the steps: obtaining a finite element calculation result of a global model corresponding to a target tire and tire material parameters representing fatigue; performing life prediction on the target tire based on the finite element calculation result and the tire material parameters to obtain an initial prediction result corresponding to the global model; determining a target area meeting a preset tire life weak condition from the global model according to the initial prediction result, and constructing a corresponding sub-model based on grid information corresponding to the target area; and carrying out life prediction on the corresponding target area by utilizing the deformation gradients and stress data of the sub-models at different moments so as to finally obtain a target prediction result corresponding to the target tire. In this way, the precision degree of tire life prediction can be optimized, and the accuracy of tire life prediction is improved.

Description

Technical Field

[0001] The present invention relates to the field of tire detection technology, and in particular to a tire life prediction method, device, equipment and storage medium. Background Art

[0002] The tire fatigue life prediction methods currently available on the market generally fail to fully meet the calculation requirements of large deformation fatigue life of tires under high load conditions. Usually, the stress-strain history data corresponding to the global model of the tire is used to directly obtain the final life prediction result of the tire; the process cannot effectively handle the stress-strain history data of the tire's multi-material composite structure and its extreme working conditions, thus affecting the accuracy and reliability of the prediction.

[0003] It can be seen that how to more accurately predict tire fatigue life is a problem to be solved in this field. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a tire life prediction method, device, equipment and storage medium, which can optimize the precision of tire life prediction and improve the accuracy of tire life prediction. The specific scheme is as follows:

[0005] In a first aspect, an embodiment of the present application provides a tire life prediction method, comprising:

[0006] Obtain finite element calculation results of the global model corresponding to the target tire and tire material parameters characterizing fatigue;

[0007] Performing life prediction on the target tire based on the finite element calculation result and the tire material parameter to obtain an initial prediction result corresponding to the global model;

[0008] Determine a target area that meets a preset tire life weakness condition from the global model according to the initial prediction result, and construct a corresponding sub-model based on grid information corresponding to the target area;

[0009] The deformation gradient and stress data of the sub-model at different times are used to predict the life of the corresponding target area, so as to finally obtain the target prediction result corresponding to the target tire.

[0010] Optionally, the step of obtaining the finite element calculation result of the global model corresponding to the target tire and the tire material parameter characterizing fatigue includes:

[0011] Calculating the global model of the target tire under preset working conditions using finite element analysis software to obtain corresponding finite element calculation results; the finite element calculation results include mesh information of the global model, tire material properties, stress-strain cloud diagrams, and deformation gradient tensors;

[0012] The tire material parameters corresponding to the target tire and representing fatigue are obtained through a preset input interface; the tire material parameters include hyperelastic model material parameters, initial crack size in the fatigue model, and critical cracking energy.

[0013] Optionally, the performing life prediction on the target tire based on the finite element calculation result and the tire material parameter to obtain an initial prediction result corresponding to the global model includes:

[0014] Based on the finite element calculation results and the tire material parameters, life prediction is performed on the target tire to obtain a result cloud map corresponding to the global model;

[0015] The result cloud map is displayed and optimized by a triangulation algorithm to obtain an initial prediction result corresponding to the global model.

[0016] Optionally, before using the deformation gradient and stress data of the sub-model at different times to predict the life of the corresponding target area, the method further includes:

[0017] The deformation data of the global model at different times in the finite element calculation results are read through a preset script to form the historical deformation data of the target area corresponding to the sub-model, so as to calculate the deformation gradient and stress data of the sub-model at different times based on the historical deformation data.

[0018] Optionally, the calculating the deformation gradient and stress data of the sub-model at different times based on the historical deformation data includes:

[0019] The historical deformation data corresponding to each moment is used as the boundary condition to calculate the deformation gradient and stress data of the sub-model at the corresponding moment.

[0020] Optionally, using the deformation gradient and stress data of the sub-model at different times to perform life prediction on the corresponding target area to finally obtain a target prediction result corresponding to the target tire includes:

[0021] Using the deformation gradient and stress data of the sub-model at different times to predict the life of the corresponding target area, to obtain a corresponding regional prediction result;

[0022] The initial prediction result is corrected based on the plurality of regional prediction results to finally obtain a target prediction result corresponding to the target tire.

[0023] Optionally, the method further includes:

[0024] Obtain modification instructions for tire material parameters through a preset interactive interface;

[0025] The relevant tire material parameters are modified according to the modification instruction to obtain modified parameters, so as to perform a life prediction operation on the target tire based on the modified parameters.

[0026] In a second aspect, an embodiment of the present application provides a tire life prediction device, comprising:

[0027] An information acquisition module, used to obtain the finite element calculation results of the target tire corresponding to the global model and tire material parameters characterizing fatigue;

[0028] A first prediction module, configured to predict the life of the target tire based on the finite element calculation result and the tire material parameter, and obtain an initial prediction result corresponding to the global model;

[0029] A sub-model construction module, used to determine a target area that meets the preset tire life weakness condition from the global model according to the initial prediction result, and to construct a corresponding sub-model based on the grid information corresponding to the target area;

[0030] The second prediction module is used to use the deformation gradient and stress data of the sub-model at different times to predict the life of the corresponding target area, so as to finally obtain the target prediction result corresponding to the target tire.

[0031] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0032] Memory, used to store computer programs;

[0033] A processor is used to execute the computer program to implement the tire life prediction method as described above.

[0034] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the tire life prediction method as described above.

[0035] It can be seen that the present application first obtains the finite element calculation results of the global model corresponding to the target tire and the tire material parameters that characterize fatigue; then, based on the finite element calculation results and the tire material parameters, the life of the target tire is predicted to obtain the initial prediction results corresponding to the global model; then, according to the initial prediction results, the target area that meets the preset tire life weakness conditions is determined from the global model, and the corresponding sub-model is constructed based on the grid information corresponding to the target area; then, the deformation gradient and stress data of the sub-model at different times are used to predict the life of the corresponding target area, so as to finally obtain the target prediction results corresponding to the target tire. In this way, the present application first combines the finite element calculation results of the global model of the tire and the corresponding tire material parameters to give the fatigue life prediction of the tire as a whole; then, a more refined sub-model is constructed using the relevant grid information and the relevant strain history data to perform fatigue life prediction; this can optimize the refinement of the tire life prediction and improve the accuracy of the tire life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0037] Figure 1 A flow chart of a tire life prediction method disclosed in this application;

[0038] Figure 2 A visual software interface diagram disclosed in this application;

[0039] Figure 3 A visual software menu page diagram disclosed in this application;

[0040] Figure 4 Another visual software menu page diagram disclosed in this application;

[0041] Figure 5 A finite element calculation result processing flow chart disclosed in this application;

[0042] Figure 6 A schematic diagram of stress distribution disclosed in this application;

[0043] Figure 7 A lifespan distribution slice cloud diagram disclosed in this application;

[0044] Figure 8 A sub-model processing flow chart disclosed in this application;

[0045] Fig. 9 A processing flow chart of a combination of a cracking energy density method and a large deformation theory disclosed in the present application;

[0046] Fig.10 This is a schematic diagram of the structure of a tire life prediction device disclosed in this application;

[0047] Fig.11 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 creative work are within the scope of protection of the present invention.

[0049] See also Figure 1 As shown, an embodiment of the present invention discloses a tire life prediction method, comprising:

[0050] Step S11, obtaining finite element calculation results of the global model corresponding to the target tire and tire material parameters characterizing fatigue.

[0051] In this embodiment, to predict the life of the tire, firstly, relevant data needs to be collected. The finite element calculation results of the tire corresponding to the global model can be obtained through finite element simulation. It can be understood that the stress and strain data of the tire under specific working conditions can be obtained through finite element analysis software. Accordingly, it is also necessary to determine the parameter variables that have a significant impact on the tire life, that is, the tire material parameters that characterize fatigue, so as to evaluate the tire service life by combining the calculation results and the tire material parameters.

[0052] In a specific embodiment, the method of obtaining the finite element calculation results of the global model corresponding to the target tire and the tire material parameters characterizing fatigue may include: calculating the global model of the target tire under preset working conditions through finite element analysis software to obtain the corresponding finite element calculation results; the finite element calculation results include the mesh information of the global model, the tire material properties, the stress-strain cloud map and the deformation gradient tensor; obtaining the tire material parameters characterizing fatigue corresponding to the target tire through a preset input interface; the tire material parameters include the material parameters of the hyperelastic model, the initial crack size in the fatigue model, and the critical cracking energy. Specifically, the finite element analysis software may simulate the global model of the target tire under a preset specific working condition (such as a large deformation working condition), and obtain the stress-strain data of the target tire under the corresponding working condition; the calculation results of the finite element analysis software may include the mesh information of the global model of the target tire, the material properties of the tire, the stress-strain cloud map of the tire under each working condition, and the deformation gradient tensor. Correspondingly, various tire material parameters that affect tire life input by relevant personnel can be obtained through a pre-set interactive interface, which may include hyperelastic model material parameters, initial crack size in fatigue model, critical cracking energy and other parameters.

[0053] Step S12: predicting the life of the target tire based on the finite element calculation result and the tire material parameters to obtain an initial prediction result corresponding to the global model.

[0054] In this embodiment, the finite element calculation results and tire material parameters corresponding to the target tire can be obtained through the above steps, and the life of the target tire can be predicted based on these data; here, the fatigue life of the target tire is predicted as a whole, and the initial prediction result corresponding to the global model is finally obtained. It should be pointed out that the life prediction method may include the strain energy density prediction method, the fracture energy density prediction method, and the cracking energy calculation method considering the crack closure effect, etc., which are not limited here.

[0055] In a specific embodiment, the life prediction of the target tire based on the finite element calculation results and the tire material parameters to obtain the initial prediction result corresponding to the global model may include: predicting the life of the target tire based on the finite element calculation results and the tire material parameters to obtain a result cloud map corresponding to the global model; and optimizing the display of the result cloud map by a triangulation algorithm to obtain the initial prediction result corresponding to the global model. Specifically, predicting the life of the target tire based on the finite element calculation results and the tire material parameters can obtain a result cloud map of the life prediction corresponding to the global model; and in order to improve the display effect of the result cloud map, this embodiment can optimize the display of the result cloud map by a triangulation algorithm, reconstruct the mesh information of the global model, and finally obtain the initial prediction result after display optimization for the target tire.

[0056] Step S13: determining a target area that meets a preset tire life weakness condition from the global model according to the initial prediction result, and constructing a corresponding sub-model based on grid information corresponding to the target area.

[0057] In this embodiment, after the initial prediction result of the target tire is obtained through the above steps, the target area that meets the weak condition of tire life can be marked in the result cloud map of the initial prediction result based on the pre-set weak condition of tire life; it can be understood that the process of selecting the target area can be combined with the regional life distribution of the target tire under different working conditions, and its expected life can be quantified, and the relatively weak (lower or higher quantified value) area can be further determined. Afterwards, the initial state of each target area is reconstructed using the grid information of the global model of the target tire corresponding to the target area, and each sub-model corresponding to each target area is obtained.

[0058] Step S14: using the deformation gradient and stress data of the sub-model at different times to perform life prediction on the corresponding target area, so as to finally obtain a target prediction result corresponding to the target tire.

[0059] Further, after constructing the sub-models of each target area, the life prediction can be re-performed in combination with the deformation gradient and stress data of the sub-model at different times, so as to finally obtain the target prediction result corresponding to the target tire. In a specific embodiment, before the life prediction of the corresponding target area is performed using the deformation gradient and stress data of the sub-model at different times, it can also include: reading the deformation data of the global model at different times in the finite element calculation results through a preset script to form the historical deformation data of the target area corresponding to the sub-model, so as to calculate the deformation gradient and stress data of the sub-model at different times based on the historical deformation data. Specifically, the finite element analysis software can be called by a script to read the deformation data of the global model corresponding to the target tire at different times to form the historical deformation data of each target area. Further, the calculation of the deformation gradient and stress data of the sub-model at different times based on the historical deformation data can include: taking the historical deformation data corresponding to each moment as a boundary condition, and calculating the deformation gradient and stress data of the sub-model at the corresponding moment. Specifically, in the process of calculating the deformation gradient and stress data of each sub-model, a calculation sequence of the sub-model can be constructed according to the deformation data at different times, and the deformation gradient and stress data of each sub-model can be calculated with the deformation data corresponding to each time as the boundary condition.

[0060] In a specific embodiment, the use of the deformation gradient and stress data of the sub-model at different times to predict the life of the corresponding target area, so as to finally obtain the target prediction result corresponding to the target tire, may include: using the deformation gradient and stress data of the sub-model at different times to predict the life of the corresponding target area, so as to obtain the corresponding regional prediction result; based on a number of regional prediction results, the initial prediction result is corrected, so as to finally obtain the target prediction result corresponding to the target tire. Specifically, for the sub-model of each target area, a more refined life prediction result can be obtained through life prediction, that is, a more accurate regional prediction result can be obtained; then these regional prediction results can be used to correct the initial prediction results obtained previously, and after correction, the target prediction result of the target tire can be finally obtained.

[0061] In another specific embodiment, the process of predicting the life of the target tire may also include: obtaining modification instructions for tire material parameters through a preset interactive interface; modifying the relevant tire material parameters according to the modification instructions to obtain modified parameters, so as to perform a life prediction operation on the target tire based on the modified parameters. It is understandable that after the relevant staff inputs the tire material parameters that characterize fatigue, they can input the modification instructions of the material parameters through the relevant interactive interface to modify the material properties of certain areas corresponding to the target tire, so that the life of the target tire can be iteratively predicted, providing a friendly life prediction interactive process.

[0062] In this way, the present application first combines the finite element calculation results of the global model of the tire and the corresponding tire material parameters to give a fatigue life prediction for the tire as a whole; then uses the relevant grid information and the relevant strain history data to build a more sophisticated sub-model for fatigue life prediction; this can optimize the precision of the tire life prediction and improve the accuracy of the tire life prediction. It can be seen that the present application determines the key variables affecting fatigue life and selects a suitable theoretical model through the stress-strain history data under large deformation conditions, and combines the precise calculation of the weak position sub-model to make a more sophisticated life prediction for the local area; it can effectively handle complex tire structures and stress-strain data under extreme conditions, and can more accurately and reliably realize the fatigue life prediction of the tire, which has important application value for tire design and safety assessment.

[0063] The embodiment of the present application discloses a tire life prediction method, which can be implemented by software and specifically includes:

[0064] In the embodiment of the present application, an independent visualization software can be developed, which can calculate the fatigue performance of the tire according to the stress-strain data in the calculation results of Abaqus (finite element software for engineering simulation) under the tire rolling condition; Figure 2 The following is a diagram of the visualization software interface. Figure 3 and Figure 4There are two types of menu page diagrams, among which the user interface design requires an intuitive layout and navigation structure; an easy-to-input interface is provided to input the tire specifications, material properties, usage conditions, loading conditions, etc. that need to be customized; and a suitable result output display is provided to help users intuitively understand the analysis results. Functional encapsulation includes encapsulating the core algorithms of fatigue analysis. Data management includes a database system designed to store and manage user data, historical analysis results, material databases, etc.; as well as secure backup of user data; ensuring that the software can remain efficient and stable when processing large amounts of data and complex calculations. User interaction is a key step in improving user experience. A real-time feedback mechanism is designed to optimize the visualization process. The tire fatigue life prediction software is developed, and the software foreground input data is associated with the background fatigue life prediction kernel algorithm through interactive technology. The specific development process is not limited here. Such as Figure 5 As shown, the stress and strain data of the tire under specific working conditions are generated by finite element analysis software such as Abaqus; then these generated data (including finite element meshes, material properties, stress-strain cloud diagrams, and deformation gradient tensors, etc.) are standardized and unified in format to facilitate import into the prepared life prediction visualization software to achieve complete data reading. Material properties, loading conditions, and mesh information are stored in text format in the inp file. Finite element calculation results (including stress, strain, temperature, etc.) are stored in binary format in the odb file. Some of the calculation results (node ​​coordinates, stress-strain data, deformation gradients, etc.) are output in text format in a csv file. The visualization software for tire life prediction can automatically read material parameters from the inp file; read the deformation gradient tensor F, stress tensor σ, and strain tensor from the csv file , to predict the tire life, display the node information of the tire corresponding to the global model and the life prediction results related to each tire material area. It should be pointed out that since the Abaqus output csv file only contains rough information of the mesh, reading the csv file alone cannot completely reconstruct the mesh used for the initial calculation. If you want to obtain the mesh information by reading the inp file, you need to make a detailed analysis of the mesh type and format of abaqus, which is too time-consuming. Since the ultimate goal is to improve the display effect, the method of reconstructing the mesh according to the coordinates can be used here. Delaunay (triangulation algorithm) can divide a given point set into multiple triangles. The characteristic of this triangulation is to maximize the minimum angle of each triangle, thereby avoiding the appearance of slender triangles. The circumscribed circle of each Delaunay triangle does not contain any other points. This is the core feature of Delaunay triangulation and the basis for the implementation of the algorithm. If there is no four-point cocircularity in the point set, then its Delaunay triangulation is unique. If there is a four-point cocircularity, it can usually be solved by appropriate perturbation; combining the triangulation algorithm to reconstruct the mesh can improve the display effect of the stress-strain result cloud map.

[0065] It is understandable that the process of realizing tire life prediction through this visualization software may include two steps. First, it is necessary to import relevant data and perform fatigue life calculations. Import the two csv files required for calculating tire life into the software; the first csv file (Import Abaqus CSV) may contain: Abaqus finite element calculation result file, this file is a required file, and the content may include: cell point coordinates, cell material name, deformation gradient tensor (arranged by cell center), stress strain tensor, von Misses stress, etc. Figure 6The figure shows the von Misses stress distribution diagram displayed by the software. The second csv file (Load Material CSV) can be named materials.csv and placed in the same folder as the Abaqus finite element result file. If the materials.csv file is not found in this finite element result folder, a new materials.csv file can be generated based on the material name in the Abaqus data file; before proceeding to the next step of importing material parameters, it is necessary to edit the materials.csv file and enter the corresponding material fatigue parameters (the tire material parameters in the above embodiment). The materials.csv file may contain the following information: material name, material parameters characterizing fatigue (may include hyperelastic model material parameters, initial crack size, critical cracking energy, etc.). These two csv files can then be used to calculate and display the result cloud map of the life prediction results (initial prediction results in the above embodiment) of the target tire; the result cloud map may specifically include the von Mises stress distribution contained in the csv file imported from Abaqus, the life distribution under relaxed conditions (relaxed) and non-relaxed conditions (nonrelaxed), etc., such as Figure 7 The figure shows the calculated life distribution slice cloud map. After the life calculation is completed, the quantitative value of the expected life of each area of ​​the target tire can be displayed, and based on the quantitative value, the weak points (the target areas in the above embodiment) with more dangerous expected life characteristics are marked in the displayed result cloud map.

[0066] Second, construct a sub-model for life prediction. Figure 8 As shown in the figure, according to the mesh information of the Abaqus global model and the selected dangerous unit points (the current area of ​​life weakness), the sub-model structure network (mesh) corresponding to each weak point and the corresponding template inp file are automatically constructed; combined with the mesh information of the inp file corresponding to the global model, Abaqus can be called to recalculate the deformation gradient and stress data of each sub-model separately to perform relevant life prediction operations according to fatigue theory. First, the fatigue life is estimated according to the rolling and ground contact data of the global model of the target tire, and a point is selected from several weak points as a local point; the mesh information of the selected local point is determined to construct the initial state of the sub-model; then the deformation data at different times in the global model are read (static ground contact is the deformation data of equivalent different circumferential angle slices) to form the strain history data of this local point; then a sub-model calculation sequence is constructed according to the deformation data at different times, and the deformation corresponding to each time is used as the boundary condition to calculate the corresponding deformation gradient and stress data; then, according to the deformation gradient and stress data at different times of the sub-model, fatigue life prediction can be performed, and then the previously obtained life prediction results can be corrected, and finally the corrected target prediction results corresponding to the target tire can be obtained.

[0067] In another specific embodiment, Fig. 9 As shown in the figure, in the process of predicting the life of tires, the cracking energy density method and the large deformation theory can be combined to calculate the life; specifically, when describing the fracture behavior of flexible materials (such as rubber, plastic film, etc.), the energy that must be applied to make the crack expand in a certain direction is called tearing energy, which is defined as:

[0068] ;

[0069] ;

[0070] Among them, k is a dimensionless parameter related to strain, a is the crack size, and Wc is the cracking energy density. Here, specific strain planes and stress planes need to be specified. The calculation formula for cracking energy density is:

[0071] ;

[0072] in, , is stress, is the cracking plane, which can be determined by the deformation gradient tensor F: ;

[0073] in, The expected fatigue life of the material is expressed as:

[0074] ;

[0075] in,

[0076] ;

[0077] ;

[0078] Therefore, the relationship between material life and cracking energy density is:

[0079] ;

[0080] in, is the initial crack size, and the general initial crack length range is 10-90μm. For the maximum crack size allowed, 5mm can be selected as the upper limit of the integral of the calculation process. Under large deformation conditions, the material properties of rubber must show nonlinear models. The simulation of rubber in Abaqus is achieved by using a viscoelastic material model. The viscoelastic model can describe the time-dependent behavior of the material when subjected to long-term loading. The viscoelastic model in Abaqus is:

[0081] ;

[0082] The room temperature static creep experiment to obtain G(t) is very time-consuming. The dynamic mechanical analysis method can be used. The static to dynamic transformation formula is:

[0083] ;

[0084] in represents the storage modulus, represents the consumption modulus, and the expressions are:

[0085] ;

[0086] ;

[0087] The Fourier transform of G(t) yields:

[0088] ;

[0089] The required change of G over time can be converted into and As the frequency increases, it saves more calculation time.

[0090] For the large deformation theory, the expression for the crack energy density increment under multiaxial stress state is as follows:

[0091] ;

[0092] Where C is the right Cauchy-Green deformation tensor: S is the second PK stress tensor at the material element, which is obtained by mapping the Cauchy stress tensor (true stress tensor, defined in the current or deformed configuration) to the reference configuration. This mapping takes into account the deformation of the material so that the stress tensor adapts to the geometry before deformation. In finite element software such as Abaqus, the second PK stress is often used to simulate materials with large deformation behavior.

[0093] Furthermore, by slicing the circumference of the circle under rolling conditions, the stress history of a slice along the circumference is equivalently simulated, and the local cracking energy density dW is calculated. cAnd integrate. Consider only the shear strain contribution and the crack closure effect:

[0094] ;

[0095] Therefore, the material life expression under large deformation conditions is finally determined as:

[0096] ;

[0097] The calculation process finds the rotation axis, calculates the corresponding circular angle, regards the center of the body with the same circular angle as a slice, and then corresponds different slices to the cyclic working conditions, and calculates the corresponding cracking energy density and fatigue life of different slices.

[0098] In this way, the present application firstly combines the finite element calculation results of the global model of the tire and the corresponding tire material parameters to give the fatigue life prediction of the tire as a whole; determines the variables that have a significant impact on the tire life, and selects the appropriate fatigue life calculation theory; again, according to the typical use conditions of the tire combined with the simulation data of the rolling conditions, the stress-strain history of each unit of the tire is given, and the prediction data is given by the large deformation fatigue life theory. Then, a more refined sub-model is constructed using the relevant grid information and the relevant strain history data for fatigue life prediction; since the occurrence of fatigue fracture is closely related to the bonding of the composite material, the calculation of the life distribution at the interface of the composite requires a more refined grid division. Therefore, the present application introduces a method of fine calculation of the weak position sub-model in the fatigue life calculation process to accurately predict the life distribution of the composite material. In addition, the tire prediction method can be integrated into the software for functional testing and optimization to ensure that the software is both accurate, reliable and user-friendly; this can optimize the precision of the tire life prediction and improve the accuracy and efficiency of the tire life prediction.

[0099] like Fig.10 As shown, the embodiment of the present application discloses a tire life prediction device, comprising:

[0100] An information acquisition module 11 is used to obtain the finite element calculation results of the global model corresponding to the target tire and tire material parameters characterizing fatigue;

[0101] A first prediction module 12, configured to perform life prediction on the target tire based on the finite element calculation result and the tire material parameter, and obtain an initial prediction result corresponding to the global model;

[0102] A sub-model construction module 13, for determining a target area that meets a preset tire life weakness condition from the global model according to the initial prediction result, and constructing a corresponding sub-model based on grid information corresponding to the target area;

[0103] The second prediction module 14 is used to use the deformation gradient and stress data of the sub-model at different times to perform life prediction on the corresponding target area, so as to finally obtain the target prediction result corresponding to the target tire.

[0104] It can be seen that the present application first combines the finite element calculation results of the global model of the tire and the corresponding tire material parameters to give the fatigue life prediction of the tire as a whole; then uses the relevant grid information and the relevant strain history data to construct a more refined sub-model for fatigue life prediction; this can optimize the refinement of the tire life prediction and improve the accuracy of the tire life prediction.

[0105] In a specific embodiment, the information acquisition module 11 may include:

[0106] A calculation result acquisition unit, used to calculate the global model of the target tire under preset working conditions through finite element analysis software to obtain corresponding finite element calculation results; the finite element calculation results include grid information of the global model, tire material properties, stress-strain cloud map and deformation gradient tensor;

[0107] The material parameter acquisition unit is used to acquire tire material parameters corresponding to the target tire and representing fatigue through a preset input interface; the tire material parameters include hyperelastic model material parameters, initial crack size in the fatigue model, and critical cracking energy.

[0108] In a specific embodiment, the first prediction module 12 may include:

[0109] A first prediction unit, configured to perform life prediction on the target tire based on the finite element calculation result and the tire material parameter, and obtain a result cloud map corresponding to the global model;

[0110] The display optimization unit is used to perform display optimization on the result cloud map through a triangulation algorithm to obtain an initial prediction result corresponding to the global model.

[0111] In a specific embodiment, the device may further include:

[0112] A data reading module is used to read the deformation data of the global model at different times in the finite element calculation results through a preset script to form the historical deformation data of the target area corresponding to the sub-model, so as to calculate the deformation gradient and stress data of the sub-model at different times based on the historical deformation data.

[0113] In another specific embodiment, the data reading module may include:

[0114] The data calculation unit is used to use the historical deformation data corresponding to each moment as boundary conditions to calculate the deformation gradient and stress data of the sub-model at the corresponding moment.

[0115] In a specific embodiment, the second prediction module 14 may include:

[0116] A second prediction unit is used to use the deformation gradient and stress data of the sub-model at different times to perform life prediction on the corresponding target area to obtain a corresponding area prediction result;

[0117] The prediction result correction unit is used to correct the initial prediction result based on the plurality of regional prediction results, so as to finally obtain a target prediction result corresponding to the target tire.

[0118] In a specific embodiment, the device may further include:

[0119] A modification instruction acquisition module, used to acquire modification instructions for tire material parameters through a preset interactive interface;

[0120] The material parameter modification module is used to modify the relevant tire material parameters according to the modification instruction to obtain modified parameters, so as to perform life prediction operation on the target tire based on the modified parameters.

[0121] Furthermore, the present application also discloses an electronic device. Fig.11 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of use of the present application.

[0122] Fig.11 The present invention provides a schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the tire life prediction method disclosed in any of the above embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0123] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0124] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0125] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, and can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program that can be used to complete the tire life prediction method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include computer programs that can be used to complete other specific tasks.

[0126] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the tire life prediction method disclosed above. The specific steps of the method can refer to the corresponding contents disclosed in the above embodiments, and will not be repeated here.

[0127] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0128] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0129] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0130] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0131] The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technicians in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A tire life prediction method, characterized in that: include: Obtain finite element calculation results of the global model corresponding to the target tire and tire material parameters characterizing fatigue; Based on the finite element calculation results and the tire material parameters, life prediction is performed on the target tire to obtain an initial prediction result corresponding to the global model; Determine a target area that meets a preset tire life weakness condition from the global model according to the initial prediction result, and construct a corresponding sub-model based on grid information corresponding to the target area; The deformation gradient and stress data of the sub-model at different times are used to predict the life of the corresponding target area, so as to finally obtain the target prediction result corresponding to the target tire.

2. The tire life prediction method according to claim 1, characterized in that: The step of obtaining the finite element calculation results of the global model corresponding to the target tire and the tire material parameters characterizing fatigue includes: Calculating the global model of the target tire under preset working conditions using finite element analysis software to obtain corresponding finite element calculation results; the finite element calculation results include mesh information of the global model, tire material properties, stress-strain cloud diagrams, and deformation gradient tensors; The tire material parameters corresponding to the target tire and representing fatigue are obtained through a preset input interface; the tire material parameters include hyperelastic model material parameters, initial crack size in the fatigue model, and critical cracking energy.

3. The tire life prediction method according to claim 1, characterized in that: The performing life prediction on the target tire based on the finite element calculation result and the tire material parameter to obtain an initial prediction result corresponding to the global model includes: Based on the finite element calculation results and the tire material parameters, life prediction is performed on the target tire to obtain a result cloud map corresponding to the global model; The result cloud map is displayed and optimized by a triangulation algorithm to obtain an initial prediction result corresponding to the global model.

4. The tire life prediction method according to claim 1, characterized in that: Before using the deformation gradient and stress data of the sub-model at different times to predict the life of the corresponding target area, the method further includes: The deformation data of the global model at different times in the finite element calculation results are read through a preset script to form the historical deformation data of the target area corresponding to the sub-model, so as to calculate the deformation gradient and stress data of the sub-model at different times based on the historical deformation data.

5. The tire life prediction method according to claim 4, characterized in that: The calculating the deformation gradient and stress data of the sub-model at different times based on the historical deformation data includes: The historical deformation data corresponding to each moment is used as the boundary condition to calculate the deformation gradient and stress data of the sub-model at the corresponding moment.

6. The tire life prediction method according to claim 1, characterized in that: The use of the deformation gradient and stress data of the sub-model at different times to predict the life of the corresponding target area, so as to finally obtain the target prediction result corresponding to the target tire, includes: Using the deformation gradient and stress data of the sub-model at different times to predict the life of the corresponding target area, to obtain a corresponding regional prediction result; The initial prediction result is corrected based on the plurality of regional prediction results to finally obtain a target prediction result corresponding to the target tire.

7. The tire life prediction method according to any one of claims 1 to 6, characterized in that: Also includes: Obtain modification instructions for tire material parameters through a preset interactive interface; The relevant tire material parameters are modified according to the modification instruction to obtain modified parameters, so as to perform a life prediction operation on the target tire based on the modified parameters.

8. A tire life prediction device, characterized in that: include: An information acquisition module, used to obtain the finite element calculation results of the target tire corresponding to the global model and tire material parameters characterizing fatigue; A first prediction module, configured to predict the life of the target tire based on the finite element calculation result and the tire material parameter, and obtain an initial prediction result corresponding to the global model; A sub-model construction module, used to determine a target area that meets the preset tire life weakness condition from the global model according to the initial prediction result, and to construct a corresponding sub-model based on the grid information corresponding to the target area; The second prediction module is used to use the deformation gradient and stress data of the sub-model at different times to predict the life of the corresponding target area, so as to finally obtain the target prediction result corresponding to the target tire.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the tire life prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, which, when executed by a processor, implements the tire life prediction method according to any one of claims 1 to 7.

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