A tire life prediction method, device, equipment and storage medium

Abstract

The application discloses a tire life prediction method, device and equipment and a storage medium, relates to the technical field of tire detection, and comprises the following steps: acquiring 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 submodel based on grid information corresponding to the target area; and performing life prediction on the corresponding target area by using deformation gradients and stress data of the submodel at different times, to finally obtain a target prediction result corresponding to the target tire. In this way, the application can optimize the fineness of tire life prediction and improve the accuracy of tire life prediction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tire detection, in particular to a tire life prediction method, device, equipment and storage medium. BACKGROUND

[0002] The tire fatigue life prediction methods on the market at present generally fail to fully cope with the calculation demand of large deformation fatigue life of the tire under high load working conditions. Generally, the final life prediction result of the tire is directly obtained by using the stress and strain history data corresponding to the global model of the tire; the multi-material composite structure of the tire and the stress and strain history data thereof under extreme working conditions cannot be effectively processed in the process, thereby affecting the accuracy and reliability of the prediction.

[0003] Therefore, how to more accurately predict the tire fatigue life is a problem to be solved in the field. SUMMARY

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

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

[0006] obtaining the finite element calculation result of the global model corresponding to the target tire and the tire material parameter representing 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] 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 the grid information corresponding to the target area;

[0009] performing life prediction on the corresponding target area by using the deformation gradient and stress data of the sub-model at different times, to finally obtain a target prediction result corresponding to the target tire.

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

[0011] performing calculation on the global model of the target tire under a preset working condition by using a finite element analysis software, to obtain a corresponding finite element calculation result; the finite element calculation result comprises grid information, tire material properties, stress and strain cloud diagram and deformation gradient tensor of the global model;

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

[0013] Optionally, the 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 includes:

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

[0015] display optimization on the result cloud map through a triangulation algorithm to obtain the initial prediction result corresponding to the global model.

[0016] Optionally, before the life prediction on the target region corresponding to the submodel at different time points by using the deformation gradient and stress data of the submodel at different time points, the method further includes:

[0017] reading, through a preset script, deformation data of the global model at different time points in the finite element calculation result to constitute historical deformation data of the target region corresponding to the submodel, so as to calculate the deformation gradient and stress data of the submodel at different time points based on the historical deformation data.

[0018] Optionally, the calculation of the deformation gradient and stress data of the submodel at different time points based on the historical deformation data includes:

[0019] taking the historical deformation data corresponding to each time point as a boundary condition to calculate the deformation gradient and stress data of the submodel at the corresponding time point.

[0020] Optionally, the life prediction on the target region corresponding to the submodel at different time points by using the deformation gradient and stress data of the submodel at different time points to finally obtain a target prediction result corresponding to the target tire includes:

[0021] the life prediction on the target region corresponding to the submodel at different time points by using the deformation gradient and stress data of the submodel at different time points to obtain a region prediction result corresponding to the target region;

[0022] the correction on the initial prediction result based on a plurality of region prediction results to finally obtain the target prediction result corresponding to the target tire.

[0023] Optionally, the method further includes:

[0024] acquiring, through a preset interaction interface, a modification instruction for tire material parameters;

[0025] According to the modification instruction, a relevant tire material parameter is modified to obtain a modified parameter, so as to perform a life prediction operation on the target tire based on the modified parameter.

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

[0027] An information acquisition module is configured to acquire a finite element calculation result of a global model corresponding to a target tire and a tire material parameter representing fatigue;

[0028] A first prediction module is configured to perform 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;

[0029] A sub-model construction module is configured to determine a target region meeting a preset tire life weak condition from the global model according to the initial prediction result, and construct a corresponding sub-model based on mesh information corresponding to the target region;

[0030] A second prediction module is configured to perform life prediction on the corresponding target region by using deformation gradient and stress data of the sub-model at different time instants, to finally obtain a target prediction result corresponding to the target tire.

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

[0032] A memory is configured to save a computer program;

[0033] A processor is configured 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 configured to save a computer program, and the computer program is executed by a processor to implement the tire life prediction method as described above.

[0035] It can be seen that the application firstly acquires the finite element calculation result of the global model corresponding to the target tire and the tire material parameter representing fatigue; then performs 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; then determines a target region meeting a preset tire life weak condition from the global model according to the initial prediction result, and constructs a corresponding submodel based on the grid information corresponding to the target region; and finally performs life prediction on the corresponding target region by using the deformation gradient and stress data of the submodel at different times, to obtain a target prediction result corresponding to the target tire. In this way, the application firstly gives the fatigue life prediction of the whole tire by combining the finite element calculation result of the global model of the tire and the corresponding tire material parameter; then constructs a more detailed submodel by using related grid information and related strain history data to perform fatigue life prediction; and thus the fineness of tire life prediction can be optimized, and the accuracy of tire life prediction can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of the provided drawings.

[0037] Figure 1 A tire life prediction method flowchart disclosed by the present application;

[0038] Figure 2 A visual software interface diagram disclosed by the present application;

[0039] Figure 3 A visual software menu page diagram disclosed by the present application;

[0040] Figure 4 Another visual software menu page diagram disclosed by the present application;

[0041] Figure 5 A finite element calculation result processing flowchart disclosed by the present application;

[0042] Figure 6 A stress distribution schematic diagram disclosed by the present application;

[0043] Figure 7 A life distribution slice cloud chart disclosed by the present application;

[0044] Figure 8 A submodel processing flowchart disclosed by the present application;

[0045] Figure 9 A processing flow chart combining a crack energy density method and a large deformation theory is disclosed in the present application.

[0046] Figure 10 A structure schematic diagram of a tire life prediction device is disclosed in the present application.

[0047] Figure 11 A structure diagram of an electronic device is disclosed in the present application. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0049] Referring to Figure 1 The embodiments of the present application disclose a tire life prediction method, which comprises the following steps:

[0050] In step S11, the finite element calculation result of the global model corresponding to the target tire and the tire material parameter representing fatigue are obtained.

[0051] In the present embodiment, to predict the life of the tire, first, relevant data needs to be collected, and the finite element calculation result of the global model corresponding to the tire can be obtained through finite element simulation. It can be understood that the stress and strain data of the tire under a specific working condition can be obtained through finite element analysis software. Correspondingly, the parameter variable having a significant impact on the life of the tire, i.e., the tire material parameter representing fatigue, also needs to be determined, so as to evaluate the service life of the tire in combination with the calculation result and the tire material parameter.

[0052] In a specific embodiment, the acquiring the finite element calculation result of the global model corresponding to the target tire and the tire material parameter representing fatigue can include: performing calculation on the global model of the target tire under a preset working condition by a finite element analysis software to obtain a corresponding finite element calculation result; the finite element calculation result includes grid information of the global model, tire material properties, stress-strain nephogram and deformation gradient tensor; and acquiring the tire material parameter corresponding to the target tire through a preset input interface; the tire material parameter includes hyperelastic model material parameter, initial crack size in a fatigue model and critical cracking energy. Specifically, the global model of the target tire can be simulated and calculated under a preset specific working condition (such as a large deformation working condition) by the finite element analysis software, and stress-strain data of the target tire under the corresponding working condition can be obtained; the calculation result of the finite element analysis software can include grid information of the global model of the target tire, material properties of the tire, stress-strain nephogram of the tire under each working condition and deformation gradient tensor and other information. Correspondingly, each tire material parameter input by relevant personnel through a preset interactive interface can be acquired, which can include hyperelastic model material parameter, initial crack size in a fatigue model and critical cracking energy and other parameters.

[0053] Step S12, 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.

[0054] In the embodiment, the finite element calculation result corresponding to the target tire and each tire material parameter can be obtained through the above steps, and life prediction can be performed on the target tire based on these data; here, the target tire is subjected to overall fatigue life prediction, and finally an initial prediction result corresponding to the global model is obtained. It should be noted that the life prediction method can include strain energy density prediction method, fracture energy density prediction method and cracking energy calculation method considering crack closure effect, and is not limited here.

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

[0056] Step S13, 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 the grid information corresponding to the target area.

[0057] In the embodiment, after obtaining the initial prediction result of the target tire through the above steps, the target area meeting the tire life weak condition can be marked in the result cloud map of the initial prediction result based on the preset tire life weak condition; it can be understood that the process of selecting the target area can combine the area life distribution of the target tire under different working conditions to quantify the expected life, and can further determine the relatively weak (quantitative value is lower or higher) area. Then, the initial state of each target area is reconstructed by using the grid information of the global model of the target tire corresponding to the target area, to obtain each sub-model corresponding to each target area.

[0058] Step S14, predicting the life of the corresponding target area by using the deformation gradient and stress data of the sub-model at different time points to finally obtain the target prediction result corresponding to the target tire.

[0059] Further, after the sub-models of the target regions are constructed, the life prediction of the target regions can be performed again by combining the deformation gradient and stress data of the sub-models at different time to finally obtain the target prediction result corresponding to the target tire. In a specific embodiment, before the life prediction of the corresponding target region is performed by using the deformation gradient and stress data of the sub-models at different time, the method can further include: reading the deformation data of the global model at different time in the finite element calculation result by a preset script to constitute the historical deformation data of the target region corresponding to the sub-model, so as to calculate the deformation gradient and stress data of the sub-model at different time based on the historical deformation data. Specifically, the deformation data of the global model at different time corresponding to the target tire can be read by a script calling a finite element analysis software to constitute the historical deformation data of each target region. Further, the calculation of the deformation gradient and stress data of the sub-model at different time based on the historical deformation data can include: taking the historical deformation data corresponding to each time as a boundary condition to calculate the deformation gradient and stress data of the sub-model at the corresponding time. Specifically, in the process of calculating the deformation gradient and stress data of each sub-model, a calculation sequence of a sub-model can be constructed according to the deformation data at different time, and the deformation gradient and stress data of each sub-model are calculated by taking the deformation data corresponding to each time as a boundary condition.

[0060] In a specific embodiment, the life prediction of the corresponding target region by using the deformation gradient and stress data of the sub-model at different time to finally obtain the target prediction result corresponding to the target tire can include: performing the life prediction of the corresponding target region by using the deformation gradient and stress data of the sub-model at different time to obtain a region prediction result; and correcting the initial prediction result based on a plurality of region prediction results to finally obtain the target prediction result corresponding to the target tire. Specifically, for the sub-models of each target region, a more detailed life prediction result, i.e., a more accurate region prediction result, can be obtained after the life prediction. Then, the initial prediction result obtained before can be corrected by using these region prediction results, and the target prediction result of the target tire can be finally obtained after the correction.

[0061] In yet another specific embodiment, in the process of life prediction of the target tire, the process can further include: obtaining a modification instruction for the tire material parameter through a preset interactive interface; modifying the relevant tire material parameter according to the modification instruction to obtain a modified parameter, so as to perform a life prediction operation on the target tire based on the modified parameter. It can be understood that after the relevant staff input the tire material parameter representing fatigue, the modification instruction of the material parameter can be input through the relevant interactive interface to modify the material properties of certain regions of the target tire. In this way, the life of the target tire can be iteratively predicted, and a friendly life prediction interactive process is provided.

[0062] In this way, the present application first gives the overall fatigue life prediction of the tire by combining the finite element calculation results of the global model of the tire and the corresponding tire material parameters; then uses the relevant grid information and the relevant strain history data to construct a more detailed submodel for fatigue life prediction; in this way, the degree of detail of the tire life prediction can be optimized, and the accuracy of the tire life prediction can be improved. As can be seen, by using the stress-strain history data under large deformation conditions, the present application determines the key variables affecting the fatigue life and selects the appropriate theoretical model, and combines the precise calculation of the weak position submodel to perform more detailed life prediction on the local area; it can effectively process complex tire structures and stress-strain data under extreme conditions, and can more accurately and reliably achieve fatigue life prediction of the tire, and has important application value for tire design and safety evaluation.

[0063] The embodiment of the present application discloses a tire life prediction method, which can be realized 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 Abaqus (finite element software for engineering simulation) calculation results under the tire rolling condition; for example, Figure 2 Fig. 1 shows a visualization software interface, Figure 3 and Figure 4Two menu pages, intuitive layout and navigation structure, easy input interface, custom tire specifications, material properties, usage conditions, loading conditions, and appropriate result output display to help users understand the analysis results. Function encapsulation includes encapsulating the core algorithm of fatigue analysis. Data management includes a database system designed to store and manage user data, historical analysis results, material database, etc.; and secure backup of user data; to ensure that the software remains efficient and stable when processing large amounts of data and complex calculations. User interaction is the key to improving user experience, real-time feedback mechanism, and optimized visualization process. The tire fatigue life prediction software is developed, and the front-end input data and back-end fatigue life prediction kernel algorithm are linked through interactive technology. The specific development process is not limited here. As shown in Figure 5 The stress and strain data of the tire under specific working conditions are generated by finite element analysis software such as Abaqus; then the generated data (including finite element mesh, material properties, stress and strain cloud map, and deformation gradient tensor, etc.) are standardized and formatted to facilitate import into the prepared life prediction visualization software, realizing complete data reading. Material properties, loading conditions, and mesh information are stored in inp files in text format. Finite element calculation results (including stress, strain, temperature, etc.) are stored in odb files in binary format. Part of the calculation results (node coordinates, stress and strain data, and deformation gradient, etc.) are output in csv files in text format. The visualization software for tire life prediction can automatically read material parameters from inp files; read deformation gradient tensor F, stress tensor σ and strain tensor ε from csv files; and read the stress and strain cloud map from odb files. The user can input the tire specifications, material properties, loading conditions, etc. in the input interface, and the software will automatically calculate the tire life prediction results and display them in the output interface. , the node information of the tire corresponding to the global model and the life prediction results related to each tire material area are displayed. It should be noted that since the Abaqus output csv file only contains rough information of the grid, only reading the csv file cannot completely reconstruct the grid used in the initial calculation. If you want to obtain the grid information by reading the inp file, you need to analyze the grid type and format of Abaqus in detail, which is too time-consuming. Since the final purpose is to improve the display effect, the method of reconstructing the grid according to the coordinates can be used here. Delaunay (triangulation algorithm) can divide the given point set into multiple triangles. The feature of this triangulation is to maximize the minimum angle of each triangle, so as to avoid the appearance of slender triangles. Each Delaunay triangle does not contain any other points in its circumscribed circle. This is the core feature of Delaunay triangulation and the basis for algorithm implementation. If there is no four-point circle in the point set, its Delaunay triangulation is unique. If there is a four-point circle, it can usually be solved by appropriate perturbation; combining the triangulation algorithm to reconstruct the grid can improve the display effect of the stress-strain result cloud chart.

[0065] It can be understood that the process of life prediction of the tire through the visualization software can include two steps. First, the relevant data needs to be imported and the fatigue life calculation is performed. Two csv files required for calculating the tire life are imported into the software; the first csv file (Import Abaqus CSV) contains the following contents: Abaqus finite element calculation result file, which is a required file, and the contents can include: element node coordinates, element material name, deformation gradient tensor (arranged according to the element center), stress-strain tensor, von Misses stress, etc. For example Figure 6The von Misses stress distribution diagram displayed by the software is shown. 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 according to the material name in the Abaqus data file; the materials.csv file needs to be edited and the corresponding material fatigue parameters (the tire material parameters in the above embodiment) are input before the material parameters are imported in the next step. The materials.csv file can include the following information: material name, material parameters representing fatigue (which can include hyperelastic model material parameters, initial crack size, critical cracking energy, etc.). Then the two csv files can be used to calculate and display the result cloud diagram of the life prediction result (the initial prediction result in the above embodiment) of the target tire; the result cloud diagram can specifically include the von Mises stress distribution contained in the csv file imported from Abaqus, the life distribution under relaxed and non-relaxed conditions, etc., as shown in Figure 7 The calculated life distribution slice cloud diagram is shown. After the life calculation is completed, the quantitative value of the expected life of each region of the target tire can be displayed, and the weak points (the target regions in the above embodiment) represented by the quantitative value of the expected life are marked in the displayed result cloud diagram.

[0066] Secondly, a sub-model is constructed for life prediction. As shown in Figure 8 According to the mesh information of the Abaqus global model and the selected dangerous unit point (the current region with weak life), 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, the deformation gradient and stress data of each sub-model can be calculated again by calling Abaqus, so as to perform related life prediction operations according to the fatigue theory. First, the fatigue life is estimated according to the rolling and grounding data of the global model of the target tire, and a point is selected from the 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 (static grounding is the deformation data of the equivalent different circumferential angle slices) are read to form the strain history data of the local point; then a sub-model calculation sequence is constructed according to the deformation data at different times, and the corresponding deformation is taken as the boundary condition to calculate the corresponding deformation gradient and stress data; then the fatigue life prediction can be performed according to the deformation gradient and stress data of the sub-model at different times, and the previously obtained life prediction result can be corrected, and finally the corrected target prediction result corresponding to the target tire can be obtained.

[0067] In another specific embodiment, as shown in FIG. 2, the process of life prediction of the tire can combine the cracking energy density method and the large deformation theory to calculate the life; specifically, in describing the fracture behavior of flexible materials (such as rubber, plastic film, etc.), the energy that must be applied in order to make a crack propagate in a certain direction is called tearing energy, defined as: Figure 9

[0068] ;

[0069] ;

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

[0071] ;

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

[0073] where . The expected fatigue life of the material is expressed as:

[0074] ;

[0075] where

[0076] ;

[0077] ;

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

[0079] ;

[0080] where is the initial crack size, and the initial crack length is generally in the range of 10-90 μm. ​For the maximum crack size allowed, 5mm can be chosen as the upper limit of integration for the calculation process. Under large deformation conditions, the material properties of rubber must show a nonlinear model. In Abaqus, the simulation of rubber is realized 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 static creep experiment at room temperature to obtain G(t) is time-consuming, and the dynamic mechanical analysis method can be used. The static-to-dynamic conversion formula is:

[0083] ;

[0084] where represents the storage modulus, represents the loss modulus, and the expressions are respectively:

[0085] ;

[0086] ;

[0087] The Fourier transform of G(t) can be obtained:

[0088] ;

[0089] This formula can convert the originally required G(t) into and as a function of frequency, which saves more calculation time.

[0090] For large deformation theory, the expression for the incremental energy density of cracking under multi-axial 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 usually used to simulate materials with large deformation behavior.

[0093] Further, by rolling the circumferential angle slice, the stress history of a slice along the circumference of a circle is simulated, and the local cracking energy density dW is calculated cand integrate. Only consider the shear strain contribution, crack closure effect:

[0094] ;

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

[0096] ;

[0097] The calculation process is to find the rotation axis, calculate the corresponding circumferential angle, regard the body centers with the same circumferential angle as a slice, correspond different slices to the cyclic working conditions, and calculate the cracking energy density and fatigue life of the superposition of different slices.

[0098] In this way, the application first gives the overall fatigue life prediction of the tire by combining the finite element calculation results of the global model of the tire and the corresponding tire material parameters; determines the variables that have a major impact on the tire life, and selects the appropriate fatigue life calculation theory; secondly, according to the typical use of the tire Working conditions combined with the simulation data of the rolling working conditions give the stress-strain history of each unit of the tire, and give the prediction data according to the large deformation fatigue life theory. Then use the related grid information and related strain history data to build a more detailed sub-model to predict the fatigue life; since the fatigue fracture is closely related to the bonding of the composite material, the life distribution of the interface of the composite needs more detailed grid division. Therefore, the application introduces the way 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. And, the tire prediction method can be integrated into the software for function test and optimization to ensure that the software is accurate, reliable and user-friendly; in this way, the fineness of the tire life prediction can be optimized, and the accuracy and efficiency of the tire life prediction can be improved.

[0099] As shown in Figure 10 , the embodiment of the application discloses a tire life prediction device, comprising:

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

[0101] The first prediction module 12 is used to predict the life of the target tire based on the finite element calculation results and the tire material parameters, and obtain the initial prediction result corresponding to the global model;

[0102] The sub-model construction module 13 is used to determine the target area meeting the preset tire life weak condition from the global model according to the initial prediction result, and construct the corresponding sub-model based on the grid information corresponding to the target area;

[0103] The second prediction module 14 is configured to perform life prediction on the target region corresponding to the sub-model at different time points by using the deformation gradient and stress data of the sub-model at the different time points, so as to finally obtain a target prediction result corresponding to the target tire.

[0104] Therefore, the present application firstly gives the fatigue life prediction of the whole tire by combining the finite element calculation result of the global model of the tire and the corresponding tire material parameters; then constructs a more detailed sub-model by using the related grid information and the related strain history data to perform the fatigue life prediction; in this way, the accuracy of the tire life prediction can be improved.

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

[0106] The calculation result acquisition unit is configured to perform calculation on the global model of the target tire under a preset working condition by using a finite element analysis software, so as to obtain corresponding finite element calculation results; the finite element calculation results include grid information, tire material properties, stress-strain cloud maps and deformation gradient tensors of the global model.

[0107] The material parameter acquisition unit is configured to acquire tire material parameters corresponding to the target tire by using 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 can include:

[0109] The first prediction unit is configured to perform life prediction on the target tire based on the finite element calculation results and the tire material parameters, so as to obtain a result cloud map corresponding to the global model.

[0110] The display optimization unit is configured to perform display optimization on the result cloud map by using a triangulation algorithm, so as to obtain an initial prediction result corresponding to the global model.

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

[0112] The data reading module is configured to read deformation data of the global model at different time points in the finite element calculation results by using a preset script, so as to constitute historical deformation data of the target region corresponding to the sub-model, so as to calculate the deformation gradient and stress data of the sub-model at different time points based on the historical deformation data.

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

[0114] The data calculation unit is configured to calculate the deformation gradient and stress data of the sub-model at the corresponding time point by taking the historical deformation data corresponding to each time point as a boundary condition.

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

[0116] The second prediction unit is configured to perform life prediction on the target region at the corresponding time point by using the deformation gradient and stress data of the sub-model at the corresponding time point, to obtain a corresponding regional prediction result.

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

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

[0119] The modification instruction acquisition module is configured to acquire a modification instruction for the tire material parameter through a preset interaction interface.

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

[0121] Further, the embodiments of the present application also disclose an electronic device, Figure 11 is the structure diagram of the electronic device 20 according to an exemplary embodiment, and the content in the figure cannot be considered as any limitation on the use range of the present application.

[0122] Figure 11 A structure schematic diagram of an electronic device 20 provided by the embodiments of the present application. The electronic device 20 specifically can 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. Wherein, the memory 22 is used to store a computer program, the computer program is loaded and executed by the processor 21, to realize the related steps in the tire life prediction method disclosed by any of the preceding embodiments. In addition, the electronic device 20 in the present embodiment can be specifically an electronic computer.

[0123] In this embodiment, the power supply 23 is configured to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 is configured to create a data transmission channel between the electronic device 20 and external devices, and the communication protocol followed by the communication interface 24 can be any communication protocol applicable to the technical solution of the present application, which will not be specifically limited herein; the input / output interface 25 is configured to obtain external input data or output data to the outside, and the specific interface type can be selected according to the specific application needs, which will not be specifically limited herein.

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

[0125] The operating system 221 is configured to manage and control each hardware device on the electronic device 20 and the computer program 222, and can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program capable of completing the tire life prediction method executed by the electronic device 20 disclosed in any of the preceding embodiments, the computer program 222 can further include a computer program capable of completing other specific work.

[0126] Further, the present application also discloses a computer readable storage medium for storing a computer program; wherein the computer program is executed by a processor to implement the tire life prediction method disclosed above. For the specific steps of the method, please refer to the corresponding content disclosed in the preceding embodiments, which will not be described here.

[0127] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. For the same or similar parts between each embodiment, please refer to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and please refer to the method part for the relevant part.

[0128] The skilled person can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of both. In order to clearly show the interchangeability of hardware and software, the components and steps of each example have been described in the above description. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0129] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM, flash memory, ROM, electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. The

[0130] Finally, it should be noted that, in the description of the application, relational terms such as first and second, and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0131] The above provides a detailed description of the technical solutions of the present application. The principles and implementation modes of the present application are described by applying specific examples. The above description of the embodiments is only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed; in summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method of tire life prediction, characterized by, The method comprises the following steps: obtaining the finite element calculation result of the global model corresponding to the target tire and the tire material parameter representing fatigue; based on the finite element calculation result and the tire material parameter, predicting the service life of the target tire to obtain the initial prediction result corresponding to the global model; determining the target area meeting the preset tire service life weak condition from the global model according to the initial prediction result, and constructing the corresponding sub-model based on the grid information corresponding to the target area; using the deformation gradient and stress data of the sub-model at different time to predict the service life of the corresponding target area, and finally obtaining the target prediction result corresponding to the target tire.

2. The tire life prediction method according to claim 1, characterized by, The method comprises the following steps: obtaining the finite element calculation result of the global model corresponding to the target tire and the tire material parameter representing fatigue; obtaining the finite element calculation result of the global model corresponding to the target tire and the tire material parameter representing fatigue through the finite element analysis software under the preset working condition, and obtaining the corresponding finite element calculation result; the finite element calculation result includes the grid information, tire material property, stress-strain nephogram and deformation gradient tensor of the global model; 3. The tire life prediction method according to claim 1, characterized by, obtaining the tire material parameter corresponding to the target tire through the preset input interface; the tire material parameter includes the hyperelastic model material parameter, the initial crack size in the fatigue model and the critical cracking energy. The method comprises the following steps: based on the finite element calculation result and the tire material parameter, predicting the service life of the target tire to obtain the initial prediction result corresponding to the global model; 4. The tire life prediction method according to claim 1, characterized by, obtaining the result nephogram corresponding to the global model by predicting the service life of the target tire based on the finite element calculation result and the tire material parameter; obtaining the initial prediction result corresponding to the global model by displaying and optimizing the result nephogram through the triangulation algorithm.

5. The method of claim 4, wherein Before the method of using the deformation gradient and stress data of the sub-model at different time to predict the service life of the corresponding target area, the method further comprises the following steps: reading the deformation data of the global model at different time in the finite element calculation result through the preset script to constitute 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 time based on the historical deformation data.

6. The tire life prediction method according to claim 1, characterized by, The method comprises the following steps: taking the historical deformation data corresponding to each time as the boundary condition to calculate the deformation gradient and stress data of the sub-model at the corresponding time. The method comprises the following steps:

7. The method of predicting the life of a tire according to any one of claims 1 to 6, characterized in that, using the deformation gradient and stress data of the sub-model at different time to predict the service life of the corresponding target area to obtain the corresponding area prediction result; based on a plurality of area prediction results, correcting the initial prediction result to finally obtain the target prediction result corresponding to the target tire. The method further comprises the following steps: obtaining the modification instruction for the tire material parameter through the preset interactive interface; According to the modification instruction, a relevant tire material parameter is modified to obtain a modified parameter, so as to perform a life prediction operation on the target tire based on the modified parameter.

8. A tire life prediction device characterized by comprising: The method comprises the steps of: an information acquisition module is configured to acquire a finite element calculation result of a global model corresponding to a target tire and a tire material parameter representing fatigue; a first prediction module is configured to perform 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; a sub-model construction module is configured to determine a target region meeting a preset tire life weak condition from the global model according to the initial prediction result, and construct a corresponding sub-model based on mesh information corresponding to the target region; a second prediction module is configured to perform life prediction on the corresponding target region by using deformation gradient and stress data of the sub-model at different time points to finally obtain a target prediction result corresponding to the target tire.

9. An electronic device, comprising: The method comprises the steps of: a memory is configured to save a computer program; a processor is 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, A computer program is saved, and the computer program is executed by a processor to implement the tire life prediction method according to any one of claims 1 to 7.

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