Tire durability simulation analysis system and method

Abstract

The present invention belongs to the technical field of tire performance simulation, and specifically relates to a tire durability simulation analysis system and method. A three-dimensional finite element model of the tire including cord-rubber interface details is used to focus on the cord-rubber interface area in the simulation to dynamically identify the first debonding event, and a debonding expansion path and failure evolution judgment mechanism are constructed through spatiotemporal monitoring of the newly added debonding area, thereby realizing tire durability performance evaluation from a microscopic interface scale and improving the accuracy of fatigue life prediction. At the same time, road conditions are introduced into the microscopic failure analysis framework based on the three-dimensional finite element model of the tire, and a stepped acceleration loading sequence is used to simulate tire responses at different speeds. By obtaining durability simulation results under different road conditions and speed combinations and visually presenting them in the form of isovalue maps, the fatigue performance distribution characteristics of the tire under complex operating conditions can be intuitively reflected.

Description

Technical Field

[0001] The present invention belongs to the technical field of tire performance simulation, and in particular relates to a tire durability simulation analysis system and method. Background Art

[0002] As the only functional component of a vehicle that comes into contact with the ground, the tire's structural performance directly impacts the vehicle's safety, driving stability, ride comfort, and fuel economy. With the development of the automotive industry and the increasing demand for product reliability, tire durability assessment has become a critical step in the product design and verification process.

[0003] Traditional tire durability testing relies on extensive physical experiments, which can be challenging, with long lead times, high costs, and poor repeatability. Therefore, conducting tire durability assessments in a virtual environment using computer simulation technology has become an important means of improving R&D efficiency and product quality.

[0004] Currently, mainstream tire durability simulation methods are mainly based on finite element modeling and stress-strain field analysis at the macroscale. For example, Chinese invention patent publication number CN104778313A proposes a tire fatigue life evaluation and prediction method. By constructing a three-dimensional finite element model of the tire, combined with numerical simulation to obtain the stress-strain response within the material, and using MATLAB for interpolation processing, a two-dimensional vector diagram of the strain energy density gradient is generated. The maximum gradient modulus and its directional information are extracted, thereby predicting the tire crack initiation location, propagation path, and fatigue life.

[0005] However, these macroscopic mechanical response-based simulation methods primarily focus on the macroscopic mechanical behavior of the tire's overall structure, ignoring the damage evolution behavior in key microscopic regions, such as the cord-rubber interface. In actual engineering practice, tire fatigue failure often begins at the bond interface between the cord and rubber, manifesting as microscale debonding. These microscopic behaviors have a decisive influence on the durability of the overall structure. Due to the lack of a microscopic mechanical model of the interfacial layers, traditional simulation methods struggle to identify the initial onset of debonding and its evolutionary path, resulting in insufficient prediction capabilities for the early stages of tire fatigue damage. This stage is often a critical window for fatigue crack initiation, and its accurate identification is crucial for simulating the subsequent failure process. Summary of the Invention

[0006] The present invention aims to overcome the shortcomings of the existing technology and proposes a tire durability simulation analysis system and method. By focusing on the tire cord-rubber interface, the microscopic failure behavior of the interface area is dynamically monitored and quantitatively evaluated during the simulation process. This breaks through the technical bottleneck of traditional reliance on macroscopic stress-strain fields to predict fatigue life, thereby achieving high-precision durability performance analysis based on the material interface scale.

[0007] The purpose of the present invention can be achieved through the following technical solutions: The first aspect of the present invention provides a tire durability simulation analysis system, including: a three-dimensional modeling module: constructing a three-dimensional tire model including the interface between the carcass cord layer and the rubber matrix.

[0008] Road condition simulation module: Configure a virtual road condition library and set a step-by-step speed increase simulation sequence within the speed range for each type of road condition.

[0009] Cord-rubber interface scanning module: At the start of each simulation, the tire cord-rubber interface is imaged using microfocus X-ray tomography at an initial interval.

[0010] First debonding identification module: Debonding is identified by detecting the crack width using cord-rubber interface imaging, and then the time T1 from the start of the simulation to the first occurrence of the debonding point is recorded.

[0011] Debonding monitoring module: After identifying debonding, continuous tomographic scanning is performed at dynamically shortened intervals. The scanning results are used to conduct extended monitoring of the first debonding area and concomitant monitoring of the newly added debonding area and the first debonding area based on time and space correlation.

[0012] Failure simulation module: Combine the crack propagation monitoring results of the first debonding area and the spatiotemporal coexistence of the newly added debonding area to perform failure simulation to obtain the time T2 from debonding to failure.

[0013] Simulation display module: Generates dual-channel tire durability maps at different speeds under different road conditions. The first channel outputs the T1 contour line, and the second channel outputs the T2 contour line.

[0014] The second aspect of the present invention proposes a tire durability simulation analysis method, comprising the following steps: S1: constructing a three-dimensional tire model including the interface between the carcass cord layer and the rubber matrix, configuring a virtual road condition library, and setting a step-by-step speed increase simulation sequence within the speed range for each type of road condition.

[0015] S2: Tire cord-rubber interface imaging is acquired by microfocus X-ray tomography at the initial interval at each simulation startup.

[0016] S3: Debonding is identified by detecting the crack width using cord-rubber interface imaging, and the time T1 from the start of the simulation to the first occurrence of the debonding point is recorded.

[0017] S4: After the debonding is identified, continuous tomographic scanning is performed at dynamically shortened intervals. The scanning results are used to perform extended monitoring of the first debonding area and concomitant monitoring of the newly added debonding area and the first debonding area based on temporal and spatial correlation.

[0018] S5: Combine the crack propagation monitoring results of the first debonding area and the spatiotemporal coexistence of the newly added debonding area to perform failure simulation to obtain the time T2 from debonding to failure.

[0019] S6: Generate a dual-channel map of tire durability at different speeds under different road conditions, with the first channel outputting a T1 contour line and the second channel outputting a T2 contour line.

[0020] Combining all the above technical solutions, the positive effects of the present invention are as follows: 1. The present invention uses a three-dimensional finite element model of the tire containing the details of the cord-rubber interface to focus on the cord-rubber interface area in the simulation to dynamically identify the first debonding event, and constructs a debonding expansion path and failure evolution judgment mechanism through spatiotemporal monitoring of the newly added debonding area, thereby realizing tire durability performance evaluation from the microscopic interface scale, which not only improves the accuracy of fatigue life prediction, but also provides a visual basis for revealing the internal damage mechanism of the tire structure.

[0021] 2. The present invention introduces road conditions within a microscopic failure analysis framework based on a three-dimensional finite element tire model and uses a stepped loading sequence to simulate tire response at different speeds. By obtaining durability simulation results under different road condition and speed combinations and visually presenting them in the form of isovalue maps, it can intuitively reflect the fatigue performance distribution characteristics of the tire under complex operating conditions, providing efficient and intuitive technical support for structural optimization and the formulation of usage restrictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the connection modules of a tire durability simulation analysis system provided in Example 1 of the present invention.

[0024] Figure 2 It is a diagram showing the implementation process of destaining identification in the present invention.

[0025] Figure 3 This is a step diagram of a tire durability simulation analysis method provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0027] Example 1

[0028] The present invention provides a tire durability simulation analysis system, which includes a three-dimensional modeling module, a road condition simulation module, a cord-rubber interface scanning module, a first debonding identification module, a debonding monitoring module, a failure simulation module and a simulation display module.

[0029] See also Figure 1 As shown in the figure, in the above module composition, the 3D modeling module provides the simulation object, the road condition simulation module provides the simulation condition environment, the cord rubber interface scanning module, the first debonding identification module, the debonding monitoring module and the failure simulation module serve as the intermediate links in the entire simulation process, and the modules are connected end to end. Finally, the durability simulation results obtained by the first debonding identification module and the failure simulation module are output to the simulation display module for visual display.

[0030] The three-dimensional modeling module is used to construct a three-dimensional tire model including the interface between the carcass cord layer and the rubber matrix.

[0031] As the specific implementation process of the above module: obtain geometric parameters according to the model specifications of the tire to be simulated. Specific geometric parameters include but are not limited to outer diameter, section width, section height and tread pattern design, and use modeling software based on the geometric parameters to establish a three-dimensional geometric model of the tire in the finite element analysis platform.

[0032] The above three-dimensional geometric model of the tire is established to ensure that the model shape is consistent with the actual tire structure.

[0033] According to the tire internal structure design drawings, the spatial distribution characteristics of the cord layer are clarified, and then the cord layer structure is constructed based on the established geometric model, and its relative position relationship embedded in the rubber matrix is ​​simulated.

[0034] It's important to note that the tire cord layer, a core component of the tire structure, is typically made of high-strength materials such as steel, nylon, or polyester fiber, primarily responsible for resisting stretching and maintaining the tire's shape. These cords are embedded in and securely fixed to the rubber matrix, enhancing the tire's overall strength and enabling it to withstand vehicle weight and dynamic loads. The interface between the two is the critical path for load transfer from the rubber to the cords. During tire operation, due to repeated dynamic loads and deformation, the interface between the cords and rubber is prone to microscopic debonding. These microscopic defects are often the starting point for fatigue cracks, which then expand into macroscopic damage, ultimately leading to tire failure.

[0035] By highlighting the tire cord structure and its relative positional relationship embedded in the rubber matrix in the established three-dimensional tire geometric model, the geometric characteristics of the cord-rubber interface can be clearly displayed, making it easier to focus on this area for initial debonding identification and subsequent monitoring during the simulation process, thereby improving the sensitivity and accuracy of debonding detection.

[0036] The material properties of each component of the tire to be simulated are obtained, and the corresponding material properties are accurately assigned to the corresponding geometric areas in the established geometric model according to different areas.

[0037] In the specific example of the above operation, the material of the cord layer is steel wire, nylon or polyester fiber, and its material properties include elastic modulus, Poisson's ratio, density and the like.

[0038] In the above operations, since different materials have different mechanical response characteristics, accurately assigning material properties can ensure that the simulation results are closer to the performance under actual working conditions and improve the accuracy of predictions.

[0039] The entire 3D tire geometry is meshed using meshing tools, with locally refined meshes applied at the interface between the cord and the rubber matrix.

[0040] The aforementioned meshing process transforms the tire model into a collection of discrete elements suitable for numerical simulation, enabling accurate interpretation of complex mechanical behavior. In particular, employing a localized mesh refinement strategy at the interface between the tire cord and the rubber matrix helps improve spatial resolution at this critical interface. Because this region is the primary site of debonding initiation and propagation, conventional mesh densities struggle to accurately capture subtle interfacial damage behavior. Localized refinement effectively reduces numerical error without significantly increasing overall computational cost, improving the computational stability and accuracy of interfacial responses.

[0041] The road condition simulation module is used to configure a virtual road condition library and set a step-by-step speed increase simulation sequence within a speed range for each type of road condition.

[0042] In the specific implementation of the above solution, the virtual road conditions can be urban roads, mountain roads, gravel roads, bumpy roads, etc. By configuring various road conditions, the durability performance of tires under different working conditions can be comprehensively evaluated, providing a scientific basis for product design optimization and usage recommendations.

[0043] Further applied to the above scheme, a step-by-step speed increase simulation sequence within a speed range is set for each type of road condition, including the following process: obtaining the rated speed range of the tire under the designed use conditions according to the tire model specifications, and determining the reference speed at the same time.

[0044] It should be noted that the rated speed range mentioned above refers to the minimum and maximum rotational frequencies allowed to operate under standard operating conditions determined according to the tire design specifications. This parameter directly reflects the tire's design load-bearing capacity. This range can usually be obtained in the product design manual or technical specification provided by the tire manufacturer.

[0045] It should be further explained that the main purpose of determining the reference speed is to provide a critical reference point for dividing the low-speed interval and the high-speed interval, thereby setting reasonable speed boundary conditions for subsequent simulation analysis.

[0046] For example, the base speed can be set to 60% of the upper limit of the rated speed range, selecting a moderate speed as the critical point. For example, if a tire's rated upper speed limit is 1000 rpm, the base speed can be set to 600 rpm. This approach effectively divides low-speed and high-speed operating ranges, ensuring that simulations cover diverse scenarios from daily use to extreme operating conditions.

[0047] As another example, consider the most common speed a vehicle experiences on regular roads, such as a 60km / h speed limit. This speed is first converted to a tire linear velocity, and then, combined with the tire's rolling radius, the corresponding rotational speed is calculated as the baseline speed. This realistic setting not only reflects the vehicle's daily driving characteristics but also more closely reflects tire performance under real-world conditions, improving the simulation's practicality and prediction accuracy.

[0048] It should be noted that the above-mentioned conversion of the driving speed into the linear speed and the calculation of the reference speed in combination with the tire rolling radius belongs to the scope of the existing technology and will not be elaborated here.

[0049] As another example, in tire durability testing, relevant industry standards both domestically and internationally define standard test speeds for evaluating tire fatigue life and structural stability. By referencing the recommended speeds in these standard test procedures and converting them to tire speeds as a baseline, simulation results can be standardized to a high degree, enhancing their universality and credibility.

[0050] Taking the reference speed as the dividing point, a low-speed interval from the lower limit of the speed range to the reference speed and a high-speed interval from the reference speed to the upper limit of the speed range are constructed respectively.

[0051] In the low-speed range, a fixed speed growth step is used to divide the range into equal intervals to generate several discrete speed points.

[0052] In the high-speed range, a number of discrete speed points are divided by using fractional multiples of a fixed speed increase step.

[0053] For example, the fractional multiple of the fixed speed increase step may be one-half, that is, half of the fixed speed increase step, which can divide the high-speed range into more discrete speed points.

[0054] In tire durability simulations, to comprehensively evaluate fatigue performance and failure evolution at different speeds, the tire's rated speed range is typically divided into several discrete speed points for step-by-step simulation. However, this division is not uniformly spaced, but rather non-uniformly divided based on a preset baseline speed.

[0055] Specifically, the entire speed range is divided into a low-speed range and a high-speed range with the reference speed as the dividing point, and different speed increase step strategies are adopted in the two ranges:

[0056] In the low-speed range, a larger speed increment can be used because tire stress distribution is relatively uniform, deformation is minimal, and structural response is relatively stable. This strategy significantly reduces the number of discrete speed points required while maintaining simulation coverage, thereby reducing computing resource consumption and improving simulation efficiency.

[0057] At high speeds, as speeds increase, the centrifugal effect and inertial loads within the tire intensify, exacerbating local stress concentration and making interfacial debonding and micro-damage more likely to occur. Using smaller speed increments for finer divisions helps obtain higher-resolution durability performance data, providing a detailed comparison of durability performance changes at different speeds.

[0058] All discrete speed points from the low-speed range and the high-speed range are combined and arranged in ascending order to form a step-by-step speed increase sequence.

[0059] For each type of preset virtual road condition, simulation is carried out in sequence according to the above-mentioned step-speed increase sequence, and each speed point corresponds to an independent simulation task.

[0060] The cord-rubber interface scanning module acquires tire cord-rubber interface imaging by micro-focus X-ray tomography at an initial interval length each time a simulation is started.

[0061] It is worth noting that the use of microfocus X-ray tomography in the simulation process can provide micron-level spatial resolution compared to other imaging methods, which can clearly observe the subtle geometric shape and contact state between the cord and the rubber matrix. This high resolution is crucial for identifying the microscopic damage behavior of early debonding.

[0062] The first debonding identification module is used to identify debonding by detecting crack width using cord-rubber interface imaging, and then record the time T1 from the start of simulation to the first occurrence of the debonding point.

[0063] Preferably, see Figure 2 As shown, the debonding identification operation is as follows: the tire cord-rubber interface obtained by real-time scanning in the simulation is imaged to locate the contact interface between the cord and the rubber, which is composed of a series of discrete points.

[0064] The local normal vector direction at each discrete point on both sides of the interface is calculated, and the minimum gap distance between the cord and the rubber is measured along the normal direction to construct a crack width distribution map, where each discrete point corresponds to a crack width value.

[0065] It should be understood that the normal vector direction represents the interaction direction of the materials on both sides of the interface, and measuring the gap distance along this direction can more truly reflect the degree of crack opening.

[0066] It's important to note that in the initial state, when debonding hasn't occurred, the materials on both sides of the cord-rubber interface are in close contact, and the crack widths between each point are extremely small, remaining largely within the microscopic range, indicating good interfacial adhesion. When debonding begins in a localized area, the materials at the interface gradually separate, causing the crack width to gradually increase at that location. This change in crack width can serve as an important characteristic parameter for determining the occurrence of debonding.

[0067] The crack width value of each point is compared with the preset debonding threshold, where the debonding threshold can be determined based on historical data or experimental calibration results. If the crack width of a point reaches or exceeds the debonding threshold, it is marked as a potential debonding point. The crack width of this potential debonding point will continue to be monitored in subsequent simulation time steps. If the crack width of this point remains above the debonding threshold at the next moment, debonding is identified at this point and the current time is recorded as the moment of debonding.

[0068] It is important to understand that when comparing crack width to a debonding threshold to detect interfacial debonding, the crack width exceeding the threshold within a single time step is not considered a direct indicator of debonding. This is because transient excitation or local stress fluctuations in complex loading environments may cause a temporary, abnormal increase in crack width, which does not necessarily indicate actual interfacial debonding. To this end, by introducing potential debonding points and continuously tracking their subsequent evolution, the risk of misjudgment caused by transient disturbances is effectively reduced, improving the robustness and stability of the debonding identification process.

[0069] If the crack width of the point falls below the debonding threshold at the next moment, the focus is on the adjacent points around the potential debonding point at the next moment. If the crack width of multiple consecutive adjacent points exceeds the threshold, for example, the multiple consecutive adjacent points are 2 or more, then the debonding phenomenon is identified in the area, and the next moment is taken as the moment when the debonding occurs. Otherwise, it is considered that no debonding has occurred in the area.

[0070] It's important to understand that when the crack width at a potential debonding point falls below the debonding threshold, it's not immediately determined that debonding has not occurred at that location. Instead, the system focuses on the area surrounding the potential debonding point and further analyzes the crack width trends of its neighboring nodes. If the crack widths of multiple consecutive adjacent points within the area exceed the set debonding threshold, it is determined that a debonding zone with a certain degree of expansion has formed in that local area, confirming the presence of debonding.

[0071] This identification strategy takes into account the spatial continuity and local diffusion characteristics of debonding evolution. The transient fluctuations or recovery of a single node cannot fully reflect the overall damage state of the interface. The introduction of a neighborhood consistency criterion effectively improves the accuracy and robustness of debonding identification, avoiding the omission of actual debonding propagation due to abnormal recovery at a local point.

[0072] The debonding monitoring module is used to continuously perform tomographic scanning at dynamically shortened intervals after identifying debonding, and use the scanning results to perform extended monitoring of the first debonding area and concomitant monitoring of the newly added debonding area and the first debonding area based on time and space correlation.

[0073] As a way to implement the above solution, the extended monitoring of the first debonding area is as follows: after the first debonding is identified, continuous tomography is performed to obtain tire cord-rubber interface imaging.

[0074] The image is focused on the first debonding area to extract the contact interface between the cord and rubber in this area, and the normal vector of the debonding interface is calculated. Then, the minimum gap distance between the cord and rubber is measured along the normal direction to obtain the corresponding crack width value.

[0075] The crack width measured at the current moment is compared with the crack width at the corresponding position at the previous scanning moment, and the crack expansion rate is calculated based on the ratio of the difference between the two and the time interval.

[0076] As a further implementation of the above scheme, the dynamically shortened interval duration is implemented as follows: after the first debonding is identified, the scanning interval adjustment mechanism is activated to shorten the initial interval duration by a preset shortening step factor. This allows for high-resolution continuous monitoring of the cord-rubber interface area after the debonding occurs by gradually increasing the imaging frequency. This more accurately captures the initial evolution of the debonding area. This is because once debonding occurs, the interface damage will enter a rapid evolution phase, and the crack width may expand rapidly. Therefore, by increasing the scanning imaging frequency, the crack propagation state in the debonding area can be more accurately captured, providing high-quality data support for subsequent failure assessment.

[0077] The preset shortening step factor is set to a value between 0 and 1, and is used to represent the degree to which the scanning time interval is compressed after the first debonding occurs.

[0078] After the first scan interval is shortened, subsequent tomographic images are collected to obtain the crack expansion rate in the first debonding area.

[0079] Dynamic adjustment is performed based on the crack growth rate obtained from two adjacent scans. The specific adjustment rules are as follows: if the difference between the current crack growth rate and the crack growth rate obtained from the previous scan is less than the configured threshold, the debonding expansion is determined to be in a stable stage, and the current scanning interval is kept unchanged. Maintaining the current scanning interval helps balance data acquisition accuracy and computational overhead, and prevents resource waste caused by over-refined scanning.

[0080] The threshold value configured above is used to measure the variation of the crack propagation rate between two adjacent scans, that is, the difference in the crack propagation rate between the current moment and the previous moment. If the threshold value is set too small, the system may be overly sensitive to transient fluctuations and frequently misjudgment. If it is set too large, some early acceleration signals may be missed, reducing the monitoring sensitivity. Specifically, the variation pattern of the crack propagation rate can be predicted based on fracture mechanics or other relevant theoretical models, and the appropriate threshold range can be derived in combination with material properties and loading conditions. For example, the threshold value is configured to be 0.2.

[0081] If the current crack growth rate is higher than the crack growth rate of the previous scan, indicating that the debonding behavior is evolving at an accelerated rate, the scanning interval is further shortened based on the growth rate of the crack growth rate to increase the imaging frequency and ensure that the rapid evolution of the debonding area can be accurately captured. This is done until the scanning interval is reduced to the set minimum time resolution or the debonding area has developed to a critical state of structural failure.

[0082] For example, the above-mentioned scanning interval is further shortened by combining the growth rate of the crack expansion rate. The new scanning interval can be expressed as , where Indicates the current scan interval duration. Indicates the growth rate of the crack expansion rate, which is calculated by taking the difference between the current crack expansion rate and the crack expansion rate of the previous scan. represents the crack expansion rate of the previous scan, It is an adjustment coefficient with a value between 0 and 1. It is used to control the rate of increase of the scanning frequency caused by the increase of the expansion rate, and prevent the scanning interval from being compressed too quickly due to local fluctuations, thereby affecting the overall computing efficiency.

[0083] In the above formula It reflects the normalized growth amplitude of the crack expansion rate and realizes the dimensionless quantification of the expansion rate change trend.

[0084] pass The adjustment coefficient is used to scale the normalized crack propagation rate growth so that the compression degree of the scanning interval is proportional to the severity of the debonding expansion, avoiding a sudden increase in the scanning frequency due to local rate mutations, thereby ensuring the smoothness of the system response and the rational allocation of computing resources.

[0085] pass This results in a negative correlation between the growth degree and the scanning interval—that is, the faster the crack expands, the shorter the required scanning interval.

[0086] The present invention takes into account that the cracks at the tire cord-rubber interface usually undergo a process from slow expansion to accelerated expansion after debonding occurs. By dynamically adjusting the scanning time interval, this nonlinear evolution process can be captured more accurately, especially increasing the monitoring frequency during the rapid expansion stage of the cracks, which helps to reveal the debonding mechanism and failure path. In addition, the minimum resolution of the scanning interval or the simulation termination condition is set to ensure that the system does not infinitely approach the zero time interval, thereby controlling the computing cost while ensuring accuracy, making the entire process highly executable and engineering feasible.

[0087] As a further implementation of the above scheme, the newly added debonding area and the first debonding area are monitored concomitantly based on the time and space correlation as follows: during the continuous scanning process after the first debonding occurs, the tire cord-rubber interface is analyzed frame by frame to identify whether there is a newly added debonding area.

[0088] When a new debonding area is identified, the new occurrence time is recorded and compared with the preset time window after the first debonding. Specifically, the preset time window can be set according to the material response characteristics. For example, it can be set to 3 seconds. If the new occurrence time falls within the preset time window after the first debonding, it is marked as a time-accompanying cluster, indicating that the new debonding may have a dynamic or stress-induced correlation with the first debonding.

[0089] The distance between the newly added debonding area and the first debonding area is obtained and compared with the spatial neighborhood threshold. The spatial neighborhood threshold can be set according to the actual tire material interface scale, cord spacing and engineering experience. For example, it can be set to 1 mm. If the distance is less than or equal to the spatial neighborhood threshold, it is marked as a spatial companion cluster, indicating that it is close to the first debonding area in geometric position and there may be a risk of structural coupling failure.

[0090] When the newly added debonding region meets the requirements of temporal associated cluster, spatial associated cluster or both, it is recorded as an associated debonding cluster.

[0091] The above-mentioned method continuously collects tomographic images of the tire cord-rubber interface after the first debonding event occurs, identifies the newly added debonding areas that appear subsequently, and introduces a time window matching mechanism and a spatial neighborhood judgment rule to determine whether the newly added debonding areas are accompanied by the first debonding.

[0092] The failure simulation module is used to combine the crack extension monitoring results of the first debonding area and the spatiotemporal concomitancy of the newly added debonding area to perform failure simulation to obtain the time length T2 from debonding to failure.

[0093] Optionally, the specific contents of the above module are as follows: in the subsequent simulated tomography, the accompanying debonding clusters are marked based on the accompanying monitoring of the spatiotemporal correlation of the newly added debonding areas, and the coverage area of ​​the accompanying debonding clusters is counted in real time, while the crack expansion of the first debonding area is continuously tracked to obtain the real-time crack width.

[0094] The failure determination criteria are set as follows: a) the crack width of the first debonding area reaches a warning multiple of the initial width, and the warning multiple is exemplarily set to 3 times.

[0095] b) The area covered by the accompanying debonding clusters reaches a warning ratio of the tire shoulder area. For example, the warning ratio is set to 10%.

[0096] When any of the above conditions is met, the tire structure is determined to have entered a critical failure state and the current simulation task is terminated immediately.

[0097] The time from the first debonding to tire failure was recorded.

[0098] The above-mentioned warning multiple and warning ratio can be determined by utilizing the area distribution characteristics of crack propagation and debonding area at the cord-rubber interface when actual tire samples fail during service, or by determining the safety critical value set by the structural designer in the tire's technical manual.

[0099] It should be understood that the debonding behavior of the cord-rubber interface during the actual operation of the tire does not usually occur in isolation, but tends to expand from the initial debonding area to the surrounding area, which may trigger chain debonding in adjacent areas. In order to more realistically reflect the evolutionary characteristics of this type of interface damage, the present invention introduces a spatiotemporal cluster recognition mechanism to capture the spatial continuity and temporal correlation between debonding events, thereby effectively restoring the dynamic process of debonding expansion. On this basis, two key parameters that are physically independent but interrelated are further adopted as comprehensive failure criteria: the crack width growth multiple is used to quantify the degree of expansion of the crack along the depth direction at the first debonding point, reflecting the longitudinal evolution trend of the debonding.

[0100] The proportion of the coverage area of ​​the accompanying debonding cluster is used to evaluate the distribution range of the newly added debonding areas in the spatial neighborhood caused by the initial debonding, reflecting the lateral expansion behavior of the debonding.

[0101] By integrating these two indicators, a multi-dimensional characterization of the tire structural failure process can be achieved, avoiding the risk of misjudgment that can result from relying solely on a single criterion. This approach not only improves the accuracy of debonding failure identification but also enhances the robustness and engineering applicability of the decision logic under complex operating conditions, making the simulation results more closely aligned with the fatigue failure mechanisms of actual tires in service.

[0102] The simulation display module is used to generate a dual-channel tire durability map at different speeds under different road conditions, with the first channel outputting a T1 contour line and the second channel outputting a T2 contour line.

[0103] The tire durability dual-channel map is constructed as follows: First channel construction: a grid coordinate system is established with the road condition type as the horizontal axis and the speed as the vertical axis, and the chromaticity value of the T1 duration is filled in at each grid point to form a gradient isosurface.

[0104] Second channel construction: superimpose the gradient isosurface of T2 duration on the same coordinate system.

[0105] By constructing a two-dimensional coordinate map with road condition type as the horizontal axis and operating speed as the vertical axis, the key durability indicators of tires under different working conditions - the first debonding time T1 and the time from debonding to failure T2 - are visualized and integrated.

[0106] Among them, T1 reflects the ability of the material to resist the initiation of fatigue damage under specific conditions, and T2 reflects the residual bearing capacity of the structure after debonding occurs.

[0107] The essence of the above operation is to integrate the key durability indicators of tires under different road conditions and speeds into a visual graph, forming an efficient and intuitive tire durability performance map that can be used for engineering decision-making.

[0108] In particular, in the two-dimensional coordinate atlas constructed based on road condition type and running speed, since the road condition type is categorical data rather than numerical data, a mapping strategy is adopted in the construction process, that is, to assign a representative identification value to each category of road condition. For example, urban roads, mountain roads, gravel roads and bumpy roads can be assigned values ​​of 1, 2, 3 and 4 respectively. It should be noted that these values ​​do not have direct physical meaning, but are only used as an indexing mechanism to facilitate visual representation in the atlas. In essence, this approach realizes the conversion of non-numerical road condition category data into a form that can be displayed in a two-dimensional coordinate system, thereby facilitating the analysis of the durability performance of tires under different working conditions.

[0109] Example 2

[0110] See also Figure 3 As shown, the present invention proposes a tire durability simulation analysis method: comprising the following steps: S1: constructing a three-dimensional tire model including the interface between the carcass cord layer and the rubber matrix, configuring a virtual road condition library, and setting a step-by-step speed increase simulation sequence within the speed range for each type of road condition.

[0111] S2: Tire cord-rubber interface imaging is acquired by microfocus X-ray tomography at the initial interval at each simulation startup.

[0112] S3: Debonding is identified by detecting the crack width using cord-rubber interface imaging, and the time T1 from the start of the simulation to the first occurrence of the debonding point is recorded.

[0113] S4: After the debonding is identified, continuous tomographic scanning is performed at dynamically shortened intervals. The scanning results are used to perform extended monitoring of the first debonding area and concomitant monitoring of the newly added debonding area and the first debonding area based on temporal and spatial correlation.

[0114] S5: Combine the crack propagation monitoring results of the first debonding area and the spatiotemporal coexistence of the newly added debonding area to perform failure simulation to obtain the time T2 from debonding to failure.

[0115] S6: Generate a dual-channel map of tire durability at different speeds under different road conditions, with the first channel outputting a T1 contour line and the second channel outputting a T2 contour line.

[0116] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0117] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0118] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0119] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0120] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0121] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A tire durability simulation analysis system, characterized in that: include: 3D modeling module: constructs a 3D tire model including the interface between the carcass cord layer and the rubber matrix; Road condition simulation module: configures a virtual road condition library and sets a step-speed increase simulation sequence within the speed range for each type of road condition; Obtain the rated speed range of the tire under the designed operating conditions according to the tire model and specifications, and determine the reference speed; Taking the reference speed as the dividing point, a low-speed interval from the lower limit of the rated speed range to the reference speed and a high-speed interval from the reference speed to the upper limit of the rated speed range are constructed respectively; In the low-speed range, a fixed speed growth step is used to divide the range into equal intervals to generate several discrete speed points; In the high-speed range, a number of discrete speed points are divided by using fractional multiples of a fixed speed increase step size; All discrete speed points from the low-speed range and the high-speed range are combined and arranged in ascending order to form a step-by-step speed increase sequence; For each type of preset virtual road condition, simulation is carried out in sequence according to the above-mentioned step-speed increase sequence, and each speed point corresponds to an independent simulation task; Cord-rubber interface scanning module: acquires tire cord-rubber interface imaging through microfocus X-ray tomography at the initial interval length at each simulation startup; First debonding identification module: Debonding is identified by detecting crack width using cord-rubber interface imaging, and the time T1 from the start of simulation to the first occurrence of debonding is recorded; Debonding Monitoring Module: After identifying a debonding event, continuous tomographic scanning is performed at dynamically shortened intervals. The scanning results are used to conduct extended monitoring of the initial debonding area and concomitant monitoring of the newly added debonding area and the initial debonding area based on temporal and spatial correlation. The extended monitoring of the first debonding area is as follows: After the first debonding is identified, continuous tomography is performed to image the tire cord-rubber interface. Focus the image on the first debonding area to extract the contact interface between the cord and rubber in this area, calculate the normal vector of the debonding interface, and then measure the minimum gap distance between the cord and rubber along the normal direction to obtain the corresponding crack width value; Compare the crack width measured at the current moment with the crack width at the corresponding position at the previous scanning moment, and calculate the crack expansion rate based on the difference between the two and the time interval; The concomitant monitoring process of the newly added debonding area and the first debonding area based on temporal and spatial correlation is as follows: During the continuous scanning process after the first debonding occurs, the tire cord-rubber interface is analyzed frame by frame to identify whether there is a new debonding area; When a new debonding area is identified, the new occurrence time is recorded and compared with the preset time window after the first debonding. If the new occurrence time falls within the preset time window after the first debonding, it is marked as a time-accompanying cluster. Obtain the distance between the newly added debonding area and the first debonding area, and compare it with the spatial neighborhood threshold. If the distance is less than or equal to the spatial neighborhood threshold, mark it as a spatial companion cluster. When the newly added debonding region meets the requirements of temporal accompanying cluster or spatial accompanying cluster or both, it is recorded as accompanying debonding cluster; Failure simulation module: Combine the crack propagation monitoring results of the first debonding area and the spatiotemporal coexistence of the newly added debonding area to perform failure simulation and obtain the time T2 from debonding to failure; Simulation display module: Generates dual-channel tire durability maps at different speeds under different road conditions. The first channel outputs the T1 contour line, and the second channel outputs the T2 contour line. The tire durability dual-channel map is constructed as follows: First channel construction: A grid coordinate system is established with the road condition type as the horizontal axis and the speed as the vertical axis. The chromaticity value of the T1 duration is filled in at each grid point to form a gradient isosurface. Second channel construction: superimpose the gradient isosurface of T2 duration on the same coordinate system.

2. The tire durability simulation analysis system according to claim 1, characterized in that: The three-dimensional modeling module is implemented as follows: Obtaining geometric parameters according to the model specifications of the tire to be simulated, and using modeling software to establish a three-dimensional geometric model of the tire based on the geometric parameters; The spatial distribution characteristics of the tire cords are determined based on the tire internal structure design drawings. The cord structure is then constructed based on the established geometric model, and its relative positional relationship within the rubber matrix is ​​simulated. Obtain the material properties of each component of the tire to be simulated, and then accurately assign the corresponding material properties to the corresponding geometric areas in the established geometric model; The entire 3D tire geometry is meshed using meshing tools, with locally refined meshes applied at the interface between the cord and the rubber matrix.

3. The tire durability simulation analysis system according to claim 1, characterized in that: The debonding identification is performed as follows: The tire cord-rubber interface image obtained by real-time scanning in the simulation is used to locate the contact interface between the cord and the rubber. The interface consists of a series of discrete points. The local normal vector direction at each discrete point on both sides of the interface is calculated, and the minimum gap distance between the cord and the rubber is measured along the normal vector direction to construct a crack width distribution map, where each discrete point corresponds to a crack width value; Compare the crack width value of each point with the preset debonding threshold. If the crack width of a point reaches or exceeds the debonding threshold, it is marked as a potential debonding point. The crack width of this potential debonding point will continue to be monitored in subsequent simulation time steps. If the crack width of this point remains above the debonding threshold at the next moment, debonding is identified at this point and the current time is recorded as the moment of debonding. If the crack width of the point at the next moment falls below the debonding threshold, the adjacent points around the potential debonding point will be focused on at the next moment. If the crack widths of multiple consecutive adjacent points exceed the threshold, debonding is identified in the area, and the next moment is taken as the moment when debonding occurs. Otherwise, it is considered that no debonding has occurred in the area.

4. The tire durability simulation analysis system according to claim 1, characterized in that: The dynamically shortened interval duration is implemented as follows: After the first debonding is identified, the scanning interval adjustment mechanism is activated to shorten the initial interval duration according to the preset shortening step factor; After the first scan interval is shortened, subsequent tomographic images are collected to obtain the crack expansion rate in the first debonding area; Dynamic adjustment is performed based on the crack expansion rate obtained from two adjacent scans. The specific adjustment rules are as follows: If the difference between the current crack growth rate and the crack growth rate obtained in the previous scan is less than the configured threshold, the current scan interval will remain unchanged. If the current crack growth rate is higher than that of the previous scan, the scanning interval is further shortened in combination with the growth rate of the crack growth rate until the scanning interval is reduced to the set minimum time resolution or the debonding area has developed to the critical state of structural failure.

5. The tire durability simulation analysis system according to claim 1, characterized in that: The specific contents of the failure simulation module are as follows: In subsequent simulated tomographic scans, the accompanying debonding clusters are marked based on the concomitant monitoring of the spatiotemporal correlation of the newly added debonding areas, and the coverage area of ​​the accompanying debonding clusters is counted in real time. At the same time, the crack extension in the first debonding area is continuously tracked to obtain the real-time crack width. The determination basis for setting failure is as follows: a) The width of the crack in the first debonding area reaches a warning multiple of the initial width; b) The area covered by the accompanying debonding cluster reaches the warning percentage of the tire shoulder area; When any of the above conditions is met, the tire structure is judged to have entered a critical failure state and the current simulation task is terminated immediately; The time from the first debonding to tire failure was recorded.

6. A tire durability simulation analysis method, performed using the tire durability simulation analysis system according to any one of claims 1 to 5, characterized in that: The steps include: S1: Build a 3D tire model that includes the interface between the carcass cord layer and the rubber matrix, configure a virtual road condition library, and set a step-by-step speed increase simulation sequence within the speed range for each road condition; S2: tire cord-rubber interface imaging is acquired by microfocus X-ray tomography at the initial interval at each simulation start; S3: Debonding is identified by crack width detection using cord-rubber interface imaging, and the time T1 from the start of the simulation to the first occurrence of the debonding point is recorded; S4: After identifying the debonding, continuous tomographic scanning is performed at dynamically shortened intervals. The scanning results are used to conduct extended monitoring of the initial debonding area and concomitant monitoring of the newly added debonding area and the initial debonding area based on temporal and spatial correlation. S5: Combine the crack propagation monitoring results of the first debonding area and the spatiotemporal coexistence of the newly added debonding area to perform failure simulation to obtain the time from debonding to failure T2; S6: Generate a dual-channel map of tire durability at different speeds under different road conditions, with the first channel outputting a T1 contour line and the second channel outputting a T2 contour line.

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