Method, system and program product for predicting aging and damage risk of tire multilayer structure

Abstract

The invention relates to a tire structure aging prediction method, in particular to a method, a system and a program product for predicting aging and damage risks of a tire multilayer structure. According to the method, nanoindentation testing is carried out on all layers (an airtight layer, a transition layer, a tread layer and the like) of the tire at different time points, elastic modulus data are obtained, and the modulus variation is calculated. And performing numerical solution on the diffusion behavior of oxygen in each layer in combination with a Fick diffusion equation, establishing a concentration-modulus coupling model, extracting an aging rate and a critical modulus change threshold, and realizing prediction of failure time of each layer, thereby evaluating the overall remaining life and safety risk of the tire. The system integrates a data acquisition module, a temperature control module and an automatic analysis module, has the advantages of high detection precision, quick response, accurate prediction and the like, and can provide a scientific basis for tire design optimization and maintenance management.

Description

Technical Field

[0001] The present invention relates to a method for predicting the aging of a tire structure, and in particular to a method, a system and a program product for predicting the aging and failure risks of a multi-layer tire structure. Background Art

[0002] With the rapid development of the modern automotive industry and transportation industry, tires, as critical safety components, have received extensive attention for their performance, durability and reliability. During long-term service, tires are subject to the combined effects of various factors such as temperature, stress, oxygen, and ozone, which can lead to material aging and performance degradation, ultimately resulting in tire failure and safety accidents. Traditionally, tire aging has relied on macroscopic fatigue tests, environmental aging tests and failure analysis, but these methods have problems such as long test cycles, high costs and difficulty in accurately reflecting the true aging state of each layer structure of the tire.

[0003] In the prior art, the assessment of tire aging mainly focuses on the detection of overall performance. However, the internal structure of a tire is usually composed of multiple layers of different materials, such as an airtight layer, a transition layer, a carcass ply, a belt layer, a base rubber and a tread, etc. Due to differences in composition and processing technology, the aging mechanisms and processes of each layer of material are significantly different. Traditional methods are difficult to capture the performance changes of each layer of material at the microscale, resulting in great limitations in the understanding of the tire aging process. In addition, the method of directly measuring the oxygen content inside the tire is also difficult to achieve real-time monitoring and accurate prediction of the tire aging state due to complex operation and high cost.

[0004] Nano-indentation technology is a new method for measuring material properties, used to study the mechanical properties of micro-nano components and the local properties within the micro-nano regions of materials. It has the advantages of direct measurement and high spatial resolution, and can be used as one of the means for analyzing the distribution of micro-nano mechanical properties of materials. This technology can be used for the detection and analysis of organic or inorganic, soft or hard materials, including metals, alloys, semiconductors, glass, minerals and organic materials, etc. At the same time, the diffusion model based on Fick's law has a mature theoretical basis for describing the diffusion behavior of molecules such as oxygen in solid materials. However, there is currently no method that combines nano-indentation testing with the Fick diffusion model for comprehensive prediction of the aging differences and failure risks of each layer in a multi-layer tire structure. How to accurately predict the remaining life and potential failure risks of a tire through comparative analysis of the modulus changes of each layer, extraction of the aging rate, and setting of the critical modulus criterion remains a technical problem that urgently needs to be solved. Summary of the Invention

[0005] To solve the above technical problems, the object of the present invention is to provide a method for predicting the aging and failure risks of a tire multi-layer structure. This method measures the elastic modulus of each layer of the tire at different time points by using a high-precision nano-indentation test technology, numerically solves the diffusion process of oxygen molecules inside the tire by combining with the Fick diffusion model, and establishes a concentration-modulus coupling model. Furthermore, it conducts a comparative analysis on the magnitude and growth rate of the modulus changes of different layers. Finally, by setting a critical modulus or a critical modulus change value, a tire failure prediction model is constructed to achieve a quantitative assessment of the tire aging risk and remaining life, providing an effective technical means for tire design optimization, maintenance management, and safety monitoring.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for predicting the aging and failure risks of a tire multi-layer structure, the method comprising the following steps:

[0008] 1) Multi-layer sample preparation and modulus measurement:

[0009] Prepare specimens from each layer of the tire material, and obtain the elastic modulus E i (t) of each layer by using nano-indentation testing at a predetermined time point t;

[0010] 2) Modulus change calculation:

[0011] Taking the modulus E i，0 of each layer in the initial or reference state as a benchmark, calculate the modulus change amount of each layer:

[0012] ΔE i (t) = E i (t) - E i，0 ,

[0013] where E i (t) represents the elastic modulus of the i-th layer at time t, and E i，0 is the initial modulus of the i-th layer;

[0014] 3) Multi-layer Fick diffusion modeling:

[0015] Divide each layer in the tire thickness direction in sequence, the thickness of the i-th layer is h i , and establish the one-dimensional Fick's second law within each layer:

[0016] x ∈ [X i-1 , X i ,

[0017] where: c i (x, t) is the oxygen concentration of the i-th layer at position x and time t; D iis the diffusion coefficient of the i-th layer; X0 = 0 and X i = X i-1 + h i ;

[0018] 4) Interlayer interface continuity condition:

[0019] At the interface x = X between adjacent layers i and i + 1, the concentration continuity and flux continuity are satisfied: i

[0020] c i (X i , t) = c i+1 (X i , t),

[0021] 5) Construction of concentration-modulus coupling model:

[0022] The obtained oxygen concentration c i (x, t) is coupled with the modulus change ΔE i (t) of each layer;

[0023] 6) Aging risk and failure prediction:

[0024] According to ΔE i (t) and its growth rate k i , k i obtained by regression fitting for each layer over time, set the critical modulus or critical modulus change threshold E crit，i or ΔE crit，i , where ΔE crit，i = E crit，i - E i，0 , by solving

[0025]

[0026] obtain the failure time t i * for each layer, and take the minimum value

[0027]

[0028] as the prediction of the remaining life of the tire as a whole;

[0029] 7) Result output and feedback correction:

[0030] Output the aging risk assessment report for each layer and the prediction result of the remaining life of the tire; if there is a large deviation between the measured data and the model prediction, then correct α i , D i or the critical threshold until the prediction is accurate.

[0031] ​Preferably, each layer of the material is selected from one or more of an airtight layer, a transition layer, a carcass ply, a belt layer, a base rubber, and a tread.

[0032] Preferably, the nanoindentation test is carried out with a loading range of 1 mN to 10 mN, and the measurement accuracy of the indentation depth is preferably less than 0.1 μm to ensure accurate force-displacement curve data.

[0033] Preferably, the diffusion coefficient D of each layer in the Fick diffusion model i can be corrected for temperature through the Arrhenius relationship, that is

[0034]

[0035] where: D i,0 is the frequency factor of the i-th layer; E a,i is the diffusion activation energy of the i-th layer; R is the gas constant; T is the absolute temperature.

[0036] Preferably, the concentration-modulus coupling model in step 5) adopts a linear approximation model:

[0037] ΔE i (t) = α i [c i (x, t) - c i，init ,

[0038] where α i is the coupling coefficient, and c i,init is the initial oxygen concentration of the i-th layer;

[0039] Or, the concentration-modulus coupling model can be described in a non-linear function form, and the formula is as follows:

[0040] ΔE i (t) = E max，i [1 - exp(-β i {c i (x, t) - c i,init ))}],

[0041] where: E max,i is the maximum modulus change limit of the i-th layer; β i is the non-linear coupling parameter, which is obtained through experimental calibration.

[0042] Preferably, in step 6), when the modulus change ΔE i (t) of any layer reaches or exceeds its critical modulus change threshold ΔE crit,i at a certain moment t, it is determined that the layer is in a high-risk aging state, and a corresponding maintenance or replacement warning is output.

[0043] Furthermore, the present invention also provides a system for implementing the tire aging and damage prediction method, which system includes:

[0044] 1) A nano-indentation test module, configured to collect force-displacement data of each layer specimen of the tire at multiple time nodes and calculate the elastic modulus E i (t);

[0045] 2) A temperature control module, configured to test the specimen under set temperature conditions and keep the temperature fluctuation controlled within ±2°C;

[0046] 3) A data acquisition and processing module, configured to denoise the test data and perform fitting calculations to obtain the modulus change ΔE i (t) of each layer and the aging rate k i ;

[0047] 4) A Fick diffusion solving unit, configured to calculate the oxygen concentration distribution c i (x,t) of each layer according to the multi-layer Fick diffusion model and boundary conditions, where the boundary conditions include concentration continuity and flux continuity;

[0048] 5) An aging risk and damage prediction module, configured to compare the ΔE i (t) obtained from testing each layer with a preset critical value ΔE crit,i , and output the aging risk prediction results of each layer of the tire and the overall remaining life prediction result according to the solved failure time t i * ;

[0049] 6) A visualization and report output unit, configured to display the modulus change curve of each layer, the critical value comparison graph, and the remaining life assessment report, and automatically send a warning message when high-risk aging is detected.

[0050] Preferably, the nano-indentation test module has an automatic multi-point test function to ensure that at least 5 test point data are collected on each layer specimen, thereby improving the statistical reliability of the data; and / or, the data acquisition and processing module is built-in with a linear or nonlinear regression algorithm for fitting the ΔE i (t) curve of each layer and automatically extracting the aging growth rate k i ; and / or, the aging risk and damage prediction module is built-in with a configurable threshold library, which stores the critical modulus or the critical modulus change value ΔE crit,i corresponding to each layer under different tire materials and usage environments, and supports dynamic update according to real-time data.

[0051] Preferably, the temperature control module has a programmable heating function, can simulate accelerated aging conditions, and supports temperature correction of the diffusion coefficient Di of each layer through the Arrhenius formula.

[0052] Furthermore, the present invention also provides a computer-readable storage medium, on which a computer program or instruction is stored, and when the computer program or instruction is executed by a processor, the method is implemented.

[0053] Furthermore, the present invention also provides a computer program product, including a computer program or instruction, and when the computer program or instruction is executed by a processor, the method is implemented.

[0054] Due to the above technical solutions adopted by the present invention, the present invention has the following remarkable technical effects:

[0055] 1. Microscopic and precise detection of aging state: By using nanoindentation testing technology, the change of elastic modulus of each layer of the tire can be accurately measured at the micron or even nanoscale, and the aging information of the material caused by oxygen penetration can be captured in time, far superior to the detection accuracy of traditional macroscopic fatigue tests.

[0056] 2. Differentiated evaluation of multi-layer structure: By establishing a multi-layer Fick diffusion model and combining it with a concentration-modulus coupling model, a quantitative comparison of the aging rate and modulus degradation of different layers of the tire (such as the airtight layer, transition layer, cord layer, belt layer, base rubber and tread) is carried out, which can comprehensively reflect the aging process and differences of each layer of material, providing a basis for optimized design.

[0057] 3. Accurate prediction of the remaining life of the tire: By setting the critical modulus or the value of critical modulus change of each layer and solving the time when each layer reaches the failure state, the present invention can scientifically predict the overall remaining service life of the tire and reduce the safety hazards caused by aging.

[0058] 4. Dynamic response and environmental adaptability: By introducing factors such as temperature and oxygen diffusion coefficient into the model, accurate evaluation under different working environments (normal temperature or accelerated aging temperature) is realized, which helps to adapt to the dynamic changes of actual working conditions and improve the reliability and practicality of prediction.

[0059] 5. Reduction of detection cost and cycle: By adopting efficient nanoindentation testing and numerical solution methods, accurate aging data can be obtained in a short time. Compared with traditional long-term fatigue tests and destructive tests, the detection cost and time cycle are greatly reduced, realizing rapid diagnosis and early warning of the tire.

[0060] In summary, the present invention not only deeply reveals the aging mechanism of the multi-layer structure of the tire theoretically, but also provides scientific, precise and real-time technical support for tire design, maintenance and safety management in practice, with broad application prospects and remarkable economic and social benefits. Specific embodiments

[0061] Combined with the embodiments of the present invention, the technical solutions in the embodiments will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0062] I. Sample Preparation

[0063] 1. Tire Sampling and Layer Separation

[0064] Select a representative area from the tire to be tested and cut out the required specimens along the radial or circumferential direction.

[0065] Use mechanical cutting, grinding, and polishing processes to layer the tire and obtain samples of each layer such as the airtight layer, transition layer, cord layer, belt layer, base rubber, and tread.

[0066] The size of each layer of specimen is preferably controlled at 10×10mm 2 , with a thickness of 1 - 3mm, and ensure that the test surface is flat and the surface roughness is less than 0.1μm to meet the test requirements of the nanoindentation instrument.

[0067] 2. Initial Data Recording

[0068] Before the tire is aged or used, conduct preliminary tests on each layer of samples to obtain the initial elastic modulus E i,0 ; at the same time, record the initial oxygen concentration c i,init (which can be obtained through auxiliary detection methods or reference documents).

[0069] II. Nanoindentation Test

[0070] 1. Instrument Calibration and Parameter Setting

[0071] Use a calibration standard sample to calibrate the nanoindentation instrument to ensure that the load sensor and displacement sensor are accurate.

[0072] Set the test parameters: the load range is 1mN to 10mN; the measurement accuracy of the indentation depth is preferably less than 0.1μm; the loading method adopts the loading - holding - unloading cycle.

[0073] 2. Data Acquisition

[0074] On the test surface of each specimen, select at least 5 evenly distributed test points and conduct nanoindentation tests in sequence.

[0075] Record the force - displacement data of each test point and calculate the elastic modulus E i (t) of each test point using the Oliver–Pharr method or other fitting algorithms.

[0076] 3. Data Processing

[0077] Statistically analyze the elastic moduli of each test point in the same layer, calculate the average value after removing outliers, and obtain the modulus E of this layer at a specific time point t i (t).

[0078] Calculate the modulus change: ΔE i (t) = E i (t) - E i，0 Where:

[0079] E i (t): The elastic modulus of the i-th layer at time t;

[0080] E i，0 : The initial reference modulus of the i-th layer;

[0081] ΔE i (t): The modulus change of the i-th layer.

[0082] III. Establishment of Multilayer Fick Diffusion Model

[0083] 1. Stratification and Definition of Spatial Coordinates

[0084] Divide the tire thickness direction into n layers, with each layer having a thickness of h i , and let the coordinate interval of each layer in the thickness direction be [X i-1 , X i , where:

[0085] X0 = 0, X i = X i-1 + h i , i = 1, 2,..., n,

[0086] 2. Diffusion Equation for Each Layer

[0087] For the i-th layer, establish the one-dimensional Fick's second law:

[0088] x ∈ [X i-1 , X i , t > 0,

[0089] Where: c i (x, t): The oxygen concentration of the i-th layer at position x and time t; Di: The diffusion coefficient of the i-th layer.

[0090] 3. Boundary and Interface Conditions

[0091] Outer surface boundary (at x = 0): Set the concentration on the atmosphere side to a constant c0, that is

[0092] c1(0, t) = c0, t > 0,

[0093] Inner boundary (innermost layer at x = X n ): Can be set to a fixed concentration or zero flux, determined according to the actual situation.

[0094] Interlayer continuity: At the interface of each layer at x = X i : Satisfy:

[0095] c i (X i , t) = c i+1 (X i , t),

[0096]

[0097] 4. Temperature correction (optional)

[0098] If the test environment temperature changes significantly, the Arrhenius formula can be used to correct the diffusion coefficient:

[0099]

[0100] Where: D i，0 is the frequency factor of the i-th layer; E a，i is the diffusion activation energy of the i-th layer; R is the gas constant; T is the absolute temperature.

[0101] 5. Numerical solution

[0102] Use the finite difference, finite volume or finite element method to discretize the above partial differential equations and boundary conditions, and set the continuity conditions at the interlayer nodes. The initial condition is: c i (x, 0) = c i，init (x), which can be simplified to a uniform distribution c i，init . After iterative solution, the oxygen concentration distribution c i (x, t) of each layer at different times t and positions x is obtained.

[0103] IV. Construction of the concentration-modulus coupling model

[0104] 1. Establishment of the coupling model

[0105] Based on experimental observations, when the rubber material absorbs oxygen, its molecular structure changes, resulting in a change in the elastic modulus. To quantitatively describe this phenomenon, a concentration-modulus coupling model is established, for example, using a linear relationship:

[0106] ΔE i (t) = α i [c i (x, t) - c i，init ,

[0107] Where αi is the coupling coefficient, c i，init is the initial oxygen concentration of the i-th layer;

[0108] 2. Nonlinear model (optional)

[0109] Of course, for a more accurate description, a nonlinear function form can also be adopted:

[0110] ΔE i (t) = E max，i [1 - exp(-β i {c i (x, t) - c i，init )}],

[0111] where: E max，i is the maximum modulus change limit of the i-th layer; β i is the nonlinear coupling parameter, obtained by experimental calibration.

[0112] V. Aging risk and failure prediction

[0113] 1. Critical threshold setting

[0114] According to the fatigue test of the tire material or industry standards, determine the critical modulus E crit，i or the critical modulus change:

[0115] ΔE crit，i = E crit，i - E i，0 ,

[0116] When any layer satisfies at a certain time t:

[0117] ΔE i (t) ≥ ΔE crit，i ,

[0118] Then it is determined that this layer enters the high-risk aging state.

[0119] 2. Failure time prediction

[0120] According to the fitting function of ΔE i (t) of each layer with time (such as the linear model ΔE i (t) = k i t + b i ), solve for the failure moment

[0121]

[0122] Get t i * _ is the time when the i-th layer reaches the critical failure state.

[0123] Define the overall failure time of the tire as:

[0124]

[0125] 3. Risk Assessment and Report Output

[0126] Compare ΔE i (t) of each layer at different time points with the critical threshold to generate an aging risk curve and a heat map, visually showing the aging degree of each layer.

[0127] The output includes: the aging level of each layer (such as mild, moderate, severe aging), the failure prediction time t i * of each layer, and the overall remaining life t min * of the tire, etc.

[0128] When it is detected that a certain layer is about to reach or has exceeded the critical value, the system automatically issues a maintenance or replacement warning.

[0129] VI. System Implementation

[0130] The system module configuration of the implementation system of the present invention includes:

[0131] Nanoindentation test module: Implement multi-point automatic testing and output force-displacement data;

[0132] Temperature control module: Provide a stable test environment, supporting normal temperature and high temperature (accelerated aging) tests;

[0133] Data acquisition and processing module: Digitally process the test data and calculate E i (t) and ΔE i (t) of each layer using built-in algorithms;

[0134] Fick diffusion solving unit: Solve the multi-layer Fick diffusion model using numerical methods such as finite difference and finite element, and output the oxygen concentration distribution c i (x, t);

[0135] Aging risk and failure prediction module: Calculate the failure time of each layer and evaluate the overall remaining life based on the concentration-modulus coupling model and the critical threshold;

[0136] Visualization and report output unit: Graphically display the modulus change curves, failure predictions, and aging risks of each layer, and generate a comprehensive report.

[0137] Each module of the present invention realizes real-time data transmission through a high-speed data bus to ensure that model parameters (such as α i , D i , ΔE crit,ietc.) to improve the prediction accuracy. The system supports automatic saving of test data and prediction results, facilitating subsequent comparison, calibration, and maintenance management.

[0138] The following presents a specific test data of the present invention, aiming to illustrate how to obtain the aging data, modulus change, and failure prediction results of each layer of materials by using the technical solution of the present invention.

[0139] Table 1 Nano-indentation test data of the airtight layer (at room temperature of 23 °C)

[0140] Test time (hours) Test point 1 E (MPa) Test point 2 E (MPa) Test point 3 E (MPa) Average E (MPa) ΔE (MPa) 0 25.0 24.5 25.2 24.9 0.0 100 26.5 26.0 26.8 26.43 1.53 500 28.0 27.8 28.2 28.0 3.10 1000 29.5 29.0 29.8 29.43 4.53

[0141] Note: E represents the elastic modulus, with the unit of MPa; ΔE = E(t) - E0, where the initial average modulus E0 = 24.9.

[0142] Table 2 Nano-indentation test data of the transition layer (at room temperature of 23 °C)

[0143] Test time (hours) Test point 1 E (MPa) Test point 2 E (MPa) Test point 3 E (MPa) Average E (MPa) ΔE (MPa) 0 22.0 22.2 21.8 22.0 0.0 100 22.8 22.7 22.6 22.70 0.70 500 23.6 23.4 23.7 23.57 1.57 1000 24.3 24.2 24.5 24.33 2.33

[0144] Note: The initial average modulus is 22.0 MPa; the data shows that the modulus of the transition layer increases relatively gently with time.

[0145] Table 3 Nano-indentation test data of the tread layer (at room temperature of 23 °C)

[0146] Test time (hours) Test point 1 E (MPa) Test point 2 E (MPa) Test point 3 E (MPa) Average E (MPa) ΔE 0 30.0 30.2 29.8 30.0 0.0 100 30.5 30.3 30.6 30.47 0.47 500 31.2 31.1 31.4 31.23 1.23 1000 31.8 31.7 31.9 31.80 1.80

[0147] Note: The initial average modulus is 30.0 MPa; the modulus change of the tread layer is small, showing good aging resistance.

[0148] Table 4 Failure prediction data (based on the linear fitting model)

[0149] Assume that the modulus change of each layer with time satisfies the linear model:

[0150] ΔE i (t) = k i t + b i , where b i is approximately 0 (based on the initial state), and the aging rate k i (unit: MPa / hour) of each layer is obtained through regression fitting:

[0151] Layer classification <![CDATA[k i (MPa / hour)]]> <![CDATA[Critical modulus change ΔE crit,i (MPa)]]> <![CDATA[Predicted failure time t i * (hours) <!-- 7 -->]]> Airtight layer 0.0045 6.0 1333 Transition layer 0.0023 4.0 1739 Tread layer 0.0018 3.0 1667

[0152] Note:

[0153] 1. The critical modulus change of the airtight layer is set to 6.0 MPa (that is, when the elastic modulus of the airtight layer increases from the initial 24.9 MPa to 30.9 MPa, it is determined as high-risk aging);

[0154] 2. The critical value between the transition layer and the tread layer is determined according to material fatigue tests or industry standards;

[0155] 3. The predicted remaining life of the entire tire is taken as the minimum value among the predicted failure times of each layer, i.e., t min * = min{1333, 1739, 1667} ≈ 1333 hours.

[0156] The above is the description of the embodiments of the present invention. Through the above description of the disclosed embodiments, those skilled in the art can implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel points disclosed herein.

Claims

1. A method for predicting the aging and damage risk of a tire multi-layer structure, characterized in that: The method comprises the following steps: 1) Multilayer sample preparation and modulus determination: Prepare samples from each layer of the tire material, and use nanoindentation testing at a predetermined time point t to obtain the elastic modulus E of each layer. i (t); 2) Calculation of modulus change: The modulus E of each layer in its initial or reference state i,0 As a benchmark, calculate the modulus change of each layer: ΔE i (t)=E i (t)-E i，0 , Among them, E i (t) represents the elastic modulus of the i-th layer at time t, E i,0 is the initial modulus of the i-th layer; 3) Multi-layer Fick diffusion modeling: Divide each layer in the tire thickness direction, and the thickness of the i-th layer is h i , and establish one-dimensional Fick's second law in each layer: Where: c i (x, t) is the oxygen concentration of the i-th layer at position x and time t; D i is the diffusion coefficient of the i-th layer; X0 = 0 and X i =X i-1 +h i ; 4) Interlayer interface continuity conditions: At the interface between adjacent layers i and i+1, x=X i At , concentration continuity and flux continuity are satisfied: c i (X i ,t)=c i+1 (X i ,t), 5) Construction of concentration-modulus coupling model: The oxygen concentration c i (x, t) and the modulus change ΔE of each layer i (t) Establishing coupling relationship; 6) Aging risk and damage prediction: According to the ΔE of each layer growing over time i (t) and its growth rate k i , k i Through regression fitting, the critical modulus or critical modulus change threshold E of each layer is set. crit,i or ΔE crit,i , where ΔE crit,i =E crit,i -E i,0 , by solving Get the failure time t of each layer i * , and take the minimum value As a prediction of the overall remaining life of the tire; 7) Result output and feedback correction: Output the aging risk assessment report of each layer and the prediction result of the remaining life of the tire; if there is a large deviation between the measured data and the model prediction, the α i , D i Or critical thresholds are modified until the prediction is accurate.

2. The method according to claim 1, characterized in that Each material layer is selected from one or more of an airtight layer, a transition layer, a cord layer, a belt layer, a base rubber and a tread; and / or, the nanoindentation test adopts a loading range of 1mN to 10mN, and the measurement accuracy of the indentation depth is preferably less than 0.1μm to ensure accurate force-displacement curve data.

3. The method according to claim 1, characterized in that The diffusion coefficient Di of each layer in the Fick diffusion model can be corrected for temperature through the Arrhenius relationship, that is, Where: D i,0 is the frequency factor of the i-th layer; E a,i is the diffusion activation energy of the i-th layer; R is the gas constant; T is the absolute temperature.

4. The method according to claim 1, characterized in that: Step 5) The concentration-modulus coupling model adopts a linear approximation model: △E i (t)=α i [c i (x,t)-C i，nit ], Among them, α i is the coupling coefficient, c i,init is the initial oxygen concentration of the i-th layer; Alternatively, the concentration-modulus coupling model is described in the form of a nonlinear function, and the formula is as follows: △E i (t)=E max，i [1-exp(-β i {c i (x,t)-c i，init })], Where: E max,i is the maximum modulus change limit of the i-th layer; β i is the nonlinear coupling parameter obtained through experimental calibration.

5. The method according to claim 1, characterized in that In step 6), when the modulus of any layer changes ΔE i (t) At a certain moment t, it reaches or exceeds its critical modulus change threshold ΔE crit,i When the layer is detected as being in a high-risk aging state, the layer is determined to be in a high-risk aging state and a corresponding maintenance or replacement warning is output.

6. A system for implementing the tire aging and damage prediction method according to any one of claims 1 to 5, characterized in that: The system includes: 1) Nanoindentation test module, used to collect force-displacement data of each layer of tire samples at multiple time points and calculate the elastic modulus E i (t); 2) Temperature control module, used to test the sample under set temperature conditions and keep the temperature fluctuation within ±2℃; 3) Data acquisition and processing module, used to denoise the test data and calculate the modulus change ΔE of each layer i (t) and aging rate k i ; 4) Fick diffusion solver, used to calculate the oxygen concentration distribution c of each layer based on the multi-layer Fick diffusion model and boundary conditions, including concentration continuity and flux continuity. i (x,t); 5) Aging risk and damage prediction module, used to convert the ΔE obtained from each layer test i (t) and the preset critical value ΔE crit,i Compare and according to the solved failure time t i * Output the aging risk of each layer of the tire and the overall remaining life prediction results; 6) Visualization and report output unit, used to display the modulus change curve of each layer, critical value comparison chart and remaining life assessment report, and automatically issue a warning message when high-risk aging is detected.

7. The system according to claim 6, characterized in that The nanoindentation test module has an automatic multi-point test function to ensure that data from at least 5 test points are collected on each layer of the sample, thereby improving the statistical reliability of the data; and / or, the data acquisition and processing module has a built-in linear or nonlinear regression algorithm for calculating the ΔE of each layer. i (t) curve is fitted to automatically extract the aging growth rate k i ; and / or, the aging risk and damage prediction module has a built-in configurable threshold library, which stores the critical modulus or critical modulus change value ΔE corresponding to each layer under different tire materials and usage environments crit,i , and supports dynamic updates based on real-time data.

8. The system according to claim 6, characterized in that The temperature control module has a programmable heating function, which can simulate accelerated aging conditions and support the diffusion coefficient D of each layer through the Arrhenius formula. i Perform temperature correction.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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