Calculation method for abrasion loss of tread rubber under wide-temperature-range complex working conditions

Abstract

The invention relates to the technical field of data processing, in particular to a method for calculating the abrasion loss of tread rubber under a wide-temperature-range complex working condition, which comprises the following steps of: processing each parameter by utilizing dimensionless analysis, and constructing a first dimensionless group and a second dimensionless group so as to eliminate dimensional interference and quantify the synergistic effect of mechanical load and material performance. On the basis of temperature data, parameters of a temperature correction function are fitted through a variable-temperature wear experiment, and the nonlinear attenuation rule of the viscoelasticity of the rubber along with the temperature change is accurately represented. And integrating the dimensionless group, the product of the hardness parameter and the dynamic modulus and the temperature correction function into a thermal and mechanical coupling integral model to realize dynamic coupling and synchronous calculation of the multi-physical field parameters. Finally, the abrasion loss is output through the model, the limitation of parameter decoupling in an existing method is overcome, and prediction precision and model adaptability are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of rubber material wear performance testing and prediction technology, and in particular to a method for calculating tire tread wear under complex working conditions over a wide temperature range. Background Technology

[0002] In complex operating conditions across a wide temperature range, assessing the wear performance of rubber materials, such as tire tread, relies on precise testing and life prediction methods. Wide temperature variations cause fluctuations in the thermodynamic properties of materials, such as changes in elastic modulus and viscoelasticity, thus affecting friction behavior and wear mechanisms. Complex operating conditions, such as road surface roughness, load fluctuations, and ambient humidity, exacerbate the nonlinear characteristics of the wear process. By simulating real-world conditions in the laboratory and employing accelerated wear tests to quantify wear, it is possible to capture the material's response under dynamic environments. Based on wear data and material degradation models, combined with fatigue life theory, tire lifespan can be inferred, providing a scientific basis for optimizing material formulations and improving durability.

[0003] Existing methods for testing rubber material wear suffer from the following technical challenges: Firstly, existing methods typically operate under single physical field conditions, such as constant temperature or fixed mechanical load. However, accurate testing and life prediction of rubber material wear performance under complex, wide-temperature-range conditions require simultaneous consideration of the dynamic coupling effects of mechanical load, sliding velocity, and temperature across multiple physical fields. Secondly, existing methods lack a comprehensive characterization of the interactions between parameters, leading to decoupling of mechanical load, temperature, and material dynamic response parameters. This prevents the effective capture of the nonlinear decay characteristics of rubber viscoelasticity with temperature changes and the synergistic effects of multiple factors. This limitation makes wear prediction models based on single-condition test data unable to accurately reflect material behavior under complex environments, ultimately resulting in significant deviations between model predictions and actual operating conditions, and significantly insufficient prediction accuracy. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for calculating tire tread wear under complex working conditions over a wide temperature range. This method solves the technical problem that existing rubber material wear testing methods suffer from decoupling of mechanical load, temperature, and material dynamic response parameters in complex multi-physics coupled environments, resulting in a significant deviation between the wear prediction model and the actual working conditions, and a significant lack of prediction accuracy.

[0005] To solve the above-mentioned technical problems, the specific contents of the present invention are as follows: The method for calculating tire tread wear under complex working conditions over a wide temperature range provided by this invention includes: Step 1: Obtain mechanical load data, sliding speed data, temperature data, hardness parameters, and dynamic modulus parameters of the tire tread rubber. Step 2: Using the mechanical load data, sliding speed data, hardness parameters and dynamic modulus parameters, construct the first dimensionless group and the second dimensionless group through dimensionless analysis; Step 3: Based on the temperature data, obtain the temperature correction function by fitting through a variable temperature wear experiment; Step 4: Calculate the product of the hardness parameter and the dynamic modulus parameter; Step 5: Input the first dimensionless group, the second dimensionless group, the product, and the temperature correction function into the thermo-mechanical coupling integral model, and calculate the wear amount of the tire tread rubber through the thermo-mechanical coupling integral model.

[0006] Furthermore, in the method for calculating tire tread wear under complex working conditions over a wide temperature range described in this invention, step 3 includes: A variable temperature wear test was conducted within a temperature range of 20℃ to 100℃, with a temperature gradient interval of no more than 10℃, to obtain wear volume data at different temperatures. Using the wear volume data, a temperature correction function is obtained by fitting a nonlinear least squares method, wherein the goodness of fit is not less than 0.95.

[0007] Furthermore, in the method for calculating tire tread wear under complex working conditions over a wide temperature range described in this invention, step 2 includes: Time parameters are obtained based on the time series of the sliding speed data. The product of sliding speed, time parameter, hardness parameter and dynamic modulus parameter is used as a variable. Through dimensionless analysis, the wear volume is normalized by using the cube of the sliding distance as a normalization factor to obtain the first dimensionless group. The dimensionless analysis is based on Buckingham's π theorem, which eliminates dimensional interference and quantifies the synergistic effect between mechanical load and material properties.

[0008] Furthermore, in the method for calculating tire tread wear under complex working conditions over a wide temperature range described in this invention, step 1 includes: The dynamic modulus parameters at the reference temperature were obtained by testing at a reference temperature of 23℃, a frequency of 1Hz, and a strain amplitude of 0.1% to 5%. Using the dynamic modulus parameters at the reference temperature, a master curve of dynamic modulus covering the temperature range of -30℃ to 100℃ is constructed in conjunction with the WLF equation to characterize the dynamic properties of the material as a function of temperature.

[0009] Furthermore, in the method for calculating tire tread wear under complex working conditions over a wide temperature range described in this invention, step 4 includes: Prepare rubber specimens with at least three different formulations, and determine the hardness and dynamic modulus parameters of the rubber specimens; Using the rubber specimens, a three-factor cross-experiment of variable load, variable speed and variable temperature was carried out on a friction testing machine. The experimental load range was 50N to 200N, the experimental speed range was 0.1m / s to 1.2m / s, and the experimental temperature range was 20℃ to 100℃. The wear volume data generated during the experiment were recorded. Using the wear volume data and corresponding experimental conditions as input, the parameters of the thermo-mechanical coupled integral model are optimized by response surface methodology, so that the average relative error between the predicted wear volume and the measured wear volume does not exceed 3.5%.

[0010] Furthermore, in the method for calculating tire tread wear under complex working conditions over a wide temperature range described in this invention, step 3 further includes: The cumulative sliding distance is calculated by performing numerical integration on the real-time collected sliding speed data over time and using the trapezoidal rule. The cumulative slip distance and wear volume data at different temperatures are used as inputs, and the parameters of the temperature correction function are fitted by the least squares method. Slip distance is introduced as a weighting factor in the fitting process to enhance the physical basis of temperature correction.

[0011] Furthermore, in the method for calculating tire tread wear under complex working conditions over a wide temperature range described in this invention, step 3 further includes: During the temperature-dependent wear test, displacement data was acquired using a GPS odometer at a sampling frequency of 1 Hz. The deviation between the GPS displacement data and the cumulative slip distance obtained by integration is calculated, and the calibration coefficient is generated by linear regression. The calibration coefficient is applied to correct the cumulative slip distance in order to eliminate the cumulative error in the integration calculation.

[0012] Furthermore, in the method for calculating tire tread wear under complex working conditions over a wide temperature range described in this invention, step 1 further includes: Dynamic modulus spectrum data were obtained by performing frequency sweep experiments in the frequency range of 0.1Hz to 100Hz using a dynamic mechanical analyzer. Using the dynamic modulus spectrum data, the fractional order is determined by fitting a fractional derivative model, with the sum of squared residuals not exceeding 5%. The fractional order is input into the fractional derivative model to characterize the dynamic modulus parameter as it changes over time.

[0013] Furthermore, in the method for calculating tire tread wear under complex working conditions over a wide temperature range described in this invention, step 4 further includes: Mechanical load, sliding speed and temperature data were synchronously acquired at a frequency of 10Hz, and noise was removed using a Kalman filter algorithm. The filtered data is written to the circular data buffer in timestamp order, and the capacity of the circular data buffer is set to 100 sets of data. The latest data is read from the circulating data buffer and input into the thermo-mechanical coupling integral model to calculate the dynamic wear amount, ensuring data synchronization and calculation continuity.

[0014] Furthermore, in the method for calculating tire tread wear under complex operating conditions over a wide temperature range, step 5 of the present invention further includes: The wear measurement value and historical change curve are displayed in real time through a graphical user interface; Compare the current wear level with a preset safety threshold, where the preset safety threshold is set based on the material fatigue limit; When the wear exceeds a preset safety threshold, an audible and visual warning signal is triggered and a log is generated for real-time monitoring of tire wear life and maintenance decisions.

[0015] Beneficial effects of this invention; This invention simultaneously acquires mechanical load data, sliding speed data, temperature data, hardness parameters, and dynamic modulus parameters, covering the mechanical, thermal, and material properties dimensions of the wear process. Dimensionless analysis is used to construct a first and second dimensionless group, eliminating dimensional interference and quantifying the synergistic effect of mechanical load and material properties. Based on temperature data, the parameters of the temperature correction function are fitted through variable-temperature wear experiments, accurately characterizing the nonlinear decay law of rubber viscoelasticity with temperature changes. The first and second dimensionless groups, the product of hardness parameters and dynamic modulus, and the temperature correction function are integrated into a thermo-mechanical coupled integral model, achieving dynamic coupling and synchronous calculation of multi-physics parameters. This overcomes the limitations of existing methods in decoupling mechanical load, temperature, and material response parameters, significantly improving the prediction accuracy and model adaptability of tire tread wear under complex working conditions across a wide temperature range. Attached Figure Description

[0016] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0017] Figure 1 A flowchart illustrating the method for calculating tire tread wear under complex working conditions in a wide temperature range, as provided in this embodiment of the invention. Detailed Implementation

[0018] To make the technical solution of the present invention clearer, the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The present invention provided by various embodiments will be described in detail below with reference to the accompanying drawings. To better understand the purpose of the present invention, the present invention will be described in further detail below.

[0019] Please see Figure 1 The present invention provides a method for calculating tire tread wear under complex working conditions over a wide temperature range, comprising: Step 1: Obtain mechanical load data, sliding speed data, temperature data, hardness parameters, and dynamic modulus parameters of the tire tread rubber. Step 2: Using the mechanical load data, sliding speed data, hardness parameters and dynamic modulus parameters, construct the first dimensionless group and the second dimensionless group through dimensionless analysis; Step 3: Based on the temperature data, obtain the temperature correction function by fitting through a variable temperature wear experiment; Step 4: Calculate the product of the hardness parameter and the dynamic modulus parameter; Step 5: Input the first dimensionless group, the second dimensionless group, the product, and the temperature correction function into the thermo-mechanical coupling integral model, and calculate the wear amount of the tire tread rubber through the thermo-mechanical coupling integral model.

[0020] The present invention provides a method for calculating tire tread wear under complex working conditions over a wide temperature range. This method achieves accurate calculation of tire tread wear through a series of interconnected technical steps. The method first acquires mechanical load data, sliding speed data, temperature data, hardness parameters, and dynamic modulus parameters of the tire tread. Mechanical load data is collected using a pressure sensor, reflecting the magnitude of the external force on the contact interface. Sliding speed data is recorded using a speed sensor, characterizing the relative motion state. Temperature data is monitored using a temperature sensor, capturing the environmental thermal effects. Hardness parameters are measured using a hardness tester, quantifying the material's resistance to deformation. Dynamic modulus parameters are tested using a dynamic mechanical analyzer, describing the material's viscoelastic behavior. All these parameters together constitute the foundation of multiphysics data, covering the mechanical, thermal, and material property dimensions of the wear process.

[0021] After acquiring multi-source data, the method uses mechanical load data, sliding velocity data, hardness parameters, and dynamic modulus parameters to construct a first and second dimensionless group through dimensionless analysis. Dimensionless analysis is based on Buckingham's π theorem, transforming dimensional physical quantities into dimensionless combinations. The first dimensionless group normalizes the wear volume by using the cube of the sliding distance, eliminating dimensional interference and highlighting the correlation between wear and movement distance. The second dimensionless group combines load and material parameters to characterize the dynamic load intensity and quantify the synergistic effect of mechanical input and material response. Dimensionless processing simplifies complex parameter relationships and enhances the model's universality.

[0022] Based on temperature data, the method obtains a temperature correction function by fitting a variable-temperature wear experiment. The variable-temperature wear experiment sets multiple temperature gradient points within a wide temperature range and measures the wear volume data at each temperature. The experimental data is fitted using a nonlinear least squares method to generate the temperature correction function parameters. The temperature correction function captures the nonlinear decay law of rubber viscoelasticity with temperature change in an exponential form, making up for the limitations of the model under a single temperature. The fitting process ensures that the function curve is in high agreement with the experimental data, improving the accuracy of the temperature effect characterization.

[0023] The method calculates the product of hardness parameter and dynamic modulus parameter; this product serves as a comprehensive index of the material's wear resistance, integrating the static resistance characterized by hardness and the viscoelastic response reflected by dynamic modulus; the product calculation strengthens the intrinsic correlation of material parameters, provides unified input parameters for the model, and avoids errors caused by isolated parameters.

[0024] Finally, the method inputs the first dimensionless group, the second dimensionless group, the product of the hardness parameter and the dynamic modulus parameter, and the temperature correction function into a thermo-mechanical coupled integral model. The model simultaneously processes the multi-physics input through integral form. The first dimensionless group provides a normalized wear benchmark, the second dimensionless group contributes the load intensity factor, the product parameters introduce the comprehensive influence of material properties, and the temperature correction function dynamically adjusts the thermal effect. The model calculates and outputs the wear amount of the tire tread rubber, directly reflecting the degree of material degradation under complex working conditions. The process of this invention, from data acquisition and parameter processing to model calculation, is closely connected through data flow and progresses step by step, realizing multi-factor coupling and accurate prediction.

[0025] Specifically, in the method for calculating tire tread wear under complex working conditions in a wide temperature range according to the present invention, step 3 includes: A variable temperature wear test was conducted within a temperature range of 20℃ to 100℃, with a temperature gradient interval of no more than 10℃, to obtain wear volume data at different temperatures. Using the wear volume data, a temperature correction function is obtained by fitting a nonlinear least squares method, wherein the goodness of fit is not less than 0.95.

[0026] In the method for calculating tire tread wear under complex working conditions over a wide temperature range described in this invention, step three includes conducting a variable-temperature wear test within a temperature range of 20 to 100 degrees Celsius, with a temperature gradient interval not exceeding 10 degrees Celsius, to obtain wear volume data at different temperatures. The variable-temperature wear test is conducted in a temperature-controlled environmental chamber, with a built-in temperature sensor monitoring temperature fluctuations in real time. The wear test begins after each temperature gradient point has stabilized. The wear test uses a standard friction testing machine, applying a constant load and sliding speed. After running for a specified time, the specimen is removed, and the wear volume data is measured using a three-dimensional surface profilometer. The test process covers the entire temperature range, ensuring that each temperature point has a corresponding wear volume record, forming a temperature-wear volume dataset.

[0027] After obtaining wear volume data at different temperatures, a temperature correction function was obtained by fitting the data using a nonlinear least squares method. The nonlinear least squares method iteratively optimized the function parameters to minimize the sum of squared residuals between the predicted and measured values. An exponential function was chosen as the basis for the temperature correction function because it effectively characterizes the nonlinear decay of rubber viscoelasticity with temperature. A goodness of fit of no less than 0.95 indicates that the function curve closely matches the experimental data, demonstrating the reliability of the fitting results.

[0028] The temperature correction function is used to correct the effect of temperature on wear in the thermo-mechanical coupled integral model. After the function parameters are fitted, the temperature correction function is integrated into the model calculation process, dynamically adjusting the predicted wear value based on the input temperature data. Step three, from experimental data acquisition to function fitting, is logically coherent and provides an accurate basis for thermal effect correction in the model.

[0029] Specifically, in the method for calculating tire tread wear under complex working conditions in a wide temperature range according to the present invention, step 2 includes: Time parameters are obtained based on the time series of the sliding speed data. The product of sliding speed, time parameter, hardness parameter and dynamic modulus parameter is used as a variable. Through dimensionless analysis, the wear volume is normalized by using the cube of the sliding distance as a normalization factor to obtain the first dimensionless group. The dimensionless analysis is based on Buckingham's π theorem, which eliminates dimensional interference and quantifies the synergistic effect between mechanical load and material properties.

[0030] In the method for calculating tire tread wear under complex working conditions in a wide temperature range described in this invention, step two includes obtaining time parameters based on the time series of sliding speed data. Sliding speed data is continuously collected by a speed sensor at a fixed sampling frequency, with each data point accompanied by a timestamp, forming a time series dataset. Time parameters are extracted from this time series and represent the cumulative time or time interval from the start of the experiment to each sampling point, used to quantify the duration of the sliding process. The acquisition of time parameters is performed synchronously with data acquisition, ensuring a strict correspondence between time information and speed data, providing a time dimension basis for subsequent analysis.

[0031] After obtaining the time parameter, the combination of sliding velocity, time parameter, and the product of hardness parameter and dynamic modulus parameter are used as variables and processed through dimensionless analysis. The sliding velocity variable represents the relative motion rate, the time parameter variable represents the duration of action, and the product of hardness parameter and dynamic modulus parameter serves as a comprehensive index of the material's wear resistance. The combinations of variables cover multiple dimensions including mechanical motion, time accumulation, and material properties. Variable selection is based on physical correlation to ensure that dimensionless analysis can effectively capture the intrinsic relationships between parameters.

[0032] Dimensionless analysis is based on Buckingham's π theorem. By identifying the fundamental dimensions of the variables, dimensional physical quantities are transformed into dimensionless combinations. The process first identifies the core variables, including sliding velocity, time parameters, the product of hardness parameters and dynamic modulus parameters, as well as wear volume and sliding distance. The wear volume is normalized using the cube of the sliding distance as a normalization factor (i.e., wear volume divided by the cube of the sliding distance), eliminating the influence of dimensions. The normalized wear volume and the variable combinations are then used to construct a dimensionless group through exponential relationships, ultimately yielding the first dimensionless group.

[0033] The construction of the first dimensionless group eliminates dimensional interference in the original data, enabling direct comparison and combination of different physical quantities. Dimensionless processing quantifies the synergistic effect of mechanical load and material properties. The first dimensionless group comprehensively reflects the coupled influence of sliding speed, application time, and material properties on wear volume. The analysis process of this invention, from data acquisition to variable combination and then to dimensionless processing, is logically connected between steps through data flow, ultimately outputting dimensionless parameters that can be used as model input.

[0034] Specifically, the method for calculating tire tread wear under complex working conditions in a wide temperature range according to the present invention includes step 1 as follows: The dynamic modulus parameters at the reference temperature were obtained by testing at a reference temperature of 23℃, a frequency of 1Hz, and a strain amplitude of 0.1% to 5%. Using the dynamic modulus parameters at the reference temperature, a master curve of dynamic modulus covering the temperature range of -30℃ to 100℃ is constructed in conjunction with the WLF equation to characterize the dynamic properties of the material as a function of temperature.

[0035] In the method for calculating tire tread wear under complex working conditions over a wide temperature range described in this invention, step one includes obtaining the dynamic modulus parameters at a reference temperature of 23 degrees Celsius, under conditions of a frequency of 1 Hz and a strain amplitude of 0.1% to 5%. The testing process is conducted using a rubber analyzer. The analyzer applies a sinusoidal load at a fixed frequency of 1 Hz, and the strain amplitude is set in segments within the range of 0.1% to 5%. The stress response is measured at each strain amplitude, and the dynamic modulus parameters are calculated. The dynamic modulus parameters include storage modulus and loss modulus, characterizing the viscoelastic behavior of the material at the reference temperature. The testing environment is controlled at a constant temperature of 23 degrees Celsius to avoid temperature fluctuations affecting the accuracy of the results. The test data is filtered to eliminate noise interference and ensure the reliability of the dynamic modulus parameters.

[0036] After obtaining the dynamic modulus parameters at the reference temperature, the WLF equation is used to construct a master dynamic modulus curve covering the temperature range from -30°C to 100°C. The WLF equation, based on the time-temperature equivalence principle, superimposes the dynamic modulus data from different temperatures onto the reference temperature master curve using a shift factor. The shift factor is calculated using the WLF equation formula, inputting the dynamic modulus parameters at the reference temperature and the temperature difference, and outputting the shift amount of each temperature relative to the reference temperature. The master curve construction process integrates the dynamic modulus data at discrete temperature points into a continuous temperature function through data fitting and curve translation. The master dynamic modulus curve fully characterizes the dynamic properties of the material as a function of temperature, including the modulus changes in the glass transition region, the rubbery plateau region, and the viscous flow region.

[0037] Step one, from dynamic modulus parameter testing to master curve construction, is logically coherent. Testing provides baseline data, the WLF equation expands the temperature range, and the master curve achieves wide-temperature-range characterization. This invention provides accurate material property input for subsequent wear calculations, supporting the reliability of the multiphysics coupling model.

[0038] Specifically, in the method for calculating tire tread wear under complex working conditions in a wide temperature range according to the present invention, step 4 includes: Prepare rubber specimens with at least three different formulations, and determine the hardness and dynamic modulus parameters of the rubber specimens; Using the rubber specimens, a three-factor cross-experiment of variable load, variable speed and variable temperature was carried out on a friction testing machine. The experimental load range was 50N to 200N, the experimental speed range was 0.1m / s to 1.2m / s, and the experimental temperature range was 20℃ to 100℃. The wear volume data generated during the experiment were recorded. Using the wear volume data and corresponding experimental conditions as input, the parameters of the thermo-mechanical coupled integral model are optimized by response surface methodology, so that the average relative error between the predicted wear volume and the measured wear volume does not exceed 3.5%.

[0039] Specifically, in the method for calculating tire tread wear under complex working conditions in a wide temperature range according to the present invention, step 3 further includes: The cumulative sliding distance is calculated by performing numerical integration on the real-time collected sliding speed data over time and using the trapezoidal rule. The cumulative slip distance and wear volume data at different temperatures are used as inputs, and the parameters of the temperature correction function are fitted by the least squares method. Slip distance is introduced as a weighting factor in the fitting process to enhance the physical basis of temperature correction.

[0040] In the method for calculating tire tread wear under complex working conditions over a wide temperature range described in this invention, step four includes preparing rubber specimens with at least three different formulations and determining the hardness and dynamic modulus parameters of the rubber specimens. The rubber specimens are prepared using a mixing process, where the base rubber compound is mixed with fillers, vulcanizing agents, and other additives in a specific ratio. The mixture is then homogenized on an open mill and vulcanized to form standard-sized specimens. The hardness parameter is measured using a Shore hardness tester, and the dynamic modulus parameter is obtained by testing with a dynamic mechanical analyzer under fixed frequency and strain amplitude conditions. Preparing specimens with multiple formulations covers different material property ranges, providing representative samples for subsequent experiments and ensuring data diversity. The hardness parameter characterizes the material's resistance to deformation, and the dynamic modulus parameter describes viscoelastic behavior; the combination of both provides the basic material performance input for the model.

[0041] After obtaining the rubber specimens, a three-factor cross-test was conducted on each specimen using a friction testing machine, involving varying load, speed, and temperature. The experimental design employed a full-factor or partial-factor scheme, with multiple gradient points set for the load within the range of 50 to 200 Newtons, segmented speed variations within the range of 0.1 m / s to 1.2 m / s, and multiple temperature gradients within the range of 20 to 100 degrees Celsius. The friction testing machine was equipped with a heating chamber and cooling system to precisely control the temperature environment. During the experiment, wear tests were performed on each specimen under different combinations of load, speed, and temperature. After a specified time, the specimens were removed, and the wear volume data was measured using a three-dimensional surface profilometer. Experimental records included mechanical load, sliding speed, temperature, and the corresponding wear volume values, forming a multi-factor experimental dataset. The three-factor cross-test captured the interactive effects of mechanical, kinematic, and thermal conditions, simulating complex real-world working conditions and providing comprehensive input for the model.

[0042] After recording the wear volume data generated during the experiment, the data and corresponding experimental conditions were used as inputs to optimize the parameters of the thermo-mechanical coupled integral model using response surface methodology (RSM). RSM established a mathematical relationship model between wear volume and experimental conditions, fitted the data using a second-order polynomial or higher-order model, and estimated the model coefficients using the least squares method. The optimization process aimed to minimize the average relative error between the predicted and measured wear volumes, iteratively adjusting the model parameters until the error did not exceed 3.5%. RSM identified the influence of key factors, verified the model's robustness, and improved prediction accuracy. Step four, from specimen preparation to experimental execution and data analysis, was logically coherent, providing a reliable foundation for model parameter calibration.

[0043] Specifically, in the method for calculating tire tread wear under complex working conditions in a wide temperature range according to the present invention, step 3 further includes: During the temperature-dependent wear test, displacement data was acquired using a GPS odometer at a sampling frequency of 1 Hz. The deviation between the GPS displacement data and the cumulative slip distance obtained by integration is calculated, and the calibration coefficient is generated by linear regression. The calibration coefficient is applied to correct the cumulative slip distance in order to eliminate the cumulative error in the integration calculation.

[0044] In the method for calculating tire tread wear under complex working conditions in a wide temperature range described in this invention, step three further includes performing numerical integration on the real-time collected sliding speed data over time, and calculating the cumulative sliding distance using the trapezoidal rule. The sliding speed data is continuously collected by a speed sensor at a fixed sampling frequency to form a time-series dataset; the numerical integration multiplies the speed value at each time point by the time interval, and the sum is obtained to obtain the cumulative sliding distance; the trapezoidal rule improves integration accuracy and reduces discrete errors by approximating the area between adjacent speed data points as a trapezoid; the cumulative sliding distance characterizes the total motion path of the specimen during the wear process, providing spatial dimension parameters for temperature correction.

[0045] After obtaining the cumulative slip distance, the cumulative slip distance and wear volume data at different temperatures are used as inputs to fit the parameters of the temperature correction function using the least squares method. The least squares method iteratively optimizes the function coefficients to minimize the sum of squared residuals between the predicted and measured values. The fitting process uses the cumulative slip distance as the independent variable and the wear volume data as the dependent variable to establish a functional relationship. The temperature correction function adopts an exponential form to describe the nonlinear change of rubber viscoelasticity with temperature. The fitting objective is to make the function curve closely fit the experimental data through mathematical optimization.

[0046] In the least squares fitting process, slip distance is introduced as a weighting factor to enhance the physical basis of temperature correction. The weighting factor is assigned based on the magnitude of the slip distance, with data points corresponding to larger slip distances assigned higher weights because they represent more significant wear accumulation. The introduction of weights adjusts the contribution in the residual calculation, making the fitting focus more on data under high slip distance conditions. This treatment strengthens the influence of motion accumulation on temperature correction and improves the model's adaptability under varying operating conditions.

[0047] Specifically, in the method for calculating tire tread wear under complex working conditions in a wide temperature range according to the present invention, step 1 further includes: Dynamic modulus spectrum data were obtained by performing frequency sweep experiments in the frequency range of 0.1Hz to 100Hz using a dynamic mechanical analyzer. Using the dynamic modulus spectrum data, the fractional order is determined by fitting a fractional derivative model, with the sum of squared residuals not exceeding 5%. The fractional order is input into the fractional derivative model to characterize the dynamic modulus parameter as it changes over time.

[0048] In the method for calculating tire tread wear under complex working conditions over a wide temperature range described in this invention, step one further includes conducting a frequency sweep experiment using a dynamic mechanical analyzer within a frequency range of 0.1Hz to 100Hz to obtain dynamic modulus spectrum data. The frequency sweep experiment uses a dynamic mechanical analyzer to scan the frequency range from 0.1Hz to 100Hz in a logarithmic or linear step manner, applying a sinusoidal load while maintaining the strain amplitude within the linear viscoelastic region of the material, and measuring the stress response and phase difference at each frequency point. The dynamic mechanical analyzer acquires data through fixed frequency intervals or continuous frequency sweep mode, obtaining spectra of storage modulus and loss modulus as a function of frequency, forming a dynamic modulus spectrum dataset. The frequency sweep experiment covers a wide frequency range, capturing the viscoelastic response of the material under different loading rates, providing a frequency-dependent characteristic basis for subsequent models. The experiment is conducted in a temperature-controlled environment to avoid temperature fluctuations affecting data consistency; the dynamic modulus spectrum data characterizes the mechanical behavior of the material under dynamic loads.

[0049] After acquiring the dynamic modulus spectrum data, the fractional order was determined by fitting the data using a fractional derivative model. The fractional derivative model employs a fractional differential equation, representing the dynamic modulus as a fractional function of frequency. Model parameters include the fractional order and relaxation time. The fitting process utilizes a nonlinear optimization algorithm, such as the Levenberg-Marquardt algorithm, to iteratively adjust the model parameters, minimizing the sum of squared residuals between the predicted and measured dynamic modulus spectrum data. A low sum of squared residuals indicates good agreement between the model and experimental data, and that the fractional order accurately reflects the fractional characteristics of the material's viscoelasticity. Once the fractional order is determined, it characterizes the strength of the material's memory effect and rate-dependent behavior, providing a mathematical description of the dynamic modulus's variation over time.

[0050] Fractional orders are input into the fractional derivative model to characterize the time-varying properties of the dynamic modulus parameter. The fractional derivative model uses fractional orders as core parameters to construct the constitutive relationship between the dynamic modulus and time. The model describes stress relaxation or creep behavior through fractional differential operators. After incorporating fractional orders into the model, it is used to simulate the time-varying response of the dynamic modulus over a wide temperature range, considering material history dependence and nonlocal effects. The model is applied to a thermo-mechanical coupled integral model to dynamically adjust the dynamic modulus parameter, enhancing its adaptability to complex operating conditions.

[0051] Specifically, in the method for calculating tire tread wear under complex working conditions in a wide temperature range according to the present invention, step 4 further includes: Mechanical load, sliding speed and temperature data were synchronously acquired at a frequency of 10Hz, and noise was removed using a Kalman filter algorithm. The filtered data is written to the circular data buffer in timestamp order, and the capacity of the circular data buffer is set to 100 sets of data. The latest data is read from the circulating data buffer and input into the thermo-mechanical coupling integral model to calculate the dynamic wear amount, ensuring data synchronization and calculation continuity.

[0052] In the method for calculating tire tread wear under complex working conditions in a wide temperature range described in this invention, step four further includes synchronously acquiring mechanical load data, sliding speed data, and temperature data at a frequency of 10 Hz, and using a Kalman filter algorithm to remove noise. The synchronous acquisition process is achieved by integrating pressure sensors, speed sensors, and temperature sensors. Each sensor simultaneously triggers data acquisition at a sampling frequency of 10 Hz, ensuring the time alignment of multi-source data. The Kalman filter algorithm is applied to the acquired raw data stream, recursively estimating the system state through a prediction-correction mechanism, filtering out measurement noise and environmental interference. The filtering algorithm uses the system dynamics model and observation model to dynamically adjust the gain coefficient and optimize the data estimate. After noise removal, the signal-to-noise ratio of the data is improved, providing clean input for subsequent processing. The data acquisition and filtering steps prepare a high-quality data foundation for model calculation and reduce error propagation.

[0053] Filtered data is written to a circular data buffer in timestamp order, with a buffer capacity of 100 data sets. Timestamps are generated by the data acquisition system, marking the precise acquisition time of each data point to ensure temporal integrity. Data writing follows a first-in, first-out (FIFO) principle, with newer data overwriting older data to maintain buffer freshness. The 100-data-set capacity of the circular data buffer balances memory resources with real-time requirements, preventing data backlog or loss. The buffer acts as a data relay, buffering differences in acquisition and processing rates to ensure a smooth data flow. The writing process ensures ordered data storage, providing structured support for continuous reading.

[0054] The latest data is read from the circular data buffer and input into the thermo-mechanical coupling integral model for dynamic wear calculation. The read operation extracts the data set with the latest timestamp from the buffer, ensuring that the model input reflects the current moment. After receiving the data, the thermo-mechanical coupling integral model performs integration to solve for the wear amount. Model calculation and data reading are triggered synchronously to reduce processing latency. The dynamic wear calculation process is continuous, with one data update corresponding to each calculation cycle, forming a closed-loop control. The data synchronization mechanism coordinates the acquisition, writing, and reading actions through a unified clock signal, maintaining pipeline continuity. The calculation output reflects the system status in real time, supporting wear monitoring and decision-making.

[0055] Specifically, in the method for calculating tire tread wear under complex working conditions in a wide temperature range according to the present invention, step 5 further includes: The wear measurement value and historical change curve are displayed in real time through a graphical user interface; Compare the current wear level with a preset safety threshold, where the preset safety threshold is set based on the material fatigue limit; When the wear exceeds a preset safety threshold, an audible and visual warning signal is triggered and a log is generated for real-time monitoring of tire wear life and maintenance decisions.

[0056] In the method for calculating tire tread wear under complex working conditions in a wide temperature range described in this invention, step five further includes real-time display of wear values ​​and historical change curves through a graphical user interface (GUI). The GUI is deployed on the monitoring terminal, integrating a data display module and a curve plotting module. Wear values ​​are dynamically updated in digital form, and historical change curves are visualized through line graphs or curves. The interface receives calculation results from the thermodynamic-mechanical coupling integral model and updates the displayed content at a fixed refresh rate to ensure real-time information. Historical data is stored in a circular buffer, retaining recent wear records and supporting curve backtracking analysis. The display process provides a human-computer interaction interface, allowing operators to intuitively grasp the wear status and providing visual support for decision-making. The GUI, as the output layer, transforms the model calculation results into readable information, completing the conversion from data to knowledge.

[0057] The current wear level is compared with a preset safety threshold, which is based on the material's fatigue limit. This preset safety threshold is determined through material fatigue testing, taking into account the stress cycle life and damage accumulation effect of the rubber material. The threshold value reflects the safe service boundary of the material. The comparison operation is performed after each wear level calculation, comparing the current wear level with the threshold to determine if it exceeds the safe range. A safety factor is introduced into the threshold setting to adapt to reliability requirements under different operating conditions. The comparison logic is embedded in the monitoring program, enabling automated judgment and reducing human intervention. The threshold based on the material fatigue limit ensures the scientific nature of the judgment standard, making the monitoring results have engineering guidance significance.

[0058] When wear exceeds a preset safety threshold, an audible and visual warning signal is triggered, and a log entry is generated. The audible and visual warning signals are implemented via a buzzer and warning lights. The audible alarm uses different frequencies to indicate the severity, and the light signals use color changes to distinguish the status. Log entries are stored in text files or a database, including timestamps, wear values, threshold information, and event types. The warning trigger mechanism responds in real time to abnormal conditions, alerting operators to intervene promptly. Log entries provide a data foundation for subsequent analysis, supporting fault diagnosis and lifespan prediction. The warning system is integrated into the monitoring platform, achieving closed-loop management from detection to response, improving the timeliness and reliability of tire wear life monitoring.

[0059] The specific implementation of this invention is as follows: In the calculation of tire tread wear under complex working conditions over a wide temperature range, mechanical load data, sliding speed data, and temperature data are first collected simultaneously by integrating pressure sensors, speed sensors, and temperature sensors. The pressure sensor is installed at the contact interface to monitor load changes in real time. The speed sensor records the relative motion rate using an encoder or laser velocimeter. The temperature sensor captures the thermal effect of the contact area using a thermocouple or infrared thermometer. Simultaneously, a hardness tester is used to determine the hardness parameters of the rubber specimen, and a dynamic modulus parameter is tested under standard frequency and strain amplitude conditions using a dynamic mechanical analyzer. These parameters constitute the foundation of multiphysics data, covering mechanical, kinematic, and thermal dimensions, providing comprehensive input for subsequent analysis.

[0060] After acquiring multi-source data, dimensionless analysis methods were used to process mechanical load data, sliding velocity data, hardness parameters, and dynamic modulus parameters. Based on Buckingham's π theorem, a first and second dimensionless group were constructed. The first dimensionless group normalized the wear volume by using the cube of the sliding distance to eliminate dimensional interference. The second dimensionless group combined load parameters and material property parameters to characterize the dynamic load intensity. Dimensionless processing transforms dimensional physical quantities into dimensionless combinations, quantifying the synergistic effect between mechanical load and material properties, and enhancing the intrinsic correlation between parameters.

[0061] To address the influence of temperature, variable-temperature wear experiments were conducted to obtain wear volume data at different temperatures. The experiments were carried out in a temperature-controlled chamber with multiple temperature gradient points, and standard wear tests were performed at each temperature point. The experimental data were fitted using a nonlinear least squares method to obtain the temperature correction function parameters. This function exponentially characterizes the nonlinear decay of rubber viscoelasticity with temperature, overcoming the limitations of a single-temperature model.

[0062] The product of the hardness parameter and the dynamic modulus parameter is calculated. This product serves as a comprehensive index of the material's wear resistance, integrating the static resistance characterized by hardness with the viscoelastic behavior reflected by the dynamic modulus. The product calculation strengthens the intrinsic relationship between material parameters, providing a unified set of input parameters for the model.

[0063] The first dimensionless group, the second dimensionless group, the product of the hardness parameter and the dynamic modulus, and the temperature correction function are input into the thermo-mechanical coupled integral model. This model simultaneously processes multi-physics inputs through integration. The first dimensionless group provides a normalization benchmark, the second dimensionless group contributes a load intensity factor, the product parameters introduce the influence of material properties, and the temperature correction function dynamically adjusts the thermal effect. The model calculates and outputs the tire tread wear, directly reflecting the degree of material degradation under complex working conditions.

[0064] During implementation, the accuracy of slip distance calculations was verified using GPS odometers, Kalman filtering algorithms were employed to improve data quality, and a cyclic data buffer was established to ensure continuous processing. Finally, a graphical user interface was used to achieve real-time display and early warning of wear, forming a complete technical solution from data acquisition to decision support. The implementation process effectively solved the parameter decoupling problem of existing methods through multi-parameter synchronous acquisition, dimensionless coupling, and dynamic correction, improving the accuracy and reliability of wear calculations under complex working conditions.

[0065] The embodiments of the present invention are as follows: Calculation method for tread wear under complex operating conditions over a wide temperature range: Construction of dimensionless groups: Two sets of dimensionless groups are established based on Buckingham's π theorem: (Normalized wear volume) characterizes the synergistic effect of mechanical load and material properties; (Dynamic load strength coefficient) reflects the contribution of external input parameters to interface deformation; Temperature correction function design: Introducing an Arrhenius-type exponential correction term: ,in: α and β were obtained by fitting the variable temperature wear experiment to characterize the decay characteristics of rubber viscoelasticity with temperature; γ is the compensation coefficient for the nonlinear thermal softening effect in the high temperature region. Synergistic characterization of material parameters: The product parameter (H·G*) of hardness (H) and dynamic modulus (G*) is used as a comprehensive wear resistance performance index to solve the characterization problem of rubber compressive-tensile asymmetry; Model Integration: Combining the dimensionless group and temperature correction term, a thermo-mechanical coupled integral model is constructed. ; Where K is the grounding mode coefficient and x is the load sensitivity index.

[0066] To solve for the transient wear of the tire, the equation is differentiated; ; in To accumulate the sliding distance; is the strain rate (h is the tread thickness); Θ is the Heaviside step function, and Tg is the glass transition temperature; The temperature field evolution equation adopts the form of a partial differential equation coupled with heat conduction and frictional heat generation: ; Boundary conditions: ; Where δ(z) is the Dirac function representing the interfacial heat source. For road surface heat flow; The time-varying characteristics of the dynamic modulus G* are characterized by a fractional derivative model: ; The fractional order α was determined through a dynamic mechanical analysis (DMA) frequency sweep experiment, with a fitting error ≤ 5%.

[0067] The establishment of a dimensionless group, as described in the construction of a dimensionless group, must satisfy the following conditions: The variables were chosen as a combination of sliding speed (v), time (t), and H·G* parameters to eliminate dimensional interference in the viscoelastic response of rubber; The exponential relationship of Π1 is normalized to the wear volume by the cube of the slip distance (S).

[0068] The method for determining the parameters of the temperature correction term in the design of the temperature correction function includes: Variable temperature wear test was conducted in the range of 20-100℃, with the temperature gradient interval between each group not exceeding 10℃. The nonlinear least squares method is used to fit α, β, and γ, and the goodness of fit R² ≥ 0.95 is required.

[0069] Experimental calibration of model parameters during model integration includes: Prepare rubber specimens with at least three different formulations and determine their H and G* values; Three-factor cross-tests were conducted on a friction testing machine, involving variable load (50-200N), variable speed (0.1-1.2m / s), and variable temperature (20-100℃). The parameters K and x are optimized using response surface methodology, with the requirement that the average relative error between the predicted and measured values ​​be ≤3.5%.

[0070] Data acquisition module: integrates a pressure sensor (range 0-500N), an infrared thermometer (accuracy ±1℃), and a GPS odometer; Dynamic parameter update module: acquires F, v, and T data in real time and corrects the cumulative error of the sliding distance S; The dynamic modulus (G*) was obtained by the following method: testing was performed using a rubber analyzer (RPA2000) at a frequency of 1 Hz and a strain amplitude of 0.1%-5%; a master curve was constructed by combining the WLF equation, covering a temperature range of -30℃ to 100℃.

[0071] This invention addresses the technical problem of insufficient prediction accuracy caused by parameter decoupling in existing rubber material wear testing methods under complex multi-physics coupled environments. It achieves synergistic coupling of mechanical load, temperature, and material dynamic response parameters through a series of technical means. Existing methods typically operate under single-physics conditions, such as constant temperature or fixed mechanical load, leading to decoupling of mechanical load, temperature, and material dynamic response parameters. This fails to capture the nonlinear decay characteristics of rubber viscoelasticity with temperature changes and the synergistic effects of multiple factors, causing the prediction model to deviate significantly from actual operating conditions. This invention simultaneously acquires mechanical load data, sliding speed data, temperature data, hardness parameters, and dynamic modulus parameters of the tire tread rubber. These parameters cover the mechanical, thermal, and material properties dimensions of the wear process, forming a multi-physics data foundation. This avoids the limitations of single-condition testing and provides complete input for parameter coupling.

[0072] Based on multi-source data, this invention uses mechanical load data, sliding velocity data, hardness parameters, and dynamic modulus parameters to construct a first and second dimensionless group through dimensionless analysis. Dimensionless analysis, based on Buckingham's π theorem, transforms dimensional physical quantities into dimensionless combinations. The first dimensionless group normalizes the wear volume by using the cube of the sliding distance, eliminating dimensional interference. The second dimensionless group characterizes the dynamic load intensity, quantifying the synergistic effect between mechanical load and material properties. This approach simplifies parameter relationships, enhances model universality, and resolves the decoupling problem caused by dimensional mismatches between parameters.

[0073] To address the influence of temperature, this invention uses temperature data to fit a temperature correction function through variable-temperature wear experiments. The experiment sets multiple temperature gradient points over a wide temperature range, measuring wear volume data at different temperatures. A nonlinear least squares method is used to fit the function parameters, accurately characterizing the nonlinear decay of rubber viscoelasticity with temperature. The temperature correction function dynamically adjusts the thermal effect exponentially, directly incorporating temperature parameters into the model and overcoming the thermal-mechanical decoupling deficiency in existing methods.

[0074] This invention calculates the product of hardness parameter and dynamic modulus parameter, which serves as a comprehensive index of the material's wear resistance performance. It integrates the static resistance characterized by hardness with the viscoelastic response reflected by dynamic modulus, strengthens the intrinsic correlation of material parameters, and avoids errors caused by isolated parameters.

[0075] Finally, the first dimensionless group, the second dimensionless group, the product of the hardness parameter and the dynamic modulus, and the temperature correction function are input into the thermo-mechanical coupled integral model. This model synchronously processes multi-physics inputs through integration. The first dimensionless group provides a normalized wear baseline, the second dimensionless group contributes a load intensity factor, the product parameters introduce the influence of material properties, and the temperature correction function dynamically adjusts the thermal effect, achieving dynamic coupling and synchronous calculation of mechanical, thermal, and material responses. The model outputs the wear amount of the tire tread rubber, directly reflecting the degree of material degradation under complex working conditions, thereby solving the prediction bias caused by parameter decoupling and improving prediction accuracy and model adaptability. The technical solution, from data acquisition and parameter processing to model calculation, is logically coherent and progressively advanced, eliminating the limitations of decoupling through multi-factor coupling.

Claims

1. A method for calculating tire tread wear under complex operating conditions over a wide temperature range, characterized in that, include: Step 1: Obtain mechanical load data, sliding speed data, temperature data, hardness parameters, and dynamic modulus parameters of the tire tread rubber. Step 2: Using the mechanical load data, sliding speed data, hardness parameters and dynamic modulus parameters, construct the first dimensionless group and the second dimensionless group through dimensionless analysis; Step 3: Based on the temperature data, obtain the temperature correction function by fitting through a variable temperature wear experiment; Step 4: Calculate the product of the hardness parameter and the dynamic modulus parameter; Step 5: Input the first dimensionless group, the second dimensionless group, the product, and the temperature correction function into the thermo-mechanical coupling integral model, and calculate the wear amount of the tire tread rubber through the thermo-mechanical coupling integral model.

2. The method for calculating tire tread wear under complex working conditions with a wide temperature range according to claim 1, characterized in that, Step 3 includes: A variable temperature wear test was conducted within a temperature range of 20℃ to 100℃, with a temperature gradient interval of no more than 10℃, to obtain wear volume data at different temperatures. Using the wear volume data, a temperature correction function is obtained by fitting a nonlinear least squares method, wherein the goodness of fit is not less than 0.

95.

3. The method for calculating tire tread wear under complex working conditions in a wide temperature range according to claim 2, characterized in that, Step 2 includes: Time parameters are obtained based on the time series of the sliding speed data. The product of sliding speed, time parameter, hardness parameter and dynamic modulus parameter is used as a variable. Dimensionless analysis is performed, and the wear volume is normalized using the cube of the sliding distance as a normalization factor to obtain the first dimensionless group.

4. The method for calculating tire tread wear under complex working conditions with a wide temperature range according to claim 3, characterized in that, Step 1 includes: The dynamic modulus parameters at the reference temperature were obtained by testing at a reference temperature of 23℃, a frequency of 1Hz, and a strain amplitude of 0.1% to 5%. Using the dynamic modulus parameters at the reference temperature, a master curve of dynamic modulus covering the temperature range of -30℃ to 100℃ is constructed in conjunction with the WLF equation to characterize the dynamic properties of the material as a function of temperature.

5. The method for calculating tire tread wear under complex working conditions with a wide temperature range according to claim 4, characterized in that, Step 4 includes: Prepare rubber specimens with at least three different formulations, and determine the hardness and dynamic modulus parameters of the rubber specimens; Using the rubber specimens, a three-factor cross-experiment of variable load, variable speed and variable temperature was carried out on a friction testing machine. The experimental load range was 50N to 200N, the experimental speed range was 0.1m / s to 1.2m / s, and the experimental temperature range was 20℃ to 100℃. The wear volume data generated during the experiment were recorded. Using the wear volume data and corresponding experimental conditions as input, the parameters of the thermo-mechanical coupled integral model are optimized by response surface methodology, so that the average relative error between the predicted wear volume and the measured wear volume does not exceed 3.5%.

6. The method for calculating tire tread wear under complex working conditions with a wide temperature range according to claim 5, characterized in that, Step 3 also includes: The cumulative sliding distance is calculated by performing numerical integration on the real-time collected sliding speed data over time and using the trapezoidal rule. The cumulative slip distance and wear volume data at different temperatures are used as inputs, and the parameters of the temperature correction function are fitted by the least squares method, with the slip distance introduced as a weighting factor during the fitting process.

7. The method for calculating tire tread wear under complex working conditions with a wide temperature range according to claim 6, characterized in that, Step 3 also includes: During the temperature-dependent wear test, displacement data was acquired using a GPS odometer at a sampling frequency of 1 Hz. The deviation between the GPS displacement data and the cumulative slip distance obtained by integration is calculated, and the calibration coefficient is generated by linear regression. The calibration coefficient is applied to correct the cumulative slip distance in order to eliminate the cumulative error in the integration calculation.

8. The method for calculating tire tread wear under complex working conditions in a wide temperature range according to claim 7, characterized in that, Step 1 further includes: Dynamic modulus spectrum data were obtained by performing frequency sweep experiments in the frequency range of 0.1Hz to 100Hz using a dynamic mechanical analyzer. Using the dynamic modulus spectrum data, the fractional order is determined by fitting a fractional derivative model, with the sum of squared residuals not exceeding 5%. The fractional order is input into the fractional derivative model to characterize the dynamic modulus parameter as it changes over time.

9. The method for calculating tire tread wear under complex working conditions with a wide temperature range according to claim 8, characterized in that, Step 4 also includes: Mechanical load, sliding speed and temperature data were synchronously acquired at a frequency of 10Hz, and noise was removed using a Kalman filter algorithm. The filtered data is written to the circular data buffer in timestamp order, and the capacity of the circular data buffer is set to 100 sets of data. The latest data is read from the circulating data buffer and input into the thermo-mechanical coupling integral model to calculate the dynamic wear amount, ensuring data synchronization and calculation continuity.

10. The method for calculating tire tread wear under complex working conditions with a wide temperature range according to claim 9, characterized in that, Step 5 further includes: The wear measurement value and historical change curve are displayed in real time through a graphical user interface; Compare the current wear level with a preset safety threshold, where the preset safety threshold is set based on the material fatigue limit; When the wear exceeds a preset safety threshold, an audible and visual warning signal is triggered and a log is generated for real-time monitoring of tire wear life and maintenance decisions.

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