Performance data analysis and evaluation method for rubber materials

Abstract

The present invention relates to the technical field of rubber performance evaluation, and in particular to a method for analyzing and evaluating performance data of rubber materials. The method comprises the following steps: acquiring X-ray scanning data of a rubber sample; performing phase contrast imaging on the X-ray scanning data of the rubber sample to obtain projection data of the rubber sample; performing back-projection reconstruction on the projection data of the rubber sample to obtain three-dimensional structural data of the rubber material; subjecting the rubber sample to multi-parameter environmental simulation loading to obtain environmental response data of the rubber material; dynamically collecting the environmental response data of the rubber material to obtain deformation process data of the rubber material; synchronously recording and analyzing the deformation process data of the rubber material to obtain a stress-strain characteristic diagram of the rubber material; and performing polarization spectral excitation on the rubber sample based on the stress-strain characteristic diagram of the rubber material to obtain fluorescence data of the rubber molecular chain. This invention improves the reliability and accuracy of rubber material performance evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of rubber performance evaluation, and in particular to a performance data analysis and evaluation method for rubber materials. Background Art

[0002] Traditional testing methods for rubber material performance primarily focus on measuring macroscopic mechanical properties, such as tensile strength, hardness, and wear resistance. While these testing methods provide some valuable insights in routine applications, they are insufficient for demanding applications in high-end fields such as aerospace, medical devices, and high-performance seals, where material performance requirements are extremely high. In the aerospace sector, rubber materials face extremely demanding operating environments. For example, during high-altitude flight, rubber materials must maintain stable performance in the complex environments of high altitude, low temperature, high vacuum, and high-intensity radiation. These extreme conditions place extremely high demands on the physical and chemical properties of rubber materials. Traditional performance evaluation methods often only allow testing under standard laboratory conditions and fail to simulate the complex environments encountered in actual use. Consequently, when these rubber materials are used in actual aerospace equipment, they can experience premature failure or unstable performance. Such failures not only lead to equipment failure but also endanger flight safety. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a performance data analysis and evaluation method for rubber materials to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a method for analyzing and evaluating performance data of rubber materials comprises the following steps:

[0005] Step S1: Acquire X-ray scanning data of a rubber sample; perform phase contrast imaging on the X-ray scanning data of the rubber sample to obtain projection data of the rubber sample; perform back-projection reconstruction on the projection data of the rubber sample to obtain three-dimensional structural data of the rubber material;

[0006] Step S2: performing multi-parameter environmental simulation loading on the rubber sample to obtain environmental response data of the rubber material; dynamically collecting the environmental response data of the rubber material to obtain deformation process data of the rubber material; synchronously recording and analyzing the deformation process data of the rubber material to obtain a stress-strain characteristic diagram of the rubber material;

[0007] Step S3: performing polarization spectral excitation on the rubber sample based on the stress-strain characteristic diagram of the rubber material to obtain fluorescence data of the rubber molecular chain; performing anisotropy calculation on the fluorescence data of the rubber molecular chain to obtain a set of orientation parameters of the rubber molecular chain; performing dynamic evolution analysis based on the orientation parameter set of the rubber molecular chain to obtain molecular chain arrangement data of the rubber material;

[0008] Step S4: performing infrared thermal imaging scanning on the rubber sample to obtain a temperature field distribution diagram of the rubber material; performing thermomechanical coupling modeling based on the molecular chain arrangement data of the rubber material and the temperature field distribution diagram of the rubber material to obtain energy dissipation data of the rubber material; performing Fourier heat conduction simulation on the energy dissipation data of the rubber material to obtain a mechanical loss characteristic diagram of the rubber material;

[0009] Step S5: constructing a multi-field coupling model for the rubber sample based on the three-dimensional structural data of the rubber material and the mechanical loss characteristic diagram of the rubber material to obtain a rubber multi-field coupling topological model; and predicting the life of the rubber sample based on the rubber multi-field coupling topological model to obtain a rubber material performance evaluation report.

[0010] This invention uses X-ray scanning and phase contrast imaging to obtain three-dimensional structural data of rubber materials. This not only covers the macroscopic morphology but also penetrates deep into the microscopic level, revealing the internal microstructural characteristics of the rubber material. This is beyond the capabilities of traditional macroscopic mechanical property testing methods. For example, in the aerospace field, microscopic defects within rubber materials (such as micropores and cracks) can become a cause of failure under extreme conditions. Multi-parameter environmental loading simulation can closely replicate the complex operating environments of rubber materials in actual applications, such as the high altitude, low temperature, high vacuum, and high-intensity radiation found in aerospace. This allows rubber material performance evaluation to be conducted in environments closer to actual use, no longer limited to conventional laboratory conditions. By dynamically collecting deformation data of rubber materials in complex environments and generating stress-strain characteristic maps, the mechanical behavior of the material in actual applications can be more accurately predicted, avoiding premature failure or performance instability caused by environmental factors. Polarization spectroscopy excitation and anisotropy calculations are used to obtain fluorescence data and orientation parameter sets of rubber molecular chains, and dynamic evolution analysis can be performed. This can delve into the molecular level and reveal the changes in molecular chain arrangement under stress. Molecular chain alignment data allows for more accurate assessment of material performance changes under complex physiological environments. Infrared thermal imaging and thermomechanical coupling modeling simultaneously consider the energy dissipation of rubber materials under mechanical loading and thermal environments. In practical applications, rubber materials are often subject to both mechanical stress and thermal environment. For example, in high-performance seals, materials must maintain sealing performance under high temperatures and high pressures. Thermomechanical coupling analysis comprehensively assesses the energy conversion and loss of rubber materials under complex operating conditions, enabling more accurate prediction of the material's mechanical loss characteristics. Based on the rubber material's three-dimensional structural data and mechanical loss characteristic maps, a multi-field coupling topological model is constructed and lifespan prediction is performed. This comprehensively considers the performance changes of rubber materials under multi-field coupling conditions, including mechanical, thermal, and chemical factors, resulting in more accurate predictions of the material's service life. Compared to traditional lifespan prediction methods, this method not only considers macroscopic mechanical properties but also incorporates multiple factors, including microstructure and energy dissipation, to more comprehensively reflect the material's performance degradation process in actual use. The multi-field coupling model allows for early prediction of the failure modes and lifespan of rubber materials under extreme environments. In summary, the present invention improves the reliability and accuracy of rubber material performance assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Other features, objects and advantages of the present invention will become more apparent from reading the detailed description made with reference to the following drawings:

[0012] Figure 1 A schematic flow chart of the steps of a method for analyzing and evaluating performance data of rubber materials according to an embodiment is shown.

[0013] Figure 2 A detailed flowchart of step S2 of an embodiment is shown.

[0014] Figure 3 A detailed flowchart of step S25 of an embodiment is shown. DETAILED DESCRIPTION

[0015] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0016] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0017] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0018] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for analyzing and evaluating performance data of rubber materials, comprising the following steps:

[0019] Step S1: Acquire X-ray scanning data of a rubber sample; perform phase contrast imaging on the X-ray scanning data of the rubber sample to obtain projection data of the rubber sample; perform back-projection reconstruction on the projection data of the rubber sample to obtain three-dimensional structural data of the rubber material;

[0020] Step S2: performing multi-parameter environmental simulation loading on the rubber sample to obtain environmental response data of the rubber material; dynamically collecting the environmental response data of the rubber material to obtain deformation process data of the rubber material; synchronously recording and analyzing the deformation process data of the rubber material to obtain a stress-strain characteristic diagram of the rubber material;

[0021] Step S3: performing polarization spectral excitation on the rubber sample based on the stress-strain characteristic diagram of the rubber material to obtain fluorescence data of the rubber molecular chain; performing anisotropy calculation on the fluorescence data of the rubber molecular chain to obtain a set of orientation parameters of the rubber molecular chain; performing dynamic evolution analysis based on the orientation parameter set of the rubber molecular chain to obtain molecular chain arrangement data of the rubber material;

[0022] Step S4: performing infrared thermal imaging scanning on the rubber sample to obtain a temperature field distribution diagram of the rubber material; performing thermomechanical coupling modeling based on the molecular chain arrangement data of the rubber material and the temperature field distribution diagram of the rubber material to obtain energy dissipation data of the rubber material; performing Fourier heat conduction simulation on the energy dissipation data of the rubber material to obtain a mechanical loss characteristic diagram of the rubber material;

[0023] Step S5: constructing a multi-field coupling model for the rubber sample based on the three-dimensional structural data of the rubber material and the mechanical loss characteristic diagram of the rubber material to obtain a rubber multi-field coupling topological model; and predicting the life of the rubber sample based on the rubber multi-field coupling topological model to obtain a rubber material performance evaluation report.

[0024] In this example, a high-resolution X-ray scanner was used to scan a rubber sample to obtain detailed data on its internal structure. Phase contrast imaging was used to convert the X-ray scan data into projection data. The projection data was processed using a back-projection reconstruction algorithm to reconstruct the three-dimensional structural data of the rubber material, revealing its internal microscopic defects and structural features. The rubber sample was placed in a multi-parameter environmental simulation loading device, simulating the complex environmental conditions encountered in real applications, such as temperature, pressure, and humidity, to obtain environmental response data of the rubber material. A dynamic acquisition system was used to record the deformation process data of the rubber material during loading in real time, capturing its mechanical behavior under different environmental conditions. The deformation process data was simultaneously recorded and analyzed to generate a stress-strain characteristic map of the rubber material, visually demonstrating the changes in the material's performance under different stress conditions. Based on the stress-strain characteristic map, a polarization spectroscopy excitation system was used to excite the rubber sample to obtain fluorescence data of the rubber molecular chains. This fluorescence data was further analyzed using anisotropy calculation software to obtain a set of orientation parameters for the rubber molecular chains, revealing how the molecular chains change in alignment under stress. The molecular chain orientation parameter set was analyzed using a dynamic evolution analysis tool to obtain dynamic evolution data of the rubber material's molecular chain alignment. At the same time, an infrared thermal imager was used to scan the rubber samples, obtaining temperature field distribution maps and understanding the material's energy dissipation under mechanical loading and thermal conditions. Combining molecular chain arrangement data and temperature field distribution maps, a model was constructed using thermomechanical coupling modeling software to calculate the rubber material's energy dissipation data. Fourier heat conduction simulation was performed on the rubber material's energy dissipation data to generate a mechanical loss characteristic map, comprehensively evaluating the material's energy conversion and loss under complex operating conditions. Finally, based on the rubber material's three-dimensional structural data and mechanical loss characteristic map, a multi-field coupling topological model of the rubber was constructed using multi-field coupling modeling software, comprehensively considering the multi-field coupling effects of mechanics, thermal energy, and chemistry. The rubber sample's lifespan was predicted, and a rubber material performance evaluation report was generated.

[0025] Preferably, step S1 includes the following steps:

[0026] Step S11: setting pre-processing parameters for the rubber sample to obtain scanning parameters for the rubber sample, and calibrating the energy of a preset X-ray light source according to the scanning parameters for the rubber sample to obtain X-ray energy spectrum data;

[0027] Specifically, for a styrene-butadiene rubber sample, basic sample information, including material type, sample size (50 mm diameter), and desired scanning accuracy (scanning resolution set to 0.05 mm), was entered into the MaterialScan Pro software. Based on this input, the software automatically generated scanning parameters, including the start and end positions of the scan and the scan step size. Subsequently, an X-Spectrum 3000 X-ray energy spectrum analyzer was used to calibrate the energy of the pre-set X-ray light source. The X-ray tube voltage was set to 80 kV and the current to 10 mA, parameters pre-selected based on the density and thickness of the rubber sample. Using the calibration function of the X-Spectrum 3000, X-ray energy spectrum data was acquired, showing that the primary energy concentration was in the 70-90 keV range.

[0028] Step S12: performing detector sensitivity mapping according to the X-ray energy spectrum data to obtain detector response matrix data;

[0029] Specifically, detector sensitivity mapping can be performed using the DetectorCal 200 detector sensitivity calibration system based on X-ray energy spectrum data. The detector is placed in the X-ray beam path, and the detector gain and bias voltage are adjusted to accurately respond to the energy range in the X-ray energy spectrum. The detector gain is set to a moderate level (a value of 5), and multiple exposure experiments are performed to record the detector response signals at different X-ray intensities. An X-Expose 500 exposure experiment system, which precisely controls X-ray exposure time and intensity, is used. During each exposure, the detector response signals are recorded at different energies (from 70 keV to 90 keV). The response signals are analyzed and the detector response matrix data is constructed using DataMatrix Builder software. This software converts the experimental data into a matrix format, detailing the detector sensitivity variations at different energies and intensities. For example, at 70 keV, the detector sensitivity is 0.8, while at 90 keV, it is 0.95.

[0030] Step S13: performing dark field correction on the detector response matrix data to obtain detector background noise data, and performing dynamic signal-to-noise ratio balance based on the detector background noise data to obtain detector optimization parameters;

[0031] Specifically, the detector response matrix data can be dark-field calibrated using the DetectorCal 200 detector sensitivity calibration device. The detector is placed in the X-ray beam path, but the X-ray source is turned off. Multiple exposure experiments (10 exposures per second) are performed to record the background noise data of the detector at different gain settings. The data is imported into the DataMatrix Analyzer software, which analyzes the background noise data and calculates the average background noise level of the detector. For example, at a gain value of 5, the average background noise of the detector is 0.02 counts / pixel. Based on the background noise data, the signal-to-noise ratio of the detector is dynamically balanced using MATLAB software. By adjusting the gain and integration time of the detector, the optimized parameters of the detector are finally obtained: a gain value of 6 and an integration time of 2 seconds.

[0032] Step S14: dividing the rubber sample into rotation angles to obtain a rotation angle sequence of the rubber sample, and performing scanning path planning on the rubber sample according to the rotation angle sequence of the rubber sample to obtain scanning trajectory data of the rubber sample;

[0033] Specifically, Python software can be used to divide the rotation angles and plan the scanning path for a rubber sample. The rubber sample was mounted on a rotating stage, and basic sample information, including the sample diameter (50 mm) and the desired scanning accuracy (0.05 mm), was input into the software. Based on this information, the software automatically generated a rotation angle sequence for the rubber sample, dividing the sample into 360 equally spaced rotation angles, each with a 1-degree interval. A spiral scanning path was selected to plan the scanning path based on the rotation angle sequence. The starting point of the scanning path was set at the bottom center of the sample, and the end point was set at the top center of the sample, with a scanning step size of 0.05 mm. These parameters were input into the control software of the X-Spectrum 3000 X-ray energy dispersive spectrometer. The resulting scanning trajectory data for the rubber sample detailed the movement path of the X-ray source and the sample's rotation angle during the scanning process.

[0034] Step S15: performing X-ray irradiation on the rubber sample according to the detector optimization parameters and the rubber sample scanning trajectory data to obtain X-ray scanning data of the rubber sample;

[0035] Specifically, the rubber sample was mounted on a rotating stage with an accuracy of 0.01 degrees, enabling precise control of the sample's rotation angle. The X-ray scanning equipment parameters were set based on the detector's optimized parameters (gain of 6, integration time of 2 seconds) and the scanning trajectory data (spiral scanning path, step size of 0.05 mm, and rotation angle interval of 1 degree). An X-Spectrum 3000 X-ray energy dispersive spectrometer was used, with the X-ray tube voltage set to 80 kV and the current set to 10 mA. These parameters were preselected based on the density and thickness of the rubber sample. The detector gain was also set to 6, and the integration time was set to 2 seconds. During the scanning process, the X-Spectrum 3000's automatic scanning program was activated, which controlled the movement of the X-ray source and detector based on the preset scanning trajectory data. The X-ray source moved along a spiral path from the bottom center to the top center of the sample, while the rotating stage rotated the sample in 1-degree intervals. After each rotation, the X-ray source performed an exposure, and the detector recorded the corresponding X-ray intensity data. The entire scanning process lasted approximately 30 minutes, ultimately generating a series of X-ray projection images. These projection images are transmitted in real time to a computer connected to the device and stored as X-ray scan data files.

[0036] Step S16: performing phase contrast imaging on the X-ray scanning data of the rubber sample to obtain rubber sample projection data, and performing back-projection reconstruction on the rubber sample projection data to obtain three-dimensional structure data of the rubber material.

[0037] Specifically, please refer to the sub-steps of step S16 for the detailed implementation process of this embodiment.

[0038] The present invention ensures energy stability and accuracy during X-ray scanning by pre-processing parameter settings and calibrating the energy of the X-ray light source. Detector sensitivity mapping and dark field correction effectively reduce detector background noise, optimize the signal-to-noise ratio, and make the acquired X-ray scanning data more accurate and clear. Rotation angle division and scanning path planning ensure that the sample is fully and evenly irradiated during the scanning process, avoiding imaging blind spots or data loss caused by unreasonable scanning paths. Compared with traditional scanning methods, this sub-scheme can more completely present the microstructural details inside the rubber material, including tiny defects, pores, and interfaces.

[0039] Preferably, step S16 includes the following steps:

[0040] Step S161: performing phase extraction on the X-ray scanning data of the rubber sample to obtain a rubber scanning phase gradient map;

[0041] Specifically, the original projection image data obtained from the X-ray scanning device can be imported into the PhaseExtractor software. The software provides a variety of phase extraction algorithms, and the phase extraction algorithm based on Fourier transform is selected. In the software, the key parameters of phase extraction are set, including the frequency range of Fourier transform and the resolution of phase extraction (set to 0.05mm). The selection of these parameters is based on the microstructural characteristics of the rubber sample and the resolution of the scanning device. By running the phase extraction algorithm, the software extracts phase information from the original projection image and generates a rubber scanning phase gradient map. The map clearly shows the phase change gradients in different areas inside the rubber sample. For example, in some areas of the sample, the phase gradient values are higher, indicating that there are microstructural changes in these areas, such as cracks or pores.

[0042] Step S162: reconstructing a phase image based on the rubber scanning phase gradient image to obtain a phase distribution map of the rubber sample;

[0043] Specifically, the phase gradient map can be imported into the Halcon software, and the phase reconstruction algorithm suitable for rubber materials, the weighted least squares algorithm, can be selected. In the software, the reconstruction parameters are set, including the image resolution (set to 0.05 mm, consistent with the scanning resolution) and the size of the reconstruction area (set to the entire cross-section of the sample, with a diameter of 50 mm). In addition, the weight parameter of the algorithm can be adjusted. After many experiments, the weight parameter was set to 0.8, which balances the clarity and noise level of the image. By running the phase reconstruction algorithm, the software generates a phase distribution map of the rubber sample. The map clearly shows the phase distribution inside the rubber sample, including the details of the microstructure and the location of defects. For example, the phase values in some areas of the phase distribution map are low, indicating that there are pores or cracks in these areas.

[0044] Step S163: performing contrast gradient compensation on the rubber sample phase distribution map to obtain an enhanced phase map of the rubber sample, and performing contrast enhancement on the enhanced phase map of the rubber sample to obtain projection data of the rubber sample;

[0045] Specifically, you can select the "Contrast Gradient Compensation" function in the Fotor software to perform contrast gradient compensation on the phase distribution map of the rubber sample, and set the compensation coefficient to 1.2. This coefficient is obtained through multiple experimental optimizations. The software automatically adjusts the contrast of the image by analyzing the gradient information in the phase distribution map, making the originally darker areas clearer while avoiding overexposure of the highlight areas. Next, select the "Histogram Equalization" method and set the enhancement intensity to 0.7 in the software. This parameter value can effectively improve the overall contrast of the image while retaining the detailed information of the image. After contrast enhancement processing, the enhanced phase map of the rubber sample is obtained. In the enhanced phase map, the microstructure inside the rubber sample, such as pores and cracks, becomes more clearly visible.

[0046] Step S164: performing geometric correction on the rubber sample projection data to obtain rubber sample corrected projection data;

[0047] Specifically, the rubber sample projection data can be imported into the GeoCorrector software. The software provides a variety of geometric correction methods, and the correction method based on feature point matching is selected. Several key feature points in the image are manually marked, such as the edges of the sample and obvious internal structures. These feature points are known in the actual sample, so the correction parameters can be calculated by comparing the difference between the feature point positions in the image and the actual positions. The correction accuracy is set to 0.01 pixels. The software automatically calculates the correction matrix based on the marked feature points and applies it to the entire image. After geometric correction, the corrected projection data of the rubber sample is obtained.

[0048] Step S165: constructing image reconstruction parameters based on the corrected projection data of the rubber sample to obtain a rubber image reconstruction control parameter set;

[0049] Specifically, the corrected projection data of the rubber sample can be imported into MATLAB software, and the reconstruction parameters can be configured based on the characteristics of the rubber sample and the scanning parameters. First, the reconstruction algorithm is set, selecting the filtered back projection algorithm (FBP) and setting the filter function to a Hamming window. The reconstruction resolution parameter is also set to 0.05 mm, consistent with the scanning resolution. Furthermore, the number of reconstruction iterations is set to 10, a parameter value that has been experimentally verified to balance computation time and reconstructed image quality. Based on these parameter settings, MATLAB software generates a set of control parameters for rubber image reconstruction and saves it as a parameter file.

[0050] Step S166: performing back-projection reconstruction on the rubber sample corrected projection data according to the rubber image reconstruction control parameter set to obtain three-dimensional structural data of the rubber material.

[0051] Specifically, the rubber image reconstruction control parameter set and the corrected projection data of the rubber sample can be imported into the WAVE-3D-Reconstruction software. Based on the settings in the parameter set, the software automatically calls the filtered back-projection algorithm (FBP) for reconstruction. During the reconstruction process, the software first applies a Hamming window filter function to the projection data. The software then performs back-projection reconstruction according to the set resolution (0.05 mm) and number of iterations (10). After approximately 30 minutes of calculation, the software generates the 3D structural data of the rubber sample. The reconstructed 3D structural data can be visualized using 3D Viewer software. In the 3D view, the internal microstructure of the rubber sample is clearly visible, including pores, cracks, and other microscopic defects.

[0052] Through phase extraction and phase image reconstruction, the present invention effectively enhances image contrast and detail clarity. Contrast gradient compensation and contrast enhancement further optimize the visual quality of the image, making the microstructural features within the rubber material more clearly visible. Geometric correction and optimization of image reconstruction parameters effectively reduce errors and artifacts during the reconstruction process. Phase extraction and enhancement processes more clearly reveal tiny defects within the rubber material, such as microcracks, pores, and uneven distribution. The optimized phase imaging and reconstruction process ensures that the acquired three-dimensional structural data achieves a high level of detail and accuracy.

[0053] Preferably, step S2 includes the following steps:

[0054] Step S21: collecting application environment data of the rubber sample to obtain characteristic data of the rubber application environment, and initializing environmental parameters of the rubber sample according to the characteristic data of the rubber application environment to obtain an initial parameter set for environmental simulation;

[0055] Specifically, assuming the rubber sample will be used in the aerospace field, an Environmental Data Acquisition System (EDAS) is used to collect relevant data. This system can simulate and record environmental conditions such as high altitude, low temperature, high vacuum, and high-intensity radiation. First, the rubber sample is placed in a simulated environment, and the environmental parameters are set to simulate the conditions of an aircraft flying at high altitude: an altitude of 10,000 meters, a temperature of -40°C, and a vacuum of Pascal, and the radiation intensity is 100 mSv / h. The EDAS system collects these environmental parameters in real time through sensors and transmits the data to the computer connected to it. The collected data is analyzed using EnviroSim software to extract key environmental characteristic parameters, such as temperature change rate, pressure change rate, and radiation intensity. Based on these characteristic data, the environmental parameters of the rubber sample are initialized in EnviroSim software. The initial parameter set for environmental simulation is set, including the initial temperature of -40°C, the initial pressure of Pascal, the initial radiation intensity is 100 mSv / h.

[0056] Step S22: performing pressure field simulation on the rubber sample based on the initial parameter set of the environmental simulation to obtain multi-axial pressure distribution data of the rubber;

[0057] Specifically, a MultiAxis Pressure Simulator (MAPS) device was used to simulate the pressure field. The rubber sample was fixed to the test platform of the MAPS device, and the device parameters were set according to the initial parameter set of the environmental simulation. The simulated pressure conditions were set, including the pressure distribution in the three main axis directions (X, Y, and Z axes). The specific parameters are as follows:

[0058] The pressure in the X-axis direction is 0.5 MPa, simulating the transverse tensile stress;

[0059] The pressure in the Y-axis direction is 0.3 MPa, simulating the longitudinal compressive stress;

[0060] The pressure in the Z-axis direction is 0.2 MPa, simulating the shear stress in the vertical direction.

[0061] The MAPS system uses built-in high-precision pressure sensors and strain gauges to monitor the response of rubber samples under multiaxial pressure in real time. The system's control system automatically adjusts the pressure-loading mechanism according to pre-set parameters. During the simulation, LS-DYNA software is used to record and analyze the multiaxial pressure distribution data of the rubber samples. The software displays a three-dimensional pressure field in real time and provides detailed pressure distribution data, including pressure values and stress directions at each point. After a period of simulated loading, the multiaxial pressure distribution data of the rubber samples is obtained. This data clearly demonstrates the pressure response of the rubber samples in different directions.

[0062] Step S23: performing radiation dose superposition mapping on the rubber multi-axial pressure distribution data to obtain a stress composite environment parameter map;

[0063] Specifically, the multi-axial pressure distribution data of rubber can be imported into MATLAB software. Then, according to the characteristic data of the rubber application environment, the radiation dose parameters are input, including a radiation intensity of 100 mSv / h and a radiation duration of 2 hours. The software uses an algorithm to superimpose the radiation dose data with the multi-axial pressure distribution data to generate a stress composite environmental parameter map. This map intuitively displays the combined effects of stress levels and radiation doses at different locations of the rubber sample in the form of colored contour lines. For example, in the figure, the overlapping parts of high stress areas (such as the pressure concentration area in the X-axis direction) and high radiation dose areas are marked in red, indicating that these areas face a higher risk of failure. Areas with low stress and low radiation dose are marked in blue. Through visualization, potential weak points of rubber samples in complex environments can be quickly identified.

[0064] Step S24: adjusting the loading step length of the rubber sample according to the stress composite environmental parameter map to obtain dynamic environmental loading sequence data, and performing multi-parameter coordinated loading on the rubber sample based on the dynamic environmental loading sequence data to obtain rubber material environmental response data;

[0065] Specifically, the loading step size can be determined based on the information in the stress-combined environmental parameter map. More precise loading step size adjustments are selected in areas of high stress and high radiation dose. The specific parameters are as follows: in the red area (high stress and high radiation dose), the loading step size is set to 0.1 MPa; in the blue area (low stress and low radiation dose), the loading step size is set to 0.5 MPa. Next, dynamic environmental loading sequence data is set in the LabVIEW system. Based on the stress-combined environmental parameter map, multiaxial pressure loading is combined with radiation dose loading to simulate the complex operating conditions of rubber samples in real applications. Using a high-precision loading control system, the equipment gradually applies pressure according to the set step size and sequence, while also adjusting the radiation dose. During the loading process, the equipment monitors the deformation and stress response of the rubber sample in real time and transmits the data to a connected computer. The collected environmental response data is analyzed using LabVIEW. The software displays the deformation process and stress distribution of the rubber sample under multi-parameter coordinated loading in real time. By analyzing this data, the dynamic response characteristics of the rubber material under complex environments are determined.

[0066] Step S25: dynamically collecting the environmental response data of the rubber material to obtain the deformation process data of the rubber material, and synchronously recording and analyzing the deformation process data of the rubber material to obtain a stress-strain characteristic diagram of the rubber material.

[0067] Specifically, please refer to the sub-steps of step S25 for the detailed implementation process of this embodiment.

[0068] By collecting and initializing environmental parameters, the present invention can highly restore the complex environmental conditions of rubber materials in actual use scenarios. This makes the performance test of rubber materials no longer limited to the simplified conditions of the laboratory, but can be carried out in an environment close to the actual working conditions. Through simulation, the performance of the material in actual use can be evaluated more accurately, avoiding the deviation of the test results caused by environmental differences. Traditional performance testing methods usually only consider the influence of stress in a single direction or a single environmental factor, while in actual applications, rubber materials are often subject to the combined effects of multi-axial stress and multiple environmental factors. Through pressure field simulation and radiation dose superposition mapping, the performance changes of rubber materials under multi-axial stress and composite environments can be comprehensively evaluated. By dynamically collecting and synchronously recording and analyzing the environmental response data of the rubber material during the multi-parameter collaborative loading process, the deformation process of the material during the loading process can be monitored in real time. This can capture the microscopic and macroscopic deformation characteristics of the material under transient loading and reveal the transient mechanical behavior of the material in a complex environment.

[0069] Preferably, step S25 includes the following steps:

[0070] Step S251: regulating the sampling frequency of the rubber material environmental response data to obtain a rubber dynamic sampling parameter set;

[0071] Specifically, the environmental response data of rubber materials can be imported into LabVIEW software. The software provides multiple sampling frequency control modes. Select "Adaptive Sampling Mode," which automatically adjusts the sampling frequency according to the deformation rate of the material. The initial value of the sampling frequency is set to 100 Hz, based on the normal response speed of rubber materials under dynamic loading. At the same time, the upper limit of the sampling frequency is set to 500 Hz. In the software, a threshold for the deformation rate is defined. When the deformation rate exceeds this threshold, the sampling frequency automatically increases. For example, when the deformation rate exceeds 0.1 mm / s, the sampling frequency automatically increases from 100 Hz to 200 Hz; when the deformation rate exceeds 0.5 mm / s, the sampling frequency further increases to 500 Hz. Through the above operations, a rubber dynamic sampling parameter set can be obtained, which includes sampling frequency settings at different deformation rates.

[0072] Step S252: performing high-speed image acquisition on the rubber sample according to the rubber dynamic sampling parameter set to obtain a deformation sequence diagram of the rubber material;

[0073] Specifically, the rubber sample can be fixed on the experimental device, and the lens of the high-speed camera system can be aimed at the sample. The frame rate of the camera system is adjusted according to the sampling frequency setting in the dynamic sampling parameter set. For example, in the stage of low deformation rate, the frame rate of the camera system is set to 100 frames per second; while in the stage of high deformation rate, the frame rate is automatically increased to 500 frames per second. During the experiment, the high-speed camera system performs high-speed image acquisition of the rubber sample according to the set frame rate and parameters. The collected image data is stored in real time on a computer connected to the camera system. Finally, a deformation sequence diagram of the rubber material is obtained. These images clearly show the deformation of the rubber sample at different loading stages.

[0074] Step S253: reconstructing the motion vector field of the rubber material deformation sequence diagram to obtain deformation material displacement field data;

[0075] Specifically, the deformation sequence diagram of the rubber material can be imported into LabVIEW software. The software provides a variety of motion vector calculation methods, and an algorithm based on the optical flow method is selected to reconstruct the motion vector field. In the software, key parameters are set, including the time step and spatial resolution. According to the dynamic deformation characteristics of the rubber material, the time step is set to 0.01 seconds (corresponding to the frame rate of the high-speed camera system), and the spatial resolution is set to 1 pixel (corresponding to the resolution of the image). By running the optical flow algorithm, the software performs displacement analysis on each pixel point in the deformation sequence diagram and calculates the displacement vector of each pixel point in the time series. Ultimately, the displacement field data of the deformed material is obtained. This data is presented in the form of a two-dimensional vector field, clearly showing the displacement of the rubber material at different positions and times.

[0076] Step S254: performing strain distribution mapping on the rubber sample based on the deformation material displacement field data to obtain rubber material strain field data, and performing stress conversion on the rubber material strain field data to obtain rubber dynamic stress distribution data;

[0077] Specifically, the displacement field data of the deformed material can be imported into the ABAQUS software. The software calculates the strain components of each pixel point, including normal strain and shear strain, by the finite difference method. Set the resolution of the calculation grid and select the same as the spatial resolution of the displacement field data, that is, 1 pixel. In order to ensure the accuracy of the strain calculation, the elastic modulus (E = 10 MPa) and Poisson's ratio (ν = 0.49) of the rubber material are input. These parameters are obtained through preliminary experimental measurements. Based on these material parameters and strain distribution data, the software converts stress through Hooke's law to obtain the dynamic stress distribution data of the rubber material. The strain field data is presented in the form of a color contour map, which clearly shows the strain level of the material at different positions. The stress distribution data is displayed in the form of a vector diagram, revealing the stress concentration of the material in different areas.

[0078] Step S255: performing a quantitative assessment of local stress concentration based on the rubber dynamic stress distribution data to obtain a rubber stress concentration factor diagram;

[0079] Specifically, the dynamic stress distribution data of rubber can be imported into MATLAB software. The software automatically identifies the stress concentration area by analyzing the stress distribution data and calculates the stress concentration factor of each area. The stress concentration factor is obtained by comparing the local stress ( ) and mean stress ( ) is determined by the ratio of: The software analysis parameters were set, including the threshold for identifying stress concentration areas (set to 1.5 times the average stress) and the calculation accuracy (set to 0.01). Running the software yielded a rubber stress concentration factor map. This map, in the form of colored contour lines, displays the stress concentration at different locations within the rubber sample. For example, in some areas, the stress concentration factor reached 2.5, indicating that the stress in these areas was 2.5 times the average stress, posing a high risk of failure.

[0080] Step S256: evaluating the deformation stability of the rubber sample based on the rubber stress concentration factor diagram to obtain rubber deformation stability data;

[0081] Specifically, the rubber stress concentration factor map can be imported into ABAQUS software. The software analyzes the stress concentration factor map and, combined with the rubber material's yield strength (5 MPa) and elongation at break (500%), calculates the deformation stability index for each region. The deformation stability index is determined by evaluating the ratio of the stress level in the stress concentration region to the material's yield strength using the following formula: Set the software's evaluation parameters, including a stability index threshold (set to 0.8). When the stability index exceeds this threshold, the software marks the area as potentially unstable. Running the software generates rubber deformation stability data, which is displayed as a two-dimensional graph, clearly identifying the deformation stability of the rubber sample at different locations. For example, in some areas of stress concentration, the stability index reaches 0.9, indicating that the stress in these areas is close to the material's yield strength, posing a high risk of deformation.

[0082] Step S257: extracting features from the rubber deformation stability data to obtain a rubber deformation feature parameter set, and performing feature space mapping based on the rubber deformation feature parameter set to obtain a stress-strain feature map of the rubber material.

[0083] Specifically, rubber deformation stability data can be imported into FeatureExtractor software. This software extracts key characteristic parameters from complex data. These parameters effectively characterize the deformation properties of rubber materials under different stress conditions. The following characteristic parameters are selected for extraction: 1. Maximum stability index: indicates the highest deformation risk among all regions of the material. 2. Average stability index: indicates the average deformation stability of the entire material. 3. Stress concentration area ratio: indicates the proportion of stress concentration areas to the total sample area. 4. Yield stress proximity: indicates the degree to which the material stress approaches the yield strength. In the software, set the range and precision of the extracted parameters. For example, set the precision of the maximum stability index to 0.01 and the precision of the stress concentration area ratio to 1%. By running FeatureExtractor software, a set of rubber deformation characteristic parameters is generated. This parameter set is output in table format, containing the specific values of all the above characteristic parameters. For example, the maximum stability index is 0.92, the average stability index is 0.65, the stress concentration area ratio is 15%, and the yield stress proximity is 0.85. Next, import the rubber deformation characteristic parameter set into FeatureMapper software and select the stress-strain curve as the base chart. The software uses interpolation and fitting algorithms to map characteristic parameters onto stress-strain curves, generating a stress-strain characteristic diagram for the rubber material. In this diagram, curves of different colors represent different levels of characteristic parameters. For example, the red curve represents the stress-strain relationship in areas of high stress concentration, while the blue curve represents the stress-strain relationship in areas of low stress.

[0084] By regulating the sampling frequency, the present invention can dynamically adjust the sampling frequency based on the material's actual response characteristics, thereby ensuring sufficient data points are obtained during critical deformation stages and avoiding information loss due to insufficient sampling. Motion vector field reconstruction can accurately reconstruct the displacement field data of the rubber material during loading. This not only intuitively demonstrates the overall deformation trend of the material but also reveals the relationship between changes in the material's internal microstructure and macroscopic deformation. For example, localized microcracks or rearrangements of molecular chains appear as stress concentration areas in the displacement field. Through strain distribution mapping and stress conversion, the strain and stress distribution of the rubber material under complex environments can be comprehensively quantified. This can reveal stress concentration areas and potential failure risk points within the material. Deformation stability assessment can quantitatively analyze the deformation stability of the material under complex environments. Feature extraction and feature space mapping can construct a stress-strain characteristic diagram of the rubber material. This can intuitively demonstrate the changes in the material's mechanical properties under complex environments.

[0085] Preferably, step S3 includes the following steps:

[0086] Step S31: identifying stress concentration areas on the stress-strain characteristic diagram of the rubber material to obtain a high stress distribution diagram of the rubber material, and performing spectral excitation point array design on the rubber sample based on the high stress distribution diagram of the rubber material to obtain light excitation point position data;

[0087] Specifically, the stress-strain characteristic map of the rubber material can be imported into the ABAQUS software. The software uses an image processing algorithm to identify areas where the stress exceeds a set threshold (for example, 1.5 times the average stress). These areas are considered to be stress concentration areas, and the software will mark these areas in red and generate a high stress distribution map. Next, the spectral excitation point array is designed based on the high stress distribution map. Using ABAQUS software, the software is able to generate light excitation point position data based on the high stress distribution map. The spacing of the light excitation points (for example, a spacing of 2 mm) and the number of excitation points (for example, 50 excitation points) are set in the software. Based on these parameters, the software evenly distributes the light excitation points in the high stress area and generates light excitation point position data. These data are output in the form of coordinates.

[0088] Step S32: performing wavelength selection on the light excitation point position data to obtain a rubber excitation spectrum parameter set, and performing polarizer angle division based on the rubber excitation spectrum parameter set to obtain polarization angle sequence data;

[0089] Specifically, the light excitation point position data can be imported into the AP Spectroscopy Suite software. The software provides a variety of wavelength options. Based on the characteristics of the rubber material, two main excitation wavelengths are selected: 488 nm (blue laser) and 532 nm (green laser). These two wavelengths can effectively excite the fluorescence signal of the rubber molecular chain. Next, determine the angle of the polarizer. Select the polarizer angle range from 0° to 180°, divided into intervals of 15°. Based on these parameters, the software generates polarization angle sequence data. For example, the polarization angle sequence includes 0°, 15°, 30°, 45°, and so on, up to 180°. It is also necessary to set the exposure time and light intensity for each excitation point. For example, the exposure time is 100 milliseconds and the light intensity is 10 mW / cm². Ultimately, the rubber excitation spectrum parameter set and polarization angle sequence data are obtained.

[0090] Step S33: reconstructing the light intensity gradient according to the polarization angle sequence data to obtain the incident light intensity distribution map of the rubber;

[0091] Specifically, the polarization angle sequence data can be imported into the AP Spectroscopy Suite software. The software analyzes the intensity changes at different polarization angles and uses the Stokes parameters to calculate the intensity gradient of each excitation point. The resolution parameter of the intensity gradient calculation is set to 0.1 mm. In the software, an algorithm based on Fourier transform is selected to handle the reconstruction of the intensity gradient. By running the software, a distribution diagram of the incident light intensity of the rubber is obtained, which clearly shows the intensity changes of each excitation point at different polarization angles. For example, in some areas, the intensity gradient is higher, indicating that the intensity changes in these areas are more significant.

[0092] Step S34: performing background noise elimination on the incident light intensity distribution diagram of the rubber to obtain background correction data of the rubber sample, and performing an excitation light stability assessment based on the background correction data of the rubber sample to obtain a light source stability parameter set;

[0093] Specifically, the incident light intensity distribution diagram of the rubber can be imported into the OAS optical analysis software. The software removes background noise using a low-pass filtering algorithm. The filter cutoff frequency is set to 0.05 Hz. After background noise elimination, the background correction data of the rubber sample is obtained. Next, the stability of the excitation light is evaluated using the LightTools software. The software calculates the stability parameters of the light source by analyzing the light intensity fluctuations in the background correction data. The time window for stability evaluation is set to 10 seconds. The software calculates the stability parameter set of the light source, including the light intensity fluctuation coefficient and the root mean square error (RMSE). For example, the light intensity fluctuation coefficient is 0.02 and the RMSE is 0.05 mW / cm². These parameters indicate that the excitation light is highly stable.

[0094] Step S35: collecting molecular chain structure fluorescence signals of the rubber sample based on the light source stability parameter set to obtain rubber molecular chain fluorescence data;

[0095] Specifically, a rubber sample can be placed on the sample stage of a fluorescence spectrometer controlled by the FluorEssence operating software, and the instrument parameters can be adjusted according to the light source stability parameter set. The excitation light wavelengths are set to 488 nm and 532 nm (the same wavelengths selected in step S32), with an exposure time of 100 milliseconds and an intensity of 10 mW / cm². During the acquisition process, the instrument detects the fluorescence signal using a highly sensitive photomultiplier tube (PMT) and converts the signal into digital data. The fluorescence intensity of each excitation point is recorded using the FluorEssence software. The software displays the fluorescence signal intensity in real time and provides data storage. By acquiring the fluorescence signals from all excitation points, fluorescence data for the rubber molecular chain is obtained. This data is output in a table format, including the coordinates of each excitation point and the corresponding fluorescence intensity value. For example, the fluorescence intensity at excitation point 1 is 120 counts, and the fluorescence intensity at excitation point 2 is 150 counts.

[0096] Step S36: performing anisotropy calculation on the rubber molecular chain fluorescence data to obtain a rubber molecular chain orientation parameter set, and performing dynamic evolution analysis based on the rubber molecular chain orientation parameter set to obtain rubber material molecular chain arrangement data.

[0097] Specifically, please refer to the sub-steps of step S36 for the detailed implementation process of this embodiment.

[0098] The present invention can accurately locate high-stress areas inside the material by identifying stress concentration areas. Compared with traditional random or uniform excitation methods, this excitation strategy based on stress distribution can more efficiently obtain molecular chain information closely related to material properties and reduce unnecessary data collection. Through wavelength selection and polarizer angle division, the optimal excitation wavelength and polarization angle can be selected according to the characteristics of the rubber material. At the same time, the optimization of the polarization angle can better capture the orientation information of the molecular chain. Through background noise elimination and excitation light stability evaluation, the interference of background noise can be effectively reduced and the purity and stability of the fluorescence signal can be improved. Through anisotropy calculation and dynamic evolution analysis, the orientation changes and dynamic evolution mechanisms of rubber molecular chains under stress can be deeply revealed. This can help researchers better understand the performance changes of rubber materials in complex environments. For example, in high-stress areas, the orientation and arrangement of molecular chains directly affect the mechanical properties and durability of the material.

[0099] Preferably, step S36 includes the following steps:

[0100] Step S361: performing signal-to-noise ratio enhancement on the fluorescence data of the rubber molecular chain to obtain a fluorescence spectrum of the rubber molecular chain;

[0101] Specifically, the fluorescence data of the rubber molecular chain can be imported into MATLAB software. The software provides multiple signal enhancement algorithms. We selected a wavelet transform-based algorithm for signal-to-noise ratio enhancement. The number of wavelet transform layers was set to 3, which has been verified to be the optimal number of layers in multiple experiments and effectively enhances the signal while reducing noise. In the software, the noise threshold was set to 0.05 counts. Running the software yielded an enhanced fluorescence spectrum of the rubber molecular chain. This spectrum clearly demonstrates how the fluorescence intensity varies with wavelength. For example, the peak fluorescence intensities at 488 nm and 532 nm are 150 counts and 180 counts, respectively.

[0102] Step S362: measuring the polarization degree of the molecular chain state of the rubber sample according to the fluorescence spectrum of the rubber molecular chain to obtain polarization intensity distribution data;

[0103] Specifically, the rubber sample can be placed on the sample stage of the fluorescence spectrometer controlled by the FluorEssence operating software, and the fluorescence spectrum of the rubber molecular chain can be imported into the control system of the device. The device detects the polarization characteristics of the fluorescence signal through a polarizer and a photomultiplier tube. The rotation angle range of the polarizer is set to 0° to 180°, and the measurement is performed at intervals of 10°. At the same time, the exposure time is set to 200 milliseconds. During the measurement process, the device automatically rotates the polarizer and records the intensity of the fluorescence signal at each angle. By analyzing the fluorescence intensity at different polarization angles, the device calculates the polarization degree of each excitation point. The polarization degree is calculated using the following formula: ,in, and The maximum and minimum values of the fluorescence intensity are shown in Figure 2. The resulting polarization intensity distribution data is presented as a two-dimensional graph, clearly identifying the polarization intensity at different locations within the rubber sample. For example, higher polarization intensity in certain regions indicates more ordered molecular chain arrangement in these areas.

[0104] Step S363: calculating the molecular orientation anisotropy index of the rubber sample based on the polarization intensity distribution data to obtain a molecular orientation anisotropy coefficient map;

[0105] Specifically, the polarization intensity distribution data can be imported into the MestReNova software. The software calculates the anisotropy index of the molecular orientation by analyzing the polarization intensity of each excitation point. The anisotropy index is calculated using the following formula: ;in, and These are the fluorescence intensity measurements parallel to and perpendicular to the polarization direction, respectively. Calculation parameters are set, including the measurement angle range (0° to 180°) and angular interval (10°) for each excitation point. Based on these parameters, the software calculates the anisotropy index for each excitation point. For example, at a certain excitation point, the fluorescence intensity in the parallel direction is 180 counts, and the fluorescence intensity in the perpendicular direction is 120 counts, resulting in a calculated anisotropy index of 0.2. By running the software, a molecular orientation anisotropy coefficient map is obtained. This map is presented as colored contour lines, clearly indicating the degree of molecular orientation anisotropy at different locations in the rubber sample. For example, certain regions have higher anisotropy coefficients (close to 0.5), indicating that the molecular chains in these regions are more orderly, while other regions have lower anisotropy coefficients (close to 0), indicating that the molecular chains in these regions are more randomly arranged.

[0106] Step S364: performing feature mapping on the molecular orientation anisotropy coefficient map to obtain a rubber molecular chain orientation parameter set;

[0107] Specifically, the molecular orientation anisotropy coefficient map obtained in step S363 can first be imported into the FeatureMapper software. The software uses image processing and data analysis algorithms to extract features from the anisotropy coefficient map. The researchers set the parameters for feature extraction, including the density of feature points (5 feature points per square millimeter) and the accuracy of feature extraction (0.01). The software maps the data in the anisotropy coefficient map into the feature space through interpolation and fitting algorithms. For example, the software extracts the following key characteristic parameters: Average anisotropy coefficient: represents the average degree of molecular orientation anisotropy for the entire sample. Maximum anisotropy coefficient: represents the degree of anisotropy in the region with the most ordered molecular orientation in the sample. Standard deviation of the anisotropy coefficient: represents the distribution range of the anisotropy coefficient, reflecting the uniformity of molecular orientation. By running the FeatureMapper software, a set of rubber molecular chain orientation parameters is obtained. These parameters are output in tabular form, containing the specific values of all the above characteristic parameters. For example, the average anisotropy coefficient is 0.3, the maximum anisotropy coefficient is 0.5, and the standard deviation is 0.1.

[0108] Step S365: performing molecular dynamics time-series evolution according to the rubber molecular chain orientation parameter set to obtain segment orientation dynamics data;

[0109] Specifically, a set of rubber chain orientation parameters can be imported into the LAMMPS software. These parameters include the mean anisotropy coefficient, the maximum anisotropy coefficient, and the standard deviation. The software initializes the simulation environment using these parameters and sets the simulation time step to 1 picosecond (ps) and the total simulation time to 10 nanoseconds (ns). During the simulation, an appropriate force field (e.g., OPLS-AA) is selected. The software integrates Newton's equations of motion to calculate the position and orientation changes of the molecular chains within each time step. The simulation temperature is set to 300 K to simulate the behavior of rubber materials at room temperature. The simulation generates segment orientation dynamics data, which are displayed as a time series, recording the orientation angle of each molecular segment at different time points. For example, within the first 2 nanoseconds of the simulation, the orientation angle of some molecular segments changes from 0° to 30°, indicating that these segments rapidly oriented initially. Subsequently, the change in orientation angle gradually slows and stabilizes.

[0110] Step S366: performing molecular multi-level response modeling on the rubber sample based on the segment orientation dynamics data to obtain a rubber microstructure stress correlation diagram;

[0111] Specifically, segment orientation dynamics data can be imported into ABAQUS software. By analyzing this data, the software constructs a multi-level model from molecular segments to the entire rubber sample. The model parameters are set, including the elastic modulus of the rubber material (10 MPa), Poisson's ratio (0.49), and the length of the molecular segments (10 nanometers). The software uses the finite element method to correlate the microstructure with the macroscopic mechanical properties. An appropriate meshing strategy is selected to divide the rubber sample into multiple tiny units, each corresponding to a molecular segment. The software calculates the stress and strain of each unit to generate a stress correlation diagram for the rubber microstructure. In the generated stress correlation diagram, the relationship between the stress distribution and molecular segment orientation in different regions can be clearly seen. For example, in areas with more consistent molecular segment orientation, the stress distribution is more uniform; in areas with more random molecular segment orientation, the stress distribution shows higher local stress concentration.

[0112] Step S367: reconstructing the spatial distribution of the rubber microstructure stress correlation diagram to obtain the molecular chain arrangement data of the rubber material.

[0113] Specifically, the stress correlation map of the rubber microstructure can be imported into the MestReNova software. Using image processing and data analysis algorithms, the software reconstructs the spatial distribution of the data in the stress correlation map. An interpolation-based reconstruction algorithm is selected, and reconstruction parameters are set, including the spatial resolution (0.1 mm) and the choice of reconstruction algorithm. The software also requires setting feature extraction parameters for the molecular chain arrangement. For example, a threshold for the degree of order of the molecular chain arrangement can be defined as 0.5. This means that when the orientation anisotropy coefficient of the molecular chain exceeds 0.5, the molecular chain arrangement in that region is considered relatively ordered. Based on these parameters, the software analyzes the molecular chain arrangement of each unit and generates molecular chain arrangement data. Ultimately, the molecular chain arrangement data for the rubber material is obtained, which is displayed as a three-dimensional visualization, clearly identifying the molecular chain arrangement at different locations in the rubber sample. For example, in some regions, the molecular chain arrangement is highly ordered, exhibiting a distinct orientation; in other regions, the molecular chain arrangement is more random.

[0114] This invention significantly improves the quality of fluorescence signals by enhancing the signal-to-noise ratio. By measuring the polarization intensity of the molecular chain state, the polarization intensity distribution of the rubber molecular chain in different directions can be accurately obtained. Polarization intensity distribution data can help researchers better understand the alignment of the molecular chains under complex stress environments. By calculating the molecular orientation anisotropy index, a molecular orientation anisotropy coefficient map can be generated. This can intuitively demonstrate the orientation differences between different regions of the rubber molecular chain and reveal the internal microstructural characteristics of the material. The anisotropy coefficient map not only reflects the degree of molecular chain orientation but also helps identify stress concentration areas and potential performance weaknesses. Feature mapping can extract the key orientation parameter set of the rubber molecular chain. Molecular dynamics temporal evolution analysis can reveal the dynamic changes of the rubber molecular chain under stress. Chain segment orientation dynamics data can provide important microscopic mechanistic support for material performance prediction and lifespan assessment. Molecular multi-level response modeling can construct a correlation map between the rubber microstructure and macroscopic stress. Spatial distribution reconstruction can generate detailed molecular chain alignment data for the rubber material. This provides a comprehensive view of the material's internal microstructure, revealing the alignment and interactions of the molecular chains in different regions.

[0115] Preferably, step S4 includes the following steps:

[0116] Step S41: dividing the molecular chain arrangement data of the rubber material into regions to obtain a rubber thermal imaging scanning region map;

[0117] Specifically, the molecular chain arrangement data of the rubber material can be imported into the ImageJ software. The software automatically divides different areas by analyzing the order and stress distribution of the molecular chain arrangement. The order threshold is set to 0.5 and the stress threshold is set to 5MPa to identify areas where the molecular chains are more orderly and the stress is higher. Based on these parameters, the software generates a rubber thermal imaging scanning area map. The map is displayed in the form of color markings, clearly identifying the areas that need to be thermally scanned. For example, the areas marked in red in the figure represent areas where the molecular chains are highly ordered and the stress is higher. These areas will be scanned first.

[0118] Step S42: setting scanning parameters according to the rubber thermal imaging scanning area map to obtain rubber thermal imaging acquisition parameters;

[0119] Specifically, the rubber thermal imaging scan area map can be imported into the Fluke SmartView IR software. The software automatically recommends appropriate scanning parameters based on the size and shape of the scan area. These parameters can be further adjusted based on the characteristics of the rubber material and experimental requirements. For example, the scan resolution (pixel size) is set to 0.1 mm, and the scan range covers the entire red-marked area. In addition, the temperature range and sensitivity of the scan also need to be set. Set the temperature range to -20°C to 80°C. Set the detector sensitivity to the highest (gain value of 10). By running the Fluke SmartView IR software, the rubber thermal imaging acquisition parameters are obtained. For example, parameters such as the scan resolution, temperature range, and detector sensitivity are recorded in the configuration file.

[0120] Step S43: performing ambient temperature calibration on the rubber thermal imaging acquisition parameters to obtain an ambient temperature calibration curve, and performing thermal radiation coefficient correction based on the ambient temperature calibration curve to obtain a thermal field radiation correction factor diagram;

[0121] Specifically, a rubber sample can be placed in the test environment of a ThermalCalibrator device. The ambient temperature range is set between -20°C and 80°C to simulate the temperature fluctuations encountered by rubber materials in actual applications. The device uses a built-in high-precision temperature sensor to record the ambient temperature in real time and transmit the data to a connected computer. Next, Fluke SmartView IR software is used to calibrate and correct the thermal imaging acquisition parameters. The software generates an ambient temperature calibration curve based on the ambient temperature data. The calibration accuracy is set to 0.1°C. The software analyzes the calibration curve and corrects the thermal emissivity in the thermal imaging acquisition parameters. The corrected thermal emissivity more accurately reflects the thermal radiation characteristics of the rubber material at different ambient temperatures. Running the software generates a thermal field radiation correction factor map. This map is displayed as colored contour lines, clearly indicating the thermal radiation correction factors at different temperatures. For example, at an ambient temperature of 20°C, the thermal radiation correction factor is 1.02; at an ambient temperature of 60°C, the thermal radiation correction factor is 1.05.

[0122] Step S44: collecting thermal images of the rubber sample according to the thermal field radiation correction factor map to obtain a rubber time-series thermal image sequence, and reconstructing the temperature gradient according to the rubber time-series thermal image sequence to obtain a temperature field distribution map of the rubber material;

[0123] Specifically, a rubber sample can be placed on the test platform of a ThermalImager instrument, and a thermal radiation correction factor map can be imported into the instrument's control system. The instrument uses an infrared detector to capture thermal images of the rubber sample according to the specified acquisition parameters (such as a resolution of 0.1 mm and a temperature range of -20°C to 80°C). The acquisition frame rate is set at 10 frames per second. During the acquisition process, the instrument applies real-time correction to each thermal image based on the thermal radiation correction factor map. The corrected thermal image data is transmitted in real time to a computer connected to the instrument, where the Thermal Image Analysis software is used to reconstruct the temperature gradient. The Thermal Image Analysis software analyzes the corrected thermal image sequence and calculates the temperature gradient at each pixel. The reconstruction resolution is set to match the thermal image acquisition resolution (0.1 mm). Using interpolation and fitting algorithms, the software generates a temperature field distribution map of the rubber material. This map is displayed as colored contour lines, clearly identifying the temperature distribution at different locations within the rubber sample. For example, higher temperature gradients in certain areas indicate concentrated thermal stress.

[0124] Step S45: performing thermomechanical coupling modeling based on the rubber material molecular chain arrangement data and the rubber material temperature field distribution diagram to obtain rubber material energy dissipation data, and performing Fourier heat conduction simulation on the rubber material energy dissipation data to obtain a rubber material mechanical loss characteristic diagram.

[0125] Specifically, please refer to the sub-steps of step S45 for the detailed implementation process of this embodiment.

[0126] Through regional segmentation, the present invention identifies key areas for thermal imaging scanning based on the material's internal microstructural characteristics. Scanning parameter settings optimize thermal imaging acquisition parameters, ensuring high resolution and sensitivity of the captured thermal image data. Ambient temperature calibration and thermal emissivity correction based on a calibration curve effectively eliminate the effects of ambient temperature fluctuations on thermal imaging data. This significantly improves the accuracy and reliability of thermal imaging data, ensuring that the temperature field distribution truly reflects the temperature variations of the rubber material under actual operating conditions. By acquiring a sequence of time-series thermal images and reconstructing the temperature gradient, the temperature field changes of the rubber material during loading can be dynamically monitored. This captures the transient thermal behavior of the material under transient loading, revealing the heat transfer and energy conversion processes within the material. The acquisition and analysis of time-series thermal image sequences allows for a more accurate assessment of the material's thermal stability under these transient conditions. Thermal-mechanical coupling modeling allows for a comprehensive assessment of the energy dissipation mechanisms of the rubber material under complex operating conditions. This approach not only considers the material's thermal properties but also incorporates its microstructural characteristics, more accurately reflecting the material's energy conversion and loss under mechanical loading and thermal conditions. Fourier heat conduction simulations can further analyze the material's transient heat transfer behavior and mechanical loss characteristics. The resulting mechanical loss characteristic diagram of the rubber material can intuitively demonstrate the changes in the material's thermal and mechanical properties under complex operating conditions.

[0127] Preferably, step S45 includes the following steps:

[0128] Step S451: reconstructing the thermal stress field of the temperature field distribution map of the rubber material to obtain a thermal stress distribution map of the rubber material;

[0129] Specifically, the temperature field distribution diagram of the rubber material can be imported into ABAQUS software. The software uses the finite element method to analyze the temperature field and calculate the thermal stress of each unit. Set the thermal expansion coefficient of the material (1.5× 1 / K) and elastic modulus (10 MPa), these parameters were obtained through previous experimental measurements. In the software, the accuracy of the grid division is set, and the spatial resolution consistent with the temperature field distribution map is selected. The software further calculates the thermal stress by calculating the temperature gradient of each unit and the strain caused by thermal expansion. For example, in areas with higher temperature gradients, the thermal stress values are higher, indicating that these areas are subject to greater thermal stress. By running the software, a thermal stress distribution map of the rubber material is obtained. The map is displayed in the form of colored contour lines, which clearly identifies the thermal stress distribution of the rubber sample at different locations. For example, in some areas, the thermal stress value reaches 3 MPa, indicating that there is thermal stress concentration in these areas.

[0130] Step S452: constructing a multi-field coupling equation for the rubber sample according to the thermal stress distribution diagram of the rubber material to obtain a rubber multi-field coupling calculation model;

[0131] Specifically, the thermal stress distribution map can be imported into ABAQUS software. The software analyzes the thermal stress distribution map and, in combination with the mechanical properties of the rubber material (such as an elastic modulus of 10 MPa and a Poisson's ratio of 0.49) and thermophysical parameters (such as a thermal conductivity of 0.15 W / m·K), constructs a multi-field coupling equation. Appropriate coupling equation forms, including heat conduction and mechanical equilibrium equations, are selected, and coupling conditions are set. Within the software, the model's boundary and initial conditions are set. For example, the initial temperature of the rubber sample is set to 20°C, and the boundary condition is an adiabatic boundary (no heat flow). The software discretizes the multi-field coupling equation using the finite element method and generates a multi-field coupling calculation model for the rubber. By running ABAQUS, the multi-field coupling calculation model for the rubber is obtained. This model is capable of simulating the complex behavior of rubber materials under multi-field coupling conditions, such as heat and force. For example, the model demonstrates how thermal stress affects the material's mechanical properties and how mechanical loading, in turn, affects the heat conduction process.

[0132] Step S453: performing energy conversion quantitative mapping on the rubber sample based on the rubber multi-field coupling calculation model to obtain a rubber thermomechanical energy conversion rate diagram;

[0133] Specifically, the rubber multi-field coupling calculation model can be imported into COMSOL Multiphysics software. The software calculates the energy conversion efficiency of each unit by analyzing the thermal field and stress field data in the model. Set the calculation parameters, including the type of energy conversion (thermal energy to mechanical energy) and the calculation accuracy (0.01%). The software analyzes the energy conversion process of each unit using the finite element method and calculates the efficiency of converting thermal energy into mechanical energy. For example, in some areas with higher thermal stress, the energy conversion rate reaches 15%, while in areas with lower thermal stress, the energy conversion rate is only 5%. By running the software, a rubber thermomechanical energy conversion rate map is obtained. The map is displayed in the form of colored contour lines, clearly indicating the energy conversion efficiency of the rubber sample at different locations.

[0134] Step S454: performing local accumulation on the rubber thermal engine energy conversion rate diagram to obtain rubber material energy dissipation data;

[0135] Specifically, the rubber thermal engine energy conversion rate diagram can be imported into ABAQUS software. The software analyzes the energy conversion rate diagram and performs local accumulation of the energy conversion efficiency of each unit. Set the regional range and accuracy of the accumulation. For example, choose to accumulate the entire rubber sample, and set the accumulation accuracy to 0.01 joules. The software accumulates the energy conversion rate of each unit through an integration algorithm to obtain the energy dissipation data of the rubber material in different areas. For example, in areas with higher thermal stress, the energy dissipation value is higher, while in areas with lower thermal stress, the energy dissipation value is lower. By running the software, the energy dissipation data of the rubber material is obtained. These data are output in tabular form, recording the energy dissipation values of the rubber sample at different positions. For example, the total energy dissipation of the entire rubber sample is 200 joules.

[0136] Step S455: constructing heat diffusion conditions based on the energy dissipation data of the rubber material to obtain a set of rubber transient heat transfer boundary conditions;

[0137] Specifically, rubber material energy dissipation data can be imported into ANSYS Fluent software. The software analyzes this energy dissipation data and combines it with the rubber material's thermophysical parameters (such as thermal conductivity and specific heat capacity) to calculate the heat diffusion process. The heat diffusion analysis is performed over a time range of 0 to 10 seconds, with a time step of 0.1 seconds. The software simulates the heat diffusion process using the finite element method and generates a set of transient heat transfer boundary conditions for the rubber. For example, in areas of high thermal stress, the boundary conditions manifest as higher heat flux density, while in areas of low thermal stress, the heat flux density is lower.

[0138] Step S456: performing Fourier series expansion based on the rubber transient heat transfer boundary condition set to obtain a rubber temperature field harmonic component diagram;

[0139] Specifically, the transient heat transfer boundary condition set can be imported into MATLAB software. The software converts the time-domain temperature data into frequency-domain data through Fourier transform. The frequency range of the Fourier series expansion is set to 0 to 100 Hz, with a frequency step size of 1 Hz. The software calculates the temperature harmonic components at each frequency point to generate a harmonic component plot of the rubber temperature field. For example, in some areas, the amplitude of the low-frequency (e.g., 10 Hz) harmonic components is high, indicating that the temperature changes in these areas are mainly caused by low-frequency heat sources; in other areas, the amplitude of the high-frequency (e.g., 50 Hz) harmonic components is high, indicating that the temperature changes in these areas are more complex.

[0140] Step S457: reconstructing the energy spectrum density of the rubber temperature field harmonic component map to obtain the energy spectrum distribution map of the rubber material, and extracting the loss characteristics of the energy spectrum distribution map of the rubber material to obtain the mechanical loss characteristic map of the rubber material.

[0141] Specifically, the harmonic component diagram of the rubber temperature field can be imported into the MATLAB software. The software generates an energy spectrum distribution diagram of the rubber material by calculating the energy spectrum density of each harmonic component. The frequency range for energy spectrum density calculation is set to 0 to 100 Hz, and the frequency resolution is set to 1 Hz. The software accumulates the energy spectrum density of each frequency point through the trapezoidal integration method to obtain the energy spectrum distribution diagram of the rubber material. For example, at certain frequency points, the energy spectrum density is high, indicating that these frequencies contribute more to the energy dissipation of the rubber material. The energy spectrum distribution diagram is further subjected to loss feature extraction to calculate the mechanical loss characteristic value of each frequency point. For example, the loss characteristic value can be calculated by the following formula: By running the software, a mechanical loss characteristic map of the rubber material is generated. This map, presented as colored contour lines, clearly identifies the mechanical loss characteristics of the rubber material at different frequencies. For example, in some areas, high-frequency loss characteristic values are higher, indicating that these areas are more susceptible to energy dissipation under high-frequency heat sources.

[0142] The present invention reconstructs the thermal stress field to intuitively display the thermal stress distribution within the material. By constructing multi-field coupling equations, the complex behavior of rubber materials under multi-field coupling conditions, such as heat and force, can be systematically analyzed, revealing the interaction mechanisms between different physical fields. Quantified energy conversion mapping clearly demonstrates the energy conversion efficiency and distribution of rubber materials under multi-field coupling conditions. Local accumulation allows precise calculation of the material's energy dissipation data, enabling identification of energy loss points in the material under different operating conditions. By constructing a set of transient heat transfer boundary conditions, the heat transfer process of rubber materials in actual operating environments can be more accurately simulated. This simulation allows for better prediction of the material's temperature changes and thermal stability under complex operating conditions. Fourier series expansion can be used to generate a harmonic component diagram of the temperature field, revealing the temperature field's characteristics at different frequencies. Energy spectral density reconstruction and extraction of mechanical loss characteristics can intuitively demonstrate the changes in the material's mechanical properties under multi-field coupling conditions. Analyzing the mechanical loss characteristics can identify potential failure risks of the material under different operating conditions.

[0143] Preferably, step S5 includes the following steps:

[0144] Step S51: performing multi-physics field coupling topological modeling on the three-dimensional structural data of the rubber material and the mechanical loss characteristic diagram of the rubber material to obtain a rubber multi-field coupling topological model;

[0145] Specifically, the three-dimensional structural data and mechanical loss characteristic maps of rubber materials can be imported into the Multiphysics software. By analyzing these data, the software identifies the microstructural characteristics and mechanical loss distribution inside the rubber material. The modeling parameters are set, including the meshing accuracy of the model (0.1 mm) and the type of coupling field (thermal field, force field, and chemical field). The software uses the finite element method to model the multi-physics field behavior of the rubber material and generates a rubber multi-field coupling topological model. For example, in the model, it can be clearly seen how the microstructure inside the rubber material affects the mechanical loss characteristics. In some areas, due to microstructural defects (such as micropores or cracks), the mechanical loss characteristic values are higher, indicating that these areas are more prone to energy dissipation. By running the software, a rubber multi-field coupling topological model is obtained, which can fully reflect the behavior of the rubber material under multi-physics field coupling conditions.

[0146] Step S52: calibrating the thermal-mechanical-chemical coupling parameters of the rubber multi-field coupling topological model to obtain a multi-field coupling parameter set of the rubber material;

[0147] Specifically, the rubber multi-field coupled topology model can be imported into LS-DYNA software. The software automatically identifies the key parameters that need to be calibrated by analyzing the coupling relationship in the model. Set the calibration parameter range and accuracy. For example, select the calibration range of thermal conductivity (k) to be 0.1 to 0.2 W / m·K, the calibration range of elastic modulus (E) to be 5 to 15 MPa, and the calibration range of chemical reaction rate constant (kchem) to be 0.01 to 0.1 The software automatically adjusts these parameters by comparing them with experimental data until the model's predictions are highly consistent with the experimental data. For example, the energy dissipation of rubber materials under different temperature and stress conditions is measured experimentally and input into the software. The software iteratively adjusts the calibration parameters through genetic algorithms and ultimately obtains a multi-field coupling parameter set. For example, the calibrated thermal conductivity is 0.15 W / m·K, the elastic modulus is 10 MPa, and the chemical reaction rate constant is 0.05 By running LS-DYNA software, the multi-field coupling parameter set of the rubber material is obtained.

[0148] Step S53: performing cross-scale experimental data fusion verification on the multi-field coupling parameter set of the rubber material to obtain a rubber material coupling model verification index;

[0149] Specifically, the multi-field coupling parameter set for the rubber material can be imported into COMSOL Multiphysics. Simultaneously, experimental data for the rubber material at different scales is collected, including microscale molecular chain alignment data, mesoscale mechanical loss characteristic maps, and macroscale mechanical property test data (such as tensile strength and hardness). This experimental data is obtained using testing equipment (such as an atomic force microscope and a dynamic thermomechanical analyzer). Within the software, validation parameters and accuracy are set. For example, a validation error range of ±5% can be selected, and the validation scale ranges can be set to micro (10 nm), meso (1 mm), and macro (100 mm). The software compares the model predictions with the experimental data and calculates validation metrics at each scale. For example, at the microscale, the predicted molecular chain alignment matches the experimental data by 95%; at the mesoscale, the error in the mechanical loss characteristic maps is 3%; and at the macroscale, the predicted error in tensile strength is 2%. By running COMSOL Multiphysics, validation metrics for the rubber material coupling model are obtained. These metrics are output in a table, documenting the validation results at different scales. For example, the validation index for the microscale is 0.95, the validation index for the mesoscale is 0.97, and the validation index for the macroscale is 0.98.

[0150] Step S54: predicting the nonlinear damage cumulative life of the rubber sample according to the rubber material coupling model verification index to obtain a nonlinear damage life spectrum of the rubber material;

[0151] Specifically, coupled model validation metrics can be imported into the FE-Safe / Rubber software. Based on these metrics, the software adjusts key model parameters. Predicted operating parameters are set, including a temperature range (-20°C to 80°C), stress level (0 to 10 MPa), and loading frequency (0.1 to 10 Hz). These parameters cover the complex environmental conditions encountered by rubber materials in real-world applications. The software uses the Manson-Halford model combined with a machine learning algorithm to simulate the damage accumulation process of rubber materials under different operating conditions. For example, under high temperature and high stress conditions, the damage accumulation rate is faster, while under low temperature and low stress conditions, the damage accumulation rate is slower. The software generates a nonlinear damage life spectrum for the rubber material, displayed as colored contour lines, clearly identifying the predicted life results for the rubber material under different operating conditions. For example, in the high temperature and high stress region, the predicted life is 1000 hours, while in the low temperature and low stress region, the predicted life is 5000 hours. By running the FE-Safe / Rubber software, the nonlinear damage life spectrum for the rubber material is obtained.

[0152] Step S55: performing failure mode feature identification on the nonlinear damage life spectrum of the rubber material to obtain a multimodal failure feature matrix of the rubber material; and performing multi-dimensional performance degradation evaluation on the multimodal failure feature matrix of the rubber material to obtain a rubber material performance evaluation report.

[0153] Specifically, the nonlinear damage life spectrum of a rubber material can be imported into the FE-Safe / Rubber software. The software analyzes the data in the life spectrum to identify the primary failure modes of the rubber material. For example, the software identifies that the primary failure modes of the rubber material under high temperature and high stress conditions are thermal oxidative aging and mechanical fatigue; while under low temperature and low stress conditions, the primary failure mode is chemical degradation. Next, Endurica software is used to perform a multi-dimensional performance degradation assessment of the rubber material. Based on the identified failure modes, the software assesses the degree of performance degradation in different dimensions (such as mechanical properties, thermal properties, and chemical stability). The assessment parameters and accuracy are set, for example, to an accuracy of ±2% for mechanical performance degradation, ±3% for thermal performance degradation, and ±4% for chemical stability degradation. By running the software, a multimodal failure characteristic matrix for the rubber material is generated, which details the failure modes and performance degradation levels under different operating conditions. For example, under high temperature and high stress conditions, the degree of mechanical performance degradation is 30%, the degree of thermal performance degradation is 20%, and the degree of chemical stability degradation is 15%. The software further generates a rubber material performance evaluation report, which details the performance, failure modes, and life prediction results of the rubber material under different working conditions.

[0154] Through multi-physics field coupling topological modeling, the present invention can construct a model that comprehensively considers the multi-field coupling effects of mechanics, thermals, and chemistry. This can more comprehensively reflect the behavior of rubber materials under complex working conditions and reveal the interaction mechanism between different physical fields. Through thermal-mechanical-chemical coupling parameter calibration, the key parameters in the model can be accurately determined to ensure the accuracy and reliability of the model. By verifying the calibration parameters through experimental data, the performance of the model can be further optimized to make it more closely resemble the behavior of the actual material. Through cross-scale data fusion, the model parameters can be further optimized to improve the versatility and reliability of the model. Through nonlinear damage cumulative life prediction, the service life of rubber materials under complex working conditions can be predicted in advance. By generating a nonlinear damage life spectrum, the life change trend of the material under different working conditions can be intuitively displayed. Through failure mode feature recognition, the potential failure modes of rubber materials under different working conditions can be identified. Through multi-dimensional performance degradation assessment, the performance changes of the material during the failure process can be comprehensively analyzed. The resulting rubber material performance evaluation report can provide a comprehensive decision-making basis for the material's engineering application.

[0155] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0156] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing and evaluating performance data of rubber materials, characterized in that: The following steps are involved: Step S1: Acquire X-ray scanning data of a rubber sample; perform phase contrast imaging on the X-ray scanning data of the rubber sample to obtain projection data of the rubber sample; and perform back-projection reconstruction on the projection data of the rubber sample to obtain three-dimensional structural data of the rubber material. The steps of performing phase contrast imaging on the X-ray scanning data of the rubber sample to obtain projection data of the rubber sample and performing back-projection reconstruction on the projection data of the rubber sample include: Step S161: performing phase extraction on the X-ray scanning data of the rubber sample to obtain a rubber scanning phase gradient map; Step S162: reconstructing a phase image based on the rubber scanning phase gradient image to obtain a phase distribution map of the rubber sample; Step S163: performing contrast gradient compensation on the rubber sample phase distribution map to obtain an enhanced phase map of the rubber sample, and performing contrast enhancement on the enhanced phase map of the rubber sample to obtain projection data of the rubber sample; Step S164: performing geometric correction on the rubber sample projection data to obtain rubber sample corrected projection data; Step S165: constructing image reconstruction parameters based on the corrected projection data of the rubber sample to obtain a rubber image reconstruction control parameter set; Step S166: performing back-projection reconstruction on the rubber sample calibrated projection data according to the rubber image reconstruction control parameter set to obtain three-dimensional structural data of the rubber material; Step S2: performing multi-parameter environmental simulation loading on the rubber sample to obtain environmental response data of the rubber material; dynamically collecting the environmental response data of the rubber material to obtain deformation process data of the rubber material; synchronously recording and analyzing the deformation process data of the rubber material to obtain a stress-strain characteristic diagram of the rubber material; Step S3: performing polarization spectral excitation on the rubber sample based on the stress-strain characteristic diagram of the rubber material to obtain fluorescence data of the rubber molecular chain; performing anisotropy calculation on the fluorescence data of the rubber molecular chain to obtain a rubber molecular chain orientation parameter set; performing dynamic evolution analysis based on the rubber molecular chain orientation parameter set to obtain rubber material molecular chain arrangement data; wherein step S3 includes the following steps: Step S31: identifying stress concentration areas on the stress-strain characteristic diagram of the rubber material to obtain a high stress distribution diagram of the rubber material, and performing spectral excitation point array design on the rubber sample based on the high stress distribution diagram of the rubber material to obtain light excitation point position data; Step S32: performing wavelength selection on the light excitation point position data to obtain a rubber excitation spectrum parameter set, and performing polarizer angle division based on the rubber excitation spectrum parameter set to obtain polarization angle sequence data; Step S33: reconstructing the light intensity gradient according to the polarization angle sequence data to obtain the incident light intensity distribution map of the rubber; Step S34: performing background noise elimination on the incident light intensity distribution diagram of the rubber to obtain background correction data of the rubber sample, and performing an excitation light stability assessment based on the background correction data of the rubber sample to obtain a light source stability parameter set; Step S35: collecting molecular chain structure fluorescence signals of the rubber sample based on the light source stability parameter set to obtain rubber molecular chain fluorescence data; Step S36: performing anisotropy calculation on the rubber molecular chain fluorescence data to obtain a rubber molecular chain orientation parameter set, and performing dynamic evolution analysis based on the rubber molecular chain orientation parameter set to obtain rubber material molecular chain arrangement data; Step S4: performing infrared thermal imaging scanning on the rubber sample to obtain a temperature field distribution diagram of the rubber material; performing thermomechanical coupling modeling based on the molecular chain arrangement data of the rubber material and the temperature field distribution diagram of the rubber material to obtain energy dissipation data of the rubber material; performing Fourier heat conduction simulation on the energy dissipation data of the rubber material to obtain a mechanical loss characteristic diagram of the rubber material; Step S5: constructing a multi-field coupling model for the rubber sample based on the three-dimensional structural data of the rubber material and the mechanical loss characteristic diagram of the rubber material to obtain a rubber multi-field coupling topological model; and predicting the life of the rubber sample based on the rubber multi-field coupling topological model to obtain a rubber material performance evaluation report.

2. The performance data analysis and evaluation method for rubber materials according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: setting pre-processing parameters for the rubber sample to obtain scanning parameters for the rubber sample, and calibrating the energy of a preset X-ray light source according to the scanning parameters for the rubber sample to obtain X-ray energy spectrum data; Step S12: performing detector sensitivity mapping according to the X-ray energy spectrum data to obtain detector response matrix data; Step S13: performing dark field correction on the detector response matrix data to obtain detector background noise data, and performing dynamic signal-to-noise ratio balance based on the detector background noise data to obtain detector optimization parameters; Step S14: dividing the rubber sample into rotation angles to obtain a rotation angle sequence of the rubber sample, and performing scanning path planning on the rubber sample according to the rotation angle sequence of the rubber sample to obtain scanning trajectory data of the rubber sample; Step S15: performing X-ray irradiation on the rubber sample according to the detector optimization parameters and the rubber sample scanning trajectory data to obtain X-ray scanning data of the rubber sample; Step S16: performing phase contrast imaging on the X-ray scanning data of the rubber sample to obtain rubber sample projection data, and performing back-projection reconstruction on the rubber sample projection data to obtain three-dimensional structure data of the rubber material.

3. The performance data analysis and evaluation method for rubber materials according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: collecting application environment data of the rubber sample to obtain characteristic data of the rubber application environment, and initializing environmental parameters of the rubber sample according to the characteristic data of the rubber application environment to obtain an initial parameter set for environmental simulation; Step S22: performing pressure field simulation on the rubber sample based on the initial parameter set of the environmental simulation to obtain multi-axial pressure distribution data of the rubber; Step S23: performing radiation dose superposition mapping on the rubber multi-axial pressure distribution data to obtain a stress composite environment parameter map; Step S24: adjusting the loading step length of the rubber sample according to the stress composite environmental parameter map to obtain dynamic environmental loading sequence data, and performing multi-parameter coordinated loading on the rubber sample based on the dynamic environmental loading sequence data to obtain rubber material environmental response data; Step S25: dynamically collecting the environmental response data of the rubber material to obtain the deformation process data of the rubber material, and synchronously recording and analyzing the deformation process data of the rubber material to obtain a stress-strain characteristic diagram of the rubber material.

4. The performance data analysis and evaluation method for rubber materials according to claim 3, characterized in that: Step S25 includes the following steps: Step S251: regulating the sampling frequency of the rubber material environmental response data to obtain a rubber dynamic sampling parameter set; Step S252: performing high-speed image acquisition on the rubber sample according to the rubber dynamic sampling parameter set to obtain a deformation sequence diagram of the rubber material; Step S253: reconstructing the motion vector field of the rubber material deformation sequence diagram to obtain deformation material displacement field data; Step S254: performing strain distribution mapping on the rubber sample based on the deformation material displacement field data to obtain rubber material strain field data, and performing stress conversion on the rubber material strain field data to obtain rubber dynamic stress distribution data; Step S255: performing a quantitative assessment of local stress concentration based on the rubber dynamic stress distribution data to obtain a rubber stress concentration factor diagram; Step S256: evaluating the deformation stability of the rubber sample based on the rubber stress concentration factor diagram to obtain rubber deformation stability data; Step S257: extracting features from the rubber deformation stability data to obtain a rubber deformation feature parameter set, and performing feature space mapping based on the rubber deformation feature parameter set to obtain a stress-strain feature map of the rubber material.

5. The performance data analysis and evaluation method for rubber materials according to claim 1, characterized in that: Step S36 includes the following steps: Step S361: performing signal-to-noise ratio enhancement on the fluorescence data of the rubber molecular chain to obtain a fluorescence spectrum of the rubber molecular chain; Step S362: measuring the polarization degree of the molecular chain state of the rubber sample according to the fluorescence spectrum of the rubber molecular chain to obtain polarization intensity distribution data; Step S363: calculating the molecular orientation anisotropy index of the rubber sample based on the polarization intensity distribution data to obtain a molecular orientation anisotropy coefficient map; Step S364: performing feature mapping on the molecular orientation anisotropy coefficient map to obtain a rubber molecular chain orientation parameter set; Step S365: performing molecular dynamics time-series evolution according to the rubber molecular chain orientation parameter set to obtain segment orientation dynamics data; Step S366: performing molecular multi-level response modeling on the rubber sample based on the segment orientation dynamics data to obtain a rubber microstructure stress correlation diagram; Step S367: reconstructing the spatial distribution of the rubber microstructure stress correlation diagram to obtain the molecular chain arrangement data of the rubber material.

6. The performance data analysis and evaluation method for rubber materials according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: dividing the molecular chain arrangement data of the rubber material into regions to obtain a rubber thermal imaging scanning region map; Step S42: setting scanning parameters according to the rubber thermal imaging scanning area map to obtain rubber thermal imaging acquisition parameters; Step S43: performing ambient temperature calibration on the rubber thermal imaging acquisition parameters to obtain an ambient temperature calibration curve, and performing thermal radiation coefficient correction based on the ambient temperature calibration curve to obtain a thermal field radiation correction factor diagram; Step S44: collecting thermal images of the rubber sample according to the thermal field radiation correction factor map to obtain a rubber time-series thermal image sequence, and reconstructing the temperature gradient according to the rubber time-series thermal image sequence to obtain a temperature field distribution map of the rubber material; Step S45: performing thermomechanical coupling modeling based on the rubber material molecular chain arrangement data and the rubber material temperature field distribution diagram to obtain rubber material energy dissipation data, and performing Fourier heat conduction simulation on the rubber material energy dissipation data to obtain a rubber material mechanical loss characteristic diagram.

7. The performance data analysis and evaluation method for rubber materials according to claim 6, characterized in that: Step S45 includes the following steps: Step S451: reconstructing the thermal stress field of the temperature field distribution map of the rubber material to obtain a thermal stress distribution map of the rubber material; Step S452: constructing a multi-field coupling equation for the rubber sample according to the thermal stress distribution diagram of the rubber material to obtain a rubber multi-field coupling calculation model; Step S453: performing energy conversion quantitative mapping on the rubber sample based on the rubber multi-field coupling calculation model to obtain a rubber thermomechanical energy conversion rate diagram; Step S454: performing local accumulation on the rubber thermal engine energy conversion rate diagram to obtain rubber material energy dissipation data; Step S455: constructing heat diffusion conditions based on the energy dissipation data of the rubber material to obtain a set of rubber transient heat transfer boundary conditions; Step S456: performing Fourier series expansion based on the rubber transient heat transfer boundary condition set to obtain a rubber temperature field harmonic component diagram; Step S457: reconstructing the energy spectrum density of the rubber temperature field harmonic component map to obtain the energy spectrum distribution map of the rubber material, and extracting the loss characteristics of the energy spectrum distribution map of the rubber material to obtain the mechanical loss characteristic map of the rubber material.

8. The performance data analysis and evaluation method for rubber materials according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing multi-physics field coupling topological modeling on the three-dimensional structural data of the rubber material and the mechanical loss characteristic diagram of the rubber material to obtain a rubber multi-field coupling topological model; Step S52: calibrating the thermal-mechanical-chemical coupling parameters of the rubber multi-field coupling topological model to obtain a multi-field coupling parameter set of the rubber material; Step S53: performing cross-scale experimental data fusion verification on the multi-field coupling parameter set of the rubber material to obtain a rubber material coupling model verification index; Step S54: predicting the nonlinear damage cumulative life of the rubber sample according to the rubber material coupling model verification index to obtain a nonlinear damage life spectrum of the rubber material; Step S55: performing failure mode feature identification on the nonlinear damage life spectrum of the rubber material to obtain a multimodal failure feature matrix of the rubber material; and performing multi-dimensional performance degradation evaluation on the multimodal failure feature matrix of the rubber material to obtain a rubber material performance evaluation report.

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