Performance data analysis and evaluation method for rubber material

Abstract

The invention relates to the technical field of rubber performance evaluation, in particular to a performance data analysis and evaluation method for a rubber material. 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; carrying out 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 pattern of the rubber material; and performing polarization spectrum excitation on the rubber sample based on the rubber material stress-strain characteristic pattern to obtain rubber molecular chain fluorescence data. According to the invention, the reliability and accuracy of rubber material performance evaluation are improved.

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 rubber material performance testing methods mainly focus on the measurement of macroscopic mechanical properties, such as tensile strength, hardness, and wear resistance. Although these test methods can provide certain reference value in conventional applications, in some high-end application fields with extremely high material performance requirements, such as aerospace, medical devices, and high-performance seal manufacturing, traditional test methods have become inadequate. In the aerospace field, rubber materials face extremely harsh working environments. For example, when an aircraft is flying at high altitudes, rubber materials need to maintain stable performance in a complex environment 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 can often only be tested under conventional laboratory conditions and cannot simulate the complex environment in actual use. Therefore, when these rubber materials are used in actual aerospace equipment, early failure or unstable performance will occur. This failure will 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 object, a performance data analysis and evaluation method for rubber materials comprises the following steps: Step S1: Acquire X-ray scanning data of the 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 structure 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: based on the stress-strain characteristic diagram of the rubber material, polarization spectrum excitation is performed on the rubber sample to obtain fluorescence data of the rubber molecular chain; anisotropy calculation is performed on the fluorescence data of the rubber molecular chain to obtain an orientation parameter set of the rubber molecular chain; dynamic evolution analysis is performed based on the orientation parameter set of the rubber molecular chain to obtain molecular chain arrangement data of the rubber material; Step S4: performing infrared thermal imaging scanning on the rubber sample to obtain a temperature field distribution diagram of the rubber material; performing thermal-mechanical 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 structure data of the rubber material and the mechanical loss characteristic diagram of the rubber material to obtain a rubber multi-field coupling topological model; predicting the life of the rubber sample based on the rubber multi-field coupling topological model to obtain a rubber material performance evaluation report.

[0005] The present invention can obtain the three-dimensional structural data of rubber materials through X-ray scanning and phase contrast imaging. This not only includes macroscopic morphology, but also can go deep into the microscopic level to reveal the microscopic structural characteristics inside the rubber material. This is something that traditional macroscopic mechanical property testing methods cannot provide. For example, in the field of aerospace, microscopic defects (such as micropores, cracks, etc.) inside rubber materials become the cause of failure under extreme environments. Multi-parameter environmental simulation loading can highly restore the complex working environment of rubber materials in practical applications, such as extreme conditions such as high altitude, low temperature, high vacuum and high-intensity radiation in aerospace. This makes the performance evaluation of rubber materials no longer limited to conventional laboratory conditions, but can be tested in an environment close to the actual use scenario. By dynamically collecting the deformation process data of rubber materials in complex environments and generating stress-strain characteristic diagrams, the mechanical behavior of materials in practical applications can be more accurately predicted to avoid early failure or performance instability caused by environmental factors. Through polarization spectral excitation and anisotropy calculation, the fluorescence data and orientation parameter set of the rubber molecular chain are obtained, and dynamic evolution analysis is performed. This can go deep into the molecular level and reveal the changes in the molecular chain arrangement of rubber materials under stress. Through the molecular chain arrangement data, the performance changes of materials under complex physiological environments can be more accurately evaluated. Through infrared thermal imaging scanning and thermomechanical coupling modeling, the energy dissipation of rubber materials under mechanical loading and thermal environment can be considered at the same time. In practical applications, rubber materials are often affected by both mechanical stress and thermal environment. For example, in high-performance seals, materials need to maintain sealing performance under high temperature and high pressure. Through thermomechanical coupling analysis, the energy conversion and loss of rubber materials under complex working conditions can be comprehensively evaluated, so as to more accurately predict the mechanical loss characteristics of materials. Based on the three-dimensional structural data and mechanical loss characteristic diagram of rubber materials, a multi-field coupling topological model is constructed and life prediction is performed. This can comprehensively consider the performance changes of rubber materials under multi-field coupling conditions such as mechanics, thermals, and chemistry, so as to more accurately predict the service life of materials. Compared with traditional life prediction methods, this method not only considers macroscopic mechanical properties, but also combines microstructure and energy dissipation factors, which can more comprehensively reflect the performance degradation process of materials in actual use. The failure mode and life of rubber materials in extreme environments can be predicted in advance through the multi-field coupling model. In summary, the present invention improves the reliability and accuracy of rubber material performance evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Other features, objects and advantages of the present invention will become more apparent from the detailed description made with reference to the following drawings: Figure 1 A schematic flow chart of the steps of a method for analyzing and evaluating performance data of a rubber material according to an embodiment is shown.

[0007] Figure 2 A detailed flow chart of step S2 of an embodiment is shown.

[0008] Figure 3 A detailed flow chart of step S25 of an embodiment is shown. DETAILED DESCRIPTION

[0009] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0010] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0011] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0012] To achieve this, please refer to Figures 1 to 3 The present invention provides a performance data analysis and evaluation method for rubber materials, comprising the following steps: Step S1: Acquire X-ray scanning data of the 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 structure 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: based on the stress-strain characteristic diagram of the rubber material, polarization spectrum excitation is performed on the rubber sample to obtain fluorescence data of the rubber molecular chain; anisotropy calculation is performed on the fluorescence data of the rubber molecular chain to obtain an orientation parameter set of the rubber molecular chain; dynamic evolution analysis is performed based on the orientation parameter set of the rubber molecular chain to obtain molecular chain arrangement data of the rubber material; Step S4: performing infrared thermal imaging scanning on the rubber sample to obtain a temperature field distribution diagram of the rubber material; performing thermal-mechanical 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 structure data of the rubber material and the mechanical loss characteristic diagram of the rubber material to obtain a rubber multi-field coupling topological model; predicting the life of the rubber sample based on the rubber multi-field coupling topological model to obtain a rubber material performance evaluation report.

[0013] In this embodiment, an X-ray scanner is used to perform high-resolution X-ray scanning on the rubber sample to obtain detailed data of its internal structure. The X-ray scanning data is converted into projection data through phase contrast imaging. The projection data is 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 characteristics. The rubber sample is placed in a multi-parameter environmental simulation loading device to simulate complex environmental conditions in actual applications, such as temperature, pressure and humidity, to obtain environmental response data of the rubber material. The deformation process data of the rubber material during loading is recorded in real time by a dynamic acquisition system to capture its mechanical behavior under different environmental conditions. The deformation process data is synchronously recorded and analyzed to generate a stress-strain characteristic diagram of the rubber material, which intuitively displays the performance changes of the material under different stress conditions. Based on the stress-strain characteristic diagram, the rubber sample is excited using a polarization spectrum excitation system to obtain the fluorescence data of the rubber molecular chain. These fluorescence data are further analyzed by anisotropy calculation software to obtain the orientation parameter set of the rubber molecular chain, revealing the arrangement changes of the molecular chain under stress. The molecular chain orientation parameter set is analyzed by a dynamic evolution analysis tool to obtain the dynamic evolution data of the molecular chain arrangement of the rubber material. At the same time, an infrared thermal imager is used to scan the rubber sample to obtain its temperature field distribution map to understand the energy dissipation of the material under mechanical loading and thermal environment. Combining the molecular chain arrangement data and the temperature field distribution map, the thermal coupling modeling software is used to model and calculate the energy dissipation data of the rubber material. The energy dissipation data of the rubber material is simulated by Fourier heat conduction to generate the mechanical loss characteristic map of the rubber material, and comprehensively evaluate the energy conversion and loss of the material under complex working conditions. Finally, based on the three-dimensional structure data and mechanical loss characteristic map of the rubber material, the multi-field coupling model construction software is used to construct the rubber multi-field coupling topological model, which comprehensively considers the multi-field coupling effects such as mechanics, thermal and chemistry. The life of the rubber sample is predicted and a rubber material performance evaluation report is generated.

[0014] Preferably, step S1 comprises 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; Specifically, for example, for styrene-butadiene rubber samples, the basic information of the sample is entered into the MaterialScan Pro software, including the material type, sample size (50 mm in diameter), and the expected scanning accuracy requirements (the scanning resolution is set to 0.05 mm). Based on this input information, the software automatically generates scanning parameters, including the start and end positions of the scan and the scanning step length. Subsequently, the energy of the preset X-ray light source is calibrated using the X-Spectrum 3000 X-ray energy spectrum analyzer. The voltage of the X-ray tube is set to 80 kV and the current is set to 10 mA. These parameters are pre-selected based on the density and thickness of the rubber sample. Through the calibration function of the X-Spectrum 3000, the X-ray energy spectrum data is obtained, and the data shows that the main energy is concentrated in the range of 70-90 keV.

[0015] Step S12: performing detector sensitivity mapping according to the X-ray energy spectrum data to obtain detector response matrix data; Specifically, the detector sensitivity mapping can be performed using the DetectorCal 200 detector sensitivity calibration device based on the X-ray energy spectrum data. The detector is placed in the X-ray beam path, and the gain and bias voltage of the detector are adjusted to enable it to accurately respond to the energy range in the X-ray energy spectrum. The gain of the detector is set to a medium level (gain value is 5), and the response signal of the detector at different X-ray intensities is recorded through multiple exposure experiments. The X-Expose 500 exposure experiment system is used, which can accurately control the exposure time and intensity of X-rays. In each exposure experiment, the response signal of the detector at different energies (from 70 keV to 90 keV) is recorded. By analyzing the response signal, the detector response matrix data is constructed using the DataMatrix Builder software. The software is able to convert the experimental data into a matrix form and record in detail the sensitivity changes of the detector at different energies and intensities. For example, at 70 keV energy, the sensitivity of the detector is 0.8, while at 90 keV energy, the sensitivity is 0.95.

[0016] 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 according to the detector background noise data to obtain detector optimization parameters; Specifically, the detector response matrix data can be dark-field corrected 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. Through multiple exposure experiments (each exposure time is 1 second, for a total of 10 times), the background noise data of the detector at different gain settings are recorded. The data is imported into the DataMatrix Analyzer software, which can analyze the background noise data and calculate the average background noise level of the detector. For example, when the gain value is 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: the gain value is 6 and the integration time is 2 seconds.

[0017] Step S14: dividing the rubber sample by rotation angle 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; Specifically, Python software can be used to divide the rotation angle and plan the scanning path of the rubber sample. The rubber sample was fixed on a rotating table, and the basic information of the sample was input through the software, including the diameter of the sample (50 mm) and the expected scanning accuracy (0.05 mm). 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 an interval of 1 degree. The spiral scanning path was selected to plan the scanning path according to the rotation angle sequence. The starting point of the scanning path was set at the bottom center of the sample, the end point was set at the top center of the sample, and the scanning step was 0.05 mm. These parameters were input into the control software of the X-Spectrum 3000 X-ray energy spectrometer. Finally, the scanning trajectory data of the rubber sample was obtained, which recorded in detail the moving path of the X-ray source during the scanning process and the rotation angle of the sample.

[0018] 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; Specifically, the rubber sample can be fixed on a rotating stage with an accuracy of 0.01 degrees, which can accurately control the rotation angle of the sample. According to the detector optimization parameters (gain value of 6, integration time of 2 seconds) and the scanning trajectory data (spiral scanning path, step size of 0.05 mm, rotation angle interval of 1 degree), the parameters of the X-ray scanning equipment are set. The X-Spectrum 3000 X-ray energy spectrum analyzer is used, and the voltage of the X-ray tube is set to 80 kV and the current is set to 10 mA. These parameters are pre-selected according to the density and thickness of the rubber sample. At the same time, the gain of the detector is set to 6 and the integration time is set to 2 seconds. During the scanning process, the automatic scanning program of the X-Spectrum 3000 is started, which controls the movement of the X-ray source and the detector according to the preset scanning trajectory data. The X-ray source moves from the bottom center of the sample to the top center along the spiral path, and the rotating stage rotates the sample step by step at an interval of 1 degree. After each rotation, the X-ray source will be exposed once, and the detector records the corresponding X-ray intensity data. The entire scanning process lasts about 30 minutes, and finally a series of X-ray projection images are generated. These projection images are transmitted in real time to a computer connected to the device and stored as X-ray scan data files.

[0019] Step S16: 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 to obtain three-dimensional structure data of the rubber material.

[0020] Specifically, please refer to the sub-steps of step S16 for the detailed implementation process of this embodiment.

[0021] The present invention can ensure the stability and accuracy of energy during X-ray scanning by setting and calibrating the energy of the X-ray light source through preprocessing parameters. Through detector sensitivity mapping and dark field correction, the detector background noise can be effectively reduced, the signal-to-noise ratio can be optimized, and the acquired X-ray scanning data can be more accurate and clear. Through rotation angle division and scanning path planning, it can 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.

[0022] Preferably, step S16 comprises the following steps: Step S161: performing phase extraction on the X-ray scanning data of the rubber sample to obtain a rubber scanning phase gradient map; Specifically, the raw 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 Fourier transform-based phase extraction algorithm is selected. In the software, set the key parameters of phase extraction, including the frequency range of the Fourier transform and the resolution of the 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 the phase information from the raw 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.

[0023] Step S162: reconstructing a phase image according to the rubber scanning phase gradient image to obtain a phase distribution map of the rubber sample; 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, set the reconstruction parameters, 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, a value that 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 figure 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.

[0024] 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; 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.

[0025] Step S164: geometrically correcting the rubber sample projection data to obtain rubber sample corrected projection data; Specifically, the projection data of the rubber sample 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. Manually mark several key feature points in the image, 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. Set the correction accuracy 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.

[0026] Step S165: constructing image reconstruction parameters according to the corrected projection data of the rubber sample to obtain a rubber image reconstruction control parameter set; Specifically, the corrected projection data of the rubber sample can be imported into the MATLAB software, and the reconstruction parameters can be configured according to the characteristics of the rubber sample and the scanning parameters. First, the reconstruction algorithm is set, the filtered back projection algorithm (FBP) is selected, and the filter function is set to the Hamming window. At the same time, the resolution parameter of the reconstruction is set to 0.05 mm, which is consistent with the scanning resolution. In addition, the number of reconstruction iterations is set to 10 times, which is an experimentally verified parameter value that can balance the calculation time and the quality of the reconstructed image. Through the setting of these parameters, the MATLAB software generates a rubber image reconstruction control parameter set and saves it as a parameter file.

[0027] Step S166: back-project and reconstruct the corrected projection data of the rubber sample according to the rubber image reconstruction control parameter set to obtain three-dimensional structure data of the rubber material.

[0028] Specifically, the rubber image reconstruction control parameter set and the rubber sample correction projection data can be imported into the WAVE-3D-Reconstruction software. The software automatically calls the filtered back projection algorithm (FBP) for reconstruction according to the settings in the parameter set. During the reconstruction process, the software first applies a Hamming window filter function to the projection data. Then, the software performs back projection reconstruction according to the set resolution (0.05 mm) and number of iterations (10 times). After about 30 minutes of calculation, the software generates the three-dimensional structural data of the rubber sample. The reconstructed three-dimensional structural data is visualized using the 3D Viewer software. In the three-dimensional view, the internal microstructure of the rubber sample is clearly visible, including pores, cracks, and other microscopic defects.

[0029] The present invention can effectively enhance the contrast and detail clarity of the image through phase extraction and phase image reconstruction. Through contrast gradient compensation and contrast enhancement processing, the visual effect of the image is further optimized, making the microscopic structural features inside the rubber material more clearly visible. Through geometric correction and optimization of image reconstruction parameters, errors and artifacts occurring during the reconstruction process can be effectively reduced. Through phase extraction and enhancement processing, tiny defects inside the rubber material, such as microcracks, pores and uneven distribution, can be more clearly displayed. Through the optimized phase imaging and reconstruction process, it can be ensured that the acquired three-dimensional structural data reaches a high level in terms of details and accuracy.

[0030] Preferably, step S2 comprises 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; Specifically, assuming that the rubber sample will be used in the aerospace field, an environmental data acquisition system (EDAS) is used to collect relevant data. The 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: the altitude is 10,000 meters, the temperature is -40°C, and the vacuum is 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 the EnviroSim software. The initial parameter set for environmental simulation is set, including an initial temperature of -40°C, an initial pressure of Pascal, the initial radiation intensity is 100 mSv / h.

[0031] 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; Specifically, the MultiAxis Pressure Simulator (MAPS) device can be used to simulate the pressure field. The rubber sample is fixed on the test platform of the MAPS device, and the parameters of the device are set according to the initial parameter set of the environmental simulation. The simulated pressure conditions are set, including the pressure distribution in the three main axis directions (X, Y, and Z axes). The specific parameters are as follows: The pressure in the X-axis direction is 0.5 MPa, simulating the transverse tensile stress; The pressure in the Y-axis direction is 0.3 MPa, simulating the longitudinal compressive stress; The pressure in the Z-axis direction is 0.2 MPa, simulating the shear stress in the vertical direction.

[0032] The MAPS device monitors the response of rubber samples under multi-axial pressure in real time through built-in high-precision pressure sensors and strain gauges. The control system of the device automatically adjusts the pressure loading device according to the set parameters. During the simulation process, LS-DYNA software is used to record and analyze the multi-axial pressure distribution data of the rubber sample. The software can display the three-dimensional distribution diagram of the pressure field in real time and provide detailed pressure distribution data, including the pressure value and stress direction of each point. After a period of simulated loading, the multi-axial pressure distribution data of the rubber sample is obtained. These data clearly show the pressure response of the rubber sample in different directions.

[0033] Step S23: performing radiation dose superposition mapping on the rubber multi-axial pressure distribution data to obtain a stress composite environment parameter map; 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 the radiation intensity of 100 mSv / h and the radiation duration of 2 hours. The software superimposes the radiation dose data with the multi-axial pressure distribution data through an algorithm to generate a stress composite environmental parameter map. The map intuitively shows the combined effects of stress levels and radiation doses of rubber samples at different positions in the form of colored contour lines. For example, in the figure, the parts where high stress areas (such as pressure concentration areas in the X-axis direction) overlap with 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.

[0034] Step S24: adjusting the loading step length of the rubber sample according to the stress composite environmental parameter diagram 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; Specifically, the loading step length can be determined according to the information in the stress composite environmental parameter diagram. A more refined loading step length adjustment is selected in the high stress and high radiation dose area. The specific parameters are as follows: in the red area (high stress and high radiation dose area), the loading step length is set to 0.1 MPa; in the blue area (low stress and low radiation dose area), the loading step length is set to 0.5 MPa. Next, the dynamic environmental loading sequence data is set in the LabVIEW system. According to the stress composite environmental parameter diagram, multi-axial pressure loading is combined with radiation dose loading to simulate the complex working conditions of rubber samples in actual applications. The equipment gradually applies pressure according to the set step length and sequence through a high-precision loading control system, and adjusts the radiation dose at the same time. During the loading process, the equipment monitors the deformation and stress response of the rubber sample in real time, and transmits the data to the computer connected to it. The collected environmental response data is analyzed using LabVIEW. The software can display the deformation process and stress distribution of the rubber sample under multi-parameter coordinated loading in real time. By analyzing these data, the dynamic response characteristics of the rubber material in a complex environment are obtained.

[0035] 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 the stress-strain characteristic diagram of the rubber material.

[0036] Specifically, please refer to the sub-steps of step S25 for the detailed implementation process of this embodiment.

[0037] The present invention can highly restore the complex environmental conditions of rubber materials in actual use scenarios by collecting and initializing environmental parameters. 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 more accurately evaluated to avoid 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 rubber materials in actual applications 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 rubber materials during multi-parameter collaborative loading, the deformation process of the material during loading 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.

[0038] Preferably, step S25 comprises the following steps: Step S251: regulating the sampling frequency of the rubber material environmental response data to obtain a rubber dynamic sampling parameter set; Specifically, the environmental response data of rubber materials can be imported into LabVIEW software. The software provides a variety of sampling frequency control modes. Select "adaptive sampling mode", which can automatically adjust the sampling frequency according to the deformation rate of the material. The initial value of the sampling frequency is set to 100 Hz, which is 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, the threshold of the deformation rate is defined. When the deformation rate exceeds the threshold, the sampling frequency will automatically increase. For example, when the deformation rate exceeds 0.1 mm / s, the sampling frequency is automatically increased from 100 Hz to 200 Hz; when the deformation rate exceeds 0.5 mm / s, the sampling frequency is further increased to 500 Hz. Through the above operations, the rubber dynamic sampling parameter set can be obtained, which includes the sampling frequency settings at different deformation rates.

[0039] 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; 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. According to the sampling frequency setting in the dynamic sampling parameter set, the frame rate of the camera system is adjusted. For example, in the stage of low deformation rate, the frame rate of the camera system is set to 100 frames / second; and in the stage of high deformation rate, the frame rate is automatically increased to 500 frames / 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, the deformation sequence diagram of the rubber material is obtained, and these images clearly show the deformation of the rubber sample at different loading stages.

[0040] Step S253: reconstructing the motion vector field of the rubber material deformation sequence diagram to obtain the displacement field data of the deformed material; Specifically, the deformation sequence diagram of the rubber material can be imported into the LabVIEW software. The software provides a variety of motion vector calculation methods, and the algorithm based on the optical flow method is selected to reconstruct the motion vector field. In the software, set key parameters, including 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 in the deformation sequence diagram and calculates the displacement vector of each pixel in the time series. Finally, the displacement field data of the deformed material is obtained, which is presented in the form of a two-dimensional vector field, clearly showing the displacement of the rubber material at different positions and times.

[0041] Step S254: performing strain distribution mapping on the rubber sample based on the deformation material displacement field data to obtain the rubber material strain field data, and performing stress conversion on the rubber material strain field data to obtain the rubber dynamic stress distribution data; 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 previous 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 locations. The stress distribution data is presented in the form of a vector diagram, revealing the stress concentration of the material in different areas.

[0042] Step S255: performing a quantitative evaluation of local stress concentration according to the rubber dynamic stress distribution data to obtain a rubber stress concentration factor diagram; 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 calculated by comparing the local stress ( ) and mean stress ( ) is determined by the ratio of: ; Set the analysis parameters of the software, including the identification threshold of the stress concentration area (set to 1.5 times the average stress) and the calculation accuracy (set to 0.01). By running the software, a rubber stress concentration factor map is obtained. The map shows the stress concentration of the rubber sample at different locations in the form of colored contour lines. For example, in some areas, the stress concentration factor reaches 2.5, indicating that the stress in these areas is 2.5 times the average stress, and there is a high risk of failure.

[0043] Step S256: evaluating the deformation stability of the rubber sample based on the rubber stress concentration factor diagram to obtain rubber deformation stability data; Specifically, the rubber stress concentration factor diagram can be imported into the ABAQUS software. The software calculates the deformation stability index of each area by analyzing the stress concentration factor diagram and combining the yield strength (5 MPa) and elongation at break (500%) of the rubber material. The deformation stability index is determined by evaluating the ratio of the stress level in the stress concentration area to the yield strength of the material, and the formula is: ; Set the evaluation parameters of the software, including the threshold of the stability index (set to 0.8). When the stability index exceeds the threshold, the software will mark the area as a potential unstable area. By running the software, the rubber deformation stability data is obtained. These data are displayed in the form of a two-dimensional graph, clearly identifying the deformation stability of the rubber sample at different locations. For example, in some stress concentration areas, the stability index reaches 0.9, indicating that the stress in these areas is close to the yield strength of the material and there is a high risk of deformation.

[0044] 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.

[0045] Specifically, the rubber deformation stability data can be imported into the FeatureExtractor software. The software can extract key characteristic parameters from complex data, which can effectively characterize the deformation characteristics of rubber materials under different stress conditions. Select the following characteristic parameters to extract: 1. Maximum stability index: indicates the highest deformation risk of the material in all areas. 2. Average stability index: indicates the average deformation stability of the material as a whole. 3. Stress concentration area ratio: indicates the proportion of the stress concentration area to the total sample area. 4. Yield stress proximity: indicates the degree to which the material stress is close to the yield strength. In the software, set the range and accuracy of the extraction parameters. For example, the accuracy of the maximum stability index is set to 0.01, and the accuracy of the stress concentration area ratio is set to 1%. By running the FeatureExtractor software, the rubber deformation characteristic parameter set is obtained, which is output in a table form, 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 the FeatureMapper software, and select the stress-strain curve as the basic chart. The software maps the characteristic parameters to the stress-strain curve through interpolation and fitting algorithms to generate a stress-strain characteristic diagram of the rubber material. In this diagram, curves of different colors represent different characteristic parameter levels. For example, the red curve represents the stress-strain relationship in the high stress concentration area, and the blue curve represents the stress-strain relationship in the low stress area.

[0046] The present invention can dynamically adjust the sampling frequency according to the actual response characteristics of the material through sampling frequency regulation, thereby ensuring that sufficient data points are obtained in the key deformation stage and avoiding information loss caused by insufficient sampling. Through motion vector field reconstruction, the displacement field data of the rubber material during loading can be accurately reconstructed. This can not only intuitively display the overall deformation trend of the material, but also reveal the relationship between the change of the microstructure inside the material and the macro deformation. For example, local microcracks or rearrangement of molecular chains will appear as stress concentration areas in the displacement field. Through strain distribution mapping and stress conversion, the strain and stress distribution of rubber materials in complex environments can be fully quantified. This can reveal the stress concentration areas and potential failure risk points inside the material. Through deformation stability evaluation, the deformation stability of the material in a complex environment can be quantitatively analyzed. Through feature extraction and feature space mapping, a stress-strain characteristic diagram of the rubber material can be constructed. This can intuitively show the changes in the mechanical properties of the material in a complex environment.

[0047] Preferably, step S3 comprises 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 dot matrix design on the rubber sample according to the high stress distribution diagram of the rubber material to obtain light excitation point position data; 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 the 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.

[0048] 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 according to the rubber excitation spectrum parameter set to obtain polarization angle sequence data; Specifically, the light excitation point position data can be imported into the AP Spectroscopy Suite software. The software provides a variety of wavelength options. According to 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 range of the polarizer angle from 0° to 180° and divide it 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°, etc., until 180°. It is also necessary to set the exposure time and light intensity of each excitation point. For example, the exposure time is 100 milliseconds and the light intensity is 10 mW / cm². Finally, the rubber excitation spectrum parameter set and polarization angle sequence data are obtained.

[0049] Step S33: reconstructing the light intensity gradient according to the polarization angle sequence data to obtain the incident light intensity distribution diagram of the rubber; Specifically, the polarization angle sequence data can be imported into the AP Spectroscopy Suite software. The software calculates the intensity gradient of each excitation point by analyzing the intensity change at different polarization angles using the Stokes parameter. The resolution parameter for the intensity gradient calculation is set to 0.1 mm. In the software, a Fourier transform-based algorithm is selected to process 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 change of each excitation point at different polarization angles. For example, in some areas, the intensity gradient is higher, indicating that the intensity change in these areas is more significant.

[0050] Step S34: performing background noise elimination on the rubber incident light intensity distribution diagram to obtain background correction data of the rubber sample, and performing excitation light stability evaluation based on the background correction data of the rubber sample to obtain a light source stability parameter set; Specifically, the incident light intensity distribution map of the rubber can be imported into the OAS optical analysis software. The software removes background noise through a low-pass filtering algorithm. The cutoff frequency of the filter is set to 0.05 Hz. After the background noise elimination process, 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 has a high stability.

[0051] 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; Specifically, the rubber sample can be placed on the sample stage of the fluorescence spectrometer controlled by the FluorEssence operating software, and the parameters of the device can be adjusted according to the light source stability parameter set. The wavelength of the excitation light is set to 488 nm and 532 nm (consistent with the wavelength selected in step S32), the exposure time is 100 milliseconds, and the light intensity is 10 mW / cm². During the acquisition process, the device detects the fluorescence signal through 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 can display the intensity of the fluorescence signal in real time and provide data storage function. By collecting the fluorescence signals of all excitation points, the fluorescence data of the rubber molecular chain is obtained. These data are output in a table form, including the coordinates of each excitation point and the corresponding fluorescence intensity value. For example, the fluorescence intensity of excitation point 1 is 120 counts, and the fluorescence intensity of excitation point 2 is 150 counts.

[0052] Step S36: performing anisotropy calculation on the fluorescence data of the rubber molecular chain 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 the rubber material molecular chain arrangement data.

[0053] Specifically, please refer to the sub-steps of step S36 for the detailed implementation process of this embodiment.

[0054] The present invention can accurately locate the high stress area inside the material by identifying the stress concentration area. Compared with the traditional random or uniform excitation method, this excitation strategy based on stress distribution can more efficiently obtain the molecular chain information closely related to the material performance 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 change and dynamic evolution mechanism of the rubber molecular chain 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.

[0055] Preferably, 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; Specifically, the fluorescence data of the rubber molecular chain can be imported into the MATLAB software. The software provides a variety of signal enhancement algorithms. The algorithm based on wavelet transform is selected for signal-to-noise ratio enhancement. The number of layers of wavelet transform is set to 3, which is the optimal number of layers verified by many experiments and can effectively enhance the signal and reduce noise. In the software, the noise threshold is set to 0.05 counts. By running the software, the enhanced fluorescence spectrum of the rubber molecular chain is obtained. The spectrum clearly shows the change of fluorescence intensity with wavelength. For example, the peak fluorescence intensity at 488 nm and 532 nm wavelengths is 150 counts and 180 counts, respectively.

[0056] 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; 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 degree of polarization of each excitation point. The degree of polarization is calculated by the following formula: ,in, and The maximum and minimum values ​​of the fluorescence intensity are obtained. The polarization intensity distribution data are finally obtained. These data are displayed in the form of a two-dimensional graph, clearly indicating the polarization intensity of the rubber sample at different positions. For example, in some areas, the polarization intensity is higher, indicating that the molecular chains in these areas are arranged in a more orderly manner.

[0057] 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 diagram; 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 by the following formula: ;in, and are the measured values ​​of fluorescence intensity in parallel and perpendicular to the polarization direction, respectively. Set the calculation parameters, including the measurement angle range (0° to 180°) and angle interval (10°) of each excitation point. The software calculates the anisotropy index for each excitation point based on these parameters. 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, and the calculated anisotropy index is 0.2. By running the software, a molecular orientation anisotropy coefficient map is obtained. The map is displayed in the form of colored contour lines, clearly indicating the degree of molecular orientation anisotropy of the rubber sample at different locations. For example, the anisotropy coefficient of some areas is higher (close to 0.5), indicating that the molecular chains in these areas are arranged in a more orderly manner; while the anisotropy coefficient of other areas is lower (close to 0), indicating that the molecular chains in these areas are arranged more randomly.

[0058] Step S364: performing feature mapping on the molecular orientation anisotropy coefficient map to obtain a rubber molecular chain orientation parameter set; Specifically, the molecular orientation anisotropy coefficient map obtained in step S363 can first be imported into the FeatureMapper software. The software extracts features from the anisotropy coefficient map through image processing and data analysis algorithms. The researchers set the parameters for feature extraction, including the density of feature points (5 feature points are extracted per square millimeter) and the accuracy of feature extraction (0.01). The software maps the data in the anisotropy coefficient map to the feature space through interpolation and fitting algorithms. For example, the software extracts the following key feature parameters: Average anisotropy coefficient: represents the average molecular orientation anisotropy degree of the entire sample. Maximum anisotropy coefficient: represents the degree of anisotropy in the area with the most ordered molecular orientation in the sample. Standard deviation of anisotropy coefficient: represents the distribution range of anisotropy coefficient, reflecting the uniformity of molecular orientation. By running the FeatureMapper software, the rubber molecular chain orientation parameter set is obtained. These parameters are output in a tabular form, containing the specific values ​​of all the above feature parameters. For example, the average anisotropy coefficient is 0.3, the maximum anisotropy coefficient is 0.5, and the standard deviation is 0.1.

[0059] Step S365: performing molecular dynamics time-series evolution according to the rubber molecular chain orientation parameter set to obtain segment orientation dynamics data; Specifically, the rubber molecular chain orientation parameter set can be imported into the LAMMPS software. These parameters include the average anisotropy coefficient, the maximum anisotropy coefficient, and the standard deviation. The software initializes the simulation environment with 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 force field) is selected. The software calculates the position and orientation changes of the molecular chains in each time step by integrating Newton's equations of motion. The simulation temperature is set to 300 K to simulate the behavior of the rubber material at room temperature. By running the simulation, the segment orientation dynamics data are obtained, which are displayed in the form of a time series, recording the orientation angle of each molecular segment at different time points. For example, in the first 2 nanoseconds of the simulation, the orientation angle of some molecular segments changes from 0° to 30°, indicating that these segments are rapidly oriented in the initial stage; in the subsequent time, the change in orientation angle gradually slows down and tends to be stable.

[0060] Step S366: performing molecular multi-level response modeling on the rubber sample based on the segment orientation dynamics data to obtain a stress correlation diagram of the rubber microstructure; Specifically, the segment orientation dynamics data can be imported into ABAQUS software. By analyzing these data, the software constructs a multi-level model from the molecular segment to the entire rubber sample. The parameters of the model are set, including the elastic modulus (10 MPa), Poisson's ratio (0.49) and the length of the molecular segment (10 nanometers) of the rubber material. The software associates the microstructure with the macroscopic mechanical properties through the finite element method. Select an appropriate meshing strategy to divide the rubber sample into multiple tiny units, each corresponding to a molecular segment. The software generates a stress correlation diagram of the rubber microstructure by calculating the stress and strain of each unit. In the generated stress correlation diagram, the relationship between the stress distribution and the molecular segment orientation in different regions can be clearly seen. For example, in areas where the molecular segment orientation is relatively consistent, the stress distribution is relatively uniform; while in areas where the molecular segment orientation is relatively random, the stress distribution shows a higher local stress concentration.

[0061] Step S367: reconstructing the spatial distribution of the rubber microstructure stress correlation diagram to obtain the molecular chain arrangement data of the rubber material.

[0062] Specifically, the stress correlation diagram of the rubber microstructure can be imported into the MestReNova software. The software reconstructs the spatial distribution of the data in the stress correlation diagram through image processing and data analysis algorithms. Select the reconstruction algorithm based on interpolation and set the reconstruction parameters, including the spatial resolution (0.1 mm) and the selection of the reconstruction algorithm. In the software, it is also necessary to set the feature extraction parameters of the molecular chain arrangement. For example, the order threshold of the molecular chain arrangement is defined as 0.5, that is, when the orientation anisotropy coefficient of the molecular chain exceeds 0.5, the molecular chain arrangement in this area is considered to be relatively orderly. Based on these parameters, the software analyzes the molecular chain arrangement of each unit and generates molecular chain arrangement data. Finally, the molecular chain arrangement data of the rubber material is obtained, which is displayed in the form of a three-dimensional visualization diagram, clearly identifying the molecular chain arrangement of the rubber sample at different positions. For example, in some areas, the molecular chain arrangement is highly ordered and shows a clear orientation; while in other areas, the molecular chain arrangement is relatively random.

[0063] The present invention can significantly improve the quality of fluorescence signals by enhancing the signal-to-noise ratio. By measuring the polarization degree of the molecular chain state, the polarization intensity distribution of the rubber molecular chain in different directions can be accurately obtained. The polarization intensity distribution data can help researchers better understand the arrangement of the molecular chain under a complex stress environment. By calculating the molecular orientation anisotropy index, a molecular orientation anisotropy coefficient map can be generated. This can intuitively show the orientation differences of the rubber molecular chain in different regions and reveal the microstructural characteristics inside the material. The anisotropy coefficient map can not only reflect the orientation degree of the molecular chain, but also help identify stress concentration areas and potential performance weaknesses. Through feature mapping, the key orientation parameter set of the rubber molecular chain can be extracted. Through molecular dynamics time-series evolution analysis, the dynamic change process of the rubber molecular chain under stress can be revealed. The segment orientation dynamics data can provide important microscopic mechanism support for material performance prediction and life assessment. Through molecular multi-level response modeling, a correlation map between the rubber microstructure and macroscopic stress can be constructed. Through spatial distribution reconstruction, detailed arrangement data of the molecular chain of the rubber material can be generated. This can provide a comprehensive view of the internal microstructure of the material and reveal the arrangement and interaction of the molecular chains in different regions.

[0064] Preferably, step S4 comprises 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; 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 chain arrangement is 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 to clearly identify the areas that need to be thermally scanned. For example, the areas marked in red in the figure represent areas where the molecular chain arrangement is highly ordered and the stress is higher, and these areas will be scanned first.

[0065] Step S42: setting scanning parameters according to the rubber thermal imaging scanning area map to obtain rubber thermal imaging acquisition parameters; 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 are further adjusted according to the characteristics of the rubber material and the 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 scan resolution, temperature range, and detector sensitivity are recorded in the configuration file.

[0066] 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 according to the ambient temperature calibration curve to obtain a thermal field radiation correction factor diagram; Specifically, the rubber sample can be placed in the test environment of the ThermalCalibrator device. The ambient temperature range is set to -20°C to 80°C to simulate the temperature changes encountered by rubber materials in actual applications. The device records the ambient temperature in real time through a built-in high-precision temperature sensor and transmits the data to a computer connected to it. Next, the 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 corrects the thermal radiation coefficient in the thermal imaging acquisition parameters by analyzing the calibration curve. The corrected thermal radiation coefficient can more accurately reflect the thermal radiation characteristics of the rubber material at different ambient temperatures. By running the software, a thermal field radiation correction factor map is obtained. The map is displayed in the form of colored contour lines, clearly identifying the thermal radiation correction factors at different temperatures. For example, when the ambient temperature is 20°C, the thermal radiation correction factor is 1.02; and when the ambient temperature is 60°C, the thermal radiation correction factor is 1.05.

[0067] 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; Specifically, the rubber sample can be placed on the test platform of the ThermalImager device, and the thermal field radiation correction factor map can be imported into the control system of the device. The device uses an infrared detector to collect thermal images of the rubber sample according to the set acquisition parameters (such as resolution 0.1 mm and temperature range -20°C to 80°C). The acquisition frame rate is set to 10 frames per second. During the acquisition process, the device corrects each thermal image in real time according to the thermal field radiation correction factor map. The corrected thermal image data is transmitted to the computer connected to the device in real time, and the temperature gradient is reconstructed using the Thermal-Image-Analysis software. The Thermal-Image-Analysis software calculates the temperature gradient of each pixel by analyzing the corrected thermal image sequence. The resolution of the reconstruction is set to be consistent with the resolution of the thermal image acquisition (0.1 mm). The software generates a temperature field distribution map of the rubber material through interpolation and fitting algorithms. The map is displayed in the form of colored contour lines, clearly identifying the temperature distribution of the rubber sample at different locations. For example, in some areas, the temperature gradient is high, indicating that there is thermal stress concentration in these areas.

[0068] Step S45: 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, and 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.

[0069] Specifically, please refer to the sub-steps of step S45 for the detailed implementation process of this embodiment.

[0070] The present invention can determine the key areas of thermal imaging scanning according to the microstructure characteristics inside the material through regional division. By setting the scanning parameters, the thermal imaging acquisition parameters can be optimized to ensure that the collected thermal image data has high resolution and high sensitivity. By calibrating the ambient temperature and correcting the thermal radiation coefficient according to the calibration curve, the influence of ambient temperature changes on the thermal imaging data can be effectively eliminated. This can significantly improve the accuracy and reliability of the thermal imaging data and ensure that the temperature field distribution diagram truly reflects the temperature changes of the rubber material under actual working conditions. By collecting a sequence of time-series thermal images and reconstructing the temperature gradient, the temperature field changes of the rubber material during the loading process can be dynamically monitored. This can capture the transient thermal behavior of the material under transient loading and reveal the heat transfer and energy conversion process inside the material. By collecting and analyzing the sequence of time-series thermal images, the thermal stability of the material under these transient conditions can be more accurately evaluated. By thermomechanical coupling modeling, the energy dissipation mechanism of the rubber material under complex working conditions can be comprehensively evaluated. This not only takes into account the thermal properties of the material, but also combines the microstructure characteristics, and can more accurately reflect the energy conversion and loss of the material under mechanical loading and thermal environment. Through Fourier heat conduction simulation, the transient heat transfer behavior and mechanical loss characteristics of the material can be further analyzed. The resulting mechanical loss characteristic diagram of the rubber material can intuitively show the changes in the thermal and mechanical properties of the material under complex working conditions.

[0071] Preferably, 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; Specifically, the temperature field distribution diagram of the rubber material can be imported into the ABAQUS software. The software analyzes the temperature field using the finite element method and calculates the thermal stress of each unit. Set the thermal expansion coefficient of the material (1.5× 1 / K) and elastic modulus (10 MPa), which are obtained through previous experimental measurements. In the software, set the accuracy of meshing and select the same spatial resolution as the temperature field distribution map. 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 presented in the form of colored contour lines, which clearly identify 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.

[0072] 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; Specifically, the thermal stress distribution map can be imported into the ABAQUS software. The software constructs a multi-field coupling equation by analyzing the thermal stress distribution map and combining the mechanical properties parameters of the rubber material (such as elastic modulus 10 MPa, Poisson's ratio 0.49) and thermophysical parameters (such as thermal conductivity 0.15 W / m·K). Select the appropriate coupling equation form, including the heat conduction equation and the mechanical equilibrium equation, and set the coupling conditions. In the software, set the boundary conditions and initial conditions of the model. For example, set the initial temperature of the rubber sample to 20°C, and the boundary condition to an adiabatic boundary (no heat flow through). The software discretizes the multi-field coupling equations through the finite element method and generates a rubber multi-field coupling calculation model. By running the ABAQUS software, the rubber multi-field coupling calculation model is obtained. This model can simulate the complex behavior of rubber materials under multi-field coupling conditions such as heat and force. For example, in the model, it can be observed how thermal stress affects the mechanical properties of the material, and how mechanical loading in turn affects the heat conduction process.

[0073] Step S453: performing energy conversion quantization mapping on the rubber sample based on the rubber multi-field coupling calculation model to obtain a rubber thermal mechanical energy conversion rate diagram; 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 accuracy of the calculation (0.01%). The software analyzes the energy conversion process of each unit through the finite element method and calculates the efficiency of converting thermal energy into mechanical energy. For example, in some areas with high thermal stress, the energy conversion rate reaches 15%, while in areas with low thermal stress, the energy conversion rate is only 5%. By running the software, a rubber thermal mechanical energy conversion rate diagram is obtained. The diagram is displayed in the form of colored contour lines, clearly identifying the energy conversion efficiency of the rubber sample at different positions.

[0074] Step S454: locally accumulating the rubber thermal engine energy conversion rate diagram to obtain rubber material energy dissipation data; Specifically, the rubber thermal engine energy conversion rate diagram can be imported into the ABAQUS software. The software locally accumulates the energy conversion efficiency of each unit by analyzing the energy conversion rate diagram. 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 integral 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 a 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.

[0075] Step S455: constructing heat diffusion conditions according to the energy dissipation data of the rubber material to obtain a set of rubber transient heat transfer boundary conditions; Specifically, the energy dissipation data of rubber materials can be imported into ANSYS Fluent software. The software calculates the heat diffusion process by analyzing the energy dissipation data and combining the thermophysical parameters of the rubber material (such as thermal conductivity and specific heat capacity). The time range of the heat diffusion analysis is set to 0 to 10 seconds, and the time step is 0.1 seconds. The software simulates the heat diffusion process through the finite element method and generates a set of transient heat transfer boundary conditions for rubber. For example, in areas with higher thermal stress, the boundary conditions are manifested as higher heat flux density, while in areas with lower thermal stress, the heat flux density is lower.

[0076] Step S456: Perform Fourier series expansion according to the rubber transient heat transfer boundary condition set to obtain a rubber temperature field harmonic component diagram; Specifically, the transient heat transfer boundary condition set can be imported into the MATLAB software. The software converts the temperature data in the time domain into frequency domain data through Fourier transform. The frequency range of the Fourier series expansion is set to 0 to 100 Hz, and the frequency step is 1 Hz. The software generates a harmonic component diagram of the rubber temperature field by calculating the temperature harmonic components at each frequency point. For example, in some areas, the amplitude of the low-frequency (such as 10 Hz) harmonic component is high, indicating that the temperature changes in these areas are mainly caused by low-frequency heat sources; while in other areas, the amplitude of the high-frequency (such as 50 Hz) harmonic component is high, indicating that the temperature changes in these areas are more complex.

[0077] Step S457: reconstructing the energy spectrum density of the rubber temperature field harmonic component diagram to obtain the energy spectrum distribution diagram of the rubber material, and extracting the loss characteristics of the energy spectrum distribution diagram of the rubber material to obtain the mechanical loss characteristic diagram of the rubber material.

[0078] 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 at each frequency point through the trapezoidal integration method to obtain the energy spectrum distribution diagram of the rubber material. For example, at some 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, and the mechanical loss characteristic value at each frequency point is calculated. For example, the loss characteristic value can be calculated by the following formula: ; By running the software, a mechanical loss characteristic diagram of the rubber material is obtained. The diagram is displayed in the form of colored contour lines, clearly identifying the mechanical loss characteristics of the rubber material at different frequencies. For example, in some areas, the high-frequency loss characteristic values ​​are higher, indicating that these areas are more likely to dissipate energy under the action of high-frequency heat sources.

[0079] The present invention can intuitively display the thermal stress distribution inside the material through thermal stress field reconstruction. 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, and the interaction mechanism between different physical fields can be revealed. Through energy conversion quantitative mapping, the energy conversion efficiency and distribution of rubber materials under multi-field coupling conditions can be clearly displayed. Through local accumulation, the energy dissipation data of the material can be accurately calculated. This can identify the energy loss link of the material under different working conditions. By constructing a transient heat transfer boundary condition set, the heat transfer process of the rubber material in the actual working environment can be more accurately simulated. Through simulation, the temperature change and thermal stability of the material under complex working conditions can be better predicted. Through Fourier series expansion, the harmonic component diagram of the temperature field can be obtained. This can reveal the change characteristics of the temperature field at different frequencies. Through energy spectral density reconstruction and extraction of mechanical loss characteristics. This can intuitively display the change of mechanical properties of the material under multi-field coupling conditions. By analyzing the mechanical loss characteristics, the potential failure risk of the material under different working conditions can be identified.

[0080] Preferably, step S5 comprises the following steps: Step S51: performing multi-physical field coupling topological modeling on the three-dimensional structure data of the rubber material and the mechanical loss characteristic diagram of the rubber material to obtain a rubber multi-field coupling topological model; Specifically, the three-dimensional structure data and mechanical loss characteristic diagram of the rubber material 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. Set the modeling parameters, 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 models the multi-physics field behavior of the rubber material through the finite element method and generates a rubber multi-field coupling topological model. For example, in the model, you can clearly see 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.

[0081] 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; Specifically, the rubber multi-field coupling topology model can be imported into the 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 the 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 a genetic algorithm and eventually 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.

[0082] Step S53: performing cross-scale experimental data fusion verification on the multi-field coupling parameter set of the rubber material to obtain a verification index of the rubber material coupling model; Specifically, the multi-field coupling parameter set of the rubber material can be imported into the COMSOL Multiphysics software. At the same time, the experimental data of the rubber material at different scales are collected, including the molecular chain arrangement data at the microscopic scale, the mechanical loss characteristic diagram at the mesoscopic scale, and the mechanical properties test data (such as tensile strength and hardness) at the macroscopic scale. These experimental data are obtained through testing equipment (such as atomic force microscope and dynamic thermomechanical analyzer). In the software, the verification parameters and accuracy are set. For example, the error range of the verification is selected as ±5%, and the scale range of the verification is set to microscopic (10 nm), mesoscopic (1 mm) and macroscopic (100 mm). The software calculates the verification index at each scale by comparing the model prediction results with the experimental data. For example, at the microscopic scale, the matching degree between the molecular chain arrangement predicted by the model and the experimental data is 95%; at the mesoscopic scale, the error of the mechanical loss characteristic diagram is 3%; at the macroscopic scale, the prediction error of the tensile strength is 2%. By running the COMSOL Multiphysics software, the verification index of the rubber material coupling model is obtained. These indicators are output in a table to record the verification 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.

[0083] Step S54: predicting the nonlinear damage cumulative life of the rubber sample according to the rubber material coupling model verification index to obtain the nonlinear damage life spectrum of the rubber material; Specifically, the coupled model verification indicators can be imported into the FE-Safe / Rubber software. Based on these indicators, the software adjusts the key parameters in the model. The predicted operating parameters are set, including the 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 practical applications. The software simulates the damage accumulation process of rubber materials under different operating conditions by combining the Manson-Halford model with a machine learning algorithm. 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 of the rubber material, which is displayed in the form of colored contours, clearly identifying the life prediction results of the rubber material under different operating conditions. For example, in the high temperature and high stress area, the life prediction value is 1000 hours; while in the low temperature and low stress area, the life prediction value is 5000 hours. By running the FE-Safe / Rubber software, the nonlinear damage life spectrum of the rubber material is obtained.

[0084] Step S55: performing failure mode feature recognition on the nonlinear damage life spectrum of the rubber material to obtain a multi-modal failure feature matrix of the rubber material; and performing multi-dimensional performance degradation evaluation on the multi-modal failure feature matrix of the rubber material to obtain a rubber material performance evaluation report.

[0085] Specifically, the nonlinear damage life spectrum of rubber materials can be imported into the FE-Safe / Rubber software. The software identifies the main failure modes of rubber materials by analyzing the data in the life spectrum. For example, the software identifies that the main failure modes of rubber materials under high temperature and high stress conditions are thermal oxidation aging and mechanical fatigue; while under low temperature and low stress conditions, the main failure mode is chemical degradation. Next, the Endurica software is used to perform a multi-dimensional performance degradation assessment on the rubber material. Based on the identified failure modes, the software assesses the degree of performance degradation of rubber materials in different dimensions (such as mechanical properties, thermal properties, chemical stability, etc.). The parameters and accuracy of the assessment are set. For example, the assessment accuracy of mechanical performance degradation is ±2%, the assessment accuracy of thermal performance degradation is ±3%, and the assessment accuracy of chemical stability degradation is ±4%. By running the software, the multi-modal failure characteristic matrix of the rubber material is obtained, which records the failure modes and performance degradation degrees under different working conditions in detail. 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 describes in detail the performance, failure mode and life prediction results of the rubber material under different working conditions.

[0086] The present invention can construct a model that comprehensively considers the multi-field coupling effects of mechanics, heat and chemistry through multi-physical field coupling topological modeling. This can more comprehensively reflect the behavior of rubber materials under complex working conditions and reveal the interaction mechanism between different physical fields. Through the calibration of thermal-mechanical-chemical coupling parameters, 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 closer to 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 the prediction of nonlinear damage cumulative life, 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 mode of the rubber material under different working conditions can be identified. Through multi-dimensional performance degradation evaluation, the performance change of the material during the failure process can be comprehensively analyzed. The rubber material performance evaluation report finally generated can provide a comprehensive decision-making basis for the engineering application of the material.

[0087] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0088] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A performance data analysis and evaluation method for rubber materials, characterized in that: The following steps are involved: Step S1: Acquire X-ray scanning data of the 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 structure 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 collect the environmental response data of the rubber material to obtain the deformation process data of the rubber material; synchronously record and analyze the deformation process data of the rubber material to obtain the stress-strain characteristic diagram of the rubber material; Step S3: performing polarization spectrum 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; Anisotropy calculation is performed on the fluorescence data of rubber molecular chains to obtain the orientation parameter set of rubber molecular chains; dynamic evolution analysis is performed based on the orientation parameter set of rubber molecular chains to obtain the molecular chain arrangement data of rubber materials; Step S4: performing infrared thermal imaging scanning on the rubber sample to obtain a temperature field distribution diagram of the rubber material; performing thermal-mechanical 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; Perform Fourier heat conduction simulation on the energy dissipation data of rubber materials to obtain the mechanical loss characteristic diagram of rubber materials; Step S5: constructing a multi-field coupling model for the rubber sample based on the three-dimensional structure data of the rubber material and the mechanical loss characteristic diagram of the rubber material to obtain a rubber multi-field coupling topological model; 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 according to the detector background noise data to obtain detector optimization parameters; Step S14: dividing the rubber sample by rotation angle 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 projection data of the rubber sample, and performing back-projection reconstruction on the projection data of the rubber sample to obtain three-dimensional structure data of the rubber material.

3. The performance data analysis and evaluation method for rubber materials according to claim 2, characterized in that: Step S16 includes the following steps: 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 according to 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: geometrically correcting the rubber sample projection data to obtain rubber sample corrected projection data; Step S165: constructing image reconstruction parameters according to the corrected projection data of the rubber sample to obtain a rubber image reconstruction control parameter set; Step S166: back-project and reconstruct the corrected projection data of the rubber sample according to the rubber image reconstruction control parameter set to obtain three-dimensional structure data of the rubber material.

4. 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 diagram 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 the stress-strain characteristic diagram of the rubber material.

5. The performance data analysis and evaluation method for rubber materials according to claim 4, 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 the displacement field data of the deformed material; Step S254: performing strain distribution mapping on the rubber sample based on the deformation material displacement field data to obtain the rubber material strain field data, and performing stress conversion on the rubber material strain field data to obtain the rubber dynamic stress distribution data; Step S255: performing a quantitative evaluation of local stress concentration according to 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.

6. The performance data analysis and evaluation method for rubber materials according to claim 1, characterized in that: 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 dot matrix design on the rubber sample according to 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 according to 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 diagram of the rubber; Step S34: performing background noise elimination on the rubber incident light intensity distribution diagram to obtain background correction data of the rubber sample, and performing excitation light stability evaluation 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 fluorescence data of the rubber molecular chain 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 the rubber material molecular chain arrangement data.

7. The performance data analysis and evaluation method for rubber materials according to claim 6, 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 diagram; 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 stress correlation diagram of the rubber microstructure; Step S367: reconstructing the spatial distribution of the rubber microstructure stress correlation diagram to obtain the molecular chain arrangement data of the rubber material.

8. 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 according to 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 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, and 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.

9. The performance data analysis and evaluation method for rubber materials according to claim 8, 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 quantization mapping on the rubber sample based on the rubber multi-field coupling calculation model to obtain a rubber thermal mechanical energy conversion rate diagram; Step S454: locally accumulating the rubber thermal engine energy conversion rate diagram to obtain rubber material energy dissipation data; Step S455: constructing heat diffusion conditions according to the energy dissipation data of the rubber material to obtain a set of rubber transient heat transfer boundary conditions; Step S456: Perform Fourier series expansion according to 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 diagram to obtain the energy spectrum distribution diagram of the rubber material, and extracting the loss characteristics of the energy spectrum distribution diagram of the rubber material to obtain the mechanical loss characteristic diagram of the rubber material.

10. 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-physical field coupling topological modeling on the three-dimensional structure 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 the nonlinear damage life spectrum of the rubber material; Step S55: performing failure mode feature recognition on the nonlinear damage life spectrum of the rubber material to obtain a multi-modal failure feature matrix of the rubber material; and performing multi-dimensional performance degradation evaluation on the multi-modal failure feature matrix of the rubber material to obtain a rubber material performance evaluation report.

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