Rubber hysteresis heat generation regulation system and method

Abstract

This application provides a system and method for regulating the hysteretic heat generation of rubber. First, under preset dynamic thermomechanical fatigue test conditions, a correlation model is established between the rubber's loss factor and dynamic heat generation performance, and a target range for loss factor regulation is determined based on the target heat generation performance. Next, based on the target range, the internal structure of the rubber is synergistically regulated by adjusting the rubber's formulation or process to change its loss factor. Then, standard dynamic thermomechanical analysis tests are performed on the regulated rubber samples to obtain its loss factor data, and its predicted heat generation performance is evaluated based on the correlation model. Finally, for rubber samples whose predicted heat generation performance meets the requirements, dynamic thermomechanical fatigue tests are conducted under the preset fatigue test conditions, and the hysteretic heat generation performance is ultimately verified by measured temperature rise or heat generation data. The above scheme can achieve precise regulation of the hysteretic heat generation of rubber.

Description

Technical Field

[0001] This application relates to the field of rubber hysteresis heat generation technology, and more specifically, to a rubber hysteresis heat generation control system and method. Background Technology

[0002] Rubber materials are widely used in tires, shock absorbers, and seals. Their hysteresis heat generation under dynamic loads directly affects the material's durability and service life. Hysteresis heat generation refers to the phenomenon where energy dissipation caused by internal friction and viscoelastic loss during cyclic deformation of rubber is converted into heat. Excessive heat generation can accelerate material aging, fatigue failure, and even cause safety hazards.

[0003] Traditional methods for controlling rubber heat generation primarily rely on empirical formulation adjustments or limited fatigue testing. While these methods optimize parameters such as fillers and vulcanization systems through repeated experiments, they cannot achieve precise control. Although existing technologies utilize Dynamic Thermomechanical Analysis (DMA) to assess the loss factor (tanδ), DMA testing is often limited to the linear region at small strains, failing to directly predict actual heat generation performance under large strain fatigue conditions. This leads to a disconnect between control results and practical applications. Furthermore, the lack of a systematic correlation model prevents the quantification of the relationship between the loss factor and dynamic heat generation. Therefore, the inability to precisely control hysteretic heat generation in rubber remains a pressing issue for the industry. Summary of the Invention

[0004] This application provides a method and system for regulating the hysteresis heat generation of rubber, which can achieve precise regulation of the hysteresis heat generation of rubber.

[0005] In a first aspect, this application provides a method for regulating the hysteresis heat generation of rubber, comprising the following steps: Under the preset dynamic thermomechanical fatigue test conditions, a correlation model between the loss factor and dynamic heat generation performance of rubber is established, and the target range for adjusting the loss factor is determined through the correlation model based on the target heat generation performance. Based on the aforementioned control target range, the internal structure of the rubber is synergistically controlled by adjusting the rubber's formulation or process to change its loss factor. The adjusted rubber samples were subjected to standard dynamic thermomechanical analysis tests to obtain their loss factor data, and their predicted heat generation performance was evaluated based on the correlation model. For rubber samples whose predicted heat generation performance meets the requirements, dynamic thermomechanical fatigue testing is conducted under the preset fatigue test conditions, and the hysteretic heat generation performance is finally verified by the measured temperature rise or heat generation data.

[0006] In some embodiments, establishing a correlation model between the loss factor and dynamic heat generation performance of rubber under preset dynamic thermomechanical fatigue test conditions specifically includes: Under standard dynamic thermomechanical analysis test conditions, the loss factor of various rubber samples in the small strain linear viscoelastic range was determined. The same sample was subjected to fatigue temperature rise test under preset dynamic thermomechanical fatigue test conditions to obtain its actual heat generation data; Based on the loss factors of multiple samples and the corresponding actual heat generation data, a correlation model between the loss factors and heat generation performance is established through regression analysis.

[0007] In some embodiments, the standard dynamic thermomechanical analysis test conditions include: strain amplitude not exceeding 5%, test frequency of 0.5–20 Hz, temperature range of -40℃ to 100℃, test process within the linear viscoelastic response range, and instrument no-load calibration and sample mounting coaxiality correction required before the test.

[0008] In some embodiments, adjusting the rubber's formulation or process to synergistically regulate its internal structure to change its loss factor specifically includes: Adjust at least one of the following in the rubber formulation: filler type, filler dosage, vulcanization system, or plasticizer content; And / or adjust at least one of the following mixing process parameters: vulcanization temperature or vulcanization time; By making the above adjustments to change the crosslinking density of the rubber, the dispersion state of the filler, or the mobility of the molecular chains, the loss factor can be controlled and adjusted.

[0009] In some embodiments, performing standard dynamic thermomechanical analysis on the regulated rubber sample to obtain its loss factor data specifically includes: On a standard dynamic thermomechanical analyzer, a small-amplitude sinusoidal strain excitation with a preset frequency and amplitude is applied to the sample. The stress response signal generated by the sample under the strain excitation was measured simultaneously. Based on the phase difference between the stress response signal and the strain excitation signal, the curve data of the loss factor of the sample changing with frequency or temperature are calculated.

[0010] In some embodiments, performing dynamic thermomechanical fatigue testing under preset fatigue test conditions specifically includes: Rubber samples were subjected to cyclic tensile or compressive loads using a fatigue testing machine, with a strain amplitude greater than 10% and a cycle count of no less than 10. 4 Second-rate; The temperature rise changes on the surface or inside of the sample are monitored in real time using an infrared thermal imager or an embedded thermocouple. The average temperature rise or temperature rise rate during the steady-state phase is recorded as the measured heat generation performance data of the sample.

[0011] In some embodiments, after evaluating its predicted heat generation performance based on the correlation model, the method further includes: If the predicted heat generation performance does not meet the target requirements, the formula or process steps should be adjusted and re-controlled and tested until the predicted heat generation performance meets the requirements before the final fatigue verification is carried out.

[0012] Secondly, this application provides a rubber hysteresis heat generation control system, which includes: The determination module is used to establish a correlation model between the loss factor and dynamic heat generation performance of rubber under preset dynamic thermomechanical fatigue test conditions, and to determine the target range for loss factor regulation through the correlation model based on the target heat generation performance. The processing module is used to adjust the internal structure of the rubber by adjusting the formulation or process based on the control target range, so as to change its loss factor. The processing module is also used to perform standard dynamic thermomechanical analysis tests on the regulated rubber sample, obtain its loss factor data, and evaluate its predicted heat generation performance based on the correlation model. The execution module is used to perform dynamic thermomechanical fatigue testing on rubber samples whose predicted heat generation performance meets the requirements under the preset fatigue test conditions, and to finally verify their hysteretic heat generation performance through measured temperature rise or heat generation data.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described method for regulating rubber hysteresis heat generation.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for regulating rubber hysteresis heat generation.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: This application addresses the following aspects in the process of regulating hysteretic heat generation in rubber: First, by establishing a correlation model between the rubber's loss factor and dynamic heat generation performance under preset dynamic thermomechanical fatigue test conditions, and determining the target range for loss factor regulation based on the target heat generation performance, a predictive correlation from linear region testing to nonlinear fatigue heat generation is achieved. Second, by adjusting the rubber's formulation or process based on the regulation target range, its internal structure is synergistically regulated to alter its loss factor, thus precisely controlling the material's viscoelastic behavior. Third, standard dynamic thermomechanical analysis tests are performed on the regulated rubber samples to obtain loss factor data, and the predicted heat generation performance is evaluated based on the correlation model, achieving rapid iterative optimization. Finally, dynamic thermomechanical fatigue tests are conducted on rubber samples whose predicted heat generation performance meets the requirements, and the final verification is performed using measured temperature rise or heat generation data to ensure the reliability of the regulation. This approach enables precise regulation of hysteretic heat generation in rubber, providing an efficient and scientific path for optimizing low heat generation in rubber materials. Attached Figure Description

[0016] Figure 1 This is an exemplary flowchart of a rubber hysteresis heat generation control method according to some embodiments of this application; Figure 2 This is an exemplary flowchart illustrating the relationship between loss factor and heat generation performance, as shown in some embodiments of this application. Figure 3 This is a schematic diagram of the structure of a rubber hysteresis heat generation control system according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a computer device for implementing a rubber hysteresis heat generation control method according to some embodiments of this application. Detailed Implementation

[0017] To better understand the above technical solutions, a detailed explanation will be provided below with reference to the accompanying drawings and specific implementation methods. (Reference) Figure 1 The figure is an exemplary flowchart of a rubber hysteresis heat generation control method according to some embodiments of this application, which mainly includes the following steps.

[0018] In step 101, under the preset dynamic thermomechanical fatigue test conditions, a correlation model between the loss factor and dynamic heat generation performance of rubber is established, and the target range for loss factor regulation is determined through the correlation model based on the target heat generation performance.

[0019] The preset dynamic thermomechanical fatigue test conditions refer to large-strain cyclic load tests that simulate actual application scenarios, such as strain amplitude of 20%-50%, frequency of 1-10 Hz, and ambient temperature controlled within ±2℃ of the target operating temperature (e.g., 60℃ for tire applications and 50℃ for shock absorber applications). The temperature is kept constant through an environmental chamber, and the number of cycles is no less than 10. 4 Next, to evaluate the heat generation behavior of rubber under dynamic fatigue, the loss factor is a key indicator in the viscoelastic response of rubber, specifically an indicator that reflects the proportion of energy loss. Dynamic heat generation performance refers to the quantitative characteristics of how rubber materials dissipate energy and convert it into heat energy due to internal friction and viscoelastic loss under dynamic load conditions. It is used to evaluate the degree of heat generation of rubber in actual service scenarios (such as the cyclic working state of tires and shock absorbers). Its specific quantitative indicators include quantitative indicators such as temperature rise rate, steady temperature rise or heat generation density, which will not be elaborated here.

[0020] In some embodiments, reference Figure 2 As shown, the specific steps for establishing the correlation model between the loss factor and dynamic heat generation performance of rubber include...

[0021] In step 1011, under standard dynamic thermomechanical analysis (TMA) test conditions, the loss factor of various rubber samples in the low-strain linear viscoelastic range is determined. Specifically, a dynamic thermomechanical analyzer can be used, applying a sinusoidal excitation with a strain amplitude not exceeding 5%, a test frequency of 0.5–20 Hz (covering the actual application frequency), and a temperature range of -40℃ to 100℃ (to avoid thermal aging). During the test, the sample temperature is kept stable, with a heating rate of 5℃ / min, ensuring the test process is within the linear viscoelastic response range and avoiding interference from nonlinear effects. Before testing, the dynamic thermomechanical analyzer was calibrated under no-load conditions and the sample installation coaxiality was corrected. The phase difference measurement accuracy was ≤ ±0.1°. By measuring the phase difference between stress and strain, the loss factor value was calculated. The phase difference directly reflects the degree of energy loss caused by molecular chain friction and filler interaction inside the rubber. The larger the phase difference, the more significant the energy loss. By calculating the tangent of this phase difference, the loss factor of the sample can be obtained. During the test, phase difference data at different frequencies or temperatures can be continuously recorded, thereby generating curve data of the loss factor changing with frequency or temperature, providing an accurate basis for subsequent correlation of dynamic heat generation performance.

[0022] In step 1012, fatigue temperature rise tests are performed on the same sample under preset dynamic thermomechanical fatigue test conditions to obtain its actual heat generation data. Specifically, a fatigue testing machine is used to apply cyclic tensile or compressive loads with a strain amplitude greater than 10%. The temperature rise curve of the sample is monitored by an infrared thermal imager or thermocouple, and the average temperature rise in the stable phase is recorded as the heat generation data. That is, the curve is observed until the temperature rise rate is ≤0.2℃ / min and lasts for ≥10 minutes (entering the stable phase). The average temperature value of this stable phase is calculated, which is the heat generation data of the sample. This will not be elaborated further here.

[0023] In step 1013, based on the loss factors and corresponding actual heat generation data of multiple sets of samples, a correlation model between the loss factors and heat generation performance is established through regression analysis. Specifically, this can be achieved as follows: Data preparation involves collecting at least 25 sets of rubber samples with different formulations, covering key variable combinations such as filler type (2 types), filler dosage (3 levels), vulcanization system (2 types), and plasticizer content (3 levels). For each set of samples, the loss factor in the small strain linear viscoelastic range is first obtained through standard dynamic thermomechanical analysis testing, and then the corresponding actual heat generation data is obtained through dynamic thermomechanical fatigue testing, ensuring that the two data points for each set of samples correspond one-to-one, and that the parameters of the two tests remain consistent. Data preprocessing removes abnormal and missing data caused by test errors, verifies whether the test conditions for all samples are consistent, ensures the authenticity and consistency of the loss factors and heat generation data, and forms a regular paired data set. Regression fitting observes the data correlation trend, and quadratic polynomial regression or support vector machine (SVM) is preferred to establish a nonlinear correlation model (model expression example: T = a × tanδ). 2 + b×tanδ + c, where T is the average temperature rise, and a, b, and c are the fitting coefficients. In specific implementation, for example, a 5-fold cross-validation method is used to verify the model to ensure that the prediction error is ≤3%. If the data shows a significant linear trend, linear regression can be selected. With the help of statistical or programming tools, multiple sets of loss factors are fitted with corresponding heat generation data to extract the correlation between the two. Model validation is to evaluate the reliability of the model through the goodness-of-fit index. If it meets the standard, it is determined to be the final correlation model. If it does not meet the standard, sample data can be supplemented or the regression method can be changed and refitted until the model meets the requirements. For example, after the fitting is completed, the goodness-of-fit index of the model is calculated using statistical or programming tools. The index is compared with the preset standard (e.g., goodness-of-fit greater than 0.85). If the index meets the standard, it means that the model can accurately reflect the correlation between loss factors and heat generation performance and is determined to be the final correlation model. If it does not meet the standard, the data needs to be adjusted or the regression method needs to be changed and refitted until the index meets the requirements. This will not be elaborated here.

[0024] It should be noted that the above observation of the correlation trend is achieved by fitting multiple sets of loss factors with corresponding heat generation data and extracting the correlation pattern between them. This is done by organizing multiple sets of paired data of loss factors and corresponding heat generation and observing the correlation trend through visualization. The regression method can be selected as follows: if the trend is approximately linear, choose linear regression; if it is non-linear, choose multinomial regression or machine learning. By inputting data using statistical software or programming tools and performing fitting operations, the correlation between the two can be extracted. For example, when fitting data using Excel (a statistical tool), first organize five sets of paired data, such as loss factors of 0.12, 0.15, 0.18, 0.21, and 0.24, corresponding to actual heat generation (average temperature rise) of 15℃, 18℃, 21℃, 24℃, and 27℃. Input these data into Excel in two columns, select the data and insert a scatter plot. You can see that the two show a clear positive correlation trend. Click on the scatter plot to add a trend line, select the linear type, and the software will automatically fit the data, finally obtaining the correlation between heat generation and the increase of loss factor. For example, for every 0.03 increase in loss factor, the heat generation increases by 3℃. This is only an example and is not intended to limit the scope of this application.

[0025] In step 102, based on the control target range, the internal structure of the rubber is controlled by adjusting the rubber formulation or process in a coordinated manner to change its loss factor.

[0026] Internal structure regulation targets the microscopic properties of rubber, such as cross-linked networks, filler polymer interfaces, and molecular chain segment movement. In some embodiments, adjusting the rubber's formulation or process to synergistically regulate its internal structure to alter its loss factor can be achieved through the following methods: Adjust at least one of the following in the rubber formulation: filler type, filler dosage, vulcanization system, or plasticizer content. For example, replace carbon black with silane-modified silica to improve filler dispersion and reduce loss factor; replace traditional carbon black with silane-modified silica, add 3–5 parts of silane coupling agent (e.g., Si69) to improve filler dispersion, control the filler dosage at 50–55 parts, and simultaneously adjust the vulcanization system to a semi-effective vulcanization system (vulcanizing agent S / accelerator CZ / DM = 1.5 / 1.2 / 0.8) to ensure mechanical properties (tensile strength ≥ 18 MPa, tear strength ≥ 35 kN / m) while reducing loss factor; optimize the vulcanization system, use high-efficiency vulcanizing agents to control crosslinking density, and avoid increased loss due to excessive crosslinking; add plasticizers such as aromatic oils to enhance molecular chain flexibility and reduce loss factor.

[0027] In addition, at least one of the following parameters can be adjusted: mixing process parameters, vulcanization temperature, or vulcanization time. For example, extending the mixing time can improve filler dispersion; reducing the vulcanization temperature from 160°C to 140°C can form a uniform cross-linked network; and optimizing the vulcanization time can avoid under-vulcanization or over-vulcanization. The above adjustments can alter the crosslinking density of the rubber, the dispersion state of the filler, or the mobility of the molecular chains, thereby enabling controllable regulation of its loss factor, for example, reducing the target loss factor from 0.20 to 0.12.

[0028] It should be noted that changing the crosslinking density of rubber, that is, adjusting the vulcanization system (type / amount of vulcanizing agent) and vulcanization temperature / time, will directly change the number of crosslinking points between rubber molecular chains. The more crosslinking points, the more restricted the molecular chain activity, and the more predictable the changes in internal friction loss (viscoelastic loss); the fewer crosslinking points, the freer the molecular chain activity, and the more controllable the loss trend, thereby precisely adjusting the loss factor.

[0029] The change in the dispersion state of the filler is due to the adjustment of the filler type (such as carbon black vs. silane-modified silica), dosage, and mixing process (time / speed). This can improve the uniformity of the filler dispersion in the rubber matrix. When the filler agglomerates, internal friction intensifies and the loss factor increases; when it is uniformly dispersed, friction decreases and the loss factor decreases, thus achieving controllable adjustment.

[0030] The ability of molecular chains to move is altered by adjusting the plasticizer content and mixing / vulcanization parameters, which affects the flexibility of the molecular chains. Therefore, when the amount of plasticizer increases or the mixing is thorough, the molecular chains are easier to slide, and the internal friction loss is reduced. Conversely, the molecular chains become stiff, friction increases, and the loss factor changes accordingly, thus achieving the purpose of controllable regulation. This will not be elaborated further here.

[0031] In step 103, the regulated rubber sample is subjected to standard dynamic thermomechanical analysis to obtain its loss factor data, and its predicted heat generation performance is evaluated based on the correlation model.

[0032] In some embodiments, the loss factor data of the regulated rubber sample can be obtained by performing standard dynamic thermomechanical analysis tests in the following manner: On a standard dynamic thermomechanical analyzer, a small-amplitude sinusoidal strain excitation with a preset frequency and amplitude is applied to the sample, for example, a frequency of 10 Hz and a strain of 0.1%. The stress response signal generated by the sample under the strain excitation was measured simultaneously. Based on the phase difference between the stress response signal and the strain excitation signal, the curve data of the loss factor of the sample changing with frequency or temperature is calculated, such as the curve data of the loss factor changing with temperature or the curve data of the loss factor changing with frequency. The obtained loss factor is input into the correlation model to predict the heat generation performance.

[0033] In step 104, for rubber samples whose predicted heat generation performance meets the requirements, dynamic thermomechanical fatigue testing is conducted under the preset fatigue test conditions, and the hysteretic heat generation performance is finally verified by the measured temperature rise or heat generation data.

[0034] In some embodiments, dynamic thermomechanical fatigue testing under preset fatigue test conditions can be performed in the following manner: Rubber samples were subjected to cyclic tensile or compressive loads using a fatigue testing machine, with a strain amplitude greater than 10% and a cycle count of no less than 10. 4 Second-rate; The temperature rise changes on the surface or inside of the sample are monitored in real time using an infrared thermal imager or an embedded thermocouple. The average temperature rise or temperature rise rate during the steady-state phase is recorded as the measured heat generation performance data of the sample.

[0035] In some embodiments, after evaluating the predicted heat generation performance based on the correlation model, the following method can also be used: if the predicted heat generation performance does not meet the target requirements, the process steps are adjusted, and the control and testing are repeated until the predicted heat generation performance meets the requirements before the final fatigue verification is performed. That is, when the correlation model evaluation finds that the predicted heat generation performance of the adjusted sample does not meet the preset target, the process steps of adjusting the rubber formulation or process need to be returned to, the sample is re-optimized and made, and a new loss factor is obtained through standard dynamic thermomechanical analysis test. The predicted heat generation performance is evaluated again using the correlation model. This process is repeated until the predicted heat generation performance meets the requirements before the final dynamic thermomechanical fatigue test (actual temperature rise verification) is performed. This ensures that the control is accurate and efficient and avoids an ineffective final verification step.

[0036] In another aspect, in some embodiments, this application provides a rubber hysteresis heat generation control system, referring to... Figure 3 The figure is a schematic diagram of the structure of a rubber hysteresis heat generation control system according to some embodiments of this application. The rubber hysteresis heat generation control system 300 includes a determination module 301, a processing module 302 and an execution module 303, which are described below.

[0037] The determination module 301 in this application is mainly used to establish a correlation model between the loss factor and dynamic heat generation performance of rubber under preset dynamic thermomechanical fatigue test conditions, and to determine the target range of loss factor control through the correlation model based on the target heat generation performance.

[0038] Processing module 302, in this application, is mainly used to adjust the internal structure of rubber by adjusting the formulation or process based on the control target range, so as to change its loss factor.

[0039] The processing module 302 is also used to perform standard dynamic thermomechanical analysis tests on the regulated rubber sample, obtain its loss factor data, and evaluate its predicted heat generation performance based on the correlation model.

[0040] The execution module 303 in this application is mainly used to perform dynamic thermomechanical fatigue testing on rubber samples whose predicted heat generation performance meets the requirements under the preset fatigue test conditions, and to finally verify their hysteretic heat generation performance through measured temperature rise or heat generation data.

[0041] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described rubber hysteresis heat generation control method.

[0042] In some embodiments, reference Figure 4 The figure is a schematic diagram of the structure of a computer device for implementing a method for regulating the hysteresis heat generation of rubber, according to some embodiments of this application. The method in the above embodiments can be implemented through... Figure 4 The computer device 400 shown is used to implement this, and the computer device 400 includes at least one processor 401, a communication bus 402, a memory 403, and at least one communication interface 404.

[0043] The processor 401 may be a general-purpose central processing unit, an application-specific integrated circuit, or one or more devices used to control the execution of the rubber hysteresis heat generation regulation method in this application.

[0044] The communication bus 402 may include a path for transmitting information between the aforementioned components.

[0045] Memory 403 may be a read-only memory or other type of static storage device capable of storing static information and instructions, random access memory or other type of dynamic storage device capable of storing information and instructions, or an electrically erasable programmable read-only memory, a read-only optical disc or other optical disc storage, optical disk storage, magnetic disk or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. Memory 403 may exist independently and be connected to processor 401 via communication bus 402. Memory 403 may also be integrated with processor 401.

[0046] The memory 403 stores program code for executing the scheme of this application, and its execution is controlled by the processor 401. The processor 401 executes the program code stored in the memory 403. The program code may include one or more software modules. In the above embodiments, the establishment of the correlation model between the loss factor and the heat generation performance can be achieved by the processor 401 and one or more software modules in the program code in the memory 403.

[0047] Communication interface 404 is used with any transceiver or similar device to communicate with other devices or communication networks, such as Ethernet, wireless access networks, wireless LANs, etc.

[0048] In a specific implementation, as one example, computer device 400 may include multiple processors, each of which may be a single-core processor or a multi-core processor. Here, a processor may refer to one or more devices, circuits, and processing cores for processing data such as computer program instructions.

[0049] The computer device 400 described above can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device 400 can be a desktop computer, a portable computer, a network server, a handheld computer, a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device 400.

[0050] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for controlling rubber hysteresis heat generation.

[0051] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0052] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of the invention. Therefore, if these modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for regulating the hysteresis heat generation of rubber, characterized in that, Includes the following steps: Under the preset dynamic thermomechanical fatigue test conditions, a correlation model between the loss factor and dynamic heat generation performance of rubber is established, and the target range for adjusting the loss factor is determined through the correlation model based on the target heat generation performance. Based on the aforementioned control target range, the internal structure of the rubber is synergistically controlled by adjusting the rubber's formulation or process to change its loss factor. The adjusted rubber samples were subjected to standard dynamic thermomechanical analysis tests to obtain their loss factor data, and their predicted heat generation performance was evaluated based on the correlation model. For rubber samples whose predicted heat generation performance meets the requirements, dynamic thermomechanical fatigue testing is conducted under the preset fatigue test conditions, and the hysteretic heat generation performance is finally verified by the measured temperature rise or heat generation data.

2. The method according to claim 1, characterized in that, The specific steps for establishing a correlation model between the loss factor and dynamic heat generation performance of rubber under preset dynamic thermomechanical fatigue test conditions include: Under standard dynamic thermomechanical analysis test conditions, the loss factor of various rubber samples in the small strain linear viscoelastic range was determined. The same sample was subjected to fatigue temperature rise test under preset dynamic thermomechanical fatigue test conditions to obtain its actual heat generation data; Based on the loss factors of multiple samples and the corresponding actual heat generation data, a correlation model between the loss factors and heat generation performance is established through regression analysis.

3. The method according to claim 2, characterized in that, Standard dynamic thermomechanical analysis test conditions include: strain amplitude not exceeding 5%, test frequency of 0.5–20 Hz, temperature range of -40℃ to 100℃, test process within the linear viscoelastic response range, and instrument no-load calibration and sample mounting coaxiality correction required before test.

4. The method according to claim 1, characterized in that, Adjusting the formulation or process of rubber to synergistically regulate its internal structure and change its loss factor specifically includes: Adjust at least one of the following in the rubber formulation: filler type, filler dosage, vulcanization system, or plasticizer content; And / or adjust at least one of the following mixing process parameters: vulcanization temperature or vulcanization time; By making the above adjustments to change the crosslinking density of the rubber, the dispersion state of the filler, or the mobility of the molecular chains, the loss factor can be controlled and adjusted.

5. The method according to claim 1, characterized in that, Standard dynamic thermomechanical analysis was performed on the regulated rubber samples to obtain their loss factor data, specifically including: On a standard dynamic thermomechanical analyzer, a small-amplitude sinusoidal strain excitation with a preset frequency and amplitude is applied to the sample; the stress response signal generated by the sample under the strain excitation is measured simultaneously; based on the phase difference between the stress response signal and the strain excitation signal, the curve data of the loss factor of the sample changing with frequency or temperature is calculated.

6. The method according to claim 1, characterized in that, The dynamic thermomechanical fatigue test under preset fatigue test conditions specifically includes: Rubber samples were subjected to cyclic tensile or compressive loads using a fatigue testing machine, with a strain amplitude greater than 10% and a cycle count of no less than 10. 4 Second-rate; The temperature rise changes on the surface or inside of the sample are monitored in real time using an infrared thermal imager or an embedded thermocouple. The average temperature rise or temperature rise rate during the steady-state phase is recorded as the measured heat generation performance data of the sample.

7. The method according to claim 1, characterized in that, After evaluating its predicted heat generation performance based on the correlation model, it also includes: If the predicted heat generation performance does not meet the target requirements, the formula or process steps should be adjusted and re-controlled and tested until the predicted heat generation performance meets the requirements before the final fatigue verification is carried out.

8. A rubber hysteresis heat generation regulation system, characterized in that, include: The determination module is used to establish a correlation model between the loss factor and dynamic heat generation performance of rubber under preset dynamic thermomechanical fatigue test conditions, and to determine the target range for loss factor regulation through the correlation model based on the target heat generation performance. The processing module is used to adjust the internal structure of the rubber by adjusting the formulation or process based on the control target range, so as to change its loss factor. The processing module is also used to perform standard dynamic thermomechanical analysis tests on the regulated rubber sample, obtain its loss factor data, and evaluate its predicted heat generation performance based on the correlation model. The execution module is used to perform dynamic thermomechanical fatigue testing on rubber samples whose predicted heat generation performance meets the requirements under the preset fatigue test conditions, and to finally verify their hysteretic heat generation performance through measured temperature rise or heat generation data.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to retrieve the code and execute the rubber hysteresis heat generation control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the rubber hysteresis heat generation control method as described in any one of claims 1 to 7.

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