A modified white carbon black silicone rubber performance prediction and formula optimization method and system

Abstract

The application provides a modified white carbon black silicone rubber performance prediction and formula optimization method and system, the method establishes a candidate formula set containing white carbon black addition amount omega, modifier type and dispersion process parameters, obtains characteristic parameters such as dispersion uniformity coefficient D, surface silane load molar number n, mechanical property correction coefficient and electrical property correction coefficient of each candidate formula; respectively through a mechanical property prediction model and an electrical property prediction model, the prediction values of tensile strength, elongation at break, Shore hardness, volume resistivity and breakdown field strength are obtained; then the prediction values are compared with the target performance requirements, unqualified formulas are removed and the target formula is determined, and the corresponding addition amount, modifier and dispersion process parameters are output; the application can effectively reduce the repeated sample preparation and test screening workload, realize performance quantitative prediction and formula collaborative optimization, improve the formula design efficiency and the performance regulation accuracy and reliability.

Description

Technical Field

[0001] This invention relates to the field of composite material performance prediction technology, and in particular to a method and system for predicting the performance and optimizing the formulation of modified silica silicone rubber. Background Technology

[0002] Silicone rubber possesses excellent weather resistance, corona resistance, electrical insulation, and elasticity, making it widely used in high-voltage composite insulators, cable accessories, seals, and electronic and electrical insulation materials. To improve the mechanical strength and electrical stability of silicone rubber, fumed silica is typically added to the silicone rubber matrix as a reinforcing filler. Among these, fumed silica, due to its large specific surface area and significant reinforcing effect, has become a commonly used inorganic filler in silicone rubber composites.

[0003] However, silica contains a large number of hydroxyl groups on its surface, making it prone to agglomeration and limiting its compatibility with the silicone rubber matrix. In actual production, silane coupling agents are typically used to modify the surface of silica to improve its dispersion and interfacial bonding properties. Different types of modifiers, surface silane loading, silica addition amounts, and mixing and dispersion processes all affect the tensile strength, elongation at break, Shore hardness, volume resistivity, and breakdown field strength of silicone rubber composites.

[0004] The formulation design of existing silicone rubber composite materials largely relies on empirical experiments. The common practice is to prepare samples by changing the amount of silica added, the type of modifier, or the mixing process, and then determine whether the formulation meets the requirements through mechanical and electrical property tests. While this method can obtain experimental results, it requires repeated sampling and testing, resulting in a long cycle and high cost. Furthermore, the various factors have coupled influences, making it difficult to accurately determine the contribution of silica addition amount, dispersion uniformity, surface modification degree, and modifier type to the overall material performance based solely on single-factor experiments.

[0005] Furthermore, existing methods often focus only on one type of performance index, such as evaluating only the mechanical reinforcement effect or only examining changes in insulation performance, lacking quantitative methods that integrate mechanical and electrical properties into the formulation optimization process. For applications such as high-voltage composite insulators, silicone rubber materials not only need sufficient tensile strength and hardness but also need to maintain high volume resistivity and breakdown field strength. Without models capable of simultaneously predicting multiple performance indicators, formulation optimization can easily lead to problems such as improved mechanical properties at the expense of decreased electrical properties, or electrical properties meeting requirements but with excessively high processing costs and filler dosages.

[0006] Therefore, there is an urgent need for a method and system for predicting the performance and optimizing the formulation of modified silica silicone rubber, which can achieve quantitative prediction of the performance of modified silica silicone rubber and synergistic optimization of the formulation, thereby reducing repeated experiments and improving the efficiency of formulation design and the accuracy of performance control. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for predicting the performance and optimizing the formulation of modified silica silicone rubber, aiming to solve the technical problems of existing silicone rubber formulations relying on trial and error, and being difficult to quantitatively predict and synergistically optimize mechanical and electrical properties.

[0008] To achieve the above objectives, in a first aspect, the present invention provides a method for predicting the properties and optimizing the formulation of modified silica silicone rubber, comprising:

[0009] S1. Establish a set of candidate formulations, wherein the candidate formulations include the amount of silica added (ω), the type of modifier, and the dispersion process parameters;

[0010] S2. Obtain the characteristic parameters corresponding to each candidate formulation. The characteristic parameters include the amount of silica added ω, the dispersion uniformity coefficient D, the number of moles of surface silane loading n, and the mechanical property correction coefficient. and electrical performance correction factor ;

[0011] S3, based on ω, D, n and Tensile strength, elongation at break, and Shore hardness predictions were obtained through a mechanical property prediction model; based on ω, D, n, and The predicted values ​​of volume resistivity and breakdown field strength are obtained through the electrical performance prediction model;

[0012] S4. Compare each predicted value with the target performance requirements, eliminate candidate formulations that do not meet the target performance requirements, determine the target formulation from the remaining candidate formulations, and output the amount of silica added, type of modifier and dispersion process parameters corresponding to the target formulation.

[0013] The mechanical property prediction model and the electrical property prediction model both include a quadratic term of the amount of silica added ω, a linear term of the number of moles of surface silane loading n, and a correction term that is the product of the dispersion uniformity coefficient D and the correction coefficient.

[0014] As a further improvement to the above technical solution, the amount of silica added ω is the mass fraction of silica relative to the silicone rubber matrix, in phr, and the value ranges from 0 to 50 phr; in the mechanical property prediction model and the electrical property prediction model, the amount of silica added ω is denoted as w.

[0015] As a further improvement to the above technical solution, the dispersion uniformity coefficient D is obtained through scanning electron microscopy image analysis, specifically including: acquiring scanning electron microscopy images of the cross-section of the silicone rubber composite material, identifying silica agglomerates in the images, statistically analyzing the equivalent particle size of the silica agglomerates, and calculating the average value d and standard deviation σ of the equivalent particle size of the silica agglomerates. d The dispersion uniformity coefficient D is calculated according to the following formula:

[0016] D = 1 - CV;

[0017] CV=σ d / d;

[0018] Wherein, CV is the coefficient of variation of the equivalent particle size of silica agglomerates, 0 < D ≤ 1.

[0019] As a further improvement to the above technical solution, the molar number n of surface silane loading is obtained by analyzing the surface elements of modified silica using X-ray photoelectron spectroscopy, with the unit being mmol / g.

[0020] As a further improvement to the above technical solution, the mechanical property correction coefficient The standard is determined based on unmodified silica-silicone rubber composites with the same silica addition amount, silicone rubber matrix, and preparation process conditions, and calculated according to the following formula:

[0021] =Tensile strength of modified system / Tensile strength of unmodified system with the same amount of additive.

[0022] As a further improvement to the above technical solution, the electrical performance correction coefficient The standard is determined based on unmodified silica-silicone rubber composites with the same silica addition amount, silicone rubber matrix, and preparation process conditions, and calculated according to the following formula:

[0023] = Volume resistivity of modified system / Volume resistivity of unmodified system with the same amount of additive.

[0024] As a further improvement to the above technical solution, the mechanical performance prediction model includes the following formula:

[0025] ;

[0026] ;

[0027] ;

[0028] in, This is a predicted tensile strength value, in MPa. 1. Elongation at break is the predicted value, in %; H is the predicted value of Shore hardness, in Shore A; w is the amount of silica added, ω.

[0029] It should be noted that the mechanical property prediction model is obtained by fitting experimental data, and The quadratic function coefficients (-0.0011, 0.145, 0.42) and the weighting coefficient (0.35) in the formula were obtained by fitting experimental data.

[0030] In the formula, 354 represents the elongation at break of pure silicone rubber (%, experimental baseline value); 3.15 represents the effect of the addition amount on the... The weakening coefficient; 5.2 is the interface combination. The adjustment coefficient was obtained by fitting experimental data.

[0031] In the formula, 40 represents the Shore A hardness of pure silicone rubber (experimental baseline value); 0.52 represents the contribution coefficient of the additive amount; and 8.3 represents the contribution coefficient of interfacial bonding. This was obtained through fitting experimental data.

[0032] As a further improvement to the above technical solution, the electrical performance prediction model includes the following formula:

[0033] ;

[0034] ;

[0035] in, This is the predicted volume resistivity, in Ω·m. The breakdown field strength is the predicted value, in kV / mm; w is the amount of silica added (ω).

[0036] It should be noted that the electrical performance prediction model was obtained by fitting experimental data, and The coefficients of the quadratic function in the formula are (-0.003, 0.18, 1.2 × 10⁻⁶). 12 ) and weighting coefficient (1.5×10 12 This was obtained by fitting experimental data; In the formula, 15.77 is the breakdown field strength of pure silicone rubber (kV / mm, experimental baseline value); the quadratic function coefficients (-0.0008, 0.12) are obtained through data fitting; and 2.8 is the contribution coefficient of interfacial bonding.

[0037] As a further improvement to the above technical solution, the modifier includes at least one of RH151, RH171, KH550, KH560, or KH570; when the candidate formulation uses unmodified silica, the mechanical property correction factor is... and electrical performance correction factor All are set to 1.

[0038] As a further improvement to the above technical solution, the candidate formulation set includes multiple candidate formulations formed by different combinations of silica addition amounts, different types of modifiers, and different dispersion process parameters.

[0039] As a further improvement to the above technical solution, the dispersion process parameters include at least one of the following: mixing method, mixing temperature, mixing time, and stirring speed; the dispersion uniformity coefficient D is changed by adjusting the dispersion process parameters.

[0040] As a further improvement to the above technical solution, the target performance requirements include at least one of the following: tensile strength threshold, elongation at break threshold, Shore hardness threshold, volume resistivity threshold, and breakdown field strength threshold.

[0041] Preferably, the tensile strength is not less than 4.5 MPa and the volume resistivity is not less than 1.8 × 10⁻⁶. 13 Ω·m, breakdown field strength not less than 28kV / mm.

[0042] As a further improvement to the above technical solution, determining the target formulation from the remaining candidate formulations includes: calculating the comprehensive evaluation value of each remaining candidate formulation, screening candidate formulations in descending order of comprehensive evaluation value; when the comprehensive evaluation values ​​are the same or the difference is lower than the preset difference, selecting the candidate formulation with the lower amount of silica ω as the target formulation.

[0043] As a further improvement to the above technical solution, the comprehensive evaluation value is calculated by weighting the predicted tensile strength, predicted elongation at break, predicted Shore hardness, predicted volume resistivity, and predicted breakdown field strength.

[0044] As a further improvement to the above technical solution, when the predicted value of any candidate formulation fails to meet the corresponding target performance requirements, the amount of silica added (ω), the type of modifier, or the dispersion process parameters are adjusted, and the corresponding dispersion uniformity coefficient (D), surface silane loading molar number (n), and mechanical property correction coefficient are re-obtained. and electrical performance correction factor Then, performance prediction was performed again.

[0045] As a further improvement to the above technical solution, the modified silica silicone rubber composite material is a silicone rubber material for high-voltage composite insulators.

[0046] Secondly, the present invention also provides a system for predicting the performance and optimizing the formulation of modified silica silicone rubber, comprising:

[0047] The candidate formulation establishment module is used to establish a set of candidate formulations, which include the amount of silica added (ω), the type of modifier, and the dispersion process parameters.

[0048] The characteristic parameter acquisition module is used to obtain the amount of silica added (ω), dispersion uniformity coefficient (D), surface silane loading molar number (n), and mechanical property correction coefficient for each candidate formulation. and electrical performance correction factor ;

[0049] Performance prediction module, used for prediction based on ω, D, n and Predicted values ​​of tensile strength, elongation at break, and Shore hardness were obtained, based on ω, D, n, and The predicted values ​​of volume resistivity and breakdown field strength are obtained;

[0050] The formulation screening module is used to compare each predicted value with the target performance requirements and eliminate candidate formulations that do not meet the target performance requirements.

[0051] The formulation output module is used to determine the target formulation from the remaining candidate formulations and output the amount of silica added, the type of modifier, and the dispersion process parameters corresponding to the target formulation.

[0052] As a further improvement to the above technical solution, the characteristic parameter acquisition module includes an image analysis unit and a surface element analysis data processing unit; the image analysis unit is used to identify silica agglomerates based on the scanning electron microscope image of the cross-section of the silicone rubber composite material and to calculate the dispersion uniformity coefficient D; the surface element analysis data processing unit is used to determine the number of moles of surface silane loading n based on the modified silica surface element analysis data obtained by X-ray photoelectron spectroscopy.

[0053] As a further improvement to the above technical solution, the performance prediction module includes a mechanical performance prediction unit and an electrical performance prediction unit.

[0054] The mechanical property prediction unit uses the following formula for prediction:

[0055] ;

[0056] ;

[0057] ;

[0058] The electrical performance prediction unit uses the following formula for prediction:

[0059] ;

[0060] .

[0061] Thirdly, the present invention also provides a computer-readable storage medium, including a computer program stored in the computer-readable storage medium, wherein the computer program, when executed by a processor, performs the steps of the method for predicting the performance and optimizing the formulation of modified silica silicone rubber as described in the first aspect.

[0062] Because the present invention adopts the above technical solutions, the beneficial effects of the present invention are as follows:

[0063] This invention provides a method for predicting the properties and optimizing the formulation of modified silica silicone rubber. The method establishes a set of candidate formulations based on the silica addition amount ω, the type of modifier, and dispersion process parameters, and then performs unified prediction and screening of these candidate formulations. Since candidate formulations can be preliminarily eliminated based on the prediction results before sample preparation, the workload of formula screening relying on reverse sampling and testing can be reduced, thus improving the efficiency of silicone rubber composite material formulation design.

[0064] Specifically, this invention relates to the following parameters: silica addition amount ω, dispersion uniformity coefficient D, surface silane loading molar number n, and mechanical property correction coefficient. and electrical performance correction factor As characteristic parameters, ω reflects the filler dosage, D reflects the dispersion state of silica in the silicone rubber matrix, and n reflects the degree of silane grafting on the silica surface. and These parameters respectively reflect the effects of the modified system on mechanical and electrical properties. By combining these parameters, the influence of modified silica on the properties of silicone rubber can be transformed from empirical judgment to quantitative calculation, facilitating comparisons between different formulations.

[0065] This invention establishes separate prediction models for mechanical and electrical properties to predict tensile strength, elongation at break, Shore hardness, volume resistivity, and breakdown field strength, and compares the predicted values ​​with the target performance requirements. Because the screening process considers both mechanical and electrical properties simultaneously, it avoids the problem of insufficient performance in other areas due to optimization focusing on only a single indicator, thus facilitating the synergistic control of the comprehensive performance of silicone rubber composite materials.

[0066] The predictive model of this invention incorporates a quadratic term for the amount of silica added (ω), a linear term for the number of moles of surface silane loading (n), and a correction term that is the product of the dispersion uniformity coefficient (D) and a correction coefficient. The quadratic term for the amount of silica added reflects the coexisting reinforcing effect and agglomeration effect after the filler content increases; the number of moles of surface silane loading reflects the degree of interfacial modification; and the dispersion uniformity coefficient and the correction coefficient together reflect the influence of dispersion state and modification type on material properties. Therefore, this model can better reflect the performance variation law of modified silica-filled silicone rubber composites.

[0067] This invention, after eliminating candidate formulations that do not meet the target performance requirements, determines the target formulation from the remaining candidate formulations and outputs the corresponding amount of silica to be added, type of modifier, and dispersion process parameters. Therefore, the optimization results not only provide performance evaluation conclusions but also directly form the formulation and process basis required for subsequent preparation, facilitating implementation in actual production or experimental verification. Attached Figure Description

[0068] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0069] Figure 1 This is a flowchart illustrating a method for predicting the performance and optimizing the formulation of modified silica silicone rubber disclosed in this invention.

[0070] The realization of the objective, functional characteristics and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] It should be noted that the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0073] Example 1

[0074] See Figure 1 This embodiment provides a method for predicting the performance and optimizing the formulation of modified silica silicone rubber. This invention provides a method for predicting the mechanical and electrical properties of different candidate formulations before preparing silicone rubber composite materials, and for screening target formulations according to target performance requirements.

[0075] Specifically, the method includes the following steps.

[0076] S1. Establish a candidate formula set:

[0077] Based on the application requirements of silicone rubber composites, a set of candidate formulations was established. Each candidate formulation includes the amount of silica added (ω), the type of modifier, and the dispersion process parameters.

[0078] Wherein, the amount of silica added, ω, represents the mass fraction of silica relative to the silicone rubber matrix, expressed in phr, with a preferred value range of 0–50 phr. The modifier can be one of RH151, RH171, KH550, KH560, or KH570, or unmodified silica. Dispersion process parameters may include one or more of the following: mixing method, mixing temperature, mixing time, and stirring speed.

[0079] By pre-establishing a set of candidate formulations, subsequent optimization can be limited to a range of prepareable and comparable formulations, avoiding aimless and repeated adjustments to the formulation.

[0080] S2. Obtain the characteristic parameters corresponding to each candidate formula:

[0081] For each candidate formulation, its corresponding characteristic parameters are obtained, including the amount of silica added ω, the dispersion uniformity coefficient D, the number of moles of surface silane loading n, and the mechanical property correction coefficient. and electrical performance correction factor .

[0082] The dispersion uniformity coefficient D is used to characterize the dispersion uniformity of silica in the silicone rubber matrix. Specifically, this can be achieved by observing the cross-section of the silicone rubber composite material using a scanning electron microscope, identifying silica agglomerates in the image, statistically analyzing the equivalent particle size of the agglomerates, and calculating the average particle size d and standard deviation σ. d The dispersion uniformity coefficient D is calculated according to the following formula:

[0083] D = 1 - CV;

[0084] CV=σ d / d;

[0085] In the formula, D is the dispersion uniformity coefficient, 0 < D ≤ 1; CV is the coefficient of variation of the equivalent particle size of the aggregates; σ d denoted by D, the standard deviation of the equivalent particle size of silica agglomerates; and d, the average equivalent particle size of silica agglomerates. A larger D value indicates a more uniform particle size distribution and better dispersion of silica in the silicone rubber matrix. Since silica agglomerates can form interfacial defects or localized stress concentration areas, introducing D reflects the impact of the dispersion process on material properties.

[0086] The molar number of silane loadings on the surface, *n*, is used to characterize the amount of silane coupling agent grafted onto the surface of silica, expressed in mmol / g. This parameter is obtained by analyzing the elemental composition of the modified silica surface using X-ray photoelectron spectroscopy. The *n* value reflects the degree of surface modification of silica; a higher silane loading generally results in stronger interfacial bonding between silica and the silicone rubber matrix. Therefore, incorporating *n* into the prediction model is beneficial for characterizing the impact of interfacial modification on macroscopic properties.

[0087] Mechanical property correction factor and electrical performance correction factor Used to characterize the performance changes of different modified systems relative to the unmodified system. Specifically, It can be determined according to the ratio of the tensile strength of the modified system to the tensile strength of the unmodified system with the same amount of additive. The volume resistivity can be determined by the ratio of the volume resistivity of the modified system to the volume resistivity of the unmodified system with the same amount of added material. When using unmodified silica, and All are taken as 1. By distinguishing and This can reflect the different effects of the same modifier on mechanical and electrical properties, avoiding the simplistic equating of modification effects.

[0088] S3. Establish and invoke the performance prediction model:

[0089] Based on the above characteristic parameters, mechanical property prediction models and electrical property prediction models are established respectively. The mechanical property prediction model is used to predict tensile strength, elongation at break, and Shore hardness; the electrical property prediction model is used to predict volume resistivity and breakdown field strength.

[0090] S4. Select the target formulation based on the target performance requirements:

[0091] The predicted values ​​of tensile strength, elongation at break, Shore hardness, volume resistivity, and breakdown field strength of each candidate formulation are compared with the preset target performance requirements, and candidate formulations that do not meet the target performance requirements are eliminated.

[0092] For the remaining candidate formulations, the target formulation can be further determined based on overall performance, silica addition amount, or cost requirements. For example, when multiple candidate formulations meet the target performance requirements, the formulation with a lower silica addition amount can be prioritized; or the formulation with superior overall performance can be determined based on the weighted evaluation results of tensile strength, elongation at break, Shore hardness, volume resistivity, and breakdown field strength. The final output includes the silica addition amount, modifier type, and dispersion process parameters corresponding to the target formulation.

[0093] Through the above processing, this embodiment can not only provide performance prediction results for each candidate formulation, but also directly convert the prediction results into formulation screening criteria, thereby reducing invalid sample preparation and repeated testing, and improving formulation design efficiency. For silicone rubber materials that need to simultaneously meet mechanical and electrical performance requirements, this method can avoid the problem of insufficient performance of other indicators due to optimization of only a single index, which is conducive to achieving synergistic regulation of the performance of modified silica silicone rubber.

[0094] As a preferred embodiment, the mechanical property correction factor This is used to characterize the effect of modified silica on the mechanical properties of silicone rubber composites compared to unmodified silica. To ensure comparability between different modified systems, The standard was determined based on unmodified silica-silicone rubber composites with the same amount of silica added, the same silicone rubber matrix, and the same preparation process conditions.

[0095] Specifically, modified silica-silicone rubber composites and corresponding unmodified silica-silicone rubber composites were prepared, with consistent silica addition, silicone rubber matrix type, mixing conditions, vulcanization conditions, and testing conditions for both. The tensile strength of both materials was then tested, and the mechanical property correction factor was calculated using the following formula. :

[0096] =Tensile strength of modified system / Tensile strength of unmodified system with the same amount of additive.

[0097] In the formula, , where is the mechanical property correction factor; the tensile strength of the modified system is the tensile strength of the silicone rubber composite material prepared with modified silica; the tensile strength of the unmodified system with the same addition amount is the tensile strength of the silicone rubber composite material prepared with unmodified silica under the same silica addition amount, the same silicone rubber matrix, and the same preparation process conditions.

[0098] Determined through the above methods This allows the influence of different silane modifiers on mechanical properties to be transformed into calculable correction parameters. Since these parameters are based on the unmodified system, they reduce the interference of silica addition, matrix differences, and preparation process variations on the comparison results, thus more directly reflecting the impact of modification treatment on interfacial bonding and mechanical reinforcement. Furthermore, [the following text is incomplete and requires further context to translate accurately]. By introducing a mechanical property prediction model, the prediction of tensile strength, elongation at break, and Shore hardness can not only take into account the amount of silica added, dispersion uniformity, and surface silane loading, but also reflect the differences in mechanical properties brought about by different types of modifiers, which is conducive to improving the targeting of formulation screening.

[0099] As a preferred embodiment, the electrical performance correction factor This was used to characterize the effect of modified silica on the electrical properties of silicone rubber composites compared to unmodified silica. To ensure comparability between different modified systems, The standard was determined based on unmodified silica-silicone rubber composites with the same amount of silica added, the same silicone rubber matrix, and the same preparation process conditions.

[0100] Specifically, modified silica-silicone rubber composites and corresponding unmodified silica-silicone rubber composites were prepared, with consistent silica addition, silicone rubber matrix type, mixing conditions, vulcanization conditions, and testing conditions for both. The volume resistivity of both materials was then measured, and the electrical performance correction factor was calculated using the following formula. :

[0101] = Volume resistivity of modified system / Volume resistivity of unmodified system with the same amount of additive.

[0102] In the formula, , where is the electrical performance correction factor; the volume resistivity of the modified system is the volume resistivity of the silicone rubber composite material prepared with modified silica; the volume resistivity of the unmodified system with the same addition amount is the volume resistivity of the silicone rubber composite material prepared with unmodified silica under the same silica addition amount, the same silicone rubber matrix, and the same preparation process conditions.

[0103] Determined through the above methods This method can transform the effects of different modifiers on electrical properties into calculable correction parameters. Since this parameter uses the unmodified system as a baseline, it reduces the influence of filler addition, matrix type, and preparation process differences on the comparison results, thus more directly reflecting the effects of modification treatment on interface defects, surface polarity, and insulation properties. The original document describes the dispersion uniformity coefficient D and the electrical correction coefficient. The larger the value, the fewer the interface defects and the better the insulation performance; at the same time, different modifiers correspond to... The differences can be used to distinguish the degree to which the modified system improves the volume resistivity.

[0104] Furthermore, By introducing an electrical performance prediction model, the prediction of volume resistivity and breakdown field strength can consider not only the amount of silica added, surface silane loading, and dispersion state, but also the influence of different types of modifiers on electrical properties. Thus, when screening candidate formulations, the modified system that meets the target electrical performance requirements can be prioritized based on the prediction results. This helps reduce the need for reverse sampling tests and improves the targeted control of the electrical properties of modified silica silicone rubber.

[0105] As a preferred embodiment, the following parameters are obtained: silica addition amount ω, dispersion uniformity coefficient D, surface silane loading molar number n, and mechanical property correction coefficient. and electrical performance correction factor Then, the above parameters are substituted into the mechanical property prediction model and the electrical property prediction model respectively to calculate the predicted mechanical property value and the predicted electrical property value corresponding to the candidate formulation.

[0106] The mechanical property prediction model includes a tensile strength prediction model, a breakage elongation prediction model, and a Shore hardness prediction model, specifically:

[0107] ;

[0108] ;

[0109] ;

[0110] in, This is a predicted tensile strength value, in MPa. 1. Elongation at break is the predicted value, in %; H is the predicted value of Shore hardness, in Shore A; w is the symbol for the amount of silica added ω in the formula, in phr; n is the number of moles of surface silane loading, used to characterize the amount of silane coupling agent grafted onto the silica surface.

[0111] In the tensile strength prediction model described above, the quadratic term of w reflects the effect of silica addition on the reinforcing effect. Appropriate addition of silica can enhance the reinforcing effect of the filler, while excessive addition can easily lead to agglomeration, resulting in a decrease in effective reinforcing efficiency. Therefore, a quadratic term is used to describe this trend. The linear term of n reflects the effect of surface silane loading on interfacial bonding. As a product correction term, it is used to comprehensively reflect the influence of the dispersion state of silica and the type of modifier on tensile strength. Therefore, tensile strength prediction no longer depends solely on the filler dosage, but can also consider the combined effects of interfacial bonding and dispersion state.

[0112] In the elongation at break prediction model, the coefficients corresponding to w and n are negative, reflecting the restrictive effect of rigid filler addition and interfacial bonding reinforcement on the movement of silicone rubber molecular chains. Subsequently, the effects of dispersion improvement and interface optimization on material flexibility can be incorporated into the calculation. When the silica is dispersed relatively uniformly and the modification effect is good, the adverse effects of local agglomeration on elongation at break can be mitigated to some extent.

[0113] In the Shore hardness prediction model, the linear term of w reflects the reinforcing effect of the silica filler network on the material's rigidity, while the linear term of n reflects the influence of interfacial bonding enhancement and system densification after surface modification on hardness. This is achieved by using D and... Introducing a model allows hardness prediction to simultaneously consider differences in filler addition amount, dispersion state, and modification type, thus facilitating quantitative comparisons between candidate formulations.

[0114] In one embodiment, the electrical performance prediction model includes a volume resistivity prediction model and a breakdown field strength prediction model, specifically:

[0115] ;

[0116] ;

[0117] in, This is the predicted volume resistivity, in Ω·m. The breakdown field strength is the predicted value, in kV / mm; w is the amount of silica added (ω).

[0118] In the volume resistivity prediction model, the quadratic term of w is used to characterize the nonlinear effect of silica addition on insulation performance. An appropriate amount of silica helps fill matrix defects and hinders carrier migration, while excessive silica may agglomerate and form interface defects, thus weakening insulation performance. The linear term of n is used to characterize the effect of surface silane loading on interfacial polarity and hygroscopicity. It is used to comprehensively reflect the effect of dispersion state and modification type on volume resistivity.

[0119] In the breakdown field strength prediction model, the quadratic term of w reflects the effect of silica addition on breakdown resistance. Appropriate filler content can hinder electrical tree development, while excessive filler agglomeration may form localized defects and concentrated electric field regions, reducing the breakdown field strength. The linear term of n characterizes how surface silane loading improves interfacial bonding and reduces the impact of defects on breakdown resistance. It is used to reflect the combined effect of dispersion state and modification type on breakdown field strength.

[0120] The quadratic function coefficients and weighting coefficients in the above models can be obtained by fitting experimental data. By incorporating the amount of silica added, the number of moles of surface silane loading, the dispersion uniformity coefficient, and the modification correction coefficient into the same prediction framework, the mechanical and electrical properties of different candidate formulations can be calculated and compared before sample preparation. This reduces the workload of relying solely on repeated experiments to screen formulations and also helps to determine a more reasonable amount of silica added, type of modifier, and dispersion process parameters based on meeting multiple performance requirements such as tensile strength, elongation at break, Shore hardness, volume resistivity, and breakdown field strength.

[0121] When the candidate formulation uses unmodified silica, the mechanical property correction factor is... and electrical performance correction factor All values ​​are set to 1. The unmodified silica system serves as the benchmark for comparison with the modified system, maintaining consistent silica addition, silicone rubber matrix type, mixing method, vulcanization conditions, and testing conditions. Therefore, in subsequent performance prediction and formulation comparison, the unmodified system does not introduce additional modification corrections, facilitating a direct reflection of the impact of silica addition and dispersion state on the properties of silicone rubber composites. The original document also states that the modification type correction coefficient K is based on the unmodified silica system, with K=1 representing the unmodified system. K includes the mechanical property correction coefficient. and electrical performance correction factor .

[0122] In a preferred embodiment, the candidate formulation set includes multiple candidate formulations formed by combinations of different silica addition amounts, different types of modifiers, and different dispersion process parameters. Silica addition amount ω represents the mass fraction of silica relative to the silicone rubber matrix, expressed in phr; the type of modifier may include one of RH151, RH171, KH550, KH560, and KH570, or unmodified silica; the dispersion process parameters may include at least one of mixing method, mixing temperature, mixing time, and stirring speed. Combining the above factors to form a candidate formulation set facilitates simultaneous examination of the effects of filler dosage, surface modification, and process conditions on material properties during the formulation screening stage.

[0123] In practical implementation, the range of silica addition, the type of selectable modifier, and the dispersion process conditions can be pre-set according to the target application scenario. For example, multiple silica addition levels can be set within the range of 0–50 phr, one or more silane modifiers can be selected, and mechanical mixing, masterbatch method, or other feasible mixing methods can be used as dispersion process conditions to form multiple candidate formulations. Subsequently, for each candidate formulation, the dispersion uniformity coefficient D, the surface silane loading molar number n, and the mechanical property correction coefficient can be obtained. and electrical performance correction factor The parameters are then substituted into the corresponding prediction model for performance calculation. This method allows for initial calculation and screening within the candidate formulation set, followed by sample verification of a small number of formulations that meet the requirements, thereby reducing invalid experiments.

[0124] In one embodiment, the dispersion process parameters include at least one of the following: mixing method, mixing temperature, mixing time, and stirring speed. Changes in these dispersion process parameters affect the agglomeration state and distribution uniformity of silica in the silicone rubber matrix, thereby altering the dispersion uniformity coefficient D. By mapping the dispersion process parameters to the dispersion uniformity coefficient D, the impact of the dispersion process on performance can be calculated using a predictive model, rather than relying solely on empirical judgment. Since silica agglomeration reduces the effective reinforcing area and may introduce interface defects and localized areas of uneven electric field, adjusting the mixing method, mixing temperature, mixing time, or stirring speed to increase the D value during candidate formulation screening provides a process basis for improving mechanical and electrical properties. This facilitates the determination of modifiers and their amounts, as well as the identification of dispersion process parameters that match the formulation, thereby achieving synergistic optimization of the formulation and process.

[0125] In a preferred embodiment, the target performance requirements are pre-set based on the actual application scenarios of the silicone rubber composite material. The target performance requirements include at least one of the following: tensile strength threshold, elongation at break threshold, Shore hardness threshold, volume resistivity threshold, and breakdown field strength threshold. That is, under different application conditions, only some of these indicators can be selected as screening criteria, or multiple indicators can be selected simultaneously as comprehensive screening criteria.

[0126] Specifically, after completing the performance prediction of each candidate formulation, the predicted tensile strength value σ is... d Predicted value of elongation at break Shore hardness prediction value H, volume resistivity prediction value and the predicted value of the breakdown field strength Each candidate formulation is compared with its corresponding target performance threshold. If at least one of the required performance indicators fails to meet the corresponding threshold, the candidate formulation is eliminated; if all selected performance indicators meet the corresponding threshold, the candidate formulation is retained for subsequent target formulation determination.

[0127] In a preferred embodiment, when the modified silica-silicone rubber composite material is used in high-voltage composite insulators, the target performance requirements can be set as follows: tensile strength not less than 4.5 MPa, and volume resistivity not less than 1.8 × 10⁻⁶. 13 The breakdown electric field strength is not less than 28 kV / mm, and the Ω·m is constant. This set of indicators can simultaneously constrain the mechanical load-bearing capacity and electrical insulation properties of materials, avoiding the problem of insufficient electrical properties due to only improving mechanical properties, or insufficient material strength due to only meeting electrical properties. In the original document examples, these performance requirements were also used as the basis for formula optimization, comparing candidate formulas under different amounts of silica and dispersion processes.

[0128] By setting the aforementioned target performance requirements, this embodiment can directly convert performance prediction results into formulation screening conditions. Candidate formulations no longer rely solely on empirical judgment, but are selected based on predicted indicators such as tensile strength, volume resistivity, and breakdown field strength. This not only eliminates formulations that clearly do not meet the requirements before sample preparation, reducing repeated experiments, but also facilitates adjustments to screening indicators according to different application scenarios, improving the targeted nature of modified silica silicone rubber formulation optimization.

[0129] In a preferred embodiment, after the performance prediction of the candidate formulations is completed, the predicted values ​​of tensile strength, elongation at break, Shore hardness, volume resistivity, and breakdown field strength of each candidate formulation are compared with the corresponding target performance requirements. Candidate formulations that do not meet the target performance requirements are eliminated; candidate formulations that meet the target performance requirements are included as remaining candidate formulations in the subsequent evaluation process.

[0130] Specifically, a comprehensive evaluation value can be calculated based on the predicted performance of each remaining candidate formulation, and the remaining candidate formulations can be sorted in descending order of comprehensive evaluation value. The comprehensive evaluation value is used to characterize the degree to which the candidate formulation comprehensively meets the target performance requirements, and can be obtained by weighted calculation of predicted tensile strength, predicted elongation at break, predicted Shore hardness, predicted volume resistivity, and predicted breakdown field strength.

[0131] In one optional embodiment, the comprehensive evaluation value F can be calculated using the following formula:

[0132] F=a1·S σ +a2·S ε +a3·S H +a4·S ρ +a5·S E ;

[0133] Where F is the comprehensive evaluation value; S σ S is the normalized evaluation value corresponding to the predicted tensile strength; ε S is the normalized evaluation value corresponding to the predicted elongation at break; H S is the normalized evaluation value corresponding to the predicted Shore hardness value; ρ S is the normalized evaluation value corresponding to the predicted volume resistivity; E Here, a1 represents the normalized evaluation value corresponding to the predicted breakdown field strength; a2, a3, a4, and a5 are the weighting coefficients for tensile strength, elongation at break, Shore hardness, volume resistivity, and breakdown field strength, respectively, and a1+a2+a3+a4+a5=1. Each normalized evaluation value can be determined based on the degree to which the predicted value approaches or exceeds the target threshold, in order to eliminate the influence of different dimensions of different performance indicators on the evaluation results.

[0134] Among them, the predicted tensile strength reflects the material's ability to withstand tensile loads; the predicted elongation at break reflects the material's deformation and flexibility; the predicted Shore hardness reflects the material's surface resistance to indentation and rigidity; the predicted volume resistivity reflects the material's ability to suppress current flow; and the predicted breakdown field strength reflects the material's ability to resist breakdown under an electric field. Through weighted evaluation of these indicators, the overall performance differences of different candidate formulations can be further compared, provided that basic performance requirements are met.

[0135] When determining the target formulation, candidate formulations with higher comprehensive evaluation values ​​are given priority. When two or more candidate formulations have the same comprehensive evaluation value, or when the difference between their comprehensive evaluation values ​​is lower than a preset difference, the candidate formulation with the lower silica addition amount ω is selected as the target formulation. This preset difference can be set according to the actual evaluation accuracy or engineering requirements. This approach, when overall performance is similar, typically helps reduce filler usage, mitigates the agglomeration risk that high filler amounts may cause, and also facilitates subsequent mixing and processing as well as cost control.

[0136] Using the above-described method for determining the target formulation, the screening of candidate formulations no longer stops at simply judging whether they "meet the standards." Instead, it further considers the balance of multiple properties and the amount of silica added in the compliant formulations. This avoids the situation where pursuing a single performance indicator affects other properties, and, while meeting the requirements for mechanical and electrical properties, outputs silica addition amounts, modifier types, and dispersion process parameters that are easier to prepare in practice, thereby improving the feasibility of the formulation optimization results.

[0137] In a preferred embodiment, when the predicted value of any candidate formulation fails to meet the corresponding target performance requirement, the candidate formulation is adjusted and re-predicted. Specifically, based on the performance items that fail to meet the requirements, the amount of silica added (ω), the type of modifier, or the dispersion process parameters can be adjusted, and the corresponding dispersion uniformity coefficient (D), surface silane loading molar number (n), and mechanical property correction coefficient can be obtained again. and electrical performance correction factor Then, substitute the values ​​into the mechanical performance prediction model and the electrical performance prediction model to calculate the predicted values ​​of each performance.

[0138] For example, if the tensile strength or Shore hardness is insufficient, the amount of silica added (ω) can be adjusted appropriately, or a mechanical property correction factor can be selected. A higher degree of modification is required; when the volume resistivity or breakdown field strength is insufficient, an electrical performance correction factor can be selected. For higher modified systems, the dispersion uniformity coefficient D can be improved by improving dispersion process parameters such as mixing method, mixing time, mixing temperature, and stirring speed. When the elongation at break decreases significantly, it is necessary to comprehensively consider the amount of silica added and the degree of interface modification to avoid excessive filler or excessive interface constraints from adversely affecting the material's flexibility.

[0139] By using the above iterative adjustment method, the formulation and process parameters can be modified in a targeted manner based on the performance shortcomings reflected in the prediction results, thereby reducing aimless reverse sampling and testing and improving the efficiency of formulation screening.

[0140] In one embodiment, the modified silica silicone rubber composite material is a silicone rubber material for high-voltage composite insulators. This type of material typically needs to simultaneously meet mechanical and electrical insulation performance requirements. Therefore, this embodiment can be used to predict and screen candidate formulations with different silica addition amounts, different types of modifiers, and different dispersion process conditions to determine the target formulation that meets the usage requirements.

[0141] It should be noted that, in this embodiment, the surface silane loading molar number n and mechanical property correction coefficient of different modified silica were obtained experimentally. and electrical performance correction factor This is to provide basic parameters for subsequent performance prediction and formulation optimization.

[0142] RH171 modified silica, KH550 modified silica, KH560 modified silica, KH570 modified silica, and RH151 modified silica were prepared separately, with unmodified silica retained as a control. Taking RH171 modified silica as an example, 50g of fumed silica was dried and dehydrated in an 80℃ drying oven for 4h; the treated silica was added to a 250ml three-necked flask, followed by 1.25L of ethanol solution with pH 4 (adjusted using 0.05mol / L HCl); the mixture was mechanically stirred for 0.5h in an 80℃ water bath at a stirring speed of 250r / min. Subsequently, 0.75g of vinyltrimethoxysilane was added, and a reflux condenser was set up. The reaction was carried out at 80℃ for 8h with a stirring speed of 150–180r / min. After the reaction was complete, the product was centrifuged at 3000 r / min for 10 min, and the supernatant was discarded. Anhydrous ethanol was added to redisperse the product, followed by centrifugation and washing, repeated three times. Finally, the product was dried in an 80℃ drying oven to constant weight to obtain RH171 modified silica. Other modified silica can be prepared using the same steps as RH171, only replacing the corresponding silane coupling agent.

[0143] X-ray photoelectron spectroscopy was used to analyze the surface elements of each modified silica sample, and the molar number of silane loading on the surface, n, was calculated based on the surface element content, as shown in Table 1.

[0144] Table 1: Surface silane loading molar number of different modified silica

[0145]

[0146] The differences in the loading of different modifiers on the surface of silica can be converted into quantitative parameters that can be used for model calculations.

[0147] Furthermore, silicone rubber composites with a silica addition of 30 phr were prepared, and the tensile strength and volume resistivity of each modified system were tested. The results were then calculated using the unmodified silica system with the same addition as a benchmark. and The specific calculation results are shown in Table 2.

[0148] Table 2. Modification type correction factor (30 phr) for different modified silica

[0149]

[0150] Through the above treatments, the effects of different modifiers on mechanical and electrical properties can be respectively... and This indicates that it facilitates comparisons between different candidate formulations.

[0151] The dispersion uniformity coefficient D is used to characterize the dispersion uniformity of modified silica in a silicone rubber matrix and can be obtained through scanning electron microscopy image analysis. In this embodiment, two dispersion processes are selected for comparison: one is the mechanical mixing method, in which silica, silicone rubber matrix, and other compounding agents are added to a mixing device in one step for mixing; the other is the masterbatch method, in which silica is first premixed with a portion of the silicone rubber matrix to form silica masterbatch, and then the remaining silicone rubber matrix and other compounding agents are added for further mixing.

[0152] Specifically, RH171 modified silica-silicone rubber composites with a silica addition of 30 phr were prepared using both mechanical mixing and masterbatch methods. Scanning electron microscopy was used to observe the cross-sections of the obtained samples, and the equivalent particle size distribution of silica agglomerates in the images was statistically analyzed. The mean value (d) and standard deviation (σ) of the equivalent particle size of the agglomerates were calculated. d And calculate the dispersion uniformity coefficient D.

[0153] Test results show that the average particle size d of the agglomerates in the mechanically mixed samples is 1.8 μm, and the standard deviation σ is... d The average particle size of agglomerates in the masterbatch method sample was 0.54 μm, with a coefficient of variation (CV) of 0.3 and a density (D) of 0.7. The average particle size of agglomerates in the masterbatch method sample was 1.5 μm, and the standard deviation (σ) was 0.54 μm. d The particle size distribution (Variance Value) is 0.3 μm, the coefficient of variation (CV) is 0.2, and the density (D) is 0.8. These results indicate that different dispersion processes affect the dispersion state of silica in a silicone rubber matrix. By incorporating D into the performance prediction model, the impact of the dispersion process on material properties can be quantitatively calculated, thus providing a basis for optimizing the dispersion process.

[0154] To further illustrate the inventive concept and beneficial effects of this invention, this embodiment uses the aforementioned performance prediction model and takes a sample with a silica addition of 30 phr as a reference sample. The surface silane loading molar number n is measured to be 0.654 mmol / g, and the mechanical property correction factor is... The electrical performance correction factor is 1.10. It is 2.35. The above... and Used to characterize the performance correction effect of the RH171 modified system relative to the unmodified system with the same addition amount, it is used as a modification type correction parameter within the prediction range of this embodiment.

[0155] The dispersion uniformity coefficient D is mainly affected by the dispersion process. When the type of modifier and the dispersion process remain consistent, D can be used as a dispersion state parameter under those process conditions. In this embodiment, the sample was prepared using the masterbatch method, and the dispersion uniformity coefficient D was taken as 0.8.

[0156] Substituting the above parameters and different silica addition amounts ω into the mechanical and electrical property prediction models, the predicted performance values ​​under different addition amounts were calculated. The predicted values ​​were then compared with experimental values, and the results are shown in Table 3. If the dispersion process or modified system is changed, the corresponding D and [other parameters] need to be re-measured or calibrated. and Then make a prediction.

[0157] Table 3: Comparison of predicted and experimental performance values ​​of RH171 modified silica-filled silicone rubber

[0158]

[0159] Table 3 shows that, under these implementation conditions, the model-calculated values ​​are close to the experimental test values. Further explanation is based on the amount of silica added (ω), the dispersion uniformity coefficient (D), the number of moles of surface silane loading (n), and the correction coefficient. , The established predictive model can be used to estimate the mechanical and electrical properties of modified silica silicone rubber composites. During formulation development, candidate formulations can be screened using the model first, followed by sample verification of a small number of candidate formulations, thereby reducing invalid experiments.

[0160] This paper uses silicone rubber material for high-voltage composite insulators as an example to illustrate the application of the method of this invention in formulation optimization. The target performance requirements are set as follows: tensile strength not less than 4.5 MPa, and volume resistivity not less than 1.8 × 10⁻⁶. 13 Ω·m, breakdown field strength not less than 28kV / mm.

[0161] First, the type of modifier was screened. Based on the parameters obtained in Example 1, RH171 modified silica... The value is 2.35, that of KH550 modified silica. The value is 1.07. Considering the comprehensive requirements of silicone rubber materials for high-voltage composite insulators for electrical insulation and mechanical properties, RH171 modified silica was selected as one of the candidate modification systems in this embodiment.

[0162] Next, the amount of silica added was optimized. In this embodiment, the surface silane loading molar number n of RH171 modified silica was 0.654 mmol / g; the mechanical property correction factor... and electrical performance correction factor Based on the experimental results at an addition of 30 phr, the values ​​were determined to be 1.10 and 2.35, respectively. Because... and This embodiment is used to characterize the modification effect of the same modified system relative to the unmodified system. Under the condition that the type of modifier, silicone rubber matrix, and preparation process remain consistent, this embodiment will... =1.10、 =2.35 is used as an empirical correction parameter for the RH171 modified system within an adjacent addition range to predict the performance change trend under different silica addition amounts. In practical applications, measurements can also be taken separately for different addition amounts. and The parameters were then substituted into the model for calculation. The dispersion uniformity coefficient D was set to 0.8. Substituting these parameters into the prediction model, the performance under different amounts of silica was calculated, and the results are shown in Table 4.

[0163] Table 4: Performance Comparison Results at Different Silica Addition Amounts

[0164]

[0165] Based on the above calculations, when ω is 30 phr, the tensile strength, volume resistivity, and breakdown field strength all meet the target performance requirements set in this embodiment; when ω is 25 phr, the volume resistivity and breakdown field strength meet the requirements, but the tensile strength is lower than 4.5 MPa, which can be considered as a candidate for further adjustment of the dispersion process or modification of the system. This step can narrow down the range of candidate formulations before sample preparation, improving the focus of subsequent validation.

[0166] Furthermore, the dispersion processes were compared. The masterbatch method was used to improve dispersion uniformity, increasing D from 0.7 to 0.8. The performance at ω=25phr was calculated using the model, and the specific results are shown in Table 5.

[0167] Table 5: Performance Comparison Results of Different D Values ​​for ω=25phr

[0168]

[0169] It is evident that, with other parameters remaining constant, increasing the dispersion uniformity coefficient D can improve prediction performance, indicating that dispersion process parameters can be used as controllable objects in formulation optimization.

[0170] This embodiment determines candidate formulations step by step through modifier screening, addition optimization, and dispersion process optimization. This makes formulation development no longer rely solely on empirical experiments, but rather on quantitative screening based on material characteristic parameters and target performance requirements, which helps reduce reverse sampling and testing.

[0171] To illustrate the ability of the method of this invention to distinguish between different modified systems, the performance of unmodified silica, KH550 modified silica, and RH171 modified silica-filled silicone rubber composites was predicted under the conditions of silica addition of 30 phr and dispersion uniformity coefficient D of 0.8. The specific performance prediction comparison is shown in Table 6.

[0172] Table 6: Performance Prediction Comparison of Different Modified Silica Systems

[0173]

[0174] The prediction results show that RH171 modified silica has the most significant effect on improving electrical properties, while KH550 modified silica has a better effect on improving mechanical properties. This is consistent with the experimental results and verifies that the model accurately describes the differences in the types of modifiers.

[0175] The above results indicate that different modified systems exhibit differences in mechanical and electrical properties. This can be mitigated by introducing the molar number n of surface silane loading and a correction factor. , This allows for the reflection of differences in modifier types in performance prediction results. For silicone rubber materials with high electrical performance requirements, it can be combined with... And screen modified systems based on predicted electrical properties; for materials that place more emphasis on mechanical properties, they can be combined with The selection is based on predicted mechanical properties. Therefore, this invention provides a quantitative basis for selecting modifiers, determining filler dosage, and optimizing dispersion processes for different application requirements.

[0176] Example 2

[0177] This invention also provides a system for predicting the performance and optimizing the formulation of modified silica silicone rubber, used to perform the aforementioned method. The system includes a candidate formulation establishment module, a characteristic parameter acquisition module, a performance prediction module, a formulation screening module, and a formulation output module.

[0178] The candidate formulation establishment module is used to establish a set of candidate formulations, which include the amount of silica added (ω), the type of modifier, and the dispersion process parameters.

[0179] The characteristic parameter acquisition module is used to obtain the amount of silica added (ω), dispersion uniformity coefficient (D), surface silane loading molar number (n), and mechanical property correction coefficient for each candidate formulation. and electrical performance correction factor Furthermore, the characteristic parameter acquisition module includes an image analysis unit and a surface element analysis data processing unit; wherein, the image analysis unit is used to identify silica agglomerates based on scanning electron microscope images of the cross-section of the silicone rubber composite material and to calculate the dispersion uniformity coefficient D; the surface element analysis data processing unit is used to determine the number of moles of surface silane loading n based on surface element analysis data obtained from X-ray photoelectron spectroscopy.

[0180] The performance prediction module includes a mechanical performance prediction unit and an electrical performance prediction unit. The mechanical performance prediction unit is used to predict performance based on ω, D, n, and... The tensile strength, elongation at break, and Shore hardness prediction values ​​are calculated according to the aforementioned mechanical property prediction model; the electrical property prediction unit is used to calculate the predicted values ​​based on ω, D, n, and The predicted values ​​of volume resistivity and breakdown field strength are calculated according to the aforementioned electrical performance prediction model. The mechanical performance prediction unit uses the following formula for prediction:

[0181] ;

[0182] ;

[0183] ;

[0184] The electrical performance prediction unit uses the following formula for prediction:

[0185] ;

[0186] .

[0187] The formulation screening module compares the predicted performance values ​​with the target performance requirements and eliminates candidate formulations that do not meet the target performance requirements. The formulation output module determines the target formulation from the remaining candidate formulations and outputs the corresponding amount of silica, type of modifier, and dispersion process parameters for the target formulation.

[0188] The above system enables a continuous processing flow for candidate formulation establishment, characteristic parameter acquisition, performance prediction, and target formulation output, providing a clear calculation basis for the formulation screening of modified silica silicone rubber and reducing the workload of determining the formulation solely by reverse sampling and testing.

[0189] Example 3

[0190] The present invention also provides a computer-readable storage medium, including a computer program stored in the computer-readable storage medium, wherein the computer program, when executed by a processor, performs the steps of a method for predicting the performance and optimizing the formulation of modified silica silicone rubber as described in Example 1.

[0191] A processor may include one or more processing units, such as an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0192] The controller can serve as the nerve center and command center of an electronic device. Based on the instruction opcode and timing signals, the controller generates operation control signals to control the fetching and execution of instructions.

[0193] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from the memory. This avoids repeated accesses, reduces processor waiting time, and thus improves system efficiency.

[0194] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct or indirect applications in other related technical fields, are within the patent protection scope of the present invention.

Claims

1. A method for predicting the properties and optimizing the formulation of modified silica silicone rubber, characterized in that, include: S1. Establish a set of candidate formulations, wherein the candidate formulations include the amount of silica added (ω), the type of modifier, and the dispersion process parameters; S2. Obtain the characteristic parameters corresponding to each candidate formulation. The characteristic parameters include the amount of silica added ω, the dispersion uniformity coefficient D, the number of moles of surface silane loading n, and the mechanical property correction coefficient. and electrical performance correction factor ; S3, based on ω, D, n and Tensile strength, elongation at break, and Shore hardness predictions were obtained through a mechanical property prediction model; based on ω, D, n, and The predicted values ​​of volume resistivity and breakdown field strength are obtained through the electrical performance prediction model; S4. Compare each predicted value with the target performance requirements, eliminate candidate formulations that do not meet the target performance requirements, determine the target formulation from the remaining candidate formulations, and output the amount of silica added, type of modifier and dispersion process parameters corresponding to the target formulation. The mechanical property prediction model and the electrical property prediction model both include a quadratic term of the amount of silica added ω, a linear term of the number of moles of surface silane loading n, and a correction term that is the product of the dispersion uniformity coefficient D and the correction coefficient.

2. The method for predicting the performance and optimizing the formulation of modified silica silicone rubber according to claim 1, characterized in that, The amount of silica added ω is the mass fraction of silica relative to the silicone rubber matrix, and the value ranges from 0 to 50 phr; in the mechanical property prediction model and the electrical property prediction model, the amount of silica added ω is denoted as w; The dispersion uniformity coefficient D is obtained through scanning electron microscopy image analysis, specifically including: acquiring scanning electron microscopy images of the cross-section of the silicone rubber composite material, identifying silica agglomerates in the images, statistically analyzing the equivalent particle size of the silica agglomerates, and calculating the average value d and standard deviation σ of the equivalent particle size of the silica agglomerates. d The dispersion uniformity coefficient D is calculated according to the following formula: D = 1 - CV; CV=σ d / d) Wherein, CV is the coefficient of variation of the equivalent particle size of silica agglomerates, 0 < D ≤ 1; The molar number n of silane loading on the surface was obtained by analyzing the surface elements of the modified silica using X-ray photoelectron spectroscopy.

3. The method for predicting the performance and optimizing the formulation of modified silica silicone rubber according to claim 1, characterized in that, The mechanical property correction coefficient The standard is determined based on unmodified silica-silicone rubber composites with the same silica addition amount, silicone rubber matrix, and preparation process conditions, and calculated according to the following formula: =Tensile strength of modified system / Tensile strength of unmodified system with the same amount of additives; The electrical performance correction factor The standard is determined based on unmodified silica-silicone rubber composites with the same silica addition amount, silicone rubber matrix, and preparation process conditions, and calculated according to the following formula: = Volume resistivity of modified system / Volume resistivity of unmodified system with the same amount of additive.

4. The method for predicting the performance and optimizing the formulation of modified silica silicone rubber according to claim 1, characterized in that, The mechanical performance prediction model includes the following formula: ； ； ； in, This is the predicted value for tensile strength. H is the predicted value of elongation at break; H is the predicted value of Shore hardness; w is the amount of silica added (ω). The electrical performance prediction model includes the following formulas: ； ； in, This is the predicted value of volume resistivity; This is the predicted value for the breakdown field strength.

5. The method for predicting the performance and optimizing the formulation of modified silica silicone rubber according to claim 1, characterized in that, The modifiers include at least one of RH151, RH171, KH550, KH560, or KH570; when the candidate formulation uses unmodified silica, the mechanical property correction factor is... and electrical performance correction factor All are set to 1.

6. The method for predicting the performance and optimizing the formulation of modified silica silicone rubber according to claim 1, characterized in that, The candidate formulation set includes multiple candidate formulations formed by different combinations of silica addition amounts, different types of modifiers, and different dispersion process parameters; The dispersion process parameters include at least one of the following: mixing method, mixing temperature, mixing time, and stirring speed; the dispersion uniformity coefficient D is changed by adjusting the dispersion process parameters.

7. The method for predicting the performance and optimizing the formulation of modified silica silicone rubber according to claim 1, characterized in that, The target performance requirements include at least one of the following: tensile strength threshold, elongation at break threshold, Shore hardness threshold, volume resistivity threshold, and breakdown field strength threshold.

8. The method for predicting the performance and optimizing the formulation of modified silica silicone rubber according to claim 1, characterized in that, Determining the target formulation from the remaining candidate formulations includes: calculating the comprehensive evaluation value of each remaining candidate formulation, and screening candidate formulations in descending order of comprehensive evaluation value; when the comprehensive evaluation values ​​are the same or the difference is lower than the preset difference, the candidate formulation with the lower amount of silica ω is selected as the target formulation. The comprehensive evaluation value is calculated by weighting the predicted tensile strength, predicted elongation at break, predicted Shore hardness, predicted volume resistivity, and predicted breakdown field strength.

9. The method for predicting the performance and optimizing the formulation of modified silica silicone rubber according to claim 1, characterized in that, When the predicted value of any candidate formulation fails to meet the corresponding target performance requirements, adjust the amount of silica added (ω), the type of modifier, or the dispersion process parameters, and re-obtain the corresponding dispersion uniformity coefficient (D), surface silane loading molar number (n), and mechanical property correction coefficient. and electrical performance correction factor Then, performance prediction was performed again.

10. A system for predicting the performance and optimizing the formulation of modified silica silicone rubber, characterized in that, include: The candidate formulation establishment module is used to establish a set of candidate formulations, which include the amount of silica added (ω), the type of modifier, and the dispersion process parameters. The characteristic parameter acquisition module is used to obtain the amount of silica added (ω), dispersion uniformity coefficient (D), surface silane loading molar number (n), and mechanical property correction coefficient for each candidate formulation. and electrical performance correction factor ; Performance prediction module, used for prediction based on ω, D, n and Predicted values ​​of tensile strength, elongation at break, and Shore hardness were obtained, based on ω, D, n, and The predicted values ​​of volume resistivity and breakdown field strength are obtained; The formulation screening module is used to compare each predicted value with the target performance requirements and eliminate candidate formulations that do not meet the target performance requirements. The formulation output module is used to determine the target formulation from the remaining candidate formulations and output the amount of silica added, the type of modifier, and the dispersion process parameters corresponding to the target formulation.

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