Polyurethane structural adhesive heat conduction performance detection method based on density analysis

Abstract

The present application relates to the technical field of performance detection, and specifically includes a polyurethane structural adhesive heat conduction performance detection method based on density analysis, which comprises: obtaining polyurethane base material information, determining heat conduction filler performance correlation indexes, and obtaining M preparation scenes and proportions; measuring the density of polyurethane and setting holes in different zones, generating heat conduction filler mixed components in combination with the particle size of superfine powder, establishing a simulation model, initializing with the initial thermal conductivity, simulating the heat conduction influence, and predicting the heat conduction performance under different proportions. The technical problem of unstable heat conduction performance caused by uneven distribution of heat conduction fillers and difficulty in accurately controlling the heat conduction path during preparation is solved, the accurate proportioning control of heat conduction fillers, the uniform distribution of filler mixed components, and the simulation of the heat conduction path of the simulation model are realized, and the technical effects of optimizing the distribution state of polyurethane structural adhesive heat conduction fillers, improving the material heat conduction performance, improving the detection accuracy and material structure stability are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of performance detection, and particularly relates to a polyurethane structural adhesive heat conduction performance detection method based on density analysis. BACKGROUND

[0002] With the rapid development of high-performance fields such as electronic equipment and power systems, the heat generation in the equipment increases sharply, and the heat dissipation problem becomes a key factor affecting the performance and service life of the equipment. Under this background, polyurethane structural adhesive, as a material commonly used in electronic devices, packaging and bonding, is widely used in heat dissipation systems due to its excellent mechanical properties and processability. Heat-conducting fillers such as alumina, boron nitride and graphite are often added to polyurethane matrix to improve thermal conductivity due to their high thermal conductivity. However, the particle size, shape and distribution of the fillers have an important influence on the overall performance of the material. The traditional heat-conducting filler ratio and distribution are often uneven, resulting in a decrease in mechanical properties and insufficient thermal conductivity of the material. Therefore, how to optimize the distribution and ratio of the fillers while ensuring the mechanical properties and processability, and ensure the uniform mixing of the fillers and the polyurethane matrix, is a difficulty in current technology development.

[0003] In summary, the existing technology has the technical problem of unstable heat conduction performance caused by uneven distribution of heat-conducting fillers, filler sedimentation and difficulty in accurately controlling the heat conduction path during preparation. SUMMARY

[0004] The present application provides a polyurethane structural adhesive heat conduction performance detection method based on density analysis, which aims to solve the technical problem of unstable heat conduction performance caused by uneven distribution of heat-conducting fillers, filler sedimentation and difficulty in accurately controlling the heat conduction path during preparation in the prior art.

[0005] In view of the above problems, the technical scheme of the present application is as follows:

[0006] The present application provides a polyurethane structural adhesive heat conduction performance detection method based on density analysis, which includes: obtaining the basic material information of the polyurethane matrix, the basic material information including the polyurethane structure and the initial thermal conductivity coefficient;

[0007] According to the material performance indicators of various heat-conducting fillers, combined with the basic material information of the polyurethane matrix, the performance correlation indicators of various heat-conducting fillers are determined;

[0008] According to the performance correlation indicators of the various heat-conducting fillers, the resin-filler system formed by the ultra-fine powder is compared to obtain M preparation scenarios, each of the M preparation scenarios corresponding to a heat-conducting filler ratio;

[0009] Density measurement is performed on the polyurethane base material, and the density-constrained partition is performed according to the polyurethane structure in the base material information, and a plurality of density cluster holes are set;

[0010] In the resin-filler system formed by the ultra-fine powder, the particle size of the ultra-fine powder and the M heat-conducting filler ratios associated with the M preparation scenes are combined to generate a heat-conducting filler mixed component;

[0011] According to the heat-conducting filler mixed component, the settling process of a plurality of heat-conducting filler components is simulated by referring to the plurality of density cluster holes, and a polyurethane structure glue simulation model is established.

[0012] Based on the polyurethane structure glue simulation model, the initial thermal conductivity in the base material information is initialized and configured, and the influence of the heat-conducting filler on the heat-conducting path is simulated in the M preparation scenes, and the thermal conductivity of the polyurethane structure glue under different heat-conducting filler ratios is predicted.

[0013] In summary, one or more technical solutions provided in the present application solve the technical problem of unstable thermal conductivity caused by uneven distribution of heat-conducting fillers, filler settling and difficulty in accurately controlling the heat-conducting path during preparation, and realize accurate control of the heat-conducting filler, uniform distribution of the filler mixed component, and simulation of the heat-conducting path of the simulation model through the density analysis-based thermal conductivity detection method, thereby achieving the technical effects of optimizing the distribution state of the polyurethane structure glue heat-conducting filler, improving the material thermal conductivity, improving the detection accuracy and material structure stability. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 A flowchart of the density analysis-based polyurethane structure glue thermal conductivity detection method is provided for the present application;

[0015] Figure 2 A flowchart of obtaining M preparation scenes in the density analysis-based polyurethane structure glue thermal conductivity detection method is provided for the present application. DETAILED DESCRIPTION

[0016] EMBODIMENT

[0017] The present application will be specifically described below with reference to the accompanying drawings, as shown in Figure 1 The present application provides a density analysis-based polyurethane structure glue thermal conductivity detection method, wherein the method comprises:

[0018] S1: Obtain the base material information of the polyurethane base material, and the base material information includes the polyurethane structure and the initial thermal conductivity; S2: According to the material performance index of various heat-conducting fillers, combined with the base material information of the polyurethane base material, determine the performance correlation index of various heat-conducting fillers.

[0019] Specifically, polyurethane is a high molecular material commonly used to manufacture various elastomers, coatings and adhesives. Polyurethane base material refers to a composite based on polyurethane. The substance filled in the base material is used to enhance its thermal conductivity. Common thermal conductive fillers include aluminum oxide, boron nitride, carbon fiber, etc. Performance-related indicators are used to reflect the performance of thermal conductive fillers in specific applications, such as thermal conductivity, particle size, density, etc. These indicators affect the performance of fillers in composite materials.

[0020] Execution steps: First, the specific structural information and initial thermal conductivity of the polyurethane base material need to be determined. The structure of the polyurethane base material includes the composition of its molecular chain, the ratio of hard segments to soft segments, etc., which directly affects the thermal conductivity of the material. The initial thermal conductivity refers to the thermal conductivity of the polyurethane material itself without adding any thermal conductive filler. This is usually measured by standard thermal conductivity testing equipment such as a laser flash analyzer. For example, the thermal conductivity of ordinary polyurethane materials is generally around 0.2 W / m·K. Based on the information of the polyurethane base material, appropriate thermal conductive fillers are selected. The material properties of each thermal conductive filler need to be considered, such as the thermal conductivity of the filler, the particle size distribution, the particle shape, the density, etc. These indicators determine the dispersibility of the filler in the base material and the compatibility with polyurethane. For example, thermal conductive fillers such as aluminum oxide have high thermal conductivity (usually 20-30 W / m·K) and large particles (50 microns), while graphene fillers not only have high thermal conductivity (>100 W / m·K), but also have excellent mechanical properties, providing data support for subsequent analysis.

[0021] S3: According to the performance-related indicators of the various thermal conductive fillers, the resin-filler system formed by the ultra-fine powder is compared to obtain M preparation scenarios, each of which corresponds to a thermal conductive filler ratio; S4: The density of the polyurethane base material is measured, and the density is partitioned according to the polyurethane structure in the base material information, and multiple density cluster holes are set; S5: In the resin-filler system formed by the ultra-fine powder, the particle size of the ultra-fine powder and the M thermal conductive filler ratios associated with the M preparation scenarios are combined to generate a thermal conductive filler mixture component.

[0022] Specifically, the performance correlation index of the heat-conducting filler guides the thermal conductivity, particle size, density, shape, and surface chemical properties of the heat-conducting filler, which determines the dispersibility and heat-conducting effect of the filler in the polyurethane base material. For example, the thermal conductivity of the filler directly affects the heat transfer efficiency, while the particle size and shape of the filler affect its distribution in the base material. The resin-filler system formed by ultra-fine powder refers to a material system in which the heat-conducting filler with ultra-fine particles is uniformly dispersed in the resin base material. The ultra-fine powder has a small particle size (usually less than 10 microns), which enables it to be more uniformly distributed in the base material, thereby improving the thermal conductivity. The density cluster hole refers to the voids or microstructures formed based on the density difference distribution. When the polyurethane base material is partitioned by density measurement, multiple regions or holes with concentrated density are formed, and the heat-conducting filler can fill these regions to improve the thermal conductivity. The M preparation scenarios refer to experimental scenarios with multiple ratios of heat-conducting fillers. For different filler ratios and characteristics, multiple material preparation schemes are generated through experiments to optimize the thermal conductivity of the polyurethane base material.

[0023] Performance data of the selected heat-conducting fillers (such as aluminum oxide, boron nitride, graphene, etc.) need to be collected, including thermal conductivity, particle size, density, etc. Each type of filler has different performance, so the heat-conducting effect in the resin-filler system will also be different. For example, aluminum oxide has high thermal conductivity but large particle size, which is prone to settling in the polyurethane base material. Boron nitride has small particle size and good dispersibility, making it suitable for use in electrically insulating materials. According to the performance correlation index of the heat-conducting filler, M preparation scenarios are designed, in each of which the heat-conducting filler is mixed in different proportions. For example, in the combination of aluminum oxide and boron nitride, 30% aluminum oxide + 70% boron nitride or 50% aluminum oxide + 50% boron nitride can be set, and so on. Each preparation scenario is associated with a specific ratio of heat-conducting fillers.

[0024] The density of the polyurethane base material affects the distribution of the filler, so density measurement can be used to accurately measure the density of different regions of the base material. Based on this, the base material is partitioned and multiple density cluster holes are established according to the molecular structure characteristics of the polyurethane. These holes are the main filling areas for the heat-conducting filler. According to the M preparation scenarios, the heat-conducting fillers are mixed in proportion based on their particle size. During mixing, it is necessary to ensure that the fillers are uniformly distributed in the density cluster holes of the polyurethane base material to maximize the heat-conducting performance. High-shear mixers or ball mills are used to complete the mixing, enabling the ultra-fine powder to form a uniform distribution system in the base material. For example, ultra-fine powders of boron nitride and aluminum oxide can be mixed in the specified proportions and uniformly dispersed in the holes of the polyurethane base material through high-speed stirring equipment.

[0025] S6: According to the heat-conducting filler mixed component, the settling process of the plurality of heat-conducting filler components is simulated by checking the plurality of density cluster hole, and a polyurethane structural adhesive simulation model is established; S7: Based on the polyurethane structural adhesive simulation model, the initial thermal conductivity in the basic material information is initialized and configured, and the influence of the heat-conducting filler on the heat-conducting path is simulated in the M preparation scenes, and the polyurethane structural adhesive heat-conducting performance under different heat-conducting filler ratios is predicted.

[0026] Specifically, the heat-conducting filler mixed component refers to a component formed by mixing different heat-conducting fillers (such as aluminum oxide, boron nitride, graphene, etc.) in different proportions, wherein the physical and chemical properties (such as thermal conductivity, particle size, etc.) of each filler are different, so that the thermal conductivity of the material can be optimized after mixing; in the polyurethane base material, due to the density difference in the material structure, these density differences will form holes or low-density areas, and the heat-conducting filler mixed component can settle in these holes to fill the low-density areas and enhance the heat-conducting path; the distribution and settling mode of the heat-conducting filler in the polyurethane base material is the key to affecting the heat-conducting performance, and after mixing, the filler will be deposited in different positions of the base material due to the density difference, so it is necessary to simulate its settling behavior in the base material; the heat-conducting path is a heat-conducting channel formed by the heat-conducting filler filling the holes and other areas in the base material, and is a decisive factor of the material's heat-conducting performance.

[0027] Execution steps: First, generate heat-conducting filler mixed components according to the proportions of different heat-conducting fillers (such as aluminum oxide, boron nitride, etc.) determined earlier, for example, set 30% aluminum oxide + 70% boron nitride as a mixed component, and in the generation process, use a ball mill or high-shear mixing equipment to mix the heat-conducting fillers uniformly to ensure that their particle sizes meet the structural requirements of the polyurethane base material; in the density cluster holes of the polyurethane base material, the heat-conducting filler mixed component will have a settling behavior due to gravity and density difference, and a mathematical model established by computer simulation software simulates the distribution, settling and heat-conducting path of the filler in the polyurethane base material, and common simulation tools include ANSYS Fluent or COMSOL Multiphysics, which can analyze the influence of different filler ratios on the heat-conducting effect through software; input the particle size, density and other physical properties of the filler in the simulation model, simulate the process of the heat-conducting filler filling different density holes in the base material through calculation, predict the settling path and final distribution of the filler, for example, aluminum oxide has a higher density and may settle in the deep layer of the base material, while boron nitride has a lower density and may be distributed in the shallow layer, and these distributions will affect the final heat-conducting path of the material. Therefore, it is necessary to accurately simulate the settling process of these fillers in the base material.

[0028] According to the settling simulation results of the heat-conducting filler mixed components, a polyurethane structural adhesive simulation model is established, which can predict the heat-conducting path of the filler in the material by combining the distribution of the filler and the geometric structure of the substrate. The basic parameters of the polyurethane substrate are initialized by inputting the basic material information (such as the initial thermal conductivity coefficient and density distribution of the polyurethane substrate) in the simulation model. The thermal conductivity coefficient and geometric distribution of the filler can be added in the model using multi-physics simulation software such as COMSOL Multiphysics to simulate the heat-conducting path inside the material. The M preparation scenarios generated previously are imported into the simulation model, and each scenario has a different effect on the heat-conducting path of the material. Through simulation, the heat-conducting path under each ratio is analyzed, and the distribution of the filler in different density cluster holes and the contribution of these holes to the heat-conducting path are observed. In the simulation, the finite element method (FEM) can be used for calculation to predict the overall heat-conducting performance of the material under different filler ratios. For example, it is found that the ratio of 50% aluminum oxide + 50% boron nitride can form a continuous heat-conducting channel in the material, thereby optimizing the heat-conducting performance of the material. Based on the simulation results of different preparation scenarios, the heat-conducting effect under each filler ratio is analyzed, and the heat-conducting performance of the polyurethane structural adhesive under different ratios is predicted by calculating the total length of the heat-conducting path, thermal resistance, and heat flow distribution. The optimal heat-conducting filler combination is determined to optimize the heat dissipation effect of the material.

[0029] Further, as shown in Figure 2 According to the performance correlation index of the various heat-conducting fillers, the resin filler system formed by the ultra-fine powder is obtained, and the method comprises:

[0030] Obtain heat dissipation demand information, which includes thermal load information and environmental condition information.

[0031] Set a first scene parameter set by combining the heat dissipation mode associated with the polyurethane structural adhesive through the thermal load information in the heat dissipation demand information.

[0032] Set a second scene parameter set by combining the heat dissipation mode associated with the polyurethane structural adhesive through the environmental condition information in the heat dissipation demand information.

[0033] Set the M preparation scenarios based on the first scene parameter set and the second scene parameter set.

[0034] Specifically, the heat dissipation requirement information includes heat load information and environmental condition information. The heat load information refers to the heat or energy demand generated by the device or system during operation, which needs to be effectively dissipated through a heat dissipation material such as a polyurethane structural adhesive to avoid overheating of the device. The environmental condition information refers to external factors such as temperature and humidity of the use environment, which have a significant impact on the heat dissipation performance of the polyurethane adhesive. The first set of scenario parameters is set based on the heat load information and includes how the heat-conducting filler meets the heat dissipation requirements of the device or system, such as how to efficiently dissipate heat under high-power devices. The second set of scenario parameters is set based on the environmental condition information and adjusts the heat dissipation performance of the material under different environmental temperature, humidity, and other conditions. The M preparation scenarios are multiple heat-conducting filler ratios and configuration scenarios designed for the polyurethane structural adhesive, each of which is optimized for different heat dissipation requirements and environmental conditions.

[0035] Execution steps: Heat dissipation requirement information needs to be obtained from the target device or application environment. For example, if a polyurethane structural adhesive is designed for a high-power electronic device, the heat load information may include the power loss generated by the device during operation, the temperature peak during operation, and the like. The environmental condition information includes the temperature range and humidity level of the environment in which the device is located. For example, the heat load of a certain device is 50W, the operating temperature range is -10°C to 80°C, and the humidity is 40% to 60%. Based on the obtained heat load information, the heat dissipation performance required by the polyurethane structural adhesive is analyzed. For example, under a heat load of 50W, it is necessary to ensure that the polyurethane structural adhesive can effectively dissipate heat without causing material failure. Then, appropriate heat-conducting fillers are selected and the proportions of the fillers are set. Tools such as heat flow simulation software (such as ANSYS, Fluent) are used to simulate the heat dissipation performance of different heat-conducting fillers to determine which fillers and proportions can optimize the heat dissipation effect under a specific heat load.

[0036] By analyzing the environmental conditions (such as temperature and humidity), the adaptability of the polyurethane structural adhesive in different environments is adjusted. For example, in a high-temperature environment, some heat-conducting fillers may fail or the efficiency of the heat dissipation path may decrease. Therefore, heat-conducting fillers that can maintain stable performance under high temperature or high humidity conditions must be selected. Material performance tests are conducted under different environmental conditions to adjust the proportions of the fillers and the structural design. Based on the heat load information and the environmental condition information, the first set of scenario parameters and the second set of scenario parameters are combined to generate different preparation scenarios. For example, for different combinations of heat load and environmental conditions, different heat-conducting filler ratios can be tested to generate multiple preparation scenarios. Each scenario tests a new filler combination, and multiple scenario simulations are performed to predict the heat dissipation performance of the polyurethane structural adhesive in each scenario, and finally the best heat-conducting filler combination is determined. Through the above steps, the best preparation scenario for different heat-conducting filler combinations under different environmental and load conditions is determined to ensure that the heat dissipation performance of the polyurethane structural adhesive meets the actual application requirements.

[0037] Further, by the heat load information in the heat dissipation requirement information, in combination with the heat dissipation mode associated with the polyurethane structural adhesive, a first set of scene parameters is set, and the method comprises:

[0038] The target heat dissipation device is connected, and a heat flow distribution map under different working conditions is obtained.

[0039] Through the heat flow distribution map under different working conditions, the heat conduction path between the polyurethane structural adhesive and the target heat dissipation device is fitted.

[0040] Based on the heat load information in the heat dissipation requirement information, the heat conduction path between the polyurethane structural adhesive and the target heat dissipation device is checked, and a first set of scene parameters is obtained.

[0041] Specifically, the heat load information refers to the heat generation or energy consumption of the target device under different working conditions, which is usually expressed in the form of power or heat flow. The heat load information is used to determine the heat that the heat dissipation system needs to handle; the heat dissipation mode refers to how the polyurethane structural adhesive and the heat dissipation device conduct, convect or radiate heat, and common heat dissipation modes include heat conduction (heat transfer through solid materials), convective heat dissipation (through liquid or gas medium) and radiative heat dissipation (through electromagnetic waves); the heat flow distribution map refers to the heat distribution of different regions of the device during operation, by monitoring the heat flow of different parts of the device, the distribution map of the heat flow in space can be obtained, which is helpful for optimizing the heat conduction material and the heat dissipation path; the heat conduction path refers to the specific conduction route of heat from the heat source to the heat dissipation material, and then from the heat dissipation material to the environment, and the optimization of the heat conduction path can improve the heat dissipation efficiency and reduce the temperature of the device.

[0042] The target heat dissipation device is connected to the monitoring system to monitor the heat flow distribution of the device under different working conditions in real time. Temperature or heat flow sensors such as thermocouples and infrared sensors are installed at key parts of the device to collect heat flow data of the device under various operating conditions. An infrared thermal imager (such as FLIR) or thermocouple array is used to generate real-time heat flow distribution maps of the device. These maps show the heat concentration and temperature gradient at different parts, which helps identify high-heat areas or low-efficiency heat dissipation areas. The process of heat conduction from the high-heat source area of the device to the surface of the heat dissipation device through polyurethane structural adhesive and finally to the external environment is simulated. Finite element analysis software (such as ANSYS or COMSOL) is used to model the heat conduction process under different thermal loads. The thickness of the polyurethane structural adhesive and the proportion of the thermal conductive filler are adjusted based on the material properties, geometric structure, and working conditions of the device. The proportion of the thermal conductive filler is adjusted to reduce thermal resistance and improve heat dissipation efficiency.

[0043] After obtaining the heat flow distribution and heat conduction path, the first scene parameter set is set based on the thermal load information of the device. The thermal load information describes the heat demand of the device under different working conditions, such as the heat generated by the device running in low-power state and high-power state. The appropriate thermal conductive material and thermal conduction path are selected to ensure good heat dissipation effect under different thermal loads. Heat flow simulation tools (such as ANSYS Fluent or SolidWorks Simulation) can simulate different heat dissipation scenarios based on thermal load conditions. In the simulation, the proportion, thickness, or heat dissipation path of the thermal conductive material is changed to verify the heat dissipation effect under different working conditions and determine the optimal thermal conductive material and heat dissipation design. Through the above steps, the optimal thermal conduction path and thermal conductive material combination can be determined to effectively improve the heat dissipation performance of the polyurethane structural adhesive in the target device and meet the heat dissipation requirements under different working conditions.

[0044] Further, based on the thermal load information in the heat dissipation requirement information, the first scene parameter set is obtained by comparing the heat conduction path between the polyurethane structural adhesive and the target heat dissipation device. The method of the present application includes:

[0045] The initial scene index is added to the heat flow distribution map under different working conditions and the resin filler system formed by the ultra-fine powder. The initial scene index includes the thermal conductivity.

[0046] Based on the initial scene index, the initial scene parameters are determined by comparing the heat conduction path between the polyurethane structural adhesive and the target heat dissipation device.

[0047] According to the heat load information in the heat dissipation requirement information, the first set of scene parameters is obtained in combination with the initial scene parameters.

[0048] Specifically, the initial scene index is an initial condition set in the heat conduction simulation analysis, mainly including the thermal conductivity, heat capacity, density and other parameters of the material. These initial conditions are used to define the heat conduction characteristics of the material in the simulation model. The thermal conductivity refers to the amount of heat transferred per unit time under a certain temperature difference, which is a key index affecting the efficiency of heat conduction. Materials with high thermal conductivity usually have better heat dissipation performance. The heat flow distribution map is the temperature and heat flow distribution of different regions of the equipment surface or interior obtained by sensors or thermal imaging technology, which can show the concentration area of heat in the equipment and facilitate the identification of high and low heat areas. The heat load information refers to the amount of heat generated or required for heat dissipation by the equipment under different working conditions. The heat load is closely related to the working state of the equipment and usually varies with the power.

[0049] Execution steps: Use sensors or infrared imaging technology to monitor the heat flow of the target equipment under different working conditions to generate a heat flow distribution map of the equipment. The heat flow distribution map shows the temperature distribution and heat flow density of different regions of the equipment under different operating conditions. Based on the heat flow distribution map, add initial scene indicators including the thermal conductivity, density, and heat capacity of the material. The thermal conductivity is the most important initial scene indicator and directly affects the efficiency of material heat transfer. For example, in a computer-aided simulation software (such as ANSYS Fluent), input the thermal conductivity of different heat-conducting materials (such as aluminum oxide and boron nitride) as initial scene indicators. For example, the thermal conductivity of aluminum oxide is about 30 W / m·K, while the thermal conductivity of boron nitride can reach 200 W / m·K.

[0050] After obtaining the heat flow distribution map and initial scene indicators, the next step is to simulate the heat conduction path, which can be done through simulation software such as finite element analysis (FEA). This simulation process models the heat transfer from the high-temperature areas of the device to the polyurethane structural adhesive, and then to the external environment. Different heat-conducting fillers (such as aluminum oxide or boron nitride) are added to the polyurethane structural adhesive to simulate the heat conduction path and observe their impact on the overall heat dissipation performance. Based on these simulation results, the most suitable initial scene parameters are determined, such as filler ratio, adhesive layer thickness, and material thermal conductivity. According to the thermal load information of the device under different working conditions (such as the heat generated by the device at low and high power), the initial scene parameters are matched with these working conditions to generate a first scene parameter set. For example, when running at high power, a higher thermal conductivity filler is required. Therefore, the first scene parameter set includes multiple material combinations and configuration schemes for different thermal load requirements. Through the above steps, the simulation model can analyze the ratio of different heat-conducting fillers and the heat dissipation path under different thermal load conditions, ultimately obtaining an optimized scene parameter set that effectively improves the heat dissipation performance of the polyurethane structural adhesive and meets the heat dissipation requirements of the target device.

[0051] Further, according to the thermal load information in the heat dissipation requirement information, in combination with the initial scene parameters, the first scene parameter set is obtained. The method of the present application comprises:

[0052] According to the thermal load information in the heat dissipation requirement information, in combination with the initial scene parameters, a first thermal cycle is added.

[0053] In the first thermal cycle, the target temperature range associated with the target heat dissipation device is configured for temperature cycling.

[0054] After completing the temperature cycle configuration in the first thermal cycle, the first scene parameter set is obtained by comparing the cycle number.

[0055] Specifically, the thermal load information determines the amount of heat that needs to be dissipated; the initial scene parameters refer to the simulation initial conditions set based on the physical properties of the material, such as thermal conductivity, density, and thickness, which affect the efficiency of heat conduction and the heat dissipation effect of the material; the first thermal cycle refers to the process of cyclically increasing and decreasing the temperature of the device or material within a specified temperature range. By simulating multiple thermal cycles, the thermal stability, expansion and contraction performance, and heat dissipation efficiency of the material at different temperatures are evaluated; the temperature cycle configuration refers to the specific temperature range and heating / cooling rate set during the thermal cycle process, which is usually set to multiple temperature intervals in device design to verify material performance.

[0056] The execution step is to collect the heat dissipation requirement information of the target device, especially the heat load information, for example, the heat generation of the device under different power or load conditions, which is calculated by sensors or device power formula. According to the different heat load information of the device, combined with the initial scene parameters (for example, the proportion of heat-conducting filler, the thermal conductivity of polyurethane adhesive, etc.) set in advance, a heat cycle strategy is formulated, that is, the first heat cycle is added. For example, an electronic device generates 5W of heat when running at low load, and generates 50W of heat when running at high load. Combine these two heat load conditions with the initial scene parameters to simulate the heat dissipation performance of polyurethane adhesive.

[0057] According to the actual working temperature range of the device, the temperature cycle configuration is configured, for example, the working temperature range of the electronic device may be -10℃ to 85℃. In the simulation model, by setting this temperature range, the temperature is repeatedly cycled to simulate the performance change of the material in actual use. The temperature cycle configuration usually includes: the temperature range is the working temperature range of the device, for example, from -10℃ to 85℃; the temperature rising and falling rate refers to the rate of temperature rising or falling per minute, for example, 5℃ per minute; the cycle number is the heat dissipation performance of the device under multiple start-stop or long-term operation, for example, 100 heat cycles are set to observe the stability of the material.

[0058] After completing the temperature cycle, record the temperature change after each heat cycle, the heat dissipation efficiency of the material, and whether there is obvious heat attenuation phenomenon. By comparing these data, the thermal conductivity of the material is gradually optimized, and the first scene parameter set is determined. Specifically, the performance change of the material after multiple heat cycles can be judged by observing the thermal stability and thermal expansion behavior of the material, for example, if the heat-conducting filler still maintains uniform distribution after multiple heat cycles, and the thermal conductivity of the polyurethane structural adhesive does not decrease significantly, it can be considered that the material has good thermal stability in this temperature range.

[0059] Further, the method of the present application comprises:

[0060] In the first heat cycle, a first thermal stability coefficient of thermal expansion behavior is set.

[0061] In the first heat cycle, a second thermal stability coefficient of cold shrinkage behavior is set.

[0062] The temperature cycle configuration index of the first heat cycle is adjusted by the first thermal stability coefficient of the thermal expansion behavior and the second thermal stability coefficient of the cold shrinkage behavior.

[0063] Specifically, thermal expansion behavior refers to the physical phenomenon of volume increase due to heat absorption at high temperatures, and different materials have different thermal expansion rates. Polyurethane structural adhesive will expand at high temperatures, affecting its thermal conductivity performance. Cold shrinkage behavior refers to the phenomenon of volume contraction due to heat loss at low temperatures. Cold shrinkage can change the internal structure of the material, thereby affecting the thermal conduction path. Thermal stability coefficient is used to describe the degree of size and volume change of the material when it experiences temperature changes. The first thermal stability coefficient is used to characterize the expansion degree of the material at high temperatures, and the second thermal stability coefficient is used to characterize the contraction degree of the material at low temperatures. Temperature cycle configuration index is a control parameter set for the performance of the material at different temperatures, such as temperature change range, cycle number, heating and cooling rate, etc., so as to accurately evaluate the thermal stability of the material in simulation or experiment.

[0064] Execution step: Set the thermal stability coefficient at high temperature through the thermal expansion coefficient of the material (i.e. the proportion of volume or length change per degree Celsius of temperature rise). When the polyurethane structural adhesive is heated, it will expand, causing the thermal conductive filler of the material to redistribute, thereby affecting the overall thermal conductivity performance. Therefore, the first thermal stability coefficient needs to be set to characterize the expansion behavior of the material at high temperatures. For example, use a thermal expansion coefficient measuring instrument (such as a Thermomechanical Analyzer) to measure the expansion of the polyurethane adhesive at high temperatures. The volume of the polyurethane adhesive expands by 0.2% per 10°C rise. According to this value, set the first thermal stability coefficient to 0.002 / °C.

[0065] At low temperature conditions, the material will shrink, which may cause micro-cracks in the material or uneven distribution of thermal conductive fillers, so the cold shrinkage behavior needs to be evaluated and the second thermal stability coefficient needs to be set to characterize the volume reduction degree of the material at low temperatures, ensuring that the material maintains a good thermal conduction path in the low temperature cycle. Test the shrinkage of the polyurethane adhesive at low temperature conditions, for example, the material shrinks by 0.1% per 10°C drop at -10°C. According to this data, set the second thermal stability coefficient to 0.001 / °C; set the cold shrinkage coefficient of the material to ensure that the simulation can reflect the possible shrinkage phenomenon of the material at extremely low temperatures.

[0066] After measuring the thermal expansion and cold shrinkage, the temperature cycle configuration needs to be adjusted according to these coefficients to ensure that each temperature change truly reflects the thermal stability of the material in actual use. The specific adjustments include temperature range, heating and cooling rate, cycle number, etc. The temperature range is set according to the working temperature range of the equipment, setting the upper and lower limits of the temperature cycle. For example, if the device operating temperature is -10°C to 85°C, the cycle test will be set to this range, and the temperature change rate will be adjusted in combination with the thermal expansion and cold shrinkage behavior of the material; the heating and cooling rate is to simulate the rapid heating and cooling process of the material in the actual environment, and a faster temperature change rate can be set, such as heating 5°C per minute and cooling 5°C per minute, to observe the thermal stability performance of the material in the rapid temperature change; the cycle number is to simulate the thermal fatigue in long-term use, and multiple temperature cycles are usually set, and according to the stability of thermal expansion and cold shrinkage, 50 or 100 cycles can be performed to ensure that the material can maintain its thermal conductivity after multiple cycles.

[0067] By observing the size change, thermal path change and thermal conductivity performance of the material, it is determined whether the material maintains the same thermal conductivity after multiple cycles. If the thermal conductivity coefficient of the material decreases after the 30th cycle, the filler ratio needs to be adjusted or other modified materials need to be used to improve the thermal stability. By measuring the thermal expansion behavior and cold shrinkage behavior of the material, the first thermal stability coefficient and the second thermal stability coefficient are set, which can more accurately adjust the temperature cycle configuration index. This not only helps to evaluate the thermal stability of the material, but also provides guidance for material modification in actual application, so as to optimize the thermal conductivity of polyurethane structural adhesive at different temperatures.

[0068] Further, in the resin-filler system formed by the ultra-fine powder, in combination with the particle size of the ultra-fine powder and the M thermal conductive filler ratios associated with the M preparation scenarios, the method further comprises:

[0069] In the resin-filler system formed by the ultra-fine powder, the settling process of the thermal conductive filler is simulated by comparing the multiple density cluster holes, and the distribution state of the thermal conductive filler mixed components is obtained.

[0070] By the distribution state of the thermal conductive filler mixed components, the M thermal conductive filler ratios associated with the M preparation scenarios are marked for distribution uniformity.

[0071] Specifically, ultra-fine powder refers to filler materials with very small particle sizes, typically in the micron or nanometer range. The fine structure of such powders allows them to effectively fill the tiny pores in the material, improving thermal conductivity. Common ultra-fine powders include aluminum oxide, carbon nanotubes, and others. Resin-filler system refers to a composite material system formed by mixing resin and filler. Resin serves as the base material, while filler is used to enhance specific properties, such as thermal conductivity. Density cluster holes refer to micro-holes or voids formed in different regions of the polyurethane base material due to density differences. These holes affect the settling and distribution of fillers. Different density regions attract different masses of filler particles to settle. The settling process guides the thermal filler to gradually settle to the bottom of the resin matrix or distribute to different regions due to gravity or other external forces. Uneven distribution of fillers can affect thermal conductivity.

[0072] Execution steps: Select appropriate ultra-fine powders based on the particle size of the thermal filler. Common ultra-fine powders have particle sizes ranging from microns or nanometers (e.g., 0.1 microns to 5 microns). Choose ultra-fine powders with different particle sizes (e.g., aluminum oxide, boron nitride, etc.). Then, set the ratio of thermal filler according to different preparation scenarios. For example, scenario 1 uses 10% ultra-fine powder and 90% polyurethane base material, scenario 2 uses 20% ultra-fine powder and 80% base material, and so on. Each scenario represents a different ratio scheme, aiming to find the optimal thermal filler ratio.

[0073] In the resin-filler system, the settling of fillers is affected by factors such as gravity, density difference, and particle size. Ultra-fine powders may settle faster in areas with higher density and slower in areas with lower density. Therefore, simulating the settling process of fillers in different density cluster holes is crucial. Use simulation tools to simulate the settling process and understand the distribution of fillers in different areas to ensure that fillers do not concentrate in certain areas, thereby affecting thermal conductivity.

[0074] By comparing different density cluster holes and monitoring the distribution of thermal fillers, ideally, fillers should be evenly distributed throughout the resin matrix, rather than concentrated in certain areas. Use data analysis tools to analyze the distribution of fillers and generate a map of the distribution state of fillers to evaluate the uniformity of filler distribution. For example, use a CT scanner to observe the distribution of fillers in different density areas. If it is found that fillers are concentrated in high-density areas and sparse in low-density areas, the filler ratio or settling process needs to be adjusted. Use data visualization tools in MATLAB or Python to visualize the distribution of thermal fillers and analyze their uniformity.

[0075] Based on the simulation and imaging results, the uniformity of the filler distribution in different preparation scenarios is marked. If the filler distribution in a certain scenario is relatively uniform, it is marked as high distribution uniformity; otherwise, if the filler distribution in a certain scenario is not uniform, it is marked as low distribution uniformity, providing a reference for subsequent optimization of the formula, so as to select the best ratio scheme with the best distribution uniformity; the uniformity of the filler distribution is quantitatively analyzed through Voronoi diagram or Delaunay triangulation, for example, in Python, the k-means algorithm in the Scikit-learn library is used for cluster analysis of the uniformity of the filler distribution, and then the uniformity scores of different preparation scenarios are obtained. By combining the particle size of the ultra-fine powder and the ratio of the thermal conductive filler under different preparation scenarios, the settling process of the thermal conductive filler in the resin system is simulated, and the distribution uniformity of the filler is marked, which can effectively evaluate the distribution state of the thermal conductive filler under different conditions, and help to optimize the thermal conductivity of the polyurethane structural adhesive, so that it can perform more stably and efficiently in actual application.

[0076] Further, the M thermal conductive filler ratios associated with the M preparation scenarios are marked for distribution uniformity, and the method of the present application further comprises:

[0077] Based on the thermal conductivity detection instance, the structural stability index of the polyurethane structural adhesive is obtained, including the processing performance index and the mechanical performance index.

[0078] The distribution of the thermal conductive filler is constrained by the structural stability index.

[0079] Specifically, the thermal conductivity detection instance refers to an instance of measuring the thermal conductivity of a material through experimental methods, which usually detects the thermal conductivity of a material in different environments through conduction, convection, radiation, etc. For example, the thermal conductivity of the polyurethane structural adhesive is measured by a thermal conductivity instrument. The structural stability index refers to the performance of the polyurethane structural adhesive in terms of mechanical properties and processing properties, which is a key parameter to ensure that the material does not crack, deform, or other problems during use. For example, tensile strength, shear strength, adhesion performance, etc. are all manifestations of structural stability. The processing performance index refers to the flowability, formability, etc. of the polyurethane structural adhesive during processing, for example, the forming speed of the material at high temperature or room temperature, the ability to fill voids, etc. The mechanical performance index refers to the reaction characteristics of the material under stress, such as tensile strength, bending modulus, fracture toughness, etc., which is a key parameter to ensure long-term reliable use of the material. Distribution constraint refers to ensuring the uniform distribution of the thermal conductive filler in the material system by setting specific conditions or restrictions. The constraint conditions can be based on mechanical properties, processing properties, density, etc. to avoid problems caused by uneven material performance.

[0080] Execution step: detect the thermal conductivity of the polyurethane structural adhesive through experiments. Use a thermal conductivity testing instrument (such as a laser thermal conductivity tester or a heat flow meter) to measure the thermal conductivity of the material at different temperatures to obtain the detection result of the thermal conductivity, and at the same time, obtain the structural stability index of the polyurethane structural adhesive, including the mechanical properties and processing properties of the material. The mechanical property index is determined by a tensile testing machine to measure the tensile strength of the material, or by a three-point bending test to measure the bending modulus, for example, the tensile strength of the polyurethane structural adhesive is 15 MPa, and the shear strength is 8 MPa. Mechanical testing instruments, such as Instron tensile testing machines, are used to measure the tensile strength and elongation of the polyurethane structural adhesive.

[0081] After obtaining the structural stability index, the distribution of the thermal conductive filler is constrained to ensure uniform distribution of the filler in the material, thereby improving the overall structural stability. For example, if excessive settling of the thermal conductive filler causes a decrease in mechanical properties in certain areas, a distribution constraint condition is set to make the distribution of the filler more uniform. Specifically, in combination with the mechanical property index, the thermal conductive filler is constrained from gathering too much in local areas to avoid causing stress concentration or leading to material fracture. For example, if a certain filler causes the tensile strength of the material to decrease to less than 10 MPa, the filler ratio needs to be adjusted to ensure that the structural stability is not affected. For example, in areas with high concentration of fillers, the tensile strength of the material may decrease, and the distribution of the fillers is constrained by simulation to avoid this phenomenon.

[0082] Based on the distribution of the fillers and the structural stability of the material, a distribution uniformity marker is set to mark the uniformity of the distribution of the thermal conductive filler in each preparation scenario by combining the uniformity of the filler distribution with the mechanical and processing properties. If the filler distribution in a certain scenario meets the structural stability requirement, it is marked as uniform; if the filler distribution in a certain scenario does not meet the requirement, it is marked as non-uniform. The uniformity of the filler distribution is classified and marked to determine the uniformity of different filler distributions and mark it in combination with the structural stability index. The structural stability index of the polyurethane structural adhesive (including processing properties and mechanical properties) is obtained by thermal performance detection, and the distribution of the thermal conductive filler is constrained to ensure that the uniformity of the filler distribution matches the structural stability. Through simulation and experimental analysis, the distribution of the thermal conductive filler is optimized by distribution constraint to ensure that the overall performance of the material under different filler ratios meets the expected requirements.

[0083] In summary, the beneficial effects of the embodiments of the present application are:

[0084] 1. The application obtains information of polyurethane base material, determines the performance correlation index of the thermal conductive filler, obtains M preparation scenes and proportions; measures the density of the polyurethane and partitions the holes, generates the thermal conductive filler mixed component combined with the particle size of the superfine powder, establishes a simulation model, initializes with the initial thermal conductivity, simulates the thermal conduction influence, predicts the thermal conductivity performance under different proportions, solves the technical problems of unstable thermal conductivity performance caused by uneven distribution of the thermal conductive filler, filler settlement and difficulty in accurately controlling the thermal conduction path during preparation, realizes the accurate proportioning control of the thermal conductive filler, the uniform distribution of the filler mixed component and the simulation of the thermal conduction path of the simulation model through the thermal conductivity performance detection method based on density analysis, and achieves the technical effects of optimizing the distribution state of the thermal conductive filler of the polyurethane structural adhesive, improving the material thermal conductivity, improving the detection accuracy and material structure stability

[0085] 2. The application obtains the structure stability index of the polyurethane structural adhesive based on the thermal conductivity performance detection instance, the structure stability index includes the processing performance index and the mechanical performance index; the distribution of the thermal conductive filler is optimized through the distribution constraint combined with simulation and experimental analysis, and the overall performance of the material under different filler proportions is ensured to meet the expected requirements.

[0086] In summary, any step can be stored in an unlimited computer memory as computer instructions or programs, and can be called and recognized by an unlimited computer processor, and no additional limitation is made here.

[0087] Further, the above technical solution only represents the preferred technical solution of the technical solution of the application, and some changes made by the technical personnel in the technical field to some parts of the application also represent the principles of the novel application. Obviously, the technical personnel in the field can make various modifications and changes to the application without departing from the scope of the application.

Claims

1. A method for testing the thermal conductivity of polyurethane structural adhesives based on density analysis, characterized in that, The method includes: Obtain basic material information for the polyurethane substrate, including the polyurethane structure and initial thermal conductivity. Based on the material performance indicators of various thermally conductive fillers and the basic material information of the polyurethane substrate, the performance correlation indicators of various thermally conductive fillers are determined. Based on the performance correlation indicators of the various thermally conductive fillers, and by referring to the resin-filler system formed by ultrafine powder, M preparation scenarios are obtained, and each of the M preparation scenarios corresponds to a thermally conductive filler ratio. The density of the polyurethane substrate is measured, and the polyurethane structure in the basic material information is divided into zones under density constraints, and multiple density cluster pores are set. In the resin-filler system formed by ultrafine powder, a thermally conductive filler mixture is generated by combining the particle size of the ultrafine powder and the M thermally conductive filler ratios associated with the M preparation scenarios. Based on the aforementioned thermally conductive filler mixture, a polyurethane structural adhesive simulation model was established by simulating the sedimentation process of multiple thermally conductive filler components in relation to the aforementioned multiple density cluster pores. Based on the polyurethane structural adhesive simulation model, the initial thermal conductivity in the basic material information is used for initial configuration, and the influence of the thermally conductive filler on the thermal conduction path is simulated in the M preparation scenarios to predict the thermal conductivity of the polyurethane structural adhesive under different thermally conductive filler ratios.

2. The method for testing the thermal conductivity of polyurethane structural adhesives based on density analysis as described in claim 1, characterized in that, Based on the performance correlation indicators of the various thermally conductive fillers, and by comparing them with the resin filler system formed from ultrafine powder, M preparation scenarios are obtained. The method includes: Obtain heat dissipation demand information, which includes heat load information and environmental condition information; Based on the heat load information in the heat dissipation demand information, and combined with the heat dissipation method associated with the polyurethane structural adhesive, a first scenario parameter set is set. By combining the environmental condition information in the heat dissipation requirement information with the heat dissipation method associated with the polyurethane structural adhesive, a second set of scene parameters is set. Based on the first scene parameter set and the second scene parameter set, the M preparation scenes are set.

3. The method for testing the thermal conductivity of polyurethane structural adhesives based on density analysis as described in claim 2, characterized in that, By using the heat load information in the heat dissipation demand information, combined with the heat dissipation method associated with the polyurethane structural adhesive, a first scenario parameter set is set, the method including: Connect to the target heat dissipation device and obtain heat flow distribution maps under different operating conditions; By using the heat flow distribution maps under different working conditions, the heat conduction path between the polyurethane structural adhesive and the target heat dissipation device is fitted. Based on the heat load information in the heat dissipation demand information, and by comparing the heat conduction path between the polyurethane structural adhesive and the target heat dissipation device, a first scenario parameter set is obtained.

4. The method for testing the thermal conductivity of polyurethane structural adhesives based on density analysis as described in claim 3, characterized in that, Based on the heat load information in the heat dissipation demand information, and by comparing the heat conduction path between the polyurethane structural adhesive and the target heat dissipation device, a first scenario parameter set is obtained. The method includes: By using heat flow distribution patterns under different working conditions and resin filler systems formed by ultrafine powder, initial scenario indicators are added, including thermal conductivity. Based on the initial scenario indicators, the initial scenario parameters are determined by comparing the heat conduction path between the polyurethane structural adhesive and the target heat dissipation device. Based on the heat load information in the heat dissipation demand information and combined with the initial scenario parameters, the first scenario parameter set is obtained.

5. The method for testing the thermal conductivity of polyurethane structural adhesives based on density analysis as described in claim 4, characterized in that, Based on the heat load information in the heat dissipation demand information and combined with the initial scenario parameters, the first scenario parameter set is obtained, and the method includes: Based on the heat load information in the heat dissipation demand information and combined with the initial scenario parameters, a first thermal cycle is added; In the first thermal cycle, the temperature cycle is configured with the target temperature range associated with the target heat dissipation device. After the first thermal cycle completes the temperature cycle configuration, the first scenario parameter set is obtained by comparing the number of cycles.

6. The method for testing the thermal conductivity of polyurethane structural adhesives based on density analysis as described in claim 5, characterized in that, In the first thermal cycle, a first thermal stability coefficient is set for the thermal expansion behavior; In the first thermal cycle, a second thermal stability coefficient for cold contraction behavior is set; The temperature cycle configuration index of the first thermal cycle is adjusted by using the first thermal stability coefficient of the thermal expansion behavior and the second thermal stability coefficient of the cold contraction behavior.

7. The method for testing the thermal conductivity of polyurethane structural adhesives based on density analysis as described in claim 1, characterized in that, In the resin-filler system formed from ultrafine powder, the method further includes, in conjunction with the particle size of the ultrafine powder and the M thermally conductive filler ratios associated with the M preparation scenarios: In the resin-filler system formed by ultrafine powder, the sedimentation process of the thermally conductive filler was simulated by the multiple density cluster pores to obtain the distribution state of the mixed components of the thermally conductive filler. The uniformity of the distribution of the thermally conductive filler mixture components is marked for the M thermally conductive filler ratios associated with the M preparation scenarios based on their distribution status.

8. The method for testing the thermal conductivity of polyurethane structural adhesives based on density analysis as described in claim 7, characterized in that, The method further includes marking the uniformity of distribution of M thermally conductive filler ratios associated with the M preparation scenarios, and the method also includes: Based on a thermal conductivity testing example, the structural stability index of polyurethane structural adhesive is obtained. The structural stability index includes processing performance index and mechanical performance index. The distribution of thermally conductive filler is constrained by the aforementioned structural stability index.

Images