Design and verification method of a multi-layer composite adhesive tape

Abstract

The application relates to a design and verification method of a multi-layer composite adhesive tape in the technical field of intelligent manufacturing, which comprises the following steps: obtaining heat conduction performance data and insulation performance data of the adhesive tape material, constructing a multi-dimensional performance database, and generating a performance distribution map according to the multi-dimensional performance database; according to the performance distribution map, a layered screening method is used to design a multi-layer structure combination, and heat accumulation simulation parameters and insulation performance simulation parameters of the multi-layer structure combination are obtained; according to the heat accumulation simulation parameters and the insulation performance simulation parameters, combined with mechanical strength test data, a comprehensive performance evaluation model is constructed, and an optimal material composite scheme is screened out through the comprehensive performance evaluation model; according to the optimal material composite scheme, heat accumulation distribution data under high load operation are obtained by using a finite element analysis method, and heat accumulation peak values and potential safety hazard points are determined.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology, and in particular to a design and verification method for multilayer composite tape. Background Technology

[0002] The lithium battery manufacturing field is crucial for improving battery performance and safety. The core of this lies in the materials and processes used during battery assembly, which directly affect the battery's thermal conductivity, insulation, and structural stability. With the rapid development of electric vehicles and energy storage systems, battery pack thermal management and safety performance have become a focus of industry attention, urgently requiring innovative materials and processes to meet high-performance demands. Currently, commonly used tape solutions in battery assembly often face the dilemma of balancing multiple performance aspects. Traditional methods often employ single materials or simple composite methods, which, while meeting certain requirements, struggle to simultaneously address the comprehensive requirements of thermal conductivity, insulation, and mechanical strength. This can lead to potential safety hazards due to heat accumulation during high-power operation, or compromise long-term reliability due to insufficient material strength.

[0003] Against this backdrop, the thermal conductivity and insulation properties of adhesive tape materials have become key technical bottlenecks. Insufficient thermal conductivity limits the heat dissipation efficiency of batteries under high loads, leading to temperature increases and consequently affecting battery life and safety. High thermal conductivity materials are often accompanied by lower insulation properties, making it difficult to meet the stringent high-voltage requirements of battery packs. Furthermore, the mechanical strength of the tape is also a significant constraint, especially during battery assembly and operation, where the tape needs to withstand complex shear stresses. Insufficient strength can lead to material delamination or adhesive failure, affecting the structural integrity of the battery. The interplay between the contradictions between thermal conductivity and insulation, and the demand for mechanical strength, makes it difficult to achieve comprehensive performance optimization using a single material or traditional composite processes.

[0004] Therefore, designing a multilayer composite tape process that can maintain high thermal conductivity while possessing excellent insulation properties and significantly improving shear strength has become a key issue in the development of lithium battery assembly technology. Summary of the Invention

[0005] This invention provides a method for designing and verifying multilayer composite tapes, mainly including:

[0006] The thermal conductivity and insulation performance data of the adhesive tape material are obtained, and a multi-dimensional performance database is constructed. A performance distribution map is generated based on the multi-dimensional performance database. Based on the performance distribution map, a multi-layer structure combination is designed using a layered screening method, and the heat accumulation simulation parameters and insulation performance simulation parameters of the multi-layer structure combination are obtained. Based on the heat accumulation simulation parameters and insulation performance simulation parameters, combined with mechanical strength test data, a comprehensive performance evaluation model is constructed. The preferred material composite scheme is selected through the comprehensive performance evaluation model. Based on the preferred material composite scheme, the heat accumulation distribution data under high-load operation is obtained using finite element analysis, and the heat accumulation peak and potential safety hazard points are determined. For the heat accumulation peak and potential safety hazard points, the local material ratio and thickness parameters of the material composite scheme are adjusted to obtain the adjusted values. The adjusted heat distribution and insulation performance data are used to determine whether they meet the safety standards for high-voltage environments, resulting in an optimized structural design. Shear force simulation tests are then performed on the optimized structural design to obtain deformation and adhesion failure data of the tape under complex stress environments, determining whether the mechanical strength meets stability requirements and obtaining performance verification results. Based on these performance verification results, the multilayer interface bonding process parameters are adjusted to obtain updated shear force and structural integrity data, determining whether they meet comprehensive performance requirements and obtaining the final multilayer composite tape process scheme. Based on the final multilayer composite tape process scheme, a correlation model between tape performance and battery life is constructed to obtain predicted data on heat accumulation and safety hazards under long-term operation, determining the reliability indicators of the process scheme. Furthermore, the acquisition of thermal conductivity and insulation performance data of the adhesive tape material, the construction of a multi-dimensional performance database, and the generation of a performance distribution map based on the multi-dimensional performance database include: conducting environmental load tests on different adhesive tape materials, collecting heat accumulation characteristic data under high load operation and insulation failure threshold data in a high-voltage environment to obtain a test dataset; constructing a multi-dimensional database for the test dataset, classifying and storing the heat accumulation characteristic data and insulation failure threshold data according to material type, and determining a benchmark value for data acquisition accuracy; analyzing the changing trend and obtaining key characteristic values ​​of material performance distribution by statistically comparing the heat distribution pattern with the threshold mapping of high-voltage insulation response based on the multi-dimensional database; using the key characteristic values ​​of material performance distribution, combined with the test condition simulation results derived from the test dataset, constructing a performance map, and determining the applicable range of the adhesive tape material in different environments.Furthermore, the step of designing a multi-layer structure combination using a stratified screening method based on the performance distribution map, and obtaining the heat accumulation simulation parameters and insulation performance simulation parameters of the multi-layer structure combination, includes: obtaining initial data from the performance distribution map, analyzing the thermal conductivity and insulation properties of materials at different layers, and determining the preliminary classification range of the thermally conductive layer material and the basic matching scheme of the insulation layer thickness; constructing a multi-layer structure model using a stratified screening method based on the preliminary classification range of the thermally conductive layer material and the basic matching scheme of the insulation layer thickness, and obtaining preliminary values ​​for interlayer heat accumulation assessment; adjusting the screening conditions for the structure combination based on the preliminary values ​​of the interlayer heat accumulation assessment, optimizing the interlayer interface contact characteristics, and determining the specific layer sequence and thickness distribution scheme of the multi-layer structure combination; after obtaining the specific layer sequence and thickness distribution scheme of the multi-layer structure combination, constructing a virtual test environment, inputting the heat accumulation assessment data and insulation layer thickness parameters, and obtaining the insulation performance value under the simulated scenario; adjusting the refined parameters of the multi-layer structure combination based on the insulation performance value and the heat accumulation assessment data, and determining the final heat accumulation simulation parameters and insulation performance simulation parameters. Furthermore, the step of constructing a comprehensive performance evaluation model based on the heat accumulation simulation parameters and insulation performance simulation parameters, combined with mechanical strength test data, and selecting preferred material composite schemes through the comprehensive performance evaluation model includes: obtaining initial parameter data of the multilayer structure combination, the initial parameter data including thickness, density, and elastic modulus; setting simulation scenarios for different combination methods; extracting key parameters; and determining the applicable range of the initial parameter data; based on the initial parameter data, using finite element analysis software to simulate the response of the multilayer structure combination under different loads; obtaining simulation results of mechanical strength test data and shear force data; correcting outliers in the simulation results; and obtaining performance data of the multilayer structure combination under various working conditions; and using the performance data... A comprehensive performance evaluation system is constructed. This system allocates weights for mechanical strength test data and shear force data based on preset weights, calculates the structural integrity score for each combination method, and introduces interlaminar bond strength distribution as an evaluation dimension to determine the stability performance of the multilayer structure combination. For the stability performance, failure mode identification features are extracted from the performance data. These features are obtained by comparing the peak interlaminar stress with a preset threshold. The preset threshold is used to classify the risk level of each combination method, obtaining a quantitative result for the adhesive failure risk of the multilayer structure combination. By comprehensively comparing the quantitative result with the structural integrity score, combination methods that meet the performance indicators are selected. Combined with the optimization and adjustment of the interlaminar bond strength distribution, the final material composite scheme is determined.Furthermore, the step of obtaining heat accumulation distribution data under high load operation using finite element analysis based on the preferred material composite scheme, and determining the heat accumulation peak and potential safety hazards, includes: extracting the density, specific heat capacity, and thermal conductivity parameters of each layer of material from the preferred material composite scheme, and simultaneously obtaining the internal heat source power density distribution and external environment convective heat transfer coefficient corresponding to high load operation, as input conditions for thermal simulation analysis; using the input conditions, constructing a three-dimensional geometric model of the material composite scheme in finite element analysis software, performing non-uniform mesh generation on the model, setting material properties in the heat conduction physical field, applying internal heat source power density distribution and surface convection boundary conditions, and establishing a transient thermal analysis model; and running the simulation. The transient thermal analysis model is described above to calculate the spatiotemporal distribution data of the temperature field of the composite material structure during a complete working cycle. This spatiotemporal distribution data is then used as a thermal load and mapped onto the structural mechanics physical field of the same finite element model. The distribution of thermal stress caused by temperature differences is calculated. Simultaneously, by traversing and comparing all node temperature values ​​in the spatiotemporal distribution data, the global and local temperature peak coordinates and corresponding temperature values ​​are identified. The temperature peak values ​​are compared with preset safe temperature thresholds for each layer of material. If the temperature in a certain area exceeds the safe temperature threshold, it is marked as an overheating risk point. Furthermore, by combining the regions in the thermal stress distribution that exceed the material's yield strength, the locations of potential safety hazards in the composite material structure are comprehensively determined.Furthermore, the adjustment of local material ratios and thickness parameters of the composite material scheme for the aforementioned heat accumulation peak and potential safety hazards, obtaining adjusted heat distribution data and insulation performance data, and determining whether the adjusted heat distribution data and insulation performance data meet the high-voltage environment safety standards, to obtain an optimized structural design, includes: establishing a thermal-electric coupling field simulation model using the finite element method based on the high-voltage environment parameter input and the initial structure of the composite tape; running the model to obtain the initial state heat distribution simulation results and electric field distribution results; extracting the heat peak location from the heat distribution simulation results, and identifying high field strength regions from the electric field distribution results as the basis for hazard identification; adjusting the material ratio adjustment parameters and thickness parameter settings for the corresponding regions in the simulation model for the local regions determined by the heat peak location and hazard identification; running the updated simulation model to obtain the adjusted heat distribution data and electric field distribution data; and using a dielectric spectrum analyzer to measure the insulation performance of the tape sample prepared according to the adjusted parameters. The process involves obtaining dielectric loss factor and volume resistivity data in the frequency domain; combining these data with the adjusted electric field distribution data, calculating the local dielectric strength using a product formula, and subtracting a preset margin threshold to obtain the safety margin, thus acquiring the insulation performance data of the tape under high voltage; comparing the adjusted heat distribution data with a preset heat accumulation threshold, and simultaneously comparing the insulation performance data with high voltage safety standards; if the heat distribution data exceeds the threshold or the insulation performance data falls below the standard, it is determined that the safety requirements are not met, and the material and thickness parameter combinations for the non-compliant areas are recorded; based on the determination results and the recorded parameter combinations, an optimization algorithm is used to iteratively search the solution space of material ratio and thickness parameters. The objective function of the optimization algorithm integrates the heat distribution uniformity index and the insulation safety margin index. When the objective function value reaches the convergence condition, a material ratio and thickness parameter combination that meets all safety standard comparison requirements is obtained, and this combination is the optimized structural design.Furthermore, the step of conducting shear force simulation tests on the optimized structural design to obtain deformation data and adhesive failure data of the tape under complex stress environments, determining whether the mechanical strength meets the stability requirements, and obtaining performance verification results includes: establishing a shear force simulation model based on the optimized structural design, inputting complex stress environment parameters, running the model to obtain tape deformation distribution data and adhesive stress values, and obtaining initial mechanical response indicators; calculating the cumulative damage degree of the tape within the battery life cycle using the finite element analysis method based on the deformation distribution data, identifying potential failure points in conjunction with the adhesive stress values, and determining the mechanical strength distribution map; extracting stability indicators of key areas from the mechanical strength distribution map, comparing them with a preset battery life cycle threshold, and adjusting model parameters if the indicators are lower than the threshold to obtain an optimized strength margin value; and generating a comprehensive report based on the strength margin value, integrating fatigue life prediction data obtained from the cumulative damage degree, determining whether the tape meets the stability requirements, and obtaining the final performance verification results. Furthermore, based on the performance verification results, the process parameters for adjusting the multilayer interface bonding are adjusted to obtain updated shear strength data and structural integrity data. This is used to determine whether the overall performance requirements are met, resulting in the final multilayer composite tape process scheme. This includes: extracting the coordinates and interface stress distribution of local bonding failure points from the performance verification results; combining the constitutive relationships of each layer of the tape; dividing the failure point neighborhood using a finite element mesh and applying boundary conditions; calculating the stress concentration factor and energy release rate; and quantifying the failure risk level. For the failure risk level, a process parameter vector space is established with curing temperature, lamination pressure, and holding time as dimensions. An orthogonal experimental design method is used to select sample points within the process parameter vector space for parameter sensitivity analysis, obtaining the process window boundary corresponding to each sample point. Based on the results... Based on the results of the parameter sensitivity analysis, a set of optimized parameter combinations within the process window boundary is selected to prepare new multilayer composite tape samples. Shear strength and peel strength data of the samples are obtained through high and low temperature cyclic shear tests and constant load peel tests. Using the shear strength and peel strength data, combined with creep deformation obtained through continuous monitoring under constant temperature and load, and the number of fatigue cycles recorded under alternating load, a multidimensional performance dataset is constructed. Each indicator in the dataset is compared one by one with a preset performance requirement threshold. If all indicators in the multidimensional performance dataset are not lower than the performance requirement threshold, the optimized parameter combination is determined to meet the comprehensive performance requirements. The parameter combination and its corresponding process window definition result are recorded as the final multilayer composite tape process scheme.Furthermore, based on the final multilayer composite tape process scheme, a correlation model between tape performance and battery life is constructed to obtain long-term heat accumulation prediction data and safety hazard prediction data, and to determine the reliability indicators of the process scheme. This includes: collecting tape performance parameters, obtaining tensile strength and heat resistance data of the tape material from various operating environments, constructing an initial database of tape performance parameters, and determining the performance benchmark of the tape under different conditions; for the tape performance parameter database, combined with battery life cycle test data, a performance correlation model is established using a support vector machine algorithm, with the input being the tensile strength and heat resistance data of the tape material, and the output being the mapping relationship with the battery life cycle, obtaining the corresponding law between the two; based on the... The performance correlation model simulates the heat accumulation distribution under long-term operation monitoring scenarios. Temperature change data is collected from different time periods of battery operation to obtain the dynamic trend of heat accumulation distribution and identify key influencing areas. Based on the heat accumulation distribution, potential triggering conditions for safety hazard indicators are analyzed. Anomalies exceeding preset thresholds are extracted from temperature change data to construct a hazard early warning mechanism and obtain the early warning threshold range for safety hazard indicators. Using the early warning threshold range of the safety hazard indicators, the optimization effect of the process scheme is evaluated in application scenario verification. Anomaly distribution is compared from battery test data under various operating environments to obtain quantitative results of reliability index values ​​and determine the stable performance of the process scheme in practical applications.

[0007] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0008] This invention discloses a design method for high-performance multilayer composite tapes, aiming to solve business scenario problems such as the contradiction between thermal conductivity and insulation performance, the safety hazards of heat accumulation, and the risk of adhesive failure. This invention constructs a multi-dimensional performance database to analyze the heat accumulation and insulation response of materials under extreme conditions. It combines a layered screening method to design multilayer structural combinations and utilizes finite element analysis and simulation testing to optimize heat distribution and mechanical strength. Material ratios and process parameters are adjusted for potential hazards in key areas, ultimately forming a process scheme that meets the stability and reliability requirements throughout the battery's lifespan. Through full-process data integration, this invention establishes a correlation model between tape performance and battery life, predicting safety risks during long-term operation and ensuring the structural integrity of the tape under complex stress environments. Its core technical effect lies in achieving a balance between thermal conductivity and insulation performance, significantly reducing peak heat accumulation and the risk of adhesive failure, providing a reliable guarantee for the efficient and safe operation of battery systems. Attached Figure Description

[0009] Figure 1 This is a flowchart of a design and verification method for a multilayer composite tape according to the present invention. Detailed Implementation

[0010] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0011] like Figure 1 The design and verification method for a multilayer composite tape in this embodiment may specifically include:

[0012] Step S101: By collecting data on the thermal conductivity and insulation properties of the tape material, a multi-dimensional performance database is constructed, which includes the heat accumulation characteristics of different materials under high load operation and the insulation response in a high-voltage environment, thus obtaining a preliminary performance distribution map.

[0013] By conducting environmental load tests on different adhesive tape materials, thermal conductivity data and insulation performance indicators were collected. The heat accumulation characteristics under high load operation and the insulation failure threshold in high-voltage environments were recorded to obtain a preliminary test dataset. For this test dataset, a multi-dimensional database was constructed, classifying and storing heat accumulation characteristics and high-voltage insulation responses according to material type, and determining the baseline value for data acquisition accuracy. Based on this multi-dimensional database, the changing trends were analyzed through statistical comparison of heat distribution patterns and threshold mapping of high-voltage insulation responses, obtaining key characteristic values ​​of material performance distribution. Using these characteristic values ​​of material performance distribution, combined with simulation results of test conditions derived from the test dataset, a performance spectrum was constructed to determine the applicable range of the adhesive tape material in different environments.

[0014] In one embodiment, the thermal conductivity data of the tape material can be collected using the heat flow meter method.

[0015] Specifically, the tape sample is placed between heat flux sensors, with a constant heat source applied to one side and the other side kept cool. The thermal conductivity is calculated by measuring the heat flux density and temperature difference. This method ensures data accuracy because it directly quantifies the material's heat transfer capacity. Under high-load operation, such as simulating continuous current flow, acquiring heat accumulation characteristics involves monitoring the sample temperature change curve over time, recording the cumulative heat from the initial state to the saturation point, thus forming a characteristic dataset. This process is applicable to the field of electrical insulation tapes, such as materials used for cable wrapping. Furthermore, the acquisition of insulation performance data focuses on the response in high-voltage environments.

[0016] For example, using high-voltage testing equipment, a tape sample is placed between electrodes, and the voltage is gradually increased until breakdown is observed, while simultaneously recording the insulation resistance and dielectric strength. These data reflect the material's ability to withstand high voltage, for example, in substation cable insulation applications, to prevent electrical faults.

[0017] It should be noted that high-voltage environment simulation can include humidity control to assess the impact of environmental factors on insulation response, thereby enriching the multidimensionality of the database. The process of constructing a multidimensional performance database based on the collected data includes data classification and storage.

[0018] In one embodiment, thermal conductivity data is first categorized, such as by material type (e.g., silicone-based or rubber-based tapes), and then heat accumulation characteristics are integrated. These characteristics are obtained by calculating the cumulative heat energy integral, for example, by integrating temperature-time curves to quantify total heat. This approach ensures that the database covers the performance of different materials under high loads, such as thermal decay characteristics under prolonged electrical loads, helping to identify tape types with excellent heat resistance. Preferably, insulation response data in high-voltage environments is included in the database.

[0019] Specifically, these responses include voltage-current curves and breakdown thresholds, with response characteristics formed through statistical analysis of the distribution of different materials, such as average breakdown voltage and standard deviation. The database uses a relational structure to store multi-dimensional information, such as linking thermal conductivity with insulation resistance, facilitating subsequent queries. In the field of electrical tape production, this database supports material screening, such as selecting tapes suitable for high-voltage transmission lines. In one possible implementation, preliminary performance distribution maps are obtained through visualization tools.

[0020] For example, thermal conductivity and insulation performance data from a database can be mapped to a two-dimensional coordinate system, with the horizontal axis representing thermal conductivity and the vertical axis representing insulation strength. A point-like distribution displays the performance points of different materials. Furthermore, heat accumulation characteristics are incorporated as color coding, with high accumulation values ​​represented by warm colors, thus visually displaying the material's thermal stability under high loads. This map generation is suitable for tape material R&D scenarios, helping engineers quickly compare the overall performance of various tapes in high-pressure environments. Understandably, the generation of this performance distribution map can also be extended to three-dimensional views.

[0021] In one embodiment, high-load operating time is added as a third dimension to show the dynamic change of heat accumulation over time, for example, by representing the insulation degradation curves of different materials under continuous high voltage using surface plots. This extension enhances the analytical depth of the graph, enabling the prediction of potential fault points in cable maintenance, thereby improving system reliability. Specifically, data verification steps are emphasized during the database construction process.

[0022] For example, the collected thermal conductivity data are repeatedly tested, and the deviation rate is calculated to ensure reliability; samples exceeding a threshold need to be re-collected. This verification is applicable to high-voltage insulation response, ensuring that the characteristics accurately reflect material behavior. In electrical equipment tape applications, this step reduces errors in performance evaluation. Furthermore, the high-load heat accumulation characteristics of different materials can be obtained through simulation experiments.

[0023] For example, a simulated current load is applied to silicone tape, and the process of heat accumulation to a steady state is monitored, characterized by peak temperature and accumulation time constant. These characteristics are integrated into a database to support a refined representation of the heat distribution in the spectrum. In one embodiment, the output of the performance distribution spectrum includes statistical summaries, such as calculating cluster centers of material properties, for classifying high-performance tape groups. This summary provides a basis for decision-making in the tape manufacturing field, such as prioritizing the development of insulating materials with low heat accumulation. It should be noted that the entire process is limited to the field of tape material performance evaluation to ensure the specificity of the technical solution. Through these embodiments, the technical features of the claims are specifically supported, demonstrating the versatility of the solution in electrical insulation scenarios.

[0024] Step S102: Based on the performance distribution map, and considering the contradiction between thermal conductivity and insulation performance, a layered screening method is used to conduct a preliminary design of the material composite scheme, determine the potential multi-layer structure combination, and obtain the simulation parameters of each combination in terms of heat accumulation and insulation performance.

[0025] To address the conflict between optimizing thermal conductivity and ensuring insulation performance, initial data is obtained from a pre-established performance distribution map. The thermal conductivity and insulation characteristics of materials at different layers are analyzed to determine the preliminary classification range of each layer, resulting in a basic combination scheme for the thermally conductive layer material and the insulation layer thickness. Based on this combination scheme, a layered screening method is used to combine materials in layered designs, constructing a multi-layer structure model. The focus is on the impact of interlayer thermal conductivity, obtaining preliminary values ​​for interlayer heat accumulation assessment. Using these interlayer heat accumulation assessment values, the structural combination screening conditions are adjusted to optimize interlayer interface contact characteristics, determining the specific layer sequence and thickness distribution scheme for the multi-layer structure combination, based on the operational requirements of balancing performance conflicts. After obtaining the specific layer sequence and thickness distribution scheme for the multi-layer structure combination, a virtual test environment is constructed to simulate insulation parameters. The heat accumulation assessment data and insulation layer thickness parameters are input to obtain the insulation performance values ​​under the simulated scenario. Based on the insulation performance values ​​and heat accumulation assessment data, combined with the performance distribution analysis benchmark, and for the comprehensive business of optimizing thermal conductivity and ensuring insulation performance, the refined parameters of the multilayer structure combination are adjusted to determine the final simulation parameter results of heat accumulation and insulation performance.

[0026] In one implementation, a performance distribution map is first constructed to map the thermal conductivity and insulation performance indicators of various materials.

[0027] Specifically, the performance distribution map is formed by collecting parameters such as thermal conductivity and insulation resistance values ​​from a material database to create a two-dimensional or multi-dimensional scatter plot, thereby visually displaying the distribution of different materials in these properties.

[0028] For example, by plotting thermal conductivity on the horizontal axis and insulation performance on the vertical axis, points indicating materials with high thermal conductivity but low insulation, and vice versa, can be marked. This type of plot helps identify regions of performance inconsistency—those areas with excellent thermal conductivity but insufficient insulation. In this way, a data foundation is provided for subsequent composite design. Furthermore, the inconsistencies between thermal conductivity and insulation performance can be analyzed.

[0029] It should be noted that the contradiction usually refers to the difficulty in simultaneously meeting the requirements of high thermal conductivity and high insulation in a single material. For example, metallic materials have good thermal conductivity but poor insulation, while ceramic materials have good insulation but weak thermal conductivity.

[0030] In one possible implementation, these points are identified through quantitative methods, such as calculating performance deviation values; points where the difference between thermal conductivity and insulation performance exceeds a preset threshold are marked as inconsistencies. This analysis process includes data preprocessing, deviation calculation, and point labeling to ensure accurate capture of areas requiring optimization, thus laying the foundation for a stratified screening method. A preliminary design of the material composite scheme is then performed using this stratified screening method.

[0031] Specifically, the layered screening method is an iterative optimization approach. First, materials with strong thermal conductivity are selected as inner layer candidates from the performance distribution map. Then, materials with strong insulation properties are selected as outer layer candidates. Through this layer-by-layer stacking, a potential multi-layer structure is formed.

[0032] For example, in the scenario of heat dissipation and insulation in electronic devices, copper-based materials are first selected as the thermally conductive layer, and then polymer materials are selected as the insulating layer, with compatibility evaluated layer by layer. The core of this method lies in the layered logic, that is, each layer is selected based on specific performance requirements, avoiding contradictions at a single level.

[0033] In one embodiment, potential multi-layer structure combinations are determined using a combination algorithm.

[0034] Understandably, the selected materials are arranged and combined according to the number of layers (e.g., 2 to 5 layers). For example, the inner layer uses a high thermal conductivity metal, the middle layer uses a transition material, and the outer layer uses a high insulating polymer, forming various combinations such as metal-ceramic-polymer structures. This combination method considers interface compatibility and thickness parameters to ensure structural stability. Through this design, multiple feasible solutions are generated, supporting application scenarios in the same field, such as battery casings or circuit boards.

[0035] Preferably, simulated parameters for heat accumulation and insulation performance of each combination are obtained.

[0036] Specifically, finite element simulation software is used to simulate the thermal conduction and electrical insulation of the multilayer structure.

[0037] For example, regarding heat accumulation parameters, the temperature distribution and accumulated heat value inside the structure are calculated by inputting heat sources and environmental conditions; for insulation performance, the resistance value and breakdown voltage under applied voltage are simulated. This simulation process includes model building, boundary condition setting, and iterative calculation to obtain quantitative parameters such as a heat accumulation value of 50 joules per cubic centimeter and an insulation resistance of 10⁻¹² ohms. These parameters are used to evaluate the overall performance of the combination. Furthermore, in another implementation, the parameters of the layered screening are adjusted for the thermal management equipment scenario, such as increasing the number of layers to optimize heat distribution.

[0038] For example, using a three-layer structure—a thermally conductive layer, a buffer layer, and an insulating layer—simulation results show a 20% reduction in heat accumulation and improved insulation performance. This flexible adjustment demonstrates the versatility of the solution.

[0039] For example, this method can achieve a balanced optimization of material properties in practice, providing a reliable design path for composite solutions.

[0040] Step S103: By analyzing the simulation parameters of the multi-layer structure combination and combining the test data of mechanical strength and shear resistance, a comprehensive performance evaluation model is constructed to judge the performance of each combination in terms of structural integrity and adhesive failure risk, and to obtain the preferred material composite scheme.

[0041] By classifying and organizing the material properties of each layer of the multilayer structure, initial parameter data of the multilayer structure, including basic attributes such as thickness, density, and elastic modulus, is obtained. Simulation scenarios are set for different combination methods, and key parameters are extracted for subsequent analysis to determine the applicability of the initial parameter data. Based on the initial parameter data, finite element analysis software is used as a numerical simulation tool to simulate the response of the multilayer structure under different loads, obtaining simulation results of mechanical strength test and shear force data. Outliers in the simulation results are corrected to obtain performance data of the multilayer structure under various working conditions. Based on the performance data, a comprehensive performance evaluation system is constructed. This system allocates weights for mechanical strength test data and shear force data based on preset weights, where the weights are determined according to the proportion of historical experimental data. The structural integrity score of each combination method is calculated, and the interlayer bond strength distribution is introduced as a new evaluation dimension to determine the stability performance of the multilayer structure under different scenarios. To assess the stability performance, potential factors contributing to adhesive failure risk are analyzed. Failure mode identification (FMID) features are extracted from the performance data. These features are obtained by comparing interlaminar stress peak values ​​with a preset threshold. The preset threshold is used to classify the risk level of each combination, resulting in a quantitative result for the adhesive failure risk of the multilayer structure combination. By comprehensively comparing the quantitative result with the structural integrity score, combination methods that meet the performance indicators are selected. Combined with the optimization and adjustment of interlaminar bond strength distribution, the final material composite scheme is determined, completing the optimization process for the multilayer structure combination.

[0042] In one implementation, for the performance evaluation of multilayer structure combinations, it is first necessary to analyze the simulation parameters of the multilayer structure to clarify the performance of each layer material under different conditions.

[0043] Specifically, multilayer structures are typically composed of layers of different materials, such as metal layers, polymer layers, or composite fiber layers, which exhibit different physical properties when subjected to stress, temperature, or humidity changes.

[0044] For example, in the field of composite building panels, the stress distribution and deformation of each layer of material under external loads can be simulated using the finite element method. Furthermore, simulation parameters include the material's elastic modulus, Poisson's ratio, and interlayer bond strength. By inputting these parameters into simulation software, stress-strain curves are generated, providing fundamental data for subsequent performance evaluation. This simulation analysis can help identify potential weaknesses in multi-layer structures in specific application scenarios.

[0045] Preferably, based on the obtained simulation parameters, further tests are conducted on mechanical strength and shear resistance to verify the actual performance of the multilayer structure.

[0046] In one possible implementation, the testing process can be carried out in a laboratory environment, for example, using a universal testing machine to apply tensile and shear loads to the sample for composite panels, and record its fracture strength and deformation.

[0047] It should be noted that the test conditions should simulate the actual usage environment; for example, the effects of wind load and temperature difference need to be considered for building exterior wall panels. These test data can quantify the mechanical properties of multi-story structures, providing experimental basis for subsequent comprehensive evaluation.

[0048] Specifically, the construction of a comprehensive performance evaluation model is the core step of the entire technical solution, used to integrate simulation parameters and test data to determine the overall performance of the multi-layer structure.

[0049] In one embodiment, the model can be based on a multi-dimensional weighted analysis method, incorporating indicators such as mechanical strength, shear resistance, and interlayer bond stability into the evaluation system.

[0050] For example, in the evaluation of building composite panels, each indicator is first standardized to avoid the influence of dimensional differences on the results. Then, weights are assigned according to the importance of the application scenario. For instance, for exterior wall panels, shear resistance may be more important, so the weight can be set to 0.4, while mechanical strength may be weighted at 0.3. A comprehensive performance score is obtained through weighted calculation, which is used for subsequent risk assessment. Furthermore, the model can also incorporate historical data for calibration to ensure that the evaluation results are consistent with actual applications. This method can systematically analyze the performance of multi-layer structures and is applicable to different types of composite materials. In addition, the impact of environmental factors on material performance can be considered during model construction, such as the weakening effect of humidity on bond strength, by adjusting model parameters to simulate performance changes under different environments.

[0051] It should be noted that the model design should be scalable to allow for the addition of more evaluation dimensions in the future, such as corrosion resistance or thermal stability. The construction of this comprehensive performance evaluation model can not only quantify the performance of multilayer structures but also provide guidance during the material design phase, laying the foundation for subsequent optimal solutions.

[0052] In one possible implementation, based on the aforementioned comprehensive performance evaluation model, the structural integrity and adhesive failure risk of the multi-layer structure are further assessed.

[0053] Specifically, structural integrity can be assessed by comparing the comprehensive score output by the evaluation model with a preset threshold. If the score is lower than the threshold, it indicates that there is a potential defect in the structure.

[0054] For example, in the application of building composite panels, if a sample's shear strength score is significantly lower than the standard value, it may indicate a risk of cracking in strong winds. Furthermore, the assessment of adhesive failure risk focuses on test data and simulation results of interlayer bond strength, such as identifying potential delamination areas by analyzing interlayer stress distribution. This assessment method helps designers identify problems during the material assembly stage and adjust solutions promptly.

[0055] For example, after obtaining the evaluation results, material composite solutions suitable for specific application scenarios can be selected based on the comprehensive performance score and risk assessment.

[0056] In one embodiment, for building exterior wall panels, a preferred option may be a metal-polymer composite structure with high mechanical strength and stable shear resistance.

[0057] Preferably, this solution can further improve overall performance by adjusting the type of interlayer adhesive. In this way, it is possible to meet the usage requirements of specific scenarios while ensuring structural safety.

[0058] Understandably, the above-mentioned technical solutions have wide applicability in the field of building composite materials. Furthermore, in different implementation scenarios, such as interior decorative panels or industrial composite structures, the weighting parameters and test conditions of the evaluation model can be adjusted according to specific needs.

[0059] For example, interior decorative panels may prioritize lightweight and aesthetics, while industrial structures focus more on durability and impact resistance. By flexibly adjusting the technical solutions, diverse application needs can be met. In one implementation, the aforementioned steps form a tight logical connection, from simulation parameter analysis to test data acquisition, and then to comprehensive model construction and risk assessment; each step provides data support for subsequent steps. Furthermore, this logical process ensures the reliability of the evaluation results, providing a scientific basis for the final selection of the optimal material composite solution.

[0060] It should be noted that the above implementation methods are merely illustrative examples, and the technical solutions can be adjusted according to specific business needs in actual applications.

[0061] For example, in certain special scenarios, additional test indicators, such as fire resistance or sound insulation, can be introduced to further improve the comprehensive performance evaluation model.

[0062] Preferably, during implementation, model parameters can be optimized through multiple iterations, such as adjusting weight allocation based on actual test results, to improve the accuracy of the evaluation. This iterative approach allows for continuous improvement of the technical solution's effectiveness over long-term application. Finally.

[0063] In one possible implementation, the effectiveness of the technical solution can be verified through actual engineering cases.

[0064] For example, after applying the preferred composite panels in a certain construction project, the structure remained stable under extreme weather conditions, indicating that the technical solution has a significant effect on improving material performance.

[0065] Step S104: Based on the preferred material composite scheme, obtain its heat accumulation distribution data under high load operation, and use the finite element analysis method to perform a refined simulation of the heat distribution to determine the heat accumulation peak and potential safety hazard points in key areas.

[0066] From the preferred material composite scheme, the density, specific heat capacity, and thermal conductivity parameters of each layer are extracted. Simultaneously, the internal heat source power density distribution and the external environmental convective heat transfer coefficient corresponding to high-load operation are obtained. These parameters serve as input conditions for thermal simulation analysis. Using these input conditions, a three-dimensional geometric model of the material composite scheme is constructed in finite element analysis software. The model is then divided into non-uniform meshes, material properties are set in the heat conduction physical field, and internal heat source power density distribution and surface convection boundary conditions are applied to establish a transient thermal analysis model. The transient thermal analysis model is run to calculate the spatiotemporal distribution data of the temperature field of the material composite structure over a complete working cycle. This spatiotemporal distribution data of the temperature field is used as a thermal load and mapped onto the structural mechanical physical field of the same finite element model. The thermal stress distribution caused by temperature differences is calculated, and by traversing and comparing all node temperature values ​​in the spatiotemporal distribution data, the global and local temperature peak coordinates and corresponding temperature values ​​are identified. The peak temperature value is compared with the preset safe temperature threshold of each layer of material. If the temperature of a certain area exceeds the safe temperature threshold, it is marked as an overheating risk point. At the same time, combined with the area in the thermal stress distribution that exceeds the material yield strength, the location of potential safety hazards in the material composite structure is comprehensively determined.

[0067] In one embodiment, according to a preferred material composite scheme, the cumulative heat distribution data under high load operation is first obtained.

[0068] Specifically, this data acquisition process is aimed at the field of building composite panels, such as in the application of exterior wall panels, by deploying temperature sensor arrays on the sample surface and between internal layers to monitor heat changes in real time under high load conditions such as continuous wind load or high temperature environment.

[0069] It should be noted that high-load operation refers to simulating the extreme pressure and thermal stress experienced by the panels in actual building scenarios, such as dynamic loads when wind speeds reach 30 meters per second. The data collected by sensors includes the temperature gradient and heat flux density of each layer of material, which are aggregated using a data logger to form a distribution map. This method ensures that the data accurately reflects the heat accumulation pattern in the multi-layered structure, providing a reliable foundation for subsequent simulations. After acquiring the data, the finite element method is further used to perform a refined simulation of the heat distribution.

[0070] Specifically, the finite element method (FEM) is a numerical simulation technique used to discretize complex structures into a finite number of elements and predict heat distribution by solving the heat conduction equation. In the context of building composite panels, such as metal-polymer composite structures, a three-dimensional model is first established, including parameters such as the thermal conductivity, specific heat capacity, and density of each layer of material.

[0071] For example, during the simulation, the sheet material model is meshed into tens of thousands of tetrahedral elements, and high-load operating data such as heat source intensity and boundary conditions are input, for example, the heat input when the external temperature is 50 degrees Celsius. Further, by iteratively calculating the temperature field of each element, a heat accumulation distribution cloud map is generated. This refined simulation can reveal the concentration of heat in the interlayer bonding region; for example, the polymer layers may experience increased stress due to differences in thermal expansion coefficients.

[0072] It should be noted that the finite element analysis framework includes preprocessing, solution, and post-processing stages. The preprocessing stage defines material properties and loads; the solution stage uses finite element software to calculate thermal equilibrium; and the post-processing stage visualizes the results. This method is applicable to different plate thicknesses in the construction field, such as 10 mm and 20 mm thick samples, and simulation accuracy can be controlled by adjusting the mesh density.

[0073] Preferably, based on the simulation, the peak heat accumulation and potential safety hazards in the key areas are determined.

[0074] Specifically, critical areas refer to the locations with the largest temperature gradients in the heat distribution, such as the edges of panels or interlayer interfaces. Peak locations are identified by analyzing simulated heat maps, such as the cumulative point where the temperature in a certain area reaches above 80 degrees Celsius. Furthermore, the identification of potential safety hazard points is based on threshold comparisons; for example, if the peak value exceeds the material's heat resistance limit, such as the polymer softening point, it is marked as a potential hazard area.

[0075] In one possible implementation, for building exterior wall panels, potential hazards might include thermal stress concentration near the fixing points, leading to the risk of adhesive failure. This identification process can guide material optimization, such as adjusting interlayer insulation to reduce peak stress levels.

[0076] For example, in the implementation scenario of indoor composite panels, similar heat data is acquired and finite element simulations are performed, focusing on heat accumulation under low temperature and high humidity environments to determine whether there is a risk of peeling off the decorative layer. This scenario demonstrates the versatility of the technical solution in interior building applications.

[0077] Understandably, the above steps form a logical chain, from data acquisition to simulation to hazard identification, with each step seamlessly connected to ensure the completeness of the analysis. In actual construction projects, this method helps improve the safety performance of composite panels.

[0078] Step S105: For the heat accumulation peak and safety hazard points, adjust the local material ratio and thickness parameters of the multilayer composite tape, obtain the adjusted heat distribution and insulation performance data, determine whether it meets the safety standards under high voltage environment, and obtain the optimized structural design.

[0079] Based on the high-pressure environmental parameters and the initial structure of the composite tape, a thermal-electric coupling field simulation model of the tape was established using the finite element method. The model includes a material ratio adjustment region and a thickness parameter setting region. The inputs to the model are the high-pressure environmental parameters and initial structural data, and the outputs are heat distribution and electric field distribution. Running the model yields the initial state heat distribution simulation results and electric field distribution results. Heat peak locations are extracted from the heat distribution simulation results, and high-field-strength regions are identified from the electric field distribution results as the basis for identifying potential hazards. For the local areas identified by the heat peak locations and potential hazard identification, the material ratio adjustment parameters and thickness parameter setting parameters of the corresponding regions in the simulation model are adjusted. These adjustments are based on the thermal conductivity and dielectric constant data of each component in the material library, and parameter changes are achieved by comparing the differences in thermal conductivity and the matching degree of dielectric constant layer by layer. The updated simulation model is run to obtain the adjusted heat distribution data and electric field distribution data. An insulation performance test is performed on the tape sample prepared according to the adjusted parameters using a dielectric spectrum analyzer to obtain the dielectric loss factor and volume resistivity data in the frequency domain. The dielectric loss factor data and volume resistivity data are combined with the adjusted electric field distribution data. The local dielectric strength is calculated using a product formula, and a preset margin threshold is subtracted to obtain the safety margin, thereby obtaining the insulation performance data of the tape under high voltage, including local dielectric strength and safety margin. The adjusted heat distribution data is compared with a preset heat accumulation threshold, and the insulation performance data is compared with high-voltage safety standards, which include temperature rise limits and breakdown voltage requirements. If the heat distribution data exceeds the threshold or the insulation performance data is lower than the standard, it is determined that the safety requirements are not met, and the material and thickness parameter combinations of the non-compliant areas are recorded and stored for later reference. Based on the determination results and the recorded parameter combinations, an optimization algorithm is used to iteratively search the solution space of material ratio and thickness parameters. The objective function of the optimization algorithm integrates the heat distribution uniformity index and the insulation safety margin index, and the search direction is adjusted by successively comparing function values. When the objective function value reaches the convergence condition, a material ratio and thickness parameter combination that meets all safety standard comparison requirements is obtained; this combination is the optimized structural design.

[0080] In one implementation, the peak heat accumulation and potential safety hazards of the multilayer composite tape are first identified.

[0081] Specifically, multi-layer composite tapes are commonly applied in the field of high-voltage cable insulation. The peak of heat accumulation refers to the point where heat accumulates to the highest value in a local area when current passes through, while the potential safety hazard points include positions that may cause insulation breakdown or material aging. By scanning the tape sample with a thermal imaging device and recording the temperature distribution data under a simulated high-voltage environment, such as monitoring the heat change under the condition of a voltage of 10 kV, these key points can be determined. This identification process helps with subsequent optimization to ensure the stability of the tape during the winding of high-voltage cables. Further, based on the identified peak of heat accumulation and potential safety hazard points, the local material ratio and thickness parameters of the multi-layer composite tape are adjusted.

[0082] Exemplarily, a hybrid material of silicone rubber and polyethylene is used in the inner layer of the tape. For the peak region, the proportion of silicone rubber is increased from 30% to 50% to enhance heat conductivity; at the same time, the thickness of this region is increased from 0.5 mm to 0.8 mm to disperse heat. This adjustment is achieved through finite element simulation software. After inputting the material property parameters, iterative calculations are performed to ensure that the local changes do not affect the overall structural integrity. On the high-voltage cable production line, this method can be applied to variants with different tape layers, demonstrating its versatility.

[0083] Preferably, the adjusted heat distribution and insulation performance data are obtained.

[0084] In a possible implementation, a high-voltage current is applied to the adjusted tape sample using a thermal simulation test bench, and the heat distribution is measured to obtain the temperature field data through an infrared thermal imager. For example, it is recorded that the peak temperature drops from the original 80°C to 65°C; the insulation performance is evaluated through a dielectric strength tester to detect whether the breakdown voltage exceeds the standard threshold. This data acquisition process emphasizes accuracy and verifies the reliability through multiple repeated experiments, which is particularly applicable in the cable insulation scenario of high-voltage substations.

[0085] It should be noted that it is determined whether the adjusted design meets the safety standards under high-voltage environments.

[0086] Specifically, the safety standards under high-voltage environments include an insulation withstand voltage not lower than 15 kV / mm and a thermal stability not exceeding 70°C during continuous operation. By comparing the obtained data with industry standards such as IEC 60243, if the heat distribution is uniform and the insulation performance indicators meet the standards, it is determined to be qualified. This judgment process can be integrated into an automated system and executed in real time during the cable manufacturing process.

[0087] In one embodiment, an optimized structural design is obtained.

[0088] For example, adjusting parameters is applied to a three-layer composite tape, with an outer layer thickness of 1.0 mm to provide mechanical protection, and the inner layer's composition optimized to form the final design drawings. This structure achieves uniform heat distribution in high-voltage transmission cable insulation, improving overall durability. Furthermore, in another embodiment, considering cables of different high-voltage levels, such as 35kV, the middle layer material composition is adjusted to 40% epoxy resin to improve insulation. After data acquisition, the criteria are determined to be met, resulting in an optimized design suitable for this scenario. This diverse range of embodiments covers variations in the high-voltage electrical field, ensuring flexibility in the technical solution.

[0089] Step S106: By conducting shear force simulation tests on the optimized structural design, data on the deformation and adhesion failure of the tape under complex stress environments are collected to determine whether its mechanical strength meets the stability requirements within the battery life cycle, and the final performance verification results are obtained.

[0090] Based on the optimized structural design, a shear force simulation model is established. Complex stress environment parameters are input, and the model is run to obtain tape deformation distribution data and adhesive stress values, yielding initial mechanical response indicators. For the deformation distribution data, finite element analysis is used to calculate the cumulative damage degree of the tape over the battery life cycle. Combined with the adhesive stress values, potential failure points are identified, and a mechanical strength distribution map is determined. Stability indicators for key areas are extracted from the mechanical strength distribution map and compared with a preset battery life cycle threshold. If the indicator is lower than the threshold, model parameters are adjusted to obtain an optimized strength margin value. Based on the strength margin value, fatigue life prediction data obtained from the cumulative damage degree is integrated to generate a comprehensive report, determining whether the tape meets stability requirements and obtaining the final performance verification results.

[0091] In one implementation, shear force simulation tests are performed on the optimized multilayer composite tape structure design.

[0092] Specifically, multilayer composite tapes are used in the field of high-voltage cable insulation, and their optimized structure includes adjusted material ratios and thickness parameters. The shear stress test process is simulated using finite element analysis software. First, a three-dimensional model of the tape is imported, and then a shear load is applied, for example, setting a shear rate of 5 mm / s in the software, to calculate the stress distribution of the tape under high-voltage cable winding conditions. This test helps identify potential mechanical weaknesses and ensures the durability of the tape in practical applications. On high-voltage cable production lines, this simulation can be performed on tape variants with different numbers of layers, demonstrating their applicability in simulated environments. Furthermore, data on tape deformation and adhesive failure under complex stress environments are collected.

[0093] Exemplarily, the complex stress environment refers to the combination of tension, torsion, and vibration encountered during the operation of high-voltage cables. A multi-axial stress is applied using an experimental platform. For example, the shear force during cable bending is simulated through a hydraulic device, and the deformation data is monitored in real time. The deformation obtains the displacement value through a laser scanner, and the adhesive failure is observed for signs of interlayer separation through an ultrasonic detector. For example, the failure point is recorded when the deformation amount exceeds 2 mm. This data acquisition process emphasizes accuracy and is particularly applicable in the cable insulation scenario of high-voltage substations through multiple tests.

[0094] It should be noted that environmental factors such as temperature changes affecting the tape need to be considered during data acquisition to ensure the comprehensiveness of the data.

[0095] Preferably, determine whether the mechanical strength of the tape meets the stability requirements within the cable life cycle.

[0096] Specifically, the cable life cycle refers to the durability period within a predicted operation time of more than 10 years. The stability requirements include a shear strength not lower than 50 MPa and a deformation rate less than 1%. Based on the collected data, a judgment is made through comparative analysis. For example, if the simulation results show no significant failure of the tape under continuous stress, it is determined to meet the requirements. This determination process can be integrated into an automated system and executed in real time during the cable manufacturing process.

[0097] In one possible implementation, for 35 kV class cables, the test parameters are adjusted to match the stress at a higher voltage, and the mechanical strength is confirmed to meet the standards.

[0098] In one embodiment, the final performance verification result is obtained.

[0099] For example, the simulation and collected data are summarized to generate a report. The report includes a deformation curve and a failure threshold chart. If all indicators meet the safety standards, the result is qualified. This verification achieves mechanical stability in high-voltage transmission cable insulation and can support the long-term application of the tape. Further, in another implementation, variants of different cable diameters are considered, such as large cross-section cables. After collecting data, the verification results are obtained to get a performance evaluation applicable to this scenario.

[0100] Specifically, the principle of the anti-shear force simulation test is to simulate the real stress path and calculate the stress-strain relationship through software. For example, after inputting the material elastic modulus, the deformation field is iteratively solved to avoid actual destructive tests. This explanation clarifies the business process of the simulation and ensures the reliability of the test. In the field of high-voltage cables, this method covers various winding methods and enhances the generality of the solution.

[0101] Understandably, the acquisition of deformation and adhesive failure data involves the arrangement of sensor arrays, such as placing strain gauges on tape samples to record real-time signal changes and analyze precursory failures such as microcrack propagation. This detailed process elucidates the operational details and helps in assessing mechanical properties.

[0102] In one embodiment, a threshold comparison algorithm is introduced to determine mechanical strength, such as comparing data with a standard curve to quantify stability indicators. This approach provides support throughout the cable's lifespan, preventing premature failure.

[0103] It should be noted that the final performance verification results are obtained through data integration, such as merging simulation outputs and experimental records to form a comprehensive evaluation report, which supports iterative optimization design.

[0104] Step S107: Based on the performance verification results, adjust the multi-layer interface bonding process parameters of the tape to address the potential risk of localized bonding failure, obtain updated shear strength and structural integrity data, determine whether it fully meets the comprehensive performance requirements under high-load operating conditions, and obtain the final multi-layer composite tape process solution.

[0105] The coordinates of local adhesion failure points and the interface stress distribution are extracted from the performance verification results. Combined with the constitutive relationship of each layer of the tape, the neighborhood of the failure point is divided using a finite element mesh, and boundary conditions are applied to calculate the stress concentration factor. The stress concentration factor is defined as the ratio of the local maximum stress to the nominal stress. Then, the J-integral method is used to calculate the energy release rate, where the energy release rate represents the energy required for unit crack propagation. The stress concentration factor and energy release rate are used to quantify the failure risk level. For the failure risk level, a process parameter vector space is established with curing temperature, lamination pressure, and holding time as dimensions. An orthogonal experimental design method is used, by setting levels for each dimension and generating an orthogonal table. Sample points are selected within the process parameter vector space for parameter sensitivity analysis. Parameter sensitivity analysis observes response changes by changing a single parameter to obtain the process window boundary corresponding to each sample point. Based on the results of the parameter sensitivity analysis, if the decreasing trend of the stress concentration factor is positively correlated with the lamination pressure, a set of optimized parameter combinations within the process window boundary is selected to prepare a new multilayer composite tape sample. The shear strength and peel strength data of the sample are obtained through high and low temperature cyclic shear tests and constant load peel tests. Using the shear strength and peel strength data, combined with creep deformation obtained through continuous monitoring under constant temperature and load, and the number of fatigue cycles recorded under alternating load, a multi-dimensional performance dataset is constructed. Each indicator in the dataset is compared one by one with a preset performance requirement threshold. If all indicators in the multi-dimensional performance dataset are not lower than the performance requirement threshold, the optimized parameter combination is determined to meet the comprehensive performance requirements. The parameter combination and its corresponding process window definition result are recorded as the final multilayer composite tape process scheme.

[0106] In one implementation, based on the risk of interlayer micro-separation of the tape at the cable bending section revealed by previous simulations and experiments, the multilayer interface bonding process parameters are specifically adjusted.

[0107] Specifically, for the interface between the polyester fiber reinforcement layer and the acrylic pressure-sensitive adhesive layer, the original hot-pressing temperature was increased from 150 degrees Celsius to 165 degrees Celsius, while the rolling pressure was adjusted from 0.4 MPa to 0.5 MPa, and the holding time was extended by 3 seconds. This adjustment aims to enhance the interfacial adhesion energy by increasing the thermal motion and interpenetration of molecular chain segments, thus addressing adhesive failure caused by repeated bending stress under high-load operation. Furthermore, an improved testing method was adopted to obtain updated performance data.

[0108] For example, a fatigue testing platform simulating the dynamic bending of a cable is constructed. This platform uses a servo motor to drive a fixture, causing a cable sample with optimized adhesive tape to bend reciprocally at a frequency of 10 times per minute at ±30 degrees. After 1000 cycles, the test is paused, and a high-precision micro-force tester is used to perform a point-to-point shear force test on the tape joint. For example, three different locations are selected on the sample, and the shear force at failure is measured and the average value is calculated.

[0109] Understandably, the acquisition of structural integrity data does not solely rely on mechanical testing.

[0110] In one possible implementation, an industrial endoscope is used in conjunction with a high-definition camera unit to periodically observe the interlayer interface state of the tape during fatigue testing.

[0111] For example, after every 200 bending cycles, images of the inner layer are acquired through a pre-set observation hole. Image analysis software is used to identify and record the initiation and expansion of microscopic defects such as bubble enlargement, adhesive layer cracking, or fiber layer warping. Based on the acquired updated data, it is determined whether the high-load operating conditions are met.

[0112] Specifically, high-load operating conditions are defined as a combined condition where the cable operates at its rated current carrying capacity, accompanied by periodic changes in ambient temperature (-20°C to 70°C) and external mechanical vibration (frequency 5-50 Hz). The criteria include: the updated average shear strength must be no less than 55 MPa, and after 1000 simulated bending cycles, no new interlayer separation areas exceeding 0.5 square millimeters in area should be found by endoscopic observation.

[0113] Preferably, if the data shows that the shear strength meets the standard but there is still a tendency for minor defects to expand, a secondary process fine-tuning can be carried out.

[0114] For example, while keeping the temperature and pressure constant, the solid content of the primer applied to the interface is increased by 5% to further enhance interfacial wettability and chemical bonding. The above testing and judgment process is then repeated. Finally, when the updated shear strength data and structural integrity observation results stably meet the preset high-load operating condition thresholds, the current hot-pressing temperature, rolling pressure, holding time, and primer formulation parameters are locked, forming a definitive multilayer composite tape process plan document. This document details the adjustment path from initial parameters to final parameters, the corresponding test conditions and result data for each round of verification, providing precise process guidance for the large-scale production of cable insulation tape.

[0115] Step S108: By integrating the data of the entire process of the final process scheme, a correlation model between tape performance and battery life is constructed, and data on heat accumulation and safety hazards under long-term operation are obtained to determine the reliability indicators of the process scheme in practical applications.

[0116] By collecting performance parameters of the adhesive tape, tensile strength and heat resistance data of the tape material are obtained from various operating environments to construct an initial database of tape performance parameters and determine the performance benchmark of the tape under different conditions. Based on this performance parameter database and combined with battery life cycle test data, a performance correlation model is established using a support vector machine algorithm. The input is the tensile strength and heat resistance data of the tape material, and the output is the mapping relationship with the battery life cycle, obtaining the corresponding pattern between the two. According to the performance correlation model, the heat accumulation distribution under long-term operation monitoring scenarios is simulated. Temperature change data is collected from different time periods of battery operation to obtain the dynamic trend of heat accumulation distribution and identify key influencing areas of heat accumulation distribution. Based on the heat accumulation distribution, potential triggering conditions for safety hazard indicators are analyzed. Anomalies exceeding preset thresholds are extracted from temperature change data to construct a hazard early warning mechanism and obtain the early warning threshold range for safety hazard indicators. Using the early warning threshold range of the safety hazard indicators, the optimization effect of the process scheme is evaluated in application scenario verification. The distribution of anomalies is compared from battery test data under various operating environments to obtain quantitative results of reliability index values ​​and determine the stable performance of the process scheme in practical applications.

[0117] In one implementation, the final process solution involves full-process data integration, which specifically refers to collecting all data related to tape application and battery performance throughout the entire manufacturing chain, from the entry of raw tape materials into the warehouse to the completion of battery aging tests.

[0118] It should be noted that the data throughout the entire process includes multiple stages.

[0119] For example, during the tape preparation stage, data on process parameters such as adhesive coating thickness, curing temperature and time, and substrate tension need to be collected. During battery assembly, application data such as pressure at the tape application site, ambient temperature and humidity during application, and the number of layers wrapping the tabs are collected. During battery formation and testing, electrical performance data such as initial capacity, internal resistance, and first charge-discharge curves are collected, as well as lifetime data such as capacity decay curves, cycle count, and test ambient temperature during long-term cycle testing. These data originate from multiple heterogeneous data sources, including production execution systems, equipment sensors, and laboratory test databases.

[0120] Specifically, data integration is achieved by establishing a unified data platform. This platform defines a standardized data model, assigning a unique identifier to each roll of tape and each battery. Through this identifier, the process parameters of the tape, its application data on the production line, and the final test lifespan data of the battery are linked together.

[0121] For example, if a specific batch of tape is used to encapsulate one hundred batteries in a batch, the cycle life data of these one hundred batteries will be correlated with the original process parameters of that tape. The data cleaning process handles missing values ​​and outliers, and aligns data of different frequencies and formats to the same time or event dimension, forming a structured dataset that can be used for modeling. Constructing a correlation model between tape performance and battery life is the core of this solution. Tape performance is a multi-dimensional concept, including but not limited to adhesive strength, thermal conductivity, thickness uniformity, electrolyte corrosion resistance, and insulation resistance. Battery life is typically characterized by the number of cycles required for capacity retention to drop to a certain threshold (e.g., 80%).

[0122] In one embodiment, the correlation model is implemented using the gradient boosting regression tree algorithm from machine learning. The model takes a series of tape performance characteristics as input and battery cycle life as the prediction target.

[0123] Preferably, the feature vectors input to the model are carefully constructed.

[0124] For example, the characteristics include not only the raw measurements of the tape's performance, but also its statistically derived characteristics.

[0125] Specifically, for the tape thickness metric, in addition to the average thickness, the thickness range and standard deviation along the entire roll length are calculated to characterize thickness uniformity. For adhesive strength, data from tests conducted at different aging temperatures (e.g., 25°C, 60°C) are collected as different features. Furthermore, specific application parameters of the tape in the battery are incorporated as contextual features, such as the pressure applied during application and the proportion of the tape covering the side of the cell. The model training process requires a large amount of completed battery cycle test data as samples.

[0126] For example, complete lifecycle data for 5,000 batteries produced over the past year using tapes with different process parameters were collected. Seventy percent of this data was used as the training set to train a gradient boosting regression tree model. The model iteratively builds multiple decision trees, each learning from the residuals of predictions from all previous trees, and finally sums the predictions of all trees to obtain the final prediction. The training objective is to minimize the mean squared error between the predicted cycle life and the actual cycle life. The remaining thirty percent of the data was used as the test set to evaluate the model's prediction accuracy and generalization ability. Through feature importance analysis, the most critical tape performance indicators affecting battery life can be identified; for example, it may be found that the adhesive strength retention rate of the tape at high temperatures has the highest correlation with battery cycle life. Furthermore, to obtain long-term heat accumulation prediction data, a simplified thermal-electric coupling model of the battery needs to be established. This model treats the battery as a heat-generating body, whose heat generation power is related to factors such as charging / discharging current and internal resistance. The performance of the tape, especially its thermal conductivity and contact thermal resistance with the cell, directly affects the efficiency of heat dissipation from the inside of the cell to the outer casing.

[0127] In one possible implementation, heat accumulation prediction is achieved through simulation calculations. Based on the battery life degradation trend predicted by the aforementioned correlation model, the increase in internal resistance of the battery after long-term cycling can be deduced. Increased internal resistance leads to increased ohmic heat.

[0128] Specifically, for a given charge / discharge condition (e.g., 1C rate charge / discharge), initial state parameters are set using battery electrochemical-thermal coupling simulation software. Key tape performance parameters derived from the correlation model analysis, such as the estimated change in contact thermal resistance between the tape and the cell interface after long-term use, are input as boundary conditions into the simulation model. The simulation model calculates the temperature field distribution of the battery in each cycle and records the peak temperature. By simulating hundreds or even thousands of cycles, the cumulative upward trend of temperature in the core region of the battery with increasing cycle count can be observed, i.e., the heat accumulation effect.

[0129] For example, simulations might show that using a tape with poor thermal conductivity that peels off easily after aging will result in a maximum internal battery temperature eight degrees Celsius higher after 500 cycles compared to using a high-performance tape. Based on heat accumulation prediction data, safety hazard prediction primarily focuses on assessing the risk of thermal runaway. Thermal runaway is an uncontrollable rise in temperature caused by a series of exothermic side reactions. Predictive data can be translated into several key safety warning indicators.

[0130] For example, the time required for a battery to reach its thermal runaway trigger temperature (e.g., 180 degrees Celsius) under simulated extreme abuse conditions (such as high temperatures or high-rate overcharging) is calculated and compared to the time taken using a baseline tape solution. If the prediction shows a significantly shorter time to reach the trigger temperature, it indicates a higher safety hazard. Another approach is to analyze the estimated temperatures of different internal battery components (such as the positive electrode, negative electrode, and separator) caused by heat accumulation to determine whether chain reactions such as separator shrinkage and electrolyte decomposition might occur, thereby predicting the probability of an internal short circuit. Determining the reliability indicators of the process solution in practical applications involves converting the above predicted data into quantifiable and assessable engineering standards.

[0131] In one embodiment, reliability metrics include safe operating cycles and a temperature rise threshold. A safe operating cycle is defined as the maximum number of cycles under standard operating conditions where the predicted cumulative battery temperature does not exceed a safe threshold (e.g., 60 degrees Celsius) and the predicted safety hazard risk level is "low". This number is determined by iteratively running a thermal simulation model until the threshold is reached.

[0132] For example, for a specific tape process scheme A, the model predicts that after its application, the highest simulated temperature of the battery will reach 59.5 degrees Celsius after 800 cycles, and the predicted thermal runaway trigger time will be higher than the industry safety benchmark. Therefore, the safe operating cycle of scheme A can be determined to be 800 cycles. Another key indicator is the maximum steady-state temperature rise, which is the maximum value of the difference between the battery surface temperature and the ambient temperature when it reaches a steady state under specified operating conditions. This indicator directly reflects the heat dissipation efficiency of the tape. By comparing the predicted maximum steady-state temperature rise under different process schemes, the scheme with the best heat dissipation performance can be selected. In addition, reliability indicators can also include the failure probability based on statistical models, such as the probability of thermal runaway caused by tape-related problems within the target life cycle (e.g., 1000 cycles) being less than one in a million.

[0133] It is understandable that the above implementation methods all revolve around the field of lithium-ion battery manufacturing. For different types of batteries, such as cylindrical batteries or square aluminum-cased batteries, the tape application methods and heat dissipation paths differ, but the basic logic of end-to-end data integration, correlation model construction, thermal simulation prediction, and reliability index determination is the same.

[0134] For example, for large-scale energy storage batteries, the impact of the creep performance of the tape under long-term static conditions on contact thermal resistance may be of greater concern. This requires incorporating relevant long-term static test data during the data integration phase and introducing time-related creep characteristics into the correlation model. By providing multiple embodiments based on the same technical framework but with different focuses, the versatility and practicality of this technical solution in evaluating the reliability of battery tape processes are demonstrated.

[0135] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. The present invention has been described in detail with reference to preferred embodiments. Those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for designing and verifying multilayer composite tapes, characterized in that, include: Acquire thermal conductivity and insulation performance data of adhesive tape materials, construct a multi-dimensional performance database, and generate a performance distribution map based on the multi-dimensional performance database. Based on the performance distribution map, a multi-layer structure combination is designed using a layered screening method to obtain the heat accumulation simulation parameters and insulation performance simulation parameters of the multi-layer structure combination. Based on the heat accumulation simulation parameters and insulation performance simulation parameters, combined with mechanical strength test data, a comprehensive performance evaluation model is constructed, and a preferred material composite scheme is selected through the comprehensive performance evaluation model. Based on the preferred material composite scheme, finite element analysis is used to obtain heat accumulation distribution data under high load operation, determining the heat accumulation peak and potential safety hazard points. For the heat accumulation peak and potential safety hazard points, the local material ratio and thickness parameters of the material composite scheme are adjusted, obtaining the adjusted heat distribution data and insulation performance data. It is then determined whether the adjusted heat distribution data and insulation performance data meet the high-voltage environment safety standards, resulting in an optimized structural design. Shear force simulation tests are performed on the optimized structural design to obtain deformation data and adhesion failure data of the tape under complex stress environments, determining whether the mechanical strength meets the stability requirements, and obtaining performance verification results. Based on the performance verification results, the multi-layer interface bonding process parameters are adjusted, updated shear strength data and structural integrity data are obtained, and it is determined whether the comprehensive performance requirements are met, thus obtaining the final multi-layer composite tape process scheme. Based on the final multi-layer composite tape process scheme, a correlation model between tape performance and battery life is constructed, and predicted data on heat accumulation and safety hazards under long-term operation are obtained to determine the reliability index of the process scheme.

2. The method as described in claim 1, characterized in that, The process of acquiring thermal conductivity and insulation performance data of adhesive tape materials, constructing a multi-dimensional performance database, and generating a performance distribution map based on the multi-dimensional performance database includes: conducting environmental load tests on different adhesive tape materials, collecting heat accumulation characteristic data under high load operation and insulation failure threshold data in high-voltage environments to obtain a test dataset; constructing a multi-dimensional database for the test dataset, classifying and storing the heat accumulation characteristic data and insulation failure threshold data according to material type, and determining a baseline value for data acquisition accuracy; analyzing the changing trend and obtaining key characteristic values ​​of material performance distribution by statistically comparing the heat distribution pattern with the threshold mapping of high-voltage insulation response based on the multi-dimensional database; and using the key characteristic values ​​of material performance distribution, combined with the test condition simulation results derived from the test dataset, constructing a performance map to determine the applicable range of the adhesive tape material in different environments.

3. The method as described in claim 1, characterized in that, The process of designing a multi-layer structure combination using a stratified screening method based on the performance distribution map, and obtaining the heat accumulation simulation parameters and insulation performance simulation parameters of the multi-layer structure combination, includes: obtaining initial data from the performance distribution map, analyzing the thermal conductivity and insulation properties of materials at different layers, and determining the preliminary classification range of the thermally conductive layer material and the basic matching scheme of the insulation layer thickness; constructing a multi-layer structure model using a stratified screening method based on the preliminary classification range of the thermally conductive layer material and the basic matching scheme of the insulation layer thickness, and obtaining preliminary values ​​for interlayer heat accumulation assessment; adjusting the screening conditions for the structure combination based on the preliminary values ​​of the interlayer heat accumulation assessment, optimizing the interlayer interface contact characteristics, and determining the specific layer sequence and thickness distribution scheme of the multi-layer structure combination; after obtaining the specific layer sequence and thickness distribution scheme of the multi-layer structure combination, constructing a virtual test environment, inputting the heat accumulation assessment data and insulation layer thickness parameters, and obtaining the insulation performance value under the simulated scenario; and adjusting the refined parameters of the multi-layer structure combination based on the insulation performance value and the heat accumulation assessment data to determine the final heat accumulation simulation parameters and insulation performance simulation parameters.

4. The method according to any one of claims 1-3, characterized in that, The step of constructing a comprehensive performance evaluation model based on the heat accumulation simulation parameters and insulation performance simulation parameters, combined with mechanical strength test data, and then selecting the preferred material composite scheme through the comprehensive performance evaluation model includes: obtaining initial parameter data of the multilayer structure combination, including thickness, density, and elastic modulus; setting simulation scenarios for different combination methods; extracting key parameters; and determining the applicable range of the initial parameter data; based on the initial parameter data, using finite element analysis software to simulate the response of the multilayer structure combination under different loads; obtaining simulation results of mechanical strength test data and shear force data; correcting outliers in the simulation results; and obtaining performance data of the multilayer structure combination under various working conditions. Based on the performance data, a comprehensive performance evaluation system is constructed. This system allocates weights for mechanical strength test data and shear strength data using preset weights, calculates the structural integrity score for each combination method, and introduces interlaminar bond strength distribution as an evaluation dimension to determine the stability performance of the multilayer structure combination. For the stability performance, failure mode identification features are extracted from the performance data. These features are obtained by comparing the peak interlaminar stress with a preset threshold. The preset threshold is used to classify the risk level of each combination method, obtaining a quantitative result for the adhesive failure risk of the multilayer structure combination. By comprehensively comparing the quantitative result with the structural integrity score, combination methods that meet the performance indicators are selected. Combined with the optimized adjustment of the interlaminar bond strength distribution, the final material composite scheme is determined.

5. The method according to any one of claims 1-3, characterized in that, The step of obtaining heat accumulation distribution data under high load operation using finite element analysis based on the preferred material composite scheme, and determining the heat accumulation peak and potential safety hazards, includes: extracting the density, specific heat capacity, and thermal conductivity parameters of each layer of material from the preferred material composite scheme, and simultaneously obtaining the internal heat source power density distribution and external environmental convective heat transfer coefficient corresponding to high load operation as input conditions for thermal simulation analysis; using the input conditions, constructing a three-dimensional geometric model of the material composite scheme in finite element analysis software, performing non-uniform mesh generation on the model, setting material properties in the heat conduction physical field, applying internal heat source power density distribution and surface convection boundary conditions, and establishing a transient thermal analysis model; running the transient thermal analysis model to calculate the spatiotemporal distribution data of the temperature field of the material composite structure within a complete working cycle; using the spatiotemporal distribution data of the temperature field as a thermal load, mapping it to the structural mechanical physical field of the same finite element model, calculating the thermal stress distribution caused by temperature differences, and simultaneously identifying the global and local temperature peak coordinates and corresponding temperature values ​​by traversing and comparing all node temperature values ​​in the spatiotemporal distribution data of the temperature field; The peak temperature value is compared with the preset safe temperature threshold of each layer of material. If the temperature of a certain area exceeds the safe temperature threshold, it is marked as an overheating risk point. At the same time, combined with the area in the thermal stress distribution that exceeds the material yield strength, the location of potential safety hazards in the material composite structure is comprehensively determined.

6. The method according to any one of claims 1-3, characterized in that, The process involves adjusting the local material ratio and thickness parameters of the composite material scheme to address the accumulated heat peak and potential safety hazards, obtaining adjusted heat distribution data and insulation performance data, and determining whether the adjusted heat distribution data and insulation performance data meet the high-voltage environment safety standards to obtain an optimized structural design. This includes: establishing a thermal-electric coupling field simulation model using the finite element method based on the high-voltage environment parameters and the initial structure of the composite tape; running the model to obtain the initial heat distribution simulation results and electric field distribution results; extracting the heat peak location from the heat distribution simulation results and identifying high-field-strength regions from the electric field distribution results as the basis for hazard identification; adjusting the material ratio adjustment parameters and thickness parameter settings for the corresponding regions in the simulation model for the local regions determined by the heat peak location and hazard identification; running the updated simulation model to obtain the adjusted heat distribution data and electric field distribution data; and using a dielectric spectrum analyzer to test the insulation performance of the tape sample prepared according to the adjusted parameters to obtain dielectric loss factor data and volume resistivity data in the frequency domain. The dielectric loss factor data and the volume resistivity data are combined with the adjusted electric field distribution data. The local dielectric strength is calculated using a product formula, and a preset margin threshold is subtracted to obtain the safety margin, thus obtaining the insulation performance data of the tape under high voltage. The adjusted heat distribution data is compared with a preset heat accumulation threshold, and the insulation performance data is compared with the high voltage safety standard. If the heat distribution data exceeds the threshold or the insulation performance data is lower than the standard, it is determined that the safety requirements are not met, and the material and thickness parameter combinations of the non-compliant areas are recorded. Based on the determination results and the recorded parameter combinations, an optimization algorithm is used to iteratively search the solution space of material ratio and thickness parameters. The objective function of the optimization algorithm integrates the heat distribution uniformity index and the insulation safety margin index. When the objective function value reaches the convergence condition, the material ratio and thickness parameter combinations that meet all safety standard comparison requirements are obtained, and the combination is the optimized structural design.

7. The method according to any one of claims 1-3, characterized in that, The process of performing shear force simulation tests on the optimized structural design to obtain deformation and adhesion failure data of the tape under complex stress environments, determining whether the mechanical strength meets stability requirements, and obtaining performance verification results includes: establishing a shear force simulation model based on the optimized structural design, inputting complex stress environment parameters, running the model to obtain tape deformation distribution data and adhesion stress values, and obtaining initial mechanical response indicators; calculating the cumulative damage degree of the tape within the battery life cycle using the finite element analysis method based on the deformation distribution data, identifying potential failure points in conjunction with the adhesion stress values, and determining a mechanical strength distribution map; extracting stability indicators of key areas from the mechanical strength distribution map, comparing them with a preset battery life cycle threshold, and adjusting model parameters if the indicators are lower than the threshold to obtain an optimized strength margin value; and generating a comprehensive report based on the strength margin value, integrating fatigue life prediction data obtained from the cumulative damage degree, determining whether the tape meets stability requirements, and obtaining the final performance verification results.

8. The method according to any one of claims 1-3, characterized in that, The process involves adjusting the multilayer interface bonding process parameters based on the performance verification results, obtaining updated shear strength and structural integrity data, determining whether the overall performance requirements are met, and obtaining the final multilayer composite tape process scheme. This includes: extracting the coordinates and interface stress distribution of local bonding failure points from the performance verification results; combining the constitutive relationships of each layer of the tape material; dividing the failure point neighborhood using a finite element mesh and applying boundary conditions; calculating the stress concentration factor and energy release rate; quantifying the failure risk level; establishing a process parameter vector space with curing temperature, lamination pressure, and holding time as dimensions for the failure risk level; using an orthogonal experimental design method; selecting sample points within the process parameter vector space; performing parameter sensitivity analysis; and obtaining the process window boundary corresponding to each sample point; based on the results of the parameter sensitivity analysis, selecting a set of optimized parameter combinations within the process window boundary; preparing new multilayer composite tape samples; and obtaining the shear strength and peel strength data of the samples through high and low temperature cyclic shear tests and constant load peel tests. Using the shear strength data and peel strength data, combined with the creep deformation obtained by continuous monitoring under constant temperature and load, and the fatigue cycle number recorded under alternating load, a multi-dimensional performance dataset is constructed. Each indicator in the dataset is compared with a preset performance requirement threshold. If all indicators in the multi-dimensional performance dataset are not lower than the performance requirement threshold, it is determined that the optimized parameter combination meets the comprehensive performance requirements. The parameter combination and its corresponding process window definition result are recorded as the final multilayer composite tape process scheme.

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