Method and system for evaluating bending durability of arc-shaped screen under action of multi-point gathering force

Abstract

The invention discloses a method for evaluating the bending durability of an arc-shaped screen under the action of multi-point aggregation force, and the method comprises the steps: simulating the local stress distribution of the arc-shaped screen under the action of the multi-point aggregation force through a finite element analysis method, and obtaining the distribution data of a stress concentration region; according to the distribution data of the stress concentration area, performing local grid encryption on the arc-shaped screen to obtain a high-resolution local stress distribution model; aiming at the local stress distribution model, acquiring non-uniform deformation data under the action of multi-point aggregation force, and determining a local deformation behavior of the screen; the stress unbalance degree of the overall structure of the arc screen is calculated through the non-uniform deformation data, and the overall stress distribution state is obtained; if the overall stress distribution state exceeds a preset unbalance threshold value, adjusting grid parameters of the local stress concentration area, and obtaining an optimized stress distribution model; and according to the optimized stress distribution model, obtaining correlation data of local aggregation deformation and overall bending performance, and determining the potential risk of the long-term failure mode.

Description

Technical Field

[0001] This invention relates to the field of curved screen testing technology, and in particular to a method and system for evaluating the bending durability of curved screens under multi-point concentrated force. Background Technology

[0002] Curved screens, as an important direction in modern display technology, have irreplaceable value in fields such as smart devices, automotive displays, and wearable devices. Their unique form factor not only enhances the user experience but also drives product innovation. However, the bending performance of curved screens directly affects their lifespan and reliability, becoming a focus of industry attention. Ensuring their stability and durability in complex usage scenarios is particularly crucial.

[0003] While some research has been conducted on the performance testing of curved screens, existing methods often overlook the complex stress environments screens face in real-world use, particularly in evaluating their performance under multi-point stress or localized pressure. These methods primarily focus on testing uniform stress, making it difficult to simulate non-uniform deformation caused by localized contact or external impacts in real-world scenarios, thus failing to fully reveal the potential risks of screens under extreme conditions.

[0004] Against this backdrop, testing the bending performance of curved screens faces significant technical challenges. The most pressing issue is accurately simulating the local stress distribution under multi-point concentrated forces. This non-uniform mechanical environment directly affects the screen's deformation behavior, and the concentration of local stress often becomes the starting point for performance degradation. Furthermore, since local deformation can trigger stress imbalances in the overall structure, studying the correlation between localized concentrated deformation and overall bending performance becomes particularly important. If this correlation is not clear, it will be difficult to predict the screen's failure modes during long-term use. Summary of the Invention

[0005] In order to solve the above-mentioned technical problems, the present invention provides a method and system for evaluating the bending durability of an arc screen under multi-point cohesive force.

[0006] The technical solution of this invention is implemented as follows: A method for evaluating the bending durability of an arc-shaped screen under multi-point cohesive force includes the following steps: The local stress distribution of an arc screen under multi-point concentrated force was simulated using the finite element analysis method to obtain the distribution data of the stress concentration area. Based on the distribution data of stress concentration areas, the local mesh of the curved screen is refined to obtain a high-resolution local stress distribution model; For the local stress distribution model, non-uniform deformation data under the action of multi-point cohesive force is obtained to determine the local deformation behavior of the screen; By using non-uniform deformation data, the stress imbalance degree of the overall structure of the curved screen is calculated, and the overall stress distribution state is obtained. If the overall stress distribution exceeds the preset imbalance threshold, the mesh parameters of the local stress concentration area are adjusted to obtain the optimized stress distribution model. Based on the optimized stress distribution model, the correlation data between localized concentrated deformation and overall bending performance are obtained to determine the potential risks of long-term failure modes.

[0007] Furthermore, the local stress distribution of the simulated curved screen under multi-point concentrated force specifically includes: A three-dimensional model of the curved screen is constructed using geometric modeling software, defining the radius of curvature, thickness, and boundary contour to obtain the geometric model of the curved screen. Finite element analysis software was used to mesh the geometric model of the curved screen, set the mesh density parameters, and generate a finite element mesh model. Based on the materials mechanics database, the Young's modulus, Poisson's ratio, and yield strength of the curved screen material are obtained to determine the material property parameters. By applying concentrated forces at multiple points in a finite element mesh model, defining the load point locations and force values, a load application model is obtained. The stress distribution in the load application model is calculated using the finite element analysis algorithm, and the local stress distribution data is obtained by using the tetrahedral element integration method.

[0008] Furthermore, the high-resolution local stress distribution model includes: A high-resolution finite element mesh model is obtained through adaptive mesh generation technology, and the mesh refinement area is determined. Based on the mesh refinement region, the Young's modulus, Poisson's ratio, and yield strength of the curved screen material are obtained from the material mechanics database, and a material property configuration file is generated. A multi-point concentrated force is applied to a high-resolution finite element mesh model using the finite element analysis algorithm. The location of the load points and the force values ​​are defined to obtain the load distribution model.

[0009] Furthermore, determining the local deformation behavior of the screen specifically includes: Based on the three-dimensional geometric model, an initial finite element mesh model is generated using adaptive mesh generation technology. Obtain material property data of the curved screen from the materials mechanics database and generate a material property configuration file; For the initial finite element mesh model, apply multi-point concentrated forces, define the load point locations and force value distribution, and generate a load distribution model; The non-uniform deformation data in the load distribution model are calculated using the finite element analysis method to obtain the local deformation distribution model. If the deformation value in the local deformation distribution model exceeds the preset threshold, it is marked as a high-risk deformation area, and a high-risk area coordinate set is generated. By processing the coordinate set of high-risk areas using data visualization tools, a heatmap of local deformation distribution is generated to determine the local deformation behavior of the screen.

[0010] Furthermore, obtaining the optimized stress distribution model specifically includes: If the overall stress distribution exceeds the preset imbalance threshold, the mesh density of the local stress concentration area is adjusted by the mesh optimization algorithm to generate an optimized mesh model. Based on the optimized mesh model, the stress distribution is recalculated using the finite element analysis method to generate an updated stress distribution model. If there are high-stress regions in the updated stress distribution model, the high-stress regions are refined using local mesh generation techniques to generate a refined mesh model. Based on the refined mesh model, the material parameters are updated using the material property configuration file to generate an updated material distribution model; Using the updated material distribution model, the local stress equilibrium distribution is calculated by the finite element analysis method to obtain the equilibrium stress model. If the stress value in the equilibrium stress model still exceeds the preset threshold, the local mesh parameters are adjusted through an iterative optimization process to generate the final stress distribution model. Based on the final stress distribution model, a stress equilibrium distribution map is generated using data visualization tools to determine the overall stress equilibrium state.

[0011] Furthermore, the potential risks of identifying long-term failure modes specifically include: Local deformation data are obtained from the optimized stress distribution model, and deformation distribution patterns are generated using data extraction methods. Based on the deformation distribution law, the overall bending performance index is calculated using finite element analysis technology to obtain the bending performance distribution; If the indicators in the bending performance distribution exceed the preset threshold, correlation parameters are extracted through data analysis technology to generate correlation data between local deformation and overall performance. Based on the correlation parameters, a machine learning regression model is used to analyze the impact of local deformation on the overall bending performance, and the distribution of the impact weights is obtained. High-weight regions are extracted from the influence weight distribution, and failure mode analysis techniques are used to generate preliminary assessment data for long-term failure risk. If the risk value in the preliminary assessment data exceeds the preset threshold, the analysis parameters of the local deformation data are adjusted through an iterative optimization algorithm to generate an optimized risk assessment result. Based on the optimized risk assessment results, a failure risk distribution map is generated using data visualization technology to identify the potential risks of long-term failure modes.

[0012] Furthermore, after obtaining the load distribution model, the process also includes: The stress distribution data in the load distribution model is calculated by the tetrahedral element integration method to obtain high-precision local stress distribution results. If there are areas in the high-precision local stress distribution results where the stress value is greater than the preset threshold, they are marked as high-risk stress concentration areas, and a high-risk area coordinate set is generated. Based on the coordinate set of high-risk areas, a high-resolution stress distribution heat map is generated using data visualization tools to obtain a visual representation of the high-risk areas. The high-resolution stress distribution heatmap is analyzed by iterative optimization algorithm, and the mesh refinement parameters are adjusted to generate an optimized high-resolution finite element mesh model.

[0013] Furthermore, it also includes: By using the potential risk data of long-term failure modes, the Monte Carlo simulation method is used to predict the durability performance of the curved screen under complex stress environment, and the failure probability distribution is obtained. Based on the failure probability distribution, local pressure performance data under extreme conditions are obtained to determine the stability of the screen's deformation behavior in extreme environments; Based on the deformation behavior stability data, data fusion technology is used to integrate local stress distribution and overall stress imbalance data to obtain a comprehensive evaluation result of the bending performance of the curved screen.

[0014] Furthermore, the obtained failure probability distribution is obtained using the Monte Carlo simulation method, specifically including: The potential risk data of long-term failure modes are used as input, including the distribution data of local stress concentration areas, non-uniform deformation data, and the overall stress distribution status. The Monte Carlo simulation method is used to generate a large number of possible stress environment scenarios through random sampling to simulate the stress response of the curved screen under complex stress environment. Based on the results of random sampling, the failure probability under each scenario is calculated, and a failure probability distribution is generated.

[0015] A system for evaluating the bending durability of an arc-shaped screen under multi-point cohesive force includes: The stress analysis and mesh optimization module simulates the local stress distribution of an arc screen under multi-point concentrated force using the finite element analysis method; it refines the local mesh of the arc screen to obtain a high-resolution local stress distribution model; it determines whether the overall stress distribution state exceeds the preset imbalance threshold, and adjusts the mesh parameters of the local stress concentration area through an iterative optimization algorithm to obtain the optimized stress distribution model. The deformation and stress imbalance analysis module acquires non-uniform deformation data under the action of multi-point concentrated forces for the local stress distribution model; and calculates the degree of stress imbalance of the overall structure of the curved screen using the non-uniform deformation data. The failure risk and correlation analysis module obtains correlation data between localized concentrated deformation and overall bending performance based on the optimized stress distribution model; and predicts the durability performance of the curved screen under complex stress environment through simulation methods using potential risk data of long-term failure modes. The extreme condition assessment module acquires local pressure performance data under extreme conditions based on the failure probability distribution, and judges the stability of the screen's deformation behavior in extreme environments. The comprehensive evaluation module integrates local stress distribution and overall stress imbalance data based on deformation behavior stability data using data fusion technology to obtain a comprehensive evaluation result of the bending performance of the curved screen.

[0016] Compared with the prior art, the present invention has the following advantages: 1. This invention simulates the local stress distribution of an arc-shaped screen under multi-point concentrated force using the finite element method. It can accurately identify stress concentration areas and solve the problem that existing technologies cannot simulate non-uniform stress distribution in real-world scenarios. This allows for precise evaluation of the stress state of the arc-shaped screen under complex usage scenarios, providing a reliable basis for subsequent performance optimization. By refining the local mesh in the stress concentration areas of the arc-shaped screen, a high-resolution local stress distribution model is generated, improving the simulation accuracy of the stress concentration areas. This allows for more detailed capture of local stress changes, thereby more accurately evaluating the mechanical properties of the arc-shaped screen in key areas and providing more precise data support for optimized design. 2. By using non-uniform deformation data, the stress imbalance of the overall structure of the curved screen is calculated, and the correlation data between local concentrated deformation and overall bending performance is further obtained. The impact of local deformation on the overall structural performance is clarified, solving the problem that the existing technology cannot clarify the correlation between the two. This provides an important basis for predicting the failure mode of the curved screen in long-term use and helps to take targeted optimization measures in advance. 3. When the overall stress distribution exceeds the preset imbalance threshold, adjust the mesh parameters of the local stress concentration area to obtain an optimized stress distribution model. This optimization process can effectively reduce the risks caused by stress concentration, making the stress distribution of the curved screen more balanced, thereby significantly improving its durability and reliability under complex stress environments and extending the product's service life. 4. This invention provides a scientific and accurate basis for the design and optimization of curved screens by comprehensively analyzing the bending durability of curved screens under multi-point concentrated force. It helps guide the structural design of curved screens, enabling them to better adapt to complex usage environments while meeting performance requirements, thereby improving the overall quality and market competitiveness of products. Attached Figure Description

[0017] Figure 1 This is a flowchart of an embodiment 1, which describes a method for evaluating the bending durability of an arc-shaped screen under multi-point cohesive force. Figure 2 This is a framework diagram of an arc screen bending durability assessment system under multi-point cohesive force in Example 2. Detailed Implementation

[0018] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. Example 1

[0019] like Figure 1 As shown, this embodiment provides a method for evaluating the bending durability of an arc-shaped screen under multi-point cohesive force, including the following steps: The local stress distribution of an arc screen under multi-point concentrated force was simulated using the finite element analysis method to obtain the distribution data of the stress concentration area. Based on the distribution data of stress concentration areas, the local mesh of the curved screen is refined to obtain a high-resolution local stress distribution model; For the local stress distribution model, non-uniform deformation data under the action of multi-point cohesive force is obtained to determine the local deformation behavior of the screen; By using non-uniform deformation data, the stress imbalance degree of the overall structure of the curved screen is calculated, and the overall stress distribution state is obtained. If the overall stress distribution exceeds the preset imbalance threshold, the mesh parameters of the local stress concentration area are adjusted to obtain the optimized stress distribution model. Based on the optimized stress distribution model, the correlation data between localized concentrated deformation and overall bending performance are obtained to determine the potential risks of long-term failure modes.

[0020] Furthermore, the local stress distribution of the simulated curved screen under multi-point concentrated force specifically includes: A three-dimensional model of the curved screen is constructed using geometric modeling software, defining the radius of curvature, thickness, and boundary contour to obtain the geometric model of the curved screen. Finite element analysis software was used to mesh the geometric model of the curved screen, set the mesh density parameters, and generate a finite element mesh model. Based on the materials mechanics database, the Young's modulus, Poisson's ratio, and yield strength of the curved screen material are obtained to determine the material property parameters. By applying concentrated forces at multiple points in a finite element mesh model, defining the load point locations and force values, a load application model is obtained. The stress distribution in the load application model is calculated using the finite element analysis algorithm, and the local stress distribution data is obtained by using the tetrahedral element integration method.

[0021] If there are regions in the local stress distribution data where the stress value is greater than a preset threshold, they are marked as stress concentration regions, and the coordinate set of stress concentration regions is obtained. Based on the coordinate set of the stress concentration region, a stress distribution heatmap is generated using data visualization tools to obtain a visual representation of the stress concentration region.

[0022] Specifically, it can be illustrated as follows: A three-dimensional model of the curved screen was constructed using geometric modeling software, defining its radius of curvature as 500mm, thickness as 1.5mm, width as 150mm, and height as 200mm, thus obtaining a complete geometric model of the curved screen. The geometric model of the curved screen was meshed using ANSYS Mechanical finite element analysis software. The mesh density parameter was set to 10 elements per millimeter, generating a finite element mesh model containing approximately 500,000 tetrahedral elements to ensure the accuracy of the simulation. Based on the materials mechanics database, the Young's modulus of the curved screen material (assumed to be high-strength glass) is obtained as 70 GPa, Poisson's ratio as 0.2, and yield strength as 500 MPa. These material property parameters are then input into the finite element analysis software to provide accurate material characteristics for subsequent stress calculations. In the finite element mesh model, a concentrated force is applied at multiple points. It is assumed that a concentrated force perpendicular to the screen surface with a value of 50N is applied at the center position of the curved screen (coordinates 75mm, 100mm, 0.75mm) to obtain the load application model. The stress distribution in the load application model is calculated using the linear static analysis module in ANSYS Mechanical and the tetrahedral element integration method. The stress σ = A / F, where F is the applied force and A is the area of ​​force application. Assuming the concentrated force acts within a circular region of radius r = 5 mm, the area of ​​force application A is:

[0023] Assuming a preset stress threshold of 300 MPa, the stress values ​​of all elements are iterated. If the stress value of an element exceeds this threshold, it is marked as a stress concentration region, and the coordinate set of these stress concentration regions is obtained. For example, the calculation results show that the stress value of the elements near the load point is 636.62 MPa, which is significantly higher than the preset threshold of 300 MPa. Therefore, these elements are marked as stress concentration regions, and their coordinate set C is recorded. s ; Finally, the post-processing module of ANSYS Mechanical is used to generate a stress distribution heatmap, in which areas with high stress values ​​are represented in red and areas with low stress values ​​are represented in blue, thus intuitively showing the location and degree of stress concentration areas and providing a visual basis for subsequent analysis and optimization.

[0024] Furthermore, the high-resolution local stress distribution model includes: A high-resolution finite element mesh model is obtained through adaptive mesh generation technology, and the mesh refinement area is determined. Based on the mesh refinement region, the Young's modulus, Poisson's ratio, and yield strength of the curved screen material are obtained from the material mechanics database, and a material property configuration file is generated. A multi-point concentrated force is applied to a high-resolution finite element mesh model using the finite element analysis algorithm. The location of the load points and the force values ​​are defined to obtain the load distribution model.

[0025] The stress distribution data in the load distribution model is calculated by the tetrahedral element integration method to obtain high-precision local stress distribution results. If there are areas in the high-precision local stress distribution results where the stress value is greater than the preset threshold, they are marked as high-risk stress concentration areas, and a high-risk area coordinate set is generated. Based on the coordinate set of high-risk areas, a high-resolution stress distribution heat map is generated using data visualization tools to obtain a visual representation of the high-risk areas. The high-resolution stress distribution heatmap is analyzed by iterative optimization algorithm, and the mesh refinement parameters are adjusted to generate an optimized high-resolution finite element mesh model.

[0026] Specifically, it can be illustrated as follows: Using ANSYS Mechanical's adaptive mesh generation function, the local mesh is refined in the stress concentration area of ​​the curved screen. It is assumed that the mesh density is increased to 20 elements per millimeter in the stress concentration area to obtain more accurate simulation results. Through adaptive mesh generation, the regions requiring mesh refinement are identified, particularly near load points and known stress concentration areas. The material properties of the curved screen material (assumed to be high-strength glass) are then retrieved from a materials mechanics database. Young's modulus E: 70 GPa Poisson's ratio ν: 0.2 Yield strength σ y 500MPa Input these material property parameters into the finite element analysis software to generate a material property configuration file, ensuring that the correct material parameters are used in the high-resolution model. Apply a multi-point concentrated force in the high-resolution finite element mesh model. Assume that a concentrated force perpendicular to the screen surface with a force value of 50N is applied at the center of the curved screen (coordinates 75mm, 100mm, 0.75mm). Define the load point locations and force values ​​to generate a load distribution model; The stress distribution of each element was calculated using the tetrahedral element integration method in finite element analysis software; the stress distribution of the high-risk area was then calculated using the post-processing module of ANSYS Mechanical or data visualization tools such as MATLAB, based on the coordinate set C of the high-risk area. h Generate a high-resolution stress distribution heatmap; in the heatmap, areas with higher stress values ​​are represented in red, and areas with lower stress values ​​are represented in blue, visually showing the location and extent of high-risk stress concentration areas; Iterative optimization algorithms (such as ANSYS's adaptive mesh optimization function) are used to analyze high-resolution stress distribution heatmaps. Based on the stress distribution in the heatmap, mesh refinement parameters are adjusted to further refine the mesh in high-risk stress concentration areas. For example, if the stress distribution in a certain high-risk area is still not accurate enough, the mesh density in that area can be further increased, and the finite element analysis can be performed again until satisfactory high-precision local stress distribution results are obtained. Finally, an optimized high-resolution finite element mesh model is generated, providing a more accurate basis for subsequent analysis and optimization.

[0027] Furthermore, determining the local deformation behavior of the screen specifically includes: Based on the three-dimensional geometric model, an initial finite element mesh model is generated using adaptive mesh generation technology. Obtain material property data of the curved screen from the materials mechanics database and generate a material property configuration file; For the initial finite element mesh model, apply multi-point concentrated forces, define the load point locations and force value distribution, and generate a load distribution model; The non-uniform deformation data in the load distribution model are calculated using the finite element analysis method to obtain the local deformation distribution model. If the deformation value in the local deformation distribution model exceeds the preset threshold, it is marked as a high-risk deformation area, and a high-risk area coordinate set is generated. By processing the coordinate set of high-risk areas using data visualization tools, a heatmap of local deformation distribution is generated to determine the local deformation behavior of the screen.

[0028] Specifically, it can be illustrated as follows: The adaptive meshing function of ANSYS Mechanical was used to mesh the three-dimensional geometric model of the curved screen. The geometric parameters of the curved screen are assumed to be: radius of curvature R=500mm, thickness T=1.5mm, width W=150mm, and height H=200mm. The initial mesh density is set to 10 elements per millimeter, generating an initial finite element mesh model containing approximately 300,000 tetrahedral elements. Obtain the material properties of the curved screen material (assuming it is high-strength glass) from a materials mechanics database: Young's modulus E: 70 GPa Poisson's ratio ν: 0.2 Yield strength σ y 500MPa Input these material property parameters into the finite element analysis software to generate a material property configuration file; Multiple concentrated forces are applied in the initial finite element mesh model. It is assumed that a concentrated force perpendicular to the screen surface with a value of 50N is applied at the center position of the curved screen (coordinates 75mm, 100mm, 0.75mm). Define the load point locations and force distribution to generate a load distribution model.

[0029] Use the linear static analysis module in ANSYS Mechanical to calculate the non-uniform deformation data in the load distribution model. The deformation δ can be calculated using the following formula:

[0030] Assume the preset deformation threshold is 10μm. Iterate through the deformation values ​​of all elements. If the deformation value of an element is greater than the threshold, mark it as a high-risk deformation region. For example, if the calculation results show that the deformation value of the element near the load point is 9.01μm, which is close to but does not exceed the preset threshold of 10μm, and assuming that the deformation value exceeds 10μm in some other elements, mark these elements as high-risk deformation regions and record their coordinate set Cd. Using the post-processing module of ANSYS Mechanical or data visualization tools such as MATLAB, a heat map of local deformation distribution is generated based on the coordinate set Cd of the high-risk area. In the heat map, areas with higher deformation values ​​are represented in red, and areas with lower deformation values ​​are represented in blue, which intuitively shows the location and extent of high-risk deformation areas. Heatmaps clearly show local deformation behavior, especially the distribution of high-risk deformation areas, providing a visual basis for subsequent analysis and optimization.

[0031] Furthermore, obtaining the optimized stress distribution model specifically includes: If the overall stress distribution exceeds the preset imbalance threshold, the mesh density of the local stress concentration area is adjusted by the mesh optimization algorithm to generate an optimized mesh model. Based on the optimized mesh model, the stress distribution is recalculated using the finite element analysis method to generate an updated stress distribution model. If there are high-stress regions in the updated stress distribution model, the high-stress regions are refined using local mesh generation techniques to generate a refined mesh model. Based on the refined mesh model, the material parameters are updated using the material property configuration file to generate an updated material distribution model; Using the updated material distribution model, the local stress equilibrium distribution is calculated by the finite element analysis method to obtain the equilibrium stress model. If the stress value in the equilibrium stress model still exceeds the preset threshold, the local mesh parameters are adjusted through an iterative optimization process to generate the final stress distribution model. Based on the final stress distribution model, a stress equilibrium distribution map is generated using data visualization tools to determine the overall stress equilibrium state.

[0032] Specifically, it can be illustrated as follows: Assume the preset stress imbalance threshold is 300 MPa.

[0033] The maximum stress value obtained from the overall stress distribution obtained by finite element analysis is 350 MPa, which exceeds the preset threshold of 300 MPa. The mesh density of the stress concentration region is adjusted using the mesh optimization algorithm of ANSYS Mechanical. For example, in the stress concentration region, the mesh density is increased from 10 elements per millimeter to 20 elements per millimeter. An optimized mesh model is generated, which has a higher mesh density in stress concentration regions to improve simulation accuracy; Using the linear static analysis module of ANSYS Mechanical, the same load conditions are reapplied to the optimized mesh model (e.g., a concentrated force of 50 N is applied at the center). The stress distribution is recalculated to generate an updated stress distribution model. It is assumed that after recalculation, the maximum stress value in the stress concentration area is reduced to 320MPa, but is still higher than the preset threshold of 300MPa. Further local mesh refinement is performed on high-stress regions (regions with stress values ​​greater than 300 MPa) in the updated stress distribution model. It is assumed that the mesh density of these regions is further increased to 30 elements per millimeter to generate a refined mesh model to more accurately capture the stress distribution. Using the previously generated material property profile (Young's modulus E=70GPa, Poisson's ratio ν=0.2, yield strength σ), y =500MPa), update the material parameters in the refined mesh model to ensure that the material properties of all elements are consistent, so as to avoid calculation errors caused by inconsistent material parameters; Using the linear static analysis module of ANSYS Mechanical, the updated material distribution model is reloaded and the stress distribution is calculated to generate an equilibrium stress model. At this time, the stress distribution is more uniform and the stress value in the stress concentration area is further reduced. It is assumed that after optimization, the maximum stress value in the stress concentration area is reduced to 280MPa, which is lower than the preset threshold of 300MPa. If the stress value in the equilibrium stress model still exceeds the preset threshold (for example, assuming that the maximum stress value is still 310 MPa after the previous optimization), then continue to perform iterative optimization. Each iteration adjusts the mesh density or mesh shape to gradually reduce the stress value in the stress concentration area. Assuming that after multiple iterations, the maximum stress value in the final stress distribution model is reduced to 270 MPa, which is lower than the preset threshold of 300 MPa. Using the post-processing module of ANSYS Mechanical or data visualization tools such as MATLAB, a stress equilibrium distribution map is generated based on the final stress distribution model. In the heat map, areas with higher stress values ​​are represented in red, and areas with lower stress values ​​are represented in blue, which intuitively shows the equilibrium state of stress distribution. Through the stress equilibrium distribution map, the optimization effect of stress distribution can be clearly seen, ensuring that the overall stress distribution state is within the preset threshold range.

[0034] Furthermore, the potential risks of identifying long-term failure modes specifically include: Local deformation data are obtained from the optimized stress distribution model, and deformation distribution patterns are generated using data extraction methods. Based on the deformation distribution law, the overall bending performance index is calculated using finite element analysis technology to obtain the bending performance distribution; If the indicators in the bending performance distribution exceed the preset threshold, correlation parameters are extracted through data analysis technology to generate correlation data between local deformation and overall performance. Based on the correlation parameters, a machine learning regression model is used to analyze the impact of local deformation on the overall bending performance, and the distribution of the impact weights is obtained. High-weight regions are extracted from the influence weight distribution, and failure mode analysis techniques are used to generate preliminary assessment data for long-term failure risk. If the risk value in the preliminary assessment data exceeds the preset threshold, the analysis parameters of the local deformation data are adjusted through an iterative optimization algorithm to generate an optimized risk assessment result. Based on the optimized risk assessment results, a failure risk distribution map is generated using data visualization technology to identify the potential risks of long-term failure modes.

[0035] Specifically, it can be illustrated as follows: Local deformation data are extracted from the optimized stress distribution model using finite element analysis software (such as ANSYS Mechanical), assuming that the extracted local deformation data includes the displacement δ and strain ϵ of each element. The extracted local deformation data is processed using data analysis tools (such as MATLAB or Python) to generate deformation distribution patterns. It is assumed that the deformation is mainly concentrated in the central and edge areas of the curved screen. The overall bending performance parameters, such as the maximum bending stress σ, are calculated using finite element analysis software. bend and bending stiffness K bend Assuming the calculated maximum bending stress σ bend =280MPa, bending stiffness K bend =1.2×10 6 The bending performance distribution map is generated using N / m, showing the bending stress and bending stiffness in different regions. It is assumed that the bending performance distribution map shows higher bending stress in the central region and lower bending stiffness in the edge regions. The preset maximum bending stress threshold is assumed to be 250 MPa, and the bending stiffness threshold is assumed to be 1.0 × 10⁻⁶ MPa. 6 N / m; Since the maximum bending stress σbend = 280MPa exceeds the preset threshold of 250MPa, further analysis is required.

[0036] Data analysis techniques (such as correlation analysis) are used to extract correlation parameters between local deformation and overall bending performance. It is assumed that the extracted correlation parameters include the correlation coefficient r between deformation and bending stress. δ−σ =0.85 and the correlation coefficient r between deformation and bending stiffness δ−K =0.78. Based on the extracted correlation parameters, correlation data between local deformation and overall performance are generated. It is assumed that the correlation data shows that the correlation between deformation and bending stress is strong, and the correlation between deformation and bending stiffness is weak. The influence of local deformation on overall bending performance is analyzed using machine learning regression models (such as random forest regression). It is assumed that the influence weight distribution obtained after model training shows that the deformation in the central region has the highest influence weight on the overall bending performance, followed by the peripheral region. Regions with influence weights exceeding a preset threshold (assumed to be 0.8) are extracted and marked as high-weight regions, assuming these high-weight regions are mainly concentrated in the central area of ​​the curved screen. Failure mode analysis techniques (such as fatigue life analysis) are used to assess the long-term failure risk of these high-weight regions. Preliminary assessment data shows a long-term failure risk value of 0.9 for the central region and 0.6 for the peripheral region, with a preset risk threshold of 0.7. Since the risk value of 0.9 in the central region exceeds the preset threshold, optimization is required. Iterative optimization algorithms (such as genetic algorithms) are used to adjust the analysis parameters of local deformation data, such as mesh density and material parameters, to reduce the risk value. It is assumed that after optimization, the risk value of the central region is reduced to 0.65. Based on the optimized parameters, the long-term failure risk is reassessed, assuming that the optimized risk assessment results show that the risk value of all areas is lower than the preset threshold of 0.7. Use data visualization tools (such as the post-processing module of ANSYS Mechanical or MATLAB) to generate failure risk distribution maps.

[0037] In the heat map, areas with higher risk values ​​are represented in red, and areas with lower risk values ​​are represented in blue, which intuitively shows the distribution of long-term failure risk. Assuming the failure risk distribution map shows, the optimized curved screen has a low failure risk in long-term use, mainly concentrated in the edge area, but the risk values ​​are all within an acceptable range. Based on the failure risk distribution map, the potential risks of long-term failure modes are determined. It is assumed that the final identified potential risk areas are mainly concentrated in the edge areas, but the risk values ​​are low, and the overall long-term failure risk of the curved screen is within a controllable range.

[0038] Furthermore, after obtaining the load distribution model, the process also includes: The stress distribution data in the load distribution model is calculated by the tetrahedral element integration method to obtain high-precision local stress distribution results. If there are areas in the high-precision local stress distribution results where the stress value is greater than the preset threshold, they are marked as high-risk stress concentration areas, and a high-risk area coordinate set is generated. Based on the coordinate set of high-risk areas, a high-resolution stress distribution heat map is generated using data visualization tools to obtain a visual representation of the high-risk areas. The high-resolution stress distribution heatmap is analyzed by iterative optimization algorithm, and the mesh refinement parameters are adjusted to generate an optimized high-resolution finite element mesh model.

[0039] Furthermore, it also includes: By using the potential risk data of long-term failure modes, the Monte Carlo simulation method is used to predict the durability performance of the curved screen under complex stress environment, and the failure probability distribution is obtained. Based on the failure probability distribution, local pressure performance data under extreme conditions are obtained to determine the stability of the screen's deformation behavior in extreme environments; Based on the deformation behavior stability data, data fusion technology is used to integrate local stress distribution and overall stress imbalance data to obtain a comprehensive evaluation result of the bending performance of the curved screen.

[0040] Furthermore, the obtained failure probability distribution is obtained using the Monte Carlo simulation method, specifically including: The potential risk data of long-term failure modes are used as input, including the distribution data of local stress concentration areas, non-uniform deformation data, and the overall stress distribution status. The Monte Carlo simulation method is used to generate a large number of possible stress environment scenarios through random sampling to simulate the stress response of the curved screen under complex stress environment. Based on the results of random sampling, the failure probability under each scenario is calculated, and a failure probability distribution is generated.

[0041] Specifically, it can be illustrated as follows: Distribution data of local stress concentration regions, such as the coordinate set C of the stress concentration region. h Non-uniform deformation data, such as the deformation value δ in a local deformation distribution model; overall stress distribution state, such as the maximum stress value σ. max and stress distribution diagram; Suppose 10,000 random stress environment scenarios are generated, each scenario containing different load sizes, orientations, and distributions. Random sampling methods (such as Latin hypercube sampling) are used to ensure the uniformity and representativeness of the samples. For example, the randomly generated load sizes range from 30N to 70N, and the orientation varies within ±10°. For each randomly generated stress environment scenario, the stress response of the curved screen is calculated using finite element analysis software (such as ANSYS Mechanical). For example, in a random scenario, a load F = 45 N is applied with a 5° deflection angle. In the calculated stress distribution, the maximum stress value is 290 MPa. Assuming the fatigue limit stress of the material is σ... fatigue =250MPa; For each scenario, if the maximum stress value exceeds the fatigue limit stress, the curved screen is considered to be likely to fail in that scenario, and the failure probability P is calculated. failureLet be the ratio of the number of failure scenarios to the total number of scenarios. Assuming that out of 10,000 scenarios, 1,200 scenarios have a maximum stress value exceeding 250 MPa, then the failure probability is:

[0042] Generate a failure probability distribution, record the failure probability at each stress level, and use statistical analysis techniques (such as histogram analysis) to extract key failure probability distributions. For example, generate the probability density function of the failure probability and find that the failure probability is concentrated between 0.1 and 0.2. By fitting the failure probability distribution, its distribution characteristics are determined, such as the mean μ=0.15 and the standard deviation σ=0.03; based on the distribution characteristics of the failure probability, a representative stress environment scenario (such as a scenario with a maximum stress value of 300MPa) is selected for finite element analysis; the stress distribution law is calculated, such as the stress value distribution in the stress concentration area. Assuming a preset stress threshold of 280 MPa, the maximum stress value in the stress concentration area is found to be 310 MPa, exceeding the preset threshold. Data mining techniques (such as cluster analysis) are used to extract material fatigue characteristics and generate fatigue characteristic parameters, such as fatigue life N. fatigue =10 6 The next loop; Using a random forest regression model, and inputting fatigue characteristic parameters and stress distribution data, the model predicts the failure probability trend in long-term service scenarios. For example, the model predicts failure rates in 10... 6 After the cycle, the failure probability will increase to 0.25, generating a failure probability trend distribution chart to show the trend of failure probability changes under different number of cycles; Identify high-risk failure modes in the failure probability trend distribution, such as failure modes in high stress concentration areas and high cycle counts; use Bayesian inference methods, combining prior knowledge and current data, to generate prediction results for failure modes, for example, predicting that in high stress concentration areas, the main failure mode is fatigue fracture, with a risk value of 0.3. Based on the failure mode prediction results, potential failure modes, such as fatigue fracture and local yielding, are identified. Assuming the preset risk threshold is 0.2, the risk value of the potential failure mode is found to be 0.3, which exceeds the preset threshold. Use iterative optimization algorithms (such as genetic algorithms) to adjust the environmental stress factor, for example, reduce the maximum stress value to 270 MPa, and re-predict the failure probability until the risk value is lower than the preset threshold. Based on the optimized environmental stress factor, regenerate the failure mode prediction data. For example, the predicted failure probability is reduced to 0.18 after optimization.

[0043] Failure mode distribution maps are generated using data visualization tools (such as MATLAB or ANSYS post-processing modules). In the heat map, areas with higher risk values ​​are represented in red, and areas with lower risk values ​​are represented in blue, visually displaying the optimized failure mode distribution. Based on the failure mode distribution map, the durability performance of the curved screen under complex stress environments is determined. For example, the optimized curved screen has a lower failure risk in long-term use, mainly concentrated in the edge area, but the risk values ​​are all within an acceptable range.

[0044] The predicted durability performance of the curved screen under complex stress environments specifically includes: Extract key failure probability distributions from the failure probability distribution, use statistical analysis techniques to generate the probability density function of failure probability, and determine the distribution characteristics of failure probability. Based on the distribution characteristics of failure probability, finite element analysis technology is used to simulate the stress response of the arc screen under complex stress environment, and the stress distribution law is obtained. If the stress concentration area in the stress distribution pattern exceeds the preset threshold, the fatigue characteristics of the material are extracted using data mining technology to generate fatigue characteristic parameters. Based on fatigue characteristic parameters, a random forest regression model is used to predict the failure probability trend of curved screen in long-term use scenarios, and the failure probability trend distribution is obtained. High-risk failure modes are extracted from the failure probability trend distribution, and failure mode prediction results are generated by Bayesian inference method to identify potential failure modes. If the risk value of a potential failure mode exceeds a preset threshold, the environmental stress factor is adjusted through an iterative optimization algorithm to generate optimized failure mode prediction data. Based on the optimized failure mode prediction data, a failure mode distribution map is generated using data visualization technology to determine the durability performance of the curved screen under complex stress environment.

[0045] Specifically, it can be illustrated as follows: Assuming the failure probability distribution obtained through Monte Carlo simulation ranges from 0.05 to 0.20, statistical analysis techniques (such as histogram analysis) are used to extract the key failure probability distribution. It is found that the failure probability is relatively concentrated between 0.10 and 0.15. The probability density function (PDF) of the failure probability is generated. It is assumed that the PDF follows a normal distribution with a mean μ=0.125 and a standard deviation σ=0.025. The failure probability distribution is determined to be a normal distribution with a mean μ = 0.125 and a standard deviation σ = 0.025. Based on the distribution characteristics of failure probability, a representative stress environment scenario (such as a scenario with a maximum stress value of 300MPa) is selected for finite element analysis. Finite element analysis using ANSYS Mechanical can be used to calculate stress distribution patterns, such as the stress value distribution in stress concentration regions. Using a random forest regression model, and inputting fatigue characteristic parameters and stress distribution data, the model predicts the failure probability trend in long-term service scenarios. It is assumed that the model predicts failure rates within 10... 6 After this cycle, the failure probability will increase to 0.25. Generate a failure probability trend distribution map to show the trend of failure probability changes under different cycle counts, assuming 10 5 The failure probability is 0.15 in the second cycle; in 10... 6 The failure probability is 0.25 in the next cycle. Identify high-risk failure modes in the failure probability trend distribution, such as failure modes in high stress concentration areas and at high cycle counts. Assume that high-risk failure modes are mainly concentrated in stress concentration areas, and at 10... 6 The failure probability reaches 0.25 after one cycle.

[0046] Using Bayesian inference, combined with prior knowledge and current data, the prediction results of failure modes are generated. Assuming that prior knowledge indicates that the failure probability of the stress concentration area is 0.20, and combined with the current data (failure probability is 0.25), the updated failure probability is 0.22. The potential failure mode was determined to be fatigue fracture in the stress concentration region. Based on the failure mode prediction results, the potential failure mode was determined to be fatigue fracture in the stress concentration region. Assuming the preset risk threshold is 0.20, the risk value of the potential failure mode was found to be 0.22, which exceeds the preset threshold. Use iterative optimization algorithms (such as genetic algorithms) to adjust the environmental stress factor, for example, reduce the maximum stress value to 280 MPa, and re-predict the failure probability until the risk value is lower than the preset threshold. Based on the optimized environmental stress factor, regenerate the failure mode prediction data, assuming that the predicted failure probability is reduced to 0.18 after optimization. Failure mode distribution maps are generated using data visualization tools (such as MATLAB or ANSYS post-processing modules). In the heat map, areas with higher risk values ​​are represented in red, and areas with lower risk values ​​are represented in blue, visually displaying the optimized failure mode distribution. Assuming the optimized failure mode distribution map shows that the risk value of the stress concentration area is 0.18, which is lower than the preset threshold of 0.20, the durability performance of the curved screen under complex stress environment is determined based on the failure mode distribution map. For example, the optimized curved screen has a lower failure risk in long-term use, mainly concentrated in the stress concentration area, but the risk values ​​are all within an acceptable range.

[0047] Furthermore, the determination of the stability of the screen's deformation behavior in extreme environments includes: Finite element analysis technology is used to extract stress concentration regions from local pressure distribution and obtain the distribution characteristics of stress concentration regions. If the distribution characteristics of the stress concentration area exceed the preset threshold, the material rigidity parameters are extracted from the characteristics of the curved screen using data mining techniques to determine the distribution law of the material rigidity parameters. Based on the distribution law of material stiffness parameters, a random forest regression model is used to predict the screen deformation trend and obtain the probability distribution of the deformation trend. High-risk deformation patterns are extracted from the probability distribution of deformation trends, and the occurrence probability of high-risk deformation patterns is analyzed using Bayesian inference methods to determine the distribution characteristics of high-risk deformation patterns. If the distribution characteristics of high-risk deformation modes exceed the preset threshold, the environmental stress factor is adjusted through an iterative optimization algorithm to generate optimized deformation trend data. Based on the optimized deformation trend data, a deformation trend distribution map is generated using data visualization technology to determine the stability of the deformation behavior of the curved screen in extreme environments. Stability assessment indicators are extracted from the deformation trend distribution map, and statistical analysis techniques are used to calculate the probability density function of the stability assessment indicators to determine the stability performance of the curved screen under extreme conditions.

[0048] Specifically, it can be illustrated as follows: Finite element analysis was performed using ANSYS Mechanical to extract stress concentration regions from the local pressure distribution. It was assumed that the maximum stress value in the stress concentration region of the local pressure distribution was 320 MPa, which exceeded the preset threshold of 300 MPa. Record the distribution characteristics of stress concentration regions, including coordinate set C. h Based on the stress value distribution, assuming a preset stress threshold of 300 MPa, it was found that the maximum stress value in the stress concentration area was 320 MPa, which exceeded the preset threshold. Data mining techniques (such as cluster analysis) are used to extract material stiffness parameters, such as Young's modulus E and Poisson's ratio ν, from the properties of the curved screen. Assume the extracted material stiffness parameters are: Young's modulus E: 70 GPa Poisson's ratio ν: 0.2 The distribution of material rigidity parameters in the curved screen is analyzed, such as the variation of Young's modulus E in the stress concentration region. It is assumed that Young's modulus E is slightly reduced in the stress concentration region, and the distribution range is 68 GPa to 72 GPa. Using a random forest regression model, the distribution of material stiffness parameters and stress values ​​in stress concentration areas are input to predict the screen's deformation trend. The model predicts the following probability distribution of screen deformation trend under extreme conditions: Maximum deformation value δ max15μm Probability distribution of deformation trend: mean μ = 10 μm, standard deviation σ = 3 μm High-risk deformation patterns are extracted from the probability distribution of deformation trends, such as regions with deformation values ​​exceeding 12 μm. It is assumed that high-risk deformation patterns are mainly concentrated in stress concentration areas with deformation values ​​ranging from 12 μm to 15 μm. Using Bayesian inference, combining prior knowledge and current data, we analyze the probability of high-risk deformation patterns. Assuming that prior knowledge indicates a probability of 0.15 for high-risk deformation patterns, and combining the current data (a probability of deformation exceeding 12 μm is 0.20), the updated probability is 0.18. Determine the distribution characteristics of high-risk deformation modes, such as their distribution range and probability in stress concentration areas. Assuming the preset threshold for high-risk deformation modes is 0.15, it is found that the updated probability of occurrence is 0.18, which exceeds the preset threshold. Use iterative optimization algorithms (such as genetic algorithms) to adjust the environmental stress factor, for example, reduce the maximum stress value to 290 MPa, and re-predict the deformation trend until the probability of high-risk deformation mode occurrence is lower than the preset threshold. Based on the optimized environmental stress factor, deformation trend data is regenerated, assuming that the maximum predicted deformation value is reduced to 12 μm and the probability of occurrence is reduced to 0.14. Use data visualization tools (such as MATLAB or ANSYS post-processing modules) to generate deformation trend distribution maps; In the heat map, areas with higher deformation values ​​are represented in red, and areas with lower deformation values ​​are represented in blue, visually showing the optimized deformation trend distribution. For example, if the optimized deformation trend distribution map shows that the deformation value of the stress concentration area is 12μm, which is lower than the preset threshold of 12μm; Extract stability assessment indicators, such as the maximum deformation value δ, from the deformation trend distribution map. max Given the standard deviation σ of the deformation distribution, we assume the extracted stability assessment index is: Maximum deformation value δ max 12μm Standard deviation of deformation distribution σ: 2μm Statistical analysis techniques (such as histogram analysis) are used to calculate the probability density function of the stability assessment index, assuming the maximum deformation value δ. max The probability density function follows a normal distribution with a mean μ = 12 μm and a standard deviation σ = 2 μm. Based on the statistical analysis results, the stability performance of the curved screen under extreme conditions is judged. It is assumed that the preset stability threshold is that the maximum deformation value does not exceed 12μm and the standard deviation of the deformation distribution does not exceed 3μm.

[0049] Since the maximum deformation value after optimization is 12μm and the standard deviation is 2μm, both of which are within the preset threshold range, it is judged that the curved screen has good stability under extreme conditions.

[0050] Furthermore, the comprehensive evaluation results of the bending performance of the curved screen include: From the local stress distribution data and the overall stress imbalance data, the principal component analysis method is used to extract the comprehensive stress characteristics and obtain the stress distribution characteristics of the arc screen. Based on the stress distribution characteristics, the kernel density estimation method is used to generate the probability density function of the stress distribution and determine the statistical characteristics of the stress distribution. If the statistical characteristics of the stress distribution exceed the preset threshold, then the stress imbalance mode is identified by cluster analysis technology, and the classification result of the stress imbalance mode is obtained. Based on the classification results of stress imbalance modes, a weighted average method is used to integrate the contribution of local stress distribution and overall stress imbalance to generate a comprehensive stress influence factor. Bending performance indicators are extracted from the comprehensive stress influence factor, and the distribution law of bending performance indicators is calculated by statistical inference technology to determine the bending performance stability of the curved screen. If the bending performance stability is lower than the preset threshold, the stress distribution parameters are adjusted through an iterative optimization algorithm to generate optimized comprehensive stress characteristics. Based on the optimized comprehensive stress characteristics, a bending performance distribution map is generated using data visualization technology to determine the bending performance of the curved screen in extreme environments.

[0051] Specifically, it can be illustrated as follows: Principal component analysis (PCA) is used to extract comprehensive stress characteristics from local stress distribution data and global stress imbalance data. It is assumed that the local stress distribution data includes the stress value σ for each element. local The overall stress imbalance data includes the overall stress distribution state σ global .

[0052] PCA analysis was used to extract the first two principal components, which explained 85% and 10% of the variance, respectively, and the stress distribution characteristics of the curved screen were obtained, including the weights and directions of the principal components. The kernel density estimation (KDE) method is used to generate the probability density function (PDF) of the stress distribution based on the stress distribution characteristics, assuming the mean μ = 280 MPa and the standard deviation σ = 20 MPa.

[0053] The generated PDF shows a normal distribution with a mean μ = 280 MPa and a standard deviation σ = 20 MPa. The statistical characteristics of the stress distribution are confirmed to be a normal distribution with a mean μ = 280 MPa and a standard deviation σ = 20 MPa. Assuming the preset stress threshold is 300 MPa, the mean μ of the stress distribution is found to be 280 MPa, which is close to the preset threshold. Clustering analysis techniques (such as K-means clustering) were used to identify stress imbalance patterns. Stress values ​​were divided into two categories: low stress regions (σ<250MPa) and high stress regions (σ≥250MPa). The classification results of stress imbalance patterns were obtained, and the high stress regions were mainly concentrated in the stress concentration areas. Using a weighted average method, based on the classification results of stress imbalance modes, the contribution of local stress distribution and overall stress imbalance is integrated, assuming that the weight of local stress distribution is 0.7 and the weight of overall stress imbalance is 0.3. Calculate the comprehensive stress influence factor σ composite :

[0054] A distribution map of the comprehensive stress influence factor is generated, showing the comprehensive stress values ​​in different regions. Bending performance indicators, such as the maximum bending stress σ, are extracted from the comprehensive stress influence factor. bend and bending stiffness K bend Assuming the maximum bending stress σ is extracted bend =270MPa, bending stiffness K bend =1.1×10 6 N / m; The distribution of bending performance indices is calculated using statistical inference techniques (such as hypothesis testing), assuming that the distribution of bending performance indices follows a normal distribution, and the maximum bending stress σ... bend The mean μ = 270 MPa, and the standard deviation σ = 15 MPa; To assess the bending performance stability of a curved screen, we assume a preset stability threshold of a maximum bending stress not exceeding 280 MPa and a bending stiffness not less than 1.0 × 10⁻⁶ MPa. 6 N / m; Due to the maximum bending stress σ bend =270MPa and bending stiffness K bend =1.1×10 6 The N / m values ​​are all within the preset threshold range, indicating that the curved screen has good bending performance stability. However, it is assumed that in some cases, the bending performance stability is lower than the preset threshold, for example, the maximum bending stress σ... bend =290MPa, exceeding the preset threshold of 280MPa; Use iterative optimization algorithms (such as genetic algorithms) to adjust stress distribution parameters, for example, reduce the stress value in stress concentration areas, recalculate the comprehensive stress influence factor and bending performance index, until the bending performance stability reaches a preset threshold. Based on the optimized stress distribution parameters, the optimized comprehensive stress characteristics are generated, assuming the optimized maximum bending stress σ. bend =275MPa, bending stiffness K bend =1.1×10 6 N / m are all within the preset threshold range; Use data visualization tools (such as MATLAB or ANSYS post-processing modules) to generate bending performance distribution maps; In the heat map, areas with higher bending stress are represented in red, and areas with lower bending stress are represented in blue, visually displaying the optimized bending performance distribution. Assuming the optimized bending performance distribution map shows that the curved screen performs well in extreme environments, mainly concentrated in stress concentration areas, but the stress values ​​are all within acceptable ranges, the bending performance of the curved screen in extreme environments can be determined based on the bending performance distribution map. For example, the optimized curved screen performs well in extreme environments, with the maximum bending stress and bending stiffness both within the preset threshold range, and the overall bending performance is stable.

[0055] Example 2 like Figure 2 As shown, this embodiment provides a system for evaluating the bending durability of an arc-shaped screen under multi-point cohesive force, comprising: The stress analysis and mesh optimization module simulates the local stress distribution of an arc screen under multi-point concentrated force using the finite element analysis method; it refines the local mesh of the arc screen to obtain a high-resolution local stress distribution model; it determines whether the overall stress distribution state exceeds the preset imbalance threshold, and adjusts the mesh parameters of the local stress concentration area through an iterative optimization algorithm to obtain the optimized stress distribution model. Finite element analysis was performed using ANSYS Mechanical to ensure the accuracy and reliability of the simulation. Adaptive mesh generation technology was used to refine the mesh locally in stress concentration areas to ensure the accuracy of the high-resolution model. A genetic algorithm was used to optimize the mesh parameters to ensure the efficiency and convergence of the optimization process. The preset imbalance threshold was 300 MPa. If the overall stress distribution exceeds this threshold, the optimization process is triggered. The optimization objective is to reduce the maximum stress value in the stress concentration area and ensure that it is below the preset threshold.

[0056] The deformation and stress imbalance analysis module acquires non-uniform deformation data under the action of multi-point concentrated forces for the local stress distribution model; and calculates the degree of stress imbalance of the overall structure of the curved screen using the non-uniform deformation data. Non-uniform deformation data are obtained using the Digital Image Correlation (DIC) method to ensure data accuracy and reliability. The stress imbalance calculation method is as follows: the stress-strain analysis algorithm in the finite element analysis software is used to calculate the stress imbalance degree of the overall structure. The stress imbalance degree is defined as the ratio of the maximum stress value to the average stress value. If the ratio exceeds 1.5, the stress imbalance degree is considered to be high. The post-processing module of ANSYS Mechanical is used to generate stress distribution maps and deformation distribution maps to intuitively display the stress imbalance and deformation.

[0057] The failure risk and correlation analysis module obtains correlation data between localized concentrated deformation and overall bending performance based on the optimized stress distribution model; and predicts the durability performance of the curved screen under complex stress environment through simulation methods using potential risk data of long-term failure modes. Principal component analysis (PCA) was used to extract the correlation data between localized clustered deformation and overall bending performance to ensure the comprehensiveness and accuracy of the analysis. Monte Carlo simulation was used to predict the durability performance of the curved screen under complex stress environment, generating a failure probability distribution. A random forest regression model was used to analyze long-term failure modes, generating failure mode prediction results and identifying potential failure modes. A risk threshold of 0.2 was defined; if the failure probability exceeded this threshold, a high-risk failure mode was considered to exist.

[0058] The extreme condition assessment module acquires local pressure performance data under extreme conditions based on the failure probability distribution, and judges the stability of the screen's deformation behavior in extreme environments. Strain gauges are used to measure local pressure performance data to ensure the accuracy and reliability of the data. Bayesian inference methods are used to analyze the probability of occurrence of high-risk deformation modes and determine the stability of deformation behavior. If the distribution characteristics of high-risk deformation modes exceed the preset threshold, the environmental stress factor is adjusted through iterative optimization algorithms to generate optimized deformation trend data.

[0059] The comprehensive evaluation module integrates local stress distribution and overall stress imbalance data based on deformation behavior stability data using data fusion technology to obtain a comprehensive evaluation result of the bending performance of the curved screen. Principal component analysis (PCA) and weighted average methods are used to integrate local stress distribution and overall stress imbalance data to generate a comprehensive stress influence factor. Bending performance indicators, such as maximum bending stress and bending stiffness, are extracted from the comprehensive stress influence factor. Statistical inference techniques are used to calculate the distribution law of bending performance indicators and determine the bending performance stability of the curved screen. If the bending performance stability is lower than a preset threshold, the stress distribution parameters are adjusted through an iterative optimization algorithm to generate optimized comprehensive stress characteristics.

[0060] The specific embodiments of the invention have been described in detail above, but these are merely examples. The invention is not limited to the specific embodiments described above. Those skilled in the art should understand that the embodiments and descriptions in the specification are only illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the bending durability of an arc-shaped screen under the action of a multipoint force, characterized in that, The method comprises the following steps: Simulate local stress distribution of the arc-shaped screen under multi-point concentrated force by finite element analysis method, and obtain distribution data of stress concentration area; According to the distribution data of the stress concentration area, locally grid the arc-shaped screen to obtain a high-resolution local stress distribution model; According to the local stress distribution model, obtain non-uniform deformation data under the action of multi-point concentrated force, and determine the local deformation behavior of the screen; Calculate the stress imbalance degree of the overall structure of the arc-shaped screen through the non-uniform deformation data to obtain the overall stress distribution state; If the overall stress distribution state exceeds the preset imbalance threshold, adjust the grid parameters of the local stress concentration area to obtain an optimized stress distribution model; According to the optimized stress distribution model, obtain the correlation data of local concentrated deformation and overall bending performance to determine the potential risk of long-term failure mode.

2. The method for evaluating the bending durability of an arc-shaped screen under the action of a multi-point converging force according to claim 1, characterized in that, The simulation of the local stress distribution of the arc-shaped screen under the action of multi-point concentrated force specifically comprises: Construct a three-dimensional model of the arc-shaped screen by a geometric modeling software, define the curvature radius, thickness and boundary profile to obtain a geometric model of the arc-shaped screen; Divide the geometric model of the arc-shaped screen into grids by using a finite element analysis software, set the grid density parameters, and generate a finite element grid model; According to the material mechanics database, obtain the Young's modulus, Poisson's ratio and yield strength of the material of the arc-shaped screen to determine the material property parameters; Apply multi-point concentrated force in the finite element grid model, define the load point position and force value to obtain a load application model; Calculate the stress distribution in the load application model by a finite element analysis algorithm, and obtain the local stress distribution data by using a tetrahedral element integral method.

3. The method of claim 1, wherein the method further comprises: The high-resolution local stress distribution model comprises: Obtain a high-resolution finite element grid model by adaptive grid division technology to determine the grid encryption area; According to the grid encryption area, generate a material property configuration file from the material mechanics database by obtaining the Young's modulus, Poisson's ratio and yield strength of the material of the arc-shaped screen; Apply multi-point concentrated force to the high-resolution finite element grid model by using a finite element analysis algorithm, define the load point position and force value to obtain a load distribution model.

4. The method of claim 1, wherein the method further comprises: The determination of the local deformation behavior of the screen specifically comprises: Generate an initial finite element grid model by using adaptive grid division technology according to the three-dimensional geometric model; Generate a material property configuration file from the material mechanics database by obtaining the material property data of the arc-shaped screen; Apply multi-point concentrated force to the initial finite element grid model, define the load point position and force value distribution to generate a load distribution model; Calculate the non-uniform deformation data in the load distribution model by using a finite element analysis method to obtain a local deformation distribution model; If the deformation value in the local deformation distribution model exceeds the preset threshold, mark it as a high-risk deformation area to generate a high-risk area coordinate set; Process the high-risk area coordinate set by a data visualization tool to generate a local deformation distribution heat map and determine the local deformation behavior of the screen.

5. The method of claim 1, wherein, The obtaining of the optimized stress distribution model specifically comprises: If the overall stress distribution state exceeds the preset imbalance threshold, adjust the grid density of the local stress concentration area to generate an optimized grid model; According to the optimized grid model, the stress distribution is recalculated by using the finite element analysis method to generate an updated stress distribution model; If there is a high stress area in the updated stress distribution model, then the high stress area is encrypted by using a local grid division technique to generate a refined grid model; According to the refined grid model, the material parameters are updated by using the material attribute configuration file to generate an updated material distribution model; By using the updated material distribution model, the local stress is evenly distributed by using the finite element analysis method to obtain an equilibrium stress model; If the stress value in the equilibrium stress model still exceeds the preset threshold, then the local grid parameters are adjusted by using an iterative optimization process to generate a final stress distribution model; According to the final stress distribution model, a stress equilibrium distribution diagram is generated by using a data visualization tool to determine the overall stress equilibrium state.

6. The method of claim 1, wherein, The determination of the potential risk of the long-term failure mode specifically includes: Local deformation data is obtained from the optimized stress distribution model, and a deformation distribution rule is generated by using a data extraction method; According to the deformation distribution rule, the overall bending performance index is calculated by using the finite element analysis technique to obtain a bending performance distribution; If the index in the bending performance distribution exceeds the preset threshold, then the correlation parameters are extracted by using a data analysis technique to generate correlation data between local deformation and overall performance; According to the correlation parameters, the influence of local deformation on overall bending performance is analyzed by using a machine learning regression model to obtain an influence weight distribution; High-weight areas are extracted from the influence weight distribution, and preliminary evaluation data of long-term failure risk are generated by using a failure mode analysis technique; If the risk value in the preliminary evaluation data exceeds the preset threshold, then the analysis parameters of the local deformation data are adjusted by using an iterative optimization algorithm to generate an optimized risk evaluation result; According to the optimized risk evaluation result, a failure risk distribution diagram is generated by using a data visualization technique to determine the potential risk of the long-term failure mode.

7. The method of claim 3, wherein the method further comprises: After obtaining the load distribution model, it further includes: The stress distribution data in the load distribution model is calculated by using a tetrahedral element integral method to obtain high-precision local stress distribution results; If there is an area with a stress value greater than the preset threshold in the high-precision local stress distribution results, then it is marked as a high-risk stress concentration area to generate a high-risk area coordinate set; According to the high-risk area coordinate set, a high-resolution stress distribution heat map is generated by using a data visualization tool to obtain a visual representation of the high-risk area; The high-resolution stress distribution heat map is analyzed by using an iterative optimization algorithm to adjust the grid encryption parameters to generate an optimized high-resolution finite element grid model.

8. The method of claim 1, wherein the method further comprises: It further includes: By using the potential risk data of the long-term failure mode, the durability performance of the arc-shaped screen in a complex stress environment is predicted by using a Monte Carlo simulation method to obtain a failure probability distribution; According to the failure probability distribution, local pressure performance data under extreme conditions are obtained to determine the deformation behavior stability of the screen in extreme environments; According to the deformation behavior stability data, the local stress distribution and the overall stress imbalance data are integrated by using a data fusion technique to obtain a comprehensive evaluation result of the bending performance of the arc-shaped screen.

9. The method of claim 8, wherein the method further comprises: The failure probability distribution is obtained by using the Monte Carlo simulation method, specifically including: The potential risk data of long-term failure mode is taken as input, including the distribution data of local stress concentration area, non-uniform deformation data and overall stress distribution state; A Monte Carlo simulation method is adopted to generate a large number of possible stress environment scenarios through random sampling, and the stress response of the arc screen under complex stress environment is simulated; Based on the results of random sampling, the failure probability under each scenario is calculated to generate a failure probability distribution.

10. A system for evaluating the bending durability of an arc screen under the action of multi-point clustering force, comprising: a stress analysis and grid optimization module that simulates the local stress distribution of the arc screen under the action of multi-point clustering force through finite element analysis method; a local grid encryption is performed on the arc screen to obtain a high-resolution local stress distribution model; it is judged whether the overall stress distribution state exceeds the preset imbalance threshold, and the grid parameters of the local stress concentration area are adjusted through an iterative optimization algorithm to obtain an optimized stress distribution model; a deformation and stress imbalance analysis module that obtains non-uniform deformation data under the action of multi-point clustering force for the local stress distribution model; Through the non-uniform deformation data, the stress imbalance degree of the overall structure of the arc screen is calculated; a failure risk and correlation analysis module that obtains the correlation data of local clustering deformation and overall bending performance according to the optimized stress distribution model; and simulates the durability performance of the arc screen under complex stress environment through the potential risk data of long-term failure mode and simulation method; an extreme condition evaluation module that obtains local stress performance data under extreme conditions for the failure probability distribution, and judges the deformation behavior stability of the screen in extreme environment; a comprehensive evaluation module that integrates local stress distribution and overall stress imbalance data to obtain a comprehensive evaluation result of the bending performance of the arc screen according to the deformation behavior stability data by using data fusion technology.

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