Bending strength evaluation method and device, electronic equipment and storage medium

Abstract

The application discloses a bending strength evaluation method and device, electronic equipment and a storage medium. The bending strength evaluation method comprises the following steps: obtaining strength test data of a test sample, the strength test data being obtained based on two-point bending test of the test sample; constructing a probability distribution model of the ultra-thin glass according to the strength test data by using a preset estimation algorithm; and predicting a predicted failure probability of the ultra-thin glass according to the probability distribution model. In the bending strength evaluation method, the two-point bending test and the solution of the probability distribution model by using the preset estimation algorithm are used to realize accurate modeling of the strength test data of the ultra-thin glass, and the accuracy of the predicted failure probability is significantly improved.

Description

Technical Field

[0001] This application relates to the field of display technology, and in particular to a method, apparatus, electronic device, and computer-readable storage medium for evaluating bending strength. Background Technology

[0002] The rapid development of flexible display technology has driven the commercialization of innovative electronic devices such as foldable and rollable screens. Ultra-thin glass (UTG) has become a core cover material for flexible organic light-emitting diode (OLED) displays due to its excellent optical transmittance, surface smoothness, temperature resistance, and mechanical strength. Compared to traditional polymer materials, UTG has significant advantages in scratch resistance, gas barrier properties, and touch sensitivity. Its thickness is typically controlled between 0.1 and 1.2 mm, and its bending performance can be further improved through chemical strengthening processes.

[0003] However, related technologies mostly focus on characterizing the fracture behavior of UTGs under single bending or short-term cyclic loading, lacking systematic research and comprehensive statistical descriptions on the failure mechanisms of materials under long-term dynamic stress. This leads to insufficient understanding of the long-term failure probability of UTGs. Summary of the Invention

[0004] In view of this, this application provides a bending strength evaluation method, a bending strength evaluation device, an electronic device, and a non-volatile computer-readable storage medium.

[0005] The bending strength evaluation method of this application includes:

[0006] Obtain strength test data of the test sample, the strength test data being obtained based on the test sample undergoing a two-point bending test;

[0007] The model parameters of the probability distribution model of the ultrathin glass are obtained by using a preset estimation algorithm based on the strength test data;

[0008] Based on the probability distribution model, the predicted failure probability of the ultrathin glass is predicted.

[0009] In some implementations, predicting the predicted failure probability of the ultrathin glass based on the probability distribution model includes:

[0010] Obtain the bending shape of the ultra-thin glass in the display module;

[0011] The simulation results of the ultrathin glass under the bending shape were obtained by finite element simulation.

[0012] The simulation results are input into the probability distribution model to obtain the predicted failure probability of the ultrathin glass under the bending shape.

[0013] In some embodiments, the bending strength assessment method further includes:

[0014] Obtain the actual failure rate of the ultrathin glass;

[0015] The accuracy of the probability distribution model is determined based on the predicted failure probability and the actual failure rate.

[0016] In some implementations, the preset estimation algorithm includes at least one of maximum likelihood estimation, least squares estimation, and Bayesian estimation.

[0017] In some implementations, the probability distribution model includes one of the WeiBull distribution model and the log-normal distribution model.

[0018] In some implementations, strength test data of the test sample is obtained, the strength test data being based on a two-point bending test of the test sample, including:

[0019] The test sample is placed between two parallel plates;

[0020] Control at least one of the parallel plates to move so that the test sample breaks;

[0021] Record the distance between the parallel plates when the test sample breaks;

[0022] The strength test data of the test sample is calculated based on the distance using the maximum tensile stress algorithm.

[0023] In some implementations, the maximum tensile stress algorithm includes:

[0024]

[0025] Where σ is the maximum tensile stress, E is the elastic modulus, and t is the thickness of the test sample.

[0026] In some embodiments, the flatness deviation of the parallel plates is greater than or equal to -0.1 mm and less than or equal to 0.1 mm; and / or

[0027] The parallelism deviation between the parallel plates is <0.1 mm; and / or

[0028] The moving speed of the parallel plate is <60mm / s.

[0029] The bending strength evaluation device according to the embodiments of this application includes:

[0030] The acquisition module is used to acquire the strength test data of the test sample, which is obtained based on the test sample undergoing a two-point bending test;

[0031] The solution module is used to solve the model parameters of the probability distribution model of the ultrathin glass based on the strength test data using a preset estimation algorithm;

[0032] A prediction module is used to predict the predicted failure probability of the ultrathin glass based on the probability distribution model. The bending strength assessment device of this application includes an electronic device comprising multiple wheels, and the bending strength assessment device includes:

[0033] The electronic device according to the embodiments of this application includes a processor and a memory, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the bending strength evaluation method.

[0034] The non-volatile computer-readable storage medium of this application includes a computer program that, when executed by a processor, causes the processor to perform the bending strength evaluation method described above.

[0035] In the bending strength assessment method, bending strength assessment device, electronic device, and readable storage medium of this application, the strength test data of the test sample is obtained by performing a two-point bending test on the test sample. Then, the model parameters of the probability distribution model of the ultra-thin glass are solved by a preset estimation algorithm, realizing accurate modeling of the strength test data of the ultra-thin glass. Thus, the failure probability of the ultra-thin glass is predicted based on the probability distribution model, which significantly improves the accuracy of the predicted failure probability and provides theoretical support for the durability design and life prediction of flexible displays.

[0036] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0037] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0038] Figure 1 This is a flowchart illustrating the bending strength evaluation method according to certain embodiments of this application;

[0039] Figure 2 This is a schematic diagram of the bending strength evaluation device according to certain embodiments of this application;

[0040] Figure 3 This is a flowchart illustrating the bending strength evaluation method according to certain embodiments of this application;

[0041] Figure 4This is a schematic diagram of a scenario in which a test sample of certain embodiments of this application is subjected to a two-point bending test;

[0042] Figure 5-6 This is a flowchart illustrating the bending strength evaluation method according to certain embodiments of this application;

[0043] Figure 7 This is a graph showing the relationship between the predicted failure probability and the actual failure rate for certain embodiments of this application. Detailed Implementation

[0044] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0045] Please see Figure 1 This application provides a method for evaluating the bending strength of ultrathin glass, the method comprising:

[0046] 01. Obtain the strength test data of the test sample. The strength test data is based on the test sample after a two-point bending test.

[0047] 02. The model parameters of the probability distribution model of ultrathin glass are solved based on the strength test data using a preset estimation algorithm.

[0048] 03. Based on the probability distribution model, predict the failure probability of ultrathin glass;

[0049] Please see Figure 2 This application provides a bending strength evaluation device 10. The bending strength evaluation device 10 includes an acquisition module 11, a solution module 12, and a prediction module 13. Step 01 can be implemented by the acquisition module 11, step 02 can be implemented by the solution module 12, and step 03 can be implemented by the prediction module 13.

[0050] Alternatively, the acquisition module 11 can be used to acquire the strength test data of the test sample, which is obtained based on the test sample undergoing a two-point bending test; the solution module 12 can be used to solve for the model parameters of the probability distribution model of the ultrathin glass based on the strength test data using a preset estimation algorithm; and the prediction module 13 can be used to predict the predicted failure probability of the ultrathin glass based on the probability distribution model.

[0051] This application also provides an electronic device, including a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the processor implements the above-mentioned bending strength evaluation method. That is, the processor is used to acquire the strength test data of the test sample, which is obtained based on the test sample undergoing a two-point bending test; and to solve the model parameters of the probability distribution model of the ultrathin glass based on the strength test data using a preset estimation algorithm; and to predict the predicted failure probability of the ultrathin glass based on the probability distribution model.

[0052] In the bending strength assessment method, bending strength assessment device, and electronic device of this application, the strength test data of the test sample is obtained by performing a two-point bending test on the test sample. Then, the model parameters of the probability distribution model of the ultra-thin glass are solved by a preset estimation algorithm, realizing accurate modeling of the strength test data of the ultra-thin glass. Thus, the failure probability of the ultra-thin glass is predicted based on the probability distribution model, which significantly improves the accuracy of the predicted failure probability and provides theoretical support for the durability design and life prediction of flexible displays.

[0053] In some embodiments, the bending strength assessment device 10 may be part of an electronic device. Alternatively, the electronic device may include the bending strength assessment device 10.

[0054] In some embodiments, the bending strength assessment device 10 may be a discrete component assembled in a certain way to have the aforementioned functions, or a chip having the aforementioned functions in the form of an integrated circuit, or a computer software code segment that enables the computer to have the aforementioned functions when run on a computer.

[0055] In some implementations, the bending strength assessment device 10 may be a standalone component or an additional peripheral element added to an electronic device. The bending strength assessment device 10 may also be integrated into the electronic device; for example, when the bending strength assessment device 10 is part of the electronic device, it may be integrated into the processor.

[0056] It should be noted that ultra-thin glass (UTG) is a glass material with a thickness typically between 1.2 mm and 0.1 mm, with some models less than 0.1 mm. It features ultra-thinness, high strength, high transparency, good flexibility, and foldability. Ultra-thin glass can be used in flexible displays such as foldable screens and curved screens.

[0057] To improve the reliability and durability of UTG (Ultra-Thin Glass) and reduce the failure rate, key specifications must be strictly controlled. For example, adding edge polishing processes to reduce edge defects, appropriately tightening thickness tolerances and ranges, and selecting glass raw materials with high surface compressive stress (CS) are necessary. Specifically, the thickness of the bending zone of ultra-thin glass can be ≤100µm. Total thickness variation (TTV) is an important indicator of glass thickness uniformity; typically, the TTV of the same product should be ≤10% of the glass thickness. A smaller TTV reduces local stress concentration and improves the glass's bending performance. Thickness tolerance refers to the allowable deviation range of glass thickness within the same batch; the thickness tolerance of the same batch of products should be ≤10% of the glass thickness. Understandably, stricter thickness tolerance helps improve the uniformity of glass strength. Surface compressive stress (CS) is used to inhibit crack propagation; higher CS values ​​can improve the overall strength of the glass. The CS of ultra-thin glass can be ≥500MPa.

[0058] Edge polishing significantly affects the edge quality of glass. Reducing edge defects (such as chipping) can lower the probability of crack initiation. To minimize the impact of excessive chipping depth on testing, the chipping amount of test samples should be ≤20µm. The material selection for ultrathin glass includes, but is not limited to, strong sodium aluminum silicon glass, strong lithium aluminum silicon glass, or microcrystalline glass.

[0059] In step 01, the two-point bending test involves applying concentrated forces to both ends of the material, causing it to bend between the two loading points. By measuring the amount of deformation under a specific load or the load required to achieve a specific deformation, the material's mechanical properties, such as bending strength and elastic modulus, are calculated.

[0060] A test sample refers to an ultra-thin glass sample used for testing. Multiple test samples may be included, and these samples can be grouped into one or more groups depending on their manufacturing process. Understandably, when test samples are divided into multiple groups, there will also be multiple sets of strength test data.

[0061] For example, in some examples, among multiple test samples, there may be test samples using the new processing technology and test samples using the old processing technology. The test samples can be divided into two groups according to the different processing technologies. After the two-point bending test, two sets of strength test data are obtained, namely the strength test data of the test sample using the new processing technology and the strength test data of the test sample using the old processing technology.

[0062] In step 02, the preset estimation algorithm may include, but is not limited to, at least one of the following: Maximum Likelihood Estimation (MLE), Least Squares, and Bayesian estimation.

[0063] It should be noted that when the strength test data includes multiple sets, the model parameters of the probability distribution model for each set can be obtained by using a preset estimation algorithm based on the strength test data of each set. For example, if the strength test data includes first strength test data obtained from test samples generated by the new processing technology and second strength test data obtained from test samples generated by the old processing technology, then the model parameters of the probability distribution model of the ultra-thin glass obtained by the new processing technology can be obtained by using the preset estimation algorithm based on the first strength test data, and the model parameters of the probability distribution model of the ultra-thin glass obtained by the old processing technology can be obtained by using the second strength test data. Thus, the probability distribution models of the ultra-thin glass with the new processing technology and the ultra-thin glass with the old processing technology are obtained.

[0064] The probability distribution model includes either the WeiBull distribution model or the log-normal distribution model. Those skilled in the art will understand that the WeiBull distribution model is a continuous probability distribution, and it may include a probability density function and a cumulative distribution function. The expression for calculating the probability density function of the WeiBull distribution model includes:

[0065]

[0066] Where β is the shape parameter, λ is the scale parameter, and γ is the position parameter.

[0067] When γ is 0, the probability density function can be expressed as:

[0068]

[0069] Where β is the shape parameter and λ is the scale parameter.

[0070] The expression for calculating the cumulative distribution function includes:

[0071]

[0072] Where β is the shape parameter and λ is the scale parameter.

[0073] The expression for calculating the probability density function of the log-normal distribution model includes:

[0074]

[0075] Where μ and σ are the mean and standard deviation of the logarithms of the variables, respectively.

[0076] Understandably, maximum likelihood estimation provides a robust and statistically reliable method to accurately predict the strength distribution of ultrathin glass under various stress scenarios. Ultrathin glass is a brittle material, and its failure is typically triggered by randomly distributed microcracks on the surface or inside, resulting in significant statistical dispersion in failure strength. The WeiBull distribution model, based on the "weakest link theory," assumes that the overall strength of a material is determined by its weakest link (the largest defect), perfectly matching the failure mechanism of brittle materials. Furthermore, the WeiBull distribution can extrapolate and predict the failure probability in large-scale production using strength data from a small sample (e.g., 30-50 samples), significantly reducing testing costs. Combined with the WeiBull distribution curve, a safe stress threshold at a specific confidence level can be determined.

[0077] Therefore, in this embodiment, the maximum likelihood estimation method is used as an example for explanation, and the probability distribution model can be illustrated using the WeiBull distribution model. That is, the model parameters of the WeiBull distribution model are solved using the maximum likelihood estimation method based on the intensity test data of the test samples, i.e., the likelihood function in the WeiBull distribution model is maximized. The calculation expression can be:

[0078]

[0079] Where, f(x) i ;β;λ) is the probability density function of the WeiBull distribution model, where β is the shape parameter and λ is the scale parameter.

[0080] By substituting the strength test data of the test sample into the above formula, the model parameters β and λ of the WeiBull distribution model can be obtained, making the WeiBull distribution model more reliable and improving its accuracy. This ensures that the WeiBull distribution model can more accurately predict the failure probability of ultrathin glass and better reflect the actual strength distribution of ultrathin glass.

[0081] Please see Figure 3 In some implementations, step 01 includes:

[0082] 011, Place the test sample between the two parallel plates;

[0083] 012, control at least one parallel plate to move so that the test sample breaks;

[0084] 013, Record the distance between the parallel plates when the test sample breaks;

[0085] 014, the strength test data of the test sample is calculated based on the distance using the maximum tensile stress algorithm.

[0086] Please combine Figure 2In some implementations, sub-steps 011-014 can be implemented by the acquisition module 11. In other words, the acquisition module 11 can be used to place the test sample between two parallel plates, control at least one parallel plate to move so that the test sample breaks, record the distance between the parallel plates when the test sample breaks, and calculate the strength test data of the test sample based on the distance using the maximum tensile stress algorithm.

[0087] In some implementations, the processor can be used to place the test sample between two parallel plates, control at least one parallel plate to move so that the test sample breaks, record the distance between the parallel plates when the test sample breaks, and calculate the strength test data of the test sample based on the distance using a maximum tensile stress algorithm.

[0088] Please see Figure 4 Specifically, during the two-point bending test, the test sample is first placed between two parallel plates, ensuring that the length direction of the test sample is perpendicular to the axis of the parallel plates. Then, a small preload (e.g., 0.1N) is applied to eliminate the contact gap, and the initial zero displacement is recorded. Next, a downward vertical load is continuously applied, while the sample response is monitored in real time using a force-displacement curve. The test is immediately terminated when the load suddenly drops (corresponding to sample breakage), and the distance D between the parallel plates at the time of breakage is recorded. The strength test data of the test sample is calculated based on this distance using the maximum tensile stress algorithm. The calculation expression for the maximum tensile stress algorithm includes:

[0089]

[0090] Where σ is the maximum tensile stress, D is the distance between the parallel plates, E is the elastic modulus of the test sample, and t is the thickness of the test sample.

[0091] Additionally, it should be noted that the flatness of the parallel plates directly affects the accuracy of the two-point bending test. Therefore, to ensure uniform stress distribution during the test, the flatness deviation of the parallel plates should be greater than or equal to -0.1 mm and less than or equal to 0.1 mm. Parallelism deviation between the parallel plates can easily lead to additional shear stress during the test; therefore, the parallelism deviation between the parallel plates should be <0.1 mm. Furthermore, the test speed affects the glass's fracture behavior; therefore, the moving speed of the parallel plates should be <60 mm / s. Moreover, to further ensure the accuracy of the two-point bending test, it is recommended to calibrate the equipment using a rebound force measurement before testing. Subsequent testing should proceed when the error between the theoretically calculated rebound force and the measured rebound force is ≤5%. During the test, a high-sensitivity sensor (such as a force sensor or displacement sensor) should be used to ensure accurate measurement of the UTG's rebound force and bending deformation. A sensitivity better than 0.01 N (force sensor) or 0.001 mm (displacement sensor) is recommended to improve the accuracy and reliability of the test data.

[0092] Please see Figure 5 In some implementations, step 03 includes:

[0093] 031, Obtain the bending shape of the ultra-thin glass in the display module;

[0094] 032, The simulation results of ultrathin glass under bending shape were obtained through finite element simulation;

[0095] 033. The simulation results are input into the probability distribution model to obtain the predicted failure probability of ultrathin glass under bending conditions.

[0096] Please combine Figure 2 In some implementations, sub-steps 031-033 can be implemented by prediction module 13. In other words, prediction module 13 can be used to obtain the bending shape of ultra-thin glass in the display module, obtain the simulation results of ultra-thin glass under bending shape through finite element simulation, and input the simulation results into the probability distribution model to obtain the predicted failure probability of ultra-thin glass under bending shape.

[0097] In some implementations, the processor can be used to obtain the bending shape of the ultra-thin glass in the display module, obtain the simulation results of the ultra-thin glass under the bending shape through finite element simulation, and input the simulation results into the probability distribution model to obtain the predicted failure probability of the ultra-thin glass under the bending shape.

[0098] It should be noted that the bending shape of ultra-thin glass in a display module refers to the actual bending shape of the ultra-thin glass when it is applied to a display module (such as an OLED module). The simulation results need to consider the stress changes of the display module during folding and unfolding, as well as the interactions between different components, to ensure that the simulation results are as close to reality as possible.

[0099] For example, in some cases, the stress of ultrathin glass under bending conditions is 1000 MPa obtained through finite element simulation. The maximum likelihood estimation method, based on the strength test data generated by the new processing technology, yields model parameters of β = 5.164 and λ = 3847. Based on the strength test data generated by the old processing technology, the model parameters are β = 4.608 and λ = 3582. The probability distribution model of ultrathin glass under the new processing technology calculates a predicted failure probability of 951 ppm for ultrathin glass under bending conditions, while the probability distribution model of ultrathin glass under the old processing technology calculates a predicted failure probability of 2792 ppm for ultrathin glass under bending conditions.

[0100] Thus, by reversing the bending shape of ultrathin glass to obtain simulation results, and then using a probability distribution model to solve for the predicted failure probability of ultrathin glass under bending conditions, theoretical support is provided for the durability design and lifespan prediction of flexible displays.

[0101] Please see Figure 6 In some embodiments, the bending strength assessment method further includes:

[0102] 04. Obtain the actual failure rate of ultrathin glass;

[0103] 05. Determine the accuracy of the probability distribution model based on the predicted failure probability and the actual failure rate.

[0104] Please combine further Figure 2 In some embodiments, the bending strength assessment device 10 further includes a determination module 14. Step 04 can be implemented by the acquisition module 11, and step 05 can be implemented by the determination module 14. Alternatively, the acquisition module 11 can also be used to acquire the actual failure rate of the ultrathin glass, and the determination module 14 can be used to determine the accuracy of the probability distribution model based on the predicted failure probability and the actual failure rate.

[0105] In some implementations, the processor can be used to obtain the actual failure rate of the ultrathin glass and determine the accuracy of the probability distribution model based on the predicted failure probability and the actual failure rate.

[0106] In step 04, sampling data of ultra-thin glass from multiple large-scale production projects can be collected and analyzed to obtain the actual failure rate. The actual failure rate may include, but is not limited to, the actual failure rate of pre-bending tests and the actual failure rate after consumer use.

[0107] In step 05, a relationship diagram can be drawn based on the predicted failure probability and the actual failure rate, and the accuracy of the probability distribution model can be determined based on the relationship diagram. If the relationship between the two is approximately linear, then the probability distribution model is accurate.

[0108] Please see Figure 7 For example, in some cases, after plotting the relationship between the predicted failure probability and the actual failure rate, the equation y = 1.9197x + 175.84 can be obtained, where y is the actual failure rate (ppm) and x is the predicted failure probability (ppm). Therefore, this linear relationship not only verifies the effectiveness of the probability distribution model, but also provides an important reference to help optimize the selection and processing technology of ultra-thin glass, thereby improving the reliability and durability of products.

[0109] This application also provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the aforementioned bending strength evaluation method.

[0110] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any other combination. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state drives (SSDs)).

[0111] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0112] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0113] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0114] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for evaluating the bending strength of ultrathin glass, characterized in that, The bending strength assessment method includes: Obtain strength test data of the test sample, the strength test data being obtained based on the test sample undergoing a two-point bending test; The model parameters of the probability distribution model of the ultrathin glass are solved using a preset estimation algorithm based on the strength test data; and Based on the probability distribution model, the predicted failure probability of the ultrathin glass is predicted.

2. The bending strength evaluation method according to claim 1, characterized in that, Based on the probability distribution model, the predicted failure probability of the ultrathin glass is predicted, including: Obtain the bending shape of the ultra-thin glass in the display module; The simulation results of the ultrathin glass under the bending shape were obtained by finite element simulation. The simulation results are input into the probability distribution model to obtain the predicted failure probability of the ultrathin glass under the bending shape.

3. The bending strength evaluation method according to claim 1, characterized in that, The bending strength assessment method further includes: Obtain the actual failure rate of the ultrathin glass; The accuracy of the probability distribution model is determined based on the predicted failure probability and the actual failure rate.

4. The bending strength evaluation method according to claim 1, characterized in that, The preset estimation algorithm includes at least one of the maximum likelihood estimation method, the least squares method, and the Bayesian estimation method.

5. The bending strength evaluation method according to claim 1, characterized in that, The probability distribution model includes either the WeiBull distribution model or the log-normal distribution model.

6. The bending strength evaluation method according to claim 1, characterized in that, Obtain strength test data for the test sample, the strength test data being obtained based on a two-point bending test of the test sample, including: The test sample is placed between two parallel plates; Control at least one of the parallel plates to move so that the test sample breaks; Record the distance between the parallel plates when the test sample breaks; The strength test data of the test sample is calculated based on the distance using the maximum tensile stress algorithm.

7. The bending strength evaluation method according to claim 6, characterized in that, The maximum tensile stress algorithm includes: Where σ is the maximum tensile stress, D is the distance between the parallel plates, E is the elastic modulus, and t is the thickness of the test sample.

8. The bending strength evaluation method according to claim 6, characterized in that, The flatness deviation of the parallel plates is greater than or equal to -0.1 mm and less than or equal to 0.1 mm; and / or The parallelism deviation between the parallel plates is <0.1 mm; and / or The moving speed of the parallel plate is <60mm / s.

9. A device for evaluating the bending strength of ultrathin glass, characterized in that, The bending strength assessment device includes: The acquisition module is used to acquire the strength test data of the test sample, which is obtained based on the test sample undergoing a two-point bending test; The solution module is used to solve the model parameters of the probability distribution model of the ultrathin glass based on the strength test data using a preset estimation algorithm; The prediction module is used to predict the failure probability of the ultrathin glass based on the probability distribution model.

10. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a computer program that, when executed by the processor, causes the processor to perform the bending strength evaluation method according to any one of claims 1-8.

11. A non-volatile computer-readable storage medium containing a computer program, characterized in that, When the computer program is executed by a processor, the processor performs the bending strength evaluation method according to any one of claims 1-8.

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