Transparent soft material tensile failure analysis method, system, terminal and medium

Abstract

The application discloses a transparent soft material drawing damage analysis method, system, terminal and medium, the method comprises the following steps: one side of the optical glue layer is bonded on any transparent sheet (such as acrylic sheet), the other side is bonded on the lower glass plate of the loading device, and after the acrylic sheet and the upper glass plate of the loading device are pressed and cured, the loading device is controlled to vertically pull the optical glue layer to complete the drawing damage test; the test image of the cavity evolution in the drawing damage test process of the optical glue layer is obtained, and the mechanical data corresponding to the test image in the drawing damage test process is obtained; the cavity feature extraction and trajectory tracking processing are carried out on the test image, the cavity time cone diagram of the cavity evolution process is obtained, and the critical stress distribution diagram of the cavity initiation is obtained according to the test image and the mechanical data. The application can realize the synchronous high-precision observation and analysis of the mechanical response and optical evolution of the material damage process.

Description

Technical Field

[0001] This application relates to the field of image analysis technology, and in particular to a method, system, terminal, and computer-readable storage medium for analyzing pull-out failure of transparent soft materials. Background Technology

[0002] Optical clear adhesive (OCA) is a key material widely used in touchscreens, display devices, and flexible electronic devices, possessing excellent light transmittance, adhesion, and mechanical flexibility. With the rapid development of flexible electronics and display technologies, OCA materials are frequently subjected to mechanical loads such as pull-out and peeling during actual service. These mechanical loads easily lead to localized deformation and damage of the adhesive layer. Typical damage forms include cavitation (external cavitation, or voids) and its propagation, material cracking, interface debonding, and fingering instability. These damage forms not only reduce the internal optical transparency of the material but can also cause overall structural and functional failure in severe cases, significantly affecting the optical performance, display effect, and reliability of the device. Currently, testing and analysis methods for pull-out damage of OCA materials are mainly in the academic research stage, generally consisting of experimental setups built by scholars themselves, with no mature commercial products available. They belong to specialized research tools within a non-standardized system. Although some academic studies have achieved preliminary simultaneous observation of mechanical loading and optical imaging, these self-built testing devices generally have many technical bottlenecks and shortcomings, which restrict the in-depth research and data reliability, and make it difficult to meet the needs of high-precision and high-reliability research on OCA materials.

[0003] Currently, one issue is the insufficient stiffness of the loading chain and low control precision. Most existing academic research designs for loading and observation systems generally have low overall stiffness of the loading chain, resulting in large deformation of the system itself when pulling thin adhesive layers, making it difficult to achieve precise speed and displacement control. This shortcoming is particularly evident when the mechanical response exhibits non-monotonic characteristics during adhesive layer deformation (such as interface debonding and finger instability). Furthermore, since OCA materials themselves have significant rate dependence and loading rate sensitivity, insufficient loading chain stiffness further amplifies the inaccuracy of speed and displacement control, seriously affecting the reliability of test results and the repeatability of experiments. Secondly, the observation is out of focus due to the deformation of the loading chain. The optical observation equipment used in the existing system is generally a fixed focal length industrial camera. Although the camera itself has sufficient focusing ability, due to the insufficient stiffness of the loading chain and the large overall deformation of the system, the area to be observed gradually drifts out of the fixed focal plane of the camera during the pulling process. This causes key phenomena such as cavitation initiation, crack propagation or interface debonding to gradually become out of focus and blurred, making it impossible to clearly capture the key details of the changes in the internal microstructure of the material. This out-of-focus problem seriously restricts the in-depth study of the failure mechanism of the adhesive layer and the failure mode of the material. Thirdly, there is a lack of systematic methods for cavitation image recognition and statistical analysis. Even in studies that have completed synchronous observation of cavitation, most work stops at a rough qualitative analysis of cavitation. Only a few studies have performed basic quantification of the geometric dimensions of a small number of cavitations in local areas, such as cross-sectional area, roundness, and boundary contour. For the evolution process of cavitation groups in the entire adhesive layer system, a systematic framework for image recognition, counting, and automated extraction of dynamic evolution has not yet been formed. Currently, there are no mature solutions and application tools in the literature and industry for global identification and large-sample statistical analysis of OCA cavitation groups. This has prevented the effective exploration of the spatiotemporal distribution characteristics of cavitation as a precursor to damage or a microscopic failure signal, which greatly limits the in-depth development of OCA adhesive layer reliability evaluation and damage mechanism modeling.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The main objective of this application is to provide a method, system, terminal, and medium for analyzing pull-out failure of transparent soft materials, aiming to solve the problem in the prior art that the low stiffness of the loading chain in the pull-out failure test of optical adhesives causes large deformation and image defocusing when pulling out thin adhesive layers, resulting in low reliability of test results.

[0006] The first aspect of this application provides a method for analyzing the pull-out failure of a transparent soft material. The method includes the following steps: one side of an optical adhesive layer is bonded to an acrylic sheet, and the other side is bonded to a lower glass plate of a loading device. After the acrylic sheet and the upper glass plate of the loading device are pressed and cured, the loading device is controlled to perform vertical pull-out loading on the optical adhesive layer to complete the pull-out failure test; test images of cavitation evolution during the pull-out failure test of the optical adhesive layer are acquired, along with mechanical data corresponding to the test images during the pull-out failure test; cavitation feature extraction and trajectory tracking processing are performed on the test images to obtain a cavitation time cone diagram of the cavitation evolution process; and a critical stress distribution diagram for cavitation initiation is obtained based on the test images and the mechanical data.

[0007] Optionally, in one embodiment of this application, the step of bonding one side of the optical adhesive layer to the acrylic sheet and the other side to the lower glass plate of the loading device, and pressing and curing the acrylic sheet with the upper glass plate of the loading device, specifically includes: bonding one side of the optical adhesive layer to the front side of the acrylic sheet; cutting the acrylic sheet with the optical adhesive layer attached into a standard geometric shape; bonding the other side of the optical adhesive layer to the lower glass plate of the loading device after removing the protective film; and pressing and curing the back side of the standard geometric acrylic sheet with the upper glass plate of the loading device using ultraviolet adhesive.

[0008] Optionally, in one embodiment of this application, the test image includes multiple image frames at different times during the pull-out failure test, and the mechanical data includes multiple loading displacements and loading loads at different times; acquiring the test image of cavitation evolution during the pull-out failure test of the optical adhesive layer, and the mechanical data corresponding to the test image during the pull-out failure test, specifically includes: receiving multiple image frames with image timestamps of the cavitation evolution of the optical adhesive layer collected by the camera during the pull-out failure test of the optical adhesive layer by the loading device; receiving multiple loading displacements and loading loads with mechanical timestamps during the pull-out failure test of the optical adhesive layer by the loading device, wherein the multiple image timestamps correspond one-to-one with the multiple mechanical timestamps.

[0009] Optionally, in one embodiment of this application, the step of performing cavitation feature extraction and trajectory tracking processing on the test image to obtain a cavitation time cone diagram of the cavitation evolution process specifically includes: extracting features from multiple image frames to obtain a structured dataset of cavitation; and performing spatiotemporal trajectory tracking based on the structured dataset to obtain a cavitation time cone diagram of the cavitation evolution process.

[0010] Optionally, in one embodiment of this application, the structured dataset includes multiple cavitation geometric parameters; the step of extracting features from multiple image frames to obtain structured data of cavitation specifically includes: performing threshold binarization on the multiple image frames after grayscale conversion to obtain a cavitation region corresponding to each image frame, wherein each cavitation region contains one or more cavitations; filling the internal holes of the multiple cavitation regions and separating the cavitations that are stuck together in each cavitation region to obtain the cavitation geometric parameters corresponding to each of the multiple cavitation regions.

[0011] Optionally, in one embodiment of this application, the step of performing spatiotemporal trajectory tracking based on the structured dataset to obtain a cavitation time cone diagram of the cavitation evolution process specifically includes: using the reverse nearest neighbor matching method to associate each current image frame cavitation with the corresponding nearest neighbor cavitation in the previous image frame based on the cavitation geometric parameters corresponding to each of the multiple cavitation regions, to obtain multiple discrete cavitation trajectories; and reorganizing the multiple discrete cavitation trajectories in chronological order to obtain a cavitation time cone diagram of the cavitation germination, growth, and evolution process.

[0012] Optionally, in one embodiment of this application, the critical stress distribution map includes a discrete distribution map and a probability distribution map; obtaining the critical stress distribution map for cavitation germination based on the test image and the mechanical data specifically includes: extracting multiple critical stresses for cavitation germination based on multiple loading displacements with mechanical timestamps, the loading loads, and corresponding multiple image frames with image timestamps; fitting the cavitation germination stress based on the multiple critical stresses to obtain the discrete distribution map and probability distribution map for cavitation germination.

[0013] A second aspect of this application also provides a transparent soft material pull-out failure analysis system, wherein the transparent soft material pull-out failure analysis system includes:

[0014] The pull-out failure test module is used to bond one side of the optical adhesive layer to an acrylic sheet and the other side to the lower glass plate of the loading device. After the acrylic sheet and the upper glass plate of the loading device are pressed and cured, the loading device is controlled to perform vertical pull-out loading on the optical adhesive layer to complete the pull-out failure test.

[0015] The image and data acquisition module is used to acquire test images of cavitation evolution during the pull-out failure test of the optical adhesive layer, as well as mechanical data corresponding to the test images during the pull-out failure test.

[0016] The analysis result output module is used to extract cavitation features and track trajectories from the test images to obtain a cavitation time cone diagram of the cavitation evolution process, and to obtain a critical stress distribution diagram for cavitation initiation based on the test images and the mechanical data.

[0017] A third aspect of this application also provides a terminal, wherein the terminal includes: a memory, a processor, and a transparent soft material pull-out failure analysis program stored in the memory and executable on the processor, wherein when the transparent soft material pull-out failure analysis program is executed by the processor, it implements the steps of the transparent soft material pull-out failure analysis method as described above.

[0018] A fourth aspect of this application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a transparent soft material pull-out failure analysis program, and when the transparent soft material pull-out failure analysis program is executed by a processor, it implements the steps of the transparent soft material pull-out failure analysis method as described above.

[0019] Beneficial effects: This application provides a method, system, terminal, and medium for analyzing the pull-out failure of transparent soft materials. In this application, one side of the adhesive layer is first adhered to an acrylic sheet (any transparent sheet), and the other side is adhered to the lower glass plate of the loading device. The back of the acrylic sheet is adhered to the upper glass plate. This bonding method allows the adhesive layer on the acrylic sheet to better contact the glass plate of the loading device, thereby enabling the high-stiffness loading device to control the loading path to reduce system deformation and avoid image defocusing caused by loading chain deformation. As a result, the camera can acquire clear images through the designed optical path, achieving the purpose of synchronous high-precision observation and analysis of the mechanical response and optical evolution of the material failure process. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a perspective view of the loading device, camera, and loading frame in a preferred embodiment of the transparent soft material pull-out failure analysis method of this application;

[0022] Figure 2 This is a schematic diagram showing the relative positions of the loading device and the camera in a preferred embodiment of the transparent soft material pull-out failure analysis method of this application;

[0023] Figure 3This is a schematic diagram of the imaging path and optical channel of the camera on the optical adhesive layer on the loading device in a preferred embodiment of the transparent soft material pull-out failure analysis method of this application;

[0024] Figure 4 This is a flowchart of a preferred embodiment of the method for analyzing pull-out failure of transparent soft materials according to this application;

[0025] Figure 5 This is a flowchart illustrating the cavitation identification image processing steps in a preferred embodiment of the method for analyzing the pull-out failure of transparent soft materials in this application.

[0026] Figure 6 This is a flowchart illustrating the steps of cavitation numbering and time cone construction in a preferred embodiment of the transparent soft material pull-out failure analysis method of this application;

[0027] Figure 7 This is a diagram showing the formation and feature identification of cavitation in the adhesive layer during a pull-out test in a preferred embodiment of the pull-out failure analysis method for transparent soft materials in this application.

[0028] Figure 8 This is a cavitation evolution time cone diagram in a preferred embodiment of the method for analyzing the pull-out failure of transparent soft materials in this application;

[0029] Figure 9 This is a dispersion distribution diagram in a preferred embodiment of the pull-out failure analysis method for transparent soft materials in this application;

[0030] Figure 10 This is a probability distribution diagram in a preferred embodiment of the transparent soft material pull-out failure analysis method of this application;

[0031] Figure 11 This is a structural diagram of a preferred embodiment of the transparent soft material pull-out failure analysis system of this application;

[0032] Figure 12 This is a structural diagram of a preferred embodiment of the terminal of this application.

[0033] Explanation of reference numerals in the attached figures:

[0034] 10. Loading device; 11. Upper base; 12. Lower base; 13. Upper glass plate; 14. Lower glass plate; 15. Reflector; 16. Light source; 20. Camera; 100. Pull-out failure test module; 200. Image and data acquisition module; 300. Analysis result output module. Detailed Implementation

[0035] To make the objectives, technical solutions, and effects of this application clearer and more explicit, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are only possible technical implementations of this application and not all possible implementations. Based on the embodiments in this application, those skilled in the art can obtain other embodiments without creative effort, and these embodiments are also within the protection scope of this application.

[0036] In related technologies, the loading devices for loading and contacting the adhesive layer are imperfect. Existing devices often suffer from insufficient contact with the OCA material and uneven stress during loading. These problems lead to unstable deformation of the adhesive layer under stress, inducing nonlinear responses in local deformation, such as interfacial debonding, irregular cavitation formation, and uncontrollable finger-like instability, thus reducing the accuracy and repeatability of experimental results. At present, synchronous observation experimental devices for OCA pull-out failure are limited to the academic research level. Most of the devices are built independently by researchers, and there are no unified technical standards or commercial products, making it difficult to widely promote and apply them, thus limiting the depth and breadth of research.

[0037] This application provides a standardized, high-precision, high-stiffness loading chain and image recognition capability, along with a commercially viable synchronous observation system for the pull-out failure of OCA materials. This system effectively addresses many shortcomings of existing technologies, enabling efficient, quantitative, and systematic research on the failure mechanism and cavitation evolution behavior of OCA materials. It promotes the reliability improvement and technological advancement of optically transparent adhesive materials in the fields of electronic device packaging and display manufacturing.

[0038] First, let's introduce the terms used in the embodiments of this application:

[0039] OCA, Optical Clear Adhesive; UV, Ultraviolet; CNC, Computer Numerical Control; DIC, Digital Image Correlation; ROI, Region of Interest, a specific region selected in image processing (.roi output); TEMAPro, a professional image analysis system with advanced target tracking, shape measurement, and structure recognition capabilities; Avizo, a professional 3D visualization and analysis software supporting 3D stereoscopic vision, DIC analysis, and particle tracking; Dragonfly, a professional 3D visualization and analysis software supporting 3D stereoscopic vision, DIC analysis, and particle tracking; MetaMorph, a professional image processing and analysis software supporting 3D stereoscopic vision, DIC analysis, and particle tracking; OpenCV, Open Computer Vision Library, an open-source image processing library providing image processing and analysis capabilities. Scikit-image, a Python-based image processing library, provides image processing and analysis functions; Image Toolbox, a data processing software, is an image processing toolbox; U-Net, a deep learning architecture used for tasks such as image recognition and segmentation; YOLO, You Only Look Once (an object detection algorithm); DeepLab, a deep learning architecture used for tasks such as image recognition and segmentation; DTW, Dynamic Time Warping, a method for measuring the similarity of time series; Kalman filtering, an algorithm for estimating system states, used for trajectory prediction and filtering; Weibull distribution (a statistical distribution) used to describe the probability distribution of failure phenomena such as material fatigue life and equipment failure time over time; micro-focus X-ray computed tomography, micro-CT, or simply micro-CT.

[0040] It should be noted that this application applies to transparent soft materials. In this embodiment, the transparent soft material is illustrated by optically transparent adhesive, but it is not limited to this. The transparent soft material can also be an elastomer, such as natural rubber or silicone rubber PDMS (polydimethylsiloxane, a high molecular weight organosilicon compound).

[0041] To address the issue of low loading chain stiffness causing significant deformation and image defocusing during optical adhesive pull-out failure tests, resulting in low test reliability, this application first adheres one side of the adhesive layer to an acrylic sheet and the other side to the lower glass plate of the loading device. The back of the acrylic sheet is then adhered to the upper glass plate. This bonding method allows for better contact between the adhesive layer on the acrylic sheet and the glass plate of the loading device. This enables the high-stiffness loading device to control the loading path, reducing system deformation and preventing image defocusing caused by loading chain deformation. Consequently, the camera can acquire clear images through a designed optical path, achieving simultaneous high-precision observation and analysis of the mechanical response and optical evolution of the material failure process.

[0042] The technical solutions of this application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0043] The structure and positional relationship of the loading device and the camera in the embodiments of this application are described below.

[0044] See Figure 1 and Figure 2 The loading device 10 is mounted on a loading frame, which controls the upper part of the loading device 10 to move up and down, thereby loading (i.e., a pull-out test) the optical adhesive layer placed in the lower part and located between the two parts. Figure 2 As shown, the loading device 10 includes a loading frame (the frame of the loading device 10), a glass clamping area (upper glass plate 13 and lower glass plate 14), a reflector optical path, and an industrial camera 20 positioned opposite to the loading device 10. A mechanical loading interface is located on the industrial camera 20. The industrial camera 20 is side-mounted, the loading chain is vertical, the reflector 14 is arranged at a 1545° angle, and an adhesive layer is sandwiched between the upper and lower glass plates 14 to improve the stability of the optical observation channel through a high-rigidity loading chain. Figure 3 As shown, an optical channel structure is formed between the light source 16, the adhesive layer, the reflector 15, and the camera 20.

[0045] Specifically, such as Figure 1 , Figure 2 and Figure 3As shown, the loading device 10 includes upper and lower loading frames. An upper base 11, an upper glass plate 13, and a light source 16 are mounted on the upper loading frame. A lower base 12, a lower glass plate 14, and a reflector 15 are mounted on the lower loading frame. The upper glass plate 13 is located below the upper base 11, the light source 16 is located above the upper base 11, and the lower glass plate 14 is located above the lower base 12. Four guide rods are provided on the lower loading frame, each connected to one of the four ends of a mounting block. The reflector 15 is mounted on the mounting block. Adjustment components are located below each of the four ends of the mounting block, and each adjustment component is threadedly connected to one of the four guide rods. Through adjustment of the four adjustment components, the reflector 15 located on the mounting block can rotate within a preset angle range (e.g., 10°) in the x, y, and z planes, respectively. This allows the camera 20 to observe a clear image of the optical adhesive layer under this optical path design, ensuring the accuracy of subsequent image recognition.

[0046] Furthermore, the device is divided into upper and lower parts, employing an integrated 8-axis alloy steel frame design to ensure the structural rigidity and symmetry of the loading path. The upper and lower quartz glass plates are fixed to the carbon steel base on both sides with eight bolts, achieving stable clamping of the sample. The base structure is a variable cross-section mechanically optimized configuration, using carbon structural steel that has undergone CNC precision machining, heat treatment (quenching + tempering), and black zinc electroplating, significantly improving structural hardness and light suppression. A vertical through hole is provided in the center of the base, inside which a 45° silver-plated reflector 15 and a flexible backlight 16 are installed, forming an observation optical path of bottom incidence—oblique reflection—lateral imaging. The camera 20 can observe the interior of the adhesive layer from the side without interference, realizing the dynamic capture of cavitation initiation, crack development, and debonding behavior, avoiding structural obstruction and focal plane drift problems. The mechanical loading anchoring method of this application uses pins and limiting bolts to anchor the upper and lower steel frames to the loading head and base of the mechanical testing machine, respectively, forming a rigid loading chain, thereby achieving high-precision displacement loading, rate control and high-resolution force response monitoring in the vertical direction.

[0047] Understandably, in this application, the high-rigidity loading chain of the loading device 10 can solve the loading error problem caused by deformation in traditional devices. The loading device 10, through an integrated design of an 8-axis alloy steel frame, combined with the rigid anchoring of pins and limit bolts, forms a high-precision vertical displacement loading path, ensuring the controllability of the loading displacement (adapting to the viscoelastic properties of OCA) and reducing the interference of system deformation on observation. The optimized optical observation channel formed by the industrial camera 20 and the loading device 10 can avoid the image defocusing problem caused by loading deformation. By setting a 45° silver-plated reflector 15 and a flexible backlight 16 in the center of the base, a "bottom incidence-oblique reflection-lateral imaging" optical path is formed. The camera 20 observes the interior of the adhesive layer from the side without obstruction, thereby achieving clear capture of dynamic behaviors such as cavitation initiation and crack propagation, and the focal plane and the microscopic response of the adhesive layer are consistent over a long period of time.

[0048] In this application, an experimental system for simultaneous observation of OCA pull-out failure, characterized by high structural stability, clear observation channels, and controllable loading precision, is presented. The system comprises four parts: tooling structure, optical imaging module, loading docking method, and sample preparation process. It is suitable for the visualization study of the microscopic damage behavior of OCA materials under thickness pull-out.

[0049] This application addresses the challenge of observing the failure behavior of optically transparent adhesive (OCA) materials under tensile loading in the perpendicular thickness direction. It proposes a system for simultaneous observation of OCA pull-out failure, integrating a high-stiffness loading chain, synchronous optical imaging, and intelligent image recognition. This system aims to achieve high spatiotemporal resolution, quantitative, and structural simultaneous observation and characterization of local failure modes (such as cavitation initiation, interface debonding, material cracking, and finger instability) that occur in OCA materials during tensile deformation, providing reliable technical support for revealing its viscoelastic behavior and nonlinear failure mechanism.

[0050] The core idea of ​​this application is to overcome the technical bottlenecks of existing systems in terms of loading stiffness, observation stability, and cavitation identification statistics, and to construct an integrated platform with high structural rigidity, precise displacement control, and superior image data processing capabilities. A high-stiffness loading mechanism effectively controls the loading path, significantly reducing system deformation and ensuring the controllability and repeatability of the loading displacement. A fixed-focus, fixed-field imaging strategy, combined with structural calibration, maintains long-term consistency between the observation focal plane and the microscopic response of the colloid layer, avoiding image defocusing caused by loading chain deformation. Furthermore, by combining image recognition and data processing algorithms, full-field cavitation features are automatically extracted, enabling temporal tracking, geometric statistics, and evolutionary modeling of the cavitation population.

[0051] This system possesses the following significant technical advantages: it enables simultaneous, high-precision observation of the mechanical response and optical evolution during material failure, establishing a connection between mechanical experiments and image data; it ensures the accuracy of loading chain stiffness and displacement control, making it particularly suitable for the micromechanical study of viscoelastic and rate-sensitive materials like OCA; it enables image recognition, parameter extraction, and spatial statistical analysis of cavitation groups, providing quantitative evidence for the internal failure process of OCA; and it has the potential for standardization of the experimental system and universality of the methods, making it suitable for research scenarios involving other vertically loaded interface materials and adhesive layers. In summary, the highly integrated, structurally stable, and data-driven testing and observation approach in this application provides a technical path for a deeper understanding of the multi-scale failure mechanism of OCA adhesive layers during the pull-out process, and also offers a new paradigm for material evaluation, structural design, and failure prediction.

[0052] This application is compatible with OCA samples with a thickness of 0.01mm-10mm, and is paired with a 15mm diameter acrylic + glass clamping system. Structural deformation solutions include using adhesives of different thicknesses (e.g., 0.2mm VHB tape), replacing them with metal interlayers or other flexible transparent materials; the reflector can be replaced with a broadband reflective film, and the backlight can be replaced with a point light source / collimated LED array; it can be compatible with loading platforms such as Zwick and Shimadzu by changing the anchoring structure; and a long working distance microscope or a full-field DIC system can be introduced to obtain the strain field under high magnification observation.

[0053] The preferred embodiment of this application describes a method for analyzing the pull-out failure of transparent soft materials, such as... Figure 4 As shown, the method for analyzing pull-out failure of transparent soft materials includes the following steps:

[0054] In step S101, one side of the optical adhesive layer is bonded to an acrylic sheet (any transparent sheet) and the other side is bonded to the lower glass plate of the loading device. After the acrylic sheet and the upper glass plate of the loading device are pressed and cured, the loading device is controlled to perform vertical pull-out loading on the optical adhesive layer to complete the pull-out failure test.

[0055] It is understandable that cavitation growth is related to the bonding substrate in some cases, so the acrylic sheet in this application is any transparent sheet.

[0056] In one possible implementation, one side of the optical adhesive layer is bonded to the front of the acrylic sheet, and the acrylic sheet with the optical adhesive layer is cut into a standard geometric shape; the protective film on the other side of the optical adhesive layer is removed and bonded to the lower glass plate of the loading device; the back side of the standard geometric acrylic sheet is pressed and cured to the upper glass plate of the loading device using ultraviolet adhesive.

[0057] It is understood that this application is applicable to any thin adhesive layer or soft material system with a certain degree of transparency and viscoelasticity, such as 3M. TM The VHB4910 series of high-transparency pressure-sensitive adhesives also includes other commercially available or homemade optically transparent polymer films, UV-curable adhesives, or flexible polymer films.

[0058] Specifically, in the sample preparation process, one side of the transparent adhesive layer is bonded to an acrylic sheet and precisely cut into a standard geometric shape (such as a 15mm diameter disc) using a CO2 laser; after removing the protective film on the other side, it is bonded to the lower quartz glass plate; the upper acrylic structure is pressed and cured with the upper quartz glass plate using UV adhesive to achieve a closed connection of the rigid loading path, ultimately forming a test sample with a stable structure, uniform thickness, and suitable for various loading modes.

[0059] In loading mode, vertical pull-out loading is adopted, and high-precision displacement control is achieved through a rigid loading chain, thereby simulating the peeling condition of OCA in actual applications and adapting to its rate sensitivity.

[0060] This application can guarantee good contact conditions and sample alignment, and can be widely adapted to OCA materials from different sources and formulations.

[0061] In step S102, test images of cavitation evolution during the pull-out failure test of the optical adhesive layer are acquired, as well as mechanical data corresponding to the test images during the pull-out failure test.

[0062] In one possible implementation, the test images include multiple image frames at different times during the pull-out failure test, and the mechanical data includes loading displacement and loading load at multiple different times. The system receives multiple image frames with image timestamps of the cavitation evolution of the optical adhesive layer during the pull-out failure test of the optical adhesive layer by the loading device; it also receives multiple loading displacements and loading loads with mechanical timestamps during the pull-out failure test of the optical adhesive layer by the loading device, with each image timestamp corresponding to one of the multiple mechanical timestamps.

[0063] This application presents a technical system integrating high-performance industrial imaging, synchronous mechanical data acquisition, cavitation identification, spatiotemporal trajectory tracking, and statistical modeling. Covering the entire process from image acquisition to physical quantity extraction, it meets the high-precision identification requirements for the multi-scale emergence, evolution, and stress response behavior of cavitation groups within the OCA adhesive layer during the drawing process. The imaging module employs a 4K industrial camera, coupled with a telecentric lens and a flexible backlight, to clearly image the OCA adhesive layer located between quartz glass plates within a lateral reflection channel constructed by a 45° silver-plated reflector. The camera and the universal testing machine's loading device achieve TTL hardware-level trigger linkage, ensuring millisecond-level synchronization between image frames and loading displacement-load data. The high-resolution imaging of this application can capture the multi-scale evolution behavior of cavitation groups, achieving micrometer-level resolution, and the synchronous loading control enables spatiotemporal consistency between mechanical signals and image signals.

[0064] Specifically, the external trigger signal line of the camera is connected to the TTL signal output terminal of the mechanical testing machine to achieve millisecond-level synchronization. Upon starting the test, the loading device of the mechanical testing machine loads according to preset parameters, simultaneously triggering the camera to begin continuous imaging. The loading device of the mechanical testing machine records the displacement and load data of the loading head in real time, with timestamps accurate to milliseconds. The camera captures images of the adhesive layer at a set frame rate, with each frame accompanied by a timestamp that corresponds one-to-one with the timestamps of the mechanical data.

[0065] In step S103, cavitation feature extraction and trajectory tracking are performed on the test image to obtain a cavitation time cone diagram of the cavitation evolution process, and a critical stress distribution diagram for cavitation initiation is obtained based on the test image and the mechanical data.

[0066] In one possible implementation, feature extraction is performed on multiple image frames to obtain a structured dataset of cavitation bubbles; spatiotemporal trajectory tracking is performed based on the structured dataset to obtain a cavitation time cone diagram of the cavitation bubble evolution process.

[0067] Understandably, this application performs preprocessing to convert the experimental images into discrete maps (a discrete dataset of cavitation bubbles), and then performs postprocessing to connect and string the cavitation bubbles together to finally form a time cone map.

[0068] In one possible implementation, the structured dataset consists of multiple cavitation geometric parameters. After grayscale conversion of multiple image frames, threshold binarization is performed to obtain cavitation regions corresponding to each image frame, wherein each cavitation region contains one or more cavitations. The internal cavitation holes of the multiple cavitation regions are filled, and the cavitations that are stuck together in each cavitation region are separated to obtain the cavitation geometric parameters corresponding to each of the multiple cavitation regions.

[0069] like Figure 5 As shown, in the process of cavitation image recognition (i.e., preprocessing in a certain open-source image processing software), the image sequence is imported, and the cavitation germination region is selected; after grayscale processing, threshold binarization is performed; morphological operations are used to fill the pixels inside the cavitation; the watershed algorithm is applied to separate adjacent cavities; the "Analyze Particles" function is used to extract the geometric features of the cavities, such as their contour, area, roundness, and center point; the minimum recognition area is set to 10 pixels (approximately 6×10). -4 mm 2 The output is in .roi format and a data table is exported.

[0070] Specifically, using a 4K industrial camera and a telecentric lens, the original image sequence (i.e., image frames) of the OCA adhesive layer deformation process is acquired in the lateral reflection optical path, ensuring that each image frame strictly corresponds to the displacement / load data of the mechanical testing machine, forming a spatiotemporally aligned original dataset. In the preprocessing cavitation feature extraction process, the color image is converted to grayscale, and threshold binarization is used to distinguish between cavitation areas (bright areas) and the adhesive matrix (dark areas); the internal pores of the cavitation are filled to eliminate noise interference; adhering cavitation is separated to ensure that each cavitation contour is independent; the geometric parameters such as the contour, area, roundness, and center point coordinates of the cavitation are output, generating a .roi format file. This application transforms the original images into structured data, providing discrete feature points for subsequent spatiotemporal tracking.

[0071] In one possible implementation, based on the cavitation geometric parameters corresponding to each of the multiple cavitation regions, the reverse nearest neighbor matching method is used to associate each current image frame cavitation with the corresponding nearest neighbor cavitation in the previous image frame to obtain multiple cavitation discrete trajectories; based on the multiple cavitation discrete trajectories, the cavitation time cone diagram of the cavitation germination, growth and evolution process is obtained by recombining the multiple cavitation discrete trajectories in chronological order.

[0072] It should be noted that since the cavitation contours exported by a certain open-source image processing software are discrete and disordered data, it is necessary to further construct a temporal numbering system for cavitation trajectories to achieve dynamic tracking.

[0073] like Figure 6 As shown, during the cavitation spatiotemporal trajectory tracking and numbering process (i.e., post-processing by a certain data processing software), anomaly cleaning is performed to remove dust artifacts and identification error points. An inverse nearest neighbor matching algorithm is adopted, based on the Euclidean distance of the cavitation center point, tracing back from the end. Specifically, assuming that the cavitation continues to grow after its emergence and its position does not change drastically, it is recursively pushed from the last frame to the first frame. For each numbered cavitation, it is matched with the spatially adjacent, unnumbered new cavitation in the previous frame until the cavitation area is less than the identification threshold. Finally, trajectory completion is performed to establish a three-dimensional linked list of number-frame number-contour for each cavitation, forming a "cavitation time cone".

[0074] See Figure 7 , Figure 7 (a) represents the cavitation process of cavitation in the adhesive layer sample. Different colors represent different cavitations, and the gray area represents the actual stress area, the area of ​​which is denoted as Atrue. Figure 7 Figure (b) shows the curve of the cross-sectional area Ac of the cavitation bubble changing with the loading time. Time segments I to V correspond to the cavitation bubble initiation stage in Figure (a), respectively. Figure 7 (c) shows the evolution of the cavitation bubble from a radial perspective, with different colors representing different cavitation bubbles. The sample diameter is 15 mm, and the vertical axis represents the projected length of the cavitation bubble in the X direction. It can be understood that the "time cone" in this application is essentially... Figure 7 A three-dimensional oblique view.

[0075] like Figure 8 In (a) and (b), the cross-section corresponds to the contour morphology, the vertical axis represents time, and the bottom cone tip represents the initial germination moment. It can be understood that the cavitation evolution morphology in the image sequence is represented as a "time cone" or "stress cone" in the 3D image, with the cross-section representing the cavitation contour, the vertical axis representing time, and the overall morphology resembling a stalactite. Figure 8 Image (a) shows the evolution of the outlines of multiple cavitation bubbles over time, exhibiting a stalactite-like structure. Figure 8 Image (b) is a top view of cavitation clusters germinating in the adhesive layer. Figure 8(c) in the graph shows the change in the cross-sectional area of ​​each cavitation bubble over time, with the initiation point set at 0.1 mm. 2 When cavitation exceeds a certain point, it is considered that cavitation has germinated. Different time cone lengths reflect the asynchronous nature of germination, and asymmetrical contour expansion reflects the difference in growth rate. For example, the "cavitation ring" image can clearly show the entire process of non-uniform expansion of cavitation.

[0076] In one possible implementation, the critical stress distribution map includes a discrete distribution map and a probability distribution map. Multiple critical stresses for cavitation germination are extracted based on multiple loaded displacements with mechanical timestamps, the loaded loads, and the corresponding multiple image frames with image timestamps. The cavitation germination stress is then fitted based on these multiple critical stresses to obtain the discrete distribution map and the probability distribution map of cavitation germination.

[0077] See Figure 9 and Figure 10 In the process of matching and statistical modeling the mechanical response of cavitation bubbles, the first identification frame of each numbered cavitation bubble can be aligned with the time sequence of mechanical data and mapped to the nominal stress level at the corresponding moment, thereby constructing a critical stress dataset for cavitation bubble initiation. To further quantitatively characterize this distribution, a Weibull statistical model is introduced to fit the cavitation bubble initiation stress. That is, the critical stress dataset is input into the statistical model, and the goal is to focus on the dispersion and probability distribution of cavitation bubble initiation in the adhesive layer from a statistical perspective. It can be understood that cavitation bubble initiation is a damage mode, but at the microscopic level it is a random event. The damage intensity is not unique, but rather a range, a probability-varying distribution intensity.

[0078] The probability density function P(σ) is:

[0079]

[0080] Where: σ is the nominal stress of the adhesive layer at the time of the first observation of cavitation; A is the size parameter (characteristic stress), i.e., 63% of cavitation occurs under stress not exceeding A; B is the shape parameter, describing the distribution slope and concentration trend. This statistical model provides quantitative critical characteristic parameters for the viscoelastic failure behavior of OCA, which is helpful for subsequent establishment of structural reliability assessment models and multi-physics coupling failure prediction mechanisms.

[0081] It is understandable that cavitation does not occur concentratedly after the stress peak, but rather begins to occur gradually near the slight inflection point of the stress-strain curve; this critical stress distribution can reflect the local yielding or local energy accumulation behavior of the material.

[0082] It should be noted that, in the comparison of loading fixtures under different systems in the prior art (from design to performance), the imaging conditions include uneven light spots at the edge of the cavitation, incomplete edge contact, uneven background tone, and observation of non-full adhesive layer. The main causes are attributed to side lighting, backplate reflection, sample flatness, or loading chain alignment. In contrast, the imaging condition of this application is clear with a uniform background free of impurities, mainly due to perspective lighting.

[0083] Understandably, this application can also employ micro-CT to observe the adhesive layer, thus enabling testing even of opaque rubber and obtaining the three-dimensional contours of cavitation bubbles. In this application, the statistical data focuses on the number of cavitation bubbles, cavitation bubble area and volume, cavitation bubble spacing distribution, and cavitation bubble size distribution within the adhesive layer under a single state.

[0084] The key differences between this application and existing technologies are: a high-stiffness, miniaturized loading chain structure design, employing an 8-axis alloy steel skeleton and a variable cross-section CNC-machined carbon steel base, with a total system length <35cm and a loading flexibility <0.01mm / 60N, providing higher accuracy under small displacement / stress loading, especially suitable for loading tests of rate-dependent and viscoelastic materials like OCA. A lateral optical observation path design reduces the space occupied by the loading chain, utilizing a central perforation, a 45° silver-plated reflector, and a backlight to construct the imaging optical path, moving the camera out of the loading channel. This avoids system softening caused by inserting an industrial camera into the loading chain, significantly shortening the loading path, improving structural stiffness, while maintaining an unobstructed field of view and stable imaging focus. Automatic full-image cavitation group identification and quantification extraction (using open-source image processing software): Using open-source image processing software to process the complete image sequence, performing ROI cropping, binarization, watershed segmentation, and particle analysis, overcomes the limitations of traditional research's "qualitative observation or individual measurement," achieving automatic extraction and geometrically structured quantification analysis of full-field cavitation groups. The project focuses on cavitation spatiotemporal trajectory tracking and numbering (using a data processing software). A trajectory tracking algorithm based on inverse nearest neighbor matching is constructed to achieve frame-by-frame backtracking and spatiotemporal numbering of cavitation growth paths. For the first time, a time cone model of the "cavitation lifecycle" is established, providing an intuitive visualization of the multi-scale growth and emergence / death processes of cavitation, thus laying the foundation for dynamic structure modeling. Critical stress matching and Weibull statistical modeling for cavitation initiation are also developed. The frame of the first appearance of a cavitation bubble is synchronized with load-displacement-time data from an arbitrary loading test machine to extract critical stress data. By constructing a probability distribution model of the cavitation population, characteristic stress parameter A and shape parameter B are obtained, serving material reliability assessment and failure prediction.

[0085] Next, referring to the accompanying drawings, a transparent soft material pull-out failure analysis system according to an embodiment of this application is described.

[0086] Figure 11 This is a structural diagram of the transparent soft material pull-out failure analysis system according to an embodiment of this application.

[0087] like Figure 11 As shown, the transparent soft material pull-out failure analysis system includes: a pull-out failure test module 100, an image and data acquisition module 200, and an analysis result output module 300.

[0088] Specifically, the pull-out failure test module 100 is used to bond one side of the optical adhesive layer to the acrylic sheet and the other side to the lower glass plate of the loading device. After the acrylic sheet and the upper glass plate of the loading device are pressed and cured, the loading device is controlled to perform vertical pull-out loading on the optical adhesive layer to complete the pull-out failure test.

[0089] The image and data acquisition module 200 is used to acquire test images of cavitation evolution during the pull-out failure test of the optical adhesive layer, as well as mechanical data corresponding to the test images during the pull-out failure test.

[0090] The analysis result output module 300 is used to extract cavitation features and track trajectories from the test image to obtain a cavitation time cone diagram of the cavitation evolution process, and to obtain a critical stress distribution diagram for cavitation initiation based on the test image and the mechanical data.

[0091] Figure 12 A structural diagram of a terminal provided in an embodiment of this application. The terminal may include:

[0092] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0093] When the processor 502 executes the program, it implements the transparent soft material pull-out failure analysis method provided in the above embodiments.

[0094] Furthermore, the terminal also includes:

[0095] Communication interface 503 is used for communication between memory 501 and processor 502.

[0096] The memory 501 is used to store computer programs that can run on the processor 502.

[0097] The memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0098] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EIS) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0099] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0100] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0101] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for analyzing pull-out failure of transparent soft materials.

[0102] One embodiment of this application provides a computer program product, including a computer program that, when executed by a processor, implements the features described in this application. Figure 1 The corresponding embodiments provide a method for analyzing the pull-out failure of transparent soft materials.

[0103] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0104] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0105] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0106] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable storage medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable storage medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable storage medium could be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0107] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0108] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0109] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0110] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

[0111] It should be understood that the application of this application is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for analyzing pull-out failure of transparent soft materials, characterized in that, The method for analyzing pull-out failure of transparent soft materials includes: One side of the optical adhesive layer is bonded to an acrylic sheet and the other side is bonded to the lower glass plate of the loading device. After the acrylic sheet and the upper glass plate of the loading device are pressed and cured, the loading device is controlled to perform vertical pull-out loading on the optical adhesive layer to complete the pull-out failure test. Acquire test images of cavitation evolution during the pull-out failure test of the optical adhesive layer, and mechanical data corresponding to the test images during the pull-out failure test; The test images are subjected to cavitation feature extraction and trajectory tracking processing to obtain a cavitation time cone diagram of the cavitation evolution process. Based on the test images and the mechanical data, a critical stress distribution diagram for cavitation initiation is obtained.

2. The method for analyzing pull-out failure of transparent soft materials according to claim 1, characterized in that, The process of bonding one side of the optical adhesive layer to the acrylic sheet and the other side to the lower glass plate of the loading device, and then pressing and curing the acrylic sheet with the upper glass plate of the loading device, specifically includes: One side of the optical adhesive layer is bonded to the front side of the acrylic sheet, and the acrylic sheet with the optical adhesive layer is cut into a standard geometric shape. After removing the protective film, the other side of the optical adhesive layer is bonded to the lower glass plate of the loading device; The back side of the standard geometric acrylic sheet is pressed and cured to the upper glass plate of the loading device using ultraviolet adhesive.

3. The method for analyzing pull-out failure of transparent soft materials according to claim 1, characterized in that, The test images include multiple image frames at different times during the pull-out failure test, and the mechanical data includes loading displacement and loading load at multiple different times. The acquisition of test images of cavitation evolution during the pull-out failure test of the optical adhesive layer, and the mechanical data corresponding to the test images during the pull-out failure test, specifically includes: The camera receives multiple image frames with image timestamps during the pull-out failure test of the optical adhesive layer by the loading device, which capture the cavitation evolution of the optical adhesive layer. The loading device receives multiple loading displacements and loading loads with mechanical timestamps during the pull-out failure test of the optical adhesive layer, and the multiple image timestamps correspond one-to-one with the multiple mechanical timestamps.

4. The method for analyzing pull-out failure of transparent soft materials according to claim 3, characterized in that, The step of extracting cavitation features and tracking trajectories from the experimental images to obtain a cavitation time cone diagram of the cavitation evolution process specifically includes: Feature extraction is performed on multiple image frames to obtain a structured dataset of cavitation bubbles; Spatiotemporal trajectory tracking is performed based on the structured dataset to obtain a cavitation time cone diagram of the cavitation evolution process.

5. The method for analyzing pull-out failure of transparent soft materials according to claim 4, characterized in that, The structured dataset includes multiple cavitation geometry parameters; The step of extracting features from multiple image frames to obtain structured data of cavitation bubbles specifically includes: After converting multiple image frames to grayscale, threshold binarization is performed to obtain the bubble region corresponding to each image frame, wherein each bubble region contains one or more bubbles; The internal pores of the multiple cavitation regions are filled, and the cavitation bubbles that are stuck together in each cavitation region are separated to obtain the cavitation geometric parameters corresponding to each of the multiple cavitation regions.

6. The method for analyzing pull-out failure of transparent soft materials according to claim 5, characterized in that, The step of performing spatiotemporal trajectory tracking based on the structured dataset to obtain a cavitation time cone diagram of the cavitation evolution process specifically includes: Based on the cavitation geometric parameters corresponding to each of the multiple cavitation regions, the reverse nearest neighbor matching method is used to associate each current image frame cavitation with the corresponding nearest neighbor cavitation of the previous image frame to obtain multiple cavitation discrete trajectories. By recombining multiple discrete cavitation trajectories in chronological order, a cavitation time cone diagram of the cavitation germination, growth, and evolution process is obtained.

7. The method for analyzing pull-out failure of transparent soft materials according to claim 4, characterized in that, The critical stress distribution map includes a dispersion distribution map and a probability distribution map; The step of obtaining the critical stress distribution map for cavitation germination based on the experimental images and the mechanical data specifically includes: Based on the multiple loading displacements with mechanical timestamps, the loading loads, and the corresponding multiple image frames with image timestamps, multiple critical stresses for cavitation initiation are extracted. By fitting the cavitation initiation stress to multiple critical stresses, a discrete distribution map and a probability distribution map of cavitation initiation are obtained.

8. A system for analyzing pull-out failure of transparent soft materials, characterized in that, The transparent soft material pull-out failure analysis system includes: The pull-out failure test module is used to bond one side of the optical adhesive layer to an acrylic sheet and the other side to the lower glass plate of the loading device. After the acrylic sheet and the upper glass plate of the loading device are pressed and cured, the loading device is controlled to perform vertical pull-out loading on the optical adhesive layer to complete the pull-out failure test. The image and data acquisition module is used to acquire test images of cavitation evolution during the pull-out failure test of the optical adhesive layer, as well as mechanical data corresponding to the test images during the pull-out failure test. The analysis result output module is used to extract cavitation features and track trajectories from the test images to obtain a cavitation time cone diagram of the cavitation evolution process, and to obtain a critical stress distribution diagram for cavitation initiation based on the test images and the mechanical data.

9. A terminal, characterized in that, The terminal includes: a memory, a processor, and a transparent soft material pull-out failure analysis program stored in the memory and executable on the processor. When the transparent soft material pull-out failure analysis program is executed by the processor, it implements the steps of the transparent soft material pull-out failure analysis as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a transparent soft material pull-out failure analysis program, which, when executed by a processor, implements the steps of the transparent soft material pull-out failure analysis as described in any one of claims 1-7.

Images