Method and system for analyzing drawing failure of transparent soft material, terminal and medium

Abstract

The invention discloses a transparent soft material drawing failure analysis method and system, a terminal and a medium, and the method comprises the steps: bonding one surface of an optical adhesive layer to any transparent thin plate (such as an acrylic thin plate), and bonding the other surface of the optical adhesive layer to a lower-layer glass plate of a loading device, after the acrylic thin plate and the upper-layer glass plate of the loading device are laminated and cured, the loading device is controlled to perform vertical drawing loading on the optical cement layer so as to complete the drawing damage test; acquiring a test image of cavitation evolution in the drawing failure test process of the optical adhesive layer and mechanical data corresponding to the test image in the drawing failure test process; and carrying out cavitation feature extraction and trajectory tracking processing on the test image to obtain a cavitation time cone graph of the cavitation evolution process, and obtaining a critical stress distribution diagram of cavitation initiation according to the test image and the mechanical data. According to the invention, synchronous high-precision observation and analysis of mechanical response and optical evolution in the material damage process can be realized.

Description

Technical Field

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

[0002] Optical Clear Adhesive (OCA) is a key material widely used in touch screens, display devices and flexible electronic devices. It has excellent light transmittance, adhesion and mechanical flexibility. With the rapid development of flexible electronics and display technology, OCA materials are often subjected to mechanical loads such as pulling and peeling during actual service. These mechanical loads can easily lead to local deformation and damage of the adhesive layer. Typical forms of damage include cavitation (also known as external cavitation, cavitation) and expansion, material cracking, interface debonding and the occurrence of fingering instability. These forms of damage will not only lead to a decrease in the optical transparency of the material, but in severe cases may even cause overall failure of the structure and function, significantly affecting the optical performance, display effect and reliability of the device. The current test and analysis methods for the pull-out damage of OCA materials mainly exist in the academic research stage. They are generally experimental devices independently built by scholars. There are no mature commercial products. They are specialized research tools in a non-standardized system. Although some academic research has achieved preliminary simultaneous observation of mechanical loading and optical imaging, these independently built testing devices generally have many technical bottlenecks and shortcomings, which restrict the in-depth research and data reliability, and are unable to meet the needs of high-precision and high-reliability research on OCA materials.

[0003] At present, one of the problems is the insufficient stiffness of the loading chain and the low control accuracy. The overall stiffness of the loading chain of most loading and observation systems designed in existing academic research is generally low, which leads to large deformation of the system itself when pulling thin adhesive layers, making it difficult to achieve accurate speed and displacement control. This shortcoming is particularly obvious when non-monotonic mechanical response characteristics appear in the deformation of the adhesive layer (such as interface debonding and finger-like instability). In addition, since the OCA material itself has obvious rate dependence and loading rate sensitivity, the insufficient stiffness of the loading chain further amplifies the inaccuracy of speed and displacement control, seriously affecting the reliability of the test results and the repeatability of the experiment. The second is the problem of observation defocus caused by deformation of the loading chain. The optical observation equipment used in the existing system is generally a fixed-focus industrial camera. Although the camera itself has sufficient focusing ability, the loading chain is not stiff enough and the overall deformation of the system is large. As a result, the observed area gradually floats out of the fixed focal plane of the camera during the pulling process, causing 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 defocus problem seriously restricts the in-depth study of the adhesive layer damage mechanism and material failure mode. The third is the lack of systematic cavitation image recognition and statistical analysis methods. Even in the studies that have completed the synchronous observation of cavitation, most of the work stops at the rough qualitative analysis of cavitation. Only a small number of studies have carried out basic quantification of the geometric dimensions of a small number of cavitations in local areas, such as cross-sectional area, roundness, boundary contour, etc. For the evolution process of the cavitation group in the entire adhesive layer system, there is no systematic image recognition, counting and dynamic evolution automatic extraction framework. At present, there are no mature solutions and application tools for global identification and large-sample statistical analysis of OCA cavitation groups in public literature and industry. This has led to the ineffective exploration of the spatiotemporal distribution characteristics of cavitation as a precursor to damage or microscopic failure signal, which has greatly restricted the in-depth development of OCA adhesive layer reliability evaluation and damage mechanism modeling.

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

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

[0006] A first aspect of an embodiment of the present application provides a method for analyzing the pull-out failure of a transparent soft material, the method comprising the following steps: bonding one side of an optical adhesive layer to an acrylic sheet and the other side to a lower glass plate of a loading device, and after the acrylic sheet and the upper glass plate of the loading device are pressed and cured, controlling the loading device to perform vertical pull-loading on the optical adhesive layer to complete a pull-out failure test; obtaining a test image of cavitation evolution during the pull-out failure test on the optical adhesive layer, and mechanical data corresponding to the test image during the pull-out failure test; performing cavitation feature extraction and trajectory tracking processing on the test image to obtain a cavitation time cone diagram of the cavitation evolution process, and obtaining a critical stress distribution diagram for cavitation initiation based on the test image and the mechanical data.

[0007] Optionally, in one embodiment of the present application, one side of the optical adhesive layer is bonded to an acrylic sheet and the other side is bonded to a lower glass plate of a loading device, and the acrylic sheet and the upper glass plate of the loading device are pressed and cured, specifically comprising: bonding one side of the optical adhesive layer to the front side of the acrylic sheet, and cutting the acrylic sheet with the optical adhesive layer 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 acrylic sheet with the standard geometric shape to the upper glass plate of the loading device through ultraviolet glue.

[0008] Optionally, in one embodiment of the present 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; the acquisition of the test image of the cavitation evolution during the pull-out failure test on the optical adhesive layer, and the mechanical data corresponding to the test image during the pull-out failure test, specifically include: 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 on the optical adhesive layer by the loading device; receiving multiple loading displacements and loading loads with mechanical timestamps during the pull-out failure test on the optical adhesive layer by the loading device, and the multiple image timestamps correspond one-to-one to the multiple mechanical timestamps.

[0009] Optionally, in one embodiment of the present application, the cavitation feature extraction and trajectory tracking processing are performed on the test image to obtain a cavitation time cone diagram of the cavitation evolution process, specifically including: feature extraction of multiple image frames to obtain a structured data set of cavitation; and spatiotemporal trajectory tracking based on the structured data set to obtain a cavitation time cone diagram of the cavitation evolution process.

[0010] Optionally, in one embodiment of the present application, the structured data set includes multiple cavitation geometric parameters; the feature extraction of the multiple image frames to obtain structured data of the cavitation specifically includes: after graying the multiple image frames, performing threshold binarization to obtain the cavitation area corresponding to each of the image frames, wherein each of the cavitation areas contains one or more cavitations; filling the internal holes of the multiple cavitation areas, and separating the adhered cavitations in each of the cavitation areas to obtain the cavitation geometric parameters corresponding to each of the multiple cavitation areas.

[0011] Optionally, in one embodiment of the present application, the spatiotemporal trajectory tracking is performed based on the structured data set to obtain a cavitation time cone diagram of the cavitation evolution process, specifically including: according to the cavitation geometric parameters corresponding to each of the multiple cavitation regions, 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 to obtain multiple cavitation discrete trajectories; according to the multiple cavitation discrete trajectories, reorganizing them in chronological order to obtain a cavitation time cone diagram of the cavitation initiation, growth and evolution process.

[0012] Optionally, in one embodiment of the present application, the critical stress distribution map includes a discreteness distribution map and a probability distribution map; the critical stress distribution map of cavitation initiation is obtained based on the test image and the mechanical data, specifically including: extracting multiple critical stresses of cavitation initiation based on the multiple loading displacements with mechanical timestamps, the loading loads and the corresponding multiple image frames with image timestamps; fitting the cavitation initiation stress based on the multiple critical stresses to obtain the discreteness distribution map and the probability distribution map of cavitation initiation.

[0013] A second aspect of the embodiments of the present application further provides a transparent soft material drawing failure analysis system, wherein the transparent soft material drawing failure analysis system includes:

[0014] A pull-out failure test module is used to bond one side of an optical adhesive layer to an acrylic sheet and the other side to a lower glass plate of a loading device, and after the acrylic sheet and the upper glass plate of the loading device are pressed and cured, control the loading device to vertically pull and load the optical adhesive layer to complete a pull-out failure test;

[0015] An image and data acquisition module, configured to acquire a test image of cavitation evolution during a pull-out failure test on the optical adhesive layer, and mechanical data corresponding to the test image during the pull-out failure test;

[0016] The analysis result output module is used to perform cavitation feature extraction and trajectory tracking processing on the test image to obtain a cavitation time cone diagram of the cavitation evolution process, and obtain a critical stress distribution diagram of cavitation initiation based on the test image and the mechanical data.

[0017] The third aspect of an embodiment of the present application also provides a terminal, wherein the terminal includes: a memory, a processor, and a transparent soft material drawing failure analysis program stored on the memory and runnable on the processor, wherein the transparent soft material drawing failure analysis program, when executed by the processor, implements the steps of the transparent soft material drawing failure analysis method described above.

[0018] The fourth aspect of the embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a transparent soft material drawing failure analysis program, and when the transparent soft material drawing failure analysis program is executed by a processor, the steps of the transparent soft material drawing failure analysis method as described above are implemented.

[0019] Beneficial effects: The present application provides a method, system, terminal and medium for analyzing the pull-out failure of transparent soft materials. The present application first adheres one side of the adhesive layer to an acrylic thin plate (any transparent thin plate) and the other side to the lower glass plate of a loading device, and the back of the acrylic thin plate is adhered to the upper glass plate. Through this bonding method, the adhesive layer on the acrylic thin plate is better in contact with the glass plate of the loading device, so that the high-rigidity loading device controls the loading path to reduce system deformation and avoid image defocus caused by deformation of the loading chain. Then, the camera can capture clear images by designing the optical path, thereby achieving the purpose of synchronous high-precision observation and analysis of the mechanical response and optical evolution of the material failure process. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 A three-dimensional diagram of a loading device, a camera, and a loading frame in a preferred embodiment of the transparent soft material pull-out failure analysis method of the present application;

[0022] Figure 2 This is a structural diagram of 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 the present application;

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

[0024] Figure 4 This is a flow chart of a preferred embodiment of the transparent soft material drawing failure analysis method of the present application;

[0025] Figure 5 This is a flow chart of the image processing step for cavitation identification in a preferred embodiment of the transparent soft material drawing failure analysis method of the present application;

[0026] Figure 6 This is a flow chart of the steps of cavitation numbering and time cone construction in a preferred embodiment of the transparent soft material drawing failure analysis method of the present application;

[0027] Figure 7 This is a diagram of cavitation initiation and characteristic identification of the adhesive layer in a drawing test in a preferred embodiment of the transparent soft material drawing failure analysis method of the present application;

[0028] Figure 8 This is a time cone diagram of cavitation evolution in a preferred embodiment of the transparent soft material drawing failure analysis method of the present application;

[0029] Figure 9 This is a dispersion distribution diagram in a preferred embodiment of the transparent soft material drawing failure analysis method of the present application;

[0030] Figure 10 It is a probability distribution diagram in a preferred embodiment of the transparent soft material drawing failure analysis method of the present application;

[0031] Figure 11 This is a structural diagram of a preferred embodiment of the transparent soft material drawing failure analysis system of the present application;

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

[0033] Description of reference numerals:

[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 test module; 200. Image and data acquisition module; 300. Analysis result output module. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and effects of this application clearer and more specific, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. The described embodiments are only possible technical implementations of this application and are not all possible implementations. Based on the embodiments in this application, those skilled in the art can fully combine the embodiments of this application to obtain other embodiments without creative work, and these embodiments are also within the scope of protection of this application.

[0036] In related technologies, the loading and contact methods of the adhesive layer are imperfect. Existing devices often suffer from insufficient contact with the OCA material and uneven force during loading. These problems lead to unstable force deformation of the adhesive layer and induce nonlinear responses of local deformation, such as interfacial debonding, irregular cavitation initiation, and difficult-to-control finger-like instability, which in turn reduces the accuracy and repeatability of experimental results. Current OCA pull-out failure simultaneous observation experimental devices are limited to academic research. Most devices are independently built by researchers, and there is no unified technical standard or commercial product. This makes it difficult to promote and apply them widely, limiting the depth and breadth of research.

[0037] The embodiment of the present application has a standardized, high-precision, high-rigidity loading chain and image recognition capabilities, and a synchronous observation system for OCA material pulling failure with commercial prospects, which can effectively solve many shortcomings of the existing technical system, realize efficient, quantitative and systematic research on the failure mechanism and cavitation evolution behavior of OCA materials, and promote the reliability improvement and technological progress of optically transparent adhesive materials in the fields of electronic device packaging and display manufacturing.

[0038] First, the nouns involved in the embodiments of this application are introduced:

[0039] OCA, Optical Clear Adhesive; UV, UltraViolet; CNC, Computer Numerical Control; DIC, Digital Image Correlation; ROI, Region Of Interest, a specific area selected during image processing (.roi output); TEMAPro, a professional image analysis system with advanced target tracking, topography measurement, and structure recognition capabilities; Avizo, professional 3D visualization and analysis software, supporting 3D stereo, DIC analysis, and particle tracking capabilities; Dragonfly, professional 3D visualization and analysis software, supporting 3D stereo, DIC analysis, and particle tracking capabilities; MetaMorph, professional image processing and analysis software, supporting 3D stereo, DIC analysis, and particle tracking capabilities; OpenCV, Open Computer Vision Library, an open source image processing library that provides image processing and analysis capabilities. Scikit-image, a Python-based image processing library that provides image processing and analysis capabilities; Image Toolbox, a data processing software; 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 time-varying failure phenomena such as material fatigue life and equipment failure time); micro-CT, also known as micro-CT.

[0040] It should be noted that the present application is applied to transparent soft materials. The transparent soft material in this embodiment is illustrated by taking optically transparent adhesive as an example, but is not limited to this. The transparent soft material can also be an elastomer, such as natural rubber, silicone rubber PDMS (polydimethylsiloxane, a high molecular weight organic silicon compound).

[0041] In order to address the problem that the low stiffness of the loading chain in the test of optical adhesive pulling and destruction causes large deformation of the thin adhesive layer when pulling and causes image defocus, resulting in low reliability of the test results, this application first sticks one side of the adhesive layer to an acrylic thin plate and the other side to the lower glass plate of the loading device, and the back of the acrylic thin plate is stuck to the upper glass plate. Through this bonding method, the adhesive layer on the acrylic thin plate is better in contact with the glass plate of the loading device, so that the high-rigidity loading device controls the loading path to reduce system deformation and avoid image defocus caused by loading chain deformation. Then, the camera can capture clear images by designing the optical path, thereby achieving the purpose of simultaneous high-precision observation and analysis of the mechanical response and optical evolution of the material destruction process.

[0042] The following specific embodiments are used to describe the technical solution of the present application in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0043] The following describes the structure and positional relationship between the loading device and the camera in the embodiment of the present application.

[0044] See also Figure 1 and Figure 2 The loading device 10 is set on the loading frame, and the loading frame can control the upper part of the loading device 10 to move up and down to load the optical adhesive layer placed on the lower part and located between the two parts (i.e., pull-out failure test). 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, an industrial camera 20 and the loading device 10 are arranged opposite each other, and a mechanical loading interface is set on the industrial camera 20; the industrial camera 20 is sideways, the loading chain is vertical, the reflector is arranged at a 1545° angle, and a glue layer is clamped between the upper and lower glass plates 14 to improve the stability of the optical observation channel through the 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 two loading skeletons, an upper base 11, an upper glass plate 13, and a light source 16 arranged on the upper loading skeleton, and a lower base 12, a lower glass plate 14 and a reflector 15 arranged on the lower loading skeleton; the upper glass plate 13 is arranged on the lower side of the upper base 11, the light source 16 is arranged on the upper side of the upper base 11, and the lower glass plate 14 is arranged on the upper side of the lower base 12. Four guide rods are provided on the lower loading skeleton, and the four guide rods are respectively connected to the four ends of the mounting block. The reflector 15 is provided on the mounting block, and adjusting parts are respectively provided below the four ends of the mounting block. The four adjusting parts are respectively threadedly connected to the four guide rods. Through the adjustment of the four adjusting parts, the reflector 15 located on the mounting block can be rotated within a preset angle range (such as 10°) on the x plane, y plane and z plane, respectively, so that the camera 20 under the designed light path can observe a clear image of the optical adhesive layer to ensure the accuracy of subsequent image recognition.

[0046] Furthermore, the device is divided into two parts, the upper and lower parts, and adopts an integrated design of an 8-axis alloy steel skeleton to ensure the structural rigidity and symmetry of the loading path. The upper and lower quartz glass plates are fixed to the two sides of the carbon steel base by 8 bolts to achieve stable clamping of the sample. The base structure is a variable-section mechanical optimization configuration. It uses carbon structural steel that has been precision-machined by CNC, heat-treated (quenching + tempering) and black zinc electroplated, which significantly improves the structural hardness and light suppression properties. A vertical through-hole is provided in the center of the base, and a 45° silver-plated reflector 15 and a flexible backlight source 16 are installed inside to form an observation light path of bottom incidence-oblique reflection-lateral imaging. The camera 20 can observe the inside of the adhesive layer from the side without interference, realizing dynamic capture of cavitation initiation, crack development and debonding behavior, avoiding structural obstruction and focal plane drift problems. The mechanical loading anchoring method of the present application uses pins and limit 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 to achieve high-precision displacement loading, rate control and high-resolution response monitoring in the vertical direction.

[0047] It is understandable that in the present application, the high-rigidity loading chain of the loading device 10 can solve the loading error problem caused by deformation of traditional devices. The loading device 10 is designed through an integrated 8-axis alloy steel skeleton, combined with the rigid anchoring of pins and limit bolts, to form a high-precision displacement loading path in the vertical direction, ensuring the controllability of the loading displacement (adapting to the viscoelastic properties of OCA) and reducing the interference of system deformation on observation. The optical observation channel formed by the industrial camera 20 and the loading device 10 is optimized to avoid the image defocus problem caused by loading deformation. By setting a 45° silver-plated reflector 15 and a flexible backlight source 16 in the center of the base, a "bottom incidence-oblique reflection-lateral imaging" optical path is formed. The camera 20 observes the inside of the glue 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 glue layer are consistent for a long time.

[0048] In this application, an OCA pulling failure and synchronous observation experimental system with high structural stability, clear observation channels, and controllable loading accuracy is proposed, which includes four parts: tooling structure, optical imaging module, loading docking method, and sample preparation process. It is suitable for the visualization study of the microscopic destruction behavior of OCA materials under pulling in the thickness direction.

[0049] This application addresses the destructive behavior and observation difficulties of optically clear adhesive (OCA) materials under pull-out loading conditions in the perpendicular thickness direction, and proposes an OCA pull-out destruction and synchronous observation system that integrates a high-rigidity loading chain, synchronous optical imaging, and image intelligent recognition. The system aims to achieve high temporal and spatial resolution, quantitative, and structured synchronous observation and characterization of local failure modes (such as cavitation initiation, interface debonding, material cracking, finger-like instability, etc.) that occur in OCA materials during the pull-out deformation process, providing reliable technical support for revealing their viscoelastic behavior and nonlinear failure mechanism.

[0050] The core idea of ​​this application is to break through the technical bottlenecks of existing systems in loading stiffness, observation stability, and cavitation identification statistics, and to build an integrated platform with strong structural stiffness, precise displacement control, and superior image data processing capabilities. The high-rigidity loading mechanism effectively controls the loading path, significantly reduces system deformation, and ensures the controllability and repeatability of loading displacement. A fixed-focus, fixed-field imaging strategy is adopted, combined with structural calibration, to maintain long-term consistency between the observation focal plane and the microscopic response of the adhesive layer, avoiding image defocus problems caused by deformation of the loading chain. Furthermore, image recognition and data processing algorithms are combined to automatically extract full-field cavitation features, enabling temporal tracking, geometric statistics, and evolutionary modeling of cavitation groups.

[0051] This system has the following significant technical effects: it realizes the simultaneous high-precision observation of the mechanical response and optical evolution of the material destruction process, opening up the association path between mechanical tests and image data; it ensures the stiffness and displacement control accuracy of the loading chain, and is especially suitable for the micromechanical research of viscoelastic and rate-sensitive materials such as OCA; it realizes image recognition, parameter extraction and spatial statistical analysis of cavitation groups, and provides a quantitative basis for the internal failure process of OCA; it has the potential for standardization of experimental systems and universality of methods, and is expected to be extended to other vertically loaded interface materials, adhesive layers and other research scenarios. In summary, in this application, the highly integrated, structurally stable, and data-driven test observation ideas provide a technical path for an in-depth understanding of the multi-scale failure mechanism of the OCA adhesive layer during the drawing process, and also provides a new paradigm for material evaluation, structural design and failure prediction.

[0052] This application is suitable for 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 (such as 0.2mm VHB tape), switching to 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; the anchoring structure can be replaced to be compatible with loading platforms such as Zwick and Shimadzu; and a long working distance microscope or full-field DIC system can be introduced under high-magnification observation to obtain the strain field.

[0053] The transparent soft material drawing failure analysis method described in the preferred embodiment of this application is as follows Figure 4 As shown, the transparent soft material drawing failure analysis method 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 vertically pull and load the optical adhesive layer to complete the pull-out destruction 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 adhered to the front side of an 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 then adhered to the lower glass plate of the loading device; and the back side of the acrylic sheet with the standard geometric shape is pressed and cured with ultraviolet glue on the upper glass plate of the loading device.

[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 highly transparent pressure-sensitive adhesives also includes other commercially available or homemade optically transparent polymer films, UV-curing adhesives (ultraviolet light-curing 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 round 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 UV glue to achieve a closed connection of the rigid loading path, ultimately forming a test sample with a stable structure, uniform thickness, and suitable for a variety of loading modes.

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

[0060] This application can ensure good contact conditions and sample neutrality, and can be widely adapted to OCA materials of different sources and formulations.

[0061] In step S102, a test image of cavitation evolution during a pull-out failure test on the optical adhesive layer and mechanical data corresponding to the test image during the pull-out failure test are obtained.

[0062] In one possible implementation, 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. A camera receives multiple image frames with image timestamps of cavitation evolution of the optical adhesive layer, captured by the loading device during the pull-out failure test on the optical adhesive layer; and receives multiple loading displacements and loading loads with mechanical timestamps during the pull-out failure test on the optical adhesive layer by the loading device, wherein the multiple image timestamps correspond one-to-one to the multiple mechanical timestamps.

[0063] In this application, a technical system integrating high-performance industrial imaging, synchronous mechanical data acquisition, cavitation identification, spatiotemporal trajectory tracking and statistical modeling is used, covering the entire process from image acquisition to physical quantity extraction, and can meet the needs of high-precision identification of the multi-scale initiation, evolution and stress response behavior of the cavitation group in the OCA adhesive layer during the drawing process. The imaging module adopts a 4K industrial camera, combined with a telecentric lens and a flexible backlight, to clearly image the OCA adhesive layer located between the quartz glass plates in the lateral reflection channel constructed by a 45° silver-plated reflector. The camera and the universal testing machine loading device realize TTL hardware-level trigger linkage to ensure millisecond synchronization of the image frame and the loading displacement-load data. The high-resolution imaging of this application can capture the multi-scale evolution behavior of the cavitation group with a resolution of micron level, and the synchronous loading control can achieve spatiotemporal consistency of the mechanical signal and the image signal.

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

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

[0066] In a possible implementation, feature extraction is performed on the plurality of image frames to obtain a structured data set of cavitation; and spatiotemporal trajectory tracking is performed based on the structured data set to obtain a cavitation time cone diagram of the cavitation evolution process.

[0067] It is understandable that the present application performs pre-processing to convert the test image into a discrete image (a data set of discrete cavitation bubbles), and then connects the cavitation bubbles together through post-processing to finally form a time cone image.

[0068] In one possible implementation, the structured dataset comprises multiple cavitation geometric parameters. The multiple image frames are grayscaled and then thresholded and binarized to obtain a cavitation region corresponding to each image frame, wherein each cavitation region contains one or more cavitations. The internal pores of the multiple cavitation regions are filled, and the adhering cavitations 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 figure, in the cavitation image recognition process (i.e., pre-processing using an open-source image processing software), the image sequence is imported and the cavitation initiation area is selected; after grayscale processing, threshold binarization is performed; morphological operations are performed to fill the internal pixels of the cavitation; the watershed algorithm is applied to separate adjacent cavitations; the “Analyze Particles” function is used to extract the geometric features of the cavitation, such as the outline, area, roundness, and center point; the minimum recognition area is set to 10 pixels (approximately 6×10 -4 mm 2 ); export to .roi format and export the data table.

[0070] Specifically, through a 4K industrial camera and a telecentric lens, the original image sequence (i.e., image frame) of the deformation process of the OCA adhesive layer is obtained in the lateral reflection light path to ensure that each frame of the image strictly corresponds to the displacement / load data of the mechanical testing machine, forming a time-space aligned original data set. In the pre-processing cavitation feature extraction process, the color image is converted to grayscale, and the cavitation area (bright area) and the adhesive layer matrix (dark area) are distinguished by threshold binarization; the internal holes of the cavitation are filled to eliminate noise interference; the adhesion cavitations are separated to ensure that the contour of each cavitation is independent; the geometric parameters such as the contour, area, roundness, and center point coordinates of the cavitation are output to generate a .roi format file. This application converts the original image into structured data to provide discrete feature points for subsequent time-space tracking.

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

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

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

[0074] See also Figure 7 , Figure 7 (a) shows the initiation process of cavitation in the adhesive layer sample. Different colors represent different cavitations. The gray area represents the true stress area, and its area is recorded as Atrue. Figure 7 (b) shows the variation curve of cavitation cross-sectional area Ac with loading time, where time segments I to V correspond to the cavitation initiation stages in (a). Figure 7 (c) in the figure shows the evolution of cavitation in radial perspective, with different colors representing different cavitations. The sample diameter is 15 mm, and the vertical axis represents the projection length of the cavitation in the X direction. It is understood that the “time cone” in this application is essentially Figure 7 Stereoscopic oblique view of .

[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 is the initial initiation moment. It can be understood that the cavitation evolution morphology in the image sequence is expressed as a "time cone" or "stress cone" in the three-dimensional image. The cross section represents the cavitation contour, the vertical axis represents time, and the overall morphology is similar to that of stalactites. Figure 8 (a) shows the evolution of the contours of multiple cavitation bubbles over time, showing a stalactite-like structure. Figure 8 (b) is a top view of the cavitation group initiating in the gel layer. Figure 8(c) is a graph showing the cross-sectional area of ​​each cavitation bubble changing with time, where the initiation point is set to 0.1 mm. 2 If the cavitation grows beyond this point, it is considered to have initiated. Different time cone lengths reflect the asynchrony of initiation, and asymmetric contour expansion reflects differences in growth rates. For example, the "cavitation ring" image can clearly show the entire process of non-uniform cavitation expansion.

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

[0077] See also Figure 9 and Figure 10 During the cavitation mechanical response matching and statistical modeling process, the first identified frame of each numbered cavitation can be aligned with the mechanical data time series based on the frame number and mapped to the nominal stress level at the corresponding moment, thereby constructing a critical stress dataset for cavitation initiation. To further quantitatively characterize this distribution, a Weibull statistical model is introduced to fit the cavitation initiation stress. In other words, the critical stress dataset is input into the statistical model. The goal is to statistically analyze the discreteness and probability distribution of cavitation initiation in the adhesive layer. It is understandable that cavitation initiation is a failure mode, but at the microscopic level it is a random event. The failure intensity is not non-unique, but rather an interval, a distribution intensity with probabilistic variations.

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

[0079]

[0080] Where σ is the nominal stress in the adhesive layer when cavitation is first observed; A is the size parameter (characteristic stress), indicating that 63% of cavitation initiation occurs when the stress does not exceed A; and B is the shape parameter, describing the distribution slope and central tendency. This statistical model provides quantitative critical characteristic parameters for the viscoelastic failure behavior of OCAs, which will facilitate the subsequent development of structural reliability assessment models and multi-physics coupled failure prediction mechanisms.

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

[0082] It should be noted that, when comparing loading tooling under different systems of the prior art (from design to performance), the imaging conditions include uneven light spots on the edge of the bubble, incomplete edge contact, uneven background color tone, and non-full glue layer observation, which are mainly attributed to side lighting, backboard reflection, sample flatness or loading chain alignment; while the imaging conditions of the present application are clear in outline and the background is uniform and free of impurities, which is mainly attributed to perspective lighting.

[0083] It is understood that the present application also utilizes micro-CT to observe the rubber layer, thereby enabling testing 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 within the rubber layer under a single state, their area and volume, and the spacing and size distribution of the cavitation bubbles.

[0084] The key difference between this application and the prior art is: a high-rigidity miniaturized loading chain structure design, using an 8-axis alloy steel skeleton and a variable-section CNC precision-machined carbon steel base, a total system length of <35cm, a loading compliance of <0.01mm / 60N, providing higher precision under small displacement / stress loading, and is especially suitable for OCA-type rate-related and viscoelastic material loading tests. The lateral optical observation path design reduces the space occupied by the loading chain, and uses a central perforation, a 45° silver-plated reflector and a backlight to construct an imaging light path. The camera is moved out of the loading channel, which can avoid the system softening caused by the insertion of an industrial camera into the loading chain, significantly shorten the loading path, and improve the structural rigidity, while keeping the observation field unobstructed and the imaging focus stable. Automatic identification and quantitative extraction of cavitation groups in the entire image (a certain open source image processing software), using open source software A certain open source image processing software is used to process the complete image sequence, perform ROI cropping, binarization, watershed segmentation and particle analysis, which can break through the limitations of "qualitative observation or individual measurement" in traditional research and realize automatic extraction of cavitation groups in the entire field and quantitative analysis of geometric structure. Cavitation spatiotemporal trajectory tracking and numbering (a data processing software) builds a trajectory tracking algorithm based on reverse nearest neighbor matching, enabling frame-by-frame tracing and spatiotemporal numbering of cavitation growth paths. This establishes a time-cone model of the "cavitation life cycle," visualizing the multi-scale growth and birth and death of cavitation, providing a foundation for dynamic structural modeling. Cavitation initiation critical stress matching and Weibull statistical modeling synchronize the frame where the cavitation first appears with the load-displacement-time data of an arbitrary loading test machine, extracting critical stress data. By constructing a probability distribution model for the cavitation population, characteristic stress parameters A and shape parameters B are obtained, serving material reliability assessment and failure prediction.

[0085] Next, a transparent soft material drawing failure analysis system proposed according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0086] Figure 11 It is a structural diagram of the transparent soft material drawing failure analysis system according to an embodiment of the present application.

[0087] like Figure 11 As shown, the transparent soft material drawing failure analysis system includes: a drawing 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 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 vertically pull and load the optical adhesive layer to complete the pull-out failure test.

[0089] An image and data acquisition module 200 is configured to acquire a test image of cavitation evolution during a pull-out failure test on the optical adhesive layer, and mechanical data corresponding to the test image during the pull-out failure test;

[0090] The analysis result output module 300 is used to perform cavitation feature extraction and trajectory tracking processing on the test image to obtain a cavitation time cone diagram of the cavitation evolution process, and obtain a critical stress distribution diagram of cavitation initiation based on the test image and the mechanical data.

[0091] Figure 12 This is a diagram of the structure of a terminal provided in an embodiment of the present application. The terminal may include:

[0092] Memory 501 , processor 502 , and computer programs stored in the memory 501 and executable on the processor 502 .

[0093] When the processor 502 executes the program, the transparent soft material drawing failure analysis method provided in the above embodiment is implemented.

[0094] Furthermore, the terminal further includes:

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

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

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

[0098] If the memory 501, processor 502, and communication interface 503 are implemented independently, the communication interface 503, memory 501, and processor 502 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EIS) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, 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, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0100] The 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 the present application.

[0101] This embodiment also provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the above-mentioned transparent soft material drawing failure analysis method is implemented.

[0102] One embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the Figure 1 The transparent soft material drawing failure analysis method provided in any embodiment of the corresponding embodiment.

[0103] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

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

[0105] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0106] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the 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 (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable storage medium" can be any device that can contain, store, communicate, propagate, or transport a program 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 the following: an electrical connection having one or N wires (electronic devices), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable storage medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0107] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0108] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

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

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

[0111] It should be understood that the application of this application is not limited to the above examples. For ordinary technicians in this field, they can make improvements or changes based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to this application.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for analyzing the pull-out failure of a transparent soft material, characterized in that: The transparent soft material drawing failure analysis method comprises: One side of the 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 vertically pull the optical adhesive layer to complete a pull-out failure test. Acquire a test image of cavitation evolution during a pull-out failure test on the optical adhesive layer, and mechanical data corresponding to the test image during the pull-out failure test; The test image is subjected to cavitation feature extraction and trajectory tracking processing to obtain a cavitation time cone diagram of the cavitation evolution process, and a critical stress distribution diagram of cavitation initiation is obtained based on the test image and the mechanical data.

2. The transparent soft material drawing failure analysis method according to claim 1, characterized in that: 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 and the upper glass plate of the loading device, specifically includes: Adhere one side of the optical adhesive layer to the front side of the acrylic sheet, and cut the acrylic sheet with the optical adhesive layer 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; The back side of the acrylic sheet with the standard geometric shape is pressed and cured with the upper glass plate of the loading device through ultraviolet glue.

3. The transparent soft material drawing failure analysis method according to claim 1, characterized in that: The test image includes a plurality of image frames at different times during the pull-out failure test, and the mechanical data includes a plurality of loading displacements and loading loads at different times; The obtaining of the test image of cavitation evolution during the pull-out failure test on the optical adhesive layer and the mechanical data corresponding to the test image during the pull-out failure test specifically includes: receiving a plurality of image frames with image timestamps of cavitation evolution of the optical adhesive layer collected by a camera during a pull-out failure test of the optical adhesive layer by the loading device; A plurality of loading displacements and loading loads with mechanical timestamps are received during a pull-out failure test performed by the loading device on the optical adhesive layer, wherein the plurality of image timestamps correspond one to one with the plurality of mechanical timestamps.

4. The transparent soft material drawing failure analysis method according to claim 3, characterized in that: The cavitation feature extraction and trajectory tracking processing are performed on the test image to obtain a cavitation time cone diagram of the cavitation evolution process, specifically including: performing feature extraction on the plurality of image frames to obtain a structured data set of cavitation; The spatiotemporal trajectory is tracked according to the structured data set to obtain a cavitation time cone diagram of the cavitation evolution process.

5. The transparent soft material drawing failure analysis method according to claim 4, characterized in that: The structured data set includes a plurality of cavitation geometry parameters; The feature extraction of the plurality of image frames to obtain structured data of cavitation specifically includes: After graying the plurality of image frames, performing threshold binarization to obtain a cavitation region corresponding to each image frame, wherein each cavitation region contains one or more cavitations; The internal pores of the plurality of cavitation regions are filled, and the cavitations adhered to each other in each of the cavitation regions are separated to obtain the cavitation geometric parameters corresponding to each of the plurality of cavitation regions.

6. The transparent soft material drawing failure analysis method according to claim 5, characterized in that: The step of tracking the spatiotemporal trajectory according to the structured data set to obtain a cavitation time cone diagram of the cavitation evolution process specifically includes: According to the cavitation geometric parameters corresponding to the plurality of cavitation regions, a reverse nearest neighbor matching method is used to associate each cavitation in the current image frame with the nearest neighbor cavitation in the corresponding previous image frame to obtain a plurality of cavitation discrete trajectories; The plurality of cavitation discrete trajectories are reassembled in time sequence to obtain a cavitation time cone diagram of the cavitation initiation, growth and evolution process.

7. The transparent soft material drawing failure analysis method according to claim 4, characterized in that: The critical stress distribution diagram includes a dispersion distribution diagram and a probability distribution diagram; The step of obtaining a critical stress distribution diagram for cavitation initiation based on the test image and the mechanical data specifically includes: extracting a plurality of critical stresses for cavitation initiation according to the plurality of loading displacements and the loading loads with mechanical time stamps and the corresponding plurality of image frames with image time stamps; The cavitation initiation stress is fitted according to the plurality of critical stresses to obtain a dispersion distribution diagram and a probability distribution diagram of cavitation initiation.

8. A transparent soft material drawing failure analysis system, characterized in that: The transparent soft material drawing failure analysis system includes: A pull-out failure test module is used to bond one side of an optical adhesive layer to an acrylic sheet and the other side to a lower glass plate of a loading device, and after the acrylic sheet and the upper glass plate of the loading device are pressed and cured, control the loading device to vertically pull and load the optical adhesive layer to complete a pull-out failure test; An image and data acquisition module, configured to acquire a test image of cavitation evolution during a pull-out failure test on the optical adhesive layer, and mechanical data corresponding to the test image during the pull-out failure test; The analysis result output module is used to perform cavitation feature extraction and trajectory tracking processing on the test image to obtain a cavitation time cone diagram of the cavitation evolution process, and obtain a critical stress distribution diagram of cavitation initiation based on the test image and the mechanical data.

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

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a transparent soft material drawing failure analysis program, and when the transparent soft material drawing failure analysis program is executed by a processor, the steps of transparent soft material drawing failure analysis according to any one of claims 1 to 7 are implemented.

Images