Crack propagation and fracture toughness detection method and related device

Abstract

The invention relates to the technical field of image detection, and particularly provides a crack propagation and fracture toughness detection method and a related device, and the method comprises the steps: obtaining a damage process image set, calibrating the proportion between the pixel of the damage process image set and the actual size, and obtaining a conversion proportion coefficient; according to the conversion proportionality coefficient, respectively determining reference points of the reference image and the image to be detected in the damage process image set, and carrying out crack edge feature processing on the damage process image set; carrying out reference point matching by utilizing a hierarchical matching method based on an image pyramid and frequency domain normalization cross-correlation, and calculating to obtain crack opening displacement of the fracture model; and obtaining a load parameter corresponding to the crack opening displacement and a preset geometric parameter, and calculating to obtain the crack initiation toughness and the instability toughness of the fracture model. And the detection efficiency and effect of crack propagation and fracture toughness can be improved.

Description

Technical Field

[0001] The present application belongs to the field of image detection technology, and in particular relates to a method and related device for detecting crack extension and fracture toughness. Background Art

[0002] The double-K fracture model is used in fracture mechanics to describe the initial fracture and unstable fracture during crack propagation. Traditional crack opening displacement measurement relies on mechanical, optical or electromagnetic extensometers. These methods usually require direct contact with the crack surface, which makes the measuring equipment easy to damage and the operation complicated, especially in harsh environments. It is difficult to ensure the stability of the measurement. In addition, although optical and electromagnetic extensometers avoid contact, they still have great limitations in measurement accuracy, automation and complex crack morphology processing.

[0003] The electronic extensometer based on image recognition is a new non-contact crack measurement tool that has emerged in recent years. By collecting and processing images of the crack propagation process, it can achieve high-precision and automated crack opening displacement measurement. This technology has significant advantages such as reducing crack surface interference, easy operation, and achieving full process automation. However, existing image recognition methods still face many challenges when dealing with complex crack morphology. Traditional image matching algorithms, such as Normalized Cross-Correlation (NCC), have poor sensitivity to morphological, scale changes, and rotational transformations during crack propagation, resulting in unstable crack boundary matching results, especially in tortuous or dynamically changing crack environments. The measurement accuracy is severely limited. Although some advanced matching algorithms, such as Scale-Invariant Feature Transform (SIFT) or Oriented FAST and Rotated BRIEF (ORB), have enhanced the processing capabilities of scale and rotation changes, due to the high computational complexity, direct application to real-time crack monitoring is still restricted by hardware computing capabilities. In addition, in the face of high-resolution crack images and large amounts of image processing, the existing algorithms have significant bottlenecks in computational efficiency and are difficult to meet the requirements of real-time monitoring and precise tracking. These problems have led to the inability of image-based electronic extensometers to achieve the expected results in applications, especially in scenarios that require real-time, high-precision monitoring. There is an urgent need to optimize the algorithm to improve adaptability and robustness.

[0004] Therefore, the existing technology still has many shortcomings in the accuracy, automation, robustness and computational efficiency of crack monitoring. It is urgent to develop a high-precision, automated image electronic extensometer and double-K fracture model calculation method that can adapt to complex crack propagation, so as to overcome the defects of existing measurement methods and improve the research efficiency and effect of crack propagation behavior. Summary of the invention

[0005] In view of this, the present application aims to provide a method and related device for detecting crack extension and fracture toughness, which can improve the detection efficiency and effect of crack extension and fracture toughness.

[0006] To achieve the above purpose, the technical solution created by this application is implemented as follows: A method for detecting crack extension and fracture toughness, the method comprising: Acquire a set of destruction process images, and calibrate the ratio between the pixels of the set of destruction process images and the actual size to obtain a conversion ratio coefficient; According to the conversion ratio coefficient, respectively determining reference points of the reference image and the image to be measured in the destruction process image set, and performing crack edge feature processing on the destruction process image set; The reference points are matched by using a preset hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation to calculate the crack opening displacement of the fracture model; The load parameters and preset geometric parameters corresponding to the crack opening displacement are obtained, and the crack initiation toughness and instability toughness of the fracture model are calculated.

[0007] In some embodiments, the reference point matching is performed by using a preset hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation to calculate the crack opening displacement of the fracture model, including: Downsampling the reference image to form an image pyramid, wherein each layer of the image in the image pyramid is arranged according to the resolution size; Performing a fast Fourier transform on a low-resolution layer of the image pyramid to form a normalized cross-correlation coefficient; Using the normalized cross-correlation coefficient, the bottom layer of the image pyramid is matched upward layer by layer to locate the position of the reference image in the image to be tested; wherein, in response to one layer of the image pyramid matching the preliminary position of the reference point, the reference image is scaled back to the size of the next layer, and the preliminary position is used as the starting point of the new position; At a high-resolution layer of the image pyramid, performing key point matching on the reference image and the image to be tested to obtain a matching result; Determine the target position of the reference point according to the matching result, and calculate the pixel distance of the reference image and the pixel distance of the image to be measured according to the target position; The crack opening displacement of the fracture model is calculated according to the pixel distance of the reference image and the pixel distance of the image to be measured.

[0008] In some embodiments, downsampling the reference image to form an image pyramid includes: Gaussian blur and down-sampling are performed on the reference image to obtain the image pyramid. The calculation formula of the image layer in the image pyramid is as follows: ; in, I s represents the image pyramid; I represents the reference image; s Represents the scaling factor.

[0009] In some embodiments, the normalized cross-correlation coefficient is as follows: ; in, For a given reference image block; is the image block to be tested; F represents Fourier transform; represents inverse Fourier transform; Indicates the image to be tested The complex conjugate of the Fourier transform of ; and Represent the amplitude of the Fourier transform of the reference image and the image to be tested respectively.

[0010] In some embodiments, performing key point matching on the reference image and the image to be tested at the high-resolution layer of the image pyramid to obtain a matching result includes: Using the Euclidean distance method, descriptors of the key points are extracted from the reference image and the image to be tested at the high-resolution layer of the image pyramid. The calculation formula for the descriptor extraction is as follows: ; in, d i Indicates the reference image i The key point and the i The descriptor between key points is used to measure the similarity between two key points in the feature space; ( x i1 , y i1 ) represents the first i The coordinates of the key points, x i1 is the first i The horizontal coordinate of the key point in the reference image, y i1is the first i The vertical coordinate of the key point in the reference image; ( x i2 , y i2 ) indicates the first i The coordinates of the key points, x i2 is the first i The horizontal coordinate of the key point in the image to be tested, y i2 The image to be tested i The vertical coordinate of a key point in the image to be tested; The matching degree between the key points is calculated, and the position and matching are refined using the RANSAC algorithm. The calculation formula of the RANSAC is as follows: ; ; in, i Represents the index of the key point pair; inliers represents the internal point, that is, the point whose error is less than the preset threshold; error i Indicates i The reprojection error of key point pairs; A function representing the measurement error; x i Indicates the reference image i Homogeneous coordinates of key points; y i Represents the homogeneous coordinates of the matched key points in the image to be tested; M is the transformation matrix to be estimated.

[0011] In some embodiments, the calculating the crack opening displacement of the fracture model according to the pixel distance of the reference image and the pixel distance of the image to be measured includes: The calculation formula of the crack opening displacement is as follows: ; ; in, CMOD is the crack opening displacement; d0 is the pixel distance of the reference image; dn is the pixel distance of the image to be measured; k Represents the conversion ratio coefficient, H is the actual distance, h is the pixel distance.

[0012] In some embodiments, the calculation expressions of the crack initiation toughness and the instability toughness are as follows: ; ; in, K in Indicates the crack initiation toughness; K un Indicates instability toughness; P Indicates concentrated load; S represents the span of the prismatic specimen; B Indicates the thickness of the specimen; D Indicates the specimen height; β is the ratio of the span of the prism specimen to the specimen height, ; It represents the fracture height ratio at the crack initiation moment. , Indicates the length of the prefabricated cut; represents the fracture height ratio at the time of instability, , and The calculation expression is as follows: ; ; ; ; ; ; ; in, is the equivalent crack length; , , , and The calculation expression is as follows:

[0013]

[0014] in, CMOD u represents the critical crack opening displacement, P u It indicates the load applied by the press when the critical crack opening displacement is reached; B Indicates the thickness of the specimen; E It represents the elastic modulus, and the specific calculation expression is as follows: ;

[0015] in, S represents the span of the prismatic specimen; D Indicates the height of the specimen; is the length of the prefabricated cut; B Indicates the thickness of the specimen; C i represents the initial elastic compliance; CMOD i It represents the initial linear part before the peak load in the P-CMOD curve; P i Reach CMOD i The press applies load; Fracture height ratio at crack initiation Calculate the crack initiation toughness; use the fracture height ratio at the instability moment Calculate the buckling toughness.

[0016] In some embodiments, the geometric parameters of the crack model include at least span, section height, section thickness, and initial crack length, and the load parameters of the fracture model include at least initial crack opening displacement and corresponding load, unstable crack opening displacement and corresponding load.

[0017] Compared with the prior art, the invention of this application can achieve the following beneficial effects: by improving the normalized cross-correlation image electronic extensometer and combining it with the double K fracture model, accurate monitoring of the crack propagation process and effective evaluation of fracture toughness parameters can be effectively achieved. It can improve the accuracy and efficiency of crack propagation monitoring, especially under non-contact measurement conditions, and has higher practicality and reliability, and can be widely used in safety assessment of engineering structures and research on fracture properties of materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings constituting part of the invention of this application are used to provide a further understanding of the invention of this application. The illustrative embodiments and descriptions of the invention of this application are used to explain the invention of this application and do not constitute an improper limitation on the invention of this application. In the drawings: Figure 1 It is a flowchart of the first embodiment of the present application; Figure 2 is a schematic diagram of a workflow of an exemplary embodiment of the present application; Figure 3 It is a flowchart of the first embodiment of the present application; Figure 4 is a schematic diagram of the image electronic extensometer setting in this embodiment of the present application; Figure 5It is a schematic diagram of the workflow of the hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation in an embodiment of the present application; Figure 6 It is a structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical scheme and advantages of the invention of the present application clearer, the invention of the present application is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the invention of the present application and do not constitute a limitation on the invention of the present application. Similar components in different embodiments use associated similar component numbers. In the following embodiments, many detailed descriptions are to enable the invention of the present application to be better understood. However, those skilled in the art can easily recognize that some of the features can be omitted in different situations, or can be replaced by other components, materials, and methods. In some cases, some operations related to the invention of the present application are not shown or described in the specification, in order to avoid the core part of the invention of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0020] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments created by the present application can be combined with each other to form various implementation methods. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a necessary sequence, unless otherwise specified that a certain sequence must be followed.

[0021] In the description of the invention of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the invention of the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the invention of the present application. In addition, the terms "first", "second", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the invention of the present application, unless otherwise specified, the meaning of "multiple" is two or more.

[0022] In the description of the invention of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installation", "connection" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the invention of this application can be understood by specific circumstances.

[0023] The invention of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments. The present application aims to provide an image electronic extensometer and a double K fracture model calculation method based on improved normalized cross-correlation, so as to overcome the problems of insufficient accuracy, low degree of automation, robustness, limited computational efficiency and ability to handle complex crack morphologies in the prior art in crack extension monitoring. Different from the normalized cross-correlation method of the prior art, the present application uses an improved hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation to perform image matching and crack extension analysis, which can significantly improve the accuracy of crack opening displacement measurement, realize full process automated analysis, and adapt to the monitoring needs of complex crack extension behavior.

[0024] See also Figure 1 , Figure 1 : is a flow chart of the first embodiment of the present application. The method for detecting crack extension and fracture toughness may include the following steps: Step S101, obtaining a set of destruction process images, and calibrating the ratio between the pixels of the set of destruction process images and the actual size to obtain a conversion ratio coefficient; As an illustrative example, this embodiment can use a high-precision industrial complementary metal oxide semiconductor camera (CMOS) to collect images of the material loading destruction process, and then select and load images in the destruction process image set to MATLAB, define the ratio and calibrate the conversion ratio relationship between pixels and actual size.

[0025] The destruction process image set may include at least one of a reference image and an image to be tested. The object of the image may be a material, such as ordinary concrete, and the specimen size is 40 mm × 40 mm × 160 mm. A 10 mm deep cut is prefabricated in the middle of the specimen to simulate the initial crack. The image of the material during the loading and destruction process is collected by a high-precision industrial CMOS camera, and the image resolution is set to dpi=500, which can ensure that the subtle changes in the crack opening can be accurately captured.

[0026] Step S102, determining reference points of the reference image and the image to be tested in the damage process image set according to the conversion ratio coefficient, and performing crack edge feature processing on the damage process image set; As an example, two reference points can be manually selected in the first frame image, located on both sides of the crack, for subsequent crack opening displacement measurement. Using these reference points, by inputting the actual physical distance of each reference point, the conversion ratio coefficient between pixels and actual size is calculated: , H is the actual distance, h is the pixel distance.

[0027] Then, the reference image and / or the image to be tested may be preprocessed to enhance the crack edge features. For example, a contrast enhancement method may be used to increase the contrast between the crack and the concrete background, making the crack features more obvious and facilitating subsequent analysis.

[0028] Step S103, performing reference point matching using a preset hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation to calculate the crack opening displacement of the fracture model; As an illustrative example, a hierarchical matching method based on an image pyramid and frequency domain normalized cross-correlation based on a Fourier (FFT)-scale rotation (SIFT / ORB)-matching pyramid may be used to perform reference point matching and calculate the crack opening displacement.

[0029] Step S104, obtaining load parameters and preset geometric parameters corresponding to the crack opening displacement, and calculating the crack initiation toughness and instability toughness of the fracture model.

[0030] As an illustrative example, the geometric parameters in this embodiment may at least include: span 100 mm, section height 40 mm, section thickness 40 mm, and initial crack length 10 mm. The input geometric parameters and load parameters are used to calculate the crack initiation toughness and instability toughness of the double K fracture model. Among them, the load parameters in this embodiment include: initial crack opening displacement ( CMOD i ) and the corresponding load ( P i ), the opening displacement of the unstable crack ( CMOD u ) and corresponding load ( P u ).

[0031] In an exemplary embodiment, please refer to Figure 2 It is a work flow chart of the present invention; Firstly, a high-precision industrial CMOS camera is used to collect images of the material loading and failure process to form a set of failure process images; then, the image data is selected and loaded into MATLAB from the failure process image set to calibrate the conversion ratio between pixels and actual size; then, two reference points of the electronic extensometer are set, and image preprocessing is performed to enhance the crack edge features; then, the hierarchical matching method based on the normalized cross-correlation of Fourier (FFT)-scale rotation (SIFT / ORB)-matching pyramid is used to match the reference points and calculate the crack opening displacement; then, the basic geometric parameters and load parameters of the double-K fracture model are input; then, based on the input geometric parameters and load parameters, the initiation toughness and instability toughness of the double-K fracture model are calculated.

[0032] This embodiment effectively realizes accurate monitoring of the crack propagation process and effective evaluation of fracture toughness parameters by improving the normalized cross-correlation image electronic extensometer and combining it with the double K fracture model. It can improve the accuracy and efficiency of crack propagation monitoring, especially under non-contact measurement conditions, and has higher practicality and reliability, and can be widely used in safety assessment of engineering structures and research on fracture properties of materials.

[0033] See also Figure 3 , Figure 3 : is a flow chart of the second embodiment of the present application. The method for detecting crack extension and fracture toughness may include the following steps: Step S301, obtaining a set of destruction process images, and calibrating the ratio between the pixels of the set of destruction process images and the actual size to obtain a conversion ratio coefficient; The implementation of step S301 may be the same as that of step S101, and will not be repeated here.

[0034] Step S302, determining reference points of the reference image and the image to be tested in the damage process image set according to the conversion ratio coefficient, and performing crack edge feature processing on the damage process image set; As an illustrative example, two reference points of the electronic extensometer can be set, such as Figure 4 As shown, two reference points are manually selected in the first frame image, located on both sides of the crack, for the subsequent measurement of crack opening displacement. The contrast enhancement method is used to improve the contrast between the crack and the concrete background, making the crack features more obvious and convenient for subsequent analysis.

[0035] Step S303, downsampling the reference image to form an image pyramid, wherein each layer of the image in the image pyramid is arranged according to the resolution size; As an illustrative example, the workflow of the hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation is as follows: Figure 5 As shown in the figure, 4 to 6 layers of image pyramids are constructed for the reference image and the image to be tested in the destruction process image. The resolution of each layer is 1 / 2 of the previous layer (2 times downsampling). Gaussian blur and downsampling are used to reduce the impact of noise. The image layer generation formula in the image pyramid is as follows: ; In the expression, s is the scaling factor, for example, 1 / 2. Each layer in the pyramid smoothes and downsamples the original image to obtain images of different resolutions.

[0036] Step S304, performing fast Fourier transform on the low-resolution layer of the image pyramid to form a normalized cross-correlation coefficient; As an illustrative example, the fast Fourier transform (FFT) can be used on the low-resolution layer to accelerate the normalized cross-correlation. Specifically, the FFT is used to accelerate the calculation of the normalized cross-correlation (NCC) at the bottom layer of the pyramid (the coarsest resolution). First, the image to be tested and the reference image block are transformed by FFT. The normalized cross-correlation coefficient is calculated to find the initial matching position. The normalized cross-correlation coefficient calculation formula is as follows: ; In the expression For a given reference image block; is the image block to be tested; F represents Fourier transform; represents inverse Fourier transform; Indicates the image to be tested The complex conjugate of the Fourier transform of ; and Represent the amplitude of the Fourier transform of the reference image and the image to be tested respectively.

[0037] Step S305, using the normalized cross-correlation coefficient, matching the bottom layer of the image pyramid layer by layer upward to locate the position of the reference image in the image to be tested; wherein, in response to one layer of the image pyramid matching the preliminary position of the reference point, scaling the reference image back to the size of the next layer, and using the preliminary position as the starting point of the new position; As an illustrative example, we can start from the bottom of the pyramid and perform matching upwards layer by layer, with each layer being refined based on the matching results of the previous layer. We can use normalized cross-correlation to calculate the matching between each layer of images and locate the position of the reference image in the image to be tested. Layer matching obtains the preliminary position of the reference point , then in the layer, scale the reference image back to The size of the layer and use that initial estimate as the starting point for the new position.

[0038] Step S306, performing key point matching on the reference image and the image to be tested at the high-resolution layer of the image pyramid to obtain a matching result; As an illustrative example, SIFT or ORB can be used for key point matching. SIFT or ORB method is used to extract key points and descriptors of the image at the higher resolution layer (i.e., the finer image layer) of the pyramid. Key point matching is performed on the reference image and the image to be tested, and the scale and rotation changes are considered. The Euclidean distance is used to measure the matching quality. The calculation expression is as follows: ; in, d i Indicates the reference image and the image to be tested. i The descriptor between the key points can also be understood as the Euclidean distance between the two key points. This value measures the similarity between the two key points in the feature space. The smaller the distance, the more similar the two key points are; ( x i1 , y i1 ) represents the first i The coordinates of the key points, x i1 is the horizontal coordinate of the key point in the reference image, y i1 is the vertical coordinate of the key point in the reference image; ( x i2 , y i2 ) indicates the first i The coordinates of the key points,x i2 is the horizontal coordinate of the key point in the image to be tested, y i2 It is the vertical coordinate of the key point in the image to be tested.

[0039] Then, the matching degree between key points can be calculated, and the position and matching can be further refined using the RANSAC algorithm. The RANSAC process is as follows: ; ; in, i Represents the index of the key point pair; inliers represents the internal point, that is, the point whose error is less than the preset threshold; error i Indicates i The reprojection error of key point pairs; A function representing the error measurement, for example, a threshold function, used to determine whether a point pair is an “inlier”; x i Indicates the reference image i Homogeneous coordinates of key points; y i Homogeneous coordinates of the matching key points in the image to be tested; M is the transformation matrix to be estimated.

[0040] Step S307, determining the target position of the reference point according to the matching result, and calculating the pixel distance of the reference image and the pixel distance of the image to be measured according to the target position; As an illustrative example, the exact position of the reference point can be determined by the matching results extracted by SIFT or ORB. Combined with the NCC method, the matching accuracy can be further refined on the high-resolution layer. Output the matching results: Output the final position of the reference point based on the optimal matching position, calculate the initial image pixel distance and the current image pixel distance to be measured; convert to actual displacement based on the scale factor.

[0041] Step S308: calculating the crack opening displacement of the fracture model according to the pixel distance of the reference image and the pixel distance of the image to be tested.

[0042] As an illustrative example, the crack opening displacement ( CMOD ), the specific calculation formula is as follows: ; in, CMOD is the crack opening displacement; d0 is the pixel distance of the reference image (in pixels); dnis the pixel distance of the image to be tested (in pixels); k Represents the conversion scale factor, defined as the actual distance H (in mm), and pixel distance h (in pixels), .

[0043] Step S309, obtaining load parameters and preset geometric parameters corresponding to the crack opening displacement, and calculating the crack initiation toughness and instability toughness of the fracture model.

[0044] As an exemplary example, the calculation expressions of the crack initiation toughness and the instability toughness are as follows: ; ; in, K in Indicates the crack initiation toughness; K un Indicates instability toughness; P Indicates concentrated load; S Indicates the span of the prismatic specimen (in mm); B Indicates the thickness of the specimen (in mm); D Indicates the specimen height (in mm); β is the ratio of the span of the prism specimen to the specimen height, ; It represents the fracture height ratio at the crack initiation moment. , Indicates the length of the prefabricated cut; represents the fracture height ratio at the time of instability, , and The calculation expression is as follows: ; ; ; ; ; ; ; in, is the equivalent crack length; , , , and The calculation expression is as follows:

[0045]

[0046] in, CMOD u represents the critical crack opening displacement (in mm), P u Indicates the load applied by the press when the critical crack opening displacement is reached (in N); B Indicates the thickness of the specimen (in mm); E It represents the elastic modulus, and the specific calculation expression is as follows: ;

[0047] in, S Indicates the span of the prismatic specimen (in mm); D Indicates the height of the specimen (in mm); is the length of the prefabricated cut; B Indicates the thickness of the specimen (in mm); C i represents the initial elastic compliance; CMOD i It represents the initial linear part before the peak load in the P-CMOD curve; P i Reach CMOD i The press applies load (in N) when Fracture height ratio at crack initiation Calculate the crack initiation toughness; use the fracture height ratio at the instability moment Calculate the buckling toughness.

[0048] In an exemplary embodiment, basic geometric parameters and load parameters of the crack model are input, wherein the geometric parameters may at least include a span of 100 mm, a section height of 40 mm, a section thickness of 40 mm, and an initial crack length of 10 mm; the load parameters may at least include an initial crack opening displacement ( CMOD i ) and the corresponding load ( P i ), the opening displacement of the unstable crack ( CMOD u ) and corresponding load ( P u ).

[0049] Through the above operation steps, the crack opening displacement and double K fracture model parameter results of the embodiment are measured as shown in Table 1: Table 1 Crack opening displacement and double K fracture model parameter results of the embodiment of the present application

[0050] The results of the crack propagation experiment show that the crack initiation toughness is relatively small, which means that the crack will begin to propagate under lower load conditions, which is consistent with the mechanical properties of brittle materials such as concrete. The image electronic extensometer-based method is more accurate than the traditional mechanical extensometer, and can more sensitively capture the initial initiation of cracks and avoid the interference of contact measurement. As the crack propagates, the material's resistance to the crack increases, and the instability toughness is significantly higher than the crack initiation toughness. The formation of the plastic zone at the crack tip and the redistribution of the stress field are the main reasons for this enhancement.

[0051] In the experimental data, the crack initiation load and instability load are 643.0 N and 1273.4 N respectively, the latter is almost twice the former, showing a significant blunting effect after the initial crack expansion, allowing the material to withstand higher loads. CMOD ) is crucial to the impact of crack propagation and material properties. The small displacement changes at the initial stage of the crack can significantly affect the stress state of the material, emphasizing the importance of accurately monitoring the crack opening displacement for early repair. The non-contact and automated measurement of the image electronic extensometer can improve the accuracy and efficiency of crack propagation monitoring, which has important value in engineering applications.

[0052] This embodiment uses a non-contact measurement method based on an image electronic extensometer, and a hierarchical matching method based on an image pyramid and frequency domain normalized cross-correlation based on a Fourier-scale rotation-matching pyramid. Fast Fourier transform (FFT) is used to accelerate the calculation. First, preliminary matching is performed on a low-resolution image, and then the pyramid image method is used to refine the matching layer by layer. Finally, the scale-invariant feature matching (SIFT / ORB) method is used to further deal with the rotation and scale invariance issues at a high-resolution level. This hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation can improve the computational efficiency and robustness of the algorithm while ensuring accuracy, and overcome the shortcomings of the traditional normalized cross correlation (NCC) method, which has a large amount of calculation and is insensitive to rotation and scale changes.

[0053] At the same time, through MATLAB's image processing and numerical calculation functions, this application can directly extract geometric parameters from the crack extension image, and automatically calculate the initial fracture toughness and instability fracture toughness of the double K fracture model in combination with load data. Compared with the traditional method that requires manual measurement and calculation, the automated processing flow of this application significantly reduces the complexity of the operation and improves the accuracy and consistency of the analysis process.

[0054] In addition, the combination of image electronic extensometer and numerical calculation is suitable for the study of complex crack propagation behavior, with high adaptability and practicality. Through image preprocessing (including contrast enhancement, Gaussian filtering, edge detection) to cope with images of different quality and characteristics, it can effectively extract crack characteristics and displacement parameters under various crack morphologies and background conditions, and is widely used in the fracture safety assessment of engineering structures and the study of mechanical properties of materials.

[0055] Accordingly, according to an embodiment of the present application, the present application also provides a computer device, a readable storage medium and a computer program product.

[0056] Figure 6 It is a structural diagram of a computer device 12 provided in an embodiment of the present application. Figure 6 A block diagram of an exemplary computer device 12 suitable for implementing embodiments of the present application is shown. Figure 6 The computer device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0057] like Figure 6 As shown, computer device 12 is in the form of a general-purpose computing device. Computer device 12 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0058] Components of computer device 12 may include, but are not limited to, one or more processors or processing units 16 , a system memory 28 , and a bus 18 that connects various system components including system memory 28 and processing unit 16 .

[0059] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0060] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0061] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 6 not shown, usually called a "hard drive"). Although Figure 6 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0062] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0063] The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may communicate with one or more devices that enable a user to interact with the computer device 12, and / or may communicate with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). This communication may be performed via an input / output (I / O) interface 22. In addition, the computer device 12 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the computer device 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0064] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the crack extension and fracture toughness detection method provided in the embodiment of the present application.

[0065] An embodiment of the present application also provides a non-transitory computer-readable storage medium storing computer instructions, on which a computer program is stored, wherein when the program is executed by a processor, the crack extension and fracture toughness detection methods provided in all the inventive embodiments of the present application are implemented.

[0066] The computer storage medium of the embodiment of the present application may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device.

[0067] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0068] The program code included in the computer-readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present application can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages ​​such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer by any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect to the Internet).

[0069] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above-mentioned crack extension and fracture toughness detection method when executed by a processor.

[0070] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps disclosed in this application can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in this application can be achieved, and this document does not limit this.

[0071] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for detecting crack extension and fracture toughness, characterized in that: The crack extension and fracture toughness detection method comprises: Acquire a set of destruction process images, and calibrate the ratio between the pixels of the set of destruction process images and the actual size to obtain a conversion ratio coefficient; According to the conversion ratio coefficient, respectively determining reference points of the reference image and the image to be measured in the destruction process image set, and performing crack edge feature processing on the destruction process image set; The reference points are matched by using a preset hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation to calculate the crack opening displacement of the fracture model; The load parameters and preset geometric parameters corresponding to the crack opening displacement are obtained, and the crack initiation toughness and instability toughness of the fracture model are calculated.

2. The method for detecting crack extension and fracture toughness according to claim 1, characterized in that: The reference point matching is performed by using a preset hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation to calculate the crack opening displacement of the fracture model, including: Downsampling the reference image to form an image pyramid, wherein each layer of the image in the image pyramid is arranged according to the resolution size; Performing a fast Fourier transform on a low-resolution layer of the image pyramid to form a normalized cross-correlation coefficient; Using the normalized cross-correlation coefficient, the bottom layer of the image pyramid is matched upward layer by layer to locate the position of the reference image in the image to be tested; wherein, in response to one layer of the image pyramid matching the preliminary position of the reference point, the reference image is scaled back to the size of the next layer, and the preliminary position is used as the starting point of the new position; At a high-resolution layer of the image pyramid, performing key point matching on the reference image and the image to be tested to obtain a matching result; Determine the target position of the reference point according to the matching result, and calculate the pixel distance of the reference image and the pixel distance of the image to be measured according to the target position; The crack opening displacement of the fracture model is calculated according to the pixel distance of the reference image and the pixel distance of the image to be measured.

3. The method for detecting crack extension and fracture toughness according to claim 2, characterized in that: The downsampling the reference image to form an image pyramid includes: Gaussian blur and down-sampling are performed on the reference image to obtain the image pyramid. The calculation formula of the image layer in the image pyramid is as follows: ； in, I s represents the image pyramid; I represents the reference image; s Represents the scaling factor.

4. The method for detecting crack extension and fracture toughness according to claim 2, characterized in that: The normalized correlation coefficient is as follows: ； in, For a given reference image block; is the image block to be tested; F represents Fourier transform; represents inverse Fourier transform; Indicates the image to be tested The complex conjugate of the Fourier transform of ; and Respectively represent the amplitudes of the Fourier transform of the reference image and the image to be tested.

5. The method for detecting crack extension and fracture toughness according to claim 2, characterized in that: The step of performing key point matching on the reference image and the image to be tested at the high-resolution layer of the image pyramid to obtain a matching result includes: Using the Euclidean distance method, descriptors of the key points are extracted from the reference image and the image to be tested at the high-resolution layer of the image pyramid. The calculation formula for the descriptor extraction is as follows: ； in, d i Indicates the reference image i The key point and the i The descriptor between key points is used to measure the similarity between two key points in the feature space; ( x i1 , y i1 ) represents the first i The coordinates of the key points, x i1 is the first i The horizontal coordinate of the key point in the reference image, y i1 is the first i The vertical coordinate of the key point in the reference image; ( x i2 , y i2 ) indicates the first i The coordinates of the key points, x i2 is the first i The horizontal coordinate of the key point in the image to be tested, y i2 The image to be tested i The vertical coordinate of a key point in the image to be tested; The matching degree between the key points is calculated, and the position and matching are refined using the RANSAC algorithm. The calculation formula of the RANSAC is as follows: ； ； in, i Represents the index of the key point pair; inliers represents the internal point, that is, the point whose error is less than the preset threshold; error i Indicates i The reprojection error of key point pairs; A function representing the measurement error; x i Indicates the reference image i Homogeneous coordinates of key points; y i Represents the homogeneous coordinates of the matched key points in the image to be tested; M is the transformation matrix to be estimated.

6. The method for detecting crack extension and fracture toughness according to claim 2, characterized in that: The step of calculating the crack opening displacement of the fracture model according to the pixel distance of the reference image and the pixel distance of the image to be measured comprises: The calculation formula of the crack opening displacement is as follows: ； ； in, CMOD is the crack opening displacement; d0 is the pixel distance of the reference image; dn is the pixel distance of the image to be tested; k Represents the conversion ratio coefficient, H is the actual distance, h is the pixel distance.

7. The method for detecting crack extension and fracture toughness according to claim 1, characterized in that: The calculation expressions of the crack initiation toughness and the instability toughness are as follows: ； ； in, K in Indicates the crack initiation toughness; K un Indicates instability toughness; P Indicates concentrated load; S represents the span of the prismatic specimen; B Indicates the thickness of the specimen; D Indicates the specimen height; β is the ratio of the span of the prism specimen to the specimen height, ; It represents the fracture height ratio at the crack initiation moment. , Indicates the length of the prefabricated cut; represents the fracture height ratio at the time of instability, , and The calculation expression is as follows: ； ； ； ； ； ； ； in, is the equivalent crack length; , , , and The calculation expression is as follows: in, CMOD u represents the critical crack opening displacement, P u It indicates the load applied by the press when the critical crack opening displacement is reached; B Indicates the thickness of the specimen; E It represents the elastic modulus, and the specific calculation expression is as follows: ； in, S represents the span of the prismatic specimen; D Indicates the height of the specimen; is the length of the prefabricated cut; B Indicates the thickness of the specimen; C i represents the initial elastic compliance; CMOD i It represents the initial linear part before the peak load in the P-CMOD curve; P i Reach CMOD i The press applies load; Fracture height ratio at crack initiation Calculate the crack initiation toughness; use the fracture height ratio at the instability moment Calculate the buckling toughness.

8. The method for detecting crack extension and fracture toughness according to claim 1, characterized in that: The geometric parameters of the crack model include at least span, section height, section thickness, and initial crack length; the load parameters of the fracture model include at least initial crack opening displacement and corresponding load, and unstable crack opening displacement and corresponding load.

9. A computer device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for detecting crack extension and fracture toughness according to any one of claims 1 to 8.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method for detecting crack extension and fracture toughness according to any one of claims 1 to 8.

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