CN111815694A - A kind of fatigue crack growth life prediction method, device, equipment and storage medium - Google Patents

Abstract

The application discloses a fatigue crack growth life prediction method, device, equipment and storage medium. The method includes: collecting a fatigue crack growth image of a component; obtaining crack image parallax information according to the collected image; After processing, the fatigue crack depth data is obtained; according to the obtained fatigue crack depth data, the fatigue crack propagation life is predicted. This application combines image processing technology with the prediction of fatigue crack growth life, and is suitable for predicting the fatigue crack growth life of various material structures. It can realize non-contact detection, simple operation and will not cause any damage to the structure, low cost and high efficiency. It has high precision and high precision, and can solve the problems of low precision, cumbersome equipment, complicated operation, poor real-time performance and harsh environment requirements of various existing methods for measuring fatigue cracks.

Description

A kind of fatigue crack growth life prediction method, device, equipment and storage medium

technical field

The invention relates to the technical field of construction engineering, in particular to a fatigue crack growth life prediction method, device, equipment and storage medium.

Background technique

About 80% of the fracture failure accidents of various engineering structures and components are caused by fatigue failure. Fatigue fracture is a problem that plagues many industries, especially in the field of civil engineering. The proportion of failure caused by fatigue in engineering failure is becoming more and more prominent. Once fracture failure occurs, it will cause huge economic losses and casualties. The consequences could be disastrous.

The detection and analysis of fatigue crack initiation and propagation is one of the main tasks of structural fatigue design and life prediction. Nowadays, fatigue crack propagation tests are carried out at home and abroad, and there are two main methods for detecting fatigue cracks: static testing and dynamic testing. Among them, when the static detection method is used for detection, the measured object needs to be in a relatively static state. The static detection methods include: surface replica method, electromagnetic eddy current method, magnetic induction method, magnetic powder method, penetration method, etc.; while the dynamic detection method is aimed at It is an object that is undergoing relative motion. The methods of dynamic detection include: ultrasonic detection method, ray detection method, acoustic emission method, modal acoustic emission method, etc. However, the various methods for measuring fatigue cracks at present either have low accuracy, or have cumbersome equipment, complicated operations, poor real-time performance and harsh environmental requirements.

Therefore, how to solve the problems of low precision, cumbersome equipment, complicated operation, poor real-time performance, and harsh environmental requirements existing in the existing measurement fatigue crack detection method is a technical problem to be solved urgently by those skilled in the art.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present invention is to provide a fatigue crack growth life prediction method, device, equipment and storage medium, which can automatically measure and dynamically monitor fatigue cracks and perform fatigue life prediction, realize real-time non-contact detection, and have low cost and high precision. High and easy to operate. Its specific plan is as follows:

A fatigue crack growth life prediction method, comprising:

Collect fatigue crack growth images of components;

According to the collected image, obtain the disparity information of the crack image;

processing the obtained crack image parallax information to obtain fatigue crack depth data;

According to the obtained fatigue crack depth data, the fatigue crack growth life is predicted.

Preferably, in the above-mentioned fatigue crack growth life prediction method provided by the embodiment of the present invention, collecting a fatigue crack growth image of a component specifically includes:

Fatigue crack propagation images of components are collected by binocular camera, TOF camera or structured light camera.

Preferably, in the above-mentioned fatigue crack propagation life prediction method provided by the embodiment of the present invention, the acquisition of crack image parallax information specifically includes:

Disparity is calculated by using the BM algorithm in OpenCV or the SGBM algorithm.

Preferably, in the above-mentioned fatigue crack growth life prediction method provided in the embodiment of the present invention, when a component fatigue crack growth image is collected by a binocular camera, the image planes of the two cameras in the binocular camera are located on the same plane, and the light The axes are parallel to each other; the fatigue crack depth data is obtained using the following formula:

为裂纹深度；

为所述双目相机的焦距；

为所述双目相机中两台相机之间的距离；

为视差。in,

is the crack depth;

is the focal length of the binocular camera;

is the distance between two cameras in the binocular camera;

for parallax.

Preferably, in the above-mentioned fatigue crack growth life prediction method provided by the embodiment of the present invention, the following formula is used to predict the fatigue crack growth life:

为疲劳裂纹扩展寿命；

为裂纹深度；

为临界裂纹尺寸；

为几何修正系数；

为循环应力幅；

为裂纹扩展参数；

为描述材料疲劳裂纹扩展性能的基本参数。in,

is the fatigue crack growth life;

is the crack depth;

is the critical crack size;

is the geometric correction coefficient;

is the cyclic stress amplitude;

is the crack propagation parameter;

It is a basic parameter to describe the fatigue crack growth performance of materials.

Preferably, in the above-mentioned fatigue crack growth life prediction method provided by the embodiment of the present invention, the following formula is used to determine the critical crack size:

为临界裂纹尺寸；

为最大循环应力；

为材料的断裂韧度；

为几何修正系数。in,

is the critical crack size;

is the maximum cyclic stress;

is the fracture toughness of the material;

is the geometric correction factor.

The embodiment of the present invention also provides a fatigue crack growth life prediction device, including:

The image acquisition module is used to acquire the fatigue crack growth image of the component;

an image processing module, configured to obtain the disparity information of the crack image according to the collected image;

a crack data calculation module, configured to process the acquired parallax information of the crack image to obtain fatigue crack depth data;

The fatigue life prediction module is used for predicting the fatigue crack growth life according to the obtained fatigue crack depth data.

An embodiment of the present invention further provides a fatigue crack growth life prediction device, including a processor and a memory, wherein, when the processor executes a computer program stored in the memory, the above-mentioned fatigue crack propagation provided by the embodiment of the present invention is realized life prediction methods.

Preferably, in the above-mentioned fatigue crack growth life prediction device provided in the embodiment of the present invention, it further includes: a camera device for collecting fatigue crack growth images of components, a display device for displaying images and related data, and a The camera device and the display device are coupled to a peripheral interface of the processor and the memory.

Embodiments of the present invention further provide a computer-readable storage medium for storing a computer program, wherein when the computer program is executed by a processor, the above-mentioned fatigue crack growth life prediction method provided by the embodiments of the present invention is implemented.

It can be seen from the above technical solutions that the method, device, equipment and storage medium for fatigue crack growth life prediction provided by the present invention include: collecting images of fatigue crack growth of components; obtaining parallax information of crack images according to the collected images; The obtained crack image parallax information is processed to obtain fatigue crack depth data; according to the obtained fatigue crack depth data, the fatigue crack growth life is predicted.

The invention is a technology that combines image processing technology with the prediction of fatigue crack growth life, automatically measures and dynamically monitors fatigue cracks and predicts fatigue life, is suitable for predicting the fatigue crack growth life of various material structures, and can realize non-contact Detection, operation is simple and will not cause any damage to the structure, low cost, high efficiency, high precision, can solve various existing methods for measuring fatigue cracks question.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following briefly introduces the accompanying drawings required for the description of the embodiments or related technologies. Obviously, the accompanying drawings in the following description are only the For the embodiments of the invention, for those of ordinary skill in the art, other drawings can also be obtained according to the provided drawings without any creative effort.

1 is a flowchart of a fatigue crack growth life prediction method provided by an embodiment of the present invention;

FIG. 2 is a two-dimensional schematic diagram of depth imaging of a binocular camera provided by an embodiment of the present invention;

3 is a three-dimensional schematic diagram of depth imaging of a binocular camera provided by an embodiment of the present invention;

4 is a schematic structural diagram of a fatigue crack growth life prediction device provided by an embodiment of the present invention;

FIG. 5 is a schematic structural diagram of a fatigue crack growth life prediction device provided by an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The present invention provides a fatigue crack growth life prediction method, as shown in FIG. 1 , comprising the following steps:

S101. Collect a fatigue crack growth image of a component;

S102, according to the collected image, obtain the disparity information of the crack image;

S103, processing the obtained crack image parallax information to obtain fatigue crack depth data;

S104 , predicting the fatigue crack growth life according to the obtained fatigue crack depth data.

In the above-mentioned fatigue crack growth life prediction method provided by the embodiment of the present invention, by collecting the fatigue crack growth image of the component, processing the obtained crack image parallax information, calculating the fatigue crack depth data, and predicting the fatigue crack growth life, It is a technology that combines image processing technology with the prediction of fatigue crack growth life to automatically measure and dynamically monitor fatigue cracks and predict fatigue life. It is suitable for predicting the fatigue crack growth life of various material structures, and can realize non-contact detection. The operation is simple and will not cause any damage to the structure, with low cost, high efficiency and high precision, which can effectively avoid the current visual inspection method, magnetic particle method, penetration method, ultrasonic method, magnetic flux leakage method, eddy current method, infrared method, acoustic emission method, etc. The crack detection method has the disadvantages of low precision, complicated operation, and harsh environmental requirements.

During specific implementation, in the above-mentioned fatigue crack growth life prediction method provided by the embodiment of the present invention, step S101 collects a fatigue crack growth image of a component, which specifically includes: collecting a component fatigue crack growth image through a binocular camera, a TOF camera or a structured light camera . In practical applications, the way to collect fatigue crack growth images of components may be, but not limited to, binocular cameras, TOF cameras, structured light cameras, and the like.

Further, in a specific implementation, when step S102 is performed to obtain the disparity information of the crack image, the disparity d may be calculated by using the BM algorithm or the SGBM algorithm in OpenCV.

和

已经校准，在左右图像上具有相同的像素坐标，x ^l 和x ^r 分别表示点在左右成像仪上的水平位置，这使得深度与视差成反比关系，即裂纹深度与双目相机的视差成反比关系，视差d=x ^l -x ^r ，利用相似三角形，可通过下列公式获取裂纹深度Z：Further, in the specific implementation, when step S103 is performed, the fatigue crack depth data can be obtained based on the parallax principle; as shown in FIG. 2 and FIG. 3 , when the component fatigue crack propagation image is collected by the binocular camera, the binocular camera The image planes of the two cameras are exactly on the same plane, and the optical axes are required to be strictly parallel to each other (the optical axis is a ray drawn from the projection center O toward the principal point c of the image, also known as the principal ray); the distance T is constant and the focal length is the same ( f _l =f _r =f ₀ ), and the principal point

and

It has been calibrated to have the same pixel coordinates on the left and right images, xl and ^xr represent the horizontal position of the point on the left and right ^imagers respectively, which makes the depth inversely proportional to the parallax, i.e. the crack depth is inversely proportional to the parallax of the binocular camera relationship, disparity d = x ^l - x ^r , and using similar triangles, the crack depth Z can be obtained by the following formula:

为裂纹深度；

为双目相机的焦距；

为双目相机中两台相机之间的距离；

为视差。in,

is the crack depth;

is the focal length of the binocular camera;

is the distance between the two cameras in the binocular camera;

for parallax.

In specific implementation, in the above-mentioned fatigue crack growth life prediction method provided by the embodiment of the present invention, when step S104 is performed, first, the critical crack size when the component breaks under the action of the load is determined; then, according to the critical crack size The fatigue crack growth formula is obtained, and the formula is integrated; finally, for the constant amplitude load, the fatigue crack growth life is obtained by integrating.

Specifically, according to the fracture criterion based on the stress intensity factor of linear elastic fracture mechanics, there are:

是最大循环应力；

是材料的断裂韧度；对于无限大中心裂纹板，若板宽

，则几何修正系数f=1；对于无限大单边裂纹板，若板宽

，f=1.1215。因此，采用下列公式确定临界裂纹尺寸：in,

is the maximum cyclic stress;

is the fracture toughness of the material; for an infinite center crack plate, if the plate width

, then the geometric correction factor f = 1; for an infinite unilateral cracked plate, if the plate width

, f = 1.1215. Therefore, the critical crack size is determined using the following formula:

为最大循环应力；

为材料的断裂韧度；

为临界裂纹尺寸；

为几何修正系数。in,

is the maximum cyclic stress;

is the fracture toughness of the material;

is the critical crack size;

is the geometric correction factor.

Further, in specific implementation, the fatigue crack propagation formula can be generally written as:

为疲劳裂纹扩展速率，∆K为应力强度因子，R为应力比。in,

is the fatigue crack growth rate, ΔK is the stress intensity factor, and R is the stress ratio.

Arrange the above formula, and then integrate both ends, there are:

为常数，若疲劳裂纹扩展速率满足Paris公式，则有：For infinite slabs with cracks, geometric correction factor

is a constant, if the fatigue crack growth rate satisfies the Paris formula, there are:

For constant amplitude loads, the fatigue crack growth life can be determined integrally using the following formula:

为疲劳裂纹扩展寿命；

为裂纹深度（即初始裂纹尺寸

）；

为临界裂纹尺寸；

为几何修正系数；

为循环应力幅；

为裂纹扩展参数；

为描述材料疲劳裂纹扩展性能的基本参数。in,

is the fatigue crack growth life;

is the crack depth (i.e. the initial crack size

);

is the critical crack size;

is the geometric correction coefficient;

is the cyclic stress amplitude;

is the crack propagation parameter;

It is a basic parameter to describe the fatigue crack growth performance of materials.

Based on the same inventive concept, an embodiment of the present invention also provides a fatigue crack growth life prediction device. Since the principle of the device to solve the problem is similar to the aforementioned fatigue crack growth life prediction method, the implementation of the device can refer to fatigue crack growth The implementation of the life prediction method will not be repeated here.

During specific implementation, the fatigue crack growth life prediction device provided by the embodiment of the present invention, as shown in FIG. 4 , specifically includes:

The image acquisition module 11 is used to acquire the fatigue crack growth image of the component;

The image processing module 12 is used for obtaining the disparity information of the crack image according to the collected image;

The crack data calculation module 13 is used for processing the obtained crack image parallax information to obtain fatigue crack depth data;

The fatigue life prediction module 14 is configured to predict the fatigue crack growth life according to the obtained fatigue crack depth data.

In the above-mentioned fatigue crack growth life prediction device provided by the embodiment of the present invention, the image processing technology can be applied to the measurement of surface fatigue cracks and the fatigue life prediction can be performed through the interaction of the above four modules, so that real-time non-contact detection can be realized. Moreover, it has low cost, high precision and easy operation, laying the foundation for the automation and intelligence of fatigue life prediction.

In specific implementation, in the above-mentioned fatigue crack growth life prediction device provided by the embodiment of the present invention, the image acquisition module 11 may be a binocular camera, a TOF camera, or a structured light camera; , by calculating the disparity by using the BM algorithm in OpenCV or the SGBM algorithm.

For more specific working processes of the above-mentioned modules, reference may be made to the corresponding contents disclosed in the foregoing embodiments, which will not be repeated here.

Correspondingly, an embodiment of the present invention also discloses a fatigue crack growth life prediction device, as shown in FIG. 5 , comprising a processor 21 and a memory 22 ; wherein, the foregoing implementation is implemented when the processor 21 executes the computer program stored in the memory 22 Example of a fatigue crack growth life prediction method disclosed.

Further, during specific implementation, in the above-mentioned fatigue crack growth life prediction device provided in the embodiment of the present invention, as shown in FIG. 5 , it may further include: a camera device 23 for collecting fatigue crack growth images of components, for displaying A display device 24 for images and related data, and a peripheral interface 25 for coupling the camera device 23 and the display device 24 to the processor 21 and the memory 22 .

Wherein, the memory 22 may be, but not limited to, random access memory (RAM), read only memory (ROM), programmable read only memory (PROM), erasable read only memory (EPROM), electrically erasable only memory Read memory (EEPROM), etc., the memory 22 is used to store the program, the processor 21 executes the program after receiving the execution instruction, and the method executed by the server defined by the process explained in any embodiment of the present invention can be applied to the processor 21, or implemented by the processor 21. The processor 21 may be an integrated chip with signal processing capability, or may be a general-purpose processor. The peripheral interface 25 is used to couple various input/output devices to the processor 21 and the memory 22 . The camera 23 may be, but not limited to, a binocular camera. The display device 24 is specifically used to realize the interaction between the user and the device, and may be, but not limited to, the display device 24 can display information such as structure images, depth images, and the like.

For a more specific process of the above method, reference may be made to the corresponding content disclosed in the foregoing embodiments, which will not be repeated here.

Correspondingly, the present invention also discloses a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, the aforementioned method for predicting fatigue crack growth life is implemented.

For a more specific process of the above method, reference may be made to the corresponding content disclosed in the foregoing embodiments, which will not be repeated here.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts of the various embodiments may be referred to each other. For the apparatuses, devices, and storage media disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and reference may be made to the descriptions of the methods for related parts.

Professionals may further realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two, in order to clearly illustrate the possibilities of hardware and software. Interchangeability, the above description has generally described the components and steps of each example in terms of function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

The steps of a method or algorithm described in conjunction with the embodiments disclosed herein may be directly implemented in hardware, a software module executed by a processor, or a combination of the two. Software modules can be placed in random access memory (RAM), internal memory, read only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other in the technical field. in any other known form of storage medium.

A fatigue crack growth life prediction method, device, equipment and storage medium provided by the embodiments of the present invention include: collecting a fatigue crack growth image of a component; obtaining crack image parallax information according to the collected image; After processing, the fatigue crack depth data is obtained; according to the obtained fatigue crack depth data, the fatigue crack propagation life is predicted. This is a technology that combines image processing technology with the prediction of fatigue crack growth life, automatically measures and dynamically monitors fatigue cracks and predicts fatigue life. It is suitable for predicting the fatigue crack growth life of various material structures and can realize non-contact detection. , the operation is simple and will not cause any damage to the structure, the cost is low, the efficiency is high, and the precision is high. .

Finally, it should also be noted that in this document, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply these entities or that there is any such actual relationship or sequence between operations. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

The fatigue crack growth life prediction method, device, equipment and storage medium provided by the present invention have been described in detail above. Specific examples are used in this paper to illustrate the principles and implementations of the present invention. The descriptions of the above embodiments are only used for Help to understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation and application scope. In summary, the content of this specification It should not be construed as a limitation of the present invention.

Claims (10)

1. a fatigue crack growth life prediction method, is characterized in that, comprises: Collect fatigue crack growth images of components; According to the collected image, obtain the disparity information of the crack image; processing the obtained crack image parallax information to obtain fatigue crack depth data; According to the obtained fatigue crack depth data, the fatigue crack growth life is predicted. 2 . The fatigue crack growth life prediction method according to claim 1 , wherein the collection of fatigue crack growth images of components specifically includes: 2 . Fatigue crack propagation images of components are collected by binocular camera, TOF camera or structured light camera. 3. The fatigue crack growth life prediction method according to claim 2, wherein obtaining the parallax information of the crack image specifically comprises: Disparity is calculated by using the BM algorithm in OpenCV or the SGBM algorithm. 4 . The fatigue crack growth life prediction method according to claim 3 , wherein when the fatigue crack growth image of the component is collected by a binocular camera, the image planes of the two cameras in the binocular camera are located on the same plane, 5 . The optical axes are parallel to each other; the fatigue crack depth data is obtained using the following formula:

in,

is the crack depth;

is the focal length of the binocular camera;

is the distance between two cameras in the binocular camera;

for parallax. 5. The fatigue crack growth life prediction method according to claim 4, wherein the following formula is used to predict the fatigue crack growth life:

in,

is the fatigue crack growth life;

is the crack depth;

is the critical crack size;

is the geometric correction coefficient;

is the cyclic stress amplitude;

is the crack propagation parameter;

It is a basic parameter to describe the fatigue crack growth performance of materials. 6. The fatigue crack growth life prediction method according to claim 5, wherein the critical crack size is determined by the following formula:

in,

is the critical crack size;

is the maximum cyclic stress;

is the fracture toughness of the material;

is the geometric correction factor. 7. A fatigue crack growth life prediction device, comprising: The image acquisition module is used to acquire the fatigue crack growth image of the component; an image processing module, configured to obtain the disparity information of the crack image according to the collected image; a crack data calculation module, configured to process the acquired parallax information of the crack image to obtain fatigue crack depth data; The fatigue life prediction module is used for predicting the fatigue crack growth life according to the obtained fatigue crack depth data. 8. A fatigue crack growth life prediction device, comprising a processor and a memory, wherein, when the processor executes a computer program stored in the memory, the device according to any one of claims 1 to 6 is implemented. Fatigue crack growth life prediction method. 9 . The fatigue crack growth life prediction device according to claim 8 , further comprising: a camera device for collecting fatigue crack growth images of components, a display device for displaying images and related data, and a device for displaying images and related data. 10 . The camera device and the display device are coupled to a peripheral interface of the processor and the memory. 10. A computer-readable storage medium, characterized in that it is used for storing a computer program, wherein, when the computer program is executed by a processor, the fatigue crack growth life prediction method according to any one of claims 1 to 6 is implemented .

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