Bolt tensile testing methods and systems

Abstract

This invention belongs to the field of testing technology, specifically relating to a bolt tensile testing method and system. The bolt tensile testing method includes: after installing the bolts to be tested into the test connection points, a control module drives a tensile testing device to stretch each bolt to detect its pressure data and tensile amount; the control module also acquires bolt surface images during the testing process using a visual inspection device; the control module identifies the type of bolt defects in the bolt surface images. This invention, by combining sensor detection and image detection during the tensile testing of the bolts, can both detect the pressure data and tensile amount of each bolt and monitor the surface changes of the bolts in real time during the tensile test, ensuring the service life of the tensile testing device and providing a basis for real-time monitoring of process defects and process optimization.

Description

Technical Field

[0001] This invention belongs to the field of testing technology, specifically relating to a method for testing using hydraulic pressure, and more particularly to a bolt tensile force testing method and system. Background Technology

[0002] Traditional bolt tensile testing machines can only detect the tensile strength and tensile force of bolts. However, during the tensile testing process, bolts may also develop defects such as cracks and local deformation. On the one hand, bolts may break or fly off during the tensile testing process, which may slightly affect the service life of the bolt tensile testing machine. On the other hand, the lack of data on the changes in defects during the tensile testing process makes it impossible to monitor process defects in real time or to delay subsequent process optimization.

[0003] Therefore, there is an urgent need to develop a new bolt tensile testing method and system to solve the technical problem of real-time monitoring of surface changes during bolt tensile testing.

[0004] It should be noted that the information disclosed in this background section is only for understanding the background technology of the present application concept, and therefore, the above description is not considered to constitute prior art information. Summary of the Invention

[0005] This disclosure provides at least one method and system for detecting bolt tension.

[0006] In a first aspect, embodiments of this disclosure provide a bolt tensile testing method, comprising: after installing the bolts to be tested into the test connection positions of each bolt, a control module drives a tensile testing device to stretch each bolt to be tested, so as to detect the pressure data and tensile amount of each bolt; the control module also acquires bolt surface images of each bolt during the testing process through a visual inspection device; when the control module configures parameters, the control module confirms the detection range in any bolt surface image, and identifies the bolt defect category in the detection range through feature extraction and image comparison, and identifies the same bolt surface images of different resolutions during image comparison, until the minimum resolution that matches the bolt defect category determined during feature extraction and image comparison is found, and the control module sets the minimum resolution as the optimal resolution, and identifies the bolt surface image of the optimal resolution during image comparison; when the control module detects the bolt surface image, the control module performs a sampling inspection of the bolt surface image through feature extraction to identify the bolt defect category in the bolt surface image, and the control module performs a full inspection of the same bolt surface image through image comparison to identify the bolt defect category in the bolt surface image.

[0007] In one optional implementation, the control module identifies the bolt defect category in the bolt surface image by feature extraction. That is, the control module scans the outline of each bolt in the detection range to detect the length of a single bolt, the shape of each thread in a single bolt, and the distance between adjacent threads, thereby identifying the bolt defect category in the detection range.

[0008] In one optional implementation, the control module identifies the type of bolt defect in the bolt surface image by image comparison. That is, the control module scans the outline of each bolt in the bolt surface image and compares it with a standard image to identify the degree of overlap between the outline of each bolt in the bolt surface image and the outline of each bolt in the standard image, thereby identifying the type of bolt defect in the bolt surface image.

[0009] In one alternative implementation, bolt defect categories include: ductile fracture, brittle fracture, thread stripping or delamination, head-to-shank connection fracture, and dents.

[0010] In one optional implementation, when the control module sets the optimal resolution, the control module identifies the bolt defect category within the detection range through feature extraction. During image comparison, the control module first identifies the detection range within the same bolt surface image at any resolution. If the bolt defect categories determined during feature extraction and image comparison are consistent, the control module reduces the resolution of the bolt surface image for image comparison and identification until the bolt defect categories determined during feature extraction and image comparison are inconsistent. Then, the previous resolution is confirmed as the optimal resolution.

[0011] In one optional implementation, when the control module sets the optimal resolution, the control module identifies the bolt defect category within the detection range through feature extraction. During image comparison, the control module first identifies the detection range within the same bolt surface image at any resolution. If the bolt defect categories determined during feature extraction and image comparison are inconsistent, the control module increases the resolution of the bolt surface image for image comparison and identification until the bolt defect categories determined during feature extraction and image comparison are consistent, at which point the current resolution is confirmed as the optimal resolution.

[0012] In one alternative implementation, the optimal resolution is set with an upper limit and a lower limit.

[0013] In one optional implementation, when the control module performs spot checks on the detection range through feature extraction, the control module scans the outline of each bolt in the bolt surface image, identifies the starting bolt in the bolt surface image, and scans the corresponding bolts in the bolt surface image at intervals from the starting bolt to identify the bolt defect category in the bolt surface image.

[0014] In one optional implementation, when the control module performs a full inspection of the same bolt surface image through image comparison, the control module adjusts the resolution of the bolt surface image to the optimal resolution, scans the outline of each bolt in the bolt surface image, and compares it with the standard image to identify the degree of overlap between the bolt outline in the bolt surface image and the bolt outline in the standard image, thereby identifying the bolt defect category in the detection range.

[0015] Secondly, this disclosure also provides a detection system employing the bolt tensile testing method described above, comprising: a control module, a tensile testing device, and a vision inspection device; wherein the tensile testing device and the vision inspection device are electrically connected to the control module, the tensile testing device is provided with a plurality of bolt test connection positions, and the vision inspection device is positioned facing each bolt test connection position; after a bolt to be tested is inserted into each of the bolt test connection positions, the control module is configured to drive the tensile testing device to stretch each bolt to be tested, so as to detect the pressure data and tensile amount of each bolt to be tested; the control module is further configured to acquire bolt surface images of each bolt to be tested during the testing process through the vision inspection device; when the control module configures parameters, the control module is configured to confirm any bolt... The control module detects bolt defects within a specific range of bolt surface images, identifying the types of bolt defects within that range through feature extraction and image comparison. During image comparison, it identifies bolt surface images of the same bolt at different resolutions until it finds the minimum resolution that matches the identified bolt defect types during feature extraction and image comparison. This minimum resolution is then set as the optimal resolution. The control module identifies the bolt surface image with the optimal resolution during image comparison. Furthermore, when detecting bolt surface images, the control module is configured to perform random sampling of bolt surface images through feature extraction to identify bolt defect types within the images. It is also configured to perform full inspection of the same bolt surface images through image comparison to identify bolt defect types within the images.

[0016] The beneficial effects of this invention are as follows: By combining sensor detection and image detection during the tensile testing of bolts, this invention can detect the pressure data and tensile amount of each bolt, and also monitor the surface changes of the bolts in real time during the tensile test. This allows for timely cessation of the tensile test when bolt defects are found on the surface, ensuring the service life of the tensile testing device. Furthermore, the real-time sensor data and image recognition data collected during the tensile test provide a basis for real-time monitoring of process defects and process optimization, thereby improving production efficiency. By combining feature extraction for random sampling of bolt surface images with image comparison for full inspection of the same bolt surface images, each bolt in the bolt surface image can be accurately identified, overcoming the problems of missed detection and low defect identification accuracy caused by traditional sampling methods. By simultaneously detecting bolt surface images through feature extraction and image comparison, the minimum resolution at which both methods determine the same bolt defect category is set as the optimal resolution. Using this optimal resolution in subsequent image comparison processes allows for reasonable allocation of computing power, meeting the requirements of accurate bolt defect identification and reduced computing power.

[0017] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a bolt tensile force testing method provided in this disclosure embodiment; Figure 2 A flowchart of control module configuration parameters provided in this embodiment of the disclosure; Figure 3 A flowchart illustrating a control module detecting bolt surface images is provided as an embodiment of this disclosure; Figure 4 A flowchart for setting an optimal resolution is provided as an embodiment of this disclosure; Figure 5This is a schematic diagram of a detection system provided in an embodiment of the present disclosure. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The terminology used herein is for the purpose of describing specific exemplary configurations only and is not intended to be limiting. As used herein, the singular articles “a,” “an,” and “the” may also be intended to include plural forms unless otherwise clearly stated herein. The terms “comprising,” “including,” and “having” are inclusive and thus specify the presence of features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein should not be construed as requiring them to be performed in the specific order discussed or shown, unless specifically identified as such. Additional or alternative steps may be employed.

[0023] As used herein, the phrases “in one embodiment,” “according to one embodiment,” “in some embodiments,” etc., generally refer to the fact that a particular feature, structure, or characteristic following the phrase can be included in at least one embodiment of this disclosure. Therefore, a particular feature, structure, or characteristic can be included in more than one embodiment of this disclosure, such that these phrases do not necessarily refer to the same embodiment. As used herein, the terms “example,” “exemplary,” etc., are used to “serve as an example, instance, or illustration.” Any implementation, aspect, or design described herein as “example” or “exemplary” is not necessarily to be construed as preferred or superior to other implementations, aspects, or designs. Rather, the use of the terms “example,” “exemplary,” etc., is intended to present concepts in a specific manner.

[0024] Research has found that traditional bolt tensile testing machines can only detect the bolt's tensile strength and load-bearing capacity. However, during the tensile testing process, bolts may also exhibit defects such as cracks and localized deformation. On the one hand, bolts may break or fly off during the tensile testing process, which can slightly affect the service life of the bolt tensile testing machine. On the other hand, the lack of data on the changes in bolt defects during the tensile testing process makes it impossible to monitor process defects in real time or leads to a delay in subsequent process optimization.

[0025] Based on the above research, this disclosure provides a bolt tensile testing method and system. By combining sensor detection and image detection during the tensile test of the bolt, it can detect the pressure data and tensile amount of each bolt, and also monitor the surface changes of the bolt in real time during the tensile test, ensuring the service life of the tensile testing device and providing a basis for real-time monitoring of process defects and process optimization.

[0026] The shortcomings of the above solutions are the result of the inventor's practical experience and careful research. Therefore, the discovery process of the above problems and the solutions proposed in this disclosure below should be considered as the inventor's contribution to this disclosure.

[0027] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0028] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0029] like Figures 1 to 5 As shown, at least one embodiment provides a bolt tensile testing method, which includes: after installing the bolts to be tested into the test connection positions of each bolt, the control module drives the tensile testing device to stretch each bolt to detect the pressure data and tensile amount of each bolt; the control module also acquires bolt surface images of each bolt during the testing process through a vision inspection device; when the control module configures parameters, the control module confirms the detection range in any bolt surface image, and identifies the bolt defect category in the detection range through feature extraction and image comparison, and identifies the same bolt surface images of different resolutions during image comparison until the minimum resolution that matches the bolt defect category determined during feature extraction and image comparison is found, and the control module sets this minimum resolution as the optimal resolution, and identifies the bolt surface image of the optimal resolution during image comparison; when the control module detects the bolt surface image, the control module performs a sampling inspection of the bolt surface image through feature extraction to identify the bolt defect category in the bolt surface image, and the control module is also configured to perform a full inspection of the same bolt surface image through image comparison to identify the bolt defect category in the bolt surface image.

[0030] Specifically, to ensure the bolts are qualified, their surface threads need to be inspected. However, bolts have many threads, and the control module's computing power is limited. Therefore, using a sampling method to inspect bolts in the bolt surface image results in missed detections and inaccurate detection. By combining feature extraction for sampling inspection of bolt surface images with image comparison for full inspection of the same bolt surface image, each bolt in the bolt surface image can be accurately identified. This overcomes the problems of missed detections and low defect identification accuracy caused by traditional sampling methods. Furthermore, by simultaneously inspecting bolt surface images through feature extraction and image comparison, the minimum resolution at which both methods determine the same bolt surface image defect category is set as the optimal resolution. Using this optimal resolution in subsequent image comparison processes allows for reasonable allocation of computing power, meeting the requirements of accurate bolt defect identification and reduced computing power.

[0031] In at least one embodiment, by combining sensor detection and image detection during the tensile test of the bolts to be tested, it is possible to detect the pressure data and tensile amount of each bolt to be tested, and also to monitor the surface changes of the bolts to be tested in real time during the tensile test. This allows the tensile test to be stopped in time when bolt defects are found on the surface of the bolts to be tested, thus ensuring the service life of the tensile testing device. At the same time, the sensor data and image recognition data collected in real time during the tensile test can provide a basis for real-time monitoring of process defects and process optimization, thereby improving production efficiency.

[0032] In at least one embodiment, the control module identifies the bolt defect category in the bolt surface image by feature extraction. That is, the control module scans the outline of each bolt in the detection range to detect the length of a single bolt, the shape of each thread in a single bolt, and the distance between adjacent threads, thereby identifying the bolt defect category in the detection range.

[0033] Specifically, the control module performs grayscale processing on the bolt surface image, which can mark the threads of each bolt in the bolt surface image. As a result, the outline of each bolt can be displayed in the bolt surface image. It can quickly scan the outline of each bolt in the detection range. At the same time, by recognizing the outline of each bolt, it can detect the length of a single bolt, the shape of each thread in a single bolt, and the distance between adjacent threads. Then, it can compare with the set parameters to identify the bolt defect category in the detection range.

[0034] In at least one embodiment, the control module identifies the bolt defect category in the bolt surface image by image comparison. That is, the control module scans the outline of each bolt in the bolt surface image and compares it with a standard image to identify the degree of overlap between the bolt outline in the bolt surface image and the bolt outline in the standard image, thereby identifying the bolt defect category in the bolt surface image.

[0035] Specifically, the control module performs grayscale processing on the bolt surface image, which marks the threads of each bolt in the bolt surface image. As a result, the outline of each bolt can be displayed in the bolt surface image, and the bolt outline is color-coded. The bolt surface image is overlaid with a standard image, while the outlines of each bolt in the standard image are marked with other colors. By checking whether each bolt in the bolt surface image coincides with the corresponding bolt in the standard image, the type of bolt defect in the bolt surface image can be quickly identified.

[0036] Specifically, during image comparison and recognition, one of the bolts is located, and if the bolt surface image is free of bolt defects, it should completely overlap with the standard image.

[0037] In at least one embodiment, the bolt defect categories include: ductile fracture, brittle fracture, thread stripping or delamination, head-to-shank connection fracture, and dents.

[0038] Specifically, there is significant plastic deformation (such as "neck") in ductile fracture.

[0039] Specifically, brittle fracture involves almost no plastic deformation, with a flat fracture surface that is either crystalline or radial.

[0040] Specifically, in the case of thread stripping or derailment, the thread is pulled flat or peeled off.

[0041] Specifically, the breakage at the head-to-rod connection occurred below the bolt head.

[0042] Specifically, dents can be found at the end of a thread, in the transition area between the bolt shank and the head, or in areas where markings (such as grade markings) are too deep.

[0043] Specifically, the higher the resolution of the bolt surface image, the more accurately each bolt outline appears in the bolt surface image. Conversely, if the resolution of the bolt surface image is low, each bolt outline will not appear clearly in the bolt surface image, which will affect the recognition of the bolt outline and thus make it impossible to accurately identify the bolt defect category. At the same time, due to the limited computing power of the control module, in order to meet the requirements of full inspection of bolt surface images while avoiding overloading the control module, the optimal resolution can be set to dynamically and reasonably allocate computing power, achieve a perfect combination of feature extraction sampling and image comparison, and improve the recognition accuracy of bolt defect categories on bolt surface images.

[0044] In at least one embodiment, please refer to Figure 4When the control module sets the optimal resolution, the control module identifies the bolt defect category in the detection range through feature extraction. During the image comparison process, the control module first identifies the detection range in the same bolt surface image at any resolution. If the bolt defect category is consistent when the feature extraction and image comparison are performed, the control module reduces the resolution of the bolt surface image for image comparison and identification until the bolt defect category is inconsistent when the feature extraction and image comparison are performed. Then, the previous resolution is confirmed as the optimal resolution.

[0045] Specifically, in setting the optimal resolution, a full inspection of the bolt surface image is first performed through feature extraction. Identifying the bolt defect category within the detection range provides a reference standard for image comparison. Then, during image comparison, the detection range of the same bolt surface image at any resolution is first identified. If the bolt defect category determined during feature extraction and image comparison is consistent, it indicates that the image comparison can accurately determine the bolt defect category. There is room for the control module to reduce computing power, thereby reducing the resolution of the bolt surface image for image comparison and identification. This continues until the bolt defect category determined during feature extraction and image comparison is inconsistent. At this point, the image comparison cannot accurately determine the bolt defect category. The previous resolution is confirmed as the minimum resolution at which the bolt defect category is consistent during feature extraction and image comparison. At this point, the computing power of the control module reaches its optimal level, while also ensuring detection accuracy.

[0046] In at least one embodiment, please refer to Figure 4 When the control module sets the optimal resolution, it identifies the bolt defect category within the detection range through feature extraction. During image comparison, the control module first identifies the detection range within the same bolt surface image at any resolution. If the bolt defect categories determined during feature extraction and image comparison are inconsistent, the control module increases the resolution of the bolt surface image for image comparison and identification until the bolt defect categories determined during feature extraction and image comparison are consistent, at which point the current resolution is confirmed as the optimal resolution.

[0047] Specifically, in setting the optimal resolution, a full inspection of the bolt surface image is first performed through feature extraction. Identifying the bolt defect category within the detection range provides a reference standard for image comparison. Then, during image comparison, the detection range of the same bolt surface image at any resolution is first identified. If the bolt defect category determined during feature extraction and image comparison is inconsistent, it indicates that the image comparison cannot accurately determine the bolt defect category. Therefore, the resolution of the bolt surface image is increased for image comparison and identification until the bolt defect category determined during feature extraction and image comparison is consistent. At this point, the image comparison can accurately determine the bolt defect category, confirming that the current resolution is the minimum resolution at which the bolt defect category is consistent during feature extraction and image comparison. At this point, the computing power of the control module reaches its optimal level, while also ensuring detection accuracy.

[0048] In at least one embodiment, the optimal resolution is set with an upper limit and a lower limit.

[0049] Specifically, because the computing power of the control module has an upper limit, the optimal resolution is set to an upper limit. When the resolution reaches the upper limit of the optimal resolution during the configuration of the control module parameters, the bolt defect categories judged during feature extraction and image comparison are still inconsistent. Therefore, the optimal resolution is set to the upper limit. During the image comparison process, multiple bolt surface images are superimposed together, and the non-overlapping parts of the bolts are compared. Then, they are compared with the standard image to identify the bolt defect categories in the bolt surface images.

[0050] In at least one embodiment, when the control module performs spot checks on the detection range through feature extraction, the control module scans the outline of each bolt in the bolt surface image, identifies the starting bolt in the bolt surface image, and scans the corresponding bolts in the bolt surface image at intervals from the starting bolt to identify the bolt defect category in the bolt surface image.

[0051] Specifically, the control module performs grayscale processing on the bolt surface image, which can separate the threaded area from other areas in the bolt surface image, so that the outline of each bolt can be displayed in the bolt surface image. The corresponding bolt outline in the detection range is scanned at intervals and the computational burden on the control module can be reduced. By recognizing the selected bolt outline, the bolt defect category in the detection range can be identified.

[0052] In at least one embodiment, when the control module performs a full inspection of the same bolt surface image through image comparison, the control module adjusts the resolution of the bolt surface image to the optimal resolution, scans the outline of each bolt in the bolt surface image, and compares it with the standard image to identify the degree of overlap between the bolt outline in the bolt surface image and the bolt outline in the standard image, thereby identifying the bolt defect category in the detection range.

[0053] Specifically, the control module performs grayscale processing on the bolt surface image and adjusts the resolution of the bolt surface image to the optimal resolution, reducing the computational burden on the control module. It can also separate the threaded and non-threaded areas in the bolt surface image, so that the outline of each bolt can be displayed in the bolt surface image. The bolt outline is color-coded, and the bolt surface image is overlaid with a standard image. At the same time, the outlines of each bolt in the standard image are marked with other colors. By checking whether each bolt in the bolt surface image coincides with the corresponding bolt in the standard image, the bolt defect category in the bolt surface image can be quickly identified.

[0054] Based on the same technological concept, such as Figures 1 to 5 As shown, at least one embodiment also provides a detection system employing the bolt tensile testing method described above, comprising: a control module, a tensile testing device, and a vision inspection device; wherein the tensile testing device and the vision inspection device are electrically connected to the control module, the tensile testing device is provided with a plurality of bolt test connection positions, and the vision inspection device is positioned facing each bolt test connection position; after the bolt to be tested is inserted into each of the bolt test connection positions, the control module is configured to drive the tensile testing device to stretch each bolt to be tested, so as to detect the pressure data and tensile amount of each bolt to be tested; the control module is also configured to acquire bolt surface images of each bolt to be tested during the testing process through the vision inspection device; when the control module configures parameters, the control module is configured to confirm any bolt The control module detects bolt defects within a specific range of bolt surface images, identifying the types of bolt defects within that range through feature extraction and image comparison. During image comparison, it identifies bolt surface images of the same bolt at different resolutions until it finds the minimum resolution that matches the identified bolt defect types during feature extraction and image comparison. This minimum resolution is then set as the optimal resolution. The control module identifies the bolt surface image with the optimal resolution during image comparison. Furthermore, when detecting bolt surface images, the control module is configured to perform random sampling of bolt surface images through feature extraction to identify bolt defect types within the images. It is also configured to perform full inspection of the same bolt surface images through image comparison to identify bolt defect types within the images.

[0055] Specifically, the vision inspection device consists of a high-definition industrial camera (8k resolution) and a line-scan LED light source. The light emitted by the line-scan LED light source is reflected on the surface of the bolt to the high-definition industrial camera, enabling the high-definition industrial camera to capture a high-definition image of the bolt surface.

[0056] Specifically, the tensile testing device consists of a tensioner, a displacement sensor, and a pressure sensor.

[0057] In summary, this invention combines sensor detection and image detection during the tensile testing of bolts. This allows for the detection of pressure and tensile strength of each bolt, as well as real-time monitoring of surface changes during the tensile test. This enables timely cessation of the tensile test upon the presence of bolt defects, ensuring the lifespan of the tensile testing device. Furthermore, the real-time sensor data and image recognition data collected during the tensile test provide a basis for real-time monitoring of process defects and process optimization, thereby improving production efficiency. By combining feature extraction for random sampling of bolt surface images with image comparison for full inspection of the same bolt surface images, each bolt in the bolt surface image can be accurately identified, overcoming the problems of missed detections and low defect identification accuracy caused by traditional sampling methods. By simultaneously detecting bolt surface images through feature extraction and image comparison, the minimum resolution at which both methods determine the same bolt defect category is set as the optimal resolution. Using this optimal resolution in subsequent image comparisons allows for reasonable allocation of computing power, meeting the requirements of accurate bolt defect identification and reduced computing power.

[0058] The disclosures and other solutions, examples, embodiments, modules, and functional operations described in this document can be implemented in digital electronic circuits, or computer software, firmware, or hardware, including the structures disclosed in this document and their structural equivalents, or combinations thereof. The disclosures and other embodiments can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a tangible and non-volatile computer-readable medium for execution by a data processing apparatus or for controlling the operation of the data processing apparatus. The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a storage device, a material composition that influences machine-readable propagated signals, or one or more of these. The terms "data processing unit" or "data processing apparatus" include all means, devices, and machines for processing data, including, for example, programmable processors, computers, or multiprocessors or computer groups. In addition to hardware, the apparatus may also include code that creates an execution environment for a computer program, such as code constituting processor firmware, a protocol stack, a database management system, an operating system, or combinations thereof. The propagated signals are artificially generated signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information for transmission to a suitable receiver device.

[0059] Computer programs (also known as programs, software, software applications, scripts, or code) can be written in any programming language (including compiled or interpreted languages) and can be deployed in any form, including as standalone programs or as modules, components, subroutines, or other units suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to that program, or in multiple coordinating files (e.g., a file storing one or more modules, subroutines, or portions of code). Computer programs can be deployed and executed on one or more computers located at a single site or distributed across multiple sites interconnected by a communication network.

[0060] The processing and logic flows described in this document can be executed by one or more programmable processors that execute one or more computer programs to perform functions by manipulating input data and generating outputs. The processing and logic flows can also be executed by special-purpose logic circuitry, and the devices can be implemented as special-purpose logic circuitry, such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits).

[0061] For example, processors suitable for executing computer programs include general-purpose and special-purpose microprocessors, as well as any one or more of any type of digital computer. Typically, the processor receives instructions and data from read-only memory or random access memory, or both. The basic components of a computer are a processor that executes instructions and one or more storage devices that store the instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or operatively coupled to receive data from or transfer data to mass storage devices, or both. However, a computer does not necessarily have such devices. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, for example, semiconductor memory devices such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and optical disc read-only memory (CD ROM) and digital versatile optical disc read-only memory (DVD-ROM). The processor and memory may be supplemented by dedicated logic circuitry or incorporated into dedicated logic circuitry.

[0062] While this patent document contains numerous details, it should not be construed as limiting the scope of any invention or claim, but rather as a description of features of specific embodiments of a particular invention. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various functions described in the context of a single embodiment may also be implemented individually in multiple embodiments, or in any suitable sub-combination. Furthermore, although the foregoing features may be described as functioning in certain combinations, or even initially claimed to be so, in certain circumstances, one or more features from a combination of claims may be removed from the combination, and a combination of claims may refer to a sub-combination or a variation of a sub-combination.

[0063] Similarly, although the operations are described in a specific order in the accompanying drawings, this should not be construed as requiring the specific order or sequence shown to perform such operations, or all the described operations, in order to obtain the desired result. Furthermore, the separation of various system components in the embodiments of this patent document should not be construed as requiring such separation in all embodiments.

[0064] Only some implementations and examples are described; other implementations, enhancements, and variations can be made based on the content described and illustrated in this patent document.

[0065] When no intermediate component exists other than a line, trace, or other medium between the first and second components, the first component is directly coupled to the second component. When an intermediate component other than a line, trace, or other medium exists between the first and second components, the first component is indirectly coupled to the second component. The term "coupling" and its variations include direct coupling and indirect coupling. Unless otherwise stated, the term "about" is used to mean a range including upper and lower 10% of the value.

[0066] While several embodiments are provided in this disclosure, it should be understood that the disclosed systems and methods may be embodied in many other specific forms without departing from the spirit or scope of this disclosure. The present examples are intended to be illustrative rather than restrictive and are not limited to the details given. For example, various elements or components may be combined or integrated into another system, or certain features may be omitted or not implemented.

[0067] In the several embodiments provided herein, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0068] Furthermore, without departing from the scope of this disclosure, the discrete or individual technologies, systems, subsystems, and methods described and illustrated in the various embodiments may be combined or integrated with other systems, modules, technologies, or methods. Other items shown or discussed as coupled may be directly connected or indirectly coupled or communicated via some interface, device, or intermediate component in an electrical, mechanical, or other manner. Those skilled in the art can identify other examples of changes, substitutions, and modifications without departing from the spirit and scope of this disclosure.

Claims

1. A method for detecting bolt tensile force, characterized in that, include: After the test connection points of each bolt are installed into the bolts to be tested, the control module drives the tensile testing device to stretch each bolt to detect the pressure data and tensile amount of each bolt. The control module also acquires bolt surface images of each bolt during the testing process through a vision inspection device; When the control module configures parameters, the control module confirms the detection range in any bolt surface image, and identifies the bolt defect category in the detection range through feature extraction and image comparison. During the image comparison process, it identifies the same bolt surface images at different resolutions until it finds the minimum resolution that matches the bolt defect category determined during feature extraction and image comparison. The control module sets this minimum resolution as the optimal resolution, and adjusts the bolt surface image according to the optimal resolution during subsequent image comparison processes. When the control module inspects the bolt surface image, it performs random checks on the bolt surface image and identifies the bolt defect category in the bolt surface image through feature extraction. The control module also performs full inspection on the same bolt surface image and identifies the bolt defect category in the bolt surface image through image comparison.

2. The bolt tensile force testing method as described in claim 1, characterized in that, The control module identifies bolt defect categories in bolt surface images through feature extraction. Specifically, the control module scans the contours of each bolt within the detection range to detect the length of a single bolt, the shape of each thread in a single bolt, and the distance between adjacent threads, thereby identifying bolt defect categories within the detection range.

3. The bolt tensile force testing method as described in claim 1, characterized in that, The control module identifies the type of bolt defects in the bolt surface image by comparing images. Specifically, the control module scans the outline of each bolt in the bolt surface image and compares it with a standard image to identify the degree of overlap between the bolt outlines in the bolt surface image and the bolt outlines in the standard image, thereby identifying the type of bolt defects in the bolt surface image.

4. The bolt tensile force testing method as described in claim 1, characterized in that, Bolt defects include: ductile fracture, brittle fracture, thread stripping or delamination, head-to-shank connection fracture, and dents.

5. The bolt tensile force testing method as described in claim 1, characterized in that, When the control module sets the optimal resolution, it identifies the bolt defect category within the detection range through feature extraction. During image comparison, the control module first identifies the detection range within the same bolt surface image at any resolution. If the bolt defect categories determined during feature extraction and image comparison are consistent, the control module reduces the resolution of the bolt surface image for image comparison and identification until the bolt defect categories determined during feature extraction and image comparison are inconsistent. Then, the previous resolution is confirmed as the optimal resolution.

6. The bolt tensile force testing method as described in claim 1, characterized in that, When the control module sets the optimal resolution, it identifies the bolt defect category within the detection range through feature extraction. During image comparison, the control module first identifies the detection range within the same bolt surface image at any resolution. If the bolt defect categories determined during feature extraction and image comparison are inconsistent, the control module increases the resolution of the bolt surface image for image comparison and identification until the bolt defect categories determined during feature extraction and image comparison are consistent, at which point the current resolution is confirmed as the optimal resolution.

7. The bolt tensile force testing method as described in claim 5 or 6, characterized in that, The optimal resolution setting has an upper and lower limit.

8. The bolt tensile force testing method as described in claim 1, characterized in that, When the control module performs random inspections on the bolt surface image, it scans the outline of each bolt in the bolt surface image, identifies the starting bolt in the bolt surface image, and scans the corresponding bolts in the bolt surface image at intervals from the starting bolt to identify the bolt defect category in the bolt surface image.

9. The bolt tensile force testing method as described in claim 1, characterized in that, When the control module performs a full inspection on the same bolt surface image, it adjusts the resolution of the bolt surface image to the optimal resolution, scans the outline of each bolt in the bolt surface image, and compares it with the standard image to identify the degree of overlap between the bolt outline in the bolt surface image and the bolt outline in the standard image, thereby identifying the bolt defect category in the bolt surface image.

10. A testing system employing the bolt tensile force testing method as described in any one of claims 1-9, characterized in that, include: Control module, tensile testing device, and visual inspection device; in The tensile testing device, the visual inspection device, and the control module are electrically connected. The tensile testing device is provided with several bolt test connection positions, and the visual inspection device is oriented towards each bolt test connection position. After the bolts to be tested are installed in each of the bolt test connection positions, the control module is configured to drive the tensile testing device to stretch each bolt to be tested in order to detect the pressure data and tensile amount of each bolt. The control module is also configured to acquire bolt surface images of each bolt under test during the testing process using a visual inspection device; When the control module is configured with parameters, it is configured to identify the detection range in any bolt surface image, and to identify the bolt defect category in the detection range through feature extraction and image comparison. During the image comparison process, it identifies the same bolt surface images at different resolutions until it finds the minimum resolution that matches the bolt defect category determined during feature extraction and image comparison. The control module sets this minimum resolution as the optimal resolution, and adjusts the bolt surface image according to the optimal resolution during subsequent image comparison processes. When the control module detects bolt surface images, it is also configured to perform random checks on the bolt surface images and identify the bolt defect categories in the bolt surface images through feature extraction. The control module is also configured to perform full checks on the same bolt surface images and identify the bolt defect categories in the bolt surface images through image comparison.

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