Deformed steel bar quality detection method and device

Abstract

The invention provides a deformed steel bar quality detection method and device, and belongs to the technical field of steel processing, and the method comprises the following steps: obtaining external defect detection data, internal defect detection data and mechanical property detection data of deformed steel bars; obtaining an external defect detection result of the deformed steel bar based on the external defect detection data, obtaining an internal defect detection result based on the internal defect detection data, and obtaining a mechanical property detection result based on the mechanical property detection data; based on the external defect detection result, the internal defect detection result and the mechanical property detection result, obtaining a quality classification result of the deformed steel bar; and outputting the quality classification result, selecting qualified target deformed steel bars based on the quality classification result, and carrying out offline detection on the target deformed steel bars. According to the deformed steel bar quality detection method and device provided by the invention, the quality detection efficiency of deformed steel bars can be improved.

Description

Technical Field

[0001] The present application belongs to the field of steel processing technology, and more specifically, relates to a method and device for detecting the quality of threaded steel. Background Art

[0002] Rebar, the common name for hot-rolled ribbed steel bars, is a common type of construction steel. Its surface features two longitudinal ribs and evenly spaced transverse ribs along its length. The transverse ribs are named for their thread-like appearance. Rebar's performance and quality are directly related to the safety and reliability of building structures. Therefore, before leaving the factory, rebar undergoes multiple quality inspections, a time-consuming and labor-intensive process. Summary of the Invention

[0003] The purpose of this application is to provide a method and device for detecting the quality of threaded steel to improve the efficiency of the quality detection of threaded steel.

[0004] A first aspect of an embodiment of the present application provides a method for detecting the quality of threaded steel, comprising: Obtain external defect detection data, internal defect detection data and mechanical property detection data of rebar; Obtain an external defect detection result of the threaded steel based on the external defect detection data, obtain an internal defect detection result based on the internal defect detection data, and obtain a mechanical property detection result based on the mechanical property detection data; Obtaining a quality classification result of the threaded steel based on the external defect detection result, the internal defect detection result, and the mechanical property detection result; The quality classification result is output, so as to select target threaded steel bars with qualified quality based on the quality classification result, and perform offline inspection on the target threaded steel bars.

[0005] A second aspect of the embodiments of the present application provides a threaded steel quality detection device, comprising: A data acquisition module is used to acquire external defect detection data, internal defect detection data and mechanical property detection data of threaded steel; a data processing module, configured to obtain an external defect detection result of the threaded steel based on the external defect detection data, obtain an internal defect detection result based on the internal defect detection data, and obtain a mechanical property detection result based on the mechanical property detection data; A quality classification module, configured to obtain a quality classification result of the threaded steel based on the external defect detection result, the internal defect detection result, and the mechanical property detection result; The data output module is used to output the quality classification result, so as to select target threaded steel with qualified quality based on the quality classification result, and perform offline detection on the target threaded steel.

[0006] A third aspect of an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned method for detecting threaded steel quality when executing the computer program.

[0007] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for detecting quality of threaded steel are implemented.

[0008] The beneficial effects of the threaded steel quality detection method and device provided by the embodiments of the present application are: The embodiment of the present application performs online automatic detection of external defects, internal defects and mechanical properties of threaded steel to obtain external defect detection results, internal defect detection results and mechanical property detection results, respectively, and determines the quality classification result of the threaded steel based on the external defect detection results, internal defect detection results and mechanical property detection results. For threaded steel with unqualified quality, subsequent offline detection is no longer performed, and only threaded steel with qualified quality is tested offline, which can reduce the workload of offline testing and improve detection efficiency.

[0009] Therefore, this embodiment combines online automatic detection with manual offline detection, which can reduce the workload of manual offline detection and improve detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0011] Figure 1 A schematic flow chart of a method for inspecting threaded steel quality provided in one embodiment of the present application; Figure 2 This is a structural block diagram of a threaded steel quality detection device provided in one embodiment of the present application; Figure 3 A schematic block diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0013] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0014] Please refer to Figure 1 , Figure 1 A flowchart of a threaded steel quality detection method provided in one embodiment of the present application, which can be executed by an electronic device, may include: S101: Obtain external defect detection data, internal defect detection data, and mechanical property detection data of the threaded steel bar.

[0015] In this embodiment, the external defect detection data of the rebar may include images of the rebar. Image acquisition equipment such as industrial cameras and line laser scanners may be set near the processing equipment of the rebar. As the rebar moves, the image acquisition equipment may capture multiple images of the rebar at different positions according to a set period.

[0016] Similarly, non-destructive testing equipment such as ultrasonic flaw detectors and X-ray detectors can be installed near the rebar processing equipment. As the rebar moves, the non-destructive testing equipment can detect internal defects of the rebar according to a set cycle and obtain internal defect detection data.

[0017] In addition, the mechanical property test data of rebar may include the rolling force corresponding to the rebar rolling process, the inlet diameter of the rebar before rolling, the outlet diameter of the rebar after rolling, and the radius of the roll. The rolling force corresponding to the rebar rolling process can be obtained by installing a pressure detection sensor near the rebar processing equipment, and the inlet diameter of the rebar before rolling, the outlet diameter of the rebar after rolling, and the radius of the roll can be obtained based on the parameters of the processing equipment.

[0018] S102: Obtaining an external defect detection result of the threaded steel based on the external defect detection data, obtaining an internal defect detection result based on the internal defect detection data, and obtaining a mechanical property detection result based on the mechanical property detection data.

[0019] In this embodiment, the external defect detection data of the threaded steel is input into a pre-trained external defect detection model to obtain the external defect detection results of the threaded steel. The external defect detection results of the threaded steel may include visual defect information such as cracks, folds, and missing ribs on the surface of the threaded steel.

[0020] Similarly, by inputting the internal defect detection data of the rebar into a pre-trained internal defect detection model, the internal defect detection results of the rebar can be obtained. The internal defect detection results of the rebar may include internal defects such as pores and slag inclusions.

[0021] In addition, based on the rolling force corresponding to the rebar rolling process, the inlet diameter of the rebar before rolling, the outlet diameter of the rebar after rolling and the radius of the roller, the real-time yield strength, tensile strength and other mechanical performance parameters of the rebar can be inferred through the Hollomon equation or empirical formula to obtain the mechanical performance test results.

[0022] S103: Obtaining a quality classification result of the threaded steel based on the external defect detection results, the internal defect detection results, and the mechanical property detection results.

[0023] In this embodiment, hierarchical determination rules may be pre-set, and the external defect detection results, internal defect detection results, and mechanical property detection results may be classified based on the pre-set hierarchical determination rules to obtain a quality classification result of the threaded steel.

[0024] Specifically, the hierarchical judgment rules may include a veto layer and an indicator weighting layer. Each test result is pre-set with a corresponding test threshold. If a test result exceeds the corresponding test threshold in the veto layer, the veto mechanism is triggered, and the quality is directly judged to be unqualified. For example, if the yield strength is less than 90% of the standard value, it is judged to be unqualified. For test results that do not trigger the veto mechanism, each test result can be assigned a value based on the degree to which each test result exceeds the corresponding test threshold to obtain the quality score of each test result. The quality scores of each test result are then weighted and summed to obtain the quality score of the rebar. The quality classification result of the rebar is determined based on the quality score. For example, rebar with a quality score greater than 70 points is determined to be qualified rebar, and rebar with a quality score less than or equal to 70 points is determined to be unqualified rebar.

[0025] S104: Outputting the quality classification result, selecting target threaded steel bars with qualified quality based on the quality classification result, and performing offline inspection on the target threaded steel bars.

[0026] In this embodiment, based on the quality classification results, obviously unqualified products can be eliminated, and only qualified rebars are subjected to offline testing (including destructive testing such as bending tests and impact tests).

[0027] From the above, it can be concluded that this embodiment performs online automatic detection of external defects, internal defects and mechanical properties of threaded steel to obtain external defect detection results, internal defect detection results and mechanical property detection results, respectively, and determines the quality classification result of the threaded steel based on the external defect detection results, internal defect detection results and mechanical property detection results. For threaded steel with unqualified quality, subsequent offline detection is no longer performed, and only threaded steel with qualified quality is tested offline, which can reduce the workload of offline detection and improve detection efficiency.

[0028] Therefore, this embodiment combines online automatic detection with manual offline detection, which can reduce the workload of manual offline detection and improve detection efficiency.

[0029] In one embodiment of the present application, the external defect detection result includes the surface roughness and scale coverage area of ​​the threaded steel bar; and the internal defect detection result obtained based on the internal defect detection data includes: performing feature extraction on the internal defect detection data to obtain internal defect feature data; Correcting the internal defect characteristic data based on the surface roughness and scale coverage area of ​​the threaded steel to obtain corrected internal defect characteristic data; The corrected internal defect feature data is input into an internal defect detection model to obtain the internal defect detection result.

[0030] In this embodiment, an ultrasonic flaw detector can be used to detect internal defects in rebar and obtain internal defect data. Ultrasonic flaw detectors use the reflection characteristics of sound waves within the material to detect internal defects (such as slag inclusions and pores). Considering that the surface roughness of rebar or the presence of oxide scale on the surface can lead to abnormal sound wave reflection, affecting the accurate extraction of internal defect feature data, for example, excessively rough rebar surfaces can cause ultrasonic beam scattering, with some energy diffusing in non-detection directions, reducing the energy reflected back to the probe and thus reducing the echo amplitude. Furthermore, a rough surface increases scattering noise, attenuates signal energy, reduces the noise-to-signal ratio, and lowers the signal-to-noise ratio (SNR). For another example, the speed of sound from oxide scale (approximately 3000-5000 m / s) is lower than that of steel (approximately 5900 m / s), causing the rebar surface echo to occur earlier, potentially interfering with the timing of defect echoes. Both the surface roughness and the oxide scale coverage area of ​​the rebar can be obtained through external defect detection.

[0031] To address this issue, a regression model can be pre-built based on historical data to link rebar surface parameters (including surface roughness and scale coverage) with internal defect feature data. This serves as a calibration model for these two parameters. By inputting the rebar surface roughness and scale coverage into the calibration model, corrected internal defect feature data can be obtained. This corrected internal defect feature data can then be input into a trained internal defect detection model to obtain the corrected internal defect feature data. The internal defect detection model can be constructed based on a decision tree model.

[0032] From the above, it can be concluded that this embodiment constructs a correlation analysis model between the surface parameters of threaded steel and the internal defect characteristic data, and accordingly corrects the internal defect characteristic data based on the surface parameters of the threaded steel, thereby improving the detection accuracy of internal defects of threaded steel.

[0033] In one embodiment of the present application, the internal defect detection data is obtained based on an ultrasonic detector; and the threaded steel quality detection method further includes: If the surface roughness of the threaded steel bar is greater than a first threshold, the detection frequency of the ultrasonic detector is reduced by a first step length.

[0034] In this embodiment, considering that rough surfaces can cause incident ultrasonic waves to scatter and reflect, some of the sound energy is dissipated, reducing the effective sound energy entering the material and lowering the strength of the defect echo signal. The propagation characteristics of ultrasonic waves indicate that the frequency (f) of an ultrasonic wave is inversely proportional to its wavelength (λ) (λ = c / f, where c is the speed of sound). Lowering the frequency increases the wavelength, and a longer wavelength increases the wave's diffraction ability (i.e., its ability to bypass obstacles), thus reducing the scattering effect caused by rough surfaces.

[0035] Therefore, a first threshold can be set in advance. When the surface roughness of the rebar is greater than the first threshold, a probe with a lower frequency can be selected. Its wavelength is longer and its sensitivity to surface roughness is relatively low. It can better penetrate the material and reduce surface reflection interference.

[0036] In one embodiment of the present application, the mechanical property test data includes the rolling force corresponding to the rebar rolling process, the inlet diameter of the rebar before rolling, the outlet diameter of the rebar after rolling, and the radius of the roller, and the mechanical property test results include yield strength and tensile strength; The obtaining of mechanical property test results based on the mechanical property test data includes: Determining the deformation zone area of ​​the threaded steel bar based on the inlet diameter of the threaded steel bar before rolling, the outlet diameter of the threaded steel bar after rolling, and the radius of the rolling roller; Determining stress information of the threaded steel based on the area of ​​the deformation zone of the threaded steel and the rolling force corresponding to the rolling process of the threaded steel; The yield strength and tensile strength are determined based on the stress information of the threaded steel bar.

[0037] In this embodiment, the deformation zone refers to the area where the rebar undergoes plastic deformation due to the action of the rollers during the rolling process. Its area is related to the cross-sectional change of the rebar and the contact range of the rollers. Specifically, when calculating the area of ​​the deformation zone, the length of the deformation zone (i.e., the length of the contact arc) can be determined in combination with the roller radius, and the projection of the circumference of the rebar on the contact arc (i.e., the width of the contact zone) can be calculated in combination with the inlet diameter of the rebar before rolling and the outlet diameter of the rebar after rolling. The area of ​​the deformation zone is then calculated based on the contact arc length and the contact zone width. For example, the following first formula can be used to calculate the area of ​​the deformation zone: ; in, represents the length of the deformation zone, Indicates the arc length of the pressure roller, Indicates the inlet diameter of rebar before rolling. Indicates the outlet diameter of rebar after rolling. Indicates the amount of pressure, represents the contact area width, Represents the area of ​​the deformation zone.

[0038] Based on this, the stress information of the rebar can be obtained as follows: ; in, Represents stress information, Indicates the rolling force corresponding to the rebar rolling process, Represents the friction factor, usually ranging from 0.3 to 0.8.

[0039] Finally, when the deformation enters the plastic stage, the stress information The relationship with yield strength needs to consider the three-dimensional compressive stress state and introduce the stress state coefficient (usually 1.1~1.3), and the yield strength is obtained for: ; At the same time, based on the Hollomon equation, when the strain ε = n, the stress information reaches the maximum value, so the tensile strength can be obtained as: ; in, It represents tensile strength, K is the strength coefficient, and n is the work hardening index. Both K and n are parameters of the threaded steel base material.

[0040] From the above, it can be concluded that this embodiment directly uses real-time data (rolling force, diameter, etc.) during the rolling process to calculate the mechanical properties of the rebar, realizes online detection of yield strength and tensile strength, and improves detection efficiency.

[0041] In one embodiment of the present application, the mechanical property test data further includes the rib height of the threaded steel bar; and before determining the yield strength and tensile strength based on the stress information of the threaded steel bar, the following is further included: Determining a first adjustment parameter based on a rib height of the threaded steel bar and an outlet diameter of the threaded steel bar after rolling; Adjusting the stress information of the threaded steel based on the first adjustment parameter to obtain adjusted stress information; The determining the yield strength and tensile strength based on the stress information of the threaded steel bar includes: The yield strength and tensile strength are determined based on the adjusted stress information.

[0042] In this embodiment, considering that the ribs on the surface of the threaded steel bar may cause non-uniform deformation of the cross section, thereby affecting the calculation accuracy of the stress information, in order to achieve high-precision stress calculation, the inventors of this application, after a large number of experiments, obtained the following second formula to correct the stress information: ; in, represents the first adjustment parameter, Indicates rib height, Indicates the outlet diameter of rebar after rolling. Indicates the adjusted stress information.

[0043] From the above, it can be concluded that this embodiment takes into account the influence of the ribs on the surface of the threaded steel bar on the calculation of stress information and determines the first adjustment parameter to correct the stress information, which is beneficial to improving the detection accuracy of the mechanical property parameters.

[0044] 6. In one embodiment of the present application, the external defect detection data includes a rebar image; The obtaining of the external defect detection result of the threaded steel based on the external defect detection data includes: The threaded steel image is subjected to image recognition to obtain an external defect detection result of the threaded steel.

[0045] 7. In one embodiment of the present application, before performing image recognition on the rebar image, the rebar quality detection method further includes: Performing grayscale processing on the threaded steel image to obtain a grayscale image; Performing Fourier transform on the grayscale image to obtain a spectrum image; Dividing the threaded steel image into a thread rib area and a smooth area based on the frequency spectrum; Performing filtering on the thread rib area based on a median filtering method; The smooth area is filtered based on a Gaussian filtering method.

[0046] In this embodiment, Corresponding to a threaded steel quality detection method in the above embodiment, Figure 2 This is a structural block diagram of a threaded steel quality inspection device provided by an embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown. Figure 2 The threaded steel quality detection device 20 includes: a data acquisition module 21, a data processing module 22, a quality classification module 23 and a data output module 24. The data acquisition module 21 is used to acquire the external defect detection data, internal defect detection data and mechanical property detection data of the threaded steel bar; A data processing module 22 is configured to obtain an external defect detection result of the threaded steel based on the external defect detection data, obtain an internal defect detection result based on the internal defect detection data, and obtain a mechanical property detection result based on the mechanical property detection data; A quality classification module 23 is configured to obtain a quality classification result of the threaded steel based on the external defect detection result, the internal defect detection result, and the mechanical property detection result; The data output module 24 is configured to output the quality classification result, so as to select target threaded steel bars with qualified quality based on the quality classification result, and perform offline detection on the target threaded steel bars.

[0047] In one embodiment of the present application, the external defect detection result includes the surface roughness and scale coverage area of ​​the threaded steel bar, and the data processing module 22 is specifically configured to: performing feature extraction on the internal defect detection data to obtain internal defect feature data; Correcting the internal defect characteristic data based on the surface roughness and scale coverage area of ​​the threaded steel to obtain corrected internal defect characteristic data; The corrected internal defect feature data is input into an internal defect detection model to obtain the internal defect detection result.

[0048] In one embodiment of the present application, the internal defect detection data is obtained based on an ultrasonic detector; the data acquisition module 21 is specifically used to: If the surface roughness of the threaded steel bar is greater than a first threshold, the detection frequency of the ultrasonic detector is reduced by a first step length.

[0049] In one embodiment of the present application, the mechanical property test data includes the rolling force corresponding to the rebar rolling process, the inlet diameter of the rebar before rolling, the outlet diameter of the rebar after rolling, and the radius of the roller, and the mechanical property test results include yield strength and tensile strength; the data processing module 22 is specifically used to: Determining the deformation zone area of ​​the threaded steel bar based on the inlet diameter of the threaded steel bar before rolling, the outlet diameter of the threaded steel bar after rolling, and the radius of the rolling roller; Determining stress information of the threaded steel based on the area of ​​the deformation zone of the threaded steel and the rolling force corresponding to the rolling process of the threaded steel; The yield strength and tensile strength are determined based on the stress information of the threaded steel bar.

[0050] In one embodiment of the present application, the mechanical property test data further includes the rib height of the threaded steel bar; before determining the yield strength and tensile strength based on the stress information of the threaded steel bar, the data processing module 22 is further configured to: Determining a first adjustment parameter based on a rib height of the threaded steel bar and an outlet diameter of the threaded steel bar after rolling; Adjusting the stress information of the threaded steel based on the first adjustment parameter to obtain adjusted stress information; The determining the yield strength and tensile strength based on the stress information of the threaded steel bar includes: The yield strength and tensile strength are determined based on the adjusted stress information.

[0051] In one embodiment of the present application, the external defect detection data includes a threaded steel image; the data processing module 22 is specifically configured to: The threaded steel image is subjected to image recognition to obtain an external defect detection result of the threaded steel.

[0052] In one embodiment of the present application, before performing image recognition on the rebar image, the data processing module 22 is further configured to: Performing grayscale processing on the threaded steel image to obtain a grayscale image; Performing Fourier transform on the grayscale image to obtain a spectrum image; Dividing the threaded steel image into a thread rib area and a smooth area based on the frequency spectrum; Performing filtering on the thread rib area based on a median filtering method; The smooth area is filtered based on a Gaussian filtering method.

[0053] See also Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided in one embodiment of the present application. Figure 3The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules / units in the above-mentioned device embodiments, such as Figure 2 The functions of the data acquisition module 21, the data processing module 22, the quality classification module 23 and the data output module 24 are shown.

[0054] It should be understood that in the embodiment of the present application, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0055] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0056] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store preset constants such as a detection threshold.

[0057] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiment of the present application can execute the implementation method described in a threaded steel quality detection method provided in the embodiment of the present application, and can also execute the implementation method of the electronic device described in the embodiment of the present application, which will not be repeated here.

[0058] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0059] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0060] Those skilled in the art will appreciate 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 interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0061] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0062] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0063] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0064] In addition, the functional modules in the various embodiments of the present application may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The above-mentioned integrated modules may be implemented in the form of hardware or software functional modules.

[0065] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for detecting the quality of threaded steel, characterized in that: include: Obtain external defect detection data, internal defect detection data and mechanical property detection data of rebar; Obtain an external defect detection result of the threaded steel based on the external defect detection data, obtain an internal defect detection result based on the internal defect detection data, and obtain a mechanical property detection result based on the mechanical property detection data; Obtaining a quality classification result of the threaded steel based on the external defect detection result, the internal defect detection result, and the mechanical property detection result; The quality classification result is output, so as to select target threaded steel bars with qualified quality based on the quality classification result, and perform offline inspection on the target threaded steel bars.

2. A method for detecting the quality of threaded steel according to claim 1, characterized in that: The external defect detection results include the surface roughness and scale coverage area of ​​the threaded steel bar; Obtaining an internal defect detection result based on the internal defect detection data includes: performing feature extraction on the internal defect detection data to obtain internal defect feature data; Correcting the internal defect characteristic data based on the surface roughness and scale coverage area of ​​the threaded steel to obtain corrected internal defect characteristic data; The corrected internal defect feature data is input into an internal defect detection model to obtain the internal defect detection result.

3. A method for detecting the quality of threaded steel according to claim 1, characterized in that: The internal defect detection data is obtained based on an ultrasonic detector; The threaded steel quality detection method further comprises: If the surface roughness of the threaded steel bar is greater than a first threshold, the detection frequency of the ultrasonic detector is reduced by a first step length.

4. A method for detecting the quality of threaded steel according to claim 1, characterized in that: The mechanical property test data includes the rolling force corresponding to the rebar rolling process, the inlet diameter of the rebar before rolling, the outlet diameter of the rebar after rolling, and the radius of the roller. The mechanical property test results include yield strength and tensile strength; The obtaining of mechanical property test results based on the mechanical property test data includes: Determining the deformation zone area of ​​the threaded steel bar based on the inlet diameter of the threaded steel bar before rolling, the outlet diameter of the threaded steel bar after rolling, and the radius of the rolling roller; Determining stress information of the threaded steel based on the area of ​​the deformation zone of the threaded steel and the rolling force corresponding to the rolling process of the threaded steel; The yield strength and tensile strength are determined based on the stress information of the threaded steel bar.

5. A method for detecting the quality of threaded steel as claimed in claim 4, characterized in that: The mechanical properties test data also includes the rib height of the threaded steel bar; Before determining the yield strength and tensile strength based on the stress information of the threaded steel bar, the method further includes: Determining a first adjustment parameter based on a rib height of the threaded steel bar and an outlet diameter of the threaded steel bar after rolling; Adjusting the stress information of the threaded steel based on the first adjustment parameter to obtain adjusted stress information; The determining the yield strength and tensile strength based on the stress information of the threaded steel bar includes: The yield strength and tensile strength are determined based on the adjusted stress information.

6. A method for inspecting the quality of threaded steel according to claim 1, characterized in that: The external defect detection data includes a threaded steel bar image; The obtaining of the external defect detection result of the threaded steel based on the external defect detection data includes: The threaded steel image is subjected to image recognition to obtain an external defect detection result of the threaded steel.

7. A method for inspecting the quality of threaded steel according to claim 6, characterized in that: Before performing image recognition on the threaded steel image, the threaded steel quality detection method further includes: Performing grayscale processing on the threaded steel image to obtain a grayscale image; Performing Fourier transform on the grayscale image to obtain a spectrum image; Dividing the threaded steel image into a thread rib area and a smooth area based on the frequency spectrum; Performing filtering on the thread rib area based on a median filtering method; The smooth area is filtered based on a Gaussian filtering method.

8. A threaded steel quality detection device, characterized in that: include: A data acquisition module is used to acquire external defect detection data, internal defect detection data and mechanical property detection data of threaded steel; a data processing module, configured to obtain an external defect detection result of the threaded steel based on the external defect detection data, obtain an internal defect detection result based on the internal defect detection data, and obtain a mechanical property detection result based on the mechanical property detection data; A quality classification module, configured to obtain a quality classification result of the threaded steel based on the external defect detection result, the internal defect detection result, and the mechanical property detection result; The data output module is used to output the quality classification result, so as to select target threaded steel with qualified quality based on the quality classification result, and perform offline detection on the target threaded steel.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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