Steel-based clad material defect detection systems, methods, devices, and media

Abstract

The application relates to the technical field of nondestructive testing of multilayer structures. The application discloses a steel-based clad material defect detection system, method, device and medium. The system comprises a parameter module, a signal generation module, a detection probe and a defect detection module. The parameter module is used for acquiring preset detection parameters, the detection parameters comprising low-frequency magnetization parameters, high-frequency excitation parameters and medium-frequency excitation parameters. The signal generation module is used for generating a multi-frequency composite excitation signal according to the detection parameters. The detection probe is used for applying the multi-frequency composite excitation signal to a steel-based clad material to be detected and collecting a response signal. The defect detection module is used for demodulating the response signal to obtain a complex response signal of each frequency band, a differential complex response signal of each frequency band, extracting a defect feature and performing defect evaluation according to the defect feature. The application utilizes the differences in skin depths at different frequencies and multi-frequency composite excitation and synchronous demodulation technology to realize layered detection of the steel-based clad material to be detected and evaluation of defect positions and defect depths or / and depths.

Description

Technical Field

[0001] This application relates to the field of nondestructive testing technology for multilayer structures, and more specifically, to a defect detection system, method, equipment, and medium for steel-based multilayer materials. Background Technology

[0002] Steel-based clad structures typically consist of an outer carbon fiber composite load-bearing layer and a metal inner liner. These gas cylinders operate for extended periods in environments involving high-pressure charging and discharging cycles, temperature fluctuations, and corrosive media, making them prone to various types and layers of damage. These damages include surface scratches and fiber breakage in the carbon fiber layer, delamination / debonding defects within the carbon fiber layer, cracks near the surface of the steel inner liner, and deeper internal defects. Because the location of these defects varies significantly, their impact on structural load-bearing capacity and sealing safety differs considerably, necessitating reliable delamination detection and quantitative assessment.

[0003] Existing electromagnetic detection methods for composite materials or metal components mostly rely on a single frequency or a single operating mode. For multilayer heterogeneous structures of steel-based cladding materials, due to the significant differences in electrical properties and geometric structures, the detection signal is often a multilayer combined effect, making it difficult for existing electromagnetic detection methods to assess defect location and depth. Therefore, a method is needed that can complete defect assessment under the same detection conditions to improve the stability, interpretability, and engineering applicability of multilayer defect detection in steel-based cladding materials. Summary of the Invention

[0004] In view of the above situation, this application provides a defect detection system, method, equipment and medium for steel-based cladding materials, which aims to solve the above problems or at least partially solve the above problems.

[0005] In a first aspect, this application provides a defect detection system for steel-based cladding materials, comprising: The parameter module is used to acquire pre-set detection parameters, including low-frequency magnetization parameters, high-frequency excitation parameters, and medium-frequency excitation parameters. The signal generation module is used to generate a multi-frequency composite excitation signal based on the detection parameters. A detection probe is used to apply the multi-frequency composite excitation signal to the steel-based cladding material under test and collect the response signal; The defect detection module is used to demodulate the response signal to obtain the complex response signal of each frequency band, perform differential processing on the complex response signal of each frequency band to obtain the differential complex response signal of each frequency band, extract defect features based on the differential complex response signal of each frequency band, and perform defect evaluation based on the defect features and the pre-set hierarchical judgment rules.

[0006] For example, the signal generation module includes a signal generation module, a signal superposition module, and a magnetization power driving module; The signal generation module is used to generate low-frequency magnetization signals, high-frequency excitation signals, and medium-frequency excitation signals according to the detection parameters. The signal superposition module is used to superimpose the high-frequency excitation signal and the intermediate-frequency excitation signal to obtain a composite excitation signal; The magnetization power drive module is used to perform adjustable power amplification or constant current drive on the low-frequency magnetization signal to apply alternating magnetization to the steel lining layer of the steel-based composite material under test to form a controllable magnetization background.

[0007] For example, the defect detection module includes a data acquisition module, a demodulation module, a feature extraction module, and a defect assessment module; The data acquisition module is used to convert the response signal into a digital detection signal; The demodulation module is used to perform three-channel synchronous quadrature demodulation on the digital detection signal to obtain the complex response signal of each frequency band. The complex response signal of each frequency band includes a high-frequency complex response signal, a mid-frequency complex response signal, and a low-frequency complex response signal. The feature extraction module is used to perform differential processing on the complex response signals of each frequency band to obtain differential complex response signals of each frequency band, and to extract defect features based on the differential complex response signals of each frequency band. The defect features include: high frequency differential amplitude, mid-frequency differential amplitude, low frequency differential amplitude, low frequency differential phase, mid-high frequency complex ratio, and mid-low frequency complex ratio. The defect assessment module is used to assess defects based on the defect characteristics and pre-set hierarchical judgment rules.

[0008] For example, the defect assessment module is also used to determine whether the defect is located on the outer layer of the steel-based composite material to be tested, based on the medium-high frequency complex ratio value and a preset first complex ratio value range. The defect assessment module is also used to determine, if the defect is located on the outer layer of the steel-based composite material to be tested, whether the defect is located on the outer surface of the steel-based composite material to be tested or inside the outer layer of the steel-based composite material to be tested, based on the comparison between the high-frequency differential amplitude and the preset first reference amplitude.

[0009] For example, the defect assessment module is further configured to calculate a first relative anomaly intensity based on the high-frequency differential amplitude and the first reference amplitude if it is determined that the defect is located on the outer surface of the steel-based composite material to be tested. The defect assessment module is also used to determine the defect burial depth if it is determined that the defect is located inside the outer layer of the steel-based composite material to be tested, based on the pre-set mapping relationship between the medium- and high-frequency composite ratio and the defect burial depth.

[0010] For example, the defect assessment module is also used to determine whether the defect is located near the inner layer of the steel-based composite material to be tested, based on the low-frequency complex ratio value and a pre-set second complex ratio value range. The defect assessment module is also used to determine the defect depth if it is determined that the defect is located near the inner surface of the steel-based composite material to be tested, based on a pre-set mapping relationship between the low-frequency complex ratio and the defect depth.

[0011] For example, the defect assessment module is further configured to compare the low-frequency differential amplitude with a preset second reference amplitude, or compare the low-frequency differential phase with a preset first reference phase, and determine whether the defect is located inside the inner layer of the steel-based composite material to be tested based on the comparison result. The defect assessment module is also used to determine the equivalent depth of the defect if it is determined that the defect is located inside the inner layer of the steel-based composite material to be tested, based on a pre-set fitting curve of low-frequency differential amplitude and defect depth.

[0012] Secondly, this application provides a method for detecting defects in steel-based cladding materials, employing the steel-based cladding material defect detection system as described in the first aspect, the method comprising: Acquire pre-set detection parameters, including low-frequency magnetization parameters, high-frequency excitation parameters, and medium-frequency excitation parameters; A multi-frequency composite excitation signal is generated based on the detection parameters; The multi-frequency composite excitation signal is applied to the steel-based composite material under test and the response signal is collected; The complex response signals of each frequency band are obtained by demodulating the response signals. Differential processing is performed on the complex response signals of each frequency band to obtain differential complex response signals of each frequency band. Defect features are extracted from the differential complex response signals of each frequency band. Defect assessment is performed based on the defect features and pre-set hierarchical judgment rules.

[0013] Thirdly, this application provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the steel-based cladding material defect detection method as described in the second aspect.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the steel-based cladding material defect detection method as described in the second aspect.

[0015] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: In this application, the higher the frequency of the alternating magnetic field, the shallower the penetration depth; and the lower the frequency of the alternating magnetic field, the deeper the detection range. This application generates a multi-frequency composite excitation signal by using low-frequency magnetization parameters, high-frequency excitation parameters, and medium-frequency excitation parameters, and applies the multi-frequency composite excitation signal to the steel-based composite material to be tested. Based on the collected response signal, demodulation and feature extraction are performed. By utilizing the difference in skin depth at different frequencies, combined with multi-frequency composite excitation and synchronous demodulation technology, the layered detection of defects at different locations in the carbon fiber layer and steel lining layer of the steel-based composite material is realized, and the location and depth of defects are assessed. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of a defect detection system for steel-based multilayer materials according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a detection probe in one embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the principle of a defect detection method for steel-based composite materials in one embodiment of the present invention; Figure 4 Figure a is a schematic diagram of the principle of defect detection in each frequency band in one embodiment of the present invention. Figure b is a schematic diagram of the principle of defect detection using high frequency excitation signal, Figure c is a schematic diagram of the principle of defect detection using medium frequency excitation signal, and Figure c is a schematic diagram of the principle of defect detection using low frequency magnetization signal. Figure 5 This is a schematic diagram of a defect assessment process for a defect assessment module in one embodiment of the present invention; Figure 6 This is a schematic diagram of another defect assessment process of the defect assessment module in one embodiment of the present invention; Figure 7 This is a schematic flowchart of a defect detection method for steel-based composite materials according to an embodiment of the present invention; Figure 8 This is a schematic flowchart of a defect detection method for steel-based composite materials in one embodiment of the present invention; Figure 9 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the term "comprising" and its variations should be interpreted as open-ended terms meaning "including but not limited to."

[0019] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0020] As mentioned earlier, current electromagnetic detection methods for composite materials or metal components mostly rely on a single frequency or a single operating mode. For multilayer heterogeneous structures of steel-based cladding materials, due to the significant differences in electrical properties and geometric structures, the detection signal is often a multilayer combined effect, making it difficult for existing electromagnetic detection methods to assess the location and depth of defects. To address this technical problem, this application provides a defect detection system for steel-based cladding materials.

[0021] In one embodiment, a defect detection system for steel-based cladding materials is provided, the steel-based cladding material defect detection system as follows: Figure 1 As shown, the defect detection system for steel-based composite materials includes a parameter module 101, a signal generation module 102, a detection probe 103, and a defect detection module 104. Detailed descriptions of each functional module are as follows: Parameter module 101 is used to acquire preset detection parameters, including low-frequency magnetization parameters, high-frequency excitation parameters, and medium-frequency excitation parameters; Signal generation module 102 is used to generate a multi-frequency composite excitation signal according to the detection parameters; The detection probe 103 is used to apply the multi-frequency composite excitation signal to the steel-based composite material to be tested and to collect the response signal; The defect detection module 104 is used to demodulate the response signal to obtain the complex response signal of each frequency band, perform differential processing on the complex response signal of each frequency band to obtain the differential complex response signal of each frequency band, extract defect features based on the differential complex response signal of each frequency band, and perform defect evaluation based on the defect features and the pre-set hierarchical judgment rules.

[0022] In this application, the higher the frequency of the alternating magnetic field, the shallower the penetration depth; and the lower the frequency of the alternating magnetic field, the deeper the detection range. This application generates a multi-frequency composite excitation signal by using low-frequency magnetization parameters, high-frequency excitation parameters, and medium-frequency excitation parameters, and applies the multi-frequency composite excitation signal to the steel-based composite material to be tested. Based on the collected response signal, demodulation and feature extraction are performed. By utilizing the difference in skin depth at different frequencies, combined with multi-frequency composite excitation and synchronous demodulation technology, the layered detection of defects at different locations in the carbon fiber layer and steel lining layer of the steel-based composite material is realized, and the location and depth of defects are assessed.

[0023] In one embodiment, the parameter module 101 includes a human-machine interface, through which the user can set the detection parameters. The low-frequency magnetization parameter includes the low-frequency frequency. Low-frequency alternating magnetization drive amplitude (current or power) and waveform; high-frequency excitation parameters including high-frequency frequency. Intermediate frequency excitation parameters include intermediate frequency. The detection parameters also include the sampling rate. Demodulation low-pass filter bandwidth, sampling duration / window length.

[0024] In one embodiment, such as Figure 1 As shown, parameter module 101, signal generation module 102, and defect detection module 104 are mounted on computer device 4, and detection probe 103 is connected to computer device 4.

[0025] In one embodiment, the steel-based composite material 5 to be tested includes an outer layer 51 and an inner layer 52. The outer layer 51 is a carbon fiber composite material layer, and the inner layer 52 is a metal liner layer. The defect location may be in the outer layer 51 or the inner layer 52. Specifically, the defect location may be on the surface of the outer layer 51, inside the outer layer 51, near the surface of the inner layer 52, or inside the inner layer 52.

[0026] In one embodiment, such as Figure 1As shown, the signal generation module 102 includes a signal generation module, a signal superposition module, and a magnetization power drive module. The signal generation module generates a low-frequency magnetization signal, a high-frequency excitation signal, and a medium-frequency excitation signal based on the detection parameters. The signal superposition module superimposes the high-frequency excitation signal and the medium-frequency excitation signal to obtain a composite excitation signal. The magnetization power drive module performs adjustable power amplification or constant current drive on the low-frequency magnetization signal to apply alternating magnetization to the steel liner of the steel-based composite material under test to form a controllable magnetization background. Specifically, the output amplitude of the low-frequency magnetization power drive module is set according to the magnetization characteristics of the steel liner material, so that the steel liner is in an unsaturated or near-saturated controllable alternating magnetization range. The signal generation module generates the low-frequency magnetization signal, the medium-frequency excitation signal, and the high-frequency excitation signal based on the detection parameters under the same reference clock or synchronous triggering conditions.

[0027] In one embodiment, the composite excitation signal generated by the signal superposition module is output to the detection probe via the magnetization power drive module to form a multi-frequency composite excitation signal. The multi-frequency composite excitation signal is a three-way composite signal consisting of a low-frequency magnetization signal, a high-frequency excitation signal, and a medium-frequency excitation signal. The low-frequency magnetization signal is not only used for detection but also applies alternating magnetization to the steel lining to form a more stable magnetization background. This reduces fluctuations caused by changes in the magnetic state of the steel material, making internal defects in the steel easier to detect and resulting in more stable results.

[0028] In one embodiment, such as Figure 2 As shown, the detection probe 103 includes an excitation coil 1, a magnetic core 2, and a receiving coil 3. The excitation coil 1 is a U-shaped excitation / magnetization coil, and the receiving coil 3 is located in the center of the excitation coil 1. The excitation coil 1 is used to apply the multi-frequency composite excitation signal to the steel-based cladding material 5 under test, and the receiving coil 3 is used to acquire the response signal. Specifically, the excitation coil 1 generates a magnetic field, which acts on the detection area below, and the receiving coil 3 is used to sense changes in the magnetic field. If the magnetic field acts on the steel lining layer of the inner layer 52, a current and a secondary magnetic field will be generated within the steel lining layer, and the magnetic field in the magnetic core 2 is the primary magnetic field (e.g., ...). Figure 3 (As shown by the dashed arrow in the middle), the steel lining will generate a secondary magnetic field under the influence of the primary magnetic field (such as...). Figure 3 (as shown by the solid black line with arrows in the middle), generating current (such as...) Figure 3 (As shown by the solid red line with arrows in the middle), the defects are as follows: Figure 3 As shown in the black square.

[0029] Specifically, such as Figure 4As shown in Figure a, the high-frequency magnetic field formed by the high-frequency excitation signal in the multi-frequency composite excitation signal has weak penetration ability, mainly concentrated in the carbon fiber composite layer (i.e., the outer layer 51 of the steel-based cladding material 5), and the magnetic field has difficulty penetrating into the underlying steel lining. This detection mode is mainly used to detect defects in the carbon fiber composite layer, such as surface cracks and delamination. When the carbon fiber composite layer is intact, the high-frequency magnetic field will pass through the material uniformly, and the receiving coil 3 will receive a stable response signal; when there are defects in the carbon fiber composite layer (such as surface cracks, delamination, pores, etc.), the material density and structure in the defect area will change, and the high-frequency magnetic field will be reflected, scattered, or attenuated at the defect, resulting in abnormalities in the amplitude, phase, and other characteristics of the received response signal.

[0030] Specifically, such as Figure 4 As shown in Figure b, the intermediate frequency (IF) magnetic field generated by the IF excitation signal in the multi-frequency composite excitation signal has moderate penetration capability, allowing it to penetrate the carbon fiber composite material and reach the shallow surface layer of the steel liner (i.e., the near-surface of the inner layer 52 of the steel-based cladding material 5). This mode can detect deep defects inside the carbon fiber composite material while also detecting surface damage to the steel liner, such as surface corrosion and shallow cracks, thus meeting the shallow detection needs of both the composite material and the steel liner. When the IF magnetic field penetrates the carbon fiber composite material and reaches the shallow surface layer of the steel liner, the presence of defects can cause abnormalities in the amplitude, phase, and other characteristics of the received response signal.

[0031] Specifically, such as Figure 4 As shown in Figure c, the low-frequency magnetic field formed by the low-frequency magnetization signal in the multi-frequency composite excitation signal has the strongest penetrating power, capable of completely penetrating the carbon fiber composite material and reaching deep into the internal region of the steel liner (i.e., the inner layer 52 of the steel-based cladding material 5). This detection mode is mainly used to detect internal defects in the steel liner, such as deep-seated problems like cracks, corrosion pits, and material degradation, and is a key detection mode for determining the structural integrity of the steel liner. When the low-frequency magnetic field penetrates the carbon fiber composite material and acts on the steel liner, it induces a current within the steel liner, which in turn generates a secondary magnetic field. If defects exist in the steel liner, the morphology of the induced current and the secondary magnetic field will change, causing abnormalities in the amplitude, phase, and other characteristics of the received response signal.

[0032] In one embodiment, such as Figure 1As shown, the defect detection module 104 includes a data acquisition module, a demodulation module, a feature extraction module, and a defect evaluation module. The data acquisition module converts the response signal into a digital detection signal. The demodulation module performs three-channel synchronous quadrature demodulation on the digital detection signal to obtain complex response signals for each frequency band, including high-frequency complex response signals, mid-frequency complex response signals, and low-frequency complex response signals. The feature extraction module performs differential processing on the complex response signals for each frequency band to obtain differential complex response signals for each frequency band, and extracts defect features based on these differential complex response signals. These defect features include: high-frequency differential amplitude, mid-frequency differential amplitude, low-frequency differential amplitude, low-frequency differential phase, mid-high frequency complex ratio, and mid-low frequency complex ratio. The defect evaluation module performs defect evaluation based on the defect features and pre-set hierarchical judgment rules.

[0033] In one embodiment, the detection probe is placed on the surface of the steel-based composite material 5 or the gas cylinder to be tested, and the measurement is performed under set lifting conditions; the response signal is collected by the receiving coil 3 of the detection probe, and the response signal is conditioned by the analog front end and then transmitted by the data acquisition module at a sampling rate. Digital detection signal obtained by sampling .

[0034] In one embodiment, the demodulation module receives the digital detection signal output by the data acquisition module. and respectively at high frequency Intermediate frequency low frequency Each corresponding reference signal is orthogonally demodulated. During the demodulation process, each path includes an orthogonal multiplication and a low-pass filter unit, outputting the I / Q components of each frequency band and obtaining the complex response signal of each frequency band. :

[0035] In equation (1), Indicates the first Complex response signals in each frequency band, including high-frequency complex response signals. Intermediate frequency complex response signal Low-frequency complex response signal ; Indicates the in-phase component (real part); Represents orthogonal components (imaginary part); It represents the imaginary unit.

[0036] Complex response signal in equation (1) amplitude With phase angle for:

[0037] In equations (2) and (3), The modulus of the complex response signal represents the intensity of the defect signal; This represents the phase angle of the complex response signal.

[0038] In one embodiment, the feature extraction module is further configured to calculate differential complex response signals for each frequency band based on pre-set reference complex response signals for each frequency band, wherein the differential complex response signals for each frequency band include high-frequency differential complex response signals, mid-frequency differential complex response signals, and low-frequency differential complex response signals.

[0039] In one embodiment, a defect-free reference region or a pre-measured defect-free reference complex response signal is selected for each frequency band. Differential analysis is performed on each frequency band. :

[0040] In one embodiment, each frequency band reference complex response signal Including high-frequency reference complex response signal Intermediate frequency reference complex response signal Low-frequency reference complex response signal High-frequency differential complex response signal Intermediate frequency differential complex response signal Low-frequency differential complex response signal .

[0041] In one embodiment, the feature extraction module is further configured to extract features based on the high-frequency differential complex response signal. Determine the high-frequency differential amplitude It is also used to base the intermediate frequency differential complex response signal on the signal. Determine the intermediate frequency differential amplitude value and the intermediate frequency differential phase It is also used to base the low-frequency differential complex response signal on... Determine the low-frequency differential amplitude value and the low-frequency differential phase It is also used to determine the high-frequency differential amplitude value. and the intermediate frequency differential amplitude value Determine the mid-to-high frequency complex ratio value It is also used to determine the intermediate frequency differential amplitude value. and the low-frequency differential amplitude value Determine the mid-low frequency complex ratio value .

[0042] In one embodiment, the differential amplitude of each frequency band and differential phase as follows:

[0043] In the formula, k represents H, M, and L.

[0044] In one embodiment, the mid-to-high frequency complex ratio The calculation is as follows:

[0045] In one embodiment, the mid-to-low frequency complex ratio The calculation is as follows:

[0046] This application reduces the impact of lift-off variations and other factors on quantitative results by constructing complex ratio characteristics.

[0047] In one embodiment, the defect assessment module is further configured to determine whether the defect is located on the outer layer 51 of the steel-based composite material 5 to be tested, based on the medium-high frequency complex ratio value and a pre-set first complex ratio value range.

[0048] Specifically, the steel-based cladding material 5 includes an outer layer 51 and an inner layer 52, and the defect may be located in the outer layer 51 or the inner layer 52. In this embodiment, the outer layer 51 of the steel-based cladding material 5 is a carbon fiber composite material layer, and the inner layer 52 is a steel liner layer. The defect may be located in the carbon fiber composite material layer or the steel liner layer.

[0049] Specifically, the defect assessment module is further configured to, if determined that the defect is located on the outer layer of the steel-based cladding material to be tested, determine, based on a comparison between the high-frequency differential amplitude and a preset first reference amplitude, whether the defect is located on the surface of the outer layer 51 of the steel-based cladding material to be tested or inside the outer layer 51 of the steel-based cladding material to be tested. Specifically, if the mid-to-high frequency complex ratio value is within a preset first complex ratio value range, it is determined that the defect is located on the outer layer 51 of the steel-based cladding material to be tested.

[0050] Specifically, the first reference amplitude is the outer layer 51 defect-free reference amplitude. If the high-frequency differential amplitude is greater than the first reference amplitude, the defect is determined to be located on the surface of the outer layer 51 of the steel-based cladding material 5 under test; if the high-frequency differential amplitude is less than or equal to the first reference amplitude, the defect is determined to be located inside the outer layer 51 of the steel-based cladding material 5 under test. The high-frequency differential amplitude... As a qualitative indicator of defects in carbon fiber composite layers, the larger the high-frequency differential amplitude, the more severe the damage to the outer layer 51, and the more likely the damage is located near the surface of the outer layer 51.

[0051] In one embodiment, the defect assessment module is further configured to determine the defect burial depth if it is determined that the defect is located inside the outer layer 51 of the steel-based composite material to be tested, based on a pre-set mapping relationship between the medium- and high-frequency composite ratio and the defect burial depth.

[0052] Specifically, the first complex ratio range is preset, and the process of obtaining the first complex ratio range includes: Calibration experiments were conducted on standard samples, and records were kept for different known burial depths. The calibration value of the mid-to-high frequency complex ratio corresponding to the defects in the carbon fiber composite layer Establish the mapping relationship between the medium- and high-frequency complex ratio and the defect burial depth:

[0053] Meanwhile, the calibration values ​​of the mid-to-high frequency complex ratios corresponding to defects in all carbon fiber composite layers on the statistical standard specimens were also determined. The range of values ​​is defined as the first complex ratio range: ,in and These are the minimum and maximum values ​​in the calibration values ​​of the medium- and high-frequency complex ratios, respectively.

[0054] Specifically, if the defect is determined to be located inside the outer layer 51 of the steel-based cladding material 5 to be tested, the defect burial depth is calculated using formula (9). The defect burial depth value is then output.

[0055] In one embodiment, the defect assessment module is further configured to, if determined that the defect is located on the surface of the outer layer 51 of the steel-based cladding material to be tested, calculate a first relative anomaly intensity based on the high-frequency differential amplitude and the first reference amplitude. In this case, the burial depth is not output; only the first relative anomaly intensity of the outer layer defect is output. The first relative anomaly intensity of the outer layer defect is the ratio of the high-frequency differential amplitude to the first reference amplitude.

[0056] In one embodiment, the defect assessment module is further configured to determine whether the defect is located near the surface of the inner layer 52 of the steel-based cladding material to be tested, based on the low-frequency complex ratio value and a pre-set second complex ratio value range; the defect assessment module is further configured to determine the defect depth if the defect is determined to be located near the surface of the inner layer 52 of the steel-based cladding material to be tested, based on a pre-set mapping relationship between the low-frequency complex ratio value and the defect depth.

[0057] Specifically, the range of the second complex ratio is preset, and the process of obtaining the range of the second complex ratio includes: Calibration experiments were conducted on standard samples, and records were kept at different known depths. The calibration value of the low-to-medium frequency complex ratio corresponding to the near-surface defects in the inner layer 52. Establish a mapping relationship between low- and medium-frequency complex ratios and defect depth:

[0058] Meanwhile, the calibration values ​​of the low-to-medium frequency complex ratios corresponding to all near-surface defects in the inner layer 52 of the statistical standard sample were also determined. The range of values ​​is defined as the effective interval of the second complex ratio range: ,in and These are the minimum and maximum values ​​in the calibration values ​​of the mid-to-low frequency complex ratio, respectively.

[0059] Specifically, if the low-frequency complex ratio value is within the range of the pre-set second complex ratio value, the defect is determined to be located near the surface of the inner layer 52 of the steel-based composite material 5 to be tested, and the defect depth is calculated using formula (10). At this time, the defect depth value is output.

[0060] In one embodiment, the defect assessment module is further configured to compare the low-frequency differential amplitude with a preset second reference amplitude, or compare the low-frequency differential phase with a preset first reference phase, and determine whether the defect is located inside the inner layer 52 of the steel-based cladding material to be tested based on the comparison result; the defect assessment module is further configured to determine the equivalent depth of the defect based on a preset fitting curve of low-frequency differential amplitude and defect depth if it is determined that the defect is located inside the inner layer 52 of the steel-based cladding material to be tested.

[0061] Specifically, the second reference amplitude is the inner layer 52 defect-free reference amplitude. The first reference phase is the defect-free reference phase of the inner layer 52. If the low-frequency differential amplitude is greater than the second reference amplitude, or the phase offset between the low-frequency differential phase and the first reference phase is greater than a preset first offset, then the defect is determined to be located inside the inner layer 52 of the steel-based composite material 5 to be tested. The low-frequency differential amplitude... Phase offset is used as a qualitative indicator of internal defects in the inner layer. The larger the low-frequency differential amplitude or the larger the offset, the more severe the damage to the inner layer.

[0062] Specifically, for internal defects in steel linings, if quantitative depth assessment is required, a series of artificial internal defects of known depth can be pre-fabricated on standard samples. The low-frequency differential amplitude values ​​corresponding to each defect are measured, and a fitting curve between the low-frequency differential amplitude values ​​and the defect depth is established, requiring a goodness of fit of [missing information]. .

[0063] Specifically, if the defect is determined to be located inside the inner layer 52 of the steel-based composite material 5 to be tested, the measured low-frequency differential amplitude value is substituted into the fitting curve to calculate the equivalent depth of the defect, and the equivalent depth value of the defect is then output.

[0064] As a calibration example, firstly, a standard specimen with a structure equivalent to the steel-based composite material 5 to be tested is prepared, which includes at least an outer layer 51 - a carbon fiber composite layer and an inner layer 52 - a steel liner layer; different layer defects are pre-fabricated in the standard specimen, including outer layer defects - (carbon fiber surface defects, defects with different burial depths within the carbon fiber layer), inner layer near-surface defects - (steel liner near-surface defects with different depths), and inner layer internal defects - (steel liner internal defects); and the true burial depth / depth of each defect is recorded as the calibration true value. Then, the multi-frequency composite excitation signal is applied to the standard sample and the response signal is acquired through the parameter module 101, signal generation module 102, and detection probe 103. Finally, the high-frequency differential amplitude, mid-frequency differential amplitude, low-frequency differential amplitude, mid-frequency differential phase, low-frequency differential phase, mid-high frequency complex ratio, and mid-low frequency complex ratio are obtained through the defect detection module 104. The mapping relationship between the mid-high frequency complex ratio and the defect burial depth, the mapping relationship between the mid-low frequency complex ratio and the defect depth, and the fitting curve between the low-frequency differential amplitude and the defect depth are constructed. The mapping relationship between the mid-high frequency complex ratio and the defect burial depth, the mapping relationship between the mid-low frequency complex ratio and the defect depth, and the fitting curve between the low-frequency differential amplitude and the defect depth are stored in the system for quantitative evaluation in subsequent detection stages.

[0065] As a detection embodiment, a multi-frequency composite excitation signal is applied to the steel-based composite material 5 to be tested through the parameter module 101, the signal generation module 102, and the detection probe 103, and the response signal is collected. Finally, the high-frequency differential amplitude, mid-frequency differential amplitude, low-frequency differential amplitude, mid-frequency differential phase, low-frequency differential phase, mid-high frequency complex ratio, and mid-low frequency complex ratio are obtained through the defect detection module 104. Based on the pre-constructed mapping relationship between the mid-high frequency complex ratio and the defect burial depth, the mapping relationship between the mid-low frequency complex ratio and the defect depth, and the fitting curve between the low-frequency differential amplitude and the defect depth, the defect location, defect burial depth, or defect depth is determined.

[0066] This application, combined with defect-free reference differential and complex ratio characteristics, can significantly reduce the impact of lift-off and coupling variations on amplitude. Simultaneously, by pre-constructing the mapping relationships between mid-to-high frequency complex ratios and defect burial depth, mid-to-low frequency complex ratios and defect depth, and fitting curves of low-frequency differential amplitude and defect depth, the complex ratio characteristics are converted into outer layer defect burial depth, inner layer near-surface defect depth, and inner layer defect depth. This achieves layered quantitative detection, enabling the determination and dimensional assessment of multiple defect locations in steel-based composite materials (5 carbon fiber layers and steel inner lining layers), making field applications more reliable.

[0067] It should be noted that defect burial depth refers to the distance between the upper edge of the defect and the surface of the workpiece being inspected, such as... Figure 3 L1 in the equation. Defect depth refers to the distance between the upper edge and the lower edge of the defect, such as... Figure 3 L2 in the middle.

[0068] In one embodiment, the defect assessment module is used to perform the following steps: ① Determine whether the defect is located on the outer layer 51 of the steel-based cladding material 5 to be tested, based on the mid-to-high frequency complex ratio value and a preset first complex ratio value range; Determination process: If the mid-to-high frequency complex ratio value is within the preset first complex ratio value range, determine that the defect is located on the outer layer 51 of the steel-based cladding material 5 to be tested and proceed to step ②; If the mid-to-high frequency complex ratio value exceeds the preset first complex ratio value range, determine that the defect is not located on the outer layer 51 of the steel-based cladding material 5 to be tested and proceed to step ④; ② Determine whether the defect is located on the surface of the outer layer 51 of the steel-based cladding material to be tested or based on the comparison between the high frequency differential amplitude value and a preset first reference amplitude value. The determination process is as follows: if the high-frequency differential amplitude is greater than the first reference amplitude, the defect is determined to be located on the surface of the outer layer 51 of the steel-based cladding material 5 to be tested; if the high-frequency differential amplitude is less than or equal to the first reference amplitude, the defect is determined to be located inside the outer layer 51 of the steel-based cladding material 5 to be tested; ③ if the defect is determined to be located on the surface of the outer layer 51 of the steel-based cladding material 5 to be tested, the first relative abnormal intensity is calculated based on the high-frequency differential amplitude and the first reference amplitude; if the defect is determined to be located inside the outer layer 51 of the steel-based cladding material 5 to be tested, the defect burial depth is determined based on the pre-set mapping relationship between the mid-to-high frequency complex ratio and the defect burial depth; ④ based on the mid-to-low frequency complex ratio and the pre-set... The second complex ratio range is set to determine whether the defect is located near the inner layer of the steel-based cladding material to be tested. The determination process is as follows: If the mid-to-low frequency complex ratio value is within the preset second complex ratio range, the defect is determined to be near the inner layer 52 of the steel-based cladding material to be tested, and the process proceeds to step ⑤; if the mid-to-low frequency complex ratio value exceeds the preset second complex ratio range, the defect is determined not to be near the inner layer 52 of the steel-based cladding material to be tested, and the process proceeds to step ⑥; ⑤ The defect depth is determined according to the preset mapping relationship between the mid-to-low frequency complex ratio value and the defect depth; ⑥ The low-frequency differential amplitude is compared with a preset second reference amplitude, or the low-frequency differential phase is compared with a preset first reference phase, and the defect is determined based on the comparison result. The defect is located inside the inner layer 52 of the steel-based cladding material to be tested. The determination process is as follows: If the low-frequency differential amplitude is greater than the second reference amplitude, or the phase offset between the low-frequency differential phase and the first reference phase is greater than a preset first offset, then the defect is determined to be located inside the inner layer 52 of the steel-based cladding material to be tested, and proceeds to step ⑦; if the low-frequency differential amplitude is not greater than the second reference amplitude, or the phase offset between the low-frequency differential phase and the first reference phase is not greater than a preset first offset, then the defect is determined not to be located inside the inner layer 52 of the steel-based cladding material to be tested, and proceeds to step ⑧; ⑦ Determine the equivalent depth of the defect based on the pre-set fitting curve of the low-frequency differential amplitude and the defect depth; ⑧ Determine if there is no defect or mark it as requiring manual verification. (See reference...) Figure 5 As shown.

[0069] In one embodiment, the defect assessment module is used to perform the following steps: ① Determine whether the defect is located on the outer layer 51 of the steel-based cladding material 5 to be tested, based on the mid-to-high frequency complex ratio value and a preset first complex ratio value range; Determination process: If the mid-to-high frequency complex ratio value is within the preset first complex ratio value range, determine that the defect is located on the outer layer 51 of the steel-based cladding material 5 to be tested and proceed to step ②; If the mid-to-high frequency complex ratio value exceeds the preset first complex ratio value range, determine that the defect is not located on the outer layer 51 of the steel-based cladding material 5 to be tested and proceed to step ⑧; ② Determine whether the defect is located on the surface of the outer layer 51 of the steel-based cladding material to be tested or on the steel-based cladding material 5 to be tested, based on the comparison between the high frequency differential amplitude value and a preset first reference amplitude value. The defect is located inside the outer layer 51 of the steel-based cladding material 5. The determination process is as follows: ① If the high-frequency differential amplitude is greater than the first reference amplitude, the defect is determined to be located on the surface of the outer layer 51 of the steel-based cladding material 5 to be tested; ② If the high-frequency differential amplitude is less than or equal to the first reference amplitude, the defect is determined to be located inside the outer layer 51 of the steel-based cladding material 5 to be tested; ③ If the defect is determined to be located on the surface of the outer layer 51 of the steel-based cladding material to be tested, the first relative abnormal intensity is calculated based on the high-frequency differential amplitude and the first reference amplitude; ④ If the defect is determined to be located inside the outer layer 51 of the steel-based cladding material to be tested, the defect burial depth is determined based on the pre-set mapping relationship between the mid-to-high frequency complex ratio value and the defect burial depth; ⑤ Based on the mid-to-low frequency complex ratio value and the pre-set range of the second complex ratio value... Determine whether the defect is located near the inner surface of the steel-based cladding material to be tested; determination process: if the mid-to-low frequency complex ratio value is within a preset second complex ratio value range, determine that the defect is located near the inner surface of the steel-based cladding material to be tested and proceed to step ⑤; if the mid-to-low frequency complex ratio value exceeds the preset second complex ratio value range, determine that the defect is not located near the inner surface of the steel-based cladding material to be tested and proceed to step ⑧; ⑤ Determine the defect depth according to the preset mapping relationship between the mid-to-low frequency complex ratio value and the defect depth; ⑥ Compare the low-frequency differential amplitude with a preset second reference amplitude, or compare the low-frequency differential phase with a preset first reference phase, and determine whether the defect is located in the inner layer of the steel-based cladding material to be tested based on the comparison result. 52 Inside; Judgment process: If the low-frequency differential amplitude is greater than the second reference amplitude, or the phase offset between the low-frequency differential phase and the first reference phase is greater than the preset first offset, then the defect is determined to be located inside the inner layer 52 of the steel-based cladding material 5 to be tested, and proceed to step ⑦; If the low-frequency differential amplitude is not greater than the second reference amplitude, or the phase offset between the low-frequency differential phase and the first reference phase is not greater than the preset first offset, then the defect is determined not to be located inside the inner layer 52 of the steel-based cladding material 5 to be tested, and proceed to step ⑧; ⑦ Determine the equivalent depth of the defect according to the pre-set fitting curve of the low-frequency differential amplitude and the defect depth; ⑧ After obtaining the determinations of all negative results in steps ①, ④, and ⑥, determine that there is no defect or mark it as requiring manual review. See Figure 6As shown.

[0070] Specifically, "the mid-to-high frequency complex ratio value exceeding the preset first complex ratio value range" means that the mid-to-high frequency complex ratio value is less than the minimum value of the first complex ratio value range. Or the mid-to-high frequency complex ratio is greater than the maximum value of the first complex ratio range. The mid-to-low frequency complex ratio value exceeding the preset second complex ratio value range means that the mid-to-low frequency complex ratio value is less than the minimum value of the second complex ratio value range. Or the mid-to-low frequency complex ratio is greater than the maximum value of the second complex ratio range. .

[0071] This invention provides a defect detection system for steel-based composite materials. Utilizing the skin effect, the higher the frequency of the alternating magnetic field, the shallower the penetration depth; conversely, the lower the frequency of the alternating magnetic field, the deeper the detection range. This application generates a multi-frequency composite excitation signal using low-frequency magnetization parameters, high-frequency excitation parameters, and mid-frequency excitation parameters. This multi-frequency composite excitation signal is applied to the steel-based composite material 5 under test. Based on the collected response signal, demodulation and feature extraction are performed, enabling layered detection of the steel-based composite material 5 under test and achieving the assessment of defect location and defect burial depth / and depth.

[0072] Specific limitations regarding the steel-based cladding material defect detection system can be found in the limitations of the steel-based cladding material defect detection method described below. Each module in the aforementioned steel-based cladding material defect detection system can be implemented entirely or partially through software, hardware, or a combination thereof. The parameter module 101, signal generation module 102, and defect detection module 104 can be embedded in hardware within or independently of the processor in the computer device, or stored in software in the memory of the computer device, so that the processor can call and execute the corresponding operations of each module. The detection probe 103 is connected to the computer device.

[0073] In one embodiment, such as Figure 7 As shown, a method for detecting defects in steel-based cladding materials is provided, employing the steel-based cladding material defect detection system described in the above embodiment. The method includes: S1: Obtain the preset detection parameters, which include low-frequency magnetization parameters, high-frequency excitation parameters, and medium-frequency excitation parameters; S2: Generate a multi-frequency composite excitation signal based on the detection parameters; S3: Apply the multi-frequency composite excitation signal to the steel-based composite material 5 to be tested and collect the response signal; S4: Demodulate the response signal to obtain the complex response signal of each frequency band, perform differential processing on the complex response signal of each frequency band to obtain the differential complex response signal of each frequency band, extract the defect features based on the differential complex response signal of each frequency band, and perform defect evaluation based on the defect features and the pre-set hierarchical judgment rules.

[0074] In one embodiment, such as Figure 8 As shown, the detection parameters in step S1 are determined by frequency selection, with high frequency being the most important factor. Intermediate frequency Low frequency .

[0075] In one embodiment, such as Figure 8 As shown, step S2, which generates a multi-frequency composite excitation signal based on the detection parameters, further includes: generating a low-frequency magnetization signal based on the detection parameters. High-frequency excitation signal Intermediate frequency excitation signal ; to use high-frequency excitation signals Intermediate frequency excitation signal The composite excitation signal is obtained by superposition. The low-frequency magnetization signal is amplified by adjustable power or driven by constant current, and the amplified low-frequency magnetization signal is combined with the composite excitation signal and applied to the detection probe to form a multi-frequency composite excitation signal. In step S3, the multi-frequency composite excitation signal is applied to the steel-based cladding material 5 to be tested through the detection probe 103, and the response signal is acquired through the detection probe 103.

[0076] In one embodiment, such as Figure 8 As shown, step S4 involves demodulating the response signal to obtain complex response signals for each frequency band, performing differential processing on the complex response signals for each frequency band to obtain differential complex response signals for each frequency band, extracting defect features based on the differential complex response signals for each frequency band, and performing defect evaluation based on the defect features and pre-set hierarchical judgment rules. This includes: converting the response signal into a digital detection signal; performing three-channel synchronous quadrature demodulation on the digital detection signal to obtain complex response signals for each frequency band, where the complex response signals for each frequency band include high-frequency complex response signals. Intermediate frequency complex response signal Low-frequency complex response signal Differential processing is performed on the complex response signals of each frequency band to obtain differential complex response signals for each frequency band. Defect features are extracted based on the differential complex response signals of each frequency band. The defect features include: high-frequency differential amplitude, mid-frequency differential amplitude, low-frequency differential amplitude, low-frequency differential phase, mid-high frequency complex ratio, and mid-low frequency complex ratio. Defect assessment is performed based on the defect features and pre-set hierarchical judgment rules.

[0077] In one embodiment, step S4 performs differential processing on the complex response signals of each frequency band to obtain differential complex response signals for each frequency band, and extracts defect features based on the differential complex response signals of each frequency band, including: calculating differential complex response signals for each frequency band based on pre-set reference complex response signals for each frequency band, wherein the differential complex response signals for each frequency band include high-frequency differential complex response signals, mid-frequency differential complex response signals, and low-frequency differential complex response signals; determining the high-frequency differential amplitude based on the high-frequency differential complex response signals, and further determining the mid-frequency differential amplitude based on the mid-frequency differential complex response signals, and further determining the low-frequency differential amplitude and the low-frequency differential phase based on the low-frequency differential complex response signals, and further determining the mid-high frequency complex ratio based on the high-frequency differential amplitude and the mid-frequency differential amplitude, and further determining the mid-low frequency complex ratio based on the mid-frequency differential amplitude and the low-frequency differential amplitude.

[0078] In one embodiment, step S4 performs defect assessment based on the defect characteristics and a pre-set layering judgment rule, including: determining whether the defect is located on the outer layer 51 of the steel-based cladding material 5 to be tested, based on the mid-to-high frequency complex ratio value and a pre-set first complex ratio value range; if the defect is determined to be located on the outer layer 51 of the steel-based cladding material 5 to be tested, then determining whether the defect is located on the surface of the outer layer 51 of the steel-based cladding material to be tested or inside the outer layer 51 of the steel-based cladding material to be tested, based on the comparison between the high frequency differential amplitude value and a preset first reference amplitude value.

[0079] In one embodiment, step S4, which assesses defects based on the defect characteristics and pre-set layering judgment rules, further includes: if the defect is determined to be located on the surface of the outer layer 51 of the steel-based composite material to be tested, calculating the first relative abnormal intensity based on the high-frequency differential amplitude and the first reference amplitude; if the defect is determined to be located inside the outer layer 51 of the steel-based composite material to be tested, determining the defect burial depth based on the pre-set mapping relationship between the mid-to-high frequency composite ratio and the defect burial depth.

[0080] In one embodiment, step S4, which evaluates defects based on the defect characteristics and pre-set layering judgment rules, further includes: determining whether the defect is located near the inner surface of the steel-based cladding material to be tested based on the low-frequency complex ratio and a pre-set second complex ratio range; if the defect is determined to be located near the inner surface of the steel-based cladding material to be tested, determining the defect depth based on the pre-set mapping relationship between the low-frequency complex ratio and the defect depth.

[0081] In one embodiment, step S4, which evaluates defects based on the defect characteristics and pre-set layering judgment rules, further includes: comparing the low-frequency differential amplitude with a preset second reference amplitude, or comparing the low-frequency differential phase with a preset first reference phase, and determining whether the defect is located inside the inner layer of the steel-based cladding material to be tested based on the comparison result; if the defect is determined to be located inside the inner layer of the steel-based cladding material to be tested, determining the equivalent depth of the defect based on the fitting curve of the pre-set low-frequency differential amplitude and the defect depth.

[0082] As can be seen, in the above scheme, by utilizing the difference in skin depth at different frequencies, the penetration depth is smaller when the frequency of the alternating magnetic field is higher, and the detection range is deeper when the frequency of the alternating magnetic field is lower. This application generates a multi-frequency composite excitation signal by using low-frequency magnetization parameters, high-frequency excitation parameters, and medium-frequency excitation parameters, and applies the multi-frequency composite excitation signal to the steel-based composite material 5 to be tested. Based on the collected response signal, demodulation and feature extraction are performed to realize the layered detection of the steel-based composite material 5 to be tested, and to realize the assessment of defect location and defect burial depth and / or depth.

[0083] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. For a detailed description of each step, please refer to the description of each module in the steel-based cladding material defect detection system.

[0084] In one embodiment, a computer device is provided, which may be a device terminal, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a defect detection method for steel-based multilayer materials at the device end.

[0085] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: S1: Obtain the preset detection parameters, which include low-frequency magnetization parameters, high-frequency excitation parameters, and medium-frequency excitation parameters; S2: Generate a multi-frequency composite excitation signal based on the detection parameters; S3: Apply the multi-frequency composite excitation signal to the steel-based composite material 5 to be tested and collect the response signal; S4: Demodulate the response signal to obtain the complex response signal of each frequency band, perform differential processing on the complex response signal of each frequency band to obtain the differential complex response signal of each frequency band, extract the defect features based on the differential complex response signal of each frequency band, perform defect evaluation based on the defect features and the pre-set hierarchical judgment rules, and perform defect evaluation based on the defect features.

[0086] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: S1: Obtain the preset detection parameters, which include low-frequency magnetization parameters, high-frequency excitation parameters, and medium-frequency excitation parameters; S2: Generate a multi-frequency composite excitation signal based on the detection parameters; S3: Apply the multi-frequency composite excitation signal to the steel-based composite material 5 to be tested and collect the response signal; S4: Demodulate the response signal to obtain the complex response signal of each frequency band, perform differential processing on the complex response signal of each frequency band to obtain the differential complex response signal of each frequency band, extract the defect features based on the differential complex response signal of each frequency band, perform defect evaluation based on the defect features and the pre-set hierarchical judgment rules, and perform defect evaluation based on the defect features.

[0087] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and device side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0088] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0089] It should be noted that any AI models, software tools, or components not belonging to this company appearing in the embodiments of this application are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this application has been authorized (with the knowledge and consent) by the relevant parties or has been fully authorized by all parties, and the executing entity may obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

[0090] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0091] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A defect detection system for steel-based composite materials, characterized in that, include: The parameter module is used to acquire pre-set detection parameters, including low-frequency magnetization parameters, high-frequency excitation parameters, and medium-frequency excitation parameters. The signal generation module is used to generate a multi-frequency composite excitation signal based on the detection parameters; A detection probe is used to apply the multi-frequency composite excitation signal to the steel-based composite material under test and collect the response signal; The defect detection module is used to demodulate the response signal to obtain the complex response signal of each frequency band, perform differential processing on the complex response signal of each frequency band to obtain the differential complex response signal of each frequency band, extract defect features based on the differential complex response signal of each frequency band, and perform defect evaluation based on the defect features and the pre-set hierarchical judgment rules.

2. The defect detection system for steel-based cladding materials according to claim 1, characterized in that, The signal generation module includes a signal generation module, a signal superposition module, and a magnetization power drive module; The signal generation module is used to generate low-frequency magnetization signals, high-frequency excitation signals, and medium-frequency excitation signals according to the detection parameters. The signal superposition module is used to superimpose the high-frequency excitation signal and the intermediate-frequency excitation signal to obtain a composite excitation signal; The magnetization power drive module is used to perform adjustable power amplification or constant current drive on the low-frequency magnetization signal to apply alternating magnetization to the steel lining layer of the steel-based composite material under test to form a controllable magnetization background.

3. The defect detection system for steel-based cladding materials according to claim 1, characterized in that, The defect detection module includes a data acquisition module, a demodulation module, a feature extraction module, and a defect assessment module; The data acquisition module is used to convert the response signal into a digital detection signal; The demodulation module is used to perform three-channel synchronous quadrature demodulation on the digital detection signal to obtain the complex response signal of each frequency band. The complex response signal of each frequency band includes a high-frequency complex response signal, a mid-frequency complex response signal, and a low-frequency complex response signal. The feature extraction module is used to perform differential processing on the complex response signals of each frequency band to obtain differential complex response signals of each frequency band, and to extract defect features based on the differential complex response signals of each frequency band. The defect features include: high frequency differential amplitude, mid-frequency differential amplitude, low frequency differential amplitude, low frequency differential phase, mid-high frequency complex ratio, and mid-low frequency complex ratio. The defect assessment module is used to assess defects based on the defect characteristics and pre-set hierarchical judgment rules.

4. The defect detection system for steel-based cladding materials according to claim 3, characterized in that, The defect assessment module is also used to determine whether the defect is located on the outer layer of the steel-based composite material to be tested, based on the medium-high frequency complex ratio value and a pre-set first complex ratio value range. The defect assessment module is also used to determine, if the defect is located on the outer layer of the steel-based composite material to be tested, whether the defect is located on the outer surface of the steel-based composite material to be tested or inside the outer layer of the steel-based composite material to be tested, based on the comparison between the high-frequency differential amplitude and the preset first reference amplitude.

5. The defect detection system for steel-based cladding materials according to claim 4, characterized in that, The defect assessment module is also used to calculate the first relative anomaly intensity based on the high-frequency differential amplitude and the first reference amplitude if it is determined that the defect is located on the outer surface of the steel-based composite material to be tested. The defect assessment module is also used to determine the defect burial depth if it is determined that the defect is located inside the outer layer of the steel-based composite material to be tested, based on the pre-set mapping relationship between the medium- and high-frequency composite ratio and the defect burial depth.

6. The defect detection system for steel-based cladding materials according to claim 3, characterized in that, The defect assessment module is also used to determine whether the defect is located near the inner layer of the steel-based composite material to be tested, based on the low-frequency complex ratio value and a pre-set second complex ratio value range. The defect assessment module is also used to determine the defect depth if it is determined that the defect is located near the inner surface of the steel-based composite material to be tested, based on a pre-set mapping relationship between the low-frequency complex ratio and the defect depth.

7. The defect detection system for steel-based cladding materials according to claim 3, characterized in that, The defect assessment module is also used to compare the low-frequency differential amplitude with a preset second reference amplitude, or to compare the low-frequency differential phase with a preset first reference phase, and to determine whether the defect is located inside the inner layer of the steel-based composite material to be tested based on the comparison result. The defect assessment module is also used to determine the equivalent depth of the defect if it is determined that the defect is located inside the inner layer of the steel-based composite material to be tested, based on a pre-set fitting curve of low-frequency differential amplitude and defect depth.

8. A method for detecting defects in steel-based composite materials, characterized in that, The method of using the steel-based cladding material defect detection system as described in any one of claims 1 to 7 includes: Acquire pre-set detection parameters, including low-frequency magnetization parameters, high-frequency excitation parameters, and medium-frequency excitation parameters; A multi-frequency composite excitation signal is generated based on the detection parameters; The multi-frequency composite excitation signal is applied to the steel-based composite material under test and the response signal is collected; The complex response signals of each frequency band are obtained by demodulating the response signals. Differential processing is performed on the complex response signals of each frequency band to obtain differential complex response signals of each frequency band. Defect features are extracted from the differential complex response signals of each frequency band. Defect assessment is performed based on the defect features and pre-set hierarchical judgment rules.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the defect detection method for steel-based cladding materials as described in claim 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the defect detection method for steel-based cladding materials as described in claim 8.

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