A method for quantitatively evaluating structural defect parameters

Abstract

The application provides a structural defect parameter quantitative evaluation method, and belongs to the technical field of nondestructive testing. Specifically, the relationship between defect characteristic parameters and local resonance characteristics is obtained by analyzing the reflection of guided waves at the defect boundary through an orthogonal modal decomposition method. On this basis, the defect parameters are reversely determined by using the local resonance characteristics in combination with a genetic algorithm, so that the local resonance can be effectively utilized to quantitatively characterize defects. The method is not affected by complex wave propagation caused by structural complexity, has wide applicability, provides a practical solution for defect detection of emerging complex structures, and promotes the application of the local resonance-based technology in the field of nondestructive evaluation and structural health monitoring.

Description

Technical Field

[0001] This application relates to the field of nondestructive testing, and in particular to a method for quantitative evaluation of structural defect parameters. Background Technology

[0002] Advanced manufacturing technologies, with their superior design flexibility and production efficiency, are increasingly widely used, driving the proliferation of structures with complex geometries across various industries. For example, in the aerospace industry, variable-thickness curved surface composite material structures manufactured using processes such as automated fiber placement are increasingly serving as major load-bearing components. However, despite significant advancements in manufacturing technology, planar defects, such as delamination and debonding in composite structures, can still occur during manufacturing and service. If these defects are not detected and remedied in a timely manner, they can severely compromise the integrity and safety of the structure. Ultrasonic guided wave detection methods are widely used due to their advantage of enabling wide-range propagation detection with single-point excitation. However, existing ultrasonic guided wave detection methods primarily rely on the extraction of specific guided wave modes. When applied to structures with complex geometries, the accurate extraction of specific guided wave modes presents a significant challenge due to complex phenomena such as wave scattering, reflection, and mode transitions. Summary of the Invention

[0003] In view of this, this application provides a method for quantitative evaluation of structural defect parameters, which solves the problems in the prior art. It uses local resonance frequency and attenuation rate to accurately evaluate defects, thereby significantly improving detection efficiency.

[0004] The method for quantitative evaluation of structural defect parameters provided in this application adopts the following technical solution:

[0005] A method for quantitatively evaluating structural defect parameters includes the following steps:

[0006] Step 1: Preset the initial theoretical defect geometric parameters of the structure under test, and establish the guided wave reflection model at the defect boundary according to the orthogonal mode decomposition method based on the elasticity of the material of the structure under test. Calculate the relationship between the reflection coefficient of each guided wave mode at the defect boundary and the defect geometric parameters.

[0007] Step 2: Obtain the initial theoretical local resonance frequency and initial theoretical local resonance energy attenuation rate under the initial theoretical defect geometric parameters through guided wave superposition and standing wave formation analysis;

[0008] Step 3: Apply broadband excitation to the surface of the structure under test to excite the guided wave response in the structure under test;

[0009] Step 4: Perform a surface scan measurement on the guided wave response within the detection area to acquire ultrasonic signals with temporal and spatial resolution;

[0010] Step 5: Process the acquired ultrasound signals to extract the measured local resonance frequency and the measured local resonance energy attenuation rate;

[0011] Step 6: Using the measured local resonance frequency and the measured local resonance energy attenuation rate as the inversion target values, construct a global optimization model based on a genetic algorithm;

[0012] Step 7: Use the global optimization model to perform a global optimization search on the defect geometric parameters within the set parameter search space. Through an iterative evolution process, obtain the individual that minimizes the fitness function. The corresponding parameters are the estimated defect geometric features.

[0013] Optionally, in step 1, based on the calculation of the relationship between the reflection coefficients of each guided wave mode at the defect boundary and the defect geometric parameters, the relationship between the defect geometric parameters and the local resonance frequency and the local resonance energy attenuation rate is calculated.

[0014] Optionally, in step 3, a broadband excitation is applied to the surface of the structure under test using a laser pulse device.

[0015] Optionally, in step 3, the laser pulse device is a 532nm Nd:YAG laser, and the applied excitation signal is a single-cycle sinusoidal pulse with a frequency of 50kHz, used to excite broadband Lamb waves in the structure.

[0016] Optionally, in step 4, a laser Doppler vibrometer is used to perform non-contact velocity measurement on the receiving point and record the time-domain waveform of the ultrasonic response caused by the excitation signal.

[0017] Optionally, in step 5, the acquired time-domain ultrasonic signal is processed by short-time Fourier transform and Hilbert transform to extract the measured local resonance frequency and the logarithmic decay rate of its envelope over time.

[0018] Optionally, in step 6, a fitness function is constructed, defined as the sum of weighted errors between the theoretical and measured values ​​of the local resonant frequency and attenuation rate, which serves as the optimization objective function of the genetic algorithm.

[0019] Optionally, in step 7, the theoretical defect geometric parameters are iteratively optimized by combining selection, crossover, and mutation genetic operations to output the optimal inversion result.

[0020] In summary, this application includes the following beneficial technical effects:

[0021] This application uses local resonant frequency and attenuation rate, along with a genetic algorithm, to simultaneously evaluate the in-plane size and thickness of defects. This method has the advantage of high efficiency; compared to traditional wavefield detection methods that rely on high spatial resolution, it does not require wave acquisition at dense sensor points. A single measurement in the defect region is sufficient for quantitative evaluation of the defect, significantly improving detection efficiency. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of the genetic algorithm for inverting defect geometric parameters in an embodiment of this application;

[0024] Figure 2 This is a graph showing the convergence curve of the loss function of the genetic algorithm in the defect parameter inversion process in the embodiments of this application. Detailed Implementation

[0025] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0026] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. 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.

[0027] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0028] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The illustrations only show the components related to this application and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0029] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0030] This application provides a method for quantitatively evaluating structural defect parameters.

[0031] A method for quantitatively evaluating structural defect parameters includes the following steps:

[0032] Step 1: Preset the initial theoretical defect geometric parameters of the structure under test, and establish the guided wave reflection model at the defect boundary according to the orthogonal mode decomposition method based on the elasticity of the material of the structure under test. Calculate the relationship between the reflection coefficient of each guided wave mode at the defect boundary and the defect geometric parameters.

[0033] Step 2: Obtain the initial theoretical local resonance frequency and initial theoretical local resonance energy attenuation rate under the initial theoretical defect geometric parameters through guided wave superposition and standing wave formation analysis.

[0034] Step 3: Apply a broadband excitation to the surface of the structure under test to excite the guided wave response in the structure under test.

[0035] Step 4: Perform a surface scan measurement on the guided wave response within the detection area to acquire ultrasonic signals with temporal and spatial resolution.

[0036] Step 5: Process the acquired ultrasound signals to extract the measured local resonance frequency and the measured local resonance energy attenuation rate.

[0037] Step 6: Using the measured local resonance frequency and the measured local resonance energy attenuation rate as the inversion target values, construct a global optimization model based on a genetic algorithm.

[0038] Step 7: Use the global optimization model to perform a global optimization search on the defect geometric parameters within the set parameter search space. Through an iterative evolution process, obtain the individual that minimizes the fitness function. The corresponding parameters are the estimated defect geometric features, which include the in-plane size and thickness of the defect.

[0039] This application uses local resonance frequency and attenuation rate, along with a genetic algorithm, to simultaneously evaluate the in-plane size and thickness of defects. This method offers significant advantages in efficiency. Compared to traditional wavefield detection methods that rely on high spatial resolution, it eliminates the need for wave acquisition at dense sensor points; a single measurement within the defect region is sufficient for quantitative assessment, resulting in a markedly improved detection efficiency. Furthermore, it employs a damage diagnosis method based on local resonance. This guided wave mode is sensitive only to the structural health within a localized area, avoiding the signal analysis difficulties caused by complex structural forms. Therefore, it is applicable to various complex structures, including composite material structures.

[0040] The damage diagnosis method using the local resonant frequency of guided wave signals in this application can eliminate the uncertainty of signal measurement caused by various interference factors, improve the robustness of damage diagnosis, and is suitable for real-time online monitoring of complex structures.

[0041] Specifically, in step 1, a guided wave reflection model based on orthogonal mode expansion theory is established. In this embodiment, the guided wave of mode A0 is taken as the research object, and its propagation and reflection model within the defect region is constructed. The guided wave undergoes partial reflection and transmission at the defect boundary, forming multiple reflected waves that propagate back and forth inside the defect. When certain wavelength conditions are met, these reflected waves coherently superimpose, inducing the formation of standing waves, which in turn excites local defect resonance. The model uses the orthogonal mode expansion method to theoretically model the reflection process at the defect boundary, and obtains the complex reflection coefficient C of mode A0 at the defect boundary by discretizing the boundary and considering propagation modes and multiple attenuation modes. The phase angle of the reflection coefficient is used to determine the local defect resonance frequency, while its modulus reflects the degree of wave energy retention at the defect boundary, laying the foundation for subsequent attenuation analysis.

[0042] Based on the relationship between the reflection coefficients of each guided mode at the defect boundary and the defect geometric parameters, the relationship between the defect geometric parameters and the local resonant frequency and the local resonant energy attenuation rate is calculated. Specifically:

[0043] The functional relationship between the reflection coefficient of the defect boundary and frequency, defect diameter and thickness is derived, and then a theoretical expression for the local resonance frequency and local resonance energy attenuation rate is constructed.

[0044] Based on the condition that a guided wave forms a standing wave in a defect region, a relationship can be established between the local defect resonance frequency and the defect size, diameter D, and thickness h. The wavenumber of the A0 mode and the phase change of the boundary reflection jointly determine the frequency at which local defect resonance occurs. This relationship can be derived from the standing wave condition formula:

[0045] cos[(D*k(f LDR )+θ(f LDR ))]=1.

[0046] In the above formula, D represents the diameter of the defect, k is the wavenumber of the A0 mode, θ represents the phase change of the A0 mode during its interaction with the defect boundary, θ is the phase of the reflection coefficient C, and f LDR The value represents the local resonant frequency. Unlike traditional boundary assumptions, this application uses numerical solutions to solve for the reflection phase, achieving a more realistic characterization of complex defect boundaries. Based on this, the accumulation and attenuation process of guided wave energy in the defect region is further analyzed, and an analytical expression for the amplitude of the local defect resonant response is constructed. The attenuation rate of the local defect resonance is mainly affected by energy leakage, and its logarithmic amplitude decreases linearly with time. Using the magnitude of the reflection coefficient and the guided wave wavelength, the attenuation rate of the local defect resonance can be quantitatively calculated as follows:

[0047]

[0048] In the above formula, A m Let f be the amplitude of the local defect resonance, λ be the frequency, λ be the wavelength of the A0 mode, and |C| be the modulus of C. Clearly, the logarithm of the amplitude is a linear function of time, ultimately achieving a quantitative correlation between the defect's geometric parameters and its response characteristics.

[0049] Step 3 specifically involves applying broadband excitation to the surface of the structure under test using a laser pulse device. The laser pulse device is a 532nm Nd:YAG laser, and the applied excitation signal is a single-cycle sinusoidal pulse with a frequency of 50kHz. This excites guided wave propagation and forms local defect resonance in the defect region, thereby exciting broadband Lamb waves in the structure. The Nd:YAG laser is a neodymium-doped yttrium aluminum garnet laser.

[0050] Step 4 specifically involves: using a laser Doppler vibrometer to perform non-contact velocity measurement at the receiving point, with a sampling frequency of 250MHz, and recording the time-domain waveform of the ultrasonic response caused by the excitation signal. This application uses a laser Doppler vibrometer to achieve non-contact response acquisition in the vertical direction. The occurrence of damage is identified by the amplitude of the response signal, providing a data basis for subsequent local resonance feature extraction. In other embodiments, a scanning laser vibrometer or an equivalent non-contact sensor can be used to perform surface scanning measurement of the guided wave response within the detection area.

[0051] Step 5 specifically involves performing short-time Fourier transform and Hilbert transform on the acquired time-domain ultrasonic signal to extract the measured local resonance frequency and the logarithmic decay rate of its envelope over time. Specifically, the resonant dominant frequency in the spectrum is extracted using short-time Fourier transform, and the logarithmic slope of the envelope is obtained by combining it with the Hilbert transform; these are the local resonance frequency and the local resonance energy decay rate.

[0052] Step 6 specifically involves constructing a fitness function, defined as the sum of weighted errors between the theoretical and measured values ​​of the local resonance frequency and attenuation rate, which serves as the optimization objective function of the genetic algorithm. This application uses the measured local resonance frequency and attenuation rate as the inversion objective to construct the fitness function and introduces a genetic algorithm to perform a global optimization search of the defect geometric parameters within a defined parameter search space. The fitness function is then used to evaluate the degree of matching between the defect parameters and the observed local defect resonance characteristics.

[0053] Step 7 specifically involves iteratively optimizing the theoretical defect geometric parameters by combining selection, crossover, and mutation genetic operations. Through the iterative evolution process, the individual that minimizes the fitness function is obtained, and the optimal inversion result is output. The corresponding parameters are the estimated defect geometric features.

[0054] This application also provides a verification process for the method of this application in its embodiments:

[0055] Step 1: A flat-bottomed hole defect is prefabricated in the center area of ​​a 6061 aluminum alloy plate with dimensions of 800mm × 800mm and a thickness of 8mm. This defect is a circular blind hole with a thickness of 1.5mm and a diameter of 20mm, used to simulate typical planar structural damage. A scanning laser vibrometer is used to perform surface scanning measurements on the structural surface to identify areas of abnormal guided wave response, thereby accurately locating the defect.

[0056] Step 2: A 532nm Nd:YAG laser is used as the excitation source, and a pulsed excitation signal is applied to the center of the defect region. The excitation signal is a single-cycle sinusoidal pulse with a frequency of 50kHz, used to excite broadband Lamb waves in the structure. The receiving point is set at the center of the defect.

[0057] Step 3: Use a laser Doppler vibrometer to perform non-contact velocity measurement at the receiving point, with a sampling frequency of 250MHz, and record the time-domain waveform of the ultrasonic response caused by the excitation signal.

[0058] Step 4: Perform a Fast Fourier Transform on the received time-domain signal to extract the resonant frequency component, and use a Hilbert Transform to obtain the signal envelope. Perform a linear fit on its logarithm to extract the energy decay rate of the signal within the 0–0.3ms time window.

[0059] Step 5: Based on orthogonal mode expansion theory, construct a reflection model of the Lamb wave at the boundary of the flat-bottomed hole, derive the reflection coefficient C as a function of frequency, defect thickness h, and diameter D, and establish theoretical expressions for the local resonance frequency and attenuation rate. Through theoretical calculations, the local resonance frequencies and attenuation rates of several defects can be obtained as follows: when D = 12 mm and h = 1 mm, f... LDR =63kHz, η=-4.32ms -1 When D = 25 mm and h = 1 mm, f LDR =16.2kHz, η=-2.0ms -1 When D = 23 mm and h = 2 mm, f LDR =31.5kHz, η=-5.0ms -1 .

[0060] like Figure 1 As shown, step 6: Construct a fitness function, defined as the sum of weighted errors between the theoretically predicted local resonance frequency and attenuation rate and the measured values, as the optimization objective function of the genetic algorithm.

[0061] Step 7: Use a genetic algorithm to search for the optimal solution within the set parameter space. The chromosome encoding includes the defect thickness h and diameter D. The algorithm uses roulette wheel selection, single-point crossover and Gaussian mutation operations. The number of generations is 100, the crossover probability is 0.8 and the mutation probability is 0.1.

[0062] Step 8: Output the defect parameters of the best individual representative as the identification result of the current defect.

[0063] like Figure 2 As shown, the method proposed in this application achieved high-precision prediction of defect thickness and diameter in experimental verification, with the maximum relative error being less than 5%, and the remaining prediction results highly consistent with the actual values. These results fully verify the accuracy and effectiveness of the established model and the genetic algorithm-based inversion evaluation method in defect parameter identification.

[0064] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for quantitatively evaluating structural defect parameters, characterized in that, Includes the following steps: Step 1: Preset the initial theoretical defect geometric parameters of the structure under test, and establish the guided wave reflection model at the defect boundary according to the orthogonal mode decomposition method based on the elasticity of the material of the structure under test. Calculate the relationship between the reflection coefficient of each guided wave mode at the defect boundary and the defect geometric parameters. Step 2: Obtain the initial theoretical local resonance frequency and initial theoretical local resonance energy attenuation rate under the initial theoretical defect geometric parameters through guided wave superposition and standing wave formation analysis; Step 3: Apply broadband excitation to the surface of the structure under test to excite the guided wave response in the structure under test; Step 4: Perform a surface scan measurement on the guided wave response within the detection area to acquire ultrasonic signals with temporal and spatial resolution; Step 5: Process the acquired ultrasound signals to extract the measured local resonance frequency and the measured local resonance energy attenuation rate; Step 6: Using the measured local resonance frequency and the measured local resonance energy attenuation rate as the inversion target values, construct a global optimization model based on a genetic algorithm; Step 7: Use the global optimization model to perform a global optimization search on the defect geometric parameters within the set parameter search space. Through an iterative evolution process, obtain the individual that minimizes the fitness function. The corresponding parameters are the estimated defect geometric features.

2. The method for quantitative evaluation of structural defect parameters according to claim 1, characterized in that, In step 1, based on the calculation of the relationship between the reflection coefficients of each guided wave mode at the defect boundary and the defect geometric parameters, the relationship between the defect geometric parameters and the local resonance frequency and the local resonance energy attenuation rate is calculated.

3. The method for quantitatively evaluating structural defect parameters according to claim 1, characterized in that, In step 3, a wideband excitation is applied to the surface of the structure under test using a laser pulse device.

4. The method for quantitative evaluation of structural defect parameters according to claim 3, characterized in that, In step 3, the laser pulse device is a 532nm Nd:YAG laser, and the applied excitation signal is a single-cycle sinusoidal pulse with a frequency of 50kHz, used to excite the broadband Lamb wave in the structure.

5. The method for quantitative evaluation of structural defect parameters according to claim 1, characterized in that, In step 4, a laser Doppler vibrometer is used to perform non-contact velocity measurement at the receiving point and record the time-domain waveform of the ultrasonic response caused by the excitation signal.

6. The method for quantitatively evaluating structural defect parameters according to claim 1, characterized in that, In step 5, the acquired time-domain ultrasonic signal is processed by short-time Fourier transform and Hilbert transform to extract the measured local resonance frequency and the logarithmic decay rate of its envelope as a function of time.

7. The method for quantitative evaluation of structural defect parameters according to claim 1, characterized in that, In step 6, a fitness function is constructed, which is defined as the sum of weighted errors between the theoretical and measured values ​​of the local resonance frequency and attenuation rate, and serves as the optimization objective function of the genetic algorithm.

8. The method for quantitative evaluation of structural defect parameters according to claim 7, characterized in that, In step 7, the theoretical defect geometric parameters are iteratively optimized by combining selection, crossover, and mutation genetic operations to output the optimal inversion result.

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