Quantitative evaluation method for structural defect parameters

Abstract

The invention provides a quantitative evaluation method for structural defect parameters, belongs to the technical field of nondestructive testing, and particularly relates to a method for analyzing reflection of guided waves at defect boundaries through an orthogonal mode decomposition method so as to obtain a relationship between defect characteristic parameters and local resonance characteristics. And defect parameters are reversely determined by using local resonance characteristics in combination with a genetic algorithm, so that the defects can be quantitatively represented by effectively using local resonance. The method is not affected by complex wave propagation caused by structural complexity, has wide applicability, provides a feasible solution for defect detection of emerging complex structures, and promotes application of a technology based on local resonance in the fields of nondestructive evaluation and structural health monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of non-destructive testing, and in particular to a method for quantitatively evaluating structural defect parameters. BACKGROUND

[0002] Advanced manufacturing techniques are increasingly being applied due to their superior design flexibility and production efficiency, which in turn has promoted the popularity of structures with complex geometries in various industries. For example, in the aerospace industry, variable-thickness curved composite structures manufactured using automated fiber placement and other processes have increasingly become the main load-bearing components. However, despite the significant progress in manufacturing techniques, such structures may still develop planar defects, such as delamination and debonding in composite structures, during the manufacturing process and service period. If not detected in time and remedial measures are not taken, these defects may seriously compromise the integrity and safety of the structure. Ultrasonic guided wave detection methods are widely used due to their advantage of large-scale propagation detection with single-point excitation. However, existing ultrasonic guided wave detection methods mainly rely on the extraction of specific guided wave modes. When applied to structures with complex geometries, it is challenging to accurately extract specific guided wave modes due to complex phenomena such as scattering, reflection, and mode conversion of guided waves. SUMMARY

[0003] Therefore, the present application provides a method for quantitatively evaluating structural defect parameters, which solves the problems in the prior art and accurately quantitatively evaluates defects using local resonance frequency and attenuation rate, thereby significantly improving detection efficiency.

[0004] The method for quantitatively evaluating structural defect parameters provided by the present 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 measured structure, establish a guided wave reflection model at the defect boundary according to the orthogonal modal decomposition method in combination with the material elasticity of the measured structure, and calculate the relationship between the reflection coefficients 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 the 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 a wideband excitation on the surface of the measured structure to excite guided wave responses in the measured structure;

[0009] Step 4: perform surface scanning measurement on the guided wave responses in the detection area to obtain time and spatial resolution ultrasonic signals;

[0010] Step 5, processing the collected ultrasonic signals to extract the measured local resonance frequency and the measured local resonance energy decay rate;

[0011] Step 6, taking the measured local resonance frequency and the measured local resonance energy decay rate as the inversion target value, and constructing a global optimization model based on a genetic algorithm;

[0012] Step 7, using the global optimization model to perform global optimization search on the defect geometric parameters within a set parameter search space, and obtaining an individual with the minimum fitness function through an iterative evolution process, and the corresponding parameters are the estimated defect geometric characteristics.

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

[0014] Optionally, in step 3, a wideband excitation is applied to the surface of the measured structure by a laser pulse device.

[0015] Optionally, in step 3, the laser pulse device is a 532nm Nd:YAG laser, and the excitation signal applied is a single-cycle sinusoidal pulse with a frequency of 50kHz, which is used to excite wideband 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 the time-domain waveform of the ultrasonic response caused by the excitation signal is recorded.

[0017] Optionally, in step 5, the collected time-domain ultrasonic signals are processed by short-time Fourier transform and Hilbert transform to extract the measured local resonance frequency and the logarithmic decay rate of the envelope curve thereof varying with time.

[0018] Optionally, in step 6, a fitness function is constructed, which is defined as the sum of weighted errors between the theoretical values of the local resonance frequency and the decay rate and the measured values, and is used 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, and the optimal inversion result is output.

[0020] In summary, the present application has the following beneficial technical effects:

[0021] The local resonance frequency and attenuation rate, and the genetic algorithm are used to evaluate the in-plane size and thickness of the defect simultaneously. The method has the advantage of high efficiency, and compared with the traditional wave field detection method which depends on high spatial resolution, it does not need to collect waves at dense sensing points, and one measurement in the defect area is enough to quantitatively evaluate the defect, and the detection efficiency is significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0023] Figure 1 The genetic algorithm flow chart for defect geometric parameter inversion in the embodiments of the present application;

[0024] Figure 2 The loss function convergence curve graph of the genetic algorithm in the defect parameter inversion process in the embodiments of the present application. DETAILED DESCRIPTION

[0025] The embodiments of the present application will be described in detail below with reference to the drawings.

[0026] The embodiments of the present application are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the disclosure. Obviously, the described embodiments are only some of the embodiments of the present application, not all. The present application can also be implemented or applied by other different specific embodiments, and the details in the specification can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0027] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this application, it should be understood by those skilled in the art that an 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 described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this apparatus and / or practice this method.

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

[0029] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples. However, one skilled in the art will appreciate that the aspects described can be practiced without these specific details.

[0030] The embodiments of the present application provide a method for quantitatively evaluating structural defect parameters.

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

[0032] Step 1: Preset the initial theoretical defect geometric parameters of the structure under test, establish a guided wave reflection model at the defect boundary according to the orthogonal modal decomposition method based on the elasticity of the material of the structure under test, and 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 broadband excitation to the surface of the structure under test to stimulate 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 obtain an ultrasonic signal with time and spatial resolution.

[0036] Step 5: Process the collected ultrasonic signal to extract the measured local resonance frequency and the measured local resonance energy attenuation rate.

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

[0038] Step 7, the global optimization model is used to perform global optimization search on the defect geometric parameters in a set parameter search space, and an individual that minimizes the fitness function is obtained through an iterative evolution process, and the corresponding parameters are the estimated defect geometric characteristics, including the in-plane size and thickness of the defect.

[0039] The application uses the local resonance frequency and the decay rate, and the genetic algorithm to evaluate the in-plane size and thickness of the defect. This method has the advantages of high efficiency, and compared with the traditional wave field detection method which relies on high spatial resolution, it does not need to collect waves at dense sensing points, and one measurement in the defect area is enough for quantitative evaluation of the defect, and the detection efficiency is significantly improved. Moreover, the damage diagnosis method based on local resonance is adopted, and the guided wave mode is only sensitive to the local structure health status, which can avoid the problem of signal analysis difficulty caused by complex structure form, and can be applied to various complex structures such as composite structures.

[0040] The method of using the local resonance frequency of the guided wave signal for damage diagnosis in the 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 modal expansion theory is established. In the embodiment of the application, A0 mode guided wave is taken as the research object to construct its propagation and reflection model in the defect area. Guided waves are partially reflected and transmitted at the defect boundary, forming multiple reflected waves propagating back and forth inside the defect. When a certain wavelength condition is met, these reflected waves are coherently superimposed, inducing standing wave formation, and then exciting local defect resonance phenomenon. The model uses the orthogonal modal expansion method to theoretically model the reflection process at the defect boundary, and through the discrete boundary and considering the propagation mode and multiple order attenuation modes, the complex reflection coefficient C of A0 mode at the defect boundary is obtained. The phase angle of the reflection coefficient is used to determine the local defect resonance frequency, and its modulus reflects the degree of wave energy preservation at the defect boundary, laying a foundation for subsequent attenuation analysis.

[0042] On the basis of calculating the relationship between the reflection coefficient 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 decay rate is calculated. Specifically as follows:

[0043] The function relationship of the defect boundary reflection coefficient with respect to frequency, defect diameter and thickness is derived, and then the theoretical expression of the local resonance frequency and the local resonance energy decay rate is constructed.

[0044] Based on the condition that the guided wave forms a standing wave in the defect region, a relationship between the local defect resonance frequency and the defect size diameter D and thickness h can be established. The wave number of the A0 mode and the phase change of the boundary reflection jointly determine the frequency of the local defect resonance, and the 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 wave number of the A0 mode, θ represents the phase change of the A0 mode during interaction with the defect boundary, θ is the phase of the reflection coefficient C, f LDR is the local resonance frequency. Unlike the traditional boundary assumption, the method of the present application uses numerical solution of the reflection phase to realize a more realistic depiction of the complex defect boundary. On this basis, the accumulation and attenuation process of the guided wave energy in the defect region is further analyzed, and an analytical expression of the amplitude of the local defect resonance response is constructed. The decay rate of the local defect resonance is mainly affected by the energy leakage, and the logarithmic amplitude decreases linearly with time. Through the modulus of the reflection coefficient and the wavelength of the guided wave, the decay rate of the local defect resonance can be quantitatively calculated as:

[0047]

[0048] In the above formula, A m is the amplitude of the local defect resonance, f is the frequency, λ is the wavelength of the A0 mode, and |C| is the modulus of C. Obviously, the logarithm of the amplitude is a linear function of time, and finally the quantitative correlation between the defect geometric parameters and the response characteristics is realized.

[0049] Step 3 specifically: a wideband excitation is applied on the surface of the measured structure by a laser pulse device, the laser pulse device is a 532nm Nd:YAG laser, the applied excitation signal is a single-cycle sinusoidal pulse, the frequency is 50kHz, the guided wave propagation is excited and the local defect resonance is formed in the defect region, and the wideband Lamb wave in the structure is excited. The Nd:YAG laser is a neodymium-doped yttrium aluminum garnet laser.

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

[0051] Step 5 is specifically: the collected time domain ultrasonic signals are processed by short-time Fourier transform and Hilbert transform to extract the measured local resonance frequency and the logarithmic decay rate of the envelope line changing with time. Specifically, the resonance main frequency in the frequency spectrum is extracted by short-time Fourier transform, and the logarithmic slope of the envelope line is obtained by combining Hilbert transform, that is, the local resonance frequency and the local resonance energy decay rate.

[0052] Step 6 is specifically: a fitness function is constructed, which is defined as the weighted error sum between the theoretical value and the measured value of the local resonance frequency and the decay rate, as the optimization objective function of the genetic algorithm. The measured local resonance frequency and the decay rate are taken as the inversion target to construct the fitness function, and the genetic algorithm is introduced to perform global optimization search on the defect geometric parameters in the set parameter search space, and the matching degree between the defect parameters and the observed local defect resonance characteristics is evaluated by constructing the fitness function.

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

[0054] The embodiment of the present application also gives the measurement and verification process for the method of the present application:

[0055] Step 1: a flat-bottom hole defect is prefabricated in the center area of a 6061 aluminum alloy plate with a size of 800mm×800mm and a thickness of 8mm. The defect is a circular blind hole with a thickness of 1.5mm and a diameter of 20mm, which is used to simulate a typical planar structure damage. The surface of the structure is measured by a scanning laser vibration meter, and the abnormal area of the guided wave response is identified to accurately locate the defect position.

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

[0057] Step 3: the laser Doppler vibration meter is used for non-contact velocity measurement of the receiving point, the sampling frequency is 250MHz, and the time domain waveform of the ultrasonic response caused by the excitation signal is recorded.

[0058] Step 4: the received time domain signal is subjected to fast Fourier transform to extract the resonance frequency component, and Hilbert transform is used to obtain the signal envelope, and the logarithm is linearly fitted to extract the energy decay rate of the signal in the 0-0.3ms time window.

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

[0060] As shown in Figure 1 , step 6: a fitness function is constructed, which is 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 genetic algorithm.

[0061] Step 7: the genetic algorithm is used to search for the optimal solution in the set parameter space, the chromosome code contains the defect thickness h and diameter D, the roulette selection, single-point crossover and Gaussian mutation operations are adopted, the genetic generation number is 100, the crossover probability is 0.8, and the mutation probability is 0.1.

[0062] Step 8: the defect parameters represented by the optimal individual are output as the identification result of the current defect.

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

[0064] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for quantitatively evaluating structural defect parameters, characterized in that: The steps include: Step 1: Preset the initial theoretical defect geometric parameters of the structure under test, establish a guided wave reflection model at the defect boundary according to the orthogonal modal decomposition method based on the elasticity of the material of the structure under test, and calculate the relationship between the reflection coefficient of each guided wave mode at the defect boundary and the defect geometric parameters; Step 2, obtaining the initial theoretical local resonance frequency and the initial theoretical local resonance energy decay rate under the initial theoretical defect geometric parameters through guided wave superposition and standing wave formation analysis; Step 3: applying a broadband excitation on the surface of the structure under test to stimulate the guided wave response in the structure under test; Step 4: perform a surface scan measurement of the guided wave response within the detection area to obtain an ultrasonic signal with time and spatial resolution; Step 5: Process the collected ultrasonic signal 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 decay rate as inversion target values, a global optimization model based on a genetic algorithm is constructed; In step 7, a global optimization model is used to perform a global optimization search on the defect geometric parameters within the set parameter search space. The individual with the minimum fitness function is obtained through an iterative evolution process. The corresponding parameters are the estimated defect geometric features.

2. The method for quantitatively evaluating structural defect parameters according to claim 1, characterized in that: In the step 1, based on the relationship between the reflection coefficient of each waveguide 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, wherein: In step 3, a broadband excitation is applied to the surface of the structure to be measured by a laser pulse device.

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

5. The method for quantitatively evaluating structural defect parameters according to claim 1, wherein: In step 4, a laser Doppler vibrometer is used to perform non-contact velocity measurement on the receiving point, and the time domain waveform of the ultrasonic response caused by the excitation signal is recorded.

6. The method for quantitatively evaluating structural defect parameters according to claim 1, wherein: In step 5, the collected time-domain ultrasonic signal is subjected to short-time Fourier transform and Hilbert transform processing to extract the measured local resonance frequency and the logarithmic decay rate of its envelope over time.

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

8. The method for quantitatively evaluating structural defect parameters according to claim 7, wherein: 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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