A sandwich structure laser ultrasonic guided wave imaging detection and debonding defect area measurement method, electronic equipment and storage medium

Abstract

A method, electronic device and storage medium for laser ultrasonic guided wave imaging detection and debonding defect area measurement of sandwich structures, belonging to the field of nondestructive testing technology. In order to solve the problem that it is difficult to extract defect information from complex ultrasonic guided wave signals in the prior art, and thus it is impossible to obtain imaging results with high reliability; the present invention is designed based on a laser ultrasonic detection method, and the influence of defects on the time-frequency energy distribution of the signal is determined through continuous wavelet transform; the Mallat algorithm is used to obtain the detail signal of the area of ​​interest; the Pearson correlation coefficient of the detail signal and the reference signal obtained by grid scanning is calculated; the Pearson correlation coefficient is gray-mapped to obtain a complete image of the area to be detected; the Otsu method is used to binarize the obtained detection image, and the binarized image is morphologically processed to realize the defect area calculation, thereby realizing the non-coupled quantitative evaluation of the internal defects of the sandwich structure.

Description

Technical Field

[0001] The present invention belongs to the technical field of nondestructive testing, and specifically relates to a sandwich structure laser ultrasonic guided wave imaging detection and debonding defect area measurement method, electronic equipment and storage medium. Background Art

[0002] Laser ultrasonic guided wave testing technology is a long-distance, non-coupled detection method. The main principle is that a pulsed laser is used to excite ultrasonic guided waves in the structure, and the ultrasonic waves are detected at a long distance by optical methods, thereby realizing non-coupled detection of internal defects of the workpiece.

[0003] In academic paper 1 (Zeng Lvming et al., Research on large-field laser ultrasonic detection of thin laminated composite materials, Rocket Propulsion, 2023, 66(22):53-59), a large-field laser ultrasonic detection system was used to detect internal defects in laminated composite materials, effectively detecting the indentation of the bonding layer and micro-void defects formed by bubbles. In academic paper 2 (Tan Hui et al., Research on laser ultrasonic real-time monitoring method of curing state of hydroxybutane liner, Journal of Beijing Institute of Technology, 2022, 42(08):175-185), the curing process of the liner was characterized by a laser ultrasonic system and ultrasonic transmission method, solving the problem of non-coupled detection of curing state of hydroxybutane liner.

[0004] In academic paper 3 (Zheng Weiwei et al., Using laser-electromagnetic ultrasound to detect debonding of titanium-steel composite plates, Applied Laser, 2022, 42(05):102-108), laser ultrasound and electromagnetic ultrasound technology were combined to detect debonding defects in the bonding surface of titanium-steel composite plates, and the debonding defects were accurately determined by the change characteristics of the signal amplitude.

[0005] The propagation mechanism of laser-excited ultrasonic guided waves in sandwich structures is relatively complex, and the signal dispersion is very serious, making it difficult to extract defect information from it. Existing technologies have little research on how to extract signal features that can be used to characterize defects, making it difficult to obtain high-quality imaging results and unable to provide information on the area of ​​debonding defects. Summary of the invention

[0006] The problem to be solved by the present invention is that it is difficult to extract defect information from complex ultrasonic guided wave signals, and thus it is impossible to obtain imaging results with high reliability. A method for sandwich structure laser ultrasonic guided wave imaging detection and debonding defect area measurement, electronic equipment and storage medium are proposed.

[0007] To achieve the above object, the present invention is implemented through the following technical solutions:

[0008] A method for sandwich structure laser ultrasonic guided wave imaging detection and debonding defect area measurement comprises the following steps:

[0009] S1. Pulse laser is incident on the workpiece surface, and a laser Doppler interferometer is used to collect typical laser ultrasonic guided wave signals at defects and non-defects;

[0010] S2. For the typical laser ultrasonic guided wave signals at the defect and non-defect locations collected in step S1, the time-frequency spectrum of the signal at the non-defect location and the time-frequency spectrum of the signal at the defect location are respectively obtained by using the continuous wavelet transform method, and the amplitudes of the corresponding positions of the two time-frequency spectrum diagrams are subtracted to generate a difference time-frequency spectrum diagram to obtain the time and frequency range of the effect of the defect on the time-frequency energy distribution of the signal;

[0011] S3. Calculate the frequency range of detail signals of different layers of the Mallat algorithm by the sampling frequency of the signal obtained in step S1, and then select the number of layers of detail signals suitable for characterizing defects and the time range of the signal according to the time and frequency range of the effect of the defect on the signal time-frequency energy distribution obtained in step S2;

[0012] S4. Perform Mallat algorithm decomposition on the typical laser ultrasonic guided wave signal at the non-defect location collected in step S1 to obtain the detail signal of the typical laser ultrasonic guided wave signal at the non-defect location, and select the detail signal of the typical laser ultrasonic guided wave signal at the non-defect location corresponding to the number of detail signal layers of the Mallat algorithm in step S3 as the reference signal S. r ;

[0013] S5. Set the laser Doppler interferometer and the pulse laser of step S1 to be fixed relative to the surface of the workpiece, perform a grid scan on the workpiece until all the areas to be detected are covered to obtain signals of all the areas to be detected, and calculate the position (x i ,y i ) i Perform Mallat algorithm decomposition to obtain the specified layer detail signal, denoted as S i ;

[0014] S6. Calculate the reference signal S obtained in step S4 r and the position (x i ,y i ) at the specified layer detail signal S i Pearson correlation coefficient R i ;

[0015] S7. The Pearson correlation coefficient R calculated in step S6 is i When the Pearson correlation coefficient is close to 0, the color is red, indicating a defect; when the Pearson correlation coefficient is close to 1, the color is blue, indicating no defect; i ,y i ), R iConvert to pixel points at the specified position in the image to obtain a complete image of the area to be detected;

[0016] S8. Use the Otsu method to binarize the complete image of the area to be inspected obtained in step S7, perform morphological processing on the binarized image, and then calculate the defect area.

[0017] Furthermore, the specific implementation method of step S1 is to use a pulsed laser to irradiate the surface of the workpiece, adjust the energy of the pulsed laser so that the surface temperature of the workpiece does not exceed its melting temperature, and excite the ultrasonic guided wave by the thermoelastic effect. After the guided wave signal passes through the defect area and the non-defect area, respectively, a laser Doppler interferometer is used to measure the frequency shift of the light beam, and the off-surface displacement of the detection point is obtained as a typical laser ultrasonic guided wave signal at the defect and non-defect areas.

[0018] Furthermore, the specific implementation method of step S2 includes the following steps:

[0019] S2.1. The generalized Morse wavelet GMW is used to perform continuous wavelet transform on the typical laser ultrasonic guided wave signals at the defect and non-defect locations collected in step S1, and the time-frequency spectrum of the signals at the non-defect and defect locations is obtained respectively. The expression of GMW in the frequency domain is:

[0020]

[0021] Among them, ψ β,γ is the frequency domain waveform of GMW, U is the unit step size, ω is the angular frequency, β is the compactness parameter, γ is the symmetry coefficient of GMW, and e is the natural logarithm;

[0022] The waveform and characteristics of GMW are adjusted by β and γ to analyze signals of different frequencies.

[0023] S2.2. Subtract the amplitudes of the corresponding positions of the time-frequency spectrum of the non-defective and defective signals obtained in step S1 to generate a difference time-frequency spectrum. The positions where the difference between the defective and non-defective signals is large appear red in the difference time-frequency spectrum, and the time and frequency range of the effect of the defect on the signal time-frequency energy distribution is obtained.

[0024] Furthermore, the specific implementation method of step S3 includes the following steps:

[0025] S3.1. Calculate the frequency range of detail signals at different layers of the Mallat algorithm based on the actual sampling frequency and base frequency of the laser Doppler interferometer obtained in step S1, and the actual number of frequency band divisions p of the Mallat algorithm z The expression is obtained by rounding the following formula to the nearest integer:

[0026]

[0027] Where p is the number of theoretical frequency band divisions, f s is the sampling frequency, f f is the fundamental frequency;

[0028] The frequency range of the detail signal of the nth layer is expressed as

[0029] S3.2. Based on the frequency range of the effect of the defect on the signal time-frequency energy distribution obtained in step S2, select the number of detail signal layers of the Mallat algorithm suitable for characterizing the defect, and then determine the time range of the detail signal based on the time range of the effect of the defect on the signal time-frequency energy distribution obtained in step S2.

[0030] Furthermore, in step S6, the Pearson correlation coefficient is defined as the quotient of the covariance and standard deviation between two variables, and the sample points (X i ,Y i ) to estimate the mean of the standard scores and obtain the Pearson correlation coefficient, R i The expression is:

[0031]

[0032] Among them, X i and Y i For the sample drawn, For X i The sample mean of X, D(X) is i The sample standard deviation of Y i The sample mean of i The sample standard deviation is , where n is the number of samples.

[0033] Furthermore, the Pearson correlation coefficient R in step S7 i After pseudo color mapping, the pixel color is expressed in RGB format, and the blue b value is obtained by rounding to the nearest integer using the following formula:

[0034]

[0035] The green g value is obtained by rounding to the nearest integer:

[0036]

[0037] The red r value is obtained by rounding to the nearest integer:

[0038]

[0039] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a method for laser ultrasonic guided wave imaging detection of sandwich structures and measurement of debonding defect areas are implemented.

[0040] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a method for sandwich structure laser ultrasonic guided wave imaging detection and debonding defect area measurement.

[0041] Beneficial effects of the present invention:

[0042] The present invention discloses a method for detecting and measuring the area of ​​debonding defects in a sandwich structure by laser ultrasonic guided wave imaging. The imaging is based on the correlation of wavelet detail signals, which solves the problem that the time domain signal amplitude imaging defect characterization effect is poor, and it is difficult to extract defect information from complex ultrasonic guided wave signals, and thus it is impossible to obtain imaging results with high reliability. The defect information is effectively extracted from the signal, and the obtained detection image can better reflect the position, morphology and area size of the defect. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of a process for laser ultrasonic guided wave imaging detection of sandwich structures and a method for measuring the debonding defect area;

[0044] Figure 2 Schematic diagram of sandwich structure specimen with artificial defects;

[0045] Figure 3 This is the time domain amplitude imaging result of the laser ultrasonic guided wave signal of the test specimen;

[0046] Figure 4 It is the distribution diagram of the influence of defects on the time-frequency energy of the signal;

[0047] Figure 5 This is the imaging result of the test specimen obtained by this method;

[0048] Figure 6 It is the detection image after morphological processing;

[0049] Figure 7 This is the imaging result of the test specimen obtained by the water immersion ultrasonic C-scan method. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the specific embodiments described are only part of the embodiments of the present invention, rather than all of the specific embodiments. The components of the specific embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0051] Therefore, the following detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected specific embodiments of the present invention. Based on the specific embodiments of the present invention, all other specific embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0052] In order to further understand the content, features and effects of the present invention, the following specific implementation methods are given as examples, and the attached Figure 1 -Attached Figure 7 The detailed instructions are as follows:

[0053] Embodiment 1:

[0054] A method for sandwich structure laser ultrasonic guided wave imaging detection and debonding defect area measurement comprises the following steps:

[0055] S1. Pulse laser is incident on the workpiece surface, and a laser Doppler interferometer is used to collect typical laser ultrasonic guided wave signals at defects and non-defects;

[0056] Furthermore, the specific implementation method of step S1 is to use a pulsed laser to irradiate the surface of the workpiece, adjust the energy of the pulsed laser so that the surface temperature of the workpiece does not exceed its melting temperature, and excite the ultrasonic guided wave by the thermoelastic effect, and after the guided wave signal passes through the defect area and the non-defect area, use a laser Doppler interferometer to measure the frequency shift of the light beam, and obtain the off-surface displacement of the detection point as a typical laser ultrasonic guided wave signal at the defect and non-defect areas;

[0057] S2. For the typical laser ultrasonic guided wave signals at the defect and non-defect locations collected in step S1, the time-frequency spectrum of the signal at the non-defect location and the time-frequency spectrum of the signal at the defect location are respectively obtained by using the continuous wavelet transform method, and the amplitudes of the corresponding positions of the two time-frequency spectrum diagrams are subtracted to generate a difference time-frequency spectrum diagram to obtain the time and frequency range of the effect of the defect on the time-frequency energy distribution of the signal;

[0058] Furthermore, the specific implementation method of step S2 includes the following steps:

[0059] S2.1. The generalized Morse wavelet GMW is used to perform continuous wavelet transform on the typical laser ultrasonic guided wave signals at the defect and non-defect locations collected in step S1, and the time-frequency spectrum of the signals at the non-defect and defect locations is obtained respectively. The expression of GMW in the frequency domain is:

[0060]

[0061] Among them, β,γ is the frequency domain waveform of GMW, U is the unit step size, ω is the angular frequency, β is the compactness parameter, γ is the symmetry coefficient of GMW, and e is the natural logarithm;

[0062] The waveform and characteristics of GMW are adjusted by β and γ to analyze signals of different frequencies.

[0063] S2.2. The amplitudes of the corresponding positions of the time-frequency spectrum of the signal at the non-defective part and the signal at the defective part obtained in step S1 are subtracted to generate a difference time-frequency spectrum. The positions where the difference between the signal at the defective part and the signal at the non-defective part is large are shown in red in the difference time-frequency spectrum, and the time and frequency range of the effect of the defect on the time-frequency energy distribution of the signal are obtained;

[0064] S3. Calculate the frequency range of detail signals of different layers of the Mallat algorithm by the sampling frequency of the signal obtained in step S1, and then select the number of layers of detail signals suitable for characterizing defects and the time range of the signal according to the time and frequency range of the effect of the defect on the signal time-frequency energy distribution obtained in step S2;

[0065] Furthermore, the specific implementation method of step S3 includes the following steps:

[0066] S3.1. Calculate the frequency range of detail signals at different layers of the Mallat algorithm based on the actual sampling frequency and base frequency of the laser Doppler interferometer obtained in step S1, and the actual number of frequency band divisions p of the Mallat algorithm z The expression is obtained by rounding the following formula to the nearest integer:

[0067]

[0068] Among them, p is the number of theoretical frequency band divisions, f s is the sampling frequency, f f is the fundamental frequency;

[0069] The frequency range of the detail signal of the nth layer is expressed as

[0070] S3.2. Based on the frequency range of the effect of the defect on the signal time-frequency energy distribution obtained in step S2, select the number of detail signal layers of the Mallat algorithm, and then determine the time range of the detail signal based on the time range of the effect of the defect on the signal time-frequency energy distribution obtained in step S2;

[0071] The frequency range corresponding to the number of detail signal layers of the selected Mallat algorithm should be included in the frequency range corresponding to the red area of ​​the difference time-frequency image obtained in step S2 as much as possible;

[0072] S4. Perform Mallat algorithm decomposition on the typical laser ultrasonic guided wave signal at the non-defect location collected in step S1 to obtain the detail signal of the typical laser ultrasonic guided wave signal at the non-defect location, and select the detail signal of the typical laser ultrasonic guided wave signal at the non-defect location corresponding to the number of detail signal layers of the Mallat algorithm in step S3 as the reference signal S. r ;

[0073] S5. Set the laser Doppler interferometer and the pulse laser of step S1 to be fixed relative to the surface of the workpiece, perform a grid scan on the workpiece until all the areas to be detected are covered to obtain signals of all the areas to be detected, and calculate the position (x i ,y i ) i Perform Mallat algorithm decomposition to obtain the specified layer detail signal, denoted as S i ;

[0074] S6. Calculate the reference signal S obtained in step S4 r and the position (x i ,y i ) at the specified layer detail signal S i Pearson correlation coefficient R i ;

[0075] Furthermore, in step S6, the Pearson correlation coefficient is defined as the quotient of the covariance and standard deviation between two variables, and the sample points (X i ,Y i ) to estimate the mean of the standard scores and obtain the Pearson correlation coefficient, R i The expression is:

[0076]

[0077] Among them, X i and Y i For the sample drawn, For X i The sample mean of X, D(X) is i The sample standard deviation of Y i The sample mean ofi The sample standard deviation of , n is the number of samples;

[0078] Furthermore, the Pearson correlation coefficient ranges from 0 to 1. When the Pearson correlation coefficient is 1, it means that there is a complete linear correlation between the two samples; when the Pearson correlation coefficient is 0, it means that there is zero correlation between the two samples. The larger the correlation coefficient, the stronger the correlation between the two samples.

[0079] S7. The Pearson correlation coefficient R calculated in step S6 is i When the Pearson correlation coefficient is close to 0, the color is red, indicating a defect; when the Pearson correlation coefficient is close to 1, the color is blue, indicating no defect; i ,y i ), R i Convert to pixel points at the specified position in the image to obtain a complete image of the area to be detected;

[0080] Furthermore, the Pearson correlation coefficient R in step S7 i After pseudo color mapping, the pixel color is expressed in RGB format, and the blue b value is obtained by rounding to the nearest integer using the following formula:

[0081]

[0082] The green g value is obtained by rounding to the nearest integer:

[0083]

[0084] The red r value is obtained by rounding to the nearest integer:

[0085]

[0086] S8. Use the Otsu method to binarize the complete image of the area to be inspected obtained in step S7, perform morphological processing on the binarized image, and then calculate the defect area.

[0087] This example uses an aluminum alloy-epoxy resin-aluminum alloy sandwich plate structure as the research object, and uses laser ultrasonic guided wave technology to detect and image the internal defects in the structure. The distance between the excitation light and the receiving light is 20mm, and the two light sources are synchronously moved 0.5mm each time during the grid scanning process, and the sampling frequency is 250MHz.

[0088] refer to Figure 2The sandwich structure specimen with artificial defects is an aluminum alloy-epoxy resin-aluminum alloy sandwich plate with a length × width × thickness (130mm × 80mm × 2.5mm), in which the thickness of the upper and lower aluminum layers is 1mm, and the thickness of the epoxy resin glue layer is 0.5mm. Two groups of artificial defects with sizes of 10×10mm and 15×15mm are prefabricated in the glue layer.

[0089] refer to Figure 3 , using a signal amplitude of 11μs for imaging, the boundary between the defect area and the non-defect area is fuzzy, and it is difficult to distinguish the morphology of the defect, indicating that the size and position of the defect cannot be quantitatively analyzed by the time domain amplitude of the signal.

[0090] refer to Figure 4 The difference in signal energy distribution between defect-free and defective areas is mainly reflected in two areas: Area A is the time-frequency range of 8-14μs and 0.2-0.7MHz; Area B is the time-frequency range after 10μs and below 0.15MHz; Since the signal components with frequencies below 0.15MHz contain a high level of noise components, only Area A can reflect the impact of defects on the signal time-frequency energy distribution.

[0091] refer to Figure 5 In the time-frequency range of 0.2-0.7MHz, the d8 and d9 detail signals decomposed by the Mallat algorithm account for the main components. The d8 and d9 signals are superimposed in time domain to obtain the d8+d9 signal. The d8+d9 signal in the time range of 8-14μs is selected, and the Pearson correlation coefficient is processed with the corresponding reference signal. The calculated correlation coefficient is used for imaging, and the obtained image can better reflect the location and morphology of the defect.

[0092] refer to Figure 6 The image obtained by Pearson correlation coefficient processing was processed by Otsu method and binarized, and morphological processing was performed on this basis. The white pixels in the processed image represent defects. There are 1204 white pixels in the image, and the calculated defect area is 301mm 2 .

[0093] refer to Figure 7 , the C-scan test of the specimen was performed using the water immersion ultrasonic method to obtain the location and morphology information of the defects. Figure 6 The results were compared to prove that this method has high reliability in the quantitative characterization of internal defects in sandwich structures, solving the problem of Figure 3 The signal time domain amplitude imaging shown in the figure cannot quantitatively analyze the size and location of the defect.

[0094] Embodiment 2:

[0095] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a method for laser ultrasonic guided wave imaging detection of a sandwich structure and measurement of a debonding defect area described in Example 1 are implemented.

[0096] The computer device of the present invention may be a device including a processor and a memory, such as a single chip microcomputer including a central processing unit, etc. Moreover, the processor is used to implement the steps of the above-mentioned method for laser ultrasonic guided wave imaging detection of sandwich structures and measurement of debonding defect area when executing the computer program stored in the memory.

[0097] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0098] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0099] Embodiment 3:

[0100] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a sandwich structure laser ultrasonic guided wave imaging detection and debonding defect area measurement method as described in Example 1.

[0101] The computer-readable storage medium of the present invention can be any form of storage medium that can be read by a processor of a computer device, including but not limited to non-volatile memory, volatile memory, ferroelectric memory, etc. The computer-readable storage medium stores a computer program. When the processor of the computer device reads and executes the computer program stored in the memory, the steps of the above-mentioned method for laser ultrasonic guided wave imaging detection of a sandwich structure and measurement of the debonding defect area can be implemented.

[0102] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer readable media do not include electric carrier signals and telecommunication signals.

[0103] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0104] Although the present application has been described above with reference to specific embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present application. In particular, as long as there is no structural conflict, the various features in the specific embodiments disclosed in the present application may be used in combination with each other in any manner, and the fact that these combinations are not exhaustively described in this specification is only for the sake of omitting space and saving resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for detecting sandwich structures by laser ultrasonic guided wave imaging and measuring the debonding defect area, characterized in that: The steps include: S1. Pulse laser is incident on the workpiece surface, and a laser Doppler interferometer is used to collect typical laser ultrasonic guided wave signals at defects and non-defects; The specific implementation method of step S1 is to use a pulsed laser to irradiate the surface of the workpiece, adjust the energy of the pulsed laser so that the surface temperature of the workpiece does not exceed its melting temperature, and excite the ultrasonic guided wave by the thermoelastic effect. After the guided wave signal passes through the defect area and the non-defect area, a laser Doppler interferometer is used to measure the frequency shift of the light beam, and the off-surface displacement of the detection point is obtained as a typical laser ultrasonic guided wave signal at the defect and non-defect areas; S2. For the typical laser ultrasonic guided wave signals at the defect and non-defect locations collected in step S1, the time-frequency spectrum of the signal at the non-defect location and the time-frequency spectrum of the signal at the defect location are respectively obtained by using the continuous wavelet transform method, and the amplitudes of the corresponding positions of the two time-frequency spectrum diagrams are subtracted to generate a difference time-frequency spectrum diagram to obtain the time and frequency range of the effect of the defect on the time-frequency energy distribution of the signal; The specific implementation method of step S2 includes the following steps: S2.

1. The generalized Morse wavelet GMW is used to perform continuous wavelet transform on the typical laser ultrasonic guided wave signals at the defect and non-defect locations collected in step S1, and the time-frequency spectrum of the signals at the non-defect and defect locations is obtained respectively. The expression of GMW in the frequency domain is: Among them, β,γ is the frequency domain waveform of GMW, U is the unit step size, ω is the angular frequency, β is the compactness parameter, γ is the symmetry coefficient of GMW, and e is the natural logarithm; The waveform and characteristics of GMW are adjusted by β and γ to analyze signals of different frequencies. S2.

2. The amplitudes of the corresponding positions of the time-frequency spectrum of the signal at the non-defective part and the signal at the defective part obtained in step S1 are subtracted to generate a difference time-frequency spectrum. The positions where the difference between the signal at the defective part and the signal at the non-defective part is large are shown in red in the difference time-frequency spectrum, and the time and frequency range of the effect of the defect on the time-frequency energy distribution of the signal are obtained; S3. Calculate the frequency range of detail signals of different layers of the Mallat algorithm by the sampling frequency of the signal obtained in step S1, and then select the number of layers of detail signals suitable for characterizing defects and the time range of the signal according to the time and frequency range of the effect of the defect on the signal time-frequency energy distribution obtained in step S2; The specific implementation method of step S3 includes the following steps: S3.

1. Calculate the frequency range of detail signals at different layers of the Mallat algorithm based on the actual sampling frequency and base frequency of the laser Doppler interferometer obtained in step S1, and the actual number of frequency band divisions p of the Mallat algorithm z The expression is obtained by rounding the following formula to the nearest integer: Among them, p is the number of theoretical frequency band divisions, f s is the sampling frequency, f f is the fundamental frequency; The frequency range of the detail signal of the nth layer is expressed as S3.

2. Based on the frequency range of the effect of the defect on the signal time-frequency energy distribution obtained in step S2, select the number of detail signal layers suitable for the Mallat algorithm for characterizing the defect, and then determine the time range of the detail signal based on the time range of the effect of the defect on the signal time-frequency energy distribution obtained in step S2; S4. Perform Mallat algorithm decomposition on the typical laser ultrasonic guided wave signal at the non-defect location collected in step S1 to obtain the detail signal of the typical laser ultrasonic guided wave signal at the non-defect location, and select the detail signal of the typical laser ultrasonic guided wave signal at the non-defect location corresponding to the number of detail signal layers of the Mallat algorithm in step S3 as the reference signal S. r ; S5. Set the laser Doppler interferometer and the pulse laser of step S1 to be fixed relative to the surface of the workpiece, perform a grid scan on the workpiece until all the areas to be detected are covered to obtain signals of all the areas to be detected, and calculate the position (x i ,y i ) i Perform Mallat algorithm decomposition to obtain the specified layer detail signal, denoted as S i ; S6. Calculate the reference signal S obtained in step S4 r and the position (x i ,y i ) at the specified layer detail signal S i Pearson correlation coefficient R i ; In step S6, the Pearson correlation coefficient is defined as the quotient of the covariance and standard deviation between two variables, and is calculated by sampling points (X i ,Y i ) to estimate the mean of the standard scores and obtain the Pearson correlation coefficient, R i The expression is: Among them, X i and Y i For the sample drawn, For X i The sample mean of X, D(X) is i The sample standard deviation of Y i The sample mean of i The sample standard deviation of , n is the number of samples; S7. The Pearson correlation coefficient R calculated in step S6 is i When the Pearson correlation coefficient is close to 0, the color is red, indicating a defect; when the Pearson correlation coefficient is close to 1, the color is blue, indicating no defect; i ,y i ), R i Convert to pixel points at the specified position in the image to obtain a complete image of the area to be detected; Pearson correlation coefficient R in step S7 i After pseudo color mapping, the pixel color is expressed in RGB format, and the blue b value is obtained by rounding to the nearest integer using the following formula: The green g value is obtained by rounding to the nearest integer: The red r value is obtained by rounding to the nearest integer: S8. Use the Otsu method to binarize the complete image of the area to be inspected obtained in step S7, perform morphological processing on the binarized image, and then calculate the defect area.

2. An electronic device, characterized in that: It comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the sandwich structure laser ultrasonic guided wave imaging detection and debonding defect area measurement method as claimed in claim 1 when executing the computer program.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the method for laser ultrasonic guided wave imaging detection of sandwich structures and measurement of debonding defect areas as described in claim 1 is implemented.

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