A metal plate defect imaging method based on laser ultrasound and surface wave number filtering analysis

Abstract

This invention discloses a metal plate defect imaging method based on laser ultrasound and surface wave wavenumber filtering analysis. Its purpose is to use surface wave echoes from the metal plate to locate the defect position and inversely interpret the defect shape. The method determines the position of a laser vibrometer's detection light on the surface of a fixed metal plate and uses a field scanning system to scan the surface on the same side of the metal plate to obtain multiple sets of ultrasonic data. The obtained surface wave data is then subjected to a wavelet transform to remove low-frequency components from the signal, and the time component is Fourier transformed to obtain a frequency spectrum. Subsequently, signal data at specific frequencies is extracted and subjected to a two-dimensional Fourier transform to obtain a wavenumber spectrum. Finally, the wavenumber spectrum is filtered and inverse Fourier transformed to obtain a defect image, achieving defect location and defect shape imaging.

Description

Technical Field

[0001] The present invention relates to laser ultrasonic nondestructive testing, and in particular to a metal plate defect imaging method based on surface wave number filtering analysis. Background Art

[0002] With the continued development of global industry, metal structural materials face increasingly harsh service environments and more stringent processing requirements. Surface and near-surface defects can easily cause permanent damage to structural components under cyclic loading. Accumulated damage can cause defects to further expand and eventually fracture, resulting in fatigue failure. Therefore, timely and effective detection of surface and near-surface defects plays a vital role in structural safety assessments.

[0003] The most significant advantage of nondestructive testing technology is that it can monitor material quality throughout its service life, even during operation, preventing further deterioration during service and, where possible, correcting it. Conventional nondestructive testing methods for surface defect detection include magnetic particle testing, radiographic testing, and ultrasonic testing. However, magnetic particle testing can only inspect ferromagnetic materials and is unsuitable for inspecting complex surfaces. Furthermore, its inspection depth generally does not exceed 2mm, resulting in low sensitivity and potential adverse effects from surface coatings, making it difficult to distinguish defect types and shapes. Radiographic testing uses radiation, which can be harmful to the human body. Furthermore, the equipment is expensive, the inspection cycle is long, and its practical cost is high. Traditional ultrasonic testing generally uses an ultrasonic probe to excite and receive ultrasonic signals, requiring a coupling agent, which can contaminate the material surface. Furthermore, due to the limitations of the probe surface, it is not suitable for inspecting complex surfaces. Laser ultrasonic testing, however, does not require water immersion or coupling agents for excitation and reception, is not restricted by surface shape, and is suitable for nondestructive testing in harsh environments such as high temperature, high pressure, and toxic environments. This allows the technology to be extended to applications where ultrasonic testing is infeasible. By controlling the laser pulse time, extremely short ultrasonic pulses can be generated on the surface of the specimen, increasing the ultrasonic frequency and greatly improving the ability to detect tiny defects. Therefore, it is suitable for high-precision non-destructive testing.

[0004] Laser surface acoustic waves propagate along the surface contour of the material, can only penetrate to a depth of one wavelength, and the intensity decays exponentially with depth. They are very sensitive to small defects and have unique advantages in detecting surface defects and sub-surface defects. Defect characterization methods can be divided into two types according to different imaging methods, namely static imaging and dynamic imaging. Static imaging has traditional B-scan and C-scan imaging methods. The disadvantage is that the positioning and imaging effects are good, but this method requires the laser excitation spot and the laser receiving spot to maintain a set distance and move synchronously for scanning. However, this method has a slow scanning efficiency, and the ultrasonic signal strength depends on the return light quality of the receiving spot on the surface of the material. The movement of the receiving spot will cause the ultrasonic signal to be unstable. In dynamic imaging, one of the excitation spot and the receiving spot is fixed, and the other performs raster scanning. For the case where the receiving spot is fixed and the excitation spot scans the field, based on the principle of acoustic reciprocity, spatial interchange can be performed to achieve dynamic reconstruction of the wave field. However, the defect image obtained by this method relies on the dynamic propagation image of the wave field. The wave field image changes with time, and the defect edge area is not clear. It is impossible to quantitatively analyze the defect and it only has a positioning effect. Other imaging methods, such as the time-domain synthetic aperture method, are easily interfered by a series of clutter such as boundary echoes, resulting in poor imaging effect. Summary of the Invention

[0005] Purpose of the Invention: To address the shortcomings of existing technologies, this invention proposes a metal plate defect imaging method based on laser ultrasound and surface wave number filtering analysis. While ensuring efficiency, it can detect and locate the location of surface defects on the plate and image the defect shape.

[0006] Technical solution: The metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis described in the present invention includes the following steps:

[0007] (1) Stably set up the metal plate, place and start a laser vibrometer on one side of the metal plate, and adjust the laser vibrometer position, laser incident angle, and energy output until a stable and high-amplitude detection light return signal is obtained;

[0008] (2) The pulsed laser emits a light beam into the galvanometer scanning system, which then reflects the incident laser light onto the metal plate to be tested. The computer controls the galvanometer scanning system to scan the surface of the metal plate with excitation light to obtain spatial and temporal sampling matrix data.

[0009] (3) Perform wavelet transform on the broadband surface wave signal to remove the low-frequency component, then perform three-dimensional Fourier transform on the time component to convert the time domain signal into a frequency domain signal, select the central peak frequency as the characteristic frequency, and after determining the characteristic frequency, perform two-dimensional Fourier transform on the spatial component of the wave field signal to obtain the wave number image at the characteristic frequency;

[0010] (4) Calculate the theoretical wave number of the surface wave, remove the wave number outside the theoretical wave number curve, reduce the noise interference imaging effect, filter the wave number of the surface wave forward wave, retain the surface wave echo wave number, perform two-dimensional inverse Fourier transform on the remaining wave number, and obtain a single frequency wave number inversion defect image;

[0011] (5) The laser-excited ultrasonic wave is a broadband signal. Selecting only one frequency will cause information loss. Therefore, the wave field data of all the frequencies where the peaks are located are selected and step (4) is repeated. Finally, all the defect images obtained are normalized and added together to obtain the final defect image.

[0012] In the metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis, the expression of the three-dimensional Fourier transform in step (3) is as follows:

[0013]

[0014] Where f is the frequency, is the wave number, t is the time, For different directions, and is the signal amplitude;

[0015] In the metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis, the wave number calculation formula for calculating the theoretical wave number of the surface wave in step (4) is as follows:

[0016]

[0017]

[0018] Where f is the frequency; c is the ultrasonic wave velocity, which can be obtained by dividing the distance between the excitation point and the detection point by the propagation time; f0 is the characteristic frequency selected in the spectrum; is the wave number; For different direction positions; is the signal amplitude;

[0019] In the metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis, in the steps (1) and (2), the position of the laser vibrometer detection light is kept unchanged, and the position of the pulse laser excitation light is moved by the galvanometer system to scan the field.

[0020] In the metal plate defect imaging method based on laser ultrasound and surface wave wavenumber filtering analysis, step (4) only retains the surface wave echo wavenumber in the theoretical wavenumber curve and filters out other interfering wavenumbers. If there is no defect, there will be no echo wavenumber in the wavenumber spectrum, and no defect will be presented after inversion.

[0021] In the metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis, the sampling matrix is ​​a sampling point matrix.

[0022] The metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis comprises an implementation device including a nanosecond pulse laser, a galvanometer scanning system, a laser vibrometer, and a computer.

[0023] In the metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis, step (3) utilizes computer software Matlab to perform data processing and wave number imaging.

[0024] The metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis is characterized in that the characteristic frequency range of step (3) is 2MHz to 4MHz; the frequency of all wave peaks in step (5) is selected from 1MHz to 5MHz.

[0025] The application of the metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis in detecting and / or locating surface defects of plate materials.

[0026] Furthermore, a metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis is implemented by a device including a nanosecond pulse laser, a galvanometer scanning system, a laser vibrometer, and a computer, and is characterized in that it includes the following steps:

[0027] (1) Stably set up the metal plate, place and start a laser vibrometer on one side of the metal plate, and adjust the laser vibrometer position, laser incident angle, and energy output until a stable and high-amplitude detection light return signal is obtained;

[0028] (2) The pulse laser emits a light beam into the galvanometer scanning system, which then reflects the incident laser light onto the metal plate to be tested. The galvanometer scanning system is controlled by a computer to scan the surface of the metal plate with an excitation light field, and obtains matrix data of M×N rows and L columns, where M is the number of spatial sampling points in the x-direction, N is the number of spatial sampling points in the y-direction, and L is the number of temporal sampling points; wherein the x-direction and y-direction are the horizontal and vertical coordinates of the plane of the metal plate.

[0029] (3) Perform wavelet transform on the broadband surface wave signal to remove the low-frequency component, then perform Fourier transform on the time component to convert the time domain signal into the frequency domain signal. Select the central peak frequency as the characteristic frequency. After determining the characteristic frequency, perform two-dimensional Fourier transform on the spatial component of the wave field signal to obtain the wave number image at the characteristic frequency. The expression of three-dimensional Fourier transform is as follows:

[0030]

[0031] Where f is the frequency, is the wave number, t is the time, For different directions, and is the signal amplitude;

[0032] (4) Calculate the theoretical wave number of the surface wave, remove the wave number outside the theoretical wave number curve, reduce the noise interference imaging effect, filter the wave number of the surface wave forward wave, retain the surface wave echo wave number, perform two-dimensional inverse Fourier transform on the remaining wave number, and obtain a single frequency wave number inversion defect image. The wave number calculation formula and two-dimensional inverse Fourier transform formula are as follows:

[0033]

[0034] Where c is the ultrasonic wave velocity, which can be obtained by dividing the distance between the excitation point and the detection point by the propagation time, and f0 is the selected frequency in the spectrum;

[0035] (5) The laser-excited ultrasonic wave is a broadband signal. Selecting only one frequency will cause information loss. Therefore, the wave field data of all the frequencies where the peaks are located are selected and step (4) is repeated. Finally, all the defect images obtained are normalized and added together to obtain the final defect image.

[0036] In the steps (1) and (2), the position of the detection light of the laser vibrometer is kept unchanged, and the position of the excitation light of the pulse laser is moved by the galvanometer system to perform field scanning.

[0037] The step (4) only leaves the surface wave echo wavenumber in the theoretical wavenumber curve and filters out other interfering wavenumbers. If there is no defect, there will be no echo wavenumber in the wavenumber spectrum, and no defect will appear after inversion.

[0038] In step (5), the defect images obtained by inverting the wave field data of all the frequencies at the wave peaks are normalized and added together to obtain the final defect image.

[0039] The algorithm principle of the present invention is as follows Figure 1 shown.

[0040] Beneficial effects: Compared with the existing technology, the present invention has the following advantages: the present invention converts the ultrasonic signal from the time domain to the wavenumber domain through Fourier transform, and filters all sound waves other than the reflected echo at the spatial level. The broadband signal of laser ultrasound can provide frequencies with different sensitivities to defects for normalized synthesis of defect images, realizing true non-destructive testing, giving full play to the advantages of laser ultrasound's broadband and high spatial resolution, and through the rapid scanning of the galvanometer system, reducing the average number of times in the signal acquisition process, improving the scanning efficiency, and combining with the Matlab program to achieve the purpose of efficient and high-quality imaging after scanning, it is expected to be used in practical industrial applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Schematic diagram of the algorithm principle;

[0042] Figure 2 Schematic diagram of the defect location of the aluminum plate and the laser scanning path;

[0043] Figure 3 Time domain signal diagram after surface wavelet transform;

[0044] Figure 4 Theoretical wave number diagram of circular defect (a) and wave number diagram after filtering (b);

[0045] Figure 5 Circular defect image;

[0046] Figure 6 Theoretical wave number diagram of rectangular defect (a) and wave number diagram after filtering (b);

[0047] Figure 7 Rectangular defect image. DETAILED DESCRIPTION

[0048] The present invention will now be further described with reference to examples and accompanying drawings:

[0049] like Figure 2 As shown in FIG, a circular countersink with a diameter of 2 mm and a rectangular countersink with a length of 8 mm and a width of 2.5 mm are respectively processed on an aluminum plate with a size of 180 mm × 100 mm × 5 mm.

[0050] Example 1

[0051] (1) Fix the laser vibrometer on the translation stage and connect it to the oscilloscope. Adjust the angle between the vibrometer and the aluminum plate to 90°. Turn on the laser vibrometer and continuously fine-tune the position of the vibrometer to ensure that the signal amplitude received by the oscilloscope is the highest and most stable, ensuring the best quality of the return light signal received by the vibrometer. At this time, the detection spot is about 30mm away from the defect and is fixed. After the vibrometer is removed from the oscilloscope, it is connected to the computer's digital oscilloscope together with the synchronous trigger source of the pulse laser. The sampling rate is 50MHz and the number of sampling points is 1000.

[0052] (2) Set the metal plate stably, adjust the distance between the pulse laser and the aluminum plate to 100 mm, and make the excitation light incident vertically at 90°. Set the pulse laser output energy to 4 mJ, adjust the galvanometer system so that the scanning area covers the surface defects of the aluminum plate, the excitation spot focus diameter is 2 mm, and the galvanometer scanning area is as follows: Figure 2 As shown, the scanning area is 18 mm × 12 mm, the initial position of the excitation spot is at the upper left corner of the defect, and the surface wave signal is collected point by point with a step size of 0.3 mm. This example collects a total of 2400 sets of data, resulting in a numerical matrix with 2400 rows and 3000 columns.

[0053] (3) Import the numerical matrix into Matlab and convert it into a 60×40×3000 three-dimensional matrix according to the zigzag scanning sequence. Fill 10 groups of zeros before and after the second dimension of the matrix to convert the numerical matrix into a 60×60×3000 numerical matrix. Use discrete wavelet transform to remove the low-frequency components in the signal and interpolate to perform smooth processing. Figure 3 After removing the low-frequency components, the third (time) dimension of the matrix is ​​Fourier transformed to convert the time domain signal into a frequency domain signal. The center frequency f0 of the signal is determined to be 2 MHz. The matrix is ​​then subjected to a two-dimensional (spatial) Fourier transform to obtain a wavenumber image at a frequency of 2 MHz.

[0054] (4) The theoretical wave number of the aluminum plate surface wave at this frequency is calculated, which is consistent with the actual measured wave number. Figure 4 As shown in (a), the wave numbers outside the theoretical wave number are removed to reduce noise interference. According to the law of symmetry of the amplitude in the wave number domain, the program can automatically identify the surface wave echo wave number and filter out the forward wave wave number. The image after filtering is as follows Figure 4 As shown in (b), the reflected wave number is subjected to a two-dimensional inverse Fourier transform to obtain a wave number inversion image in a single frequency domain.

[0055] (5) The signal excited by the laser is a broadband signal. In order to retain more information, the frequency signal corresponding to the peak with higher amplitude in step (3) is Fourier transformed, and step (4) is repeated. The wave number inversion images obtained at each single frequency are normalized and superimposed. The final circular defect image is as follows: Figure 5 shown.

[0056] Example 2

[0057] (1) Fix the laser vibrometer on the translation stage and connect it to the oscilloscope. Adjust the angle between the vibrometer and the aluminum plate to 90°. Turn on the laser vibrometer and continuously fine-tune the position of the vibrometer to ensure that the signal amplitude received by the oscilloscope is the highest and most stable, ensuring the best quality of the return light signal received by the vibrometer. At this time, the detection spot is about 20mm away from the defect and is fixed. After the vibrometer is pulled out of the oscilloscope, it is connected to the computer's digital oscilloscope together with the synchronous trigger source of the pulse laser. The sampling rate is 50MHz and the number of sampling points is 1000.

[0058] (2) Set the metal plate stably, adjust the distance between the pulse laser and the aluminum plate to 100 mm, and make the excitation light incident vertically at 90°. Set the pulse laser output energy to 4 mJ, adjust the galvanometer system so that the scanning area covers the surface defects of the aluminum plate, the excitation spot focus diameter is 2 mm, and the galvanometer scanning area is as follows: Figure 2As shown, the scanning area is 24 mm × 12 mm. The initial position of the excitation spot is at the upper left corner of the defect. Surface wave signals are collected point by point with a step size of 0.3 mm. This example collects 3200 sets of data, resulting in a numerical matrix with 3200 rows and 3000 columns.

[0059] (3) Import the numerical matrix into Matlab and convert it into a three-dimensional matrix of 80 × 40 × 3000 using a zigzag scanning sequence. Padded 20 groups of zeros before and after the second dimension of the matrix to convert the numerical matrix into an 80 × 80 × 3000 numerical matrix. Use discrete wavelet transform to remove the low-frequency components in the signal and perform interpolation for smoothing. After removing the low-frequency components, perform Fourier transform on the third (time) dimension of the matrix to convert the time domain signal into a frequency domain signal. The center frequency f0 of the signal is determined to be 2.7 MHz. Then perform a two-dimensional (spatial) Fourier transform on the matrix to obtain a wavenumber image at a frequency of 2.7 MHz.

[0060] (4) The theoretical wave number of the aluminum plate surface wave at this frequency is calculated, which is consistent with the actual measured wave number. Figure 6 As shown in (a), the wave numbers outside the theoretical wave number are removed to reduce noise interference. According to the law of symmetry of the amplitude in the wave number domain, the program can automatically identify the surface wave echo wave number and filter out the forward wave wave number. The image after filtering is as follows Figure 6 As shown in (b), the reflected wave number is subjected to a two-dimensional inverse Fourier transform to obtain a wave number inversion image in a single frequency domain.

[0061] (5) The signal excited by the laser is a broadband signal. In order to retain more information, the frequency signal corresponding to the peak with higher amplitude in step (3) is Fourier transformed, and step (4) is repeated. The wave number inversion images obtained at each single frequency are normalized and superimposed. The final rectangular defect image is as follows: Figure 7 shown.

Claims

1. A metal plate defect imaging method based on laser ultrasound and surface wave number filtering analysis, characterized in that: The steps include: (1) Stably set up the metal plate, place and start a laser vibrometer on one side of the metal plate, and adjust the laser vibrometer position, laser incident angle, and energy output until a stable and high-amplitude detection light return signal is obtained; (2) The pulsed laser emits a light beam into the galvanometer scanning system, which then reflects the incident laser light onto the metal plate to be tested. The computer controls the galvanometer scanning system to scan the surface of the metal plate with excitation light to obtain spatial and temporal sampling matrix data. (3) Perform wavelet transform on the broadband surface wave signal to remove the low-frequency component, then perform three-dimensional Fourier transform on the time component to convert the time domain signal into a frequency domain signal, select the central peak frequency as the characteristic frequency, and after determining the characteristic frequency, perform two-dimensional Fourier transform on the spatial component of the wave field signal to obtain the wave number image at the characteristic frequency; (4) Calculate the theoretical wave number of the surface wave, remove the wave number outside the theoretical wave number curve, reduce the noise interference imaging effect, filter the wave number of the surface wave forward wave, retain the surface wave echo wave number, perform two-dimensional inverse Fourier transform on the remaining wave number, and obtain a single frequency wave number inversion defect image; (5) The laser-excited ultrasonic wave is a broadband signal. Selecting only one frequency will cause information loss. Therefore, the wave field data of all the frequencies where the peaks are located are selected and step (4) is repeated. Finally, all the defect images obtained are normalized and added together to obtain the final defect image.

2. The metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis according to claim 1 is characterized in that: The expression of the three-dimensional Fourier transform in step (3) is as follows: Where f is the frequency, is the wave number, t is the time, For different directions, and is the signal amplitude.

3. The metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis according to claim 1 is characterized in that: The wave number calculation formula for calculating the theoretical wave number of the surface wave in step (4) is as follows: Where f is the frequency; c is the ultrasonic wave velocity, which can be obtained by dividing the distance between the excitation point and the detection point by the propagation time; f0 is the characteristic frequency selected in the spectrum; is the wave number; For different direction positions; is the signal amplitude.

4. The metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis according to claim 1 is characterized in that: In the steps (1) and (2), the position of the detection light of the laser vibrometer is kept unchanged, and the position of the excitation light of the pulse laser is moved by the galvanometer system to perform field scanning.

5. The metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis according to claim 1 is characterized in that: The step (4) only leaves the surface wave echo wavenumber in the theoretical wavenumber curve and filters out other interfering wavenumbers. If there is no defect, there will be no echo wavenumber in the wavenumber spectrum, and no defect will appear after inversion.

6. The metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis according to claim 1, characterized in that: The sampling matrix is ​​a sampling point matrix.

7. The metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis according to claim 1 is characterized in that: The implementation device includes a nanosecond pulse laser, a galvanometer scanning system, a laser vibrometer, and a computer.

8. The metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis according to claim 1 is characterized in that: Step (3) Use computer software Matlab to perform data processing and wave number imaging.

9. The metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis according to claim 1, characterized in that: The characteristic frequency in step (3) is in the range of 2MHz to 4MHz; the frequencies of all the peaks in step (5) are selected from 1MHz to 5MHz.

10. Application of the metal plate defect imaging method based on laser ultrasound and surface wave wave number filtering analysis as claimed in claim 1 in detecting and / or locating surface defects of plate materials.

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