A three-dimensional ultrasonic imaging method for woven composite materials based on singular ultrawavelets

Abstract

The invention discloses a three-dimensional woven composite material ultrasonic imaging method based on singular superwavelet, which relates to the technical field of non-destructive testing, comprising: S1, performing ultrasonic C scanning on a test piece to obtain a signal set of a scanning area; S2, determining a defect echo through a data gate; S3, performing a singular superwavelet transform on the defect echo to obtain a time-frequency distribution of the defect echo; S4, selecting a plurality of single-frequency points near a center frequency according to the time-frequency distribution of the defect echo to perform ultrasonic C scanning imaging respectively; S5, fusing a plurality of single-frequency point ultrasonic C scanning images through a spatial multi-scale multi-frequency fusion imaging method to obtain a fused C scanning image; S6, determining a defect threshold in the fused C scanning image according to generalized Pareto distribution fitting, and reconstructing the fused C scanning image; the invention solves the problem that structural noise interference exists in ultrasonic C scanning imaging of three-dimensional woven composite materials, resulting in a low signal-to-noise ratio of the imaging image, and can obtain a clearer and higher-resolution ultrasonic C scanning image.

Description

Technical Field

[0001] The invention relates to the technical field of nondestructive testing, and in particular to a three-dimensional woven composite material ultrasonic imaging method based on singular ultra-wavelets. Background Art

[0002] Three-dimensional woven composite materials are cutting-edge composite materials. They are made of two or more layers of the same or different materials by laminating and pressing using three-dimensional weaving technology. They not only have the advantages of high specific strength, high specific stiffness, corrosion resistance and fatigue resistance of ordinary laminated composite materials, but also exhibit excellent out-of-plane mechanical properties due to the distribution of their fibers in the out-of-plane direction. They have been widely used in aerospace, automobile manufacturing, shipbuilding, wind power, nuclear power and medical equipment. Due to the complexity of the internal woven structure of the material, damage research on three-dimensional woven composite materials faces many challenges. Therefore, it is of great significance to carry out performance testing, failure analysis, damage detection and life prediction of three-dimensional woven composite materials during use.

[0003] At present, the main methods for composite material defect detection include ultrasonic detection, acoustic emission detection, X-ray detection, eddy current detection and infrared thermal imaging detection, which are suitable for different application scenarios. The acoustic emission detection method is a technology based on the release of local energy and the generation of acoustic signals when the material is deformed or damaged under the action of external force. This method has the advantages of simple operation and no material restrictions, but it has the limitations that its signal will decay with distance and cannot effectively detect static objects. The X-ray detection method uses X-rays to penetrate the inside of the test piece to detect whether there are defects inside the test piece. It has the characteristics of high detection accuracy, but the radiation to the human body is relatively large. In addition, it also has the limitations of low detection efficiency and high detection cost. The eddy current detection method is based on the principle of electromagnetic induction. During the detection process, there is no need to contact the test piece, nor does it require a coupling agent. It has the characteristics of fast detection speed, but it is only suitable for the detection of metal materials and thin layers of materials. The infrared thermal imaging detection method generates a temperature distribution image by detecting the infrared radiation emitted by the object to identify defects. It has the advantages of non-contact, fast and high sensitivity, but it is greatly affected by the environment and has limited ability to detect deep defects. In comparison, the ultrasonic detection method also has a wide detection range and strong applicability. It is often used to detect defects such as debonding, stratification, voids and bubbles between layers of three-dimensional woven composite materials, and can accurately locate defects.

[0004] In ultrasonic testing, ultrasonic C-scan imaging is an efficient non-destructive testing technology that uses the propagation characteristics of ultrasonic waves in the medium to accurately image the internal structure of the object being tested, thereby providing important information about its material properties, defects and integrity. However, C-scan imaging often uses direct amplitude imaging of the center frequency aliasing signal, which causes signal noise interference and results in low image resolution. In addition, since the threshold is usually set artificially for the defect area, the statistics of the defect area are not very accurate.

[0005] The background description provided herein is for the purpose of generally presenting the context of the present disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims of this application and are not admitted to be prior art by inclusion in this section. Summary of the invention

[0006] In view of the above technical problems in the related art, the present invention proposes a three-dimensional woven composite material ultrasonic imaging method based on singular ultralet.

[0007] In order to achieve the above-mentioned invention object, the present invention adopts the following technical scheme:

[0008] The present invention provides a three-dimensional woven composite material ultrasonic imaging method based on singular ultra-wavelet, comprising:

[0009] S1. Perform an ultrasonic C scan on the specimen to obtain an ultrasonic A scan signal of the scanned area;

[0010] S2, obtaining defect echo through data gate according to ultrasonic A-scan signal and bottom surface echo;

[0011] S3, performing singular ultrawavelet transform on the defect echo to obtain the time-frequency distribution of the defect echo;

[0012] S4. According to the time-frequency distribution of the defect echo, several single-frequency points near the center frequency are selected for ultrasonic C-scan imaging to obtain several single-frequency ultrasonic C-scan images;

[0013] S5. A plurality of single-frequency ultrasound C-scan images are fused by a spatial multi-scale multi-frequency fusion imaging method to obtain a fused C-scan image.

[0014] Specifically, step S2 specifically includes:

[0015] S21, finding the bottom surface echo in the ultrasonic A-scan signal and performing ultrasonic C-scan imaging to obtain a bottom surface echo C-scan image;

[0016] S22. Adjust the data gate to the defect wave position of the ultrasonic A-scan signal according to the bottom surface echo C-scan image. The A-scan signal corresponding to the defect wave position is the defect echo.

[0017] Specifically, step S3 is as follows: performing singular ultrawavelet transform on the defect echo by preset transformation parameters to obtain the time-frequency distribution of the defect echo, wherein the transformation parameters include the order and the baseline number.

[0018] Specifically, step S4 specifically includes:

[0019] S41, expanding the single frequency point range to both sides of the center frequency according to a preset ratio; the center frequency is the center frequency of the ultrasound probe;

[0020] S42, traversing the single frequency point range at fixed frequency intervals to sequentially extract the single frequency points in the time-frequency distribution of the defect echo, and then performing ultrasonic C-scan imaging on the single frequency points to obtain a plurality of single frequency point ultrasonic C-scan images.

[0021] Specifically, step S5 specifically includes:

[0022] S51, using two-dimensional discrete wavelet transform to perform multi-scale decomposition on each single-frequency ultrasonic C-scan image to obtain characteristic information at each frequency;

[0023] S52, fusing the feature information at each frequency using an averaging method to obtain a fused feature map;

[0024] S53, reconstructing the fused feature map into a fused C-scan map through two-dimensional discrete wavelet inverse transform.

[0025] The characteristic information includes: low frequency component information, horizontal high frequency component information, vertical high frequency component information and diagonal high frequency component information;

[0026] Specifically, the method further includes: S6, determining a defect threshold in the fused C-scan image according to generalized Pareto distribution fitting, and performing binarization reconstruction on the fused C-scan image.

[0027] Specifically, step S6 also includes:

[0028] The binary reconstructed fused C-scan image is segmented to obtain defective parts, and the area of ​​the defect is obtained according to the pixel size and scale of the defective parts.

[0029] Specifically, the test piece in step S1 is a three-dimensional woven composite material test piece.

[0030] Specifically, the preset transformation parameters can be selected as the preset transformation parameters of the singular ultrawavelet transform by comparing the changes in the time domain and frequency domain signals of the defect echo under different transformation parameters, and the transformation parameters that can best reveal the defect characteristics are selected.

[0031] Specifically, the preset ratio in step S41 is 0.2.

[0032] Specifically, the fixed frequency interval in step S42 is 0.1 MHz.

[0033] The three-dimensional woven composite material ultrasonic imaging method based on singular ultrawavelet of this embodiment combines the advantages of singular ultrawavelet technology and spatial multi-scale multi-frequency fusion technology, and gives full play to the advantages of multi-frequency fusion, so as to obtain a clearer and higher-resolution ultrasonic C-scan imaging image;

[0034] Furthermore, compared with other time-frequency analysis methods, the three-dimensional woven composite material ultrasonic imaging method based on singular ultrawavelets in this embodiment has a higher signal-to-noise ratio of the image and a better imaging effect;

[0035] Furthermore, this embodiment also uses a super-threshold distribution structural noise removal method to automatically set a threshold, remove non-defect information, and accurately retain defect information, so that the area result of the defect part is more accurate.

[0036] In addition, by comparing the positions of the bottom surface echo and the defect echo, the present invention can more accurately determine the depth of the defect in the test piece, effectively solving the problem that the defect echo is not obvious when the defect is located at the bottom surface, and improving the accuracy and reliability of the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 is a schematic diagram of a three-dimensional woven composite material ultrasonic imaging method based on singular ultra-wavelets according to an embodiment of the present invention;

[0039] Figure 2 is a schematic diagram of testing a three-dimensional woven composite material specimen provided in an embodiment of the present invention;

[0040] Figure 3 is a schematic diagram of using a data gate in an ultrasonic A-scan signal according to an embodiment of the present invention;

[0041] Figure 4 is a schematic diagram of a time-frequency diagram obtained by performing singular ultrawavelet transform on defect echoes under different transformation parameters provided by an embodiment of the present invention; wherein, Figure 4 (a) is a schematic diagram of the time-frequency diagram with transformation parameters o=3, a=3; Figure 4 (b) is a schematic diagram of the time-frequency diagram with transformation parameters o=20, a=3; Figure 4 (c) is a schematic diagram of the time-frequency diagram with transformation parameters o=50, a=3; Figure 4(d) is a schematic diagram of the time-frequency diagram with transformation parameters o=100, a=3; Figure 4 (e) is a schematic diagram of the time-frequency diagram with transformation parameters o=3, a=10; Figure 4 (f) is a schematic diagram of the time-frequency diagram with transformation parameters o=3, a=20; Figure 4 (g) is a schematic diagram of the time-frequency diagram with transformation parameters o=3, a=50; Figure 4 (h) is a schematic diagram of the time-frequency diagram with transformation parameters o=3, a=100; Figure 4 (i) is a schematic diagram of the time-frequency diagram with transformation parameters o=10, a=10; Figure 4 (j) is a schematic diagram of the time-frequency diagram with transformation parameters o=20, a=20; Figure 4 (k) is a schematic diagram of the time-frequency diagram with transformation parameters o=50, a=50; Figure 4 (l) is a schematic diagram of the time-frequency diagram with transformation parameters o=100, a=100;

[0042] Figure 5 is a single-frequency ultrasonic C-scan image at different frequencies provided by an embodiment of the present invention; Figure 5 (a) is a single-frequency ultrasonic C-scan image at a frequency of 3 MHz; Figure 5 (b) is a single-frequency ultrasonic C-scan image at a frequency of 4 MHz; Figure 5 (c) is a single-frequency ultrasonic C-scan image at a frequency of 5 MHz; Figure 5 (d) is a single-frequency ultrasonic C-scan image at a frequency of 6 MHz; Figure 5 (e) is a single-frequency ultrasonic C-scan image at a frequency of 7 MHz;

[0043] Figure 6 is a schematic diagram of a fused C-scan image provided according to an embodiment of the present invention;

[0044] Figure 7 is a schematic diagram of a discrete two-dimensional wavelet transform principle diagram provided according to an embodiment of the present invention;

[0045] Figure 8 is a schematic diagram of a C-scan comparison diagram under different time-frequency processing methods provided by an embodiment of the present invention; wherein, Figure 8 (a) is the original C-scan image; Figure 8 (b) is the STFT ultrasound C-scan imaging image; Figure 8 (c) is the CWT ultrasound C-scan imaging image; Figure 8 (d) is the ASLT ultrasound C-scan imaging image;

[0046] Fig. 9 is a schematic diagram of super-threshold distribution fitting provided according to an embodiment of the present invention; wherein, Fig. 9 (a) is the density map, Fig. 9 (b) is the cumulative probability plot;

[0047] Fig.10 It is a schematic diagram of a reconstructed fused C-scan image provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0049] In the description of the present invention, if there is a description of first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0050] In the description of the present invention, it should be understood that descriptions involving orientation, such as orientation or positional relationship indicated as up, down, etc., are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0051] In the description of the present invention, it should be noted that, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific content of the technical solution.

[0052] Embodiment 1.

[0053] refer to Figure 1 This embodiment provides a three-dimensional woven composite material ultrasonic imaging method based on singular ultra-wavelet, comprising the following steps:

[0054] S1. Performing an ultrasonic C scan on a specimen to obtain an ultrasonic A scan signal of a scanning area; the specimen is a three-dimensional woven composite material specimen;

[0055] In this embodiment, a JSR PureView-pulse transmitter and receiver, a WS5 wireless oscilloscope, an ultrasonic probe, a Staubli TX2-60 six-axis robotic arm, a computer and a three-dimensional woven composite material specimen are used to build an experimental platform; the water immersion focusing probe is mounted on the end of the robotic arm; the experimental platform also includes an oscilloscope and a pulse transmitter and receiver, and the ultrasonic probe uses an Olympus 5M water immersion focusing probe.

[0056] like Figure 2First, the operator sets the test parameters on the computer, then the six-axis robot moves to the specified position according to the test parameters and starts the ultrasonic pulse to scan the test piece; finally, the collected signal is processed by the oscilloscope and presented on the computer screen, thus completing the comprehensive inspection of the test piece.

[0057] In this embodiment, the three-dimensional woven composite material specimen is prepared at the Composite Materials Research Institute of Tianjin Polytechnic University. The specimen size is 210 mm×70 mm×5 mm, and the defect size area is 10 mm×15 mm×5 mm.

[0058] In this embodiment, during ultrasonic testing, the test piece is immersed in water. After the main testing parameters are set, a six-axis robotic arm is used to scan the defective parts of the test piece according to a preset scanning path to collect ultrasonic A-scan signals at all positions of the defective parts.

[0059] In this embodiment, the defective part of the test piece is scanned by ultrasonic C scanning, and the preset scanning path is planned in advance according to the approximate range of the defective part.

[0060] Ultrasonic C-scanning is a non-destructive testing technology used to detect defects, cracks or other discontinuities inside materials. C-scanning is a two-dimensional imaging technology that uses a probe to move on the surface of the material while emitting and receiving ultrasonic signals. The signal set composed of these signals is used to construct a two-dimensional image of the scanned area. C-scan imaging can show the distribution of defects inside the material. In the C-scan image, each pixel represents a small area at a specific depth inside the material, and the color or brightness indicates the intensity of the echo signal in that area.

[0061] Ultrasonic A-scan signal is the most basic ultrasonic imaging mode, which shows the waveform of the echo signal generated by different interfaces when ultrasonic waves propagate inside the material. The A-scan signal provides one-dimensional information about the internal structure of the material and can show the location and size of defects.

[0062] Therefore, the signal set obtained when performing ultrasonic C-scan on the test piece is actually composed of multiple A-scan signals. Each A-scan signal corresponds to the waveform received by the probe at a specific position, and the entire signal set is used to construct a C-scan image of the entire scan area. By analyzing these A-scan signals, detailed information about the internal defects of the material can be obtained, and finally a C-scan image is formed.

[0063] Therefore, by performing an ultrasonic C scan on the specimen, a signal set of the scanned area can be obtained, namely, an ultrasonic A scan signal.

[0064] The settings of the main detection parameters are shown in Table 1:

[0065]

[0066] S2, obtaining defect echo through data gate according to ultrasonic A-scan signal and bottom surface echo;

[0067] Step S2 specifically includes:

[0068] S21, finding the bottom surface echo in the ultrasonic A-scan signal and performing ultrasonic C-scan imaging to obtain a bottom surface echo C-scan image;

[0069] S22, adjusting the data gate to the defect wave position of the ultrasonic A-scan signal according to the bottom surface echo C-scan image, wherein the A-scan signal corresponding to the defect wave position is the defect echo;

[0070] First, import the collected ultrasonic A-scan signals of all positions into the data processing software, and display the ultrasonic A-scan signals of one position. Select the data gate to perform ultrasonic C-scan imaging on the bottom echo, observe the bottom echo C-scan imaging diagram, and continuously adjust the gate position to adjust the data gate to the defect wave position. The A-scan signal corresponding to the defect wave position is the defect echo. Figure 3 As shown;

[0071] Defect wave: Defect wave refers to the reflected wave generated when the ultrasonic wave encounters the internal defects of the material (such as cracks, pores, inclusions, etc.) during the ultrasonic testing process. These waves appear as peaks that suddenly appear from the normal signal in the ultrasonic A-scan signal. The size, shape and position of the defect wave can provide information about the nature of the defect (such as size, shape and depth).

[0072] Data Gate: Data gate is a technique used in ultrasonic testing systems to select and analyze signals from specific areas. The data gate is a sliding window. By setting the gate, the operator can focus on the signal at a specific depth inside the material and ignore other irrelevant signals. In C-scan imaging, the data gate is used to determine which parts of the A-scan signal will be used to form an image. By adjusting the position of the gate, the ultrasonic wave reflected from the defect location can be "captured".

[0073] Bottom echo: Bottom echo refers to the reflected wave generated when the ultrasonic wave penetrates the material being tested and reaches the bottom surface of the material. In the ultrasonic A-scan signal, the bottom echo is usually a strong signal, indicating that the ultrasonic wave has penetrated the entire material. The position and intensity of the bottom echo can be used to determine the thickness and integrity of the material. In C-scan imaging, the bottom echo is often used as a reference point to help determine the relative position of other defects.

[0074] Because the test block in this embodiment is a three-dimensional woven composite material.

[0075] When the defect is located at the bottom surface of the test block, direct observation of the defect echo will not be obvious, while the bottom surface echo C scan provides a clear image of the bottom surface of the test piece. In this embodiment, the position of the defect echo can be better confirmed by observing the bottom surface echo in the A scan signal, which helps the operator to accurately identify and locate the position of the defect wave (defect echo). By comparing the positions of the bottom surface echo and the defect echo, the depth of the defect in the test piece can be determined more accurately, improving the accuracy and reliability of the detection results.

[0076] In addition, this example also performs C-scan imaging on the defect echo to obtain the original ultrasonic C-scan imaging image of the defect position of the three-dimensional woven composite material.

[0077] S3, performing singular ultrawavelet transform on the defect echo to obtain the time-frequency distribution of the defect echo;

[0078] Specifically, the defect echo is subjected to singular ultrawavelet transformation by preset transformation parameters to obtain the time-frequency distribution of the defect echo, wherein the transformation parameters include an order and a baseline number.

[0079] Specifically, the preset transformation parameters can be selected as the preset transformation parameters of the singular ultrawavelet transform by comparing the changes in the time domain and frequency domain signals of the defect echo under different transformation parameters, and the transformation parameters that can best reveal the defect characteristics are selected.

[0080] The defect echo time-frequency distribution in this embodiment is a super-resolution defect echo time-frequency diagram;

[0081] Singular superlet transform (SST) is a signal processing technique based on wavelet theory. Compared with other time-frequency processing methods, it can achieve highly localized representation of fast and short bursts to provide finer frequency and time resolution, reduce the number of operations and improve signal processing capabilities. The definition follows formula (1);

[0082] ;

[0083] is the singular ultrawavelet transform in the time-frequency domain; where t is the time, is the frequency; * represents convolution; is the order that adaptively adjusts the number of periods in the singular ultrawavelet set to achieve a uniform frequency representation as the frequency increases. is the normalized singular ultra-wavelet basis function, which is defined as shown in formula (2):

[0084] ;

[0085] is the normalized singular ultrawavelet basis function, where represents normalization; Indicates time The differential element, or infinitesimal time interval; is the number of cycles, and is the baseline number used to construct the adaptive increment; and in the above equation, is the singular ultra-wavelet basis function before normalization, which can be calculated according to formula (3):

[0086]

[0087] Among them, The smaller it is, the higher the temporal resolution; is the number of cycles. As a increases, the frequency resolution becomes better. is a correction factor in the log envelope to ensure And is empirically taken as half of the sampling interval; j is an imaginary unit; the term in square brackets is to ensure that the wavelet decays to zero towards infinity in time.

[0088] Specifically, observe the original ultrasonic C-scan image, select the ultrasonic A-scan signal at the defect location, and obtain the defect echo according to the data gate. Perform singular ultrawavelet transform on the defect echo under different transformation parameters (order o, baseline number a), and observe the time-frequency changes under different parameters, such as Figure 4 The figure shows the changes of time domain and frequency domain signals based on voltage signals (in V) under different transformation parameters. The horizontal axis in each figure is time, in ms; the vertical axis is frequency, in MHz. After comprehensively comparing the time domain and frequency domain signals under different parameters, the transformation parameters that can best reveal the defect characteristics are selected as the preset transformation parameters of the singular ultrawavelet transform.

[0089] In a time-frequency diagram, only one of the time and frequency can be kept. This selection rule uses two indicators:

[0090] In order to improve the resolution of the imaging image, a relatively high time resolution is selected. Increasing the order o will increase the time resolution. When the order o reaches a certain value, the effect of improving the time resolution will become less and less obvious, but the corresponding computer calculation time will continue to increase; when a keeps increasing, the frequency resolution will increase, but the corresponding time resolution will be reduced. Because we need to locate the defects, we cannot choose a frequency resolution that is too low; by constantly changing the order o and the baseline number a, the time resolution and calculation time are combined to select the optimal order o and baseline number a.

[0091] Preferably, the order o of the transformation parameters in this example is 50, and the baseline number a is 3.

[0092] It can be understood that in different original ultrasonic C-scan imaging images, the order o and the baseline number a in the transformation parameters can be changed according to specific circumstances.

[0093] S4. According to the time-frequency distribution of the defect echo, several single-frequency points near the center frequency are selected for ultrasonic C-scan imaging to obtain several single-frequency ultrasonic C-scan images;

[0094] S41, expanding the single frequency point range to both sides of the center frequency according to a preset ratio range; the center frequency is the center frequency of the ultrasound probe;

[0095] S42, traversing the single frequency point range at a fixed frequency interval and sequentially extracting the single frequency points in the time-frequency distribution of the defect echo, and then performing ultrasonic C-scan imaging on the single frequency points to obtain a plurality of single frequency point ultrasonic C-scan images;

[0096] The preset ratio in this example is 0.2; it can be increased or decreased according to specific needs.

[0097] The fixed frequency interval in this example is 0.1 MHz, which can be adjusted to increase or decrease according to specific accuracy requirements.

[0098] The center frequency in this example is determined according to the center frequency of the ultrasonic probe. In this embodiment, the center frequency of the ultrasonic probe is 5 MHz. Then, a single frequency point of the defect echo time-frequency diagram in the range of 3 MHz to 7 MHz (incremented by 0.1 MHz) near the 5 MHz range is selected, and ultrasonic C-scan imaging is performed on the single frequency point to obtain a plurality of single frequency point ultrasonic C-scan images, such as Figure 5 shown.

[0099] S5. A fused C-scan image is obtained by fusing several single-frequency ultrasound C-scan images through a spatial multi-scale multi-frequency fusion imaging method; Figure 6 As shown;

[0100] S51, using two-dimensional discrete wavelet transform to perform multi-scale decomposition on each single-frequency ultrasonic C-scan image to obtain characteristic information at each frequency;

[0101] The characteristic information includes: low frequency component information, horizontal high frequency component information, vertical high frequency component information and diagonal high frequency component information;

[0102] S52, fusing the feature information at each frequency using an averaging method to obtain a fused feature map;

[0103] S53, reconstructing the fused feature map into a fused C-scan map through two-dimensional discrete wavelet inverse transform.

[0104] The spatial multi-scale multi-frequency fusion imaging method is an imaging technology that comprehensively utilizes information of different scales and frequencies, aiming to improve imaging quality and information extraction capabilities. This method mainly includes:

[0105] (1) Multiscale decomposition: The image is decomposed into different frequency bands using two-dimensional discrete wavelet transform to analyze the characteristics of different frequency components.

[0106] (2) Information fusion: The extracted feature information is fused by averaging technology. The fused image can better retain the key information in the original data and remove noise and irrelevant features.

[0107] (3) The fused feature map is reconstructed into a high-quality image through inverse wavelet transform.

[0108] The fused C-scan image in this embodiment is the high-quality image obtained in step (3);

[0109] 2D Discrete Wavelet Transform (2D DWT) is a technique used for image processing and signal analysis. It is based on the extension of 1D discrete wavelet transform. It decomposes image signals into different frequency components while maintaining their spatial information, thus providing rich multi-scale and multi-resolution analysis capabilities. , the definition of two-dimensional discrete wavelet transform is shown in formula (4):

[0110] ;

[0111] in, It is the coefficient of the two-dimensional wavelet transform, which represents the local characteristics of the signal at that position and scale; Represents the spatial position of the transform coefficients; is the wavelet basis function, are the scale parameters and location parameters of the wavelet basis; Indicates the width and height of the image. The flow chart is as follows Figure 7 As shown, the circle 2 in the figure represents 2 times downsampling. Each time the wavelet decomposition of the image is performed, it will be divided into 1 low-frequency component information (denoted as LL), which represents the outline of the image; and 3 high-frequency component information, namely horizontal high-frequency component information (denoted as HL), vertical high-frequency component information (denoted as LH), and diagonal high-frequency component information (denoted as HH), which represents the detail information of the image. Different fusion rules will produce different fusion effects. This embodiment only uses simple averaging method to fuse the above component information.

[0112] Specifically, the singular ultrawavelet transform in step S3 is replaced by continuous wavelet transform or short-time Fourier transform or adaptive ultrawavelet transform.

[0113] In other possible implementations, the singular ultrawavelet transform in step S3 may be replaced by a time-frequency transform method, and steps S4 and S5 remain consistent, for comparing the signal-to-noise ratio of ultrasonic C-scan imaging processed by each time-frequency method.

[0114] In this embodiment, the obtained fused C-scan image is compared with different time-frequency processing methods: the original C-scan image, the CWT ultrasound C-scan image after continuous wavelet transform (CWT) processing, the STFT ultrasound C-scan image after short-time Fourier transform (STFT) processing, and the ASLT ultrasound C-scan image after adaptive superlets transform (ASLT) processing. Figure 8 As shown, the results show that the signal-to-noise ratio of the method of the present invention is higher than that of other time-frequency processing methods, and is superior to other methods. The ultrasonic C-scan images under different time-frequency processing methods are shown in Table 2:

[0115]

[0116] The three-dimensional woven composite material ultrasonic imaging method based on singular ultralet in this embodiment effectively solves the problem of signal noise interference in C-scan imaging, which leads to low imaging resolution, and can obtain clearer and high-resolution ultrasonic C-scan images.

[0117] S6. Determine the defect threshold in the fused C-scan image according to the generalized Pareto distribution fitting, and perform binarization reconstruction on the fused C-scan image.

[0118] The binary reconstructed fused C-scan image is segmented to obtain defective parts, and the area of ​​the defect is obtained according to the pixel size and scale of the defective parts.

[0119] The fused C-scan image is subjected to a structural noise removal method using a super-threshold distribution, the core of which is a generalized Pareto distribution.

[0120] The generalized Pareto distribution (GPD) is a probability distribution used to describe extreme values. It is often used to model data that exceeds a certain threshold. In the fields of risk management and insurance actuarial science, GPD is often used to estimate the probability of occurrence and loss size of extreme events.

[0121] Generalized Pareto distribution principle: Let the random variable The distribution function of is shown in formula (5):

[0122] ;

[0123] in Represents a random variable The probability of is the observed random number; is the location parameter, which determines the starting point or threshold of the distribution; is the scale parameter, which controls the variation in the magnitude of the distribution function; is the shape parameter, which determines the change in the shape of the distribution function; hour, ;when hour, , then it is called a random variable It obeys the three-parameter generalized Pareto distribution. From the distribution function, we can know that the corresponding probability density function of the three-parameter generalized Pareto distribution is shown in formula (6):

[0124] ;

[0125] in, is a given parameter , and Next, the random variable The probability density function of . Fig. 9 As shown, the fused C-scan image is fitted with a generalized Pareto distribution for super-threshold data, and the threshold boundary between defects and non-defects is obtained. The value corresponding to the threshold boundary is used as the defect threshold.

[0126] Fig. 9 (a) is a density diagram, which shows the distribution of super-threshold data. The data should be basically consistent with the generalized Pareto distribution fitting. However, since the amplitude of the ultrasonic defect signal is higher than that of the non-defect signal, the area around 0.3 is convex. However, this only shows that this is a defect area and cannot explain the threshold limit. The maximum difference between the fitting line and the density is the threshold limit, but the figure only found it and cannot give a specific value.

[0127] Fig. 9 (b) is the cumulative probability plot, which is Fig. 9 (a) is a supplement, where the intersection F is the maximum difference point, which is also the required threshold limit between defects and non-defects; then binary differentiation is performed based on this threshold limit, the non-defective area can be removed, and the defective part can be further accurately retained, thereby reconstructing a more accurate fused C-scan image, such as Fig.10As shown, the area of ​​the defect can be obtained based on the pixel size and the scale.

[0128] The three-dimensional woven composite material ultrasonic imaging method based on singular ultrawavelet of this embodiment combines the advantages of singular ultrawavelet technology and spatial multi-scale multi-frequency fusion technology, and gives full play to the advantages of multi-frequency fusion, so as to obtain a clearer and higher-resolution ultrasonic C-scan imaging image;

[0129] Furthermore, compared with other time-frequency analysis methods, the three-dimensional woven composite material ultrasonic imaging method based on singular ultrawavelets in this embodiment has a higher signal-to-noise ratio of the image and a better imaging effect;

[0130] Furthermore, this embodiment also uses a super-threshold distribution structural noise removal method to automatically set a threshold, remove non-defect information, and accurately retain defect information, so that the area result of the defect part is more accurate.

[0131] In addition, this embodiment can more accurately determine the depth of the defect in the test piece by comparing the positions of the bottom surface echo and the defect echo, effectively solving the problem that the defect echo is not obvious when the defect is located at the bottom surface, thereby improving the accuracy and reliability of the detection results.

[0132] In addition, an embodiment of the present invention further provides a control device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor and the memory may be connected via a bus or other means.

[0133] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0134] The non-transient software program and instructions required to implement the three-dimensional woven composite material ultrasonic imaging method based on singular ultralet in the above-mentioned embodiment are stored in the memory. When executed by the processor, the three-dimensional woven composite material ultrasonic imaging method based on singular ultralet in the above-mentioned embodiment is executed.

[0135] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] In addition, an embodiment of the present invention also provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are executed by a processor or controller, for example, by the processor of the above-mentioned embodiment, so that the above-mentioned processor can execute the three-dimensional woven composite material ultrasonic imaging method based on singular ultralet in the above-mentioned embodiment.

[0137] It will be appreciated by those skilled in the art that all or some of the steps and systems in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all physical components may be implemented as a microprocessor, such as a central processing unit, a digital signal processor, or software executed by a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0138] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A three-dimensional woven composite material ultrasonic imaging method based on singular ultra-wavelet, characterized in that: The following steps are involved: S1. Perform an ultrasonic C scan on the specimen to obtain an ultrasonic A scan signal of the scanned area; S2, obtaining defect echo through data gate according to ultrasonic A-scan signal and bottom surface echo; S3, performing singular ultrawavelet transform on the defect echo to obtain the time-frequency distribution of the defect echo; S4. According to the time-frequency distribution of the defect echo, several single-frequency points near the center frequency are selected for ultrasonic C-scan imaging to obtain several single-frequency ultrasonic C-scan images; S5, fusing a plurality of single-frequency ultrasound C-scan images by a spatial multi-scale multi-frequency fusion imaging method to obtain a fused C-scan image; Step S3 specifically includes: performing singular ultrawavelet transform on the defect echo by preset transformation parameters to obtain the time-frequency distribution of the defect echo, wherein the transformation parameters include order and baseline number; Step S4 specifically includes: S41, expanding the single frequency point range to both sides of the center frequency according to a preset ratio; the center frequency is the center frequency of the ultrasound probe; S42, traversing the single frequency point range at a fixed frequency interval and sequentially extracting the single frequency points in the time-frequency distribution of the defect echo, and then performing ultrasonic C-scan imaging on the single frequency points to obtain a plurality of single frequency point ultrasonic C-scan images; Step S5 specifically includes: S51, using two-dimensional discrete wavelet transform to perform multi-scale decomposition on each single-frequency ultrasonic C-scan image to obtain characteristic information at each frequency; S52, fusing the feature information at each frequency using an averaging method to obtain a fused feature map; S53, reconstructing the fused feature map into a fused C-scan map through two-dimensional discrete wavelet inverse transform.

2. The ultrasonic imaging method according to claim 1, characterized in that: Step S2 specifically includes: S21, finding the bottom surface echo in the ultrasonic A-scan signal and performing ultrasonic C-scan imaging to obtain a bottom surface echo C-scan image; S22. Adjust the data gate to the defect wave position of the ultrasonic A-scan signal according to the bottom surface echo C-scan image. The A-scan signal corresponding to the defect wave position is the defect echo.

3. The ultrasonic imaging method according to claim 1, characterized in that: The method further includes: S6, determining a defect threshold in the fused C-scan image according to generalized Pareto distribution fitting, and performing binarization reconstruction on the fused C-scan image.

4. The ultrasonic imaging method according to claim 3, characterized in that: Step S6 also includes: The binary reconstructed fused C-scan image is segmented to obtain defective parts, and the area of ​​the defect is obtained according to the pixel size and scale of the defective parts.

5. The ultrasonic imaging method according to claim 1, characterized in that: By comparing the changes in the time domain and frequency domain signals of the defect echo under different transformation parameters, the transformation parameters that can best reveal the defect characteristics are selected as the preset transformation parameters of the singular ultrawavelet transform.

6. The ultrasonic imaging method according to claim 1, characterized in that: The fixed frequency interval in step S42 is 0.1 MHz.

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