A method and system for defect ultrasonic imaging in a strong noise background

Abstract

The application relates to the technical field of defect ultrasonic detection, and provides a defect ultrasonic imaging method and system under a strong noise background, which comprises the following steps: performing iterative scanning ultrasonic detection on a to-be-detected defect sample to obtain a first initial signal point by point, and performing iterative scanning ultrasonic detection on a reference sample to obtain a second initial signal point by point; performing noise reduction pretreatment on the first initial signal to obtain a first noise reduction signal, and performing noise reduction pretreatment on the second initial signal to obtain a second noise reduction signal; performing signal matching operation on the first noise reduction signal and the second noise reduction signal, and then performing background difference subtraction calculation to obtain a scattering characteristic signal; performing multi-directional adjacent wave difference subtraction calculation on the scattering characteristic signal to obtain a defect characteristic signal; and drawing an image according to the defect characteristic signal; and judging whether the to-be-detected defect sample has defects according to the image. The method and system can realize clear imaging of defects, and improve the precision and detection efficiency of online detection of defects.

Description

Technical Field

[0001] This application relates to the field of defect detection technology, and in particular to a defect ultrasonic imaging method and system under strong noise background. Background Technology

[0002] With the development of intelligent manufacturing, online inspection can promptly detect product defects and automatically or manually intervene in the manufacturing process to improve product quality. Therefore, to effectively detect product defects and improve product quality, online inspection is widely used for product quality control. Online inspection methods mainly include laser ultrasonic testing, infrared thermal imaging, and CCD imaging, with laser ultrasonic testing being the most commonly used.

[0003] However, due to the strong scattering of the material's microstructure and the strong environmental noise, a strong noise background is formed during the defect detection process, which often leads to a low signal-to-noise ratio of the laser ultrasonic detection signal, resulting in a blurry defect edge image and affecting the accuracy of online real-time detection. Summary of the Invention

[0004] This application provides a method and system for ultrasonic imaging of defects under strong noise background, which can achieve clear imaging of defects and improve the accuracy and efficiency of online detection.

[0005] To achieve the above objectives, in a first aspect, this application provides a defect ultrasound imaging method under strong noise background, comprising:

[0006] The test sample with defects is subjected to traversal scanning ultrasonic testing to obtain a point-by-point first initial signal, and the reference sample is subjected to traversal scanning ultrasonic testing to obtain a point-by-point second initial signal.

[0007] The first initial signal is subjected to noise reduction preprocessing to obtain a first noise-reduced signal, and the second initial signal is subjected to noise reduction preprocessing to obtain a second noise-reduced signal;

[0008] Signal matching operation is performed on the first denoised signal and the second denoised signal, and then background difference subtraction calculation is performed to obtain the scattering feature signal;

[0009] The defect feature signal is obtained by performing multi-directional adjacent wave differential subtraction calculation on the scattering feature signal;

[0010] An image is drawn based on the defect feature signals;

[0011] Based on the image, determine whether the test sample has a defect and evaluate its defect characteristics.

[0012] In an implementable embodiment, the first initial signal is obtained by performing a full scan ultrasonic detection on the test sample, and the second initial signal is obtained by performing a full scan ultrasonic detection on the reference sample.

[0013] The single-point multi-excitation is used to obtain the first initial signal when performing the full scan ultrasonic detection on the test sample, and the single-point multi-excitation is used to obtain the second initial signal when performing the full scan ultrasonic detection on the reference sample.

[0014] In an implementable embodiment, the first initial signal is obtained by performing a full scan ultrasonic detection on the test sample, and the second initial signal is obtained by performing a full scan ultrasonic detection on the reference sample.

[0015] The first initial signal is obtained by performing a full scan ultrasonic detection on the test sample, and the second initial signal is obtained by performing a full scan ultrasonic detection on the reference sample.

[0016] The first initial signal is obtained by performing a full scan ultrasonic detection on the test sample, and the second initial signal is obtained by performing a full scan ultrasonic detection on the reference sample.

[0017] In an implementable embodiment, the first initial signal is obtained by performing a full scan ultrasonic detection on the test sample, and the second initial signal is obtained by performing a full scan ultrasonic detection on the reference sample.

[0018] The first initial signal is obtained by performing a full scan ultrasonic detection on the test sample, and the second initial signal is obtained by performing a full scan ultrasonic detection on the reference sample.

[0019] The first initial signal is obtained by performing a full scan ultrasonic detection on the test sample, and the second initial signal is obtained by performing a full scan ultrasonic detection on the reference sample.

[0020] The first initial signal is obtained by performing a full scan ultrasonic detection on the test sample, and the second initial signal is obtained by performing a full scan ultrasonic detection on the reference sample.

[0021] The first initial signal is obtained by performing a full scan ultrasonic detection on the test sample, and the second initial signal is obtained by performing a full scan ultrasonic detection on the reference sample.

[0022] In an implementable embodiment, the first initial signal is obtained by performing a full scan ultrasonic detection on the test sample, and the second initial signal is obtained by performing a full scan ultrasonic detection on the reference sample.

[0023] The first initial signal is obtained by performing a full scan ultrasonic detection on the test sample, and the second initial signal is obtained by performing a full scan ultrasonic detection on the reference sample.

[0024] The first initial signal is obtained by performing a full scan ultrasonic detection on the test sample, and the second initial signal is obtained by performing a full scan ultrasonic detection on the reference sample.

[0025] judging the size of the first maximum cross-correlation coefficient and the set cross-correlation coefficient threshold value, if the first maximum cross-correlation coefficient is less than the set cross-correlation coefficient threshold value, then performing difference operation on the first noise reduction signal and the matching signal to obtain the scattering characteristic signal; if the first maximum cross-correlation coefficient is greater than the set cross-correlation coefficient threshold value, then the scattering characteristic signal is recorded as zero.

[0026] In a feasible implementation, the multi-directional adjacent wave difference subtraction calculation on the scattering characteristic signal to obtain the defect characteristic signal comprises:

[0027] Obtaining the 0° adjacent wave signal of the scattering characteristic signal, matching the 0° adjacent wave signal with the scattering characteristic signal to obtain the 0° matching signal, and obtaining the 0° defect characteristic signal according to the scattering characteristic signal and the 0° matching signal.

[0028] Obtaining the 45° adjacent wave signal of the scattering characteristic signal, matching the 45° adjacent wave signal with the scattering characteristic signal to obtain the 45° matching signal, and obtaining the 45° defect characteristic signal according to the scattering characteristic signal and the 45° matching signal.

[0029] Obtaining the 90° adjacent wave signal of the scattering characteristic signal, matching the 90° adjacent wave signal with the scattering characteristic signal to obtain the 90° matching signal, and obtaining the 90° defect characteristic signal according to the scattering characteristic signal and the 90° matching signal.

[0030] In a feasible implementation, the obtaining the 0° adjacent wave signal of the scattering characteristic signal, matching the 0° adjacent wave signal with the scattering characteristic signal to obtain the 0° matching signal, and obtaining the 0° defect characteristic signal according to the scattering characteristic signal and the 0° matching signal comprises:

[0031] Selecting the waveforms adjacent to the scattering characteristic signal along the 0° direction as the 0° adjacent wave signal, performing cross-correlation operation on the 0° adjacent wave signal and the scattering characteristic signal to obtain a second maximum cross-correlation coefficient and a corresponding second delay time;

[0032] Matching the 0° adjacent wave signal with the scattering characteristic signal according to the second delay time to obtain the 0° matching signal;

[0033] Performing difference operation on the scattering characteristic signal and the 0° matching signal to obtain the 0° defect characteristic signal.

[0034] In a feasible implementation, the drawing an image according to the defect characteristic signal comprises:

[0035] Setting a tomographic interval and a tomographic depth;

[0036] According to the tomographic interval and the tomographic depth, the defect characteristic signal is mapped to a two-dimensional tomographic image based on a variable time window energy mapping method;

[0037] The two-dimensional tomographic image is binarized;

[0038] According to the position information of each layer image, the binarized pixel points in the each layer image are mapped to a three-dimensional space based on a spatial relationship, forming discrete three-dimensional volume pixel points;

[0039] A three-dimensional image is obtained according to the discrete three-dimensional volume pixel points.

[0040] In a feasible implementation, the judging whether the defect sample to be measured has defects and the defect feature evaluation according to the image include:

[0041] It is judged whether there is a defect by image recognition technology or comparing pixel values of the image, and the position and size of the defect are confirmed.

[0042] To achieve the above object, in a second aspect, the application provides a defect ultrasonic imaging system in a strong noise background, comprising:

[0043] A scanning ultrasonic detection module is configured to perform traversing scanning ultrasonic detection on a defect sample to be measured to obtain an initial first initial signal, and perform traversing scanning ultrasonic detection on a reference sample to obtain an initial second initial signal;

[0044] A signal processing module is configured to perform noise reduction preprocessing on the first initial signal to obtain a first noise reduction signal, and perform noise reduction preprocessing on the second initial signal to obtain a second noise reduction signal; perform signal matching operation on the first noise reduction signal and the second noise reduction signal, and then perform background difference subtraction calculation to obtain a scattering characteristic signal; perform multi-directional adjacent wave difference subtraction calculation on the scattering characteristic signal to obtain a defect characteristic signal; draw an image according to the defect characteristic signal; judge whether the defect sample to be measured has defects according to the image, and perform defect feature evaluation.

[0045] The application has the following beneficial effects:

[0046] This application provides a method and system for ultrasonic imaging of defects under strong noise background. First, a scanning ultrasonic test is performed on the sample to be tested to obtain a point-by-point first initial signal. Then, a traversal scanning ultrasonic test is performed on a reference sample to obtain a point-by-point second initial signal. The first initial signal is pre-processed for noise reduction to obtain a first denoised signal, and the second initial signal is pre-processed for noise reduction to obtain a second denoised signal. Signal matching operations are performed on the first and second denoised signals, followed by background subtraction to obtain a scattering feature signal. The scattering feature signal is then subjected to multi-directional adjacent-wave differential subtraction to obtain a defect feature signal. An image is plotted based on the defect feature signal. The presence of a defect in the sample to be tested is determined based on the image, and defect features are evaluated. This application integrates background subtraction and multi-directional adjacent-wave differential methods. On the one hand, background subtraction suppresses strong noise background in the material's microstructure, achieving noise reduction of the material's microstructure noise; on the other hand, it enables rapid extraction of defect scattering feature signals. On the other hand, multi-directional adjacent wavelet difference is used to enhance the boundary detail features of micro-defects, improve the sensitivity to the direction of micro-defects such as cracks, thereby improving the efficiency of online detection while effectively avoiding the problem of low defect imaging accuracy, and thus improving the quality of defect imaging. Attached Figure Description

[0047] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0048] Figure 1 A flowchart of a defect ultrasound imaging method under strong noise background, which is one of the embodiments provided in this application;

[0049] Figure 2 A diagram illustrating the point-by-point signal processing process in a defect ultrasound imaging method under strong noise background, one of the embodiments provided in this application.

[0050] Figure 3 A flowchart of a method for obtaining a first noise-reduced signal according to some embodiments provided in this application;

[0051] Figure 4 A flowchart illustrating another method for obtaining a first noise-reduced signal according to some embodiments provided in this application;

[0052] Figure 5 A flowchart of a method for obtaining scattering characteristic signals according to some embodiments provided in this application;

[0053] Figure 6 A method flowchart for obtaining a respective directional defect signature for some embodiments provided herein;

[0054] Figure 7 A method flowchart for obtaining a 0° defect signature for some embodiments provided herein;

[0055] Figure 8 A method flowchart for rendering an image from defect signatures for some embodiments provided herein;

[0056] Figure 9 A method flowchart for obtaining a two-dimensional tomographic image for some embodiments provided herein;

[0057] Figure 10 A method flowchart for obtaining a three-dimensional image from a two-dimensional tomographic image for some embodiments provided herein;

[0058] Figure 11 A block diagram of a defect ultrasound imaging system in a strong noise background for some embodiments provided herein. DETAILED DESCRIPTION

[0059] The technical solutions of the present application will be described clearly and completely below in conjunction with the embodiments. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0060] In addition, the terms "first", "second", etc. are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0061] With the development of intelligent manufacturing, additive manufacturing technology has the advantages of short cycle, high efficiency, and material saving compared with traditional subtractive and equal material manufacturing technology, and can realize "macro-micro structure integration" and "material-design-manufacturing" integrated net forming of complex components, which has become one of the key core technologies to improve the design and manufacturing capability of high-performance complex components in high-end fields such as aerospace. However, due to the forming process, the moving point heat source is instantaneously heated and melted, and then rapidly cooled, and involves complex thermal physical phenomena such as solid-thermal gas coupling, which is easy to produce metallurgical defects in the printing process. The formation mechanism is complex, and there are many types of defects, including thermal cracks, solidification cracks, metallurgical porosity, spoon holes, shrinkage holes, and incomplete fusion, which are distributed in layers with a cross-scale of tens of microns to several millimeters.

[0062] In order to realize the detection of the product quality of additive manufacturing production, the commonly used detection methods are offline detection and online detection. Since online detection can timely find product defects and automatically or manually intervene in the manufacturing process, it can improve the product manufacturing quality. In order to effectively detect product defects and improve product quality, online detection is widely used in the detection of product quality. The online detection methods mainly include laser ultrasonic method, infrared thermal imaging method, CCD imaging method, etc., among which the laser ultrasonic method is more commonly used.

[0063] However, as the main reason for providing a strong noise environment in a strong noise background, additive manufacturing can cause low resolution of online detection. Specifically, the complex online detection environment in the additive manufacturing printing forming cavity, such as high temperature, high pressure, dust, vibration, etc., is easy to introduce strong environmental noise; the surface roughness of the additive manufacturing part can affect the penetration of the ultrasonic signal. These factors will cause the ultrasonic signal received by the ultrasonic signal receiver to usually have strong noise signals. These noise signals are often stronger than the ultrasonic signals generated by micro defects, so that the defect signals are submerged in the noise signals, causing misjudgment and missed detection of defects. At the same time, since online detection is carried out synchronously during the manufacturing process, the manufacturing process of additive manufacturing can also affect the signal-to-noise ratio of the ultrasonic signal and affect the detection of defects.

[0064] In addition, the main reason for forming a strong noise background not only includes a strong noise environment, but also the characteristics of the material itself. For materials with strong scattering, such as duplex materials, the material microstructure inhomogeneity and obvious anisotropy can easily introduce material structure scattering noise; internal and external factors comprehensively cause the ultrasonic scattering signal-to-noise ratio of internal micro-defects to be low, and the characteristic signal is difficult to identify and extract. The products formed by additive manufacturing or subtractive manufacturing production process of such materials have strong scattering characteristics due to the material itself. The spectral characteristics of the internal microstructure scattering noise and the micro-defect scattering characteristic signal are similar, which cannot effectively suppress the strong scattering material structure noise, resulting in low micro-defect detection resolution or even difficulty in detection.

[0065] Further, the defects inside the product are often micro-defects. Due to the weak amplitude of the photoacoustic scattering characteristic signal of the micro-defects, the edge of the defect imaging is often blurred, and the contrast is not high, thereby reducing the defect imaging quality and the quantitative characterization accuracy. The conventional synthetic aperture focusing, full focusing and other defect characteristic signal enhancement methods used in photoacoustic detection often need to store the full matrix data of the scanning points, and the data storage amount is large, and the real-time imaging efficiency is low.

[0066] In summary, under the influence of strong noise background and micro-defect characteristics, it is urgent to provide a defect imaging method which can overcome the influence of strong noise background and can realize clear imaging of micro-defects.

[0067] Therefore, the present application provides a defect ultrasonic imaging method and system in a strong noise background, which comprises: performing scanning ultrasonic detection on a to-be-tested defect sample to obtain a first initial signal point by point, and performing scanning ultrasonic detection on a reference sample to obtain a second initial signal point by point; performing noise reduction preprocessing on the first initial signal to obtain a first noise reduction signal, and performing noise reduction preprocessing on the second initial signal to obtain a second noise reduction signal; performing signal matching operation on the first noise reduction signal and the second noise reduction signal, and then performing background difference subtraction calculation to obtain a scattering characteristic signal; performing multi-directional neighbor wave difference subtraction calculation on the scattering characteristic signal to obtain a defect characteristic signal; drawing an image according to the defect characteristic signal; judging whether the to-be-tested defect sample has defects according to the image, and evaluating the defect characteristics. That is, by combining the background difference and the multi-directional neighbor wave difference method, on the one hand, the background difference is used to suppress the strong noise background of the material microstructure, realize the noise reduction processing of the material organization structure noise, and also realize the rapid extraction of the defect scattering characteristic signal. On the other hand, the multi-directional neighbor wave difference is used to enhance the boundary detail characteristics of the micro-defects, improve the sensitivity to the direction of cracks and other micro-defects, thereby improving the online detection efficiency, effectively avoiding the problem of low defect imaging precision, and further improving the defect imaging quality.

[0068] In the following, a defect ultrasonic imaging method and system in a strong noise background provided by the present application will be introduced in combination with the drawings and specific embodiments.

[0069] Please refer to Figure 1 and Figure 2 The present application provides a defect ultrasonic imaging method and system in a strong noise background, which comprises:

[0070] Step S1, performing scanning ultrasonic detection on a to-be-tested defect sample to obtain a first initial signal point by point, and performing scanning ultrasonic detection on a reference sample to obtain a second initial signal point by point.

[0071] In the embodiment, the to-be-tested defect sample is a sample of a to-be-tested product, which can be obtained by additive manufacturing or other manufacturing methods. Meanwhile, the reference sample is a final formed sample of a target product that meets the quality requirements, so that the ultrasonic signal realized by the reference is used as a reference to prepare for subsequent background difference processing.

[0072] Further, the ultrasonic detection in the embodiment is laser ultrasonic detection. Laser ultrasonic detection is a non-contact, high-temperature-resistant and high-precision detection technology. Compared with traditional ultrasonic detection, laser ultrasonic detection has obvious improvements in detection efficiency and detection precision, and its non-contact and high-temperature-resistant characteristics can meet the online detection requirements of additive manufacturing parts and the detection requirements of pipelines and containers in dangerous environments.

[0073] The first initial signal and the second initial signal are ultrasonic signals generated by exciting the surface of the sample. The first initial signal and the second initial signal obtained by the traversal scanning are point-by-point, and the subsequent processing and calculation of the first initial signal and the second initial signal are point-by-point corresponding calculation. Each signal obtained is a signal corresponding to each point, and the subsequent details are omitted.

[0074] Specifically, step S1 can include:

[0075] When the to-be-tested defect sample is subjected to traversal scanning ultrasonic detection, single-point multiple excitation is used to obtain a plurality of first initial signals point by point. When the reference sample is subjected to traversal scanning ultrasonic detection, single-point multiple excitation is used to obtain a plurality of second initial signals point by point.

[0076] In the embodiment, obtaining a plurality of first initial signals point by point and a plurality of second initial signals point by point by single-point multiple excitation can facilitate subsequent noise reduction processing of obvious noise in the signals.

[0077] Step S2, the first initial signal is subjected to noise reduction preprocessing to obtain a first noise reduction signal, and the second initial signal is subjected to noise reduction preprocessing to obtain a second noise reduction signal.

[0078] In the embodiment, since the detection signal is affected by the environment, the generator and the receiver and other factors during generation and reception, obvious noise such as high-frequency electrical noise and low-frequency oscillation signal can be generated. Therefore, when the first initial signal and the second initial signal are obtained, noise reduction preprocessing is needed to remove the obvious noise included therein, and the first noise reduction signal and the second noise reduction signal are obtained correspondingly to improve the efficiency and precision of subsequent signal processing.

[0079] Please refer to Figure 3 In an example, step S2 can be realized by the following method:

[0080] Step S211, average operation is performed on the plurality of first initial signals to obtain a first de-noised signal;

[0081] Step S212, average operation is performed on the plurality of second initial signals to obtain a second de-noised signal.

[0082] In the embodiment, when the first initial signal and the second initial signal are de-noised and pre-processed, a single-point multi-stimulation mean value method can be used to weaken the influence of a signal with obvious noise in the mean value of the plurality of first initial signals obtained through single-point multi-stimulation, so as to realize the de-noising pre-processing of the first initial signal. Similarly, a single-point multi-stimulation mean value method can be used to weaken the influence of a signal with obvious noise in the mean value of the plurality of second initial signals obtained through multi-stimulation, so as to realize the de-noising pre-processing of the second initial signal.

[0083] It is worth noting that the order of obtaining the first de-noised signal and the second de-noised signal can be adjusted. The first de-noised signal can be obtained first, and then the second de-noised signal can be obtained. Alternatively, the second de-noised signal can be obtained first, and then the first de-noised signal can be obtained. Alternatively, the first de-noised signal and the second de-noised signal can be obtained simultaneously. That is, the order of steps S211 to S212 can be adjusted at will, and is not limited in the embodiment.

[0084] Please refer to Figure 4 In another example, step S2 can also be implemented by the following method:

[0085] Step S221, average operation is performed on the plurality of first initial signals to obtain a first average signal;

[0086] Step S222, one de-noising and two de-noising are performed on the first average signal to obtain a first de-noised signal;

[0087] Step S223, average operation is performed on the plurality of second initial signals to obtain a second average signal;

[0088] Step S224, one de-noising and two de-noising are performed on the second average signal to obtain a second de-noised signal.

[0089] In the embodiment, after the plurality of first initial signals and the plurality of second initial signals are respectively averaged, the first average signal and the second average signal can be obtained. In order to improve the de-noising effect, one de-noising and two de-noising can be further performed on the first average signal and the second average signal respectively to improve the de-noising efficiency and the de-noising effect, so as to obtain ideal first de-noised signal and second de-noised signal.

[0090] Specifically, the primary noise reduction and secondary noise reduction processes described above can be wavelet noise reduction, Hilbert Huang noise reduction, and deep learning auto-encoding noise reduction, etc. The primary noise reduction and secondary noise reduction processes can be different, so as to further improve the noise reduction effect and reduce the influence of obvious noise on the subsequent defect imaging accuracy.

[0091] It is worth noting that the order of obtaining the first noise reduction signal and the second noise reduction signal described above can be adjusted. The first noise reduction signal can be obtained first, and then the second noise reduction signal can be obtained. Alternatively, the second noise reduction signal can be obtained first, and then the first noise reduction signal can be obtained. Alternatively, the first noise reduction signal and the second noise reduction signal can be obtained simultaneously. The order of steps S221 to S224 described above can be adjusted at will, and the order is not limited in the embodiment.

[0092] In step S3, signal matching operation is performed on the first noise reduction signal and the second noise reduction signal, and background difference subtraction calculation is performed to obtain a scattering characteristic signal.

[0093] In the embodiment, the background difference method is mainly based on the reciprocity difference principle. The basic idea is to compare the differences between the ultrasonic wave field of the non-uniform medium containing scattering sources such as grains or defects and the ultrasonic wave field of the uniform reference medium, to measure the degree of scattering wave field disturbance caused by the non-uniformity of the medium, and to obtain the scattering signal amplitude.

[0094] Since the first noise reduction signal is the signal of the defect sample to be tested, which may have defects, and the second noise reduction signal is the signal of the reference sample, which does not have or has negligible defects, in order to determine whether the first noise reduction signal includes defect information, the first noise reduction signal can be subtracted from the second noise reduction signal, that is, the background signal characteristics in the first noise reduction signal are subtracted, and only the defect signal characteristics in the first noise reduction signal are left, to obtain the scattering characteristic signal of the defect, so as to facilitate the subsequent defect detection and imaging operation. The scattering characteristic signal is the scattering characteristic signal wave emitted in all directions by the secondary sound source due to the difference in acoustic impedance between the defect and the grain, and between the grains, during the ultrasonic wave propagation process. In the embodiment, the point-by-point scattering characteristic signal of the defect sample to be tested can be obtained by the above method, so as to facilitate subsequent processing.

[0095] Please refer to Figure 5 Further, the embodiment also provides a method capable of implementing step S3, comprising:

[0096] In step S31, cross-correlation operation is performed on the first noise reduction signal and the second noise reduction signal to obtain a first maximum cross-correlation coefficient and a corresponding first delay time.

[0097] In the embodiment, since the first noise reduction signal and the second noise reduction signal can have a certain delay, a phase difference exists between the first noise reduction signal and the second noise reduction signal, and if the first noise reduction signal and the second noise reduction signal are directly subtracted at this time, the scattering characteristic signal obtained is inaccurate. Therefore, first, the first noise reduction signal and the second noise reduction signal need to be cross-correlated to obtain the first maximum cross-correlation coefficient and the corresponding first delay time, so as to subsequently adjust the phase difference between the first noise reduction signal and the second noise reduction signal.

[0098] Step S32, matching the second noise reduction signal to the first noise reduction signal according to the first delay time to obtain a matching signal.

[0099] In the embodiment, after the first delay time is obtained, the second noise reduction signal can be matched to the first noise reduction signal according to the first delay time, so as to eliminate the phase difference between the first noise reduction signal and the second noise reduction signal, align the second noise reduction signal with the first noise reduction signal, and obtain the matching signal.

[0100] Step S33, judging the size of the first maximum cross-correlation coefficient and the set cross-correlation coefficient threshold value, if the first maximum cross-correlation coefficient is less than the set cross-correlation coefficient threshold value, performing difference operation on the first noise reduction signal and the matching signal to obtain the scattering characteristic signal; if the first maximum cross-correlation coefficient is greater than the set cross-correlation coefficient threshold value, the scattering characteristic signal is recorded as zero.

[0101] In the embodiment, when the first maximum cross-correlation coefficient is less than the set cross-correlation coefficient threshold value, it indicates that there is a defect at the position of the point, and then the difference operation needs to be performed on the first noise reduction signal and the matching signal to obtain the scattering characteristic signal, so as to facilitate subsequent imaging of the defect; when the first maximum cross-correlation coefficient is greater than the set cross-correlation coefficient threshold value, the difference result of the point can be directly recorded as 0, that is, the point is considered to have no defect, so that subsequent operation is not necessary, and the detection efficiency is improved.

[0102] Preferably, the cross-correlation coefficient threshold value can be divided into a defective area and a non-defective area based on a machine learning clustering algorithm, and the first maximum cross-correlation coefficient in the defective area is set as the cross-correlation coefficient threshold value.

[0103] Step S4, performing multi-directional neighbor wave difference subtraction calculation on the scattering characteristic signal to obtain a defect characteristic signal.

[0104] In the embodiment, the scattering characteristic signal of the micro-defect is weak, resulting in blurred edges of the defect imaging and affecting the resolution of the imaging. The basic principle of the multi-directional adjacent wave difference method is to use the difference between the ultrasonic scattering characteristic signals of the defective and non-defective positions, to amplify the abnormal wave of the defect scattering by differentially calculating the time-domain waveforms of the adjacent detection points in different directions, to suppress the incident signal and the grain scattering signal, to enhance the defect characteristic signal, and to improve the signal strength of the micro-defect and facilitate clear imaging of the defect.

[0105] Please refer to Figure 6 Further, the embodiment also provides a method capable of realizing step S4, comprising:

[0106] Step S41, obtaining the 0° adjacent wave signal of the scattering characteristic signal, matching the 0° adjacent wave signal with the scattering characteristic signal to obtain a 0° matching signal, and obtaining a 0° defect characteristic signal according to the scattering characteristic signal and the 0° matching signal.

[0107] In the embodiment, in order to compare the time-domain waveforms of the adjacent points in different directions and enhance the signal strength at the edges of the defect, the 0° direction adjacent wave can be preferred for comparison. The 0° matching signal is obtained by matching the 0° adjacent wave signal with the scattering characteristic signal, so as to reduce the error caused by the phase difference between the two signals, thereby improving the accuracy of the 0° defect characteristic signal.

[0108] It is worth noting that the 0° direction adjacent wave signal described above can be the signal of the adjacent point found to the right along the 0° direction, or the signal of the adjacent point found to the left, or the signal of the adjacent point found in both directions, or the 0° adjacent wave signal found along other specified directions, which is not limited in the embodiment.

[0109] Please refer to Figure 7 For example, the step S41 described above can be realized by the following method, comprising:

[0110] Step S411, selecting the waveform adjacent to the scattering characteristic signal along the 0° direction as the 0° adjacent wave signal, and performing cross-correlation operation on the 0° adjacent wave signal and the scattering characteristic signal to obtain a second maximum cross-correlation coefficient and a corresponding second delay time.

[0111] In the embodiment, since the scattering characteristic signal and the 0° adjacent wave signal can have a certain delay, a phase difference exists between the scattering characteristic signal and the 0° adjacent wave signal. If the scattering characteristic signal and the 0° adjacent wave signal are directly subtracted, the scattering characteristic signal obtained is inaccurate. Therefore, first, the scattering characteristic signal and the 0° adjacent wave signal need to be cross-correlated to obtain a second maximum cross-correlation coefficient and a corresponding second delay time, so as to subsequently adjust the phase difference between the scattering characteristic signal and the 0° adjacent wave signal.

[0112] In step S412, the 0° adjacent wave signal is matched to the scattering characteristic signal according to the second delay time to obtain a 0° matched signal.

[0113] In the embodiment, after the second delay time is obtained, the 0° adjacent wave signal can be matched to the scattering characteristic signal according to the second delay time, so as to eliminate the phase difference between the scattering characteristic signal and the 0° adjacent wave signal, align the 0° adjacent wave signal with the first denoised signal, and obtain the 0° matched signal.

[0114] In step S413, the scattering characteristic signal and the 0° matched signal are subtracted to obtain a 0° defect characteristic signal.

[0115] In the embodiment, by subtracting the scattering characteristic signal and the 0° matched signal, the difference between the scattering characteristic signal and the 0° adjacent wave can be amplified, the 0° defect characteristic signal is obtained, and the defect characteristic of the scattering characteristic signal in the direction can be highlighted, so as to facilitate clear imaging of the defect.

[0116] In step S42, a 45° adjacent wave signal of the scattering characteristic signal is obtained, the 45° adjacent wave signal is matched to the scattering characteristic signal to obtain a 45° matched signal, and a 45° defect characteristic signal is obtained according to the scattering characteristic signal and the 45° matched signal.

[0117] In the embodiment, by observing the grid data, the points adjacent to the scattering characteristic signal also include the points adjacent in the 45° direction. Therefore, the 45° adjacent wave can be selected for comparison. The 45° adjacent wave signal is matched to the scattering characteristic signal to obtain the 45° matched signal, so as to reduce the error caused by the phase difference between the two signals, thereby improving the accuracy of the 45° defect characteristic signal.

[0118] It can be understood that the search direction of the 45° adjacent wave signal is the same as the search direction of the 0° adjacent wave signal, which is not described herein again. Meanwhile, the specific acquisition process of the 45° defect characteristic signal can refer to the acquisition process of the 0° defect characteristic signal, which is not described herein again.

[0119] Step S43, obtaining a 90° adjacent wave signal of the scattering characteristic signal, matching the 90° adjacent wave signal with the scattering characteristic signal to obtain a 90° matching signal, and obtaining a 90° defect characteristic signal according to the scattering characteristic signal and the 90° matching signal.

[0120] In the embodiment, in addition to the 0° direction and the 45° direction, the point adjacent to the scattering characteristic signal also includes the 90° direction. Therefore, the 90° adjacent wave needs to be selected for comparison. The 90° matching signal is obtained by matching the 90° adjacent wave signal with the scattering characteristic signal, so as to reduce the error caused by the phase difference between the two signals, thereby improving the accuracy of the 90° defect characteristic signal.

[0121] It can be understood that the searching direction of the 90° adjacent wave signal is the same as the searching direction of the 0° adjacent wave signal, which will not be described herein. Meanwhile, the specific obtaining process of the 90° defect characteristic signal can refer to the obtaining process of the 0° defect characteristic signal, which will not be described herein.

[0122] In the embodiment, in some embodiments, after obtaining the defect characteristic signal, it can be firstly judged by the defect characteristic signal whether the point has a defect, and a simple judgment is performed.

[0123] For example, the defect characteristic signal includes the 0° defect characteristic signal, the 45° defect characteristic signal and the 90° defect characteristic signal. Therefore, when judging whether there is a defect, the point needs to be comprehensively judged according to the defect characteristic signals in three different directions. Meanwhile, because the defect characteristic signals generated by different defects are different, especially obviously different in different directions, the defect characteristic signals in different directions can also represent the main form of the defect, thereby obtaining the defect type. Specifically, in order to improve the judgment efficiency of the defect characteristic, after obtaining the 0° defect characteristic signal, the 45° defect characteristic signal and the 90° defect characteristic signal, the defect characteristic signals in three directions can be summed to obtain a total defect characteristic signal. When judging whether the point has a defect, the total defect characteristic signal obtained by the summation can be firstly judged. If the total defect characteristic signal has no defect, it means that the point has no defect. If the total defect characteristic signal has a defect, it means that the point has a defect, and the defect characteristic signals in different directions are further judged, thereby obtaining the direction of the defect and the defect type. In the embodiment, by firstly judging the total defect characteristic signal and then judging the defect characteristic signals in different directions, the judgment efficiency of the defect can be effectively improved, thereby further improving the defect detection efficiency.

[0124] Step S5, drawing an image according to the defect characteristic signal.

[0125] In the embodiment, when the sample to be tested has defects, the defect image is drawn, and if there is no defect, no drawing is needed.

[0126] Please refer to Figure 8 For example, the embodiment further provides an implementation method of step S5, including:

[0127] Step S51, drawing a two-dimensional tomographic image according to the defect characteristic signal;

[0128] In the embodiment, when drawing the image, the two-dimensional tomographic image can be drawn first, so that the image of the defect on each layer can be obtained, facilitating the obtaining of the three-dimensional image. The two-dimensional tomographic image is an image formed by sequentially arranging a plurality of two-dimensional images according to the position information of each layer image.

[0129] Please refer to Figure 9 Specifically, the above step S51 includes:

[0130] Step S511, setting a tomographic interval and a tomographic depth.

[0131] In the embodiment, when drawing the two-dimensional tomographic image, the tomographic interval and the tomographic depth need to be set according to the desired resolution, so as to obtain the two-dimensional image corresponding to the number of layers.

[0132] Step S512, obtaining the two-dimensional tomographic image based on the variable time window energy mapping method according to the tomographic interval and the tomographic depth.

[0133] In the embodiment, the variable time window energy mapping method refers to calculating the time window start time according to the tomographic depth, determining the time window length according to the tomographic interval, summing the square of the amplitude of the defect characteristic signal in the time window of the detection point, obtaining the defect energy value at the corresponding depth of the detection point, obtaining the defect energy value of all detection points in the same depth according to the mapping relationship between the detection point defect energy value and the detection point position, and drawing the two-dimensional image of the defect at the corresponding depth. The two-dimensional defect images of each layer are arranged according to their depth positions, so as to obtain the two-dimensional tomographic image of the defect.

[0134] Step S52, three-dimensional reconstruction is performed according to the two-dimensional tomographic image to obtain a three-dimensional image.

[0135] In the embodiment, after obtaining the two-dimensional tomographic image, three-dimensional reconstruction can be performed according to the two-dimensional tomographic image to obtain a three-dimensional image. The three-dimensional image is an image of the defect with a three-dimensional spatial structure.

[0136] Please refer to Figure 10 Specifically, the above step S52 includes:

[0137] Step S521, performing binaryzation processing on the two-dimensional tomographic image.

[0138] In the embodiment, the obtained two-dimensional tomographic image is a color image, and the two-dimensional color image includes multiple layers. In order to improve the efficiency of subsequent three-dimensional reconstruction, the color image can be binarized, so that each two-dimensional image is converted from a color image to a black-and-white image.

[0139] For example, since the edges of the defect in the color two-dimensional image are clear, the color value of the pixel point at the edge of the color image can be selected as a threshold, the defect part is changed to white, and the non-defect part is changed to black; or the defect part is changed to black, and the non-defect part is changed to white. At this time, the points on the two-dimensional image are black or white, and do not include gray values; that is, the two-dimensional image after binarization processing only includes black and white colors, thereby reducing the data processing pressure of subsequent three-dimensional reconstruction and improving the imaging efficiency.

[0140] Step S522, according to the position information of each layer image, the pixel point after binarization processing in each layer image is mapped to three-dimensional space based on the spatial relationship, and a discrete three-dimensional voxel point is formed.

[0141] In the embodiment, after the two-dimensional image is binarized, according to the position information of each point of each layer image and the color information of the pixel point after binarization processing, the spatial relationship is mapped to three-dimensional space, and a discrete three-dimensional voxel point is formed.

[0142] It can be understood that the points in each two-dimensional image of each layer include position coordinate information of the plane where the points are located, together with depth information of the layer where the plane is located, forming three-dimensional position information of each point. In addition, after binarization processing, each point also includes pixel information of black or white, and the above information is mapped in three-dimensional space, that is, a discrete three-dimensional body with only black and white colors in three-dimensional space can be formed.

[0143] Step S523, obtaining a three-dimensional image according to the discrete three-dimensional voxel point.

[0144] In the embodiment, after the discrete three-dimensional voxel point is formed, it can be converted into a continuous three-dimensional image.

[0145] Step S6, judging whether the to-be-tested defect sample has a defect according to the image, and evaluating the defect characteristics.

[0146] In the embodiment, after the image is obtained, whether the to-be-tested defect sample has a defect inside can be judged according to the scanning image, and after confirming that there is a defect, the position, size and other characteristics of the defect need to be evaluated.

[0147] Further, the above step S6 includes:

[0148] The image recognition technology or the pixel value of the contrast image is used to determine whether there is a defect, and the position and size of the defect are confirmed.

[0149] In the embodiment, the image recognition technology is used to determine whether there is a defect in the image, and the position and size of the defect and other defect characteristics are obtained. Alternatively, the pixel values of the contrast image are read first, and then compared. According to the comparison result, whether there is a defect in the image is determined, and the position and size of the defect and other defect characteristics are obtained.

[0150] The image recognition process or the pixel value reading process of the image described above can be implemented by a computer, so as to further improve the detection efficiency and accuracy of the defect. It can be understood that the above process can also be realized by manual operation, which is not limited in the embodiment.

[0151] Due to the strong scattering of the microstructure of the material and environmental noise, a strong noise background is formed in the defect detection process, which often leads to low signal-to-noise ratio of the laser ultrasonic detection signal, so that the defect edge imaging is relatively blurred, and the accuracy of online real-time detection is affected. At the same time, the existing laser ultrasonic online detection needs to be scanned point by point, which seriously affects the detection efficiency. The defect ultrasonic imaging method and system in a strong noise background provided in the embodiment can include the case of strong noise signal caused by the material itself and the case of strong noise environment caused by environmental characteristics, which can include only one of the above cases, or both.

[0152] The method provided in the application comprises the following steps: scanning ultrasonic detection is performed on a defect sample to be detected to obtain a first initial signal point by point, and scanning ultrasonic detection is performed on a reference sample to obtain a second initial signal point by point; the first initial signal is preprocessed by noise reduction to obtain a first noise reduction signal, and the second initial signal is preprocessed by noise reduction to obtain a second noise reduction signal; signal matching operation is performed on the first noise reduction signal and the second noise reduction signal, and then background difference subtraction calculation is performed to obtain a scattering characteristic signal; multi-directional neighbor wave difference subtraction calculation is performed on the scattering characteristic signal to obtain a defect characteristic signal; an image is drawn according to the defect characteristic signal; whether the defect sample to be detected has a defect is determined according to the image, and a defect characteristic evaluation is performed.

[0153] That is, by combining the background difference and multi-directional neighboring wave difference methods, on the one hand, the strong noise background of the material microstructure is suppressed by the background difference. Without considering the full wave field inside the medium, only the scattered wave field difference of the non-uniform medium and the reference medium detection surface is calculated, without introducing the Born approximation and single scattering source assumption, the noise reduction processing of the material structure noise is realized, and the fast extraction of the defect scattering characteristic signal is also realized. On the other hand, the multi-directional neighboring wave difference realizes the enhancement of the boundary detail features of the micro-defects, and improves the sensitivity to the direction of the crack and other micro-defects, so as to improve the online detection efficiency, effectively avoid the low defect imaging precision problem, and further improve the defect imaging quality.

[0154] In other words, the present application not only retains the fast and efficient advantage of the background difference method defect scattering characteristic signal extraction algorithm, but also further combines the multi-directional neighboring wave difference method to match the waveforms of the adjacent two points in different directions and then calculate the difference, so as to fully exert the advantages of the method, such as the enhancement of the defect boundary detail features and the sensitivity to the direction of the crack defects. The organic combination of the two can effectively avoid the low defect characterization precision caused by improper background selection, reduce the shadow area of the crack defect detection, and does not need to collect full matrix scanning data or perform spectral analysis on the data, so that the real-time storage, time domain analysis and real-time imaging of the detection data can be realized, the data storage amount of large components is greatly reduced, and the detection efficiency is significantly improved. Finally, the high-precision photoacoustic tomography and three-dimensional reconstruction of the micro-defect features in the metal material are realized.

[0155] Please refer to Figure 11 The second aspect of the embodiment also provides a defect ultrasonic imaging system 200 in a strong noise background. The system comprises a scanning ultrasonic detection module 201 and a signal processing module 202. The scanning ultrasonic detection module 201 and the signal processing module 202 are in communication connection. The scanning ultrasonic detection module 201 is used for scanning ultrasonic detection of a to-be-tested defect sample to obtain a first initial signal point by point, and is used for performing traversal scanning ultrasonic detection on a reference sample to obtain a second initial signal point by point. The signal processing module 202 is used for performing noise reduction preprocessing on the first initial signal to obtain a first noise reduction signal, and is used for performing noise reduction preprocessing on the second initial signal to obtain a second noise reduction signal. The signal processing module 202 is used for performing signal matching operation on the first noise reduction signal and the second noise reduction signal, and is used for performing background difference subtraction calculation to obtain a scattering characteristic signal. The signal processing module 202 is used for performing multi-directional neighboring wave difference subtraction calculation on the scattering characteristic signal to obtain a defect characteristic signal. The signal processing module 202 is used for drawing an image according to the defect characteristic signal. The signal processing module 202 is used for judging whether the to-be-tested defect sample has defects according to the image, and is used for performing defect characteristic evaluation.

[0156] The system realizes suppression of strong noise background of material microstructure by background difference, realizes noise reduction processing of material structure noise, and realizes fast extraction of defect scattering characteristic signals. On the other hand, multi-directional adjacent wave difference realizes enhancement of boundary detail features of microdefects, improves the sensitivity to the direction of cracks and other microdefects, thereby improving the online detection efficiency, effectively avoiding the problem of low defect imaging accuracy, and further improving the defect imaging quality.

[0157] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A defect ultrasound imaging method under strong noise background, characterized in that, include: The test sample with defects is subjected to traversal scanning ultrasonic testing to obtain a point-by-point first initial signal, and the reference sample is subjected to traversal scanning ultrasonic testing to obtain a point-by-point second initial signal. The first initial signal is subjected to noise reduction preprocessing to obtain a first noise-reduced signal, and the second initial signal is subjected to noise reduction preprocessing to obtain a second noise-reduced signal; Perform cross-correlation operation on the first denoised signal and the second denoised signal to obtain the first maximum cross-correlation coefficient and the corresponding first delay time; The second noise reduction signal is matched to the first noise reduction signal according to the first delay time to obtain a matched signal; The first maximum cross-correlation coefficient is compared with a set cross-correlation coefficient threshold. If the first maximum cross-correlation coefficient is less than the set cross-correlation coefficient threshold, a differential operation is performed on the first denoised signal and the matched signal to obtain a scattering feature signal. If the first maximum cross-correlation coefficient is greater than the set cross-correlation coefficient threshold, the scattering feature signal is recorded as zero. The 0° neighboring wave signal of the scattering feature signal is obtained, and the 0° neighboring wave signal is matched with the scattering feature signal to obtain the 0° matching signal. The 0° defect feature signal is obtained based on the scattering feature signal and the 0° matching signal. Obtain the 45° adjacent wave signal of the scattering feature signal, match the 45° adjacent wave signal with the scattering feature signal to obtain the 45° matching signal, and obtain the 45° defect feature signal based on the scattering feature signal and the 45° matching signal; Obtain the 90° adjacent wave signal of the scattering feature signal, match the 90° adjacent wave signal with the scattering feature signal to obtain the 90° matching signal, and obtain the 90° defect feature signal based on the scattering feature signal and the 90° matching signal; An image is drawn based on the defect feature signals; Based on the image, determine whether the test sample has a defect and evaluate its defect characteristics.

2. The defect ultrasound imaging method under strong noise background according to claim 1, characterized in that, The process of obtaining a first initial signal by performing traversal scanning ultrasonic testing on the defective sample to be tested, and obtaining a second initial signal by performing traversal scanning ultrasonic testing on the reference sample, includes: When performing traversal scanning ultrasonic testing on the defective sample to be tested, multiple excitations at a single point are used to obtain multiple first initial signals. When performing traversal scanning ultrasonic testing on the reference sample, multiple excitations at a single point are used to obtain multiple second initial signals.

3. The defect ultrasound imaging method under strong noise background according to claim 2, characterized in that, The step of performing noise reduction preprocessing on the first initial signal to obtain a first noise-reduced signal, and performing noise reduction preprocessing on the second initial signal to obtain a second noise-reduced signal includes: The first noise-reduced signal is obtained by averaging multiple first initial signals. The second noise-reduced signal is obtained by averaging multiple second initial signals.

4. The defect ultrasound imaging method under strong noise background according to claim 2, characterized in that, The step of performing noise reduction preprocessing on the first initial signal to obtain a first noise-reduced signal, and performing noise reduction preprocessing on the second initial signal to obtain a second noise-reduced signal includes: A first average signal is obtained by averaging multiple first initial signals. The first average signal is subjected to first and second noise reduction processes to obtain the first noise-reduced signal. A second average signal is obtained by averaging multiple second initial signals. The second average signal is subjected to first and second noise reduction processes to obtain the second noise-reduced signal.

5. The defect ultrasound imaging method under strong noise background according to claim 1, characterized in that, The steps of acquiring the 0° adjacent wave signal of the scattering feature signal, matching the 0° adjacent wave signal with the scattering feature signal to obtain the 0° matched signal, and obtaining the 0° defect feature signal based on the scattering feature signal and the 0° matched signal include: The waveform adjacent to the scattering feature signal along the 0° direction is selected as the 0° neighboring wave signal. Cross-correlation operation is performed on the 0° neighboring wave signal and the scattering feature signal to obtain the second maximum cross-correlation coefficient and the corresponding second delay time. The 0° adjacent wave signal is matched with the scattering feature signal according to the second delay time to obtain the 0° matched signal; The scattering feature signal and the 0° matching signal are differentially processed to obtain the 0° defect feature signal.

6. The defect ultrasound imaging method under strong noise background according to any one of claims 1-5, characterized in that, The step of drawing an image based on the defect feature signal includes: Set the chromatography interval and chromatography depth; Based on the tomographic interval and the tomographic depth, the defect feature signal is used to obtain a two-dimensional tomographic image using the variable time window energy mapping method; The two-dimensional tomographic image is binarized; Based on the position information of each layer of the image, the binarized pixels in each layer of the image are mapped to three-dimensional space based on spatial relationships to form discrete three-dimensional volume pixels. A three-dimensional image is obtained based on the discrete three-dimensional volume pixels.

7. The defect ultrasound imaging method under strong noise background according to any one of claims 1-5, characterized in that, The step of determining whether the test sample has a defect based on the image and evaluating its defect characteristics includes: The presence of defects is determined by image recognition technology or by comparing the pixel values ​​of the image, and the location and size of the defects are confirmed.

8. A defect ultrasound imaging system under strong noise background, characterized in that, include: The scanning ultrasonic testing module is used to perform traversal scanning ultrasonic testing on the defective sample to be tested to obtain a point-by-point first initial signal, and to perform traversal scanning ultrasonic testing on the reference sample to obtain a point-by-point second initial signal. The signal processing module is configured to perform noise reduction preprocessing on the first initial signal to obtain a first noise-reduced signal, perform noise reduction preprocessing on the second initial signal to obtain a second noise-reduced signal, and perform cross-correlation operation on the first noise-reduced signal and the second noise-reduced signal to obtain a first maximum cross-correlation coefficient and a corresponding first delay time. The second noise-reduced signal is matched with the first noise-reduced signal according to the first delay time to obtain a matched signal; the magnitude of the first maximum cross-correlation coefficient and the set cross-correlation coefficient threshold is determined; if the first maximum cross-correlation coefficient is less than the set cross-correlation coefficient threshold, a difference operation is performed on the first noise-reduced signal and the matched signal to obtain a scattering feature signal; if the first maximum cross-correlation coefficient is greater than the set cross-correlation coefficient threshold, the scattering feature signal is recorded as zero; the 0° adjacent wave signal of the scattering feature signal is obtained, and the 0° adjacent wave signal is matched with the scattering feature signal to obtain a 0° matched signal; a 0° defect feature signal is obtained based on the scattering feature signal and the 0° matched signal. Obtain the 45° adjacent wave signal of the scattering feature signal, match the 45° adjacent wave signal with the scattering feature signal to obtain the 45° matching signal, and obtain the 45° defect feature signal based on the scattering feature signal and the 45° matching signal; Obtain the 90° adjacent wave signal of the scattering feature signal, match the 90° adjacent wave signal with the scattering feature signal to obtain the 90° matching signal, and obtain the 90° defect feature signal based on the scattering feature signal and the 90° matching signal; An image is drawn based on the defect feature signal; the presence of a defect in the test sample is determined based on the image, and the defect feature is evaluated.

Images