Full-matrix microscopic ultrasonic leakage defect three-dimensional reconstruction method

Abstract

The invention belongs to the technical field of nondestructive testing, and particularly relates to a full-matrix microscopic ultrasonic leakage defect three-dimensional reconstruction method. According to the leakage defect three-dimensional reconstruction method, precise positioning and three-dimensional shape reconstruction of tiny complex defects in the austenite thin-wall stainless steel material are achieved, and technical support is provided for leakage micropore detection and three-dimensional representation of the austenite thin-wall stainless steel material. The leakage micropore three-dimensional reconstruction method comprises the following steps: recording an A-Scan original signal and position information of each path point to obtain full-matrix microscopic ultrasonic data; taking the minimum envelope entropy as an optimization target, performing variational mode decomposition and reconstruction on the A-Scan original signal of each path point, and calculating to obtain an optimal solution of a variational model; performing two-dimensional mask extraction on the processed full-matrix microscopic ultrasonic data to generate C scanning images with different depths; extracting to obtain two-dimensional defect edge masks under different depths; and carrying out splicing and volume rendering on a two-dimensional defect edge masking result.

Description

Technical Field

[0001] The present invention belongs to the technical field of nondestructive testing, and particularly relates to a three-dimensional reconstruction method for full-matrix microscopic ultrasonic leakage defects. Background Art

[0002] As a new type of metal material with excellent mechanical properties, good wear resistance and temperature insulation performance, austenitic thin-walled stainless steel materials are widely used in the fields of chemical engineering, architectural decoration and ocean engineering technology. However, in the actual use process, affected by the transported medium and working conditions, austenitic thin-walled stainless steel materials will generate leakage defects due to corrosion. To avoid the leakage of the transported medium, technicians need to detect the defects of austenitic thin-walled stainless steel materials in time before or at the initial stage of leakage (micro-holes).

[0003] After further research, it is found that affected by the unique physical properties of austenitic thin-walled stainless steel materials (such as large and uneven internal grain sizes; in addition, the material also exhibits obvious anisotropy, etc.), existing ultrasonic defect detection technologies face many challenges in defect determination, qualitative analysis and positioning. For example: for the welds of austenitic thin-walled stainless steel materials, the uneven degree of internal grains is relatively high, resulting in serious scattering noise during the propagation of ultrasonic waves, significantly reducing the signal-to-noise ratio of the detection signal. In addition, due to the complex frequency distribution and non-stationary characteristics of the scattering noise of austenitic thin-walled stainless steel materials, while traditional defect detection technologies usually rely on preset parameters or specific filter designs, it is difficult to effectively cope with the above-mentioned dynamically changing noise interference. Finally, the leakage micro-hole sizes of austenitic thin-walled stainless steel materials are usually small and irregular in shape, further leading to image artifacts easily appearing during C-scan imaging, making it difficult for existing ultrasonic defect detection technologies to accurately extract the regional features of the target, seriously affecting the three-dimensional morphology reconstruction accuracy of leakage defects. Summary of the Invention

[0004] The present invention provides a three-dimensional reconstruction method for full-matrix microscopic ultrasonic leakage defects. This three-dimensional reconstruction method for leakage defects realizes the accurate positioning and three-dimensional morphology reconstruction of tiny and complex defects in austenitic thin-walled stainless steel materials, providing technical support for the detection and three-dimensional characterization of leakage micro-holes in austenitic thin-walled stainless steel materials.

[0005] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a three-dimensional reconstruction method for leakage micro-holes based on full-matrix microscopic ultrasonic, including the following steps: Step P1: Use a point-focusing microscopic ultrasonic probe to perform surface scanning detection on the test piece to be inspected; record the A-Scan original signals and position information of each path point to obtain full-matrix microscopic ultrasonic data; Step P2: Perform variational mode decomposition on the A-Scan raw signals of each path point, and decompose them into a series of intrinsic mode function components; Step P3: Perform Hilbert transform on each intrinsic mode function component to obtain its single-sided spectrum; based on the mixed-predicted center frequency of each modal spectrum analytical signal, modulate the frequency of each modal spectrum analytical signal to the corresponding base frequency band; and estimate the sum of the bandwidths of each modal spectrum analytical signal; Step P4: Introduce the augmented Lagrangian function, and solve the variational model reconstructed after variational mode decomposition; obtain the saddle point through the ADMM algorithm, and calculate the optimal solution of the variational model; Step P5: Use the adaptive sparrow search algorithm, with the minimum envelope entropy as the optimization goal, repeat Step P2 to Step P4, perform variational mode decomposition and reconstruction on the A-Scan raw signals of each path point until the processing of all matrix micro-ultrasonic data is completed, and iteratively solve to obtain the optimal parameters of variational mode decomposition; Step P6: Perform two-dimensional mask extraction on the all matrix micro-ultrasonic data processed in Step P5, and layer by layer convert the all matrix micro-ultrasonic data into C-scan images of different depths; smooth the C-scan images using a Gaussian filter, and obtain the gradient intensity and direction of the C-scan images using the first-order differential coefficient; Step P7: Determine the evaluation function of the first double threshold based on the gradient magnitude histogram and the minimum within-class variance; determine the evaluation function of the second double threshold based on the gradient magnitude histogram and the maximum within-class variance; Step P8: Extract the defect mask image within the threshold based on the evaluation function of the first double threshold and the evaluation function of the second double threshold; Step P9: Perform edge detection layer by layer on the obtained defect mask image to obtain two-dimensional defect edge masks at different depths; Step P10: Stitch and volume render the two-dimensional defect edge mask results to achieve three-dimensional reconstruction of the defect structure of the specimen to be inspected.

[0006] More preferably, the process of estimating the sum of the bandwidths of each modal spectrum analytical signal in Step P3 is specifically described as: While ensuring that the decomposition sequence is a modal component with a center frequency and a finite bandwidth, minimize the sum of the bandwidths of each modal spectrum analytical signal; among them, for the variational problem subject to constraints, it satisfies: Equation (4); In Equation (4), K is the number of modes to be decomposed; respectively correspond to the k-th modal component and the center frequency after decomposition; is the unit impulse function; * is the convolution operator; For partial derivative calculation; For obtaining the analytic signal of the original function; It is expressed as shifting the spectrum to the base frequency band; j is the imaginary unit.

[0007] More preferably, the process of solving the variational model reconstructed after variational mode decomposition in step P4 is specifically described as follows: Continuously update the mixing - estimated center frequency and the base frequency band of the analytic signals of each modal spectrum until its iteration meets the stopping condition: Equation (7); For what is obtained by solving equation (7) Perform inverse Fourier transform to obtain the variational model reconstructed after variational mode decomposition.

[0008] More preferably, the process of extracting the defect mask image within the threshold in step P8 is specifically described as follows: When the mask extraction algorithm based on Otsu threshold segmentation cannot extract the main body of the target defect in the defect mask image, use the mask segmentation algorithm based on SAM. Train the defect mask image by labeling the target defect area and the non - target defect area, and accurately obtain the target defect mask image layer by layer.

[0009] The present invention provides a three - dimensional reconstruction method for full - matrix microscopic ultrasonic leakage defects. Among them, this three - dimensional reconstruction method for leakage defects specifically includes the following steps: Record the A - Scan original signals and position information of each path point to obtain full - matrix microscopic ultrasonic data; Take the minimum envelope entropy as the optimization target, perform variational mode decomposition and reconstruction on the A - Scan original signals of each path point, and calculate the optimal solution of the variational model; Perform two - dimensional mask extraction on the processed full - matrix microscopic ultrasonic data to generate C - scan images of different depths; Extract the two - dimensional defect edge masks at different depths; Perform stitching and volume rendering on the two - dimensional defect edge mask results.

[0010] The three - dimensional reconstruction method for leakage micropores based on full - matrix microscopic ultrasonic with the above - mentioned step features has at least the following technical advantages compared with the prior art: (1) The three - dimensional reconstruction method for full - matrix microscopic ultrasonic leakage defects provided by the present invention realizes the effective extraction of defect information of austenitic thin - wall stainless steel materials under a high - noise background, and realizes the transformation of ultrasonic data from one - dimensional to two - dimensional to three - dimensional.

[0011] (2) The three - dimensional reconstruction method for full - matrix microscopic ultrasonic leakage defects provided by the present invention solves the technical defects of large scattering noise and difficult accurate characterization of defects in austenitic thin - wall stainless steel materials in the prior art, and is applicable to the detection of various leakage defects under a high - noise background.

[0012] (3) The three-dimensional reconstruction method for full matrix microscopic ultrasonic leakage defects provided by the present invention realizes the three-dimensional reconstruction of the leakage defect structure of the specimen to be inspected of austenitic thin-walled stainless steel material, and provides assistance for the accurate characterization and analysis of leakage defects. Description of the Drawings

[0013] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the following drawings: Figure 1 is a schematic flow chart of the three-dimensional reconstruction method for full matrix microscopic ultrasonic leakage defects provided by the present invention; Figure 2 is a detection schematic diagram of the surface scanning detection of the specimen to be inspected by a point-focus microscopic ultrasonic probe; Figure 3 is a physical diagram of a stainless steel surfacing layer defect specimen; Figure 4 is a design drawing of a stainless steel surfacing layer defect specimen; Figure 5 is a physical diagram of a stainless steel base material defect specimen; Figure 6 is a design drawing of a stainless steel base material defect specimen; Figure 7 is a schematic diagram of the convergence curve of the minimum envelope entropy; Figure 8a is a schematic diagram of the result of variational mode decomposition of a stainless steel surfacing layer defect specimen; Figure 8b is a schematic diagram of the result of variational mode decomposition of a stainless steel base material defect specimen; Figure 9a is Figure 8a a schematic diagram of the effect after denoising; Figure 9b is Figure 8b a schematic diagram of the effect after denoising; Figure 10a is a schematic diagram of the C-scan image of the near-surface layer of a stainless steel surfacing layer defect specimen; Figure 10b is a schematic diagram of the C-scan image of the crack layer of a stainless steel surfacing layer defect specimen; Figure 10c is a schematic diagram of the C-scan image of the rectangular machining groove layer of a stainless steel surfacing layer defect specimen; Figure 10d is a schematic diagram of the C-scan image of the bottom layer of a stainless steel surfacing layer defect specimen; Figure 11a is a schematic diagram of the C-scan image of the surface layer of a stainless steel base material defect specimen; Figure 11bSchematic diagram of C-scan image of near-surface layer of stainless steel base metal defect specimen; Figure 11c Schematic diagram of C-scan image inside the leakage micropores of stainless steel base metal defect specimen; Figure 11d Schematic diagram of C-scan image of near-bottom layer of stainless steel base metal defect specimen; Figure 12 Effect diagram of defect mask obtained by mask extraction algorithm based on Otsu threshold segmentation; Figure 13 Effect diagram of defect mask obtained by mask segmentation algorithm based on SAM; Figure 14 Schematic diagram of reconstruction result obtained by reconstructing stainless steel surfacing layer defect specimen with existing technology; Figure 15 Schematic diagram of reconstruction result obtained by reconstructing stainless steel surfacing layer defect specimen with the reconstruction method of the present invention; Figure 16 Schematic diagram of reconstruction result obtained by reconstructing stainless steel base metal defect specimen with existing technology; Figure 17 Schematic diagram of reconstruction result obtained by reconstructing stainless steel base metal defect specimen with the reconstruction method of the present invention. Detailed implementation manner

[0014] The present invention provides a full matrix microscopic ultrasonic leakage defect three-dimensional reconstruction method. This leakage defect three-dimensional reconstruction method realizes the accurate positioning and three-dimensional shape reconstruction of tiny and complex defects in austenitic thin-walled stainless steel materials, and provides technical support for the detection and three-dimensional characterization of leakage micropores in austenitic thin-walled stainless steel materials.

[0015] As Figure 1 shown, the present invention provides a full matrix microscopic ultrasonic leakage defect three-dimensional reconstruction method, which includes the following steps: Step P1: Use a point-focus microscopic ultrasonic probe to perform surface scanning detection on the specimen to be inspected. Record the A-Scan original signals and position information of each path point to obtain full matrix microscopic ultrasonic data.

[0016] Specifically, referring to as Figure 2 shown, where Figure 2 is the detection schematic diagram of the point-focus microscopic ultrasonic probe performing surface scanning detection on the specimen to be inspected. Through step P1, full matrix microscopic ultrasonic data recording the A-Scan original signals and position information of each path point is obtained.

[0017] To verify the reconstruction effect of the full matrix microscopic ultrasonic leakage defect three-dimensional reconstruction method provided by the present invention, a set of defect specimens are provided as follows to simulate the potential leakage crack defects in the surfacing layer (using a 5Mhz probe). Among them, the physical diagram and design diagram of the defect specimen are as shown in Figure 3 , Figure 4 . A rectangular groove is machined on the back of the specimen; then artificial crack defects are machined in the groove, with a length of 3mm, a width of 0.3mm, and a defect burial depth of 4mm. In addition, a set of stainless steel base material defect specimens are further provided to simulate the microporous leakage of the base material (using a 15Mhz probe). The physical diagram and design diagram of the defect specimen are as shown in Figure 5 , Figure 6 . The specimen is completed by metal 3D printing. The stainless steel thin plate base material is 4mm thick, and the aperture of the spiral leakage micropores is 0.8mm.

[0018] Step P2: Perform variational mode decomposition on the A-Scan original signals of each path point, and decompose them into a series of intrinsic mode function components.

[0019] On the basis of completing step P1, step P2 is further implemented to perform variational mode decomposition on the A-Scan original signals of each path point. Specifically, the purpose of performing variational mode decomposition on the A-Scan original signals of each path point is to decompose the A-Scan original signals (input signals) of the path points into multiple intrinsic mode function components (output sub-signals).

[0020] In this process, the intrinsic mode function component satisfies: Equation (1); In equation (1) thereof, is the instantaneous amplitude, is the phase angle.

[0021] Step P3: Perform Hilbert transform on each intrinsic mode function component to obtain its single-sided spectrum. Based on the mixing-predicted center frequency of the analytical signals of each mode spectrum, the frequency of each mode spectrum analytical signal is modulated to the corresponding base frequency band, and the sum of the bandwidths of each mode spectrum analytical signal is estimated.

[0022] On the basis of completing step P2, step P3 is further implemented to estimate the sum of the bandwidths of each mode spectrum analytical signal. Specifically, performing Hilbert transform on each intrinsic mode function component to obtain the single-sided spectrum, which can be expressed as: Equation (2); In equation (2) thereof, , .

[0023] Then, using the mixed-predicted center frequency of each modal spectrum analysis signal as a reference, the frequency of each modal spectrum analysis signal is modulated to the corresponding base frequency band. Among them, the modulated base frequency band can be expressed as: Equation (3); In Equation (3), is the vector description of the center frequency in the complex plane, is the center frequency.

[0024] Then, estimate the sum of the bandwidths of each modal spectrum analysis signal. As an alternative implementation, select to calculate the L² norm of the squared gradient of the modulated signal to estimate the sum of the bandwidths of each modal spectrum analysis signal.

[0025] It should be noted that, as a more preferred implementation, it is preferred to minimize the sum of the bandwidths of each modal spectrum analysis signal while ensuring that the decomposition sequence is a modal component with a finite bandwidth having a center frequency. At this time, the variational problem subject to the constraint conditions satisfies: Equation (4); In Equation (4), K is the number of modes to be decomposed; correspond to the k-th modal component and the center frequency after decomposition respectively; is the unit impulse function; * is the convolution operator; is to take the partial derivative; is the analytic signal of finding the original function; represents shifting the spectrum to the base frequency band; j is the imaginary unit.

[0026] Step P4: Introduce the augmented Lagrangian function to solve the variational model reconstructed after variational mode decomposition; obtain the saddle point through the ADMM algorithm and calculate the optimal solution of the variational model.

[0027] On the basis of completing Step P3, further implement Step P4 to calculate the optimal solution of the variational model. Specifically, the purpose of introducing the augmented Lagrangian function to solve the variational model reconstructed after variational mode decomposition is to transform the constrained variational problem into an unconstrained variational problem.

[0028] Among them, as a more preferred implementation of the present invention, the process of solving the variational model reconstructed after variational mode decomposition is specifically described as: Continuously update the mixed-predicted center frequency and the base frequency band of each modal spectrum analysis signal until its iteration satisfies the stop condition: Equation (7); For the obtained by solving Equation (7) ( Specifically, it satisfies ) Perform an inverse Fourier transform to obtain the variational model reconstructed after variational mode decomposition.

[0029] It should be noted that during the continuous update and iteration process, the variational model update equation and the center frequency can be respectively expressed as: Equation (5); Equation (6); In Equations (5) and (6), is the Fourier transform, n is the number of iterations, is the fidelity coefficient, is the augmented Lagrangian multiplier, is the penalty coefficient.

[0030] Step P5: Use the adaptive sparrow search algorithm, with the minimum envelope entropy as the optimization goal, repeat steps P2 to P4, perform variational mode decomposition and reconstruction on the A-Scan original signals of each path point until the processing of the full matrix microscopy ultrasonic data is completed, and iteratively solve to obtain the optimal parameters of the variational mode decomposition.

[0031] On the basis of completing step P4, further implement step P5 to iteratively solve the optimal parameters of the variational mode decomposition. Among them, the convergence curve of the minimum envelope entropy can be referred to as shown in Figure 7 ; The results of the variational mode decomposition of the stainless steel surfacing layer defect specimen and the stainless steel base material defect specimen can be referred to as shown in Figure 8a Figure 8b ; Further perform adaptive denoising on Figure 8a , Figure 8b , and its denoising effect can be referred to as shown in Figure 9a Figure 9b ; It should be noted that by performing variational mode decomposition and reconstruction on the A-Scan original signals of each path point, the noise signals in the full matrix microscopy ultrasonic data that are not related to the useful signals can be effectively removed (precisely separating the noise components and the effective signal components in the frequency domain), avoiding the problems of signal distortion or noise residue caused by the fixed filter bandwidth in the prior art; in addition, instead of manually setting parameters for trial and error, the optimal parameters are calculated iteratively and adaptively, realizing the adaptive denoising of the echo signals of high-scattering noise materials and preventing it from interfering with the calculations of subsequent steps.

[0032] Step P6: Perform two-dimensional mask extraction on the full matrix microscopy ultrasonic data processed in step P5, layer by layer convert the full matrix microscopy ultrasonic data into C-scan images of different depths; use a Gaussian filter to smooth the C-scan images and use the first-order differential coefficient to obtain the gradient intensity and direction of the C-scan images.

[0033] On the basis of completing step P5, step P6 is further implemented to perform two-dimensional mask extraction on the full matrix microscopic ultrasonic data. In this process, it should be additionally noted that during the process of smoothing the C-scan image using a Gaussian filter, the Gaussian filter can effectively remove the two-dimensional noise therein, thereby performing non-maximum suppression on the gradient direction of the gradient amplitude matrix of the C-scan image and refining the edges. The processing result can roughly divide the gradient amplitude values of the C-scan image into the following three categories: C0 (low gradient amplitude, belonging to non-edge points), C1 (medium gradient amplitude, possibly edge points), and C2 (high gradient amplitude, belonging to edge points). Among them, the C-scan images of the stainless steel surfacing layer defect specimens at different depths (near-surface layer, crack layer, rectangular machining groove layer, bottom layer) can be referred to as Figure 10a - Figure 10d as shown; the C-scan images of the stainless steel base material defect specimens at different depths (surface layer, near-surface layer, inside the leakage micropores, near-bottom layer) can be referred to as Figure 11a - Figure 11d as shown.

[0034] Step P7: Determine the evaluation function of the first double threshold based on the gradient amplitude histogram and the minimum within-class variance; determine the evaluation function of the second double threshold based on the gradient amplitude histogram and the maximum within-class variance.

[0035] As a relatively preferred implementation manner of the present invention, the following evaluation functions of the first double threshold and the second double threshold are respectively provided as examples. Among them, the evaluation function of the first double threshold determined based on the gradient amplitude histogram and the minimum within-class variance satisfies: Equation (8); In its Equation (8), , , , is the proportion of non-edge points in the total number of pixels, is the proportion of points suspected to be edge points in the total number of pixels, is the proportion of edge points in the total number of pixels, is the within-class variance of each class.

[0036] And the evaluation function of the second double threshold determined based on the gradient amplitude histogram and the maximum within-class variance satisfies: Equation (9); In its Equation (9), is the gray mean value of each class j, is the gray mean value of the entire image.

[0037] It should be pointed out that the first double threshold evaluation function and the second double threshold evaluation function The parameters k and m therein respectively refer to the optimal thresholds of statistical significance. That is to say, by traversing all possible k and m (where k < m), the k and m that optimize the variational mode decomposition are found, and then the low threshold thrLow and the high threshold thrHigh can be obtained respectively.

[0038] Step P8: Based on the evaluation function of the first double threshold and the evaluation function of the second double threshold, extract the defect mask image within the threshold.

[0039] On the basis of completing Step P7, Step P8 is further implemented to extract the defect mask image within the threshold. As a relatively preferred implementation manner of the present invention, the process of extracting the defect mask image within the threshold in this Step P8 is specifically described as follows: When the target defect body in the defect mask image cannot be extracted by the mask extraction algorithm based on Otsu threshold segmentation (that is, the C-scan image effect is complex and there is interference), use the mask segmentation algorithm based on SAM. By labeling the target defect area and the non-target defect area, the defect mask image is trained, and the target defect mask image is accurately obtained layer by layer.

[0040] It should be noted that the reason for using the mask segmentation algorithm based on SAM is that in the case where the defect body is not clear enough, the traditional mask extraction algorithm based on Otsu threshold segmentation will be greatly affected. As Figure 12 shown, Figure 12 is the defect mask effect diagram obtained by the mask extraction algorithm based on Otsu threshold segmentation. It can be found that for the stainless steel base material defect specimen, due to the complex morphology of its spiral micropores and the continuous influence of the surface inlet holes on the data of each layer in the tomography, the mask extraction algorithm based on Otsu threshold segmentation is no longer used, but the mask segmentation algorithm based on SAM is used instead.

[0041] Among them, the internal spiral holes of the stainless steel base material can be simply divided into four regions: background, upper surface micropore inlet, effective range of a certain layer slice, and artifacts. Therefore, the main target of detection is to extract the data within the effective range of the slice. Specifically, the SAM model is used to mark this area, and the non-interested areas are also removed by labeling, thus completing the mask extraction of the stainless steel base material defect specimen by the mask segmentation algorithm based on SAM. Refer to Figure 13 shown.

[0042] Step P9: Perform edge detection on the obtained defect mask image layer by layer to obtain two-dimensional defect edge masks at different depths.

[0043] On the basis of completing step P8, step P9 is further implemented to obtain two-dimensional defect edge masks at different depths. Specifically, the two-dimensional defect edge masks are used for the volume construction of defects. Among them, edge detection can be achieved by the Canny operator.

[0044] Step P10: Stitch and volumetrically render the two-dimensional defect edge mask results to achieve three-dimensional reconstruction of the defect structure of the test piece to be inspected.

[0045] On the basis of completing step P9, step P10 is further implemented to perform three-dimensional reconstruction of the defect structure of the test piece to be inspected. As an alternative implementation, a three-dimensional reconstruction method is provided as follows for illustration. First, use the smart pointer "vtkPNGReader" to read the slice (sequence image) data. Among them, the slice data is an image sequence of two-dimensional target defect masks, and each slice corresponds to a cross-sectional view at a specific position. Assuming each slice is a two-dimensional matrix, the slice sequence can be represented as a three-dimensional array. Among them, z is the index of the slice, and N is the number of slice sequences. Then, set the pixel interval, use the smart pointer filter "vtkContourFilter" to take the image data set as the input, and output the isosurface or isoline. The isosurface extracted next is mapped into a triangular mesh, which can be further processed (such as smoothed, shaded), and then the final visualization result is generated through the lighting model and camera settings.

[0046] Specifically, refer to Figure 14 - 17 as shown, where Figure 14 is a schematic diagram of the reconstruction result obtained by reconstructing a stainless steel surfacing layer defect test piece by the prior art, as Figure 15 is a schematic diagram of the reconstruction result obtained by reconstructing a stainless steel surfacing layer defect test piece by the reconstruction method of the present invention; Figure 16 is a schematic diagram of the reconstruction result obtained by reconstructing a stainless steel base material defect test piece by the prior art, as Figure 17 is a schematic diagram of the reconstruction result obtained by reconstructing a stainless steel base material defect test piece by the reconstruction method of the present invention. After comparison, it can be found that through the above process, a three-dimensional reconstruction method for leakage micropores based on full matrix micro-ultrasound provided by the present invention realizes the effective extraction of defect information under a high-noise background, realizes the transformation of signals from one-dimensional to two-dimensional to three-dimensional, solves the problems of large scattering noise of austenitic thin-walled stainless steel materials and difficult accurate characterization of defects, and its three-dimensional reconstruction result has been significantly improved compared with the prior art. Therefore, it can be used for the detection and three-dimensional characterization of various defects under a high-noise background.

[0047] The present invention provides a three-dimensional reconstruction method for full matrix microscopic ultrasonic leakage defects. Among them, this three-dimensional reconstruction method for leakage defects specifically includes the following steps: recording the A-Scan original signals and position information of each path point to obtain full matrix microscopic ultrasonic data; taking the minimum envelope entropy as the optimization target, performing variational mode decomposition and reconstruction on the A-Scan original signals of each path point, and calculating the optimal solution of the variational model; performing two-dimensional mask extraction on the processed full matrix microscopic ultrasonic data to generate C-scan images of different depths; extracting two-dimensional defect edge masks at different depths; and splicing and volume rendering the results of the two-dimensional defect edge masks.

[0048] The three-dimensional reconstruction method for leakage micropores based on full matrix microscopic ultrasonic with the above step characteristics, compared with the prior art. At least has the following technical advantages: (1). The three-dimensional reconstruction method for full matrix microscopic ultrasonic leakage defects provided by the present invention realizes the effective extraction of defect information of austenitic thin-walled stainless steel materials under a high-noise background, and realizes the transformation of ultrasonic data from one-dimensional to two-dimensional to three-dimensional.

[0049] (2). The three-dimensional reconstruction method for full matrix microscopic ultrasonic leakage defects provided by the present invention solves the technical defects of large scattering noise and difficult accurate characterization of defects in austenitic thin-walled stainless steel materials in the prior art, and is applicable to the detection of various leakage defects under a high-noise background.

[0050] (3). The three-dimensional reconstruction method for full matrix microscopic ultrasonic leakage defects provided by the present invention realizes the three-dimensional reconstruction of the leakage defect structure of the austenitic thin-walled stainless steel material to be inspected, and provides help for the accurate characterization and analysis of leakage defects.

[0051] As mentioned above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. Three-dimensional reconstruction method for full matrix microscopic ultrasonic leakage defects, characterized in that It includes the following steps: Step P1: Use a point-focusing micro-ultrasonic probe to perform surface scanning detection on the test specimen to be inspected; record the A-Scan original signals and position information of each path point to obtain full matrix micro-ultrasonic data; Step P2: Perform variational mode decomposition on the A-Scan original signals of each path point and decompose them into a series of intrinsic mode function components; Step P3: Perform Hilbert transform on each intrinsic mode function component to obtain its single-sided spectrum; based on the mixing-predicted center frequency of the analytical signals of each modal spectrum, modulate the frequency of each modal spectrum analytical signal to the corresponding base frequency band; and estimate the sum of the bandwidths of each modal spectrum analytical signal; Step P4: Introduce an augmented Lagrangian function to solve the variational model reconstructed after variational mode decomposition; obtain the saddle point through the ADMM algorithm and calculate the optimal solution of the variational model; Step P5: Use the adaptive sparrow search algorithm with the minimum envelope entropy as the optimization goal, repeat steps P2 to P4, perform variational mode decomposition and reconstruction on the A-Scan original signals of each path point until the full matrix micro-ultrasonic data processing is completed, and iteratively solve to obtain the optimal parameters of variational mode decomposition; Step P6: Perform two-dimensional mask extraction on the full matrix micro-ultrasonic data processed in step P5, and layer by layer convert the full matrix micro-ultrasonic data into C-scan images of different depths; use a Gaussian filter to smooth the C-scan images and use the first-order differential coefficient to obtain the gradient intensity and direction of the C-scan images; Step P7: Determine the evaluation function of the first double threshold based on the gradient magnitude histogram and the minimum within-class variance; Determine the evaluation function of the second double threshold based on the gradient magnitude histogram and the maximum within-class variance; Step P8: Extract the defect mask image within the threshold based on the evaluation function of the first double threshold and the evaluation function of the second double threshold; Step P9: Perform edge detection on the obtained defect mask image layer by layer to obtain two-dimensional defect edge masks at different depths; Step P10: Stitch and volume render the two-dimensional defect edge mask results to realize three-dimensional reconstruction of the defect structure of the test specimen to be inspected.

2. The three-dimensional reconstruction method for full matrix microscopic ultrasonic leakage defects according to claim 1, wherein The process of estimating the sum of the bandwidths of each modal spectrum analytical signal in step P3 is specifically described as: While ensuring that the decomposition sequence is a modal component with a finite bandwidth having a center frequency, minimize the sum of the bandwidths of each modal spectrum analytical signal; Among them, the variational problem subject to the constraint conditions satisfies: Formula (4); In formula (4), K is the number of modes to be decomposed; respectively corresponding to the k-th modal component and the center frequency after decomposition; is the unit impulse function; * is the convolution operator; is to take the partial derivative; is to find the analytic signal of the original function; represents shifting the spectrum to the base frequency band; j is the imaginary unit.

3. The three-dimensional reconstruction method for full matrix microscopic ultrasonic leakage defects according to claim 1, wherein The process of solving the variational model reconstructed after variational mode decomposition in step P4 is specifically described as: Continuously update the mixed-predicted center frequency and fundamental frequency band of the spectral analysis signals of each modality until its iteration meets the stop condition: Equation (7); The solution to Equation (7) gives Performing the inverse Fourier transform gives the variational model reconstructed after variational mode decomposition.

4. The three-dimensional reconstruction method of full matrix microscopic ultrasonic leakage defects according to claim 1, characterized in that, The process of extracting the defect mask image within the threshold in step P8 is specifically described as: When the mask extraction algorithm based on Otsu threshold segmentation cannot extract the main body of the target defect in the defect mask image, use the mask segmentation algorithm based on SAM, train the defect mask image by labeling the target defect area and the non-target defect area, and layer by layer accurately obtain the target defect mask image.

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