A multi-array ultrasonic three-dimensional visual enhanced imaging method for weld defect

Abstract

The present application relates to the field of weld defect detection, and particularly relates to a kind of weld defect multi-element ultrasonic three-dimensional visual enhancement imaging characterization method.The technical scheme includes: obtaining the multi-element ultrasonic detection data of the weld to be detected;Identify the data organization form of the multi-element ultrasonic detection data, and select the corresponding three-dimensional body data generation strategy based on the identification result, generate original three-dimensional ultrasonic body data;The original three-dimensional ultrasonic body data is subjected to visual enhancement processing, and the three-dimensional ultrasonic body data after visual enhancement is obtained;Based on the three-dimensional visual enhancement imaging result of weld defect of the three-dimensional ultrasonic body data after visual enhancement, the spatial quantitative parameter of weld defect is extracted;The spatial quantitative parameter is input into the defect characterization analysis model, the weld defect state characterization result is generated, and the weld defect three-dimensional visual characterization report is output.The present application improves the spatial explainability and engineering analysis capability of weld defect imaging result, and is suitable for weld defect detection.

Description

Technical Field

[0001] This invention relates to the field of weld defect detection, and specifically to a multi-element ultrasonic three-dimensional visual enhancement imaging method for characterizing weld defects. Background Technology

[0002] During manufacturing, installation and long-term service, the weld area is easily affected by factors such as welding heat input, corrosive media and alternating loads, which may result in defects or deterioration areas such as porosity, slag inclusions, lack of fusion, cracks and corrosion thinning.

[0003] Among existing inspection methods, conventional ultrasonic testing and radiographic testing have been widely used for weld defect detection. Conventional ultrasonic testing has advantages such as portable equipment and high safety, but its results are mostly presented in the form of A-scan waveforms, two-dimensional cross-sections, or local images, which are insufficient in expressing the three-dimensional spatial morphology, extension direction, and volumetric characteristics of defects. Although radiographic testing can obtain more intuitive projected images, it is easily limited by factors such as safety protection, inspection angle, shutdown conditions, and obstruction by complex structures in field applications.

[0004] Multi-element ultrasonic testing utilizes electronic delay control of multi-element probes to achieve beam deflection, dynamic focusing, and multi-angle tracing, enabling the acquisition of rich internal echo information. With the development of array acquisition and multi-channel time-delay aggregation imaging technologies, point-by-point focusing calculations using complete channel data between transmitting and receiving elements can improve defect imaging resolution and provide a data foundation for 3D reconstruction. However, in practical testing, multi-element ultrasonic data typically exists in two different forms: one is the trajectory positioning coordinates output by the testing equipment or trajectory system, and the other is array acquisition data containing transmitting channels, receiving channels, and sampling point sequences. Existing methods often only process one type of data, lacking a technical solution that is compatible with both types of data and uniformly generates 3D visual enhancement imaging results for weld defects.

[0005] Furthermore, ultrasonic testing results often suffer from issues such as strong surface echoes, grain scattering noise, structural echoes, and localized high-amplitude obstruction. Directly displaying ultrasonic data in three dimensions can easily lead to unclear internal defect areas, ambiguous boundaries, and difficulty in extracting spatial parameters. Existing testing methods rely heavily on manual image interpretation, and the results are easily affected by the experience level of the inspectors, the display method, and batch variations, making it difficult to maintain consistency in batch testing scenarios.

[0006] Therefore, it is necessary to provide a multi-element ultrasonic three-dimensional visual enhancement imaging characterization method for weld defects, which can be compatible with trajectory positioning coordinates and array acquisition data. Through three-dimensional ultrasonic volume data reconstruction, multi-channel time-delay aggregation imaging, three-dimensional visual enhancement, extraction of spatial parameters of weld defects, and defect characterization analysis, it can realize three-dimensional visual enhancement imaging, spatial quantitative analysis and characterization output of weld defects. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a multi-element ultrasonic three-dimensional visual enhancement imaging method for characterizing weld defects, thereby improving the spatial interpretability and engineering analysis capability of weld defect imaging results.

[0008] The present invention achieves the above objectives by adopting the following technical solution: The present invention provides a multi-element ultrasonic three-dimensional visual enhancement imaging method for characterizing weld defects, comprising the following steps:

[0009] Acquire multi-element ultrasonic testing data of the weld to be inspected;

[0010] The data organization format of the multi-element ultrasound detection data is identified, and the corresponding three-dimensional volume data generation strategy is selected based on the identification result to generate the original three-dimensional ultrasound volume data.

[0011] The original three-dimensional ultrasound body data is subjected to visual enhancement processing to obtain visually enhanced three-dimensional ultrasound body data;

[0012] Based on the visually enhanced three-dimensional ultrasonic body data, a three-dimensional visually enhanced imaging result of weld defects is generated, and the spatial quantitative parameters of weld defects are extracted.

[0013] The spatial quantitative parameters are input into the defect characterization analysis model to generate the weld defect state characterization results and output a three-dimensional visual characterization report of the weld defect.

[0014] Furthermore, the method for acquiring the multi-element ultrasonic detection data is as follows:

[0015] A multi-element ultrasonic probe is acoustically coupled to the surface of the weld workpiece to be inspected via a wedge or coupling medium.

[0016] The probe is controlled to move along a preset trajectory and the coordinates of the trajectory encoder corresponding to each detection position are recorded synchronously.

[0017] Multi-element ultrasonic echo data is collected at each encoder sampling point to form trajectory positioning coordinates containing track encoder coordinates, beam number and sampling point sequence, or the transmitting elements in the array are excited sequentially and the receiving elements receive the echo signals to form array acquisition data containing the transmitting channel, receiving channel and sampling point sequence.

[0018] The trajectory positioning coordinates and at least one of the array acquisition data are associated and stored with the corresponding imaging parameters and detection parameters to form multi-element ultrasound detection data to be processed.

[0019] Furthermore, the multi-element ultrasound detection data includes one or more of the following: A-scan, B-scan, or C-scan cross-sectional data, trajectory positioning coordinates, and array acquisition data;

[0020] The trajectory positioning coordinates include trajectory encoder coordinates, beam number, and sampling point sequence;

[0021] The array collects data including transmission channels, reception channels, and sampling point sequences;

[0022] The identification of the data organization form of the multi-element ultrasound detection data includes: determining whether the input data is A-scan, B-scan, or C-scan cross-sectional data, trajectory positioning coordinates, or array acquisition data, and respectively matching the trajectory positioning coordinate regular voxel reconstruction path or the array acquisition data multi-channel time delay aggregation imaging path;

[0023] When the input data is A-scan, B-scan, or C-scan cross-sectional data or trajectory positioning coordinates, the first three-dimensional ultrasound volume data is generated according to the trajectory positioning coordinate regular voxel reconstruction path. The method for generating the first three-dimensional ultrasound volume data includes: extracting all trajectory encoder coordinate sequences, beam number sets, and sampling point sequence sets from the trajectory positioning coordinates to establish a three-dimensional regular voxel grid; mapping the original amplitude response to the three-dimensional regular voxel grid according to the corresponding trajectory encoder coordinates, beam numbers, and sampling point sequences; performing zero-padding, null value marking, or neighborhood interpolation on positions in the three-dimensional regular voxel grid where no original records exist; and rearranging the mapped and padded data into a regular three-dimensional array as the first three-dimensional ultrasound volume data.

[0024] Furthermore, when the input data is array-acquired data, it is processed according to the multi-channel time-delay aggregation imaging path of array-acquired data. First, standardized array-acquired data is generated. The methods for generating standardized array-acquired data include:

[0025] The array-acquired data is structured and organized according to the sequence of transmission channels, reception channels, and sampling points;

[0026] The A-scan signal corresponding to each set of transmit-receive channels is subjected to DC removal and baseline correction.

[0027] The A-scan signal is filtered to suppress random noise and irrelevant frequency band components;

[0028] Time zero-point correction is performed on the signals of each channel based on the surface echo location, sampling frequency, and system delay parameters;

[0029] The signals of each channel are truncated within a time window based on the depth range of the area to be detected.

[0030] Amplitude standardization is performed on the echo amplitudes of different channels;

[0031] The processed array acquisition data is used as standardized array acquisition data.

[0032] Furthermore, the multi-channel time-delay aggregation imaging path processing of array-acquired data also includes generating two-dimensional time-delay aggregation imaging slices. The methods for generating two-dimensional time-delay aggregation imaging slices include:

[0033] A two-dimensional imaging grid is established within the area of ​​the weld to be inspected;

[0034] Based on the positions of the transmitting array elements, the receiving array elements, the imaging grid points, and the sound velocity of the material, the transmission propagation time and the reception propagation time corresponding to each imaging grid point are calculated and summed to obtain the total propagation delay.

[0035] The sampling position in the corresponding A-scan signal is determined based on the total propagation delay and sampling frequency.

[0036] When the sampling position is not an integer sampling point, the amplitude of adjacent sampling points is interpolated to obtain the echo amplitude corresponding to the propagation delay.

[0037] Time delay aggregation is performed on the echo amplitude values ​​of all transmit-receive channel combinations at the same imaging grid point to obtain the time delay aggregated imaging intensity of that imaging grid point;

[0038] Repeat the above calculation for all imaging grid points in the two-dimensional imaging grid to generate a two-dimensional time-delay aggregated imaging slice;

[0039] When wedge coupling or multi-layer media propagation conditions exist during the detection process, the propagation delay is segmented and corrected based on the wedge sound velocity, workpiece sound velocity, and interface position.

[0040] Furthermore, the processing of multi-channel time-delay aggregation imaging data acquired by the array also includes generating second-dimensional ultrasound volume data. The methods for generating second-dimensional ultrasound volume data include:

[0041] Acquire multiple two-dimensional time-delayed aggregated imaging slices with spatial order relationships;

[0042] Read the trajectory encoder coordinates, trajectory sampling sequence number, slice number or slice spacing corresponding to each two-dimensional time-delay aggregated imaging slice;

[0043] Establish the correspondence between the pixel coordinates of the two-dimensional time-delayed aggregated imaging slices and the actual spatial coordinates of the weld inspection area; fill each two-dimensional time-delayed aggregated imaging slice into a three-dimensional voxel mesh according to its spatial position;

[0044] Neighborhood interpolation or linear interpolation is performed on the missing voxels between adjacent two-dimensional time-delayed aggregated imaging slices;

[0045] The three-dimensional voxel mesh after space filling and interpolation processing is used as the second three-dimensional ultrasound body data.

[0046] Furthermore, the method for visually enhancing the original three-dimensional ultrasound body data includes:

[0047] The amplitude of the original three-dimensional ultrasound volume data is normalized so that the volume data amplitude is within the preset display range;

[0048] Based on the spatial region where the surface echo of the weld workpiece is located, the strong echo region on the surface is shielded, weakened, or trimmed.

[0049] Locally high-response voxels are retained based on a preset threshold or an adaptive threshold to form candidate defect voxels;

[0050] Enhance suspected weld defect areas based on voxel neighborhood amplitude differences and spatial gradient changes;

[0051] Local cropping of the 3D ultrasonic body data is performed based on the target area of ​​the weld inspection.

[0052] The processed volume data is used as visually enhanced 3D ultrasound volume data.

[0053] Furthermore, specific methods for generating 3D visual enhancement imaging results of weld defects include:

[0054] Volume rendering is performed on the visually enhanced 3D ultrasound volume data to generate a 3D volume imaging image;

[0055] Isosurfaces are extracted from the visually enhanced 3D ultrasound body data to generate isosurface imaging images;

[0056] Orthogonal slice images are generated along the length, width, and thickness directions of the weld inspection area, respectively.

[0057] Enlarge the display of a specific area as needed;

[0058] The volume rendering image, isosurface imaging image, and orthogonal slice image are displayed in a linked manner to obtain a three-dimensional visual enhancement imaging result of weld defects.

[0059] Furthermore, specific methods for extracting the spatial quantitative parameters of weld defects include:

[0060] Based on the voxel spacing and spatial coordinate calibration relationship of the three-dimensional ultrasound body data, a conversion relationship between voxel size and actual physical size is established;

[0061] Extract suspected missing voxels from the amplitude threshold conditions in the visually enhanced 3D ultrasound volume data;

[0062] Connectivity is marked based on the three-dimensional adjacency relationship between suspected defective voxels to obtain one or more candidate regions for weld defects.

[0063] Remove isolated noise regions where the number of voxels is below a preset threshold;

[0064] For each candidate region of weld defect, extract boundary voxels to generate the spatial profile of the weld defect.

[0065] Calculate the center coordinates and spatial distribution range of the defect based on the spatial profile of the weld defect.

[0066] Based on the maximum projected distance of the candidate weld defect region in the length, width and thickness directions, calculate the defect length, defect width and defect height respectively;

[0067] The defect volume is calculated based on the number of voxels contained in the candidate region of weld defect and the actual volume of a single voxel.

[0068] Calculate the burial depth of the defect based on the distance between the center coordinates of the defect and the surface of the welded workpiece;

[0069] Based on the echo amplitude distribution within the candidate area of ​​weld defect, the maximum echo amplitude and average echo amplitude of the defect are extracted.

[0070] Based on the voxel gradient change at the weld defect spatial contour, extract the defect boundary gradient features;

[0071] The defect length, width, height, volume, burial depth, maximum echo amplitude, average echo amplitude, and boundary gradient features are combined into defect characterization features.

[0072] Furthermore, the defect characterization analysis model is a classification model or regression model obtained based on historical detection samples, or a comprehensive scoring model that is initialized based on expert rules and then corrected by samples.

[0073] The defect characterization features are input into the defect characterization analysis model. The model is used to comprehensively analyze the defect spatial size, echo intensity and boundary changes to generate a defect characterization score.

[0074] Based on the preset characterization threshold, the weld defect status is characterized as low level, medium level or high level;

[0075] The output 3D visual characterization report of weld defects includes:

[0076] Mark suspected weld defect areas in three-dimensional volume imaging images, isosurface imaging images, and multi-plane reconstructed images;

[0077] The spatial coordinates, length, width, height, volume, burial depth, amplitude characteristics, boundary gradient characteristics, and weld defect status characterization results corresponding to the suspected weld defect area are displayed in a correlated manner.

[0078] The information of the weld to be inspected, the multi-element ultrasonic testing parameters, the three-dimensional visual enhancement image, the spatial contour of the weld defect, the quantitative parameters of the weld defect, and the defect status characterization results are summarized to generate a three-dimensional visual characterization report of the weld defect, which includes the three-dimensional visual enhancement image of the weld defect, the spatial quantitative parameters of the weld defect, the defect status characterization results, the inspection conclusion, and the re-inspection suggestions.

[0079] The beneficial effects of this invention are as follows:

[0080] This invention is compatible with cross-sectional data, trajectory positioning coordinates, and array acquisition data from A-scan, B-scan, or C-scan scans. It can achieve rapid three-dimensional voxel reconstruction under existing trajectory positioning coordinates and multi-channel time-delay aggregation imaging and high-resolution three-dimensional reconstruction under complete array acquisition data conditions, thus exhibiting stronger data adaptability.

[0081] This invention establishes a three-dimensional regular voxel grid by using trajectory encoder coordinates, beam numbers, and sampling point sequences. This can convert discrete tabular trajectory positioning coordinates into regular three-dimensional ultrasonic body data, solving the problem that existing trajectory positioning coordinates are difficult to display directly in three dimensions.

[0082] This invention improves the consistency of data from different transmit-receive channels by structuring and standardizing the data collected by the array, providing a stable input for subsequent propagation delay calculation, interpolation extraction, and point-by-point delay aggregation.

[0083] This invention performs point-by-point dynamic focusing calculations on the weld inspection area based on a multi-channel time delay aggregation model, which can make fuller use of the information from the transmitting and receiving channels, and improve the boundary representation ability and local response clarity of the defect area.

[0084] This invention extends two-dimensional time-delayed aggregated imaging slices into three-dimensional ultrasound volume data through coordinate mapping between two-dimensional time-delayed aggregated imaging slices and three-dimensional voxel space, which is beneficial for expressing the spatial location, extension direction and volume characteristics of defects.

[0085] This invention reduces the obstruction of surface strong echoes and background noise on the display of defects inside the weld by amplitude normalization, surface strong echo suppression, target imaging area clipping, and local defect response enhancement, thereby improving the clarity of the three-dimensional visual enhancement imaging results.

[0086] This invention combines volumetric rendering, isosurface imaging, and multi-plane reconstruction display, enabling observation of the overall spatial distribution of internal echo intensity, highlighting the boundary contours of deficient high-response regions, and providing auxiliary interpretation from multiple directions.

[0087] This invention extracts parameters such as defect length, width, height, volume, and burial depth based on three-dimensional voxel coordinates and spatial calibration relationships, thus expanding the imaging results from simple image observation to quantifiable spatial characterization results.

[0088] This invention inputs the defect spatial parameters, amplitude characteristics, and boundary gradient characteristics into the defect characterization analysis model, which can generate weld defect state characterization results, providing auxiliary basis for manual review, quality assessment, re-inspection suggestions, and subsequent maintenance decisions. Attached Figure Description

[0089] Figure 1 This is a flowchart of a multi-element ultrasonic three-dimensional visual enhancement imaging method for characterizing weld defects provided by the present invention;

[0090] Figure 2 This is a schematic diagram of the structure of the device for acquiring multi-element ultrasonic testing data of weld seams provided by the present invention;

[0091] Figure 3 This is a flowchart of regular voxel reconstruction of A-scan, B-scan, or C-scan cross-sectional data and imaging results including trajectory positioning coordinates provided by the present invention;

[0092] Figure 4 This is a flowchart of the structured organization and preprocessing of array acquisition data provided by the present invention;

[0093] Figure 5 This is a schematic diagram of the multi-channel delay aggregation model provided by the present invention;

[0094] Figure 6 This is a schematic diagram of the reconstruction of two-dimensional time-delayed aggregation imaging slice spatial registration to three-dimensional ultrasound body data provided by the present invention;

[0095] Figure 7 This is a flowchart of the three-dimensional ultrasound body data artifact suppression and visual enhancement processing provided by the present invention;

[0096] Figure 8 This is a schematic diagram of the volume rendering, isosurface imaging, and multi-plane reconstruction linkage display provided by the present invention;

[0097] Figure 9 This is a schematic diagram of the extraction of weld defect space contour and space dimension parameters provided by the present invention.

[0098] In the attached diagram, 1 represents the guide rail, 2 represents the sliding frame, 3 represents the track encoder, 4 represents the data acquisition and control unit, 5 represents the processing workstation, 6 represents the multi-element ultrasonic probe, 7 represents the wedge, 8 represents the coupling layer, 9 represents the weld area, 10 represents the defect, 101 represents the transmitting element, and 102 represents the receiving element. Detailed Implementation

[0099] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0100] This invention provides a multi-element ultrasonic three-dimensional visual enhancement imaging method for characterizing weld defects, such as... Figure 1 As shown, it includes the following steps:

[0101] Acquire multi-element ultrasonic testing data of the weld to be inspected;

[0102] The data organization format of the multi-element ultrasound detection data is identified, and the corresponding three-dimensional volume data generation strategy is selected based on the identification result to generate the original three-dimensional ultrasound volume data.

[0103] The original three-dimensional ultrasound body data is subjected to visual enhancement processing to obtain visually enhanced three-dimensional ultrasound body data;

[0104] Based on the visually enhanced three-dimensional ultrasonic body data, a three-dimensional visually enhanced imaging result of weld defects is generated, and the spatial quantitative parameters of weld defects are extracted.

[0105] The spatial quantitative parameters are input into the defect characterization analysis model to generate the weld defect state characterization results and output a three-dimensional visual characterization report of the weld defect.

[0106] The present invention will be further described in detail below with reference to specific embodiments.

[0107] In this embodiment, the object to be inspected is the weld area in a pressure pipeline, pressure vessel, or load-bearing structure. The system first acquires multi-element ultrasonic testing data and reads imaging and detection parameters such as multi-element ultrasonic probe parameters, wedge parameters, element parameters, sampling frequency, material sound velocity, sound beam angle parameters, and trajectory encoder position information. Subsequently, the system determines whether the data is trajectory positioning coordinates or array acquisition data based on its organization. If the input is trajectory positioning coordinates, the system generates first three-dimensional ultrasonic volume data through regular voxel reconstruction; if the input is array acquisition data, the system generates two-dimensional time-delayed aggregated imaging slices through standardization processing and multi-channel time-delayed aggregated imaging, and further reconstructs them into second three-dimensional ultrasonic volume data. Afterward, the system performs artifact suppression and visual enhancement on the three-dimensional ultrasonic volume data, generating three-dimensional visually enhanced imaging results, extracting defect spatial parameters, and using a defect characterization analysis model to generate weld defect status assessment results.

[0108] The specific steps of this embodiment are as follows:

[0109] Acquire multi-element ultrasonic testing data of the weld to be inspected, wherein the multi-element ultrasonic testing data includes one or more of the following: A-scan, B-scan, or C-scan cross-sectional data, trajectory positioning coordinates, and array acquisition data;

[0110] The imaging parameters and detection parameters of the weld to be inspected are read. The imaging parameters and detection parameters include multi-element ultrasonic probe parameters, wedge parameters, array element parameters, sampling frequency, material sound velocity, sound beam angle parameters, trajectory encoder position information, beam number information, and geometric parameters of the weld detection area.

[0111] Identify the data organization format of the multi-element ultrasound detection data, determine whether it is A-scan, B-scan or C-scan cross-sectional data, imaging result data containing trajectory positioning coordinates or array acquisition data, and respectively match the trajectory positioning coordinate regular voxel reconstruction path or the array acquisition data multi-channel time delay aggregation imaging path;

[0112] When the input data is cross-sectional data of A scan, B scan or C scan, or trajectory positioning coordinates, a three-dimensional regular voxel grid is established according to the trajectory encoder coordinates, beam number and sampling point sequence. The original amplitude response is mapped to the corresponding voxel position, and zero-padding, null value marking or neighborhood interpolation processing is performed on the uncovered voxels to generate the first three-dimensional ultrasound body data.

[0113] When the input data is array acquisition data, the array acquisition data is structured and organized according to the sequence of transmission channel, reception channel and sampling point, and DC removal, baseline correction, filtering, time zero point correction, time window truncation and amplitude standardization are performed to generate standardized array acquisition data;

[0114] A multi-channel time delay aggregation model is constructed based on standardized array acquisition data. The propagation time delay is calculated according to the position of the transmitting array element, the position of the receiving array element, the position of the imaging grid point, and the sound velocity of the material. The time delay aggregation calculation is performed point by point on the weld detection area to generate a two-dimensional time delay aggregation imaging slice.

[0115] Based on the coordinates of the trajectory encoder, the trajectory sampling sequence number, the slice spacing or the spatial coordinate transformation relationship, the correspondence between the two-dimensional time-delayed aggregated imaging slices and the three-dimensional voxel space is established, and multiple two-dimensional time-delayed aggregated imaging slices are spatially registered and organized into a second three-dimensional ultrasound volume data.

[0116] At least one of the first three-dimensional ultrasound volume data and the second three-dimensional ultrasound volume data is subjected to amplitude normalization, surface strong echo suppression, target imaging region cropping, candidate defect voxel screening and local defect response enhancement to obtain visually enhanced three-dimensional ultrasound volume data.

[0117] The visually enhanced 3D ultrasonic volume data is used for volume rendering, isosurface imaging, and multi-plane reconstruction display to obtain the 3D visually enhanced imaging results of weld defects.

[0118] Based on the visually enhanced 3D ultrasonic body data and the 3D visually enhanced imaging results of weld defects, the spatial coordinates, contour boundary, length, width, height, volume, equivalent size, and burial depth parameters of the weld defects are extracted. The spatial parameters of the weld defects, echo amplitude characteristics, and boundary gradient characteristics are then input into the defect characterization analysis model to generate the weld defect state characterization results. The output is a 3D visual characterization report of weld defects that includes a 3D image of the weld defects, spatial quantitative parameters, defect state characterization results, detection conclusions, and re-inspection suggestions.

[0119] The weld defects include at least one of porosity, slag inclusion, lack of fusion, cracks, and corrosion thinning.

[0120] like Figure 2 As shown, the multi-element ultrasonic probe 6 establishes acoustic coupling with the surface of the weld workpiece to be inspected via the wedge block 7 and the coupling layer 8. The multi-element ultrasonic probe 6 is controlled to move along a preset probe movement trajectory by the sliding frame 2 mounted on the guide rail 1, and the coordinates of the trajectory encoder corresponding to each detection position are recorded simultaneously.

[0121] In one implementation, multi-element ultrasonic echo data is acquired at each sampling point of the track encoder 3 to form multi-element ultrasonic detection data containing track encoder coordinates, beam numbers, and sampling point sequences. The acquired data is sent to the processing workstation 5 via the data acquisition and control unit 4.

[0122] In another implementation, the transmitting elements in the array are sequentially excited at each of the three sampling points of the track encoder, and the echo signals are received by the receiving elements to form array acquisition data containing the transmitting channel, the receiving channel, and the sampling point sequence.

[0123] Let the set of detection parameters be:

[0124] ;

[0125] in, This indicates the number of elements in a multi-element ultrasonic probe. Indicates the sampling frequency. This indicates the velocity of sound in the material of the inspected workpiece. Indicates the spacing between array elements. Represents the coordinate sequence of the trajectory encoder. This represents the geometric parameters of the detection area.

[0126] Using the above set of parameters, the correspondence between sampling points, channel data, spatial location, and three-dimensional voxels can be established.

[0127] After reading the multi-element ultrasound detection data, the system determines the data type based on the data index structure.

[0128] When the detection data mainly consists of trajectory encoder coordinates, beam numbers, and sampling point sequences, it is determined to be trajectory positioning coordinates, denoted as:

[0129] ;

[0130] in, Indicates the first Each trajectory encoder coordinate, Indicates the first Beam number, Indicates the first Each sampling point number This indicates the corresponding echo amplitude.

[0131] When the detection data mainly consists of the transmission channel, the receiving channel, and the sampling point sequence, it is identified as array-acquired data and denoted as:

[0132] ;

[0133] in, Indicates the first Each element is launched, the first The A-scan echo signal obtained by each array element during reception. The number of array elements. Sampling time.

[0134] Based on the above judgment, the system enters either the trajectory positioning coordinate three-dimensional reconstruction path or the array acquisition data multi-channel time delay aggregation imaging path.

[0135] like Figure 3 As shown, when the input data is trajectory positioning coordinates, the entire trajectory encoder coordinate sequence, the entire beam number set, and the entire sampling point sequence set are extracted from the trajectory positioning coordinates, and a three-dimensional regular voxel mesh is established.

[0136] Let the voxel coordinates in a 3D regular voxel mesh be... The mapping relationship between the three-dimensional data index and the voxel coordinate in the trajectory positioning coordinates is as follows:

[0137] ;

[0138] in, This represents a spatial mapping function determined by the coordinates of the trajectory encoder, the beam number, and the sequence of sampling points.

[0139] The first three-dimensional ultrasound body data can be represented as:

[0140] ;

[0141] in, This represents the first three-dimensional ultrasound body data. This represents the set of valid voxels covered by the original trajectory positioning coordinates.

[0142] For locations in the 3D regular voxel mesh where no original record exists, the system performs zero-padding or null-filling. Through this process, the original tabular or sequential trajectory data is converted into regular 3D ultrasound volume data, providing a foundation for subsequent volume rendering, isosurface imaging, and slice observation.

[0143] like Figure 4 As shown, when the input data is array acquisition data, the data is first structured and organized according to the sequence of transmission channels, receiving channels and sampling points to form a unified channel-level data organization form.

[0144] For the The first launch element and the first A-scan signal corresponding to each receiving array element First, DC removal is performed:

[0145] ;

[0146] in, This indicates the total number of sampling points for the A-scan signal. This indicates the signal after DC removal.

[0147] Then, amplitude standardization is performed:

[0148] ;

[0149] in, This represents the standardized A-scan signal. To prevent extremely small constants with a denominator of zero.

[0150] In practical implementation, baseline correction, bandpass filtering, time zero-point correction, and time window truncation can also be performed on the A-scan signal. Time window truncation can be expressed as:

[0151] ;

[0152] in, and These represent the start and end times of the effective detection time window, respectively. This is an indicator function.

[0153] The array acquisition data after the above processing is used as standardized array acquisition data for subsequent multi-channel time-delay aggregation imaging calculations.

[0154] like Figure 5As shown, the multi-element ultrasonic probe includes multiple transmitting elements 101 and receiving elements 102.

[0155] A two-dimensional imaging grid is established within the weld area 9 to be inspected. Let any imaging point in the imaging grid be:

[0156] ;

[0157] Let the first The positions of the transmitting array elements are:

[0158] ;

[0159] No. The positions of the receiving array elements are:

[0160] ;

[0161] Under homogeneous medium conditions, the first From each emission element to the imaging point Then from the imaging point To the The total propagation delay of each receiving array element is:

[0162] ;

[0163] in, The ultrasonic propagation speed in the weld workpiece material.

[0164] When wedge coupling or multi-layer media propagation conditions exist during the detection process, the total propagation delay can be expressed in a segmented form:

[0165] ;

[0166] in, For the speed of sound of the wedge, For the sound velocity of the workpiece, and These represent the propagation distances of the transmission path and the reception path within the wedge, respectively. and These represent the propagation distances of the transmission path and the receiving path within the workpiece, respectively.

[0167] Since propagation delays typically do not correspond exactly to integer sampling points, they are converted into sampling indices:

[0168] ;

[0169] ;

[0170] in, Sampling frequency, For integer sampling points, These are the interpolation weights.

[0171] The echo amplitude at the corresponding propagation delay position is obtained through linear interpolation:

[0172] ;

[0173] In a two-dimensional time-delay convergence imaging slice, the imaging point The time-delayed convergence imaging intensity is:

[0174] ;

[0175] in, Represents the imaging point The multi-channel time-delay aggregated imaging intensity is calculated. The above calculation is repeated for all imaging points in the two-dimensional imaging grid to generate a two-dimensional time-delay aggregated imaging slice.

[0176] like Figure 6 As shown, multiple two-dimensional time-delay aggregated imaging slices with spatial order are obtained, and the trajectory encoder coordinates, trajectory sampling sequence number, slice number or slice spacing corresponding to each two-dimensional time-delay aggregated imaging slice are read.

[0177] Let the first Zhang's two-dimensional time-delayed polymerase cross-section is:

[0178] ;

[0179] Its corresponding trajectory sampling coordinates are:

[0180] ;

[0181] The second three-dimensional ultrasound data can then be represented as:

[0182] ;

[0183] When there are missing voxels between adjacent slices, linear interpolation is used for compensation:

[0184]

[0185] in:

[0186] ;

[0187] Through the above processing, multiple two-dimensional time-delayed aggregated imaging slices are organized into a second three-dimensional ultrasound body data with spatial continuity.

[0188] like Figure 7As shown, artifact suppression and visual enhancement are performed on the first and second 3D ultrasound volume data. Let the input 3D ultrasound volume data be... First, amplitude normalization is performed:

[0189] ;

[0190] in, and These represent the minimum and maximum amplitude values ​​in the volume data, respectively.

[0191] For areas with strong surface echoes, a depth suppression weighting function is introduced:

[0192] ;

[0193] in, The surface suppression depth threshold, This is the surface strong echo suppression coefficient.

[0194] The volume data after suppressing strong surface echoes are as follows:

[0195] ;

[0196] Furthermore, the suspected deficient regions are enhanced based on local spatial gradients:

[0197] ;

[0198] in, This is the local defect response enhancement coefficient. This represents the magnitude of the gradient in three-dimensional space.

[0199] By using the aforementioned artifact suppression and visual enhancement techniques, we can reduce the obstruction of the defective areas inside the weld by the strong surface echoes, while highlighting the boundaries of local defects and high-response areas.

[0200] like Figure 8 As shown, the three-dimensional ultrasound body data after visual enhancement. Perform volume rendering, isosurface imaging, and multi-plane reconstruction display.

[0201] Volume rendering is used to show the overall spatial distribution of internal echo intensity. Isosurface imaging is used to extract high-response regions below a specific amplitude threshold; its set of isosurfaces can be represented as:

[0202] ;

[0203] in, The isosurface threshold, This is the set of corresponding isosurfaces.

[0204] Multiplanar reconstruction display involves generating orthogonal slice images along the length, width, and thickness directions, which can be represented as follows:

[0205] ;

[0206] ;

[0207] ;

[0208] in, , , These represent the slice positions in the corresponding directions.

[0209] By using volume rendering, isosurface imaging, and multi-plane reconstruction and linkage display, the overall spatial distribution of weld defects, local contour boundaries, and internal response characteristics on different cross sections can be observed simultaneously.

[0210] like Figure 9 As shown, suspected defect voxels are extracted based on visually enhanced 3D ultrasound volume data. The binarization result of the candidate defect voxels can be represented as:

[0211] ;

[0212] in, The threshold for missing candidate voxels.

[0213] In one implementation, the threshold can be determined adaptively:

[0214] ;

[0215] in, To enhance the average amplitude of the post-3D volumetric data, The standard deviation of the amplitude. This is the threshold adjustment coefficient.

[0216] Three-dimensional connected component labeling is performed on suspected defect voxels that meet the conditions to obtain one or more candidate weld defect regions. Let a certain candidate weld defect region be:

[0217] ;

[0218] in, This represents the number of voxels within the candidate region of the weld defect.

[0219] The length, width, and height of the defect are as follows:

[0220] ;

[0221] ;

[0222] ;

[0223] The missing volume is:

[0224] ;

[0225] in, , , These represent the voxel spacing in three directions.

[0226] The coordinates of the missing center are:

[0227] ;

[0228] Let the depth coordinates of the defect center be... The workpiece surface depth coordinates are The weld defect depth is:

[0229] ;

[0230] This allows us to obtain quantitative spatial parameters such as the spatial coordinates, three-dimensional profile, length, width, height, volume, and burial depth of the weld defect.

[0231] The system inputs the defect spatial parameters, amplitude characteristics, and boundary characteristics into the defect characterization and analysis model. For a candidate defect region of a weld, a defect feature vector is constructed:

[0232] ;

[0233] in, The maximum echo amplitude in the missing region. The average echo amplitude of the missing region. This represents the gradient feature of the missing boundary.

[0234] The defect characterization analysis model outputs a defect characterization score:

[0235] ;

[0236] in, For the feature weight vector, For bias terms, This is the normalized mapping function.

[0237] In one implementation, Using the Sigmoid function:

[0238] ;

[0239] The weld defect status assessment results are generated based on the characterization score:

[0240] ;

[0241] in, and This is a preset risk threshold.

[0242] Finally, the system summarizes the information of the detected object, multi-element ultrasonic testing parameters, three-dimensional imaging images, spatial contour of weld defects, quantitative parameters of defects, and characterization results of defect status, and outputs a three-dimensional visual characterization report of weld defects that includes three-dimensional images of weld defects, quantitative parameters of weld defects, characterization results of defect status, detection conclusions, and re-inspection suggestions.

[0243] Implementation Results and Comparative Analysis:

[0244] In this embodiment, the method of the present invention is used to process multi-element ultrasonic testing data of welds. For trajectory positioning coordinates, the present invention can complete regular voxel reconstruction based on trajectory encoder coordinates, beam number, and sampling point sequence to form first three-dimensional ultrasonic volume data, and improve the problem of strong surface echo obstruction through three-dimensional artifact suppression and visual enhancement. For array acquisition data, the present invention can perform multi-channel time-delay aggregation imaging calculation based on transmit-receive channel signals to generate two-dimensional time-delay aggregation imaging slices, and further reconstruct them into second three-dimensional ultrasonic volume data.

[0245] Compared to conventional two-dimensional multi-element ultrasound display methods, this invention can not only observe the response of defects on a single cross-section, but also observe the spatial distribution, extension direction, and boundary contour of defects through volume rendering, isosurface imaging, and multi-plane reconstruction. Compared to ordinary three-dimensional volume data stacking methods, this invention further introduces amplitude normalization, surface strong echo suppression, local defect response enhancement, and defect spatial parameter extraction, enabling the three-dimensional results not only for observation but also to output quantitative information such as length, width, height, volume, burial depth, and characterization score.

[0246] The comparative analysis of the prior art of this invention is shown in Table 1 below:

[0247] Table 1 Technical Comparison Analysis

[0248] Data compatibility Primarily relies on a single display of data Primarily relies on continuously sliced ​​data Compatible with trajectory positioning coordinates and array acquisition data Imaging method Two-dimensional cross-section display Simple space stacking Regular voxel reconstruction combined with multi-channel time-delayed convergence imaging Three-dimensional expressive ability limited Preliminary three-dimensional representation can be performed. It can be combined with multi-channel time-delay convergence imaging, volume rendering, and isosurface imaging for 3D representation. Surface strong echo processing Usually relies on manual window adjustment Limited processing capacity It can suppress strong surface echoes and enhance local defect responses. Defect boundary display Not intuitive enough Dependence on display threshold Contours can be highlighted using isosurfaces and gradients. Quantitative parameter output less Partial support It can output length, width, height, volume, and burial depth. Defective characterization judgment Mainly relies on human experience Mainly relies on human experience It can output deficiency characterization scores and state characterization results.

[0249] Therefore, the present invention can improve the three-dimensional spatial representation, visual enhancement display and spatial quantitative characterization of weld defects, and is applicable to multi-element ultrasonic three-dimensional visual enhancement imaging characterization of weld defects in pressure pipelines, pressure vessels and load-bearing structures.

[0250] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for characterizing weld defects using multi-element ultrasonic three-dimensional visual enhancement imaging, characterized in that, Includes the following steps: Acquire multi-element ultrasonic testing data of the weld to be inspected; The data organization format of the multi-element ultrasound detection data is identified, and the corresponding three-dimensional volume data generation strategy is selected based on the identification result to generate the original three-dimensional ultrasound volume data. The original three-dimensional ultrasound body data is subjected to visual enhancement processing to obtain visually enhanced three-dimensional ultrasound body data; Based on the visually enhanced three-dimensional ultrasonic body data, a three-dimensional visually enhanced imaging result of weld defects is generated, and the spatial quantitative parameters of weld defects are extracted. The spatial quantitative parameters are input into the defect characterization analysis model to generate the weld defect state characterization results and output a three-dimensional visual characterization report of the weld defect.

2. The multi-element ultrasonic three-dimensional visual enhancement imaging characterization method for weld defects according to claim 1, characterized in that, The method for acquiring the multi-element ultrasonic detection data is as follows: A multi-element ultrasonic probe is acoustically coupled to the surface of the weld workpiece to be inspected via a wedge or coupling medium. The probe is controlled to move along a preset trajectory and the coordinates of the trajectory encoder corresponding to each detection position are recorded synchronously. Multi-element ultrasonic echo data is collected at each encoder sampling point to form trajectory positioning coordinates containing track encoder coordinates, beam number and sampling point sequence, or the transmitting elements in the array are excited sequentially and the receiving elements receive the echo signals to form array acquisition data containing the transmitting channel, receiving channel and sampling point sequence. The trajectory positioning coordinates and at least one of the array acquisition data are associated and stored with the corresponding imaging parameters and detection parameters to form multi-element ultrasound detection data to be processed.

3. The multi-element ultrasonic three-dimensional visual enhancement imaging characterization method for weld defects according to claim 1, characterized in that, The multi-element ultrasonic detection data includes one or more of the following: A-scan, B-scan, or C-scan cross-sectional data, trajectory positioning coordinates, and array acquisition data; The trajectory positioning coordinates include trajectory encoder coordinates, beam number, and sampling point sequence; The array collects data including transmission channels, reception channels, and sampling point sequences; The identification of the data organization form of the multi-element ultrasound detection data includes: determining whether the input data is A-scan, B-scan, or C-scan cross-sectional data, trajectory positioning coordinates, or array acquisition data, and respectively matching the trajectory positioning coordinate regular voxel reconstruction path or the array acquisition data multi-channel time delay aggregation imaging path; When the input data is A-scan, B-scan, or C-scan cross-sectional data or trajectory positioning coordinates, the first three-dimensional ultrasound volume data is generated according to the trajectory positioning coordinate regular voxel reconstruction path. The method for generating the first three-dimensional ultrasound volume data includes: extracting all trajectory encoder coordinate sequences, beam number sets, and sampling point sequence sets from the trajectory positioning coordinates to establish a three-dimensional regular voxel grid; mapping the original amplitude response to the three-dimensional regular voxel grid according to the corresponding trajectory encoder coordinates, beam numbers, and sampling point sequences; performing zero-padding, null value marking, or neighborhood interpolation on positions in the three-dimensional regular voxel grid where no original records exist; and rearranging the mapped and padded data into a regular three-dimensional array as the first three-dimensional ultrasound volume data.

4. The multi-element ultrasonic three-dimensional visual enhancement imaging characterization method for weld defects according to claim 3, characterized in that, When the input data is array-acquired data, it is processed according to the multi-channel time-delay aggregation imaging path of array-acquired data. First, standardized array-acquired data is generated. The methods for generating standardized array-acquired data include: The array-acquired data is structured and organized according to the sequence of transmission channels, reception channels, and sampling points; The A-scan signal corresponding to each set of transmit-receive channels is subjected to DC removal and baseline correction. The A-scan signal is filtered to suppress random noise and irrelevant frequency band components; Time zero-point correction is performed on the signals of each channel based on the surface echo location, sampling frequency, and system delay parameters; The signals of each channel are truncated within a time window based on the depth range of the area to be detected. Amplitude standardization is performed on the echo amplitudes of different channels; The processed array acquisition data is used as standardized array acquisition data.

5. The multi-element ultrasonic three-dimensional visual enhancement imaging characterization method for weld defects according to claim 4, characterized in that, The processing of multi-channel time-delay aggregation imaging based on array-acquired data also includes generating two-dimensional time-delay aggregation imaging slices. Methods for generating two-dimensional time-delay aggregation imaging slices include: A two-dimensional imaging grid is established within the area of ​​the weld to be inspected; Based on the positions of the transmitting array elements, the receiving array elements, the imaging grid points, and the sound velocity of the material, the transmission propagation time and the reception propagation time corresponding to each imaging grid point are calculated and summed to obtain the total propagation delay. The sampling position in the corresponding A-scan signal is determined based on the total propagation delay and sampling frequency. When the sampling position is not an integer sampling point, the amplitude of adjacent sampling points is interpolated to obtain the echo amplitude corresponding to the propagation delay. Time delay aggregation is performed on the echo amplitude values ​​of all transmit-receive channel combinations at the same imaging grid point to obtain the time delay aggregated imaging intensity of that imaging grid point; Repeat the above calculation for all imaging grid points in the two-dimensional imaging grid to generate a two-dimensional time-delay aggregated imaging slice; When wedge coupling or multi-layer media propagation conditions exist during the detection process, the propagation delay is segmented and corrected based on the wedge sound velocity, workpiece sound velocity, and interface position.

6. The multi-element ultrasonic three-dimensional visual enhancement imaging characterization method for weld defects according to claim 5, characterized in that, The processing of multi-channel time-delay aggregation imaging path based on array-acquired data also includes generating second three-dimensional ultrasound volume data. Methods for generating second three-dimensional ultrasound volume data include: Acquire multiple two-dimensional time-delayed aggregated imaging slices with spatial order relationships; Read the trajectory encoder coordinates, trajectory sampling sequence number, slice number or slice spacing corresponding to each two-dimensional time-delay aggregated imaging slice; Establish the correspondence between the pixel coordinates of the two-dimensional time-delayed aggregated imaging slices and the actual spatial coordinates of the weld inspection area; fill each two-dimensional time-delayed aggregated imaging slice into a three-dimensional voxel mesh according to its spatial position; Neighborhood interpolation or linear interpolation is performed on the missing voxels between adjacent two-dimensional time-delayed aggregated imaging slices; The three-dimensional voxel mesh after space filling and interpolation processing is used as the second three-dimensional ultrasound body data.

7. The multi-element ultrasonic three-dimensional visual enhancement imaging characterization method for weld defects according to claim 1, characterized in that, The method for visually enhancing the original three-dimensional ultrasound body data includes: The amplitude of the original three-dimensional ultrasound volume data is normalized so that the volume data amplitude is within the preset display range; Based on the spatial region where the surface echo of the weld workpiece is located, the strong echo region on the surface is shielded, weakened, or trimmed. Locally high-response voxels are retained based on a preset threshold or an adaptive threshold to form candidate defect voxels; Enhance suspected weld defect areas based on voxel neighborhood amplitude differences and spatial gradient changes; Local cropping of the 3D ultrasonic body data is performed based on the target area of ​​the weld inspection. The processed volume data is used as visually enhanced 3D ultrasound volume data.

8. The multi-element ultrasonic three-dimensional visual enhancement imaging method for characterizing weld defects according to claim 1, characterized in that, Specific methods for generating 3D visual enhancement imaging results of weld defects include: Volume rendering is performed on the visually enhanced 3D ultrasound volume data to generate a 3D volume imaging image; Isosurfaces are extracted from the visually enhanced 3D ultrasound body data to generate isosurface imaging images; Orthogonal slice images are generated along the length, width, and thickness directions of the weld inspection area, respectively. Enlarge the display of a specific area as needed; The volume rendering image, isosurface imaging image, and orthogonal slice image are displayed in a linked manner to obtain a three-dimensional visual enhancement imaging result of weld defects.

9. The multi-element ultrasonic three-dimensional visual enhancement imaging characterization method for weld defects according to claim 1, characterized in that, Specific methods for extracting spatial quantitative parameters of weld defects include: Based on the voxel spacing and spatial coordinate calibration relationship of the three-dimensional ultrasound body data, a conversion relationship between voxel size and actual physical size is established; Extract suspected missing voxels from the amplitude threshold conditions in the visually enhanced 3D ultrasound volume data; Connectivity is marked based on the three-dimensional adjacency relationship between suspected defective voxels to obtain one or more candidate regions for weld defects. Remove isolated noise regions where the number of voxels is below a preset threshold; For each candidate region of weld defect, extract boundary voxels to generate the spatial profile of the weld defect. Calculate the center coordinates and spatial distribution range of the defect based on the spatial profile of the weld defect. Based on the maximum projected distance of the candidate weld defect region in the length, width and thickness directions, calculate the defect length, defect width and defect height respectively; The defect volume is calculated based on the number of voxels contained in the candidate region of weld defect and the actual volume of a single voxel. Calculate the burial depth of the defect based on the distance between the center coordinates of the defect and the surface of the welded workpiece; Based on the echo amplitude distribution within the candidate area of ​​weld defect, the maximum echo amplitude and average echo amplitude of the defect are extracted. Based on the voxel gradient change at the weld defect spatial contour, extract the defect boundary gradient features; The defect length, width, height, volume, burial depth, maximum echo amplitude, average echo amplitude, and boundary gradient features are combined into defect characterization features.

10. The multi-element ultrasonic three-dimensional visual enhancement imaging characterization method for weld defects according to claim 1, characterized in that, The defect characterization analysis model is a classification model or regression model based on historical detection samples, or a comprehensive scoring model that is initialized based on expert rules and then corrected by samples. The defect characterization features are input into the defect characterization analysis model. The model is used to comprehensively analyze the defect spatial size, echo intensity and boundary changes to generate a defect characterization score. Based on the preset characterization threshold, the weld defect status is characterized as low level, medium level or high level; The output 3D visual characterization report of weld defects includes: Mark suspected weld defect areas in three-dimensional volume imaging images, isosurface imaging images, and multi-plane reconstructed images; The spatial coordinates, length, width, height, volume, burial depth, amplitude characteristics, boundary gradient characteristics, and weld defect status characterization results corresponding to the suspected weld defect area are displayed in a correlated manner. The information of the weld to be inspected, the multi-element ultrasonic testing parameters, the three-dimensional visual enhancement image, the spatial contour of the weld defect, the quantitative parameters of the weld defect, and the defect status characterization results are summarized to generate a three-dimensional visual characterization report of the weld defect, which includes the three-dimensional visual enhancement image of the weld defect, the spatial quantitative parameters of the weld defect, the defect status characterization results, the inspection conclusion, and the re-inspection suggestions.

Images