Ultrasonic detection and damage evaluation method and system for curved surface composite material component

Abstract

The invention relates to an ultrasonic detection and damage evaluation method and system for a curved-surface composite component, and the method comprises the steps: collecting three-dimensional point cloud data of a surface damage region of the curved-surface composite component, and constructing a detection path point sequence; the industrial robot and the ultrasonic phased array are controlled to move and scan synchronously according to time, and ultrasonic A scanning signals and robot tail end pose information are collected; three-dimensional space coordinates of all reflection points in the curved surface composite material component are calculated in combination with pose derivation, and an initial three-dimensional damage point cloud is constructed; setting a segmentation threshold to construct an initial segmentation damage point cloud; a traditional DBSCAN clustering algorithm is improved for clustering segmentation, and point clouds of all independent damage areas are obtained; and voxelizing the point cloud of the independent damage area into a three-dimensional voxel model to realize quantitative assessment of the damage. According to the method, automatic and high-precision three-dimensional damage detection and evaluation of the complex curved surface component are realized, and the problems that a traditional ultrasonic detection method depends on manual operation and is poor in adaptability, the detection result is not visual and the like are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing technology, and in particular to a method and system for ultrasonic testing and damage assessment of curved composite material components. Background Technology

[0002] In recent years, composite materials have been increasingly widely used in aerospace, rail transportation, and other fields due to their advantages such as high specific stiffness, strength, lightweight properties, and significant fracture resistance. However, unlike metal components, composite components have weak interlaminar strength, making them prone to delamination, porosity, and debonding during manufacturing, assembly, and service. Under complex alternating loads, these defects can become crack propagation sources, weakening the overall mechanical properties of the components and seriously jeopardizing service safety. Therefore, it is necessary to use methods such as ultrasonic testing to conduct timely and accurate non-destructive testing on the interior of composite materials to eliminate potential safety hazards. However, the complex manufacturing process and diverse structures of composite structural components place higher demands on ultrasonic non-destructive testing and damage assessment technologies.

[0003] Traditional ultrasonic nondestructive testing techniques often employ fixed scanning devices or manual operation, making it difficult to achieve high-precision three-dimensional imaging and damage assessment of complex curved surfaces. In traditional techniques, the synchronization of ultrasonic echo signals and scanning positions relies heavily on mechanical encoders or preset path planning, resulting in poor adaptability to complex surfaces, unintuitive test results, and reliance on human experience for damage assessment. Therefore, there is an urgent need for a high-precision, highly adaptive, and highly automated ultrasonic testing and damage assessment method. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for ultrasonic testing and damage assessment of curved composite material components. It solves the problems of traditional ultrasonic nondestructive testing technology, such as difficulty in achieving high-precision three-dimensional imaging and damage assessment of complex curved components, poor adaptability to complex curved surfaces, unintuitive test results, and reliance on human experience for damage assessment. It can achieve adaptive ultrasonic testing of complex curved components and accurately assess the damage.

[0005] To achieve the above technical objectives, the present invention provides the following technical solution: a method for ultrasonic testing and damage assessment of curved composite material components, comprising the following steps: S1. The industrial robot collects three-dimensional point cloud data of the surface damage area of ​​the curved composite material component. Through preprocessing, layer slicing, feature point extraction and B-spline curve fitting, the scanning direction is determined and the detection path point sequence is generated. S2. Control the industrial robot to move along the detection path point sequence, and use the ultrasonic phased array to scan the damaged area synchronously along the scanning direction, collect the ultrasonic A-scan signal at each sampling time, and record the robot end pose information at each sampling time, and construct a synchronous dataset containing ultrasonic A-scan signal and robot end pose information indexed by the sampling time. The robot end-effector pose information includes the three-dimensional spatial coordinates and attitude rotation matrix of the industrial robot end-effector. S3. Based on the robot end pose information in the synchronous data, calculate the three-dimensional spatial coordinates of the ultrasonic probe center and its sound beam direction vector. Combine the material sound velocity of the curved composite material component and the sound wave flight time in the collected ultrasonic A-scan signal to calculate the three-dimensional spatial coordinates of each reflection point inside the curved composite material component. Based on the ultrasonic echo amplitude corresponding to each reflection point, perform color mapping and rendering to generate an initial three-dimensional damage point cloud. S4. Based on the statistical characteristics of the ultrasonic echo amplitude of the undamaged area of ​​the curved composite material component, a segmentation threshold is set to initially separate the damage and background of the initial three-dimensional damage point cloud and obtain the initial segmented damage point cloud. S5. For the initially segmented damage point cloud, the similarity of ultrasonic echo amplitude between points is introduced as a constraint to improve the traditional DBSCAN clustering algorithm, resulting in the improved DBSCAN algorithm. The initially segmented damage point cloud is then clustered and segmented to obtain the point cloud of each independent damage region. S6. Voxelize the point cloud of the independent damaged regions after clustering and segmentation, convert the discrete point cloud into a regular three-dimensional voxel model, and accurately calculate the characterization parameters of each independent damaged region based on the three-dimensional voxel model to realize the quantitative assessment of damage of curved composite material components.

[0006] Optionally, step S1 may include the following steps: S11. The industrial robot collects 3D point cloud data as the raw point cloud. The raw point cloud is preprocessed by statistical outlier removal and voxel mesh downsampling to improve data quality and reduce computational complexity, thus obtaining the preprocessed point cloud. S12. The principal component analysis algorithm is used to extract the main direction of the preprocessed point cloud, establish a local scanning coordinate system for scanning, and determine the scanning direction and stepping direction. S13. Generate a set of parallel planes at fixed intervals along the stepping direction, slice the preprocessed point cloud into layers, and extract the contour point set formed by the intersection of each slice and the point cloud as candidate path points. S14. Fit B-spline curves to the candidate path points, and resample along the fitted B-spline curve with equal arc lengths to obtain uniformly distributed path points. S15. Solve the surface normal vector of each path point and sort the path points according to the bow-shaped scanning strategy to generate an ordered sequence of detection path points.

[0007] Optionally, the calculation of the surface normal vector of each path point includes: for each path point, searching for its K nearest neighbor points in the preprocessed point cloud, fitting the path point and its K nearest neighbor points into a local plane using the least squares method, and calculating the normal vector of the local plane as the surface normal vector of the path point. Optionally, the detection path point sequence includes the coordinates of each path point and its surface normal vector.

[0008] Optionally, the ultrasonic phased array has a probe that triggers transmission and reception at fixed time intervals to acquire ultrasonic A-scan signals.

[0009] Optionally, step S3 specifically includes the following steps: S31. Obtain the offset distance from the center of the ultrasonic probe to the end effector of the industrial robot, and perform pose transformation based on the pose information of the end effector of the robot to obtain the three-dimensional spatial coordinates of the center of the ultrasonic probe. S32. The beam direction vector at the center of the ultrasonic probe is obtained by multiplying the posture rotation matrix of the end effector of the industrial robot with the unit vector. S33. Process the ultrasonic A-scan signal to obtain the sound wave flight time and ultrasonic echo amplitude at various points in the damaged area, and calculate the damage depth by combining the material sound velocity of the curved composite material component. S34. Add the damage depth to the coupling water layer thickness, vectorize it based on the sound beam direction vector, and then superimpose it onto the three-dimensional spatial coordinates of the center of the ultrasonic probe to obtain the three-dimensional spatial coordinates of the imaging points corresponding to each aperture of the ultrasonic phased array, that is, the three-dimensional spatial coordinates of each reflection point inside the curved composite material component. S35. Based on the ultrasonic echo amplitude corresponding to each reflection point, calculate the RGB values ​​of each imaging point and render them to generate an initial three-dimensional damage point cloud.

[0010] Optionally, step S4 may include the following steps: S41. Select a reference point cloud in the undamaged area of ​​the curved composite material component, and calculate the statistical characteristics of the ultrasonic echo amplitude of each point in the reference point cloud. The statistical characteristics of the ultrasonic echo amplitude include the mean and standard deviation of the ultrasonic echo amplitude. S42. Based on the statistical characteristics of the ultrasonic echo amplitude at each point in the reference point cloud, a segmentation threshold is set. The segmentation threshold is set as follows: ; in, Indicates the segmentation threshold; , These represent the mean and standard deviation of the ultrasonic echo amplitude, respectively. This is an empirical coefficient; S43. Traverse each point in the initial three-dimensional damage point cloud, read the ultrasonic echo amplitude value corresponding to each point and compare it with the segmentation threshold. If the ultrasonic echo amplitude value is greater than the segmentation threshold, the point is determined as a damage candidate point; otherwise, it is determined as a background point. S44. Construct a set with all damage candidate points and output the initial segmented damage point cloud. The initial segmented damage point cloud contains the three-dimensional spatial coordinates and ultrasonic echo amplitude of each damage candidate point, which is used for subsequent clustering processing.

[0011] Optionally, step S5 specifically includes the following steps: S51. Set the initial parameters of the improved DBSCAN algorithm, including the neighborhood radius, minimum number of neighborhood points, and ultrasonic echo amplitude similarity tolerance. At the same time, mark all points in the initial segmentation damage point cloud as unvisited. S52. Select an unvisited point from the initial segmented damaged point cloud. Mark it as visited and read its ultrasound echo amplitude to find all points related to it. The Euclidean distance is no greater than the neighborhood radius and is related to the point Points where the absolute value of the difference in ultrasonic echo amplitude is not greater than the ultrasonic echo amplitude similarity tolerance constitute a point. Effective neighborhood set ; S53. Determine the valid neighborhood set Is the number of points in the neighborhood not less than the set minimum neighborhood number? If so, then move the points to the nearest neighbor. Classification is the core point, and it is added to a new cluster. Then with points With this as the core point, based on its effective neighborhood set Perform cluster If not, then extend the point; Temporarily classify it as a noise point and return to step S52 to select the next unvisited point; S54. Repeat steps S52 to S53 until all points in the initial segmented damage point cloud are marked as visited and classified. Output all generated clusters to complete the clustering segmentation process. Each cluster represents an independent damage region segmented by clustering, forming an independent damage region point cloud.

[0012] Optionally, in step S53, the point With this as the core point, based on its effective neighborhood set Perform cluster The extension includes the following steps: S531, From the effective neighborhood set Select an unclassified point Query point Has it been visited? S532, if point If it has not been accessed, proceed to step S5321. If the site has already been visited, proceed to step S5322. S5321, Marker Point For points already visited, construct points using the same search criteria as in step S52. Effective neighborhood set Judgment point Effective neighborhood set Is the number of points in the neighborhood not less than the set minimum number of neighborhood points? If so, then place the point. They are also categorized as core points and added to clusters. At the same time, the point Effective neighborhood set Merge entry point Effective neighborhood set Return to step S531 to select the next unclassified point. until the effective neighborhood set All points have been classified; otherwise, proceed to step S5322. S5322, Read Point Information, if click If it has not yet joined any cluster, then the point will be... Classify as boundary points and add to cluster If point If a point has already been added to a cluster, return to step S531 to select the next unclassified point. until the effective neighborhood set All points have been categorized.

[0013] The present invention also provides an ultrasonic testing and damage assessment system for curved composite material components, for applying the ultrasonic testing and damage assessment method for curved composite material components, comprising: an industrial robot, a multi-functional end effector, and a host computer; The industrial robot is a moving component of the system, used to drive a multi-functional end effector to perform ultrasonic detection and feed back robot end pose information. The multi-functional end effector is installed on the end flange of an industrial robot and is used to perform point cloud scanning and ultrasonic testing on curved composite material components. The multi-functional end effector includes an ultrasonic testing module, a coupling module, a flexible floating module, a point cloud scanning module, and a flange connection module. The host computer is used to integrate and control industrial robots and end effectors, and to complete path planning, data acquisition, imaging and damage assessment.

[0014] Optionally, the ultrasonic testing module is used to transmit and receive ultrasonic signals and acquire ultrasonic testing data of curved composite material components; Optionally, the coupling module is used to provide a coupling environment between the ultrasonic probe and the composite material component to be tested. Optionally, the flexible floating module is used to achieve adaptive fitting between the detection end and the curved composite material component; Optionally, the point cloud scanning module is used to acquire the surface data of the curved composite material component; Optionally, the flange connection module shown is used to integrate the ultrasonic testing module, coupling module, flexible floating module, and point cloud scanning module into a whole and connect it to the end flange of the industrial robot.

[0015] By employing the above technical solution, the present invention provides a method and system for ultrasonic testing and damage assessment of curved composite material components, which has at least the following beneficial effects: (1) The present invention acquires surface point clouds based on visual sensors, and can realize automated ultrasonic testing path planning for composite material components with arbitrary curved surfaces through point cloud slicing algorithm based on layered slicing. It does not rely on workpiece CAD models and manual teaching, and has strong adaptability. (2) This invention ensures the accurate correspondence between ultrasound data and spatial position through time synchronization and pose mapping, thereby improving imaging accuracy; (3) This invention constructs the initial segmentation damage point cloud of the curved composite material component as a three-dimensional point cloud model of internal damage, thereby realizing intuitive visualization and spatial positioning of defects; (4) This invention improves the DBSCAN algorithm by introducing neighborhood ultrasonic echo amplitude similarity constraints, which effectively avoids the oversegmentation or undersegmentation problems of traditional clustering algorithms and effectively improves the ability to identify small-sized, low-density defects. (5) This invention achieves quantitative damage assessment of curved composite material components through voxelization, providing conditions for accurate assessment of parameters such as damage volume, location, and morphology; (6) The overall method of the present invention has the characteristics of high precision and intelligence, and is applicable to the in-service inspection and maintenance of large curved composite structures in aerospace and other fields. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of an ultrasonic testing and damage assessment method for curved composite material components according to the present invention; Figure 2 This is a schematic diagram illustrating the process of obtaining path points from the original point cloud using the method of the present invention. Figure 3 This is a flowchart illustrating the improved DBSCAN algorithm of this invention. Figure 4 This is a schematic diagram of the clustering and segmentation results in an embodiment of the present invention; Figure 5 This is a schematic diagram of the ultrasonic testing and damage assessment system for curved composite material components according to the present invention; Figure 6 This is a schematic diagram of the multifunctional end effector structure of the system of the present invention; in Figure 6 In the middle: 1-Ultrasonic testing module; 2-Coupled module; 3-Flexible floating module; 4-Point cloud scanning module; 5-Flange connection module. Detailed Implementation

[0017] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This will allow for a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects, and to facilitate its implementation.

[0018] Those skilled in the art will understand that all or part of the steps in the implementation of the methods of the embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0019] Please refer to Figures 1-6 This invention illustrates a specific implementation of the present embodiment. In this embodiment, three-dimensional point cloud data of the damaged area on the surface of a curved composite material component is collected to construct a detection path point sequence. An industrial robot and an ultrasonic phased array are controlled to move and scan synchronously over time, collecting ultrasonic A-scan signals and robot end-effector pose information. The three-dimensional spatial coordinates of each reflection point inside the curved composite material component are calculated based on the pose derivation, and rendered into an initial three-dimensional damage point cloud. A segmentation threshold is set to construct an initial segmented damage point cloud. The traditional DBSCAN clustering algorithm is improved to perform clustering and segmentation to obtain point clouds of each independent damage region. The point clouds of the clustered independent damage regions are voxelized into a three-dimensional voxel model to achieve quantitative damage assessment. This invention achieves automated, high-precision three-dimensional damage detection and assessment of complex curved surface components, effectively solving the problems of traditional ultrasonic testing methods such as reliance on manual operation, poor adaptability, and unintuitive detection results.

[0020] Please refer to Figure 1This embodiment proposes an ultrasonic testing and damage assessment method for curved composite material components, which includes the following steps: S1. The industrial robot collects three-dimensional point cloud data of the surface damage area of ​​the curved composite material component. Through preprocessing, layer slicing, feature point extraction and B-spline curve fitting, the scanning direction is determined, and a continuous, equal arc length detection path point sequence with the probe axis consistent with the surface normal is generated, which is suitable for ultrasonic phased array probe scanning.

[0021] As a preferred embodiment of step S1, the specific process includes: S11. Use a vision sensor installed at the end of an industrial robot to acquire three-dimensional point cloud data of the surface damage area of ​​the curved composite material component. Use the acquired three-dimensional point cloud data as the original point cloud. Perform statistical outlier removal and voxel grid downsampling on the original point cloud to improve data quality and reduce computational complexity, and obtain the preprocessed point cloud.

[0022] This embodiment uses a high-precision binocular vision camera as the vision sensor.

[0023] S12. The principal component analysis algorithm is used to extract the principal direction of the preprocessed point cloud, and a local scanning coordinate system is established for ultrasonic phased array probe scanning to determine the scanning direction and stepping direction.

[0024] To facilitate subsequent layered slicing calculations, the direction of the original point cloud coordinate axis is usually selected as the scanning or slicing direction (stepping direction). In this embodiment, the direction with greater curvature along the original point cloud coordinate axis is selected as the scanning direction, and the direction with less curvature is selected as the stepping direction.

[0025] S13. Generate a set of parallel planes at fixed intervals along the stepping direction, slice the preprocessed point cloud into layers, and extract the contour point set formed by the intersection of each slice and the point cloud as candidate path points.

[0026] The spacing between the slice planes is typically 0.8 to 0.95 times the width that a single detection by an ultrasonic phased array probe can cover, allowing adjacent detection paths to slightly overlap and avoid missed detections. In this embodiment, the fixed spacing is chosen to be 0.9 times the width of the ultrasonic sound field.

[0027] S14. Fit B-spline curves to the candidate path points, and resample along the fitted B-spline curve with equal arc lengths to obtain uniformly distributed path points. S15. Solve the surface normal vector of each path point and sort the path points according to the bow-shaped scanning strategy to generate an ordered sequence of detection path points.

[0028] The calculation of the surface normal vector for each path point includes: for each path point In the preprocessed point cloud, search for the K nearest neighbor points (K can be an integer value between 10 and 20 depending on the point cloud density; in this embodiment, K is 10). Use the least squares method to find the path points. The path point and its K nearest neighbors are fitted to a local plane, and the normal vector of this local plane is calculated as the surface normal vector of the path point. .

[0029] The detection path point sequence includes the coordinates and surface normal vector information of each path point, mathematically represented as: ; in, Represents the sequence of detected path points. Indicates the first one Path points, The total number of path points. , , respectively path points x, y, z coordinates It is the corresponding surface normal vector.

[0030] The process of obtaining path points from the original point cloud described above can be found in [reference needed]. Figure 2 This invention acquires surface point clouds based on visual sensors and uses a point cloud slicing algorithm based on layered slicing to achieve automated ultrasonic testing path planning for composite material components with arbitrary curved surfaces. It does not rely on workpiece CAD models or manual teaching and has strong adaptability.

[0031] S2. Control the industrial robot to move along the detection path point sequence, and use the ultrasonic phased array to scan the damaged area synchronously along the scanning direction, collect the ultrasonic A-scan signal at each sampling time, and record the robot end pose information at each sampling time, and construct a synchronous dataset containing ultrasonic A-scan signal and robot end pose information indexed by the sampling time.

[0032] The robot end-effector pose information includes the three-dimensional spatial coordinates and attitude rotation matrix of the industrial robot end-effector. As a preferred embodiment of step S2, it specifically includes: A time synchronization mechanism is employed to control the movement of the industrial robot and the ultrasonic phased array scanning process, ensuring a strict correspondence between spatial position and ultrasonic signal. The ultrasonic phased array probe is configured to trigger transmission and reception at fixed time intervals. Value and preset scan speed and scan resolution Relatedly, the preset scanning speed in this embodiment The scanning speed is 0.1 m / s, and the scanning resolution is... If the time interval is 1mm, then... The faster the scanning speed and the higher the scanning resolution, the better. The higher the value, the longer the time interval. The shorter the length, the higher the requirements for communication latency and data throughput between system modules.

[0033] Ultrasonic A-scan signals were acquired at the above sampling intervals, and the pose information of the industrial robot end effector at each sampling time was recorded simultaneously. A synchronous dataset containing ultrasonic A-scan signals and robot end effector pose information, indexed by the sampling time, was constructed, represented as follows: ; in, This represents the constructed synchronized dataset. For the first Each sampling time, This represents the total number of sampling times. , Sampling time The three-dimensional spatial coordinates and attitude rotation matrix of the end effector of an industrial robot. These are the ultrasound A-scan signals acquired at the corresponding time points. This indicates the aperture sequence number of the ultrasonic phased array.

[0034] This invention ensures a precise correspondence between ultrasound data and spatial location through time synchronization and pose mapping, thereby improving imaging accuracy.

[0035] S3. Based on the robot end pose information in the synchronous data, calculate the three-dimensional spatial coordinates of the ultrasonic probe center and its sound beam direction vector. Combine the material sound velocity of the curved composite material component and the sound wave flight time in the acquired ultrasonic A-scan signal to calculate the three-dimensional spatial coordinates of each reflection point inside the curved composite material component. Based on the ultrasonic echo amplitude corresponding to each reflection point, perform color mapping and rendering to generate an initial three-dimensional damage point cloud.

[0036] As a preferred embodiment of step S3, the specific process includes: S31. Obtain the offset distance from the center of the ultrasonic probe to the end effector of the industrial robot, and perform pose transformation based on the robot end effector pose information to obtain the three-dimensional spatial coordinates of the ultrasonic probe center; the mathematical representation of this process is as follows: ; in, Indicates the first The three-dimensional spatial coordinates of the center of the ultrasound probe at each sampling time and They represent the first The three-dimensional spatial coordinates and attitude rotation matrix of the end effector of the industrial robot at each sampling moment; This indicates the offset distance from the center of the ultrasonic probe to the end effector of the industrial robot.

[0037] S32. The beam direction vector at the center of the ultrasonic probe is obtained by multiplying the posture rotation matrix of the industrial robot's end effector by the unit vector; this process is mathematically represented as follows: ; in, Indicates the first The acoustic beam direction vector at the center of the ultrasonic probe at each sampling time. For a unit vector, it is defined as follows: , This is the transpose symbol.

[0038] S33. Process the ultrasonic A-scan signal to obtain the sound wave flight time and ultrasonic echo amplitude at various points in the damaged area, and calculate the damage depth by combining the material sound velocity of the curved composite component; the formula for calculating the damage depth is: ; in, , They represent the first ultrasonic phased array aperture at each sampling time The corresponding damage depth and sound wave flight time, Indicates the speed of sound in the material.

[0039] The ultrasonic echo amplitude represents the intensity of the ultrasonic signal reflected at that point, and is positively correlated with the degree of damage to the material.

[0040] S34. Add the damage depth to the coupling water layer thickness, vectorize the vector based on the sound beam direction vector, and then superimpose it onto the three-dimensional spatial coordinates of the ultrasonic probe center to obtain the three-dimensional spatial coordinates of the imaging points corresponding to each aperture of the ultrasonic phased array, that is, the three-dimensional spatial coordinates of each reflection point inside the curved composite material component; the mathematical representation of this process is as follows: ; in, Indicates the first ultrasonic phased array aperture at each sampling time The corresponding three-dimensional spatial coordinates of the imaging point The coupling water layer thickness refers to the vertical distance between the water layer (or other liquid coupling agent) between the ultrasonic phased array probe and the surface of the workpiece under test. In this embodiment, the ultrasonic phased array probe type is a linear array phased array probe, and the coupling water layer thickness is set to 20 mm.

[0041] S35. Based on the ultrasonic echo amplitude values ​​corresponding to each reflection point, calculate the RGB values ​​(color mapping) of each imaging point and render them to generate an initial three-dimensional damage point cloud; the mathematical representation of the color mapping process is as follows: ; in, , They represent the first ultrasonic phased array aperture at each sampling time The ultrasonic echo amplitude and RGB values ​​of the corresponding imaging point; This is a color mapping function; , These represent the maximum and minimum values ​​of the ultrasound echo amplitude at all imaging points, respectively.

[0042] The color mapping used in this embodiment is grayscale mapping, and its specific color mapping function is as follows: ; In the formula Represents RGB values. For the floor function, This indicates the amplitude of the ultrasonic echo.

[0043] This embodiment uses OpenGL for rendering. The detailed rendering process is as follows: Three-dimensional coordinate data of all imaging points Color data with RGB three channels A vertex array is constructed by arranging the vertices in a crosswise order; a vertex buffer object (VBO) is created, and the vertex array is transferred from computer memory to the video memory of the graphics processing unit (GPU); the pixel size of the point cloud display is set, and the drawing instructions are called, specifying the drawing primitive type as point primitives (GL_POINTS); the GPU rendering pipeline reads the vertex data in the video memory, performs vertex transformation and rasterization processing, and draws the colored discrete points onto the frame buffer, finally presenting a visualized 3D damaged point cloud on the display device, which is the initial 3D damaged point cloud.

[0044] S4. Based on the statistical characteristics of the ultrasonic echo amplitude of the undamaged area of ​​the curved composite material component, a segmentation threshold is set to perform preliminary separation of the damage and the background, and the initial segmented damage point cloud is obtained.

[0045] As a preferred embodiment of step S4, the specific process includes: S41. Select a reference point cloud in the undamaged area of ​​the curved composite material component, and calculate the statistical characteristics of the ultrasonic echo amplitude at each point in the reference point cloud. The statistical characteristics of the ultrasonic echo amplitude include the mean and standard deviation of the ultrasonic echo amplitude.

[0046] The non-damaged area can be determined by comparison with standard test blocks, prior knowledge, or manual selection. In this embodiment, the non-damaged area is determined by manual selection.

[0047] S42. Based on the statistical characteristics of the ultrasonic echo amplitude at each point in the reference point cloud, a segmentation threshold is set. The segmentation threshold is set as follows: ; in, Indicates the segmentation threshold; , These represent the mean and standard deviation of the ultrasonic echo amplitude, respectively. This is an empirical coefficient, with a value range of [2,3]; in this embodiment, Take 3.

[0048] S43. Traverse each point in the initial three-dimensional damage point cloud, read the ultrasonic echo amplitude corresponding to each point and compare it with the segmentation threshold. If the ultrasonic echo amplitude is greater than the segmentation threshold, the point is determined as a damage candidate point; otherwise, it is determined as a background point.

[0049] S44. Construct a set with all damage candidate points and output the initial segmented damage point cloud. The initial segmented damage point cloud contains the three-dimensional spatial coordinates and ultrasonic echo amplitude of each damage candidate point, which is used for subsequent clustering processing.

[0050] At this point, the damage point has been initially separated from the background signal, but it may contain isolated noise points or multiple damage areas that are not distinguished. This invention constructs an initial segmentation damage point cloud of a curved composite material component as a three-dimensional point cloud model of internal damage, thereby achieving intuitive visualization and spatial localization of defects.

[0051] S5. Use the initial segmented damage point cloud as the damage point cloud dataset. The traditional DBSCAN clustering algorithm is improved by introducing the similarity of ultrasonic echo amplitude between points as a constraint, resulting in the improved DBSCAN algorithm. This improved algorithm performs clustering segmentation on the initially segmented damage point cloud to obtain point clouds of each independent damage region. The process of the improved DBSCAN algorithm is as follows: Figure 3 As shown.

[0052] As a preferred embodiment of step S5, the specific process includes: S51. Load the damage point cloud dataset Set the initial parameters for the improved DBSCAN algorithm, including the neighborhood radius. Minimum number of neighborhood points Ultrasonic echo amplitude similarity tolerance At the same time, the damage point cloud dataset All points in the initial array are marked as unvisited. S52, From the damage point cloud dataset Select an unvisited point. Mark it as visited and read its ultrasound echo amplitude to find all points related to it. The Euclidean distance is no greater than the neighborhood radius. And with point The absolute value of the difference between the ultrasonic echo amplitudes is not greater than the ultrasonic echo amplitude similarity tolerance. The points that constitute the points Effective neighborhood set ; S53. Determine the valid neighborhood set Is the number of points in the neighborhood not less than the set minimum number of neighborhood points? If so, then the point will be... Classification is the core point, and it is added to a new cluster. Then with points With this as the core point, based on its effective neighborhood set Perform cluster If not, then extend the point; Temporarily classify it as a noise point (it may be reclassified as a boundary point later), and return to step S52 to select the next unvisited point; As a preferred embodiment of step S53, the point-based With this as the core point, based on its effective neighborhood set Perform cluster The expansion, specifically the process includes: S531, From the effective neighborhood set Select an unclassified point Query point Has it been visited? S532, if point If it has not been accessed, proceed to step S5321. If the site has already been visited, proceed to step S5322. S5321, Marker Point For points already visited, construct points using the same search criteria as in step S52. Effective neighborhood set Judgment point Effective neighborhood set Is the number of points in the neighborhood not less than the set minimum number of neighborhood points? : If so, then place the point. They are also categorized as core points and added to clusters. At the same time, the point Effective neighborhood set Merge entry point Effective neighborhood set Return to step S531 to select the next unclassified point. until the effective neighborhood set All points have been classified; otherwise, proceed to step S5322. S5322, Read Point Information, if click If it has not yet joined any cluster, then the point will be... Classify as boundary points and add to cluster If point If a point has already been added to a cluster, return to step S531 to select the next unclassified point. until the effective neighborhood set All points have been categorized.

[0053] S54. Repeat steps S52 to S53 until the damaged point cloud dataset is complete. All points are marked as visited and classified, at which point the current cluster... It cannot be expanded further, meaning that all neighborhood points of the core point that are reachable by density have already been incorporated into the cluster. Output all generated clusters. , Given the total number of clusters, the clustering and segmentation process is completed. Each cluster represents an independent damaged region segmented by the clustering, forming an independent damaged region point cloud.

[0054] In this embodiment, the initial segmentation damage point cloud of a carbon fiber curved surface composite component is used as the object. After applying the improved DBSCAN algorithm, five independent clusters are obtained, corresponding to five pre-embedded defects, without over-segmentation, as shown below. Figure 4 As shown ( Figure 4 The initial segmentation of the defect point cloud used for clustering has 701,029 points. This invention improves the DBSCAN algorithm by introducing the similarity of ultrasonic echo amplitude between points as a constraint, effectively avoiding the over-segmentation or under-segmentation problems of traditional clustering algorithms, and effectively improving the ability to identify small-sized, low-density defects.

[0055] S6. Voxelize the point cloud of the independent damaged regions after clustering and segmentation, convert the discrete point cloud into a regular three-dimensional voxel model, and accurately calculate the characterization parameters of each independent damaged region based on the three-dimensional voxel model to realize the quantitative assessment of damage of curved composite material components.

[0056] As a preferred embodiment of step S6, the specific process includes: The point cloud of each independent damaged region after clustering and segmentation is voxelized, converting the discrete point cloud into a regular three-dimensional voxel model; voxel side length Based on the resolution setting of the ultrasonic probe, the empirical value is half the width of the ultrasonic phased array element; in this embodiment, it is set to 0.5 mm. Based on this three-dimensional voxel model, the characterization parameters of each independent damage region are accurately calculated. These characterization parameters include, but are not limited to, damage volume. Location of the center of mass And the maximum echo amplitude, etc.; damage volume relative to the position of the center of mass The specific calculation formula is as follows: ; in, The number of voxels in a single 3D voxel model; These are the center coordinates of a single voxel in a 3D voxel model. , , This represents the x, y, and z coordinates.

[0057] This invention enables quantitative damage assessment of curved composite material components through voxelization, providing conditions for accurate assessment of parameters such as damage volume, location, and morphology.

[0058] This application also provides an ultrasonic testing and damage assessment system for curved composite material components, used to apply the ultrasonic testing and damage assessment method for curved composite material components, including: an industrial robot, a multi-functional end effector, and a host computer, such as... Figure 5 As shown.

[0059] The industrial robot is a moving component of the system, used to drive a multi-functional end effector to perform ultrasonic detection and feed back robot end pose information. The multi-functional end effector is mounted on the end flange of an industrial robot and is used for point cloud scanning and ultrasonic testing of curved composite material components; the structure of the multi-functional end effector can be referred to... Figure 6 It includes an ultrasonic testing module 1, a coupling module 2, a flexible floating module 3, a point cloud scanning module 4, and a flange connection module 5; Specifically, the ultrasonic testing module 1 has a built-in ultrasonic host, which is used to transmit and receive ultrasonic signals, acquire ultrasonic testing data of curved composite material components, and send them to the host computer. Specifically, the coupling module 2 is used to provide a coupling environment between the ultrasonic probe and the composite material component to be tested. Specifically, the flexible floating module 3 is used to achieve adaptive fitting between the detection end and the curved composite material component; Specifically, the point cloud scanning module 4 is internally equipped with a device (such as a binocular camera) for performing point cloud scanning to acquire surface data of curved composite material components. Specifically, the flange connection module 5 shown is used to integrate the ultrasonic testing module 1, coupling module 2, flexible floating module 3, and point cloud scanning module 4 into a whole and connect it to the end flange of the industrial robot.

[0060] The host computer is used to integrate and control the industrial robot and the end effector, and to perform real-time pose and trajectory planning information interaction with the industrial robot to achieve robot control, complete path planning, data acquisition, imaging, and damage assessment. In this embodiment, the host computer is a PC.

[0061] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0062] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0063] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for ultrasonic testing and damage assessment of curved composite material components, characterized in that, include: S1. The industrial robot collects three-dimensional point cloud data of the surface damage area of ​​the curved composite material component. Through preprocessing, layer slicing, feature point extraction and B-spline curve fitting, the scanning direction is determined and the detection path point sequence is generated. S2. Control the industrial robot to move along the detection path point sequence, and use the ultrasonic phased array to scan the damaged area synchronously along the scanning direction, collect the ultrasonic A-scan signal at each sampling time, and record the robot end pose information at each sampling time, and construct a synchronous dataset containing ultrasonic A-scan signal and robot end pose information indexed by the sampling time. The robot end-effector pose information includes the three-dimensional spatial coordinates and attitude rotation matrix of the industrial robot end-effector. S3. Based on the robot end pose information in the synchronous data, calculate the three-dimensional spatial coordinates of the ultrasonic probe center and its sound beam direction vector. Combine the material sound velocity of the curved composite material component and the sound wave flight time in the collected ultrasonic A-scan signal to calculate the three-dimensional spatial coordinates of each reflection point inside the curved composite material component. Based on the ultrasonic echo amplitude corresponding to each reflection point, perform color mapping and rendering to generate an initial three-dimensional damage point cloud. S4. Based on the statistical characteristics of the ultrasonic echo amplitude of the undamaged area of ​​the curved composite material component, a segmentation threshold is set to initially separate the damage and background of the initial three-dimensional damage point cloud and obtain the initial segmented damage point cloud. S5. For the initially segmented damage point cloud, the similarity of ultrasonic echo amplitude between points is introduced as a constraint to improve the traditional DBSCAN clustering algorithm, resulting in the improved DBSCAN algorithm. The initially segmented damage point cloud is then clustered and segmented to obtain the point cloud of each independent damage region. S6. Voxelize the point cloud of the independent damaged regions after clustering and segmentation, convert the discrete point cloud into a regular three-dimensional voxel model, and accurately calculate the characterization parameters of each independent damaged region based on the three-dimensional voxel model to realize the quantitative assessment of damage of curved composite material components.

2. The ultrasonic testing and damage assessment method for curved composite material components according to claim 1, characterized in that: Step S1 specifically includes the following steps: S11. The industrial robot collects 3D point cloud data as the raw point cloud. The raw point cloud is preprocessed by statistical outlier removal and voxel mesh downsampling to improve data quality and reduce computational complexity, thus obtaining the preprocessed point cloud. S12. The principal component analysis algorithm is used to extract the main direction of the preprocessed point cloud, establish a local scanning coordinate system for scanning, and determine the scanning direction and stepping direction. S13. Generate a set of parallel planes at fixed intervals along the stepping direction, slice the preprocessed point cloud into layers, and extract the contour point set formed by the intersection of each slice and the point cloud as candidate path points. S14. Fit B-spline curves to the candidate path points, and resample along the fitted B-spline curve with equal arc lengths to obtain uniformly distributed path points. S15. Solve the surface normal vector of each path point and sort the path points according to the bow-shaped scanning strategy to generate an ordered sequence of detection path points.

3. The ultrasonic testing and damage assessment method for curved composite material components according to claim 2, characterized in that: The calculation of the surface normal vector of each path point includes: for each path point, searching for its K nearest neighbor points in the preprocessed point cloud, using the least squares method to fit the path point and its K nearest neighbor points into a local plane, and calculating the normal vector of the local plane as the surface normal vector of the path point. The detection path point sequence includes the coordinates of each path point and its surface normal vector.

4. The ultrasonic testing and damage assessment method for curved composite material components according to claim 1, characterized in that: Step S2 includes: The ultrasonic phased array has a probe that triggers transmission and reception at fixed time intervals to acquire ultrasonic A-scan signals.

5. The ultrasonic testing and damage assessment method for curved composite material components according to claim 1, characterized in that: Step S3 specifically includes the following steps: S31. Obtain the offset distance from the center of the ultrasonic probe to the end effector of the industrial robot, and perform pose transformation based on the pose information of the end effector of the robot to obtain the three-dimensional spatial coordinates of the center of the ultrasonic probe. S32. The beam direction vector at the center of the ultrasonic probe is obtained by multiplying the posture rotation matrix of the end effector of the industrial robot with the unit vector. S33. Process the ultrasonic A-scan signal to obtain the sound wave flight time and ultrasonic echo amplitude at various points in the damaged area, and calculate the damage depth by combining the material sound velocity of the curved composite material component. S34. Add the damage depth to the coupling water layer thickness, vectorize it based on the sound beam direction vector, and then superimpose it onto the three-dimensional spatial coordinates of the center of the ultrasonic probe to obtain the three-dimensional spatial coordinates of the imaging points corresponding to each aperture of the ultrasonic phased array, that is, the three-dimensional spatial coordinates of each reflection point inside the curved composite material component. S35. Based on the ultrasonic echo amplitude corresponding to each reflection point, calculate the RGB values ​​of each imaging point and render them to generate an initial three-dimensional damage point cloud.

6. The ultrasonic testing and damage assessment method for curved composite material components according to claim 1, characterized in that: Step S4 specifically includes the following steps: S41. Select a reference point cloud in the undamaged area of ​​the curved composite material component, and calculate the statistical characteristics of the ultrasonic echo amplitude of each point in the reference point cloud. The statistical characteristics of the ultrasonic echo amplitude include the mean and standard deviation of the ultrasonic echo amplitude. S42. Based on the statistical characteristics of the ultrasonic echo amplitude at each point in the reference point cloud, a segmentation threshold is set. The segmentation threshold is set as follows: ； in, Indicates the segmentation threshold; , These represent the mean and standard deviation of the ultrasonic echo amplitude, respectively. This is an empirical coefficient; S43. Traverse each point in the initial three-dimensional damage point cloud, read the ultrasonic echo amplitude value corresponding to each point and compare it with the segmentation threshold. If the ultrasonic echo amplitude value is greater than the segmentation threshold, the point is determined as a damage candidate point; otherwise, it is determined as a background point. S44. Construct a set with all damage candidate points and output the initial segmented damage point cloud. The initial segmented damage point cloud contains the three-dimensional spatial coordinates and ultrasonic echo amplitude of each damage candidate point, which is used for subsequent clustering processing.

7. The ultrasonic testing and damage assessment method for curved composite material components according to claim 1, characterized in that: Step S5 specifically includes the following steps: S51. Set the initial parameters of the improved DBSCAN algorithm, including the neighborhood radius, minimum number of neighborhood points, and ultrasonic echo amplitude similarity tolerance. At the same time, mark all points in the initial segmentation damage point cloud as unvisited. S52. Select an unvisited point from the initial segmented damaged point cloud. Mark it as visited and read its ultrasound echo amplitude to find all points related to it. The Euclidean distance is no greater than the neighborhood radius and is related to the point Points where the absolute value of the difference in ultrasonic echo amplitude is not greater than the ultrasonic echo amplitude similarity tolerance constitute a point. Effective neighborhood set ; S53. Determine the valid neighborhood set Is the number of points in the neighborhood not less than the set minimum neighborhood number? If so, then move the points to the nearest neighbor. Classification is the core point, and it is added to a new cluster. Then with points With this as the core point, based on its effective neighborhood set Perform cluster If not, then extend the point; Temporarily classify it as a noise point and return to step S52 to select the next unvisited point; S54. Repeat steps S52 to S53 until all points in the initial segmented damage point cloud are marked as visited and classified. Output all generated clusters to complete the clustering segmentation process. Each cluster represents an independent damage region segmented by clustering, forming an independent damage region point cloud.

8. The ultrasonic testing and damage assessment method for curved composite material components according to claim 7, characterized in that: In step S53, the point With this as the core point, based on its effective neighborhood set Perform cluster The extension includes the following steps: S531, From the effective neighborhood set Select an unclassified point Query point Has it been visited? S532, if point If it has not been accessed, proceed to step S5321. If the site has already been visited, proceed to step S5322. S5321, Marker Point For points already visited, construct points using the same search criteria as in step S52. Effective neighborhood set Judgment point Effective neighborhood set Is the number of points in the neighborhood not less than the set minimum number of neighborhood points? If so, then place the point. They are also categorized as core points and added to clusters. At the same time, the point Effective neighborhood set Merge entry point Effective neighborhood set Return to step S531 to select the next unclassified point. until the effective neighborhood set All points have been classified; otherwise, proceed to step S5322. S5322, Read Point Information, if click If it has not yet joined any cluster, then the point will be... Classify as boundary points and add to cluster If point If a point has already been added to a cluster, return to step S531 to select the next unclassified point. until the effective neighborhood set All points have been categorized.

9. An ultrasonic testing and damage assessment system for curved composite material components, used to apply the ultrasonic testing and damage assessment method for curved composite material components as described in any one of claims 1-8, characterized in that, include: Industrial robots, multi-functional end effectors, and host computers; The industrial robot is a moving component of the system, used to drive a multi-functional end effector to perform ultrasonic detection and feed back robot end pose information. The multi-functional end effector is installed on the end flange of the industrial robot and is used to perform point cloud scanning and ultrasonic testing on curved composite material components. The multi-functional end effector includes an ultrasonic testing module (1), a coupling module (2), a flexible floating module (3), a point cloud scanning module (4), and a flange connection module (5). The host computer is used to integrate and control industrial robots and end effectors, and to complete path planning, data acquisition, imaging and damage assessment.

10. The ultrasonic testing and damage assessment system for curved composite material components according to claim 9, characterized in that: The ultrasonic testing module (1) is used to transmit and receive ultrasonic signals and acquire ultrasonic testing data of curved composite material components. The coupling module (2) is used to provide a coupling environment between the ultrasonic probe and the composite material component to be tested. The flexible floating module (3) is used to achieve adaptive fitting between the detection end and the curved composite material component; The point cloud scanning module (4) is used to acquire the surface data of the curved composite material component; The flange connection module (5) shown is used to combine the ultrasonic testing module (1), coupling module (2), flexible floating module (3), and point cloud scanning module (4) into a whole and connect them to the end flange of the industrial robot.

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