An ultrasonic phased array inspection path planning method and system for an aeronautical material component

Abstract

This invention relates to the field of industrial manufacturing, providing a method and system for ultrasonic phased array detection path planning of aerospace material components. The method includes: acquiring the three-dimensional geometric data of the component, dividing it into regions and extracting surface geometric parameters; calculating the sound field distribution dataset corresponding to multiple detection sub-regions using a sound field propagation model; selecting the best subarray configuration schemes of the reconfigurable phased array based on the sound field distribution dataset to obtain multiple target subarray configuration parameters, and calculating the coupling medium deposition scheme based on surface tilt characteristic parameters; generating a probe movement trajectory sequence and matching control commands to multiple trajectory nodes to obtain an integrated detection execution scheme; driving the probe to perform detection, acquiring ultrasonic echo signals and identifying suspected defects, adjusting the subarray configuration for suspected defect areas, and generating a denser scanning path. This invention solves the problems of beam distortion on complex curved surfaces and uneven coupling agent distribution, improving detection quality and efficiency.

Description

Technical Field

[0001] This invention relates to the field of industrial manufacturing technology, and in particular to a method and system for ultrasonic phased array detection path planning of aerospace material components. Background Technology

[0002] Aerospace composite materials, due to their high specific strength, high specific modulus, and excellent fatigue resistance, have been widely used in the main load-bearing structures of modern aerospace vehicles, such as wing skins, fuselage panels, and tail fins. However, composite materials are prone to internal defects such as delamination, porosity, and fiber breakage during manufacturing and service, which can seriously affect the load-bearing capacity and service life of components. Ultrasonic phased array testing technology, as an advanced non-destructive testing method, can achieve rapid imaging and quantitative assessment of internal defects in composite materials through electronic scanning, playing an important role in the quality control of aerospace composite materials. With the increasingly complex geometry of aerospace composite components, especially in areas such as the chord-root interface of wings and the transition zone of stiffeners where there are significant curvature changes and thickness gradients, higher requirements are placed on the path planning and parameter optimization of ultrasonic phased array testing.

[0003] Existing ultrasonic phased array testing methods mainly rely on fixed-aperture transducer arrays and preset scanning paths, which have significant shortcomings in the detection of complex curved surfaces. On the one hand, when the probe moves in high-curvature regions, the fixed-aperture array can cause some array elements to fail to couple with the curved surface, resulting in beam diffraction distortion and focus distortion, making it difficult to guarantee detection sensitivity and resolution. On the other hand, existing methods separate the couplant application process from the detection path planning, requiring operators to frequently apply couplant manually, which is not only inefficient but also prone to problems such as uneven couplant distribution, loss, or bubble formation on inclined and complex curved surfaces, seriously affecting the effective propagation of ultrasound and the quality of the detection signal. In addition, traditional detection path planning lacks comprehensive consideration of the geometric characteristics of the components and the propagation law of the sound field, and cannot adaptively adjust the array configuration and scanning strategy according to the detection needs of different areas, leading to the risk of missed detections or wasted time due to over-detection during the detection process. Summary of the Invention

[0004] This invention provides a method and system for ultrasonic phased array detection path planning of aerospace material components, in order to overcome the shortcomings of the prior art.

[0005] This invention provides a method for ultrasonic phased array detection path planning of aerospace material components, comprising:

[0006] S1: Obtain the three-dimensional geometric data of the aerospace material components, divide the three-dimensional geometric data into regions and extract the surface geometric parameters, and calculate the sound field distribution dataset corresponding to multiple detection sub-regions through the sound field propagation model;

[0007] S2: Based on the sound field distribution dataset, the subarray configuration scheme of the reconfigurable phased array is selected to obtain the configuration parameters of multiple target subarrays corresponding to multiple detection sub-regions, and the coupling medium deposition scheme is calculated based on the surface tilt feature parameters.

[0008] S3: Generate a probe movement trajectory sequence based on the configuration parameters of multiple target subarrays and the coupling medium deposition scheme, and match control commands for multiple trajectory nodes to obtain an integrated detection execution scheme;

[0009] S4: Drive the probe to perform detection according to the integrated detection execution scheme, collect ultrasonic echo signals and identify suspected defects, adjust the sub-array configuration for suspected defect areas and generate encrypted scanning paths to complete the ultrasonic phased array detection path planning.

[0010] According to the ultrasonic phased array detection path planning method for aerospace material components provided by the present invention, step S1 further includes:

[0011] S11: Acquire three-dimensional geometric data of aerospace material components;

[0012] S12: The three-dimensional geometric data is divided into regions using a semantic segmentation algorithm to obtain multiple detection sub-regions;

[0013] S13: Extract the surface geometric parameters of multiple detection sub-regions;

[0014] S14: Establish a sound field propagation model, input the surface geometric parameters of multiple detection sub-regions into the sound field propagation model to perform sound field response simulation calculation, and obtain the sound field distribution dataset corresponding to multiple detection sub-regions.

[0015] According to the ultrasonic phased array detection path planning method for aerospace material components provided by the present invention, step S12 further includes:

[0016] S121: Input the three-dimensional geometric data into a deep learning segmentation network to identify the planar regions, curved regions, stiffener regions and edge transition regions on the surface of the component, and obtain the initial segmentation result;

[0017] S122: Extract the radius of curvature, surface normal vector, and thickness distribution information from multiple segmented regions in the initial segmentation result;

[0018] S123: When the radius of curvature of the current segmented region is less than the preset curvature threshold, the current segmented region is marked as a high curvature detection sub-region. When the radius of curvature of the current segmented region is greater than or equal to the preset curvature threshold, the current segmented region is marked as a low curvature detection sub-region, thus obtaining an optimized segmentation result with high curvature and low curvature labels.

[0019] S124: Obtain composite material process parameters for multiple segmented regions in the optimized segmentation result area. The process parameters include fiber layup direction, number of laminate layers, and thickness distribution information. Combine historical defect statistics to assign defect risk weight coefficients to multiple segmented regions, resulting in multiple detection sub-regions with risk level identifiers.

[0020] According to the ultrasonic phased array detection path planning method for aerospace material components provided by the present invention, step S14 further includes:

[0021] S141: For various subarray aperture shapes of reconfigurable phased arrays, the curvature radius, surface normal vector and thickness distribution information of multiple detection sub-regions are input into the finite element acoustic field simulation module to calculate the sound wave propagation path and energy distribution of different subarray aperture shapes in the current detection sub-region;

[0022] S142: For multiple subarray aperture shapes, extract the focal position coordinates, focal spot size parameters and side lobe energy ratios from the simulation results. When the focal spot size parameter of the current subarray aperture shape in the preset detection sub-region is less than the size threshold and the side lobe energy ratio is lower than the energy threshold, establish an adaptation mapping relationship between the current subarray aperture shape and the current detection sub-region.

[0023] S143: Summarize the focal position coordinates, focal spot size parameters, and side lobe energy ratios of multiple detection sub-regions and the adapted sub-array aperture shapes to construct a sound field distribution dataset corresponding to multiple detection sub-regions.

[0024] According to the ultrasonic phased array detection path planning method for aerospace material components provided by the present invention, step S2, the step of selecting the optimal subarray configuration scheme of the reconfigurable phased array based on the sound field distribution dataset, further includes:

[0025] S211: Extract the sub-array aperture shape and corresponding array unit number of multiple detection sub-regions adapted from the sound field distribution dataset. For regions marked as high curvature detection sub-regions, select compact sub-array configurations with array unit numbers within a preset low range. For regions marked as low curvature detection sub-regions, select extended sub-array configurations with array unit numbers within a preset high range.

[0026] S212: Establish a multi-objective optimization function, which includes a detection sensitivity index, a resolution index, and a sound intensity constraint term. Substitute the compact subarray configuration and the extended subarray configuration into the multi-objective optimization function, and use a genetic algorithm to solve for the optimal subarray unit numbering combination that maximizes the detection sensitivity index and the resolution index and keeps the sound intensity below the material damage threshold.

[0027] S213: For the optimal subarray unit number combination corresponding to the multiple detection sub-regions, calculate the excitation delay sequence of the multiple array units according to the distance difference between the multiple array units and the focusing target point, and determine the transmission frequency and pulse width parameters to obtain the configuration parameters of the multiple target subarrays corresponding to the multiple detection sub-regions.

[0028] According to the ultrasonic phased array detection path planning method for aerospace material components provided by the present invention, step S2, the step of calculating the coupling medium deposition scheme based on surface tilt characteristic parameters, further includes:

[0029] S221: Calculate the angle between the surface normal vector and the gravity direction for multiple detection sub-regions to obtain the surface tilt angle. When the surface tilt angle is greater than a preset angle threshold, mark the current detection sub-region as a region prone to loss.

[0030] S222: Based on the surface area, surface roughness, and surface tilt angle of the multiple detection sub-regions, the theoretical amount of coupling medium attached per unit area is calculated using a fluid attachment model. For the detection sub-regions marked as easily lost areas, a loss compensation coefficient is introduced based on the theoretical amount of attachment to obtain the target amount of coupling medium deposited in the multiple detection sub-regions.

[0031] S223: Based on the surface tilt angle of the multiple detection sub-regions, determine the spray angle and spray pressure parameters of the coupling medium spray nozzle to obtain a coupling medium deposition scheme, wherein the coupling medium deposition scheme includes the target deposition amount of the coupling medium in the multiple detection sub-regions, the spray angle and the spray pressure parameters.

[0032] According to the ultrasonic phased array detection path planning method for aerospace material components provided by the present invention, step S3, the step of generating a probe movement trajectory sequence based on multiple target subarray configuration parameters and the coupling medium deposition scheme, further includes:

[0033] S311: Establish a kinematic model of the robotic arm, set the physical size constraints of the probe, the position constraints of the coupling medium nozzle, and the flexibility constraints of the pipeline as the boundary conditions for path planning, and generate the initial probe movement trajectory connecting multiple detection sub-regions by combining a genetic algorithm with time-optimal trajectory planning.

[0034] S312: Identify the boundary transition segment of adjacent detection sub-regions in the initial probe movement trajectory. When there is a difference in the activation unit number in the target sub-array configuration parameters corresponding to adjacent detection sub-regions, insert an array configuration smooth transition node in the boundary transition segment to make the number of sub-array activation units and the excitation delay sequence change in a gradient, thereby obtaining a probe movement trajectory sequence containing array configuration smooth transition.

[0035] According to the ultrasonic phased array detection path planning method for aerospace material components provided by the present invention, step S3, the step of matching control commands for multiple trajectory nodes, further includes:

[0036] S321: Calculate the probe moving speed vector and the robotic arm posture change rate for multiple trajectory nodes in the probe moving trajectory sequence. When the trajectory node is located in the detection sub-region with a high defect risk weight coefficient and the moving speed is lower than the speed threshold, set a first control command to extend the coupling medium spraying time for the current trajectory node.

[0037] S322: When the trajectory node is located in the detection sub-region marked as a region prone to loss, a second control command for coupling medium flow compensation is added to the current trajectory node, and the nozzle spray angle is adjusted according to the surface tilt angle;

[0038] S323: Insert a coupling medium recovery node in the retraction path segment and the repeated scanning path segment of the probe movement trajectory sequence, and introduce a third control command to the coupling medium recovery node to activate the absorption device to remove excess coupling medium.

[0039] S324: Integrate the probe movement trajectory sequence, the target subarray configuration parameters corresponding to multiple trajectory nodes, the first control command, the second control command, and the third control command to obtain an integrated detection execution scheme.

[0040] According to the ultrasonic phased array detection path planning method for aerospace material components provided by the present invention, step S4 further includes:

[0041] S41: Drive the probe to move according to the integrated detection execution scheme, execute control commands synchronously, collect ultrasonic echo signals and perform defect identification to obtain the suspected defect location;

[0042] S42: Obtain the surface geometry parameters of the detection sub-region where the suspected defect location is located, input the sound field propagation model to calculate the local sound field distribution parameters, select the refined sub-array combination according to the local sound field distribution parameters, generate a densified scanning path around the suspected defect location and increase the amount of coupling medium deposition;

[0043] S43: Collects coupling medium flow data, probe contact pressure and ultrasonic echo signal quality indicators through sensors, and adjusts coupling medium pump drive voltage, robotic arm posture and subarray configuration parameters according to the deviation between the collected data and preset values ​​to complete ultrasonic phased array detection path planning.

[0044] This invention also provides an ultrasonic phased array detection path planning system for aerospace material components, used to execute an ultrasonic phased array detection path planning method for aerospace material components as described in any of the above claims, comprising:

[0045] The segmentation module is used to acquire three-dimensional geometric data of aerospace material components, divide the three-dimensional geometric data into regions and extract surface geometric parameters, and calculate the sound field distribution dataset corresponding to multiple detection sub-regions through a sound field propagation model.

[0046] Configuration module: Used to select the best subarray configuration scheme of reconfigurable phased array based on sound field distribution dataset, obtain multiple target subarray configuration parameters corresponding to multiple detection sub-regions, and calculate the coupling medium deposition scheme based on surface tilt feature parameters;

[0047] The generation module is used to generate a probe movement trajectory sequence based on the configuration parameters of multiple target subarrays and the coupling medium deposition scheme, and to match control commands for multiple trajectory nodes to obtain an integrated detection execution scheme.

[0048] Optimization module: Used to drive the probe to perform detection according to the integrated detection execution scheme, collect ultrasonic echo signals and identify suspected defects, adjust the subarray configuration of suspected defect areas and generate encrypted scanning paths to complete the ultrasonic phased array detection path planning.

[0049] This invention establishes a sound field propagation model to simulate and calculate the sound field response of detection sub-regions with different geometric features, obtaining the adaptation mapping relationship between each region and different sub-array aperture shapes. It can automatically switch to a compact sub-array configuration when the probe moves to a high curvature region and use an extended sub-array configuration in a low curvature region, effectively avoiding beam diffraction distortion and focus distortion problems caused by fixed aperture arrays on complex curved surfaces, significantly improving the stability of detection sensitivity and resolution. Simultaneously, this invention inserts smooth transition nodes in the array configuration at the boundary transition section between adjacent detection sub-regions, causing a gradient change in the number of active sub-array units and the excitation delay sequence. This avoids sound field discontinuities and detection blind spots caused by abrupt array configuration changes, ensuring consistent detection quality throughout the entire process. Furthermore, this invention inputs the target subarray configuration parameters of each detection sub-region and the coupling medium deposition scheme into the path planning model, achieving integrated planning of the detection path and coupling agent application. It dynamically adjusts the coupling medium spraying time, flow rate, and spray angle based on the probe's moving speed vector and surface tilt angle, ensuring uniform and stable coupling quality across various complex curved surfaces. This solves problems such as uneven coupling agent distribution, easy loss, and bubble formation in traditional methods, significantly reducing signal quality fluctuations and misjudgment risks caused by poor coupling. In addition, this invention avoids excess coupling agent accumulation and contamination by inserting coupling medium recovery nodes into the retraction and repetitive scanning path segments, reducing coupling agent consumption and component surface cleaning workload. This invention also uses multimodal sensing feedback to monitor coupling medium flow rate, probe contact pressure, and ultrasonic signal quality in real time. Based on deviations, it automatically adjusts the pump drive voltage, robotic arm posture, and subarray configuration, achieving closed-loop adaptive control of the detection process. This further improves the reliability and automation level of complex component detection, significantly shortens the detection cycle, and reduces reliance on operator skill levels. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0051] Figure 1 A schematic flowchart of an ultrasonic phased array detection path planning method for aerospace material components provided by the present invention;

[0052] Figure 2 This invention provides a schematic diagram of an ultrasonic phased array detection path planning system for aerospace material components. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, embodiments of this invention, and should not be construed as limiting the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0054] The embodiments of the present invention are described below with reference to the figures.

[0055] like Figure 1 As shown, this invention provides a method for ultrasonic phased array detection path planning of aerospace material components, comprising:

[0056] S1: Obtain the three-dimensional geometric data of the aerospace material components, divide the three-dimensional geometric data into regions and extract the surface geometric parameters, and calculate the sound field distribution dataset corresponding to multiple detection sub-regions through the sound field propagation model.

[0057] Step S1 further includes:

[0058] S11: Acquire three-dimensional geometric data of aerospace material components.

[0059] In step S11, the present invention first acquires complete three-dimensional geometric data of the aerospace composite material component by using a three-dimensional laser scanning device or by directly reading the CAD model file of the component. Specifically, the three-dimensional laser scanning device emits a laser beam to the surface of the component, measures the time and angle of the laser reflection, calculates the spatial coordinates of each point on the component surface, and sets the sampling point density to no less than 10 points per square millimeter. For the CAD model, the present invention directly reads the geometric entity data in the model file and extracts the surface mesh node coordinates and topological connection relationships. After scanning or reading is completed, the present invention aligns and calibrates all spatial coordinate points according to the global coordinate system of the component, eliminates positional deviations and rotation errors, and generates an original point cloud dataset containing tens of millions of coordinate points.

[0060] S12: The three-dimensional geometric data is divided into regions using a semantic segmentation algorithm to obtain multiple detection sub-regions.

[0061] Step S12 further includes:

[0062] S121: Input the three-dimensional geometric data into a deep learning segmentation network to identify planar regions, curved regions, stiffener regions, and edge transition regions on the surface of the component, and obtain the initial segmentation result.

[0063] In step S121, this invention employs the PointNet++ deep learning segmentation network to process 3D point cloud data. First, the original point cloud data undergoes voxelization preprocessing, dividing the space into a regular voxel grid with a side length of 0.5 mm. The number of points and the average coordinates within each voxel are then counted. Subsequently, the voxelized data is input into the PointNet++ network, which contains four ensemble abstraction layers. Each layer selects neighboring points within a set radius using a ball query method: the first layer has a radius of 2 mm, the second layer 5 mm, the third layer 10 mm, and the fourth layer 20 mm, extracting local geometric features. The network uses a multilayer perceptron to map the local features of each point into a 128-dimensional feature vector, and then aggregates the neighboring features through max pooling, extracting geometric patterns from local to global layers. In the network's output layer, a 4-class probability vector is generated for each input point, corresponding to planar regions, curved surfaces, reinforcing rib regions, and edge transition regions, respectively. After output, this invention selects the category with the highest probability as the segmentation label for that point. After classifying all points, the connected component analysis method is used to aggregate spatially adjacent points with the same label into independent segmentation regions. When the Euclidean distance between two points is less than 3mm and the labels are the same, they are determined to be connected, and finally the initial segmentation result of the independent segmentation region is obtained.

[0064] S122: Extract the radius of curvature, surface normal vector, and thickness distribution information from multiple segmented regions in the initial segmentation result.

[0065] In step S122, the present invention aims to extract geometric features individually for each segmented region in the initial segmentation result. For the radius of curvature calculation, the present invention randomly samples no less than 1000 points as feature points within each segmented region. For each feature point, it searches for all its neighboring points within a 5mm neighborhood to form a neighborhood point set. Subsequently, the coordinate data of the neighborhood point set are used to construct a covariance matrix, the expression of which is:

[0066]

[0067] in, Let covariance matrix be the variance matrix. For the neighborhood point set, For the first point belonging to the neighborhood point set Neighboring points, The coordinates of the centroid of the neighborhood point set are given. Subsequently, eigenvalue decomposition of the covariance matrix yields three eigenvalues. and the corresponding feature vector , , Minimum eigenvalue corresponding feature vector That is, the surface normal vector of that point.

[0068] Subsequently, this invention utilizes the principal curvature and The radius of curvature is calculated by fitting a neighborhood point set to a quadratic surface and obtaining the eigenvalues ​​of the Hessian matrix. The radius of curvature is calculated using the formula... The median of the curvature radii of all feature points within the segmented region is calculated and taken as the representative curvature radius value of that region.

[0069] The surface normal vector is the weighted average of the feature point normal vectors, with the weight being the number of neighboring points of that point.

[0070] Regarding thickness distribution information, this invention reads the laminate ply design data from the CAD model of the component, extracts the total number of layers and the thickness of a single layer at the corresponding position of each segmented area, and calculates the total thickness according to the formula H = total number of layers × thickness of a single layer.

[0071] S123: When the radius of curvature of the current segmented region is less than the preset curvature threshold, the current segmented region is marked as a high curvature detection sub-region. When the radius of curvature of the current segmented region is greater than or equal to the preset curvature threshold, the current segmented region is marked as a low curvature detection sub-region, thus obtaining an optimized segmentation result with high curvature and low curvature labels.

[0072] Furthermore, the present invention first sets a preset radius of curvature threshold. After setting, each segmented region in the initial segmentation result is traversed, and the representative radius of curvature of that region calculated in step S122 is read. Subsequent execution of the judgment logic: If Write the identifier "high curvature area" in the attribute field of this area. Write the identifier "low curvature region". After marking all regions, generate the optimized segmentation result, which adds curvature classification attribute to the initial segmentation result. Each segmented region carries three types of information: region number, geometry type label, and curvature classification identifier.

[0073] S124: Obtain composite material process parameters for multiple segmented regions in the optimized segmentation result area. The process parameters include fiber layup direction, number of laminate layers, and thickness distribution information. Combine historical defect statistics to assign defect risk weight coefficients to multiple segmented regions, resulting in multiple detection sub-regions with risk level identifiers.

[0074] In step S124, the present invention reads the composite material process parameters corresponding to each segmented region from the component manufacturing process database. Specifically, for the fiber layup direction, the present invention extracts the fiber angle sequence of each layer of the layup in each segmented region and records it as an angle array. The laminate layer number and thickness distribution information have been obtained in step S122, and the present invention directly calls this data. Subsequently, the present invention accesses the historical defect database, which stores the location coordinates, defect type, and defect size information of defects found in previous inspections. The present invention spatially matches the center coordinates of each segmented region with the historical defect locations and counts the number of historical defects within each segmented region.

[0075] Subsequently, this invention calculates defect risk weight coefficients based on the number of defects. Specifically, all segmented regions are sorted from highest to lowest according to the number of historical defects. The top 20% of regions with the most defects are assigned a weight coefficient of 0.9 to 1.0, the middle 60% are assigned a weight coefficient of 0.5 to 0.9, and the bottom 20% of regions with the fewest defects are assigned a weight coefficient of 0.1 to 0.5. Finally, this invention writes the calculated defect risk weight coefficients into the attributes of each segmented region, forming multiple detection sub-regions with risk level identifiers. Each detection sub-region contains complete information including region number, geometric type, curvature classification, process parameters, and defect risk weight coefficient.

[0076] S13: Extract the surface geometric parameters of multiple detection sub-regions.

[0077] Furthermore, this invention extracts surface geometric parameters for subsequent sound field simulation calculations from multiple detection sub-regions with risk level identifiers generated in step S124. For each detection sub-region, this invention reads its representative radius of curvature, surface normal vector, and total thickness calculated in step S122, and simultaneously reads the fiber layup direction angle array obtained in step S124. After reading, these parameters are organized into a structured data table according to the detection sub-region number, with each row corresponding to one detection sub-region. The column fields include region number, representative radius of curvature, three-dimensional components of surface normal vector, total thickness, fiber layup direction angle array, and curvature classification identifier.

[0078] S14: Establish a sound field propagation model, input the surface geometric parameters of multiple detection sub-regions into the sound field propagation model to perform sound field response simulation calculation, and obtain the sound field distribution dataset corresponding to multiple detection sub-regions.

[0079] Step S14 further includes:

[0080] S141: For various subarray aperture shapes of reconfigurable phased arrays, the curvature radius, surface normal vector and thickness distribution information of multiple detection sub-regions are input into the finite element acoustic field simulation module to calculate the sound wave propagation path and energy distribution of different subarray aperture shapes in the current detection sub-region.

[0081] Furthermore, this invention predefines three sub-array aperture shapes: an 8x16 rectangular array, an 8x8 circular array, and a 6x10 elliptical array. For each detection sub-region, this invention reads its representative radius of curvature, surface normal vector, and total thickness, and establishes a two-dimensional acoustic simulation model of that region in finite element simulation software. After establishing the model, the composite material is divided into several layers, and each layer is set with anisotropic sound velocity parameters according to the fiber layup direction: the longitudinal wave velocity is 3000 m / s in the fiber direction and 2500 m / s perpendicular to the fiber direction; the transverse wave velocity is 1500 m / s in the fiber direction and 1200 m / s perpendicular to the fiber direction.

[0082] Subsequently, this invention simplifies the curved geometry of the detection sub-region to an arc surface with a representative radius of curvature, with an arc length of 50 mm. A phased array transducer model is placed above the arc surface. For a rectangular array, this invention sets 128 array elements arranged in 8 rows and 16 columns, with an element spacing of 0.6 mm and a total aperture size of 9.6 mm × 4.8 mm. For a circular array, 64 array elements are evenly distributed within a circular region with a diameter of 8 mm. For an elliptical array, 60 array elements are distributed within an elliptical region with a major axis of 12 mm and a minor axis of 6 mm.

[0083] This invention sets a pulse excitation signal with a center frequency of 5MHz for each array element. The excitation delay of each element is calculated based on the focusing depth being equal to half the total thickness of the detection sub-region, ensuring the sound beam is focused on the center of the material. After setting this, the invention runs a finite element simulation with a calculation time step of 0.01 microseconds and a total simulation duration of 20 microseconds, recording the change in sound pressure amplitude at each grid node within the material over time. Finally, after the simulation is completed, the sound pressure distribution cloud map inside the material at the focusing moment is extracted to analyze the sound wave propagation path and energy concentration region.

[0084] S142: For multiple subarray aperture shapes, extract the focal position coordinates, focal spot size parameters and side lobe energy ratios from the simulation results. When the focal spot size parameter of the current subarray aperture shape in the preset detection sub-region is less than the size threshold and the side lobe energy ratio is lower than the energy threshold, establish an adaptation mapping relationship between the current subarray aperture shape and the current detection sub-region.

[0085] In step S142, the present invention first searches for the location with the largest sound pressure amplitude in the sound pressure distribution cloud map, which is the focal point, and records its three-dimensional coordinates. Then, a statistical region is delineated around the focal point, and the boundary point where the sound pressure amplitude drops to half of the peak value, i.e., -6 dB, is found. The major axis of the region enclosed by the boundary point is calculated. and short axis The average of the two is defined as the focal spot size parameter. .

[0086] For sidelobe energy analysis, this invention defines the region where the sound pressure amplitude outside the focal point is greater than 20% of the peak value as the sidelobe region, and statistically analyzes the sidelobe regions. The sum of squares of sound pressure levels at all grid nodes, divided by the entire simulation domain. The sum of squares of sound pressure at all nodes within the cavity yields the sidelobe energy ratio. The expression is:

[0087]

[0088] in, This represents a grid node. Subsequently, this invention sets a size threshold. Energy threshold For each subarray aperture shape, a judgment is made based on the simulation results in each detection sub-region: when and At that time, a matching mapping relationship record between the subarray aperture shape and the detection sub-region is created in the database. The record includes the detection sub-region number, subarray aperture shape type, focal position coordinates, focal spot size parameters, and sidelobe energy ratio.

[0089] S143: Summarize the focal position coordinates, focal spot size parameters, and side lobe energy ratios of multiple detection sub-regions and the adapted sub-array aperture shapes to construct a sound field distribution dataset corresponding to multiple detection sub-regions.

[0090] In step S143, the present invention iterates through all the adaptation mapping relationship records established in step S142 and groups and summarizes them according to the detection sub-region number. For each detection sub-region, the present invention extracts the aperture shape of all sub-arrays with which it has established an adaptation relationship, as well as their corresponding focal position coordinates, focal spot size parameters, and sidelobe energy ratios, and organizes them into a sound field distribution dataset for that detection sub-region. The dataset is stored in a nested structure. The first layer is indexed by the detection sub-region number, and the second layer contains multiple sub-array configuration records. Each record contains the sub-array aperture shape, focal position coordinates, focal spot size parameters, and sidelobe energy ratios. After summarizing all detection sub-regions, a sound field distribution dataset containing the sound field characteristic information of all detection sub-regions is generated. This dataset serves as the input data for sub-array configuration optimization in the subsequent step S2.

[0091] S2: Based on the sound field distribution dataset, the subarray configuration schemes of the reconfigurable phased array are selected to obtain multiple target subarray configuration parameters corresponding to multiple detection sub-regions, and the coupling medium deposition scheme is calculated based on the surface tilt feature parameters.

[0092] In step S2, the step of selecting the optimal subarray configuration scheme of the reconfigurable phased array based on the sound field distribution dataset further includes:

[0093] S211: Extract the subarray aperture shape and corresponding array unit number of multiple detection sub-regions adapted from the sound field distribution dataset. For regions marked as high curvature detection sub-regions, select compact subarray configurations with array unit numbers within a preset low range. For regions marked as low curvature detection sub-regions, select extended subarray configurations with array unit numbers within a preset high range.

[0094] Furthermore, this invention iterates through each detection sub-region record in the sound field distribution dataset and reads the curvature classification identifier obtained in step S123 for that region. For a detection sub-region marked as "high curvature region", this invention filters sub-array configuration records with the number of array elements ranging from 32 to 48 from the sound field distribution dataset of that region. Specifically, the filtering logic is as follows: read the sub-array aperture shape type in each sub-array configuration record, query the total number of array elements for that aperture shape, and when the total number of array elements satisfies 32 ≤ total number of array elements ≤ 48, add the configuration record to the high curvature region candidate configuration list. For a detection sub-region marked as "low curvature region", this invention filters sub-array configurations with the number of array elements ranging from 64 to 128, with the filtering condition being 64 ≤ total number of array elements ≤ 128. Configuration records that meet the condition are added to the low curvature region candidate configuration list. After the screening is completed, each detection sub-region obtains a candidate configuration list. The list contains several sub-array configuration records that meet the curvature characteristics. Each record carries information such as the sub-array aperture shape, the total number of array elements, the focal position coordinates, the focal spot size parameters, and the sidelobe energy ratio.

[0095] S212: Establish a multi-objective optimization function, which includes a detection sensitivity index, a resolution index, and a sound intensity constraint term. Substitute the compact subarray configuration and the extended subarray configuration into the multi-objective optimization function, and use a genetic algorithm to solve for the optimal subarray unit number combination that maximizes the detection sensitivity index and the resolution index while keeping the sound intensity below the material damage threshold.

[0096] In step S212, the present invention establishes a multi-objective optimization function for each detection sub-region, wherein the detection sensitivity index is defined as the logarithm of the ratio of the sound pressure amplitude at the focal point to the background noise. The focal position coordinates of each configuration are read from the candidate configuration list, and the peak sound pressure amplitude at that coordinate is queried according to the simulation results of step S141. Divide by the set background noise level Take the logarithm and multiply by 20 to get the sensitivity value in decibels. The expression is:

[0097]

[0098] The resolution metric is directly taken from the focal spot size parameter recorded in the candidate configuration list. The smaller the focal spot size, the higher the resolution.

[0099] The sound intensity constraint is calculated by reading the total number of elements in the subarray and the transmission power of each element. The transmission power of a single element is set to 0.1W. The total transmission power is calculated as the total number of elements multiplied by 0.1W, then divided by the total aperture area of ​​the subarray to obtain the sound intensity. The sound intensity must be less than the material damage threshold of 0.8W / cm². 2 The specific expression is:

[0100]

[0101] in, For sound intensity, Total transmission power, The total aperture of the subarray, The number of activated array elements. The transmit power of a single array element is set to 0.1W. The aperture length of the subarray. The aperture width of the subarray.

[0102] This invention uses the negative reciprocals of the detection sensitivity index and the resolution index as the two minimization objectives of the objective function, and expresses the sound intensity constraint as an inequality constraint to construct a multi-objective optimization problem, expressed as:

[0103]

[0104]

[0105]

[0106] in, For a multi-objective optimization function, For configuration The corresponding focal spot size parameters, The material damage threshold To set the lower and upper limits for the number of active array elements, the high curvature region is [32, 48] and the low curvature region is [64, 128].

[0107] Subsequently, this invention employs a genetic algorithm to solve the optimization problem. The genetic algorithm population size is set to 100, and each individual is encoded as a binary string with a length equal to the total number of array elements (128) of the reconfigurable phased array. A binary bit of 1 indicates activation of the array element, and 0 indicates inactivation. The initial population is randomly generated. For each individual in the population, its corresponding detection sensitivity index, resolution index, and sound intensity are calculated. Individuals with higher fitness are selected based on non-dominated sorting and crowding distance for crossover and mutation operations, with a crossover probability of 0.8 and a mutation probability of 0.1. After 200 generations of genetic algorithm iteration, the solution with the highest combined sensitivity and resolution score and sound intensity that meets the constraints is selected from the Pareto front solution set as the optimal subarray element number combination. This combination is a Boolean array containing 128 elements, and the index of the position with a true value in the array is the activated array element number.

[0108] S213: For the optimal subarray unit number combination corresponding to the multiple detection sub-regions, calculate the excitation delay sequence of the multiple array units according to the distance difference between the multiple array units and the focusing target point, and determine the transmission frequency and pulse width parameters to obtain the configuration parameters of the multiple target subarrays corresponding to the multiple detection sub-regions.

[0109] In step S213, the total thickness of the detection sub-region is first read from the surface geometric parameters extracted in step S13, and the depth of the focusing target is set to half of the total thickness. Then, according to the optimal sub-array element number combination, the physical position coordinates of the activated array element in the phased array are determined. The origin of the phased array is located at the center of the array, and the position coordinates of each array element are calculated based on its row and column index and the array element spacing of 0.6 mm.

[0110] For a representative point on the surface of the detection sub-region, the present invention obtains its surface normal vector in step S122, places the probe above the point, and sets the distance between the probe center and the surface point to 1 mm. Subsequently, the acoustic path from each activated array element to the focusing target point is calculated. The acoustic path is equal to the straight-line distance from the array element to the surface point plus the depth distance from the surface point to the focusing target point along the normal vector direction.

[0111] Then, the average sound velocity in the composite material is obtained from step S141, with the average sound velocity of the longitudinal wave taken as 2750 m / s. The expression for the excitation delay of each activated array element is as follows: ,in For the first The excitation delay of each activated array element The maximum acoustic path from all activated array elements to the focusing target point. For the first The acoustic path from each activated array element to the focused target point Let be the average sound velocity in the composite material. By calculating the excitation delay, it is possible to ensure that the sound waves emitted by all array elements arrive at the focusing target point simultaneously.

[0112] The present invention then determines the transmission frequency based on the total thickness of the detection sub-region. When the total thickness is less than 5 mm, the transmission frequency is set to 7.5 MHz; when the total thickness is between 5 mm and 10 mm, it is set to 5 MHz; and when the total thickness is greater than 10 mm, it is set to 2.5 MHz. The pulse width is set to three times the period corresponding to the transmission frequency.

[0113] Finally, this invention summarizes the optimal subarray unit number combination, excitation delay sequence, transmission frequency and pulse width parameters to form the target subarray configuration parameters for the detection sub-region. The parameters are stored in the form of a structure, which includes four fields: an array of activation unit numbers, an array of delays, a frequency value and a pulse width value.

[0114] In step S2, the step of calculating the coupling medium deposition scheme based on the surface tilt characteristic parameters further includes:

[0115] S221: Calculate the angle between the surface normal vector and the gravity direction for multiple detection sub-regions to obtain the surface tilt angle. When the surface tilt angle is greater than a preset angle threshold, mark the current detection sub-region as a region prone to loss.

[0116] In step S221, the present invention reads the surface normal vector of each detection sub-region from the surface geometric parameters extracted in step S122. The normal vector is represented in the form of a three-dimensional unit vector, and the gravity direction is defined as a vertically downward unit vector (0, 0, -1). After definition, the present invention calculates the dot product of the surface normal vector and the gravity direction. The dot product result is equal to the negative of the cosine of the angle between the two vectors. Then, the radian value of the angle is calculated by the inverse cosine function and converted into an angle value. After obtaining the above angle value, the present invention defines the surface tilt angle as the absolute value of the difference between the angle and 90 degrees. When the angle between the surface normal vector and the gravity direction is 90 degrees, the surface is horizontal and the tilt angle is 0 degrees. When the angle is 0 degrees or 180 degrees, the surface is vertical and the tilt angle is 90 degrees.

[0117] Furthermore, the formula for calculating the surface tilt angle is:

[0118]

[0119] in, The surface tilt angle, For the surface normal vector, Let be the unit vector in the direction of gravity. , Let be the vector dot product, where and Let be the magnitudes of the two vectors, with the unit vector having a magnitude of 1. Given an inverse cosine function, output the value in radians. This is the conversion factor from radians to degrees.

[0120] After calculating the surface tilt angle, this invention sets a preset angle threshold of 30 degrees, traverses all detection sub-regions, and when the surface tilt angle of a certain region is greater than 30 degrees, adds a "easily lost region" mark to the attribute field of that region.

[0121] S222: Based on the surface area, surface roughness, and surface tilt angle of the multiple detection sub-regions, the theoretical amount of coupling medium attached per unit area is calculated using a fluid attachment model. For the detection sub-regions marked as easily lost areas, a loss compensation coefficient is introduced based on the theoretical amount of attachment to obtain the target amount of coupling medium deposited in the multiple detection sub-regions.

[0122] In step S222, the present invention calculates the theoretical adhesion amount of the coupling medium for each detection sub-region. Specifically, firstly, the number of point clouds contained in each detection sub-region is extracted from the initial segmentation result of step S121, and the number of point clouds is multiplied by the area represented by a single point, 0.01 mm. 2 Obtain the surface area of ​​the region. Then, read the surface roughness from the component surface quality inspection report.

[0123] Subsequently, this invention establishes a fluid adhesion model, and the formula for calculating the theoretical adhesion amount per unit area of ​​coupled medium is:

[0124]

[0125] in, The theoretical adhesion amount of the coupling medium per unit area. Based on the adhesion thickness, The roughness coefficient is set to 0.3. For surface roughness, 5 indicates Used as a reference length for normalization. The cosine of the surface tilt angle represents the effect of gravity on adhesion. The tilt correction factor is set to 0.15. For reference tilt angle, set to 45 degrees, exponent term This formula represents the nonlinear effect of the tilt angle on adhesion. It shows that the adhesion thickness of the coupling medium increases with increasing surface roughness and decreases with increasing surface tilt angle.

[0126] After calculating the theoretical deposition amount per unit area, this invention checks whether the area is marked as a region prone to loss. If so, the theoretical deposition amount is multiplied by a loss compensation coefficient. The loss compensation coefficient is calculated using linear interpolation based on the surface tilt angle; specifically, the coefficient is 1.2 when the tilt angle is 30 degrees, 1.5 when the tilt angle is 60 degrees, and 2.0 when the tilt angle is 90 degrees. After calculation, the deposition amount per unit area is multiplied by the surface area of ​​the region to obtain the target deposition amount of the coupling medium.

[0127] S223: Based on the surface tilt angle of the multiple detection sub-regions, determine the spray angle and spray pressure parameters of the coupling medium spray nozzle to obtain a coupling medium deposition scheme, wherein the coupling medium deposition scheme includes the target deposition amount of the coupling medium in the multiple detection sub-regions, the spray angle and the spray pressure parameters.

[0128] Furthermore, in step S223, the present invention sets nozzle control parameters based on the surface tilt angle of the detected sub-region. The spray angle is defined as the angle between the nozzle axis and the surface normal vector. The present invention sets the spray angle to be equal to the projection angle of the surface normal vector onto the horizontal plane. The calculation method is to read the horizontal component of the surface normal vector and calculate the angle between the horizontal component and the vertical axis; this angle is the spray angle. When the surface tilt angle is less than 15 degrees, the spray angle is set to 0 degrees, i.e., spraying vertically downwards. When the tilt angle is between 15 and 45 degrees, the spray angle is equal to the tilt angle. When the tilt angle is greater than 45 degrees, the spray angle is fixed at 45 degrees to prevent spray rebound.

[0129] The jet pressure parameter is calculated based on the target deposition amount in the coupling medium and the spraying time. The spraying time is set as the probe's expected residence time in the area, which is estimated by dividing the area by the scanning velocity. The formula for calculating the jet pressure is:

[0130]

[0131] in, For injection pressure, The target deposition amount in the coupling medium. The nozzle flow coefficient is set to 0.5 mL / (s·bar). The estimated spraying time is in seconds, calculated as follows: , To scan the area, To improve scanning speed, The nozzle efficiency coefficient is set to 0.85, representing the ratio of actual flow rate to theoretical flow rate.

[0132] Finally, this invention organizes the target deposition amount, jetting angle, and jetting pressure parameters of the coupling medium in each detection sub-region into a coupling medium deposition scheme. The scheme is stored in tabular form, with each row corresponding to a detection sub-region and columns including region number, target deposition amount, jetting angle, jetting pressure, and easy-to-bleed marker.

[0133] S3: Generate a probe movement trajectory sequence based on the configuration parameters of multiple target subarrays and the coupling medium deposition scheme, and match control commands for multiple trajectory nodes to obtain an integrated detection execution scheme.

[0134] Step S3, the step of generating the probe movement trajectory sequence based on the configuration parameters of multiple target subarrays and the coupling medium deposition scheme, further includes:

[0135] S311: Establish a kinematic model of the robotic arm, set the physical size constraints of the probe, the position constraints of the coupling medium nozzle, and the flexibility constraints of the pipeline as the boundary conditions for path planning, and generate the initial probe movement trajectory connecting multiple detection sub-regions through a genetic algorithm combined with time-optimal trajectory planning.

[0136] In step S311, the present invention first establishes a kinematic model of a six-degree-of-freedom robotic arm. The robotic arm comprises five main components: a base, shoulder joint, elbow joint, wrist joint, and end effector. The kinematic model is represented using the DH parameter method. A local coordinate system is established for each joint, and four parameters are recorded: joint rotation angle, link length, link torsion angle, and link offset. The present invention reads the DH parameter table provided by the robotic arm and constructs a transformation matrix sequence from the base coordinate system to the end effector coordinate system. Subsequently, a probe is mounted on the end effector. The probe has physical dimensions of 80mm in length, 60mm in width, and 40mm in height. The present invention models the probe as a cuboid envelope box, and in path planning, this envelope box is constrained to prevent collisions with obstacles outside the component surface. A coupling medium nozzle is installed at the front end of the probe, 20mm from the center of the probe. Simultaneously, the present invention records the relative position vector of the nozzle in the end effector coordinate system.

[0137] The flexible constraint of the pipeline is reflected by limiting the maximum speed and acceleration of the robotic arm's end effector. The maximum linear velocity is set at 100 mm / s, the maximum angular velocity at 30 degrees / s, and the maximum linear acceleration at 50 mm / s. 2 The maximum angular acceleration is 15 degrees / s². 2 Subsequently, the present invention reads the center coordinates of all detection sub-regions generated in step S124 and uses these coordinates as the necessary nodes for path planning.

[0138] After obtaining the above data, this invention uses a genetic algorithm to optimize the node access order. The population size is set to 50, and each individual is encoded as a permutation of the detection sub-region numbers. The initial population is generated by randomly shuffling the order. The total path length is calculated for each individual, which is equal to the sum of the Euclidean distances connecting each node in that order. The path length is used as the fitness function value. The genetic algorithm performs selection, crossover, and mutation operations. Crossover uses a partial mapping crossover operator, and mutation involves swapping genes at two positions. After 100 generations, the individual with the best fitness is selected as the optimal access order.

[0139] After obtaining the access order, this invention performs time-optimal trajectory planning. For movement between two adjacent detection sub-regions, a fifth-order polynomial interpolation is used to generate a joint space trajectory. The polynomial coefficients are solved using the position, velocity, and acceleration boundary conditions of the starting and ending points, requiring that the velocity and acceleration at the starting and ending points of the trajectory be zero, and that the intermediate process satisfies velocity and acceleration constraints. This invention uses the time parameter of each trajectory segment as an optimization variable, minimizing the total time using the gradient descent method to obtain the optimal execution time for each trajectory segment. Finally, this invention connects all trajectory segments in chronological order, sampling at 10ms intervals on each trajectory segment to generate discrete trajectory points. Each trajectory point contains a timestamp, end effector position coordinates, and attitude quaternions, forming the initial probe movement trajectory.

[0140] Specifically, the expression for fifth-order polynomial trajectory interpolation is:

[0141]

[0142]

[0143] in, The joint space trajectory represents the time at which a certain joint moves. Angle, For time variables, The total time for the trajectory segment is [time]. These are the polynomial coefficients, obtained by solving a system of linear equations. The joint angle position of the trajectory starting point. The joint angular velocity at the starting point of the trajectory. Let be the joint angular acceleration at the starting point of the trajectory. Let be the joint angle position at the endpoint of the trajectory. The joint angular velocity at the endpoint of the trajectory. This is the joint angular acceleration at the end of the trajectory.

[0144] S312: Identify the boundary transition segment of adjacent detection sub-regions in the initial probe movement trajectory. When there is a difference in the activation unit number in the target sub-array configuration parameters corresponding to adjacent detection sub-regions, insert an array configuration smooth transition node in the boundary transition segment to make the number of sub-array activation units and the excitation delay sequence change in a gradient, thereby obtaining a probe movement trajectory sequence containing array configuration smooth transition.

[0145] In step S312, the present invention traverses all trajectory points in the initial probe movement trajectory and identifies the boundary position of the probe entering from one detection sub-region to another. The specific identification method is to calculate the distance from each trajectory point to the center of all detection sub-regions, and take the closest region as the region to which the trajectory point belongs. When the region number of two consecutive trajectory points changes, it is determined that there is a boundary transition segment between the two points.

[0146] Subsequently, for each boundary transition segment, this invention reads the target subarray configuration parameters corresponding to the two detection sub-regions before and after the transition, extracts the activation unit number array, and compares the differences between the two arrays. Specifically, the difference is calculated by counting the number of units activated only in the first region, the number of units activated only in the second region, and the number of units activated in both regions. If the number of units activated only in a certain region is greater than zero, a difference is determined to exist. When a difference exists, this invention inserts several transition nodes into the boundary transition segment. The number of transition nodes is set to 10, evenly distributed over time between the trajectory points at both ends of the boundary.

[0147] Subsequently, for each transition node, the present invention calculates its position ratio in the transition segment, with the ratio ranging from 0 to 1. Then, the subarray configuration of the node is calculated by linear interpolation based on the position ratio. Specifically, for a cell that is activated only in the next region, when the result of multiplying the position ratio by 10 and rounding down is equal to the activation priority number of the cell, the cell is marked as activated. The activation priority is sorted from near to far based on the distance between the cell and the array center.

[0148] Specifically, the expression for array configuration linear interpolation is:

[0149]

[0150]

[0151] in, Positional proportions The number of activated array elements at that location This refers to the positional proportion within the transition section. The number of active array elements in the pre-transition region. The number of active array elements in the transition region. This is the floor function. For the first The proportion of each element in position Incentive delay at the location, and These represent the excitation delays of the array element in the regions before and after the transition.

[0152] For cells that are activated only in the preceding region, activation is maintained when the position ratio is less than the cell's deactivation threshold. The deactivation threshold is linearly distributed from 0.1 to 0.9 based on the distance between the cell and the array edge, from far to near. Cells that are co-activated remain active. After calculating the activation cell number for each transition node, the invention recalculates the excitation delay sequence for that node. The delay calculation method is the same as in step S213, determined based on the acoustic path difference from the activation cell to the focusing target point. After processing all boundary transition segments, the invention merges the original trajectory points and the inserted transition nodes in chronological order to form a probe movement trajectory sequence that includes a smooth transition of the array configuration.

[0153] In step S3, the step of matching control commands for multiple trajectory nodes further includes:

[0154] S321: Calculate the probe moving speed vector and the robotic arm posture change rate for multiple trajectory nodes in the probe moving trajectory sequence. When the trajectory node is located in the detection sub-region with a high defect risk weight coefficient and the moving speed is lower than the speed threshold, set a first control command to extend the coupling medium spraying time for the current trajectory node.

[0155] In step S321, for each trajectory node in the trajectory sequence, the present invention reads the timestamp and position coordinates of that node, calculates the position difference vector between that node and the previous node, divides it by the time difference between the two nodes to obtain the probe movement velocity vector. The velocity vector contains three directional components, and then the magnitude of the velocity vector is calculated to obtain the probe movement rate. For the robotic arm attitude change rate, the present invention reads the attitude quaternions of the current node and the previous node, calculates the relative rotation quaternion of the two quaternions, extracts the rotation angle of the relative rotation, and divides it by the time difference to obtain the angular velocity.

[0156] Specifically, the formula for calculating the probe's moving speed vector is:

[0157]

[0158]

[0159] in, The index of the trajectory node represents the k-th discrete sampling point in the probe movement trajectory sequence. It is the timestamp corresponding to the k-th trajectory node. For a moment The probe's moving velocity vector, For a moment The probe position coordinates, For time intervals, The magnitude of the velocity vector, i.e., the speed of movement. , , These represent the changes in position in the x, y, and z directions, respectively. These are the components of the velocity vector in the x, y, and z directions, respectively.

[0160] The formula for calculating the rate of change of the robotic arm's posture is:

[0161]

[0162]

[0163] in, Angular velocity, They are time points and The posture quaternion, This represents quaternion multiplication. Represents the inverse of a quaternion. For relative rotation quaternions, Let be the real part of the relative rotation quaternion. It is an inverse cosine function, outputting the value in radians.

[0164] The present invention then determines the detection sub-region to which the current node belongs, and reads the defect risk weight coefficient of that region from step S124. When the weight coefficient is greater than 0.7, it is determined to be a high-risk region. A speed threshold of 20 mm / s is set. When the node is located in a high-risk region and its movement speed is less than 20 mm / s, the present invention generates a first control command for that node. The final first control command includes the command type field "extended spraying" and an extension time parameter. The expression for the extension time is:

[0165]

[0166] in, To extend the spraying time, The baseline spraying time is set to 0.5 seconds. The probe's movement speed at the current trajectory node. The speed threshold is set to 20 mm / s. The defect risk weight coefficient for the current detection sub-region is represented by the sine term. As a nonlinear enhancement term, the extension time is further increased when the risk weight is high. Finally, the invention stores the first control command in the command list of the trajectory node.

[0167] S322: When the trajectory node is located in the detection sub-region marked as a region prone to loss, a second control command for coupling medium flow compensation is added to the current trajectory node, and the nozzle spray angle is adjusted according to the surface tilt angle.

[0168] Furthermore, the present invention continues to check the bleed-prone marker of the detection sub-region to which each trajectory node belongs, and reads the marker information from step S221. When the marker is a bleed-prone region, the present invention generates a second control command. The second control command includes two parameters: flow compensation coefficient and injection angle adjustment value.

[0169] Specifically, the flow compensation coefficient is read from the coupling medium deposition scheme in step S222, which is used directly as the flow compensation parameter. The injection angle adjustment value is calculated based on the surface tilt angle of the area. The surface tilt angle is read from step S221. When the tilt angle is between 15 degrees and 45 degrees, the injection angle adjustment value is equal to the tilt angle. When the tilt angle is greater than 45 degrees, the adjustment value is fixed at 45 degrees. In this invention, the instruction type field of the second control instruction is set to "flow compensation", with two parameters: the flow compensation coefficient and the injection angle adjustment value, which are stored in the instruction list of the trajectory node. For nodes that simultaneously meet the conditions of steps S321 and S322, their instruction list contains both the first and second control instructions.

[0170] S323: Insert a coupling medium recovery node in the retraction path segment and the repeated scanning path segment of the probe movement trajectory sequence, and introduce a third control command to the coupling medium recovery node to activate the absorption device to remove excess coupling medium.

[0171] Furthermore, in step S323, the present invention aims to identify retreat path segments and repeated scan path segments in the trajectory sequence. Specifically, the retreat path segment identification method is as follows: for three consecutive trajectory nodes, calculate the direction vectors formed by the first two nodes and the direction vectors formed by the last two nodes. If the dot product of the two vectors is negative, it indicates that the direction of movement has reversed, and the intermediate node is determined to be the retreat starting point. Starting from the retreat starting point, search backward along the trajectory until the direction of movement reverses again or reaches the boundary of the visited detection sub-region; this trajectory segment is marked as a retreat path segment.

[0172] The method for identifying repeated scan path segments is as follows: for each trajectory node, search all nodes before that node. If there is a historical node whose distance from the current node is less than 5mm and belongs to the same detection sub-region, the path where the current node is located is determined to be a repeated scan path segment.

[0173] For identified pullback and re-scanning path segments, this invention inserts a coupling medium recovery node at the starting point of these path segments. The location coordinates of the recovery node are equal to the starting coordinates of the path segment, and the timestamp is 0.1 seconds less than the starting time, indicating that the recovery action is performed before the pullback or re-scan begins. This invention generates a third control command for each recovery node, with the command type field set to "Activate Recovery," including absorption device startup parameters, absorption negative pressure set to -50 kPa, and absorption duration set to 0.2 seconds. Finally, the third control command is stored in the command list of the recovery node.

[0174] S324: Integrate the probe movement trajectory sequence, the target subarray configuration parameters corresponding to multiple trajectory nodes, the first control command, the second control command, and the third control command to obtain an integrated detection execution scheme.

[0175] In step S324, the present invention sorts all trajectory nodes in the probe movement trajectory sequence by timestamp and summarizes all the information carried by each node. The node information includes: timestamp, position coordinates, attitude quaternion, the number of the detection sub-region to which it belongs, target subarray configuration parameters, and instruction list. Among them, the target subarray configuration parameters are read from the parameters calculated by the region or transition segment to which the node belongs in step S213, including the active unit number array, excitation delay array, transmission frequency, and pulse width. The instruction list includes the instructions applicable to the node in the first, second, and third control instructions generated in steps S321 to S323.

[0176] This invention serializes all node information into a JSON format data structure. The outermost layer is a trajectory node array, and each array element is a node object. The object fields include timestamp, position, attitude, region number, subarray configuration, and instruction list. The final generated JSON data is the integrated detection execution scheme, which fully describes the position, attitude, phased array configuration, and coupling medium control actions of the probe at every moment in the entire detection process.

[0177] S4: Drive the probe to perform detection according to the integrated detection execution scheme, collect ultrasonic echo signals and identify suspected defects, adjust the sub-array configuration for suspected defect areas and generate encrypted scanning paths to complete the ultrasonic phased array detection path planning.

[0178] Step S4 further includes:

[0179] S41: Drive the probe to move according to the integrated detection execution scheme, execute control commands synchronously, collect ultrasonic echo signals and perform defect identification to obtain the suspected defect location.

[0180] In step S41, the present invention reads the integrated detection execution scheme JSON data generated in step S324, parses out the trajectory node array, sets the initial value of the real-time clock to zero, and starts the detection process.

[0181] During testing, this invention traverses trajectory nodes in timestamp order. For each node, it first reads the position coordinates and attitude quaternions, calculates the joint angles using inverse kinematics of the robotic arm, and sends joint angle commands to the robotic arm controller to drive the probe to the target position. Simultaneously, it reads the target subarray configuration parameters for that node, sending the activation unit number array, excitation delay array, transmission frequency, and pulse width parameters to the phased array controller. The controller configures the phased array transmission circuit according to these parameters. The invention also reads the node's instruction list and executes each instruction sequentially. Specifically, if a first control instruction exists, it reads the extension time parameter and sets the coupling medium nozzle opening time to the base time plus the extension time; if a second control instruction exists, it reads the flow compensation coefficient and injection angle adjustment value, multiplies the flow setpoint of the coupling medium pump by the flow compensation coefficient, and adjusts the nozzle servo motor angle to the injection angle adjustment value; if a third control instruction exists, it starts the vacuum pump of the absorption device and sets the negative pressure value and duration.

[0182] After the probe is in position, this invention triggers the phased array to transmit pulses and simultaneously starts the data acquisition system to record the echo signals of each receiving array element. The sampling rate is set to 100MHz and the sampling duration is 20 microseconds. After acquisition, this invention processes the echo signals. First, bandpass filtering is applied to the signals of each array element, with the filter center frequency equal to the transmission frequency and the bandwidth being 40% of the center frequency. Then, the filtered signals are delayed and superimposed. The delay parameters are calculated according to the receiving focusing algorithm to coherently superimpose the reflected signals at a specific depth.

[0183] The superimposed signals form A-scan data. This invention calculates the envelope of the A-scan data, which is obtained by using Hilbert transform to obtain the analytic signal magnitude. After calculation, this invention searches for peak values ​​on the envelope. When the peak amplitude exceeds three times the mean of the background noise and the depth corresponding to the peak is within the material thickness range, it is determined to be a suspected defect echo. Finally, the trajectory node position coordinates and depth values ​​corresponding to the suspected defect echo are recorded, and the three-dimensional coordinates of the defect in the component coordinate system are calculated and stored as the suspected defect location.

[0184] Specifically, the expression for the signal envelope calculated using the Hilbert transform is as follows:

[0185]

[0186]

[0187] in, The signal envelope represents time. The envelope amplitude, The original ultrasonic echo signal. The result is the Hilbert transform of the signal, where PV represents the Cauchy principal value integral. It is the integral variable.

[0188] S42: Obtain the surface geometry parameters of the detection sub-region where the suspected defect location is located, input the sound field propagation model to calculate the local sound field distribution parameters, select a refined sub-array combination based on the local sound field distribution parameters, generate a densified scanning path around the suspected defect location and increase the amount of coupling medium deposition.

[0189] In step S42, the present invention reads the coordinates of the suspected defect location identified in step S41, calculates the distance from the location to the center of each detection sub-region, selects the closest region as the region where the defect is located, and extracts the surface geometric parameters of the region from step S13, including the representative radius of curvature, surface normal vector and total thickness.

[0190] The present invention inputs these parameters into the sound field propagation model established in step S14, and runs finite element simulation to calculate the sound field distribution under a higher resolution configuration. In the simulation, the present invention restricts the shape of the sub-array aperture to a circular array, sets the number of array elements to three configurations of 16, 24 and 32 respectively for simulation, increases the emission frequency to 1.5 times the original frequency, and sets the focusing depth to the actual depth of the suspected defect.

[0191] After simulation, this invention extracts the focal spot size parameters of three configurations, selects the configuration with the smallest focal spot size as the refined subarray combination, and reads the activation unit number and excitation delay sequence corresponding to this configuration. Subsequently, this invention generates a dense scanning path around the suspected defect location. The dense path adopts an Archimedean spiral pattern, with the spiral center located at the point projected onto the surface from the suspected defect location. The spiral coil spacing is set to 1 mm, the spiral unfolds 5 times, and the total radius is 10 mm. Furthermore, this invention discretizes the spiral, sampling a path point every 0.5 mm. Each path point inherits the surface normal vector of the suspected defect area as the probe orientation.

[0192] For each point in the encrypted path, this invention calculates the required amount of coupling medium deposition at that point using the fluid adhesion model from step S222. Due to the refined scanning process, the theoretical adhesion amount per unit area is multiplied by an enhancement factor of 1.5 to obtain the target amount of coupling medium deposition for the encrypted path. Finally, this invention inserts the encrypted path into the current detection execution plan, suspends the original planned path, and prioritizes the execution of the encrypted scan.

[0193] S43: Collects coupling medium flow data, probe contact pressure and ultrasonic echo signal quality indicators through sensors, and adjusts coupling medium pump drive voltage, robotic arm posture and subarray configuration parameters according to the deviation between the collected data and preset values ​​to complete ultrasonic phased array detection path planning.

[0194] During the detection process, this invention continuously collects data from multiple sensors. The coupling medium flow sensor is installed in the nozzle's front-end pipeline, outputting real-time flow values ​​at a sampling frequency of 10Hz. This invention reads the flow sensor data every 100ms, calculates the average flow rate over the most recent second, and compares it with the preset flow rate value in the coupling medium deposition scheme corresponding to the current trajectory node. Subsequently, the deviation rate is calculated, which is the actual flow rate minus the preset flow rate, then divided by the preset flow rate. When the absolute value of the deviation rate is greater than 0.15, the flow rate is considered abnormal. Then, this invention adjusts the driving voltage of the coupling medium pump based on the deviation rate. The adjustment amount is calculated as: voltage adjustment amount = reference voltage × deviation rate × 0.5, increasing or decreasing the driving voltage by this adjustment amount.

[0195] Specifically, the formulas for calculating the flow deviation rate and the voltage adjustment are as follows:

[0196]

[0197]

[0198]

[0199] in, For flow deviation rate, The average flow rate as actually measured. To preset the flow rate value, This is the voltage adjustment amount. The reference voltage, To adjust the gain factor, set it to 0.5. This is a sign function that returns 1 or -1. The current driving voltage, This is the adjusted new driving voltage.

[0200] A pressure sensor is installed at the bottom of the probe to measure the normal pressure between the probe and the component surface, with a sampling frequency of 50Hz. This invention continuously monitors the pressure value, setting a lower pressure threshold of 2N. If the pressure value remains below the threshold for more than 0.2 seconds, poor contact is determined. If poor contact is found, this invention calculates the current surface normal vector direction, instructs the robotic arm to move 2mm along the normal vector direction to increase the contact pressure, and then remeasures the pressure value until the threshold requirement is met.

[0201] For ultrasonic echo signal quality indicators, this invention calculates the initial amplitude and signal-to-noise ratio (SNR) of each acquired A-scan signal. The initial amplitude is the peak value of the envelope of the surface reflected wave, and the SNR is the initial amplitude divided by the root mean square (RMS) value of the mid-segment noise. This invention sets the normal range for the initial amplitude to be 0.5 to 0.9 V and the normal range for the SNR to be above 20 dB. When the initial amplitude or SNR is below the normal range, this invention determines it to be due to poor coupling or improper array configuration. If there is also an abnormal flow rate, the flow rate is adjusted first; if the flow rate is normal but the signal quality is poor, this invention recalculates the optimal subarray configuration for the current position, calls the multi-objective optimization function in step S212, uses the currently measured signal quality as a constraint, re-solves for the combination of activation unit numbers and the excitation delay sequence, updates the phased array configuration, and then re-acquires data.

[0202] This also includes: S44 recording the subarray activation unit number, excitation delay sequence, probe attitude angle, actual consumption of coupling medium, and ultrasonic echo signal quality index of multiple trajectory nodes during the detection process, constructing a reinforcement learning training sample set, and obtaining the updated subarray configuration selection strategy and coupling medium deposition strategy through deep Q network training, which are used to correct the prediction parameters of the sound field propagation model.

[0203] Furthermore, throughout the entire detection process, this invention creates a data record for each executed trajectory node. The record includes: node number, the number of the detection sub-region to which it belongs, an array of actual subarray activation unit numbers used, an array of actual excitation delays, probe attitude angle, actual consumption of coupling medium, initial wave amplitude, signal-to-noise ratio, and defect detection flag. After detection, this invention summarizes the data records of all nodes into a training sample set. The training sample set is in tabular form, with each row representing one sample and columns representing various feature fields. This invention uses the detection sub-region number, radius of curvature, surface tilt angle, and number of activation units as state features, and the initial wave amplitude, signal-to-noise ratio, and defect detection flag as the basis for calculating the reward signal.

[0204] After establishing the sample set, this invention constructs a deep Q-network. The network input layer receives state features and contains 4 neurons. The hidden layer consists of two fully connected layers, each with 64 neurons, using ReLU as the activation function. The number of neurons in the output layer equals the number of selectable subarray configurations, and it outputs the Q-value for each configuration. Then, this invention uses an empirical replay mechanism to train the network. 32 samples are randomly selected from the training sample set as a batch. The target Q-value for each sample is calculated as the product of the current reward plus a discount factor and the maximum Q-value of the next state. The network weights are updated using the mean squared error loss function and the Adam optimizer.

[0205] After 1000 training iterations, the network converges to obtain an updated subarray configuration selection strategy. This strategy is manifested in that, given the features of the detection sub-region, the network outputs a ranking of the Q values ​​of each configuration scheme, with the scheme having the highest Q value being the optimal selection. This invention extracts the ratio of the actual consumption of the coupling medium to the preset deposition amount in the training samples, calculates the average ratio by grouping by detection sub-region, and uses this ratio as the correction coefficient to update the loss compensation coefficient in the fluid adhesion model in step S222.

[0206] Meanwhile, this invention statistically analyzes the average signal quality under different subarray configurations in each detection sub-region, updates the measured correction values ​​of the focal spot size parameters and sidelobe energy ratios of each configuration in the sound field distribution dataset in step S143, and the correction values ​​are equal to the equivalent focal spot size derived from the measured signal quality index, replacing the original simulation prediction values, so that the prediction parameters of the sound field propagation model are closer to the actual detection effect.

[0207] like Figure 2 As shown, the present invention also provides an ultrasonic phased array detection path planning system for aerospace material components, used to execute an ultrasonic phased array detection path planning method for aerospace material components as described in any of the above claims, comprising:

[0208] The partitioning module 100 is used to acquire three-dimensional geometric data of aerospace material components, partition the three-dimensional geometric data into regions and extract surface geometric parameters, and calculate the sound field distribution dataset corresponding to multiple detection sub-regions through a sound field propagation model.

[0209] Configuration module 200: Based on the sound field distribution dataset, it selects the best subarray configuration scheme of the reconfigurable phased array to obtain multiple target subarray configuration parameters corresponding to multiple detection sub-regions, and calculates the coupling medium deposition scheme based on the surface tilt feature parameters.

[0210] Generation module 300: is used to generate a probe movement trajectory sequence based on the configuration parameters of multiple target subarrays and the coupling medium deposition scheme, and to match control commands for multiple trajectory nodes to obtain an integrated detection execution scheme;

[0211] Optimization module 400: is used to drive the probe to perform detection according to the integrated detection execution scheme, collect ultrasonic echo signals and identify suspected defects, adjust the subarray configuration of the suspected defect area and generate an encrypted scanning path to complete the ultrasonic phased array detection path planning.

[0212] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0213] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the ultrasonic phased array detection path planning method for aerospace material components described in various embodiments or some parts of embodiments.

[0214] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An ultrasonic phased array inspection path planning method for an aeronautical material component, characterized in that, include: S1: Obtain the three-dimensional geometric data of the aerospace material components, divide the three-dimensional geometric data into regions and extract the surface geometric parameters, and calculate the sound field distribution dataset corresponding to multiple detection sub-regions through the sound field propagation model; S2: Based on the sound field distribution dataset, the subarray configuration scheme of the reconfigurable phased array is selected to obtain the configuration parameters of multiple target subarrays corresponding to multiple detection sub-regions, and the coupling medium deposition scheme is calculated based on the surface tilt feature parameters. Step S2, the step of selecting the optimal subarray configuration scheme of the reconfigurable phased array based on the sound field distribution dataset, further includes: S211: Extract the sub-array aperture shape and corresponding array unit number of multiple detection sub-regions adapted from the sound field distribution dataset. For regions marked as high curvature detection sub-regions, select compact sub-array configurations with array unit numbers within a preset low range. For regions marked as low curvature detection sub-regions, select extended sub-array configurations with array unit numbers within a preset high range. S212: Establish a multi-objective optimization function, which includes a detection sensitivity index, a resolution index, and a sound intensity constraint term. Substitute the compact subarray configuration and the extended subarray configuration into the multi-objective optimization function, and use a genetic algorithm to solve for the optimal subarray unit numbering combination that maximizes the detection sensitivity index and the resolution index and keeps the sound intensity below the material damage threshold. S213: For the optimal subarray unit number combination corresponding to the multiple detection sub-regions, calculate the excitation delay sequence of the multiple array units according to the distance difference between the multiple array units and the focusing target point, and determine the transmission frequency and pulse width parameters to obtain the configuration parameters of the multiple target subarrays corresponding to the multiple detection sub-regions; S3: Generate a probe movement trajectory sequence based on the configuration parameters of multiple target subarrays and the coupling medium deposition scheme, and match control commands for multiple trajectory nodes to obtain an integrated detection execution scheme; S4: Drive the probe to perform detection according to the integrated detection execution scheme, collect ultrasonic echo signals and identify suspected defects, adjust the sub-array configuration for suspected defect areas and generate encrypted scanning paths to complete the ultrasonic phased array detection path planning.

2. The method of claim 1, wherein, Step S1 further includes: S11: Acquire three-dimensional geometric data of aerospace material components; S12: The three-dimensional geometric data is divided into regions using a semantic segmentation algorithm to obtain multiple detection sub-regions; S13: Extract the surface geometric parameters of multiple detection sub-regions; S14: Establish a sound field propagation model, input the surface geometric parameters of multiple detection sub-regions into the sound field propagation model to perform sound field response simulation calculation, and obtain the sound field distribution dataset corresponding to multiple detection sub-regions.

3. The method of claim 2, wherein: Step S12 further includes: S121: Input the three-dimensional geometric data into a deep learning segmentation network to identify the planar regions, curved regions, stiffener regions and edge transition regions on the surface of the component, and obtain the initial segmentation result; S122: Extract the radius of curvature, surface normal vector, and thickness distribution information from multiple segmented regions in the initial segmentation result; S123: When the radius of curvature of the current segmented region is less than the preset curvature threshold, the current segmented region is marked as a high curvature detection sub-region. When the radius of curvature of the current segmented region is greater than or equal to the preset curvature threshold, the current segmented region is marked as a low curvature detection sub-region, thus obtaining an optimized segmentation result with high curvature and low curvature labels. S124: Obtain composite material process parameters for multiple segmented regions in the optimized segmentation result area. The process parameters include fiber layup direction, number of laminate layers, and thickness distribution information. Combine historical defect statistics to assign defect risk weight coefficients to multiple segmented regions, resulting in multiple detection sub-regions with risk level identifiers.

4. The method of claim 3, wherein, Step S14 further includes: S141: For various subarray aperture shapes of reconfigurable phased arrays, the curvature radius, surface normal vector and thickness distribution information of multiple detection sub-regions are input into the finite element acoustic field simulation module to calculate the sound wave propagation path and energy distribution of different subarray aperture shapes in the current detection sub-region; S142: For multiple subarray aperture shapes, extract the focal position coordinates, focal spot size parameters and side lobe energy ratios from the simulation results. When the focal spot size parameter of the current subarray aperture shape in the preset detection sub-region is less than the size threshold and the side lobe energy ratio is lower than the energy threshold, establish an adaptation mapping relationship between the current subarray aperture shape and the current detection sub-region. S143: Summarize the focal position coordinates, focal spot size parameters, and side lobe energy ratios of multiple detection sub-regions and the adapted sub-array aperture shapes to construct a sound field distribution dataset corresponding to multiple detection sub-regions.

5. The method of claim 1, wherein, Step S2, the step of calculating the coupling medium deposition scheme based on surface tilt characteristic parameters, further includes: S221: Calculate the angle between the surface normal vector and the gravity direction for multiple detection sub-regions to obtain the surface tilt angle. When the surface tilt angle is greater than a preset angle threshold, mark the current detection sub-region as a region prone to loss. S222: Based on the surface area, surface roughness, and surface tilt angle of the multiple detection sub-regions, the theoretical amount of coupling medium attached per unit area is calculated using a fluid attachment model. For the detection sub-regions marked as easily lost areas, a loss compensation coefficient is introduced based on the theoretical amount of attachment to obtain the target amount of coupling medium deposited in the multiple detection sub-regions. S223: Based on the surface tilt angle of the multiple detection sub-regions, determine the spray angle and spray pressure parameters of the coupling medium spray nozzle to obtain a coupling medium deposition scheme, wherein the coupling medium deposition scheme includes the target deposition amount of the coupling medium in the multiple detection sub-regions, the spray angle and the spray pressure parameters.

6. The method of claim 1, wherein, Step S3, the step of generating the probe movement trajectory sequence based on the configuration parameters of multiple target subarrays and the coupling medium deposition scheme, further includes: S311: Establish a kinematic model of the robotic arm, set the physical size constraints of the probe, the position constraints of the coupling medium nozzle, and the flexibility constraints of the pipeline as the boundary conditions for path planning, and generate the initial probe movement trajectory connecting multiple detection sub-regions by combining a genetic algorithm with time-optimal trajectory planning. S312: Identify the boundary transition segment of adjacent detection sub-regions in the initial probe movement trajectory. When there is a difference in the activation unit number in the target sub-array configuration parameters corresponding to adjacent detection sub-regions, insert an array configuration smooth transition node in the boundary transition segment to make the number of sub-array activation units and the excitation delay sequence change in a gradient, thereby obtaining a probe movement trajectory sequence containing array configuration smooth transition.

7. The method of claim 6, wherein: Step S3, the step of matching control commands for multiple trajectory nodes, further includes: S321: Calculate the probe moving speed vector and the robotic arm posture change rate for multiple trajectory nodes in the probe moving trajectory sequence. When the trajectory node is located in the detection sub-region with a high defect risk weight coefficient and the moving speed is lower than the speed threshold, set a first control command to extend the coupling medium spraying time for the current trajectory node. S322: When the trajectory node is located in the detection sub-region marked as a region prone to loss, a second control command for coupling medium flow compensation is added to the current trajectory node, and the nozzle spray angle is adjusted according to the surface tilt angle; S323: Insert a coupling medium recovery node in the retraction path segment and the repeated scanning path segment of the probe movement trajectory sequence, and introduce a third control command to the coupling medium recovery node to activate the absorption device to remove excess coupling medium. S324: Integrate the probe movement trajectory sequence, the target subarray configuration parameters corresponding to multiple trajectory nodes, the first control command, the second control command, and the third control command to obtain an integrated detection execution scheme.

8. The ultrasonic phased array detection path planning method for aerospace material components according to claim 1, characterized in that, Step S4 further includes: S41: Drive the probe to move according to the integrated detection execution scheme, execute control commands synchronously, collect ultrasonic echo signals and perform defect identification to obtain the suspected defect location; S42: Obtain the surface geometry parameters of the detection sub-region where the suspected defect location is located, input the sound field propagation model to calculate the local sound field distribution parameters, select the refined sub-array combination according to the local sound field distribution parameters, generate a densified scanning path around the suspected defect location and increase the amount of coupling medium deposition; S43: Collects coupling medium flow data, probe contact pressure and ultrasonic echo signal quality indicators through sensors, and adjusts coupling medium pump drive voltage, robotic arm posture and subarray configuration parameters according to the deviation between the collected data and preset values ​​to complete ultrasonic phased array detection path planning.

9. An ultrasonic phased array detection path planning system for aerospace material components, used to execute the ultrasonic phased array detection path planning method for aerospace material components as described in any one of claims 1 to 8, characterized in that, include: The segmentation module is used to acquire three-dimensional geometric data of aerospace material components, divide the three-dimensional geometric data into regions and extract surface geometric parameters, and calculate the sound field distribution dataset corresponding to multiple detection sub-regions through a sound field propagation model. Configuration module: Used to select the best subarray configuration scheme of reconfigurable phased array based on sound field distribution dataset, obtain multiple target subarray configuration parameters corresponding to multiple detection sub-regions, and calculate the coupling medium deposition scheme based on surface tilt feature parameters; When the configuration module performs the optimal selection of subarray configuration schemes for the reconfigurable phased array based on the sound field distribution dataset, it is further used for: Extract the subarray aperture shape and corresponding array unit number of multiple detection sub-regions from the sound field distribution dataset. For regions marked as high curvature detection sub-regions, select compact subarray configurations with array unit numbers within a preset low range. For regions marked as low curvature detection sub-regions, select extended subarray configurations with array unit numbers within a preset high range. A multi-objective optimization function is established, which includes a detection sensitivity index, a resolution index, and a sound intensity constraint term. The compact subarray configuration and the extended subarray configuration are substituted into the multi-objective optimization function, and the optimal subarray unit numbering combination that maximizes the detection sensitivity index and the resolution index and keeps the sound intensity below the material damage threshold is obtained by using a genetic algorithm. For the optimal subarray unit number combination corresponding to the multiple detection sub-regions, the excitation delay sequence of the multiple array units is calculated based on the distance difference between the multiple array units and the focusing target point, and the transmission frequency and pulse width parameters are determined to obtain the configuration parameters of the multiple target subarrays corresponding to the multiple detection sub-regions; The generation module is used to generate a probe movement trajectory sequence based on the configuration parameters of multiple target subarrays and the coupling medium deposition scheme, and to match control commands for multiple trajectory nodes to obtain an integrated detection execution scheme. Optimization module: Used to drive the probe to perform detection according to the integrated detection execution scheme, collect ultrasonic echo signals and identify suspected defects, adjust the subarray configuration of suspected defect areas and generate encrypted scanning paths to complete the ultrasonic phased array detection path planning.

Images