A multi-path adaptive full-focusing ultrasound imaging method for composite material corner regions

Abstract

This invention relates to a multipath adaptive full-focus ultrasound imaging method for corner areas of composite materials. The method includes: acquiring full-matrix ultrasound data of the corner area of ​​the composite material; constructing a quasi-P-wave group velocity sound velocity model based on the characteristic parameters of the composite material; constructing a surface-layered discrete model with a local coordinate system, and abstracting it into a directed graph G based on the quasi-P-wave group velocity sound velocity model; searching for a set of candidate propagation paths from array elements to arbitrary nodes in the directed graph G, and constructing multiple full-focus imaging TFM images based on the candidate propagation paths and the full-matrix ultrasound data; calculating path score factors based on the multiple full-focus imaging TFM images, and performing pixel-by-pixel weighted fusion of the multiple full-focus imaging TFM images based on the path score factors to obtain a fused imaging result. Compared with existing technologies, this invention has advantages such as strong adaptability and improved focusing stability and imaging reliability in corner areas.

Description

Technical Field

[0001] This invention relates to the field of ultrasonic testing of composite materials, and in particular to a multipath adaptive full-focus ultrasonic imaging method for corner areas of composite materials. Background Technology

[0002] Carbon fiber reinforced resin matrix composites (CFRPs) are widely used in aerospace load-bearing structures due to their high specific strength and specific modulus. Among them, L-shaped / corner components are key geometric parts for achieving connection and load transfer. These components are prone to internal defects such as delamination during manufacturing and service, and the morphology and distribution of defects are significantly affected by the ply structure and geometric constraints, resulting in higher requirements for the stability of defect detection and quantitative characterization.

[0003] The core of phased array ultrasound and delayed-stacking total focusing imaging (TFM) technology lies in achieving point-by-point focusing of the imaging area based on the delay rule. The accuracy of the delay rule directly depends on travel time prediction and acoustic ray path modeling. Traditional acoustic ray tracing often uses Snell's law to iteratively solve for the reflection and refraction of the sound beam at the interface. However, in the corner region of curved CFRP surfaces (including L-shaped corners and their rounded transition R-region), the coupling effect of three factors—curvature shape, anisotropy, and layered structure—leads to a complex solution process and makes it difficult to clarify the propagation mechanism in the R-region. To balance computational efficiency and feasibility, a more practical approach is to construct a discretized model that can characterize the material properties and then introduce a shortest path search algorithm from computer graph theory to replace the traditional acoustic ray tracing method. The effectiveness of this approach is mainly constrained by two factors: first, whether the discretized model can fully characterize the spatial anisotropy and interlayer structure characteristics of the material; and second, whether the candidate path can approximate the actual propagation path.

[0004] To address the aforementioned issues, existing solutions can be broadly categorized into two types: The first type focuses on relatively low-frequency ultrasonic testing, treating the corner region of multi-ply structures as an anisotropic homogeneous medium. It performs mesh discretization in the circumferential and thickness directions and establishes an angle-dependent velocity model through experiments to characterize the equivalent anisotropy. The second type treats the corner region as an anisotropic non-homogeneous medium, discretizing the region into fan-shaped micro-elements and assuming approximately fixed fiber orientations within each element to more fully reflect the circumferential gradient effect and layered stacking characteristics. Although the two methods differ in their discretization modeling, they generally approximate the true path using the shortest acoustic time path. This approximation is often acceptable in planar components, but in curved corner regions, due to the coupling effects of curvature, anisotropy, and layered structures, multiple candidate acoustic ray paths satisfying Snell's law may exist simultaneously locally, meaning the true path does not necessarily correspond to the single solution of the shortest time path.

[0005] From the perspective of wave theory, Fermat's principle states that sound waves propagate along paths with a stationary (extreme) travel time. In other words, the actual path of light propagation between two points is the path where the optical path reaches its extreme value (maximum, minimum, or inflection point). Conversely, in cases of complex media, non-planar interfaces, and potentially non-unique solutions, the degenerate conclusion that the shortest travel time path is equivalent to the actual propagation path no longer holds. Existing research has further extracted the shortest travel time and higher-energy propagation paths from finite element simulations of corner regions for imaging. Results show that the higher-energy path can achieve better imaging quality than the shortest path in some regions. In summary, addressing the problem of non-uniqueness of local acoustic ray paths at corners, more effectively approximating the actual propagation path and improving the reliability and quantitative consistency of defect detection in corner regions are urgent issues that need to be resolved. Summary of the Invention

[0006] The purpose of this invention is to provide a multipath adaptive full-focus ultrasound imaging method for corner areas of composite materials that achieves efficient and high-quality imaging.

[0007] The objective of this invention can be achieved through the following technical solutions: A multipath adaptive full-focus ultrasound imaging method for corner regions of composite materials includes the following steps: Acquire full-matrix ultrasonic data of the corner area of ​​composite materials; Based on the characteristic parameters of single-layer composite materials, a quasi-longitudinal wave group velocity sound speed model related to the propagation angle is constructed. A surface-layered discrete model with a local coordinate system is constructed, and it is abstracted into a directed graph G based on the quasi-longitudinal wave group velocity and sound speed model. Search for a set of candidate propagation paths from array elements to any node in the directed graph G, and construct multiple full-focus TFM images based on the set of candidate propagation paths and the ultrasound full matrix data; The path score factor is calculated based on the multiple full-focus TFM images, and the multiple full-focus TFM images are then weighted and fused pixel by pixel based on the path score factor to obtain the fused imaging result.

[0008] Furthermore, the steps for constructing the quasi-longitudinal wave group velocity sound speed model include: The characteristic parameters are organized into a stiffness coefficient matrix C0 of 0° ply in Voigt form, wherein the characteristic parameters include material density, elastic modulus, shear modulus and Poisson's ratio; The stiffness coefficient matrix C0 of the 0° ply is transformed by Bond to obtain the stiffness coefficient matrix of the ply at any angle. Based on the stiffness coefficient matrix of the arbitrary angle ply, the quasi-P-wave phase velocity is solved using the Christoffel equation, and the quasi-P-wave group velocity is calculated based on the quasi-P-wave phase velocity, forming an angle-group velocity relationship, which serves as the quasi-P-wave group velocity sound velocity model, expressed as: , In the formula, For the quasi-longitudinal wave group velocity vector, For wave vector, For wave vector gradient operator, ω is the angular frequency.

[0009] Furthermore, the construction steps of the surface layered discrete model include: Standard full-focus imaging of the imaging region is performed using the sound velocity of the coupling medium to obtain standard full-focus imaging results; Discrete points of the composite material corner area surface are uniformly extracted from the standard full-focus imaging results. The surface contour function of the composite material corner area is obtained by fitting the discrete points. ,in It is a sequence of circumferential nodes with equal spacing. These are the surface height coordinates at the corresponding node. For nodes and The functional relationship between them; Accumulate the thickness of each layer along the normal direction to generate the coordinates of the interface nodes of each layer, forming a mesh with dimensions of [missing value]. Surface layered discrete model ,in, Let be the number of discrete nodes along the thickness direction. Let be the number of discrete nodes on the hierarchical interface, and be the th The coordinates of nodes on each interface are represented as follows: , In the formula, For the first Each interface's forward node The horizontal coordinates of the location For the first Each interface's forward node The vertical coordinates at that location To reach the first The cumulative layer thickness of each interface, For nodes in The angle between the tangent direction on the plane and the horizontal direction is also the transformation angle between the local coordinate system and the global coordinate system. .

[0010] Further, the directed graph G includes nodes and directed edges containing edge weights. The nodes are nodes in the layered discrete model of the surface, the directed edges are quasi-P-wave propagation paths between adjacent layer nodes, and the edge weights are the propagation times of the quasi-P-wave between adjacent layer nodes. The directed edges only allow nodes to create directed edges with nodes in the next layer, prohibiting lateral connections between nodes in the same layer. Furthermore, a maximum propagation spread angle constraint is set to eliminate cross-layer jump directed edges that do not conform to physical propagation. The propagation time is determined by the ratio of the geometric distance between adjacent layer nodes to the quasi-P-wave group velocity at the corresponding propagation angle. Therefore, the expression for calculating the propagation time is: , In the formula, For directed edges The edge weights, For nodes With nodes The straight-line distance between them For nodes layer index, The global propagation angle is the angle from the node in the local coordinate system. Pointing to node The direction angle of wave propagation; for Intra-layer propagation angle is Effective speed of sound at that time; Wherein, the global propagation angle The calculation process includes: Take any two nodes in adjacent layers , Calculate the direction angle of the line connecting the two nodes, and use it as the global propagation angle. ; According to the conversion angle The global propagation angle Convert to local propagation angle , represented as: .

[0011] Furthermore, the candidate propagation path set includes the shortest acoustic time path set. and weak refraction path set The set of shortest acoustic time paths The steps for obtaining each shortest acoustic path include: Each array element is taken as the source point. Based on the principle of the shortest total acoustic time, Dijkstra's shortest path search is performed on the directed graph G to obtain the shortest propagation time from the source point to each node. The corresponding shortest acoustic time path is obtained by backtracking through the predecessor node. The set of weak refraction paths The steps for obtaining each weak refraction path include: For the outer surface nodes of the corner area Calculate the distance from the source node to the target node via the nodes. The propagation time along the formed straight path is used to obtain the propagation time curve. ; Based on all nodes Propagation time curve Find the minimum propagation time ; Set time tolerance threshold Combined with the minimum propagation time Construct the upper bound of the time window ; The minimum propagation time As the lower bound of the time window, combined with the upper bound of the time window This gives you the time window; Based on the aforementioned time window, nodes that meet the following conditions will be selected. Forming a candidate set : , The nodes for determining the projection entry point are determined based on geometric projection relationships. For the candidate set Each node Calculate its relative to the node The surface arc length offset is calculated using the following expression: , In the formula, This is the surface arc length offset. For nodes Position coordinates on the curved surface For nodes The arc length coordinates; The node that minimizes the surface arc length offset is selected as the weak refraction incident point. , represented as: , According to the weak refraction incident point Construct a weak refraction path and output the propagation trajectory and total propagation time of the weak refraction path, wherein the weak refraction path is composed of two parts: including the path from the array element to the weak refraction incident point in the coupling medium, and the path from the weak refraction incident point to the target node in the corner area of ​​the composite material.

[0012] Furthermore, the steps for acquiring the total focusing imaging TFM image include: For the shortest acoustic time path and the weakest refraction path in the candidate propagation path set, calculate the distance from the array element to each pixel in the imaging region. transmission time And each forms its own delayed field; Based on the principle of total focusing imaging, the ultrasound full matrix data is superimposed with time delay using the delayed fields corresponding to the shortest acoustic time path and the weak refraction path to obtain multiple total focusing imaging TFM images, including the shortest acoustic time path TFM image and the weak refraction path TFM image. The principle of total focusing imaging is expressed as follows: , In the formula, Pixels in a TFM image The signal amplitude at that location, The number of array elements. For the first Individual element incentives, the first The time-domain echo signal obtained by each array element during reception, i.e., the A-scan signal of the corresponding transmit-receive array element pair in the full ultrasound matrix data. , The ultrasound waves are respectively from the first Each array element and the first Individual elements propagate to pixels The spread time.

[0013] Furthermore, the calculation steps for the path score factor include: Based on the delay compensation channel data corresponding to the multiple full-focus TFM images, a phase consistency evaluation index is calculated, which includes an amplitude consistency factor, a weighted phase consistency factor, and an effective channel number factor. The path score factor is obtained by multiplying or weighting the amplitude consistency factor, weighted phase consistency factor, and effective channel number factor.

[0014] Furthermore, the calculation steps for the phase consistency evaluation index include: For each pixel within the imaging region Based on the shortest acoustic time path set in the candidate propagation path set and weak refraction path set Construct the path to be evaluated , represented as: , For the path to be evaluated any path in With pixels The communication signal of the ultrasound full matrix data is denoted as In the path Extract complex sample values ​​at the corresponding delay positions. and the set of valid channels participating in the scoring The complex sampled values Represented as: , In the formula, For the path Up to the pixel The time of propagation, subscript Channel number; Based on the complex sample values and effective channel set Constructing amplitude consistency factor , represented as: , In the formula, The set of valid channels for participating in the scoring. To prevent positive numbers with a denominator of zero; Based on the complex sample values Calculate the unit phase vector for each channel. , represented as: , Based on the unit phase vector Introducing amplitude weights Constructing a weighted phase consistency factor , represented as: , In the formula, The set of channels participating in the phase consistency evaluation; To characterize pixels The number of reliable channel evidences is calculated using the effective channel number factor based on the form of the effective sample number in Kish. , represented as: , In the formula, the subscript To participate in this pixel The first coherent statistics Channel index.

[0015] Furthermore, the path scoring factor Represented as: , In the formula, For amplitude consistency factor, As a weighted phase consistency factor, The effective channel number factor, This represents the number of valid channels.

[0016] Furthermore, the step of obtaining the fused imaging result includes: For each pixel within the imaging region Based on the path score factor, the path score factors for the shortest acoustic time path and the weak refraction path in the candidate propagation path set are obtained respectively. , ; According to the path scoring factor , The pixel-level path fusion weights for the shortest acoustic time path and the weak refraction path are obtained by normalization. , , represented as: , In the formula, To prevent positive numbers with a denominator of zero; Full-focus reconstruction was performed in the time-delay fields of the shortest acoustic time path and the weak refraction path, respectively. A phase consistency evaluation index was introduced to weight the imaging amplitude, resulting in the noise-reduced phase consistency-weighted path imaging outputs for the shortest acoustic time path and the weak refraction path, respectively. , , represented as: , In the formula, Representing a path The corresponding path imaging output is or ,path Indicates the shortest acoustic path or the weakest refraction path. For path The reconstructed pixel values ​​of the corresponding full-focus TFM image, Amplitude consistency factor; The pixel-level path fusion weights are used , Path imaging output , Perform pixel-by-pixel weighted fusion to obtain the amplitude of the final fused image. , represented as: , Based on the amplitude of the fused image Pixel-level fusion is performed to obtain the fused imaging result.

[0017] Compared with the prior art, the present invention has the following beneficial effects: (1) In view of the anisotropic characteristics of composite materials, especially in the corner area, the propagation path and speed of ultrasonic waves will change with the change of the incident angle. This invention abandons the traditional assumption of a single constant sound speed and establishes a quasi-longitudinal wave group velocity sound speed model that dynamically adjusts with the propagation angle, which greatly improves the simulation accuracy of ultrasonic wave propagation characteristics. It can effectively correct the phase error and position deviation caused by the change of wave speed in the corner area. On this basis, a surface layer discretization model is established and abstracted into a directed graph. This discretization and graph theory transformation method successfully solves the detection problem caused by complex three-dimensional curved surface structure. It can clearly sort out and describe the refraction and reflection law of sound waves in this irregular space. Then, the directed graph is used to search a large number of potential propagation paths, and the path score factor is introduced to perform pixel-by-pixel weighted fusion of multiple full-focus imaging TFM images, thereby realizing efficient and high-quality imaging.

[0018] (2) By simultaneously considering the shortest propagation time path and the weak refraction path, and performing pixel-level adaptive selection / fusion, this invention avoids the failure of the single path assumption in different spatial regions, thereby improving the focusing stability and imaging reliability in corner areas.

[0019] (3) Compared with existing single-path imaging that relies solely on the shortest path or weak refraction path, such as patent CN112083067B, this invention can inherit the complementary advantages of the two types of paths in different regions, reduce the shrinkage or expansion of the defect boundary caused by the mismatch of the local propagation model, and significantly improve the signal-to-noise ratio of the defect while maintaining the millimeter-level precision of the defect length.

[0020] (4) In total focusing imaging, traditional methods often only consider direct waves or a few main paths. This invention searches for a large number of potential propagation paths in a directed graph. This path search strategy ensures that effective signal contributions can be captured even in corner blind areas where sound waves are severely scattered or diffracted. This not only expands the effective detection area but also greatly improves the detection sensitivity for small or tilted defects.

[0021] (5) The present invention performs pixel-by-pixel weighted fusion based on path score factors, which means that the reliability of each path is evaluated. Paths with high reliability are given high weights and those with low reliability are given low weights. This "adaptive" weighting mechanism filters out a large number of artifact interference caused by noise or error propagation assumptions, and retains the real physical signal. The final output fusion imaging result has both extremely high signal-to-noise ratio and resolution, and avoids unnecessary computational overhead, achieving a perfect balance between computational resources and imaging quality.

[0022] (6) The present invention uses a quasi-longitudinal wave group velocity sound speed model based on single-ply, a directed graph of surface layering and discretization, and a multi-path search framework based on Fermat's principle, so that the method of the present invention can be extended to the detection of corner regions of composite materials with different ply sequences, different curvatures and different coupling methods (such as water / plexiglass coupling). Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 The quasi-longitudinal wave group velocities at different incident angles within the 0°, 45°, and 90° plies calculated in this invention; Figure 3 This is a schematic diagram of the corner region sound ray tracing method based on the shortest acoustic time path and weak refraction path of the present invention, wherein (a) is a local coordinate system, (b) is a surface layered discrete model, and (c) is a search for multiple sound ray paths; Figure 4 This is a schematic diagram of the weak refraction point selection algorithm for the weak refraction path of the present invention; Figure 5 These are the shortest time path TFM images and weak refraction path TFM images of the present invention; Figure 6 The image shows a comparison between the PCA-MPF-TFM imaging results of the present invention and the imaging results of existing acoustic tracing methods. In the image, (a) is the TFM imaging result based on the weak refraction path under the acoustic tracing method, (b) is the PCA-MPF-TFM imaging result of the present invention, (c) is the quantitative TFM defect based on the weak refraction path under the acoustic tracing method, and (d) is the quantitative TFM defect of the present invention. Figure 7 The images show a comparison of the total focusing imaging quality of the method of the present invention with that of several existing acoustic tracing methods, where (a) represents the accuracy of the defect length and (b) represents the comparison of the defect signal-to-noise ratio. Detailed Implementation

[0024] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0025] This embodiment provides a multipath adaptive full-focus ultrasound imaging method for corner areas of composite materials, such as... Figure 1 As shown, the method includes the following steps: Step 1: Place the linear array probe above the corner area and acquire full-matrix ultrasound data through the coupling medium.

[0026] In this embodiment, the composite material is carbon fiber reinforced composite material (CFRP). A linear array ultrasonic probe is set above the corner area of ​​the multi-directional plate. The probe is precisely aligned with the top of the defect, and all array elements are sequentially excited. Ultrasonic full matrix data (FMC) is collected through coupling media such as water or plexiglass.

[0027] In this embodiment, the center frequency of the probe is selected as 2 to 5 MHz based on the sample thickness and the thickness of the single layer.

[0028] Step 2: Based on the characteristic parameters of single-layer carbon fiber materials, use the Christophian equation and Bond transform to establish a quasi-longitudinal wave group velocity sound speed model related to the propagation angle.

[0029] In this embodiment, the material characteristic parameters include at least density. In addition to the elastic modulus, shear modulus, and Poisson's ratio used to establish the elastic stiffness matrix of a single-ply layer, to establish an angle-dependent quasi-P-wave group velocity sound velocity model, the characteristic parameters are first rearranged into a stiffness coefficient matrix C0 of Voigt form for a 0° ply. At this point, the stiffness coefficient matrix for any angle ply can be obtained by performing a Bond transformation on C0. Then, the quasi-P-wave phase velocity is solved using the Christoffel equation. Finally, the quasi-P-wave group velocity is calculated from the quasi-P-wave phase velocity using the formula below, forming an angle-group velocity relationship, which serves as the quasi-P-wave group velocity sound velocity model: , In the formula, For the quasi-longitudinal wave group velocity vector, For wave vector, For wave vector gradient operator, ω is the angular frequency.

[0030] Step 3: Construct a surface layered discrete model with a local coordinate system, and abstract the surface layered discrete model into a directed graph G.

[0031] In this step, firstly, standard full-focus imaging is performed on the imaging area using the sound velocity of the coupling medium. Then, discrete points of the composite material corner area surface are uniformly extracted from the standard full-focus imaging results. The surface contour function of the composite material corner area is obtained by fitting the discrete points. ,in It is a sequence of circumferential nodes with equal spacing. These are the surface height coordinates at the corresponding node. For nodes and The functional relationship between them; Then, the thickness of each layer is accumulated along the normal direction to form a depth sequence. subscript If the total number of interfaces is represented, then the first one is... The coordinates of a node on an interface can be represented as: , in For nodes in The angle between the tangent direction on the plane and the horizontal direction is also the transformation angle between the local coordinate system and the global coordinate system. The sound propagation angle between any two nodes in adjacent layers can be transformed from the global coordinate system to the local coordinate system using this angle. This process can be described by the following formula: , In the formula, The local propagation angle represents the angle from the node in the local coordinate system. Pointing to node The direction angle of wave propagation; The global propagation angle is the angle between any two nodes in adjacent layers. , The direction angle of the connecting line.

[0032] After completing the above steps, a mesh size of [size to be filled in] can be obtained. Surface layered discrete model (like Figure 3 (as shown in (b)). Let be the number of discrete nodes along the thickness direction. This represents the number of discrete nodes on the hierarchical interface. (Combined with...) Figure 3 It can be seen that the surface layer discretization model is discretized along the normal direction of the corner surface according to the ply interface, and a local coordinate system is established at each grid node (e.g., Figure 3 As shown in (a) in the figure, the local normal axis is aligned with the surface normal at the node, which is used to convert the global propagation angle into the local propagation angle.

[0033] To improve route search efficiency, the discrete hierarchical model is transformed into a directed graph G according to the following rules: First, for each node, lateral connections within the same interface are prohibited; directed edges are created only for nodes in the next row to ensure a unidirectional propagation topology from the surface inwards. The nodes are defined in the layered discrete model of the curved surface. Second, directed edges are set as quasi-longitudinal wave propagation paths between adjacent layer nodes, and a maximum propagation spread angle is set. Constraints are applied to constrain the sound propagation process in the local coordinate system, eliminating directed edges that do not conform to physical propagation rules to prevent erroneous cross-layer sound path occurrences, thus obtaining the final sound path, such as... Figure 3 As shown in (c); finally, the edge weight of the directed edge is defined as the time of the sound wave in the anisotropic monopile. This refers to the propagation time of the quasi-P-wave between adjacent layer nodes, which is determined by the ratio of the geometric distance between adjacent layer nodes to the quasi-P-wave group velocity in the corresponding propagation direction. , In the formula, For directed edges The edge weights, For nodes With nodes The straight-line distance between them for Intra-layer propagation angle is Effective speed of sound at that time For nodes The layer index, ( Indicates the coupling layer; Indicates the first Layer by layer).

[0034] Step 4: Based on Fermat's principle, search for multiple propagation paths from array elements to any node in the directed graph G, forming a candidate set of propagation paths.

[0035] In this step, the candidate propagation path set must include at least the shortest acoustic time path set. and weak refraction path set .in The shortest path is obtained by performing Dijkstra's search on the directed graph G. The specific steps are as follows: Using each array element as a source point, and taking the shortest total acoustic time as the principle, Dijkstra's shortest path search is performed on the directed graph G to obtain the shortest propagation time from the array element to each grid node. The corresponding shortest time propagation path, i.e. the shortest acoustic time path, can be obtained by backtracking from the predecessor node.

[0036] By determining the weak refraction entrance index To obtain it, the specific steps are as follows: a. Discrete nodes on the outer surface of the corner (Each node on the outer surface is used as its index during the calculation process), the calculation from the source point to the target point via surface points is performed. The propagation time along the formed straight path is used to obtain a time curve. .

[0037] b. In all Find the minimum propagation time And record the index that makes it reach the minimum value. .

[0038] c. Set time tolerance threshold Construct the upper bound of the time window , to obtain The lower bound of the time window The time window is the upper bound of the time window, and then all surfaces that meet the following conditions are indexed. Forming a candidate set .

[0039] , d. Determine the index of the projection entry point based on the geometric projection relationship. For candidate set Each Calculate its relative Surface arc length offset: , In the formula, This is the surface arc length offset. For nodes Position coordinates on the curved surface For projection point The arc length coordinates; The index that minimizes the arc length offset is selected as the weak refraction incident point. As shown in the following formula: , e. Construct a weak refraction path based on the weak refraction point, and output the propagation trajectory, total propagation time and delay field of the weak refraction path. The weak refraction path is composed of two parts: the path in the coupling medium (i.e. the path from the array element to the weak refraction incident point) and the path inside the workpiece from the weak refraction incident point to the target node.

[0040] Step 5: Reconstruct at least two full-focus TFM images based on the time-delay fields corresponding to different candidate propagation paths.

[0041] For the shortest acoustic time path and the weak refraction path in the candidate propagation path set, calculate the distance from the array element to each pixel in the imaging region. transmission time Each of these data forms its own time-delay field. Then, based on the principle of full-focus imaging, the ultrasound full matrix data is superimposed with time-delayed data using the time-delayed fields corresponding to the shortest acoustic time path and the weakest refraction path, resulting in multiple full-focus TFM images, including the shortest acoustic time path TFM image and the weakest refraction path TFM image. The imaging formula is as follows: , In the formula, Pixels in a TFM image The signal amplitude at that location, The number of array elements. For the first Individual element incentives, the first The time-domain echo signal obtained by each array element during reception, i.e., the A-scan signal of the corresponding transmit-receive array element pair in the full ultrasound matrix data. , The ultrasound waves are respectively from the first Each array element and the first Individual elements propagate to pixels The spread time.

[0042] Step 6: Construct a phase consistency evaluation index for the delay compensation channel data corresponding to at least two full-focus TFM images, and define the path score factor accordingly.

[0043] Using phase consistency as a criterion for judging path rationality, the phase consistency evaluation index should at least include the amplitude consistency factor. With weighted phase consistency factor It also includes the effective channel number factor ( The calculation steps for this phase consistency evaluation index include: For each pixel within the imaging area Based on the two sets of candidate propagation paths obtained in step four: the shortest acoustic time path set and the weak refraction path set, the path to be evaluated is denoted as: , Based on the path to be evaluated For any path With pixels The channel signals of the full ultrasound matrix data are denoted as... Extract complex sample values ​​at the delay positions corresponding to the path. : , The set of valid channels participating in the scoring is denoted as , where subscript This is the channel number.

[0044] For pixels With path The amplitude consistency factor can be constructed according to the following formula. : , In the formula, The set of valid channels for participating in the scoring. To prevent tiny positive numbers with a denominator of zero; Calculate the unit phase vector for each channel. : , And introduce amplitude weights Construct a weighted phase consistency factor As shown in the following formula: , In the formula, The set of channels participating in the phase consistency evaluation; To characterize pixels The "reliable channel evidence quantity" is calculated based on the effective channel number factor in the form of the Kish effective sample number. : , In the formula, the subscript Indicates the first Each pixel involved The imaging channel.

[0045] Based on the amplitude consistency factor obtained above The path score factor is constructed by jointly using the weighted phase consistency factor and the effective channel number factor. ,Should It is composed of the product or weighted combination of the three factors and is used to represent a pixel. In the candidate path The focusing quality is expressed by the following formula: , In the formula, This represents the number of valid channels.

[0046] Step 7: Construct pixel-wise path adaptive weights based on the path score factor, and perform pixel-wise weighted fusion on at least two full-focus TFM images to obtain the fused imaging result.

[0047] For each pixel Based on step six, the path score factors corresponding to the shortest acoustic time path and the weak refraction path are obtained respectively. , The pixel-level path fusion weights were obtained by normalization. and As shown in the following formula: , In the formula, To prevent tiny positive numbers with a denominator of zero.

[0048] For each pixel Full-focus reconstruction was performed in the delayed fields of the shortest acoustic time path and the weak refraction path, respectively. A phase consistency evaluation index was introduced to weight the imaging amplitude, resulting in the noise-reduced two-path phase consistency weighted imaging output. and , where any path For example, its phase-consistency weighted path imaging output is expressed as: , In the formula, Representing a path The corresponding path imaging output is or , For path The reconstructed pixel values ​​of the corresponding total focusing imaging TFM image.

[0049] The pixel-level path fusion weights obtained using the above steps and The imaging region is weighted and fused pixel by pixel to obtain the amplitude of the final fused image. : , Finally, the amplitude of each pixel in the fused image is fused to obtain the fused imaging result.

[0050] To verify the effectiveness of the above method, this embodiment uses an L-shaped carbon fiber reinforced composite specimen as the research object and performs the following verification steps: S1. The L-shaped carbon fiber reinforced composite specimen was stably coupled to a 5L64-A32 linear ultrasonic probe via water immersion. A robotic arm was used to precisely align the probe directly above the defect. All array elements were sequentially excited one by one, and all elements were received to collect ultrasonic echo data in full matrix form. The probe center frequency was 5MHz, and the 64 excitation elements were linearly arranged with a center-to-center spacing of 0.5mm.

[0051] S2. The characteristic parameters of the sample material, density, elastic modulus, shear modulus, and Poisson's ratio, are rearranged into a 0° ply stiffness coefficient matrix C0 as shown below: , Then, by applying the Bond transformation to C0, the stiffness coefficient matrix corresponding to unidirectional layups at different angles can be obtained. Based on this, and combining the Christophian equation and the angular relationship between phase velocity and group velocity in anisotropic media, the quasi-P-wave group velocity sound velocity models varying with the propagation angle in unidirectional layups at 0°, 45°, and 90° can be calculated, such as... Figure 2 As shown, the anisotropy is most pronounced when propagating in the horizontal direction, resulting in the highest speed of sound for the corresponding quasi-longitudinal wave group.

[0052] S3. To characterize the continuous circumferential variation of elastic properties caused by the curvature of the corner region, this embodiment first fits the profile of the concave side surface: standard full-focus imaging is performed with a sound velocity of 1500 m / s in water as the imaging area, and discrete points of the sample surface are uniformly extracted from the standard full-focus imaging results with a step size of 0.05 mm. The coordinates of the discrete points are then fitted to obtain a polynomial function. Subsequently, nodes of each layer interface are accumulated along the normal with a single-layer interface thickness of 0.2 mm to form a surface layered mesh (surface layered discrete model). The tangent angle and normal direction corresponding to each discrete surface point are calculated to establish a local coordinate system for each mesh node, aligning the local normal with the local principal axis of the material, so as to convert the global propagation angle into a local propagation angle and query the corresponding group velocity.

[0053] The entire model has 21,454 nodes at the layered interface. Based on this, the curved layered mesh is abstracted into a directed graph: only unidirectional connections between nodes and nodes in the next layer are allowed, and horizontal connections within the same layer are prohibited; and a maximum propagation diffusion angle constraint is set to eliminate cross-layer jump connections that do not conform to physical propagation; the weight of each edge is defined as the ratio of the distance between adjacent nodes to the local group velocity, thus obtaining a propagation time graph model oriented towards Fermat's principle.

[0054] S4. Multipath propagation time calculation and generation of two types of candidate delay fields: (1) For each array element as the source point, perform Dijkstra's shortest path search on the directed graph to obtain the shortest propagation time from the array element to each grid node, and the corresponding shortest time propagation path can be obtained by backtracking from the predecessor node. This propagation time field is used to construct the first type of delay hypothesis, i.e. the shortest acoustic time path.

[0055] (2) The second type is the time-delay field based on the weak refraction path. Taking the weak refraction path search process of the node with the index 10431 in the 15th row and 335th column below the surface as an example, with the projection point 335 of node 10431 on the surface as the center, the search window radius is 10 nodes, such as Figure 4 As shown in (a) to (c), surface indices are obtained by discretizing along the outer surface of the corner region. and index each surface Calculate the corresponding propagation time curve .Depend on Figure 4 From (b) we can see that, exist Minimum value is obtained at Furthermore, set a time tolerance. =0.07 ,by and Constructing time windows (window bounds) will include surface indices that meet the following conditions. Included in the candidate set (like Figure 4 As shown in point (b) in the diagram, entry points with significantly longer propagation times are eliminated, thereby ensuring that the selected entry point satisfies the "near-stationary phase" constraint of Fermat's principle.

[0056] .

[0057] Based on this, such as Figure 4 Figures (b) and (c) show the determination of the surface index corresponding to the geometric projection point. Then, for the candidate set Candidate entry points within Calculate its surface arc length offset relative to the projection point. (Unit: mm), and weakly refracted incident points are selected using the minimum arc length offset criterion. From Figure 4 (c) in the text can be read as: when hour, ;when Deviation As the arc length offset increases, for example, in or The arc length offset is approximately ,exist or The arc length offset is approximately Therefore, in the candidate set Inside, satisfy The weak refraction incident point is (like Figure 4 (As indicated by the star in (c)). The weak refraction path is formed by splicing together the path in the water and the path between the weak refraction incident point and the target node 10431.

[0058] S5. Based on the two types of time-delay fields and the FMC data, each pixel in the imaging area is reconstructed using the two types of time-delay fields through time-delay superposition, resulting in two TFM images: the shortest time path TFM image and the weak refraction path TFM image, as shown below. Figure 5 As shown. The principle of total focusing imaging is shown in the following equation: , in, Indicates the number of elements. Time delay. and These represent the ultrasonic waves from the first... Each array element and the first Each element propagates to the pixels in the TFM image. The spread time.

[0059] S6. Phase Consistency Evaluation and Pixel-by-Pixel Adaptive Fusion: For each pixel, extract the channel complex samples after delay compensation under two types of delay assumptions. Construct a phase consistency evaluation index according to the method in step six, and form a path score factor by combining the amount of effective channel evidence. ; S7. Then normalize the path score factor into pixel-wise weights to obtain... and The two TFM results are then weighted pixel-wise and fused to output the final fused image. A comparison is then performed using the isotropic standard total focusing imaging result as the baseline. Figure 6 As shown.

[0060] In this embodiment, traditional isotropic TFM, when used for corner region imaging, easily generates striped coherent structure noise and exhibits a certain degree of signal masking and size assessment deviation for layered defects near the boundary. While TFM imaging with curved layered anisotropic propagation time correction can improve defect focusing to some extent, the presence of multiple propagation paths in corner regions can still lead to focusing inaccuracies due to mismatched single-path delay assumptions. After employing the phase-consistency adaptive multipath fusion imaging method of this invention, the imaging results are as follows: Figure 6 As shown in (b), the defect indication exhibits enhanced continuity and clearer boundaries, and the stripe noise in the corner region is effectively suppressed, resulting in a simultaneous improvement in defect detection capability and the stability of quantitative characterization; whereby the defect length is determined using... The dB drop method is used for estimation, and a comprehensive evaluation is performed in conjunction with indicators such as defect signal-to-noise ratio (SNR). This embodiment compares and validates the phase-consistent adaptive multipath imaging algorithm (PCA-MPF-TFM) with existing anisotropic model-based phase / coherence weighted shortest path acoustic ray tracing methods (CF-RT-TFM), phase / coherence weighted weak refraction path acoustic ray tracing methods (CF-WR-RT-TFM), and fixed candidate path global fusion weight method (FW-MPF-TFM). The evaluation results are as follows: Figure 7 As shown, it can be demonstrated that the embodiments of the present invention significantly improve the signal-to-noise ratio of defects in the corner area while maintaining millimeter-level precision in detecting layered defects, compared with other existing methods.

[0061] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0062] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0063] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0064] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0065] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0066] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0067] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A multipath adaptive full-focus ultrasound imaging method for corner regions of composite materials, characterized in that, Includes the following steps: Acquire full-matrix ultrasonic data of the corner area of ​​composite materials; Based on the characteristic parameters of single-layer composite materials, a quasi-longitudinal wave group velocity sound speed model related to the propagation angle is constructed. A surface-layered discrete model with a local coordinate system is constructed, and it is abstracted into a directed graph G based on the quasi-longitudinal wave group velocity and sound speed model. Search for a set of candidate propagation paths from array elements to any node in the directed graph G, and construct multiple full-focus TFM images based on the set of candidate propagation paths and the ultrasound full matrix data; The path score factor is calculated based on the multiple full-focus TFM images, and the multiple full-focus TFM images are then weighted and fused pixel by pixel based on the path score factor to obtain the fused imaging result.

2. The multipath adaptive full-focus ultrasound imaging method for corner regions of composite materials according to claim 1, characterized in that, The steps for constructing the quasi-longitudinal wave group velocity sound speed model include: The characteristic parameters are organized into a stiffness coefficient matrix C0 of 0° ply in Voigt form, wherein the characteristic parameters include material density, elastic modulus, shear modulus and Poisson's ratio; The stiffness coefficient matrix C0 of the 0° ply is transformed by Bond to obtain the stiffness coefficient matrix of the ply at any angle. Based on the stiffness coefficient matrix of the arbitrary angle ply, the quasi-P-wave phase velocity is solved using the Christoffel equation, and the quasi-P-wave group velocity is calculated based on the quasi-P-wave phase velocity, forming an angle-group velocity relationship, which serves as the quasi-P-wave group velocity sound velocity model, expressed as: ， In the formula, For the quasi-longitudinal wave group velocity vector, For wave vector, For wave vector gradient operator, ω is the angular frequency.

3. The multipath adaptive full-focus ultrasound imaging method for corner regions of composite materials according to claim 1, characterized in that, The construction steps of the surface layered discrete model include: Standard full-focus imaging of the imaging region is performed using the sound velocity of the coupling medium to obtain standard full-focus imaging results; Discrete points of the composite material corner area surface are uniformly extracted from the standard full-focus imaging results. The surface contour function of the composite material corner area is obtained by fitting the discrete points. ,in It is a sequence of circumferential nodes with equal spacing. These are the surface height coordinates at the corresponding node. For nodes and The functional relationship between them; Accumulate the thickness of each layer along the normal direction to generate the coordinates of the interface nodes of each layer, forming a mesh with dimensions of [missing value]. Surface layered discrete model ,in, Let be the number of discrete nodes along the thickness direction. Let be the number of discrete nodes on the hierarchical interface, and be the th The coordinates of nodes on each interface are represented as follows: ， In the formula, For the first Each interface's forward node The horizontal coordinates of the location For the first Each interface's forward node The vertical coordinates at that location To reach the first The cumulative layer thickness of each interface, For nodes in The angle between the tangent direction on the plane and the horizontal direction is also the transformation angle between the local coordinate system and the global coordinate system. .

4. The multipath adaptive full-focus ultrasound imaging method for corner regions of composite materials according to claim 3, characterized in that, The directed graph G includes nodes and directed edges containing edge weights. The nodes are nodes in the layered discrete model of the surface. The directed edges are quasi-P-wave propagation paths between adjacent layer nodes. The edge weights are the propagation times of the quasi-P-wave between adjacent layer nodes. Directed edges are only allowed to connect nodes to the next layer; lateral connections between nodes in the same layer are prohibited. Furthermore, a maximum propagation spread angle constraint is set to eliminate cross-layer jump directed edges that do not conform to physical propagation. The propagation time is determined by the ratio of the geometric distance between adjacent layer nodes to the quasi-P-wave group velocity at the corresponding propagation angle. Therefore, the expression for calculating the propagation time is: ， In the formula, For directed edges The edge weights, For nodes With nodes The straight-line distance between them For nodes layer index, The global propagation angle is the angle from the node in the local coordinate system. Pointing to node The direction angle of wave propagation; for Intra-layer propagation angle is Effective speed of sound at that time; Wherein, the global propagation angle The calculation process includes: Take any two nodes in adjacent layers , Calculate the direction angle of the line connecting the two nodes, and use it as the global propagation angle. ; According to the conversion angle The global propagation angle Convert to local propagation angle , represented as: 。 5. The multipath adaptive full-focus ultrasound imaging method for corner regions of composite materials according to claim 1, characterized in that, The propagation path candidate set includes the shortest acoustic time path set. and weak refraction path set The set of shortest acoustic time paths The steps for obtaining each shortest acoustic path include: Each array element is taken as the source point. Based on the principle of the shortest total acoustic time, Dijkstra's shortest path search is performed on the directed graph G to obtain the shortest propagation time from the source point to each node. The corresponding shortest acoustic time path is obtained by backtracking through the predecessor node. The set of weak refraction paths The steps for obtaining each weak refraction path include: For the outer surface nodes of the corner area Calculate the distance from the source node to the target node via the nodes. The propagation time along the formed straight path is used to obtain the propagation time curve. ; Based on all nodes Propagation time curve Find the minimum propagation time ; Set time tolerance threshold Combined with the minimum propagation time Construct the upper bound of the time window ; The minimum propagation time As the lower bound of the time window, combined with the upper bound of the time window This gives you the time window; Based on the aforementioned time window, nodes that meet the following conditions will be selected. Forming a candidate set : ， The nodes for determining the projection entry point are determined based on geometric projection relationships. For the candidate set Each node Calculate its relative to the node The surface arc length offset is calculated using the following expression: ， In the formula, This is the surface arc length offset. For nodes Position coordinates on the curved surface For nodes The arc length coordinates; The node that minimizes the surface arc length offset is selected as the weak refraction incident point. , represented as: ， Based on the weak refraction incident point Construct a weak refraction path and output the propagation trajectory and total propagation time of the weak refraction path, wherein the weak refraction path is composed of two paths spliced ​​together: including the path from the array element to the weak refraction incident point in the coupling medium, and the path from the weak refraction incident point to the target node in the corner region of the composite material.

6. A multipath adaptive full-focus ultrasound imaging method for corner regions of composite materials according to claim 5, characterized in that, The steps for acquiring the total focusing imaging TFM image include: For the shortest acoustic time path and the weakest refraction path in the candidate propagation path set, calculate the distance from the array element to each pixel in the imaging region. transmission time And each forms its own delayed field; Based on the principle of total focusing imaging, the ultrasound full matrix data is superimposed with time delay using the delayed fields corresponding to the shortest acoustic time path and the weak refraction path to obtain multiple total focusing imaging TFM images, including the shortest acoustic time path TFM image and the weak refraction path TFM image. The principle of total focusing imaging is expressed as follows: ， In the formula, Pixels in a TFM image The signal amplitude at that location, The number of array elements. For the first Individual element incentives, the first The time-domain echo signal obtained by each array element during reception, i.e., the A-scan signal of the corresponding transmit-receive array element pair in the full ultrasound matrix data. , The ultrasound waves are respectively from the first Each array element and the first Individual elements propagate to pixels The spread time.

7. The multipath adaptive full-focus ultrasound imaging method for corner regions of composite materials according to claim 1, characterized in that, The calculation steps for the path score factor include: Based on the delay compensation channel data corresponding to the multiple full-focus TFM images, a phase consistency evaluation index is calculated, which includes an amplitude consistency factor, a weighted phase consistency factor, and an effective channel number factor. The path score factor is obtained by multiplying or weighting the amplitude consistency factor, weighted phase consistency factor, and effective channel number factor.

8. A multipath adaptive full-focus ultrasound imaging method for corner regions of composite materials according to claim 7, characterized in that, The calculation steps for the phase consistency evaluation index include: For each pixel within the imaging region Based on the shortest acoustic time path set in the candidate propagation path set and weak refraction path set Construct the path to be evaluated , represented as: ， For the path to be evaluated any path in With pixels The communication signal of the ultrasound full matrix data is denoted as In the path Extract complex sample values ​​at the corresponding delay positions. and the set of valid channels participating in the scoring The complex sampled values Represented as: ， In the formula, For the path Up to the pixel The time of propagation, subscript Channel number; Based on the complex sample values and effective channel set Constructing amplitude consistency factor , represented as: ， In the formula, The set of valid channels for participating in the scoring. To prevent positive numbers with a denominator of zero; Based on the complex sample values Calculate the unit phase vector for each channel. , represented as: ， Based on the unit phase vector Introducing amplitude weights Constructing a weighted phase consistency factor , represented as: ， In the formula, The set of channels participating in the phase consistency evaluation; To characterize pixels The number of reliable channel evidences is calculated using the effective channel number factor based on the form of the effective sample number in Kish. , represented as: ， In the formula, the subscript To participate in this pixel The first coherent statistics Channel index.

9. A multipath adaptive full-focus ultrasound imaging method for corner regions of composite materials according to claim 7, characterized in that, The path scoring factor Represented as: ， In the formula, For amplitude consistency factor, As a weighted phase consistency factor, The effective channel number factor, This represents the number of valid channels.

10. A multipath adaptive full-focus ultrasound imaging method for corner regions of composite materials according to claim 1, characterized in that, The steps for obtaining the fused imaging result include: For each pixel within the imaging region Based on the path score factor, the path score factors for the shortest acoustic time path and the weak refraction path in the candidate propagation path set are obtained respectively. , ; According to the path scoring factor , The pixel-level path fusion weights for the shortest acoustic time path and the weak refraction path are obtained by normalization. , , represented as: ， In the formula, To prevent positive numbers with a denominator of zero; Full-focus reconstruction was performed in the time-delay fields of the shortest acoustic time path and the weak refraction path, respectively. A phase consistency evaluation index was introduced to weight the imaging amplitude, resulting in the noise-reduced phase consistency-weighted path imaging outputs for the shortest acoustic time path and the weak refraction path, respectively. , , represented as: ， In the formula, Representing a path The corresponding path imaging output is or ,path Indicates the shortest acoustic path or the weakest refraction path. For path The reconstructed pixel values ​​of the corresponding full-focus TFM image, Amplitude consistency factor; The pixel-level path fusion weights are used , Path imaging output , Perform pixel-by-pixel weighted fusion to obtain the amplitude of the final fused image. , represented as: ， Based on the amplitude of the fused image Pixel-level fusion is performed to obtain the fused imaging result.

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