An improved full-focusing imaging method based on metasurface modified stack plate buffer rod

Abstract

The application provides a full-focusing imaging method based on a super surface improved stack plate buffer rod, relates to the technical field of nondestructive testing, and combines a semi-empirical parameterization method with a meta-heuristic search, parameterizes a side wall structure through a sine function family, and cooperates with time domain finite element simulation and experimental verification to realize directional suppression and engineering manufacturability design of a stack plate trailing wave. The application overcomes the limitation of the trailing wave artifact in the traditional buffer rod full-focusing imaging, combines the advantages of the acoustic super surface technology and the meta-heuristic algorithm, can maintain the physical interpretability of the design, and can automatically search for the super surface structure parameter set that performs best under the actual working environment limitation, so that the trailing wave is significantly weakened, the artifact is almost completely eliminated, the signal-to-noise ratio and the positioning reliability of the imaging are significantly improved, and a more feasible design scheme is provided for the popularization and application of the stack plate buffer rod in the phased array nondestructive testing.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing technology, and more specifically, to a full-focus imaging method based on a metasurface-modified stacked plate buffer bar. Background Technology

[0002] In recent years, industrial online monitoring and in-situ monitoring have become the core directions for the development of ultrasonic phased array imaging technology. Various real-time monitoring solutions have emerged in research and engineering practice, including fixed systems that fix the phased array probe to the measurement point, flexible arrays that can conform to complex curved surfaces, coating-based solutions that achieve coupling through functional coatings, implantable sensors, and non-contact or weak-contact monitoring methods represented by electromagnetic-acoustic coupling. Fixed phased array solutions demonstrate high reliability in many scenarios due to the maturity of commercial equipment and the sophistication of imaging algorithms. However, in some practical situations, permanently attaching or mounting the phased array probe to the object under inspection is neither practical nor feasible, and also presents engineering risks and maintenance difficulties. Examples include dynamic surfaces during polymer mixing or curing processes, long-term monitoring under high-temperature environments, characterization of melts or high-viscosity fluids, and detection in narrow or inaccessible areas inside equipment. Therefore, in these non-destructive testing scenarios, buffer rods are often used as alternative means of coupling and sound wave transmission.

[0003] Traditional monolithic buffer bars are effective in some applications, but they introduce focusing problems, and the volume waves propagating inside are easily attenuated, making them unsuitable for long-distance detection. In addition, reflections from the sidewalls inside the buffer bar can introduce trailing waves, which often mask interface or defect echoes, weakening the signal-to-noise ratio and limiting the widespread adoption of the method.

[0004] To address the focusing issues and trailing wave interference of traditional bulk buffer rods in phased array imaging, the industry has proposed dividing the buffer rod into a stacked plate structure corresponding one-to-one with the phased array probe wafers. By dividing the entire material into several thin plates and coupling them separately, certain reflection / refractive paths originating from within the bulk can be largely suppressed. The energy of the bulk wave, which originally propagated as rapidly decaying volume waves, is converted into guided waves propagating along the thin plates. This allows the signal reaching the surface of the object under test to retain more energy and phase information, thus making the imaging results under stacked plate conditions close to the imaging effect when the probe directly contacts the workpiece. Although stacking significantly improves the propagation loss problem, both practical and simulation results show that stacking plates still produce trailing wave artifacts. While increasing the width of the stacking plates can delay the appearance time of the trailing waves, large stacking plates are not suitable for applications in confined environments.

[0005] Acoustic metasurfaces, as an emerging technology for precise manipulation of acoustic wavefronts, energy flow, and modes through microstructures or parameterized boundaries, are gradually showing potential in ultrasonic nondestructive testing to improve resolution and achieve directional energy focusing and mode conversion.

[0006] Therefore, there is an urgent need for a full-focus imaging method based on metasurface-modified stacked plate buffer bars to solve the above-mentioned technical problems. Summary of the Invention

[0007] To address the aforementioned technical problems in related technologies, this invention proposes a full-focus imaging method based on a metasurface-modified stacked plate buffer rod.

[0008] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0009] This invention provides a full-focus imaging method based on a metasurface-modified stacked plate buffer bar, comprising:

[0010] S1. Construct the metasurface boundary expression for the sidewall of the metasurface stack based on the family of sine functions;

[0011] S2. Construct a metaheuristic algorithm test model based on the metasurface boundary expression, and set the evaluation function of the metaheuristic algorithm test model. Then, test the metaheuristic algorithm test model in sequence based on multiple metaheuristic optimization algorithms, and select the metaheuristic optimization algorithm with the smallest evaluation function value as the optimal metaheuristic algorithm.

[0012] S3. Based on the optimal metaheuristic algorithm and the metasurface boundary expression, the optimal parameter combination of the metasurface boundary expression is obtained by performing optimization simulation based on COMSOL to construct the final metasurface boundary expression of the metasurface stack plate sidewall.

[0013] S4. The stacked plate is processed according to the curve structure corresponding to the final metasurface boundary expression to obtain the metasurface stacked plate;

[0014] S5. According to the probe unit spacing of the linear array probe, the metasurface stack plate is bonded to obtain a buffer rod, which is placed between the linear array probe and the defect test block for detection to obtain full matrix data. Then, the defect test block is fully focused imaged using a dual-media imaging algorithm.

[0015] Specifically, the expression for the metasurface boundary of the sidewall in step S1 is shown in formula (1):

[0016] (1),

[0017] in, For the first amplitude, For the second amplitude, The m-th amplitude; For the first phase, For the second phase, For the m-th phase; For the first cycle, For the second cycle, The first period multiple factor indicates that the first period and the second period are integer multiples of each other; For the m-th period, is the (m-1)th period multiple factor, indicating that the first period and the mth period are integer multiples of each other; m is the polynomial order; x represents the x-coordinate of the hypersurface boundary point; y represents the y-coordinate of the hypersurface boundary point.

[0018] Specifically, the evaluation function of the metaheuristic algorithm test model is shown in formula (3):

[0019] (3),

[0020] Where f(x) is the model convergence value. Since the convergence of the model is evaluated, the smaller the value, the higher the convergence. Var represents the variance of solving the formula in [].

[0021] Specifically, the optimal meta-heuristic algorithm is a mutated basketball team optimization algorithm.

[0022] Specifically, step S3 includes the following steps:

[0023] S31. An example of writing an optimal metaheuristic algorithm using MATLAB, wherein the example provides the initial optimization parameter combination for the metasurface boundary expression;

[0024] S32. Then, it is linked with COMSOL, and the boundary structure is changed to the optimized structure constructed by the initial optimization parameter combination, and then the simulation is performed.

[0025] S33. After the simulation is completed, the time-domain signal of the simulation result is exported through MATLAB and the objective function value is calculated. Then, the time-domain signal and the objective function value are fed back to the program instance. The program instance then automatically predicts the next batch of optimized parameter combinations based on the time-domain signal and the objective function value, and increments the iteration number by 1.

[0026] S34. If the number of iterations is greater than the preset maximum number of iterations, proceed to step S35; otherwise, proceed to step S32 and replace the initial optimal parameter combination in step S32 with the next batch of optimized parameter combinations in step S33.

[0027] S35. Obtain the optimal parameter combination corresponding to the optimal solution in all iteration results as the optimal parameter combination, and construct the final metasurface boundary expression of the metasurface stack sidewall based on the optimal parameter combination.

[0028] Specifically, the objective function is the root mean square error between the time-domain waveform amplitude of the optimized structure and the time-domain waveform amplitude of the ideal absorbing boundary.

[0029] Specifically, the dual-medium imaging algorithm includes: first, extracting the complex analytic signal of the imaging point at the theoretical arrival time of each transmit-receive channel of the phased array and estimating the instantaneous phase; then, mapping the instantaneous phase to a unit phase vector according to the phase ring statistical vector and summing them in the channel dimension to obtain a synthetic phase vector; then, using the phase consistency index obtained by normalizing the magnitude of the synthetic phase vector as the coherence weight of the imaging point; and then, weighting and suppressing the amplitude superposition result according to the coherence weight to obtain the signal amplitude after phase ring statistical vector weighting; and finally, performing envelope detection, normalization, thresholding, and chromaticity mapping on the signal amplitude after phase ring statistical vector weighting to obtain a fully focused image.

[0030] Specifically, the process first extracts the complex analytical signal of the imaging point at the theoretical arrival time of each transmit-receive channel of the phased array and estimates the instantaneous phase. Then, based on the phase ring statistical vector, the instantaneous phase is mapped to a unit phase vector and summed over the channel dimension to obtain the synthetic phase vector as shown in formulas (9)-(10):

[0031] According to Euler's formula, the Hilbert transform signal components It can be represented as

[0032] (9),

[0033] in, For the full matrix signal components, i represents the number of channels, and j represents the time of the sampled signal; For Hilbert transform signal components; For the full matrix signal components The model after Hilbert transformation; The component phase angle represents the signal components of the entire matrix. The corresponding phase angle; Represents the imaginary unit; cosine Sine These are the component phase angles. The phase information of the real and imaginary parts; e is the natural base;

[0034] Based on the phase ring statistical vector, the component phase angle in formula (9) is... If we consider the sample points as randomly distributed and sum them vectorwise on the complex plane, then the composite phase vector of the phase angles in the full matrix data is... for:

[0035] (10)

[0036] in, represents the phase angle of different sample points; M is the number of sample points in the full matrix data; m is the index of the phase angle.

[0037] Specifically, the phase consistency index obtained by normalizing the magnitude of the synthesized phase vector is shown in formula (11):

[0038] The unit vector of the phase angle in the complex plane at time t can be expressed as:

[0039] (11),

[0040] in, The composite vector of phase angles The component with index m; For mold taking operation; is a unit vector of phase angles, used to characterize the phase consistency index at time t.

[0041] Specifically, the signal amplitude after weighting the amplitude superposition result according to the coherence weight is as shown in formula (12):

[0042] (12),

[0043] in, is the signal amplitude after weighting by the phase ring statistical vector; n is the number of phased array elements.

[0044] This invention proposes a full-focus imaging method based on a metasurface improved stacked plate buffer rod, comprising: S1, constructing a metasurface boundary expression for the sidewall of the metasurface stacked plate according to a family of sine functions; S2, constructing a metaheuristic algorithm test model based on the metasurface boundary expression, setting an evaluation function for the metaheuristic algorithm test model, and then testing the metaheuristic algorithm test model sequentially based on multiple metaheuristic optimization algorithms, selecting the metaheuristic optimization algorithm with the smallest evaluation function value as the optimal metaheuristic algorithm; S3, constructing the final metasurface boundary expression for the sidewall of the metasurface stacked plate by performing optimization simulation based on COMSOL according to the optimal metaheuristic algorithm and the metasurface boundary expression; S4, processing the stacked plate according to the curve structure corresponding to the final metasurface boundary expression to obtain a metasurface stacked plate; S5, bonding the metasurface stacked plate according to the probe unit spacing of the linear array probe to obtain a buffer rod, placing it between the linear array probe and the defect test block for detection to obtain full matrix data, and then performing full-focus imaging on the defect test block through a dual-medium imaging algorithm. This invention overcomes the limitation of trailing wave artifacts in traditional full-focus imaging of buffer bars. Combining the advantages of acoustic metasurface technology and metaheuristic algorithms, this invention chooses to combine semi-empirical parameterization methods with metaheuristic search. By parameterizing the sidewall structure and combining it with time-domain finite element simulation and experimental verification, it achieves directional suppression of trailing waves in stacked plates and engineered manufacturable design. This approach of the invention can maintain the physical interpretability of the design and automatically search for the metasurface structure parameter set that performs optimally under the constraints of the actual working environment, thus providing a more feasible design scheme for the widespread application of stacked plate buffer bars in phased array non-destructive testing.

[0045] Furthermore, this invention applies the advantages of metasurface wavefront modulation to full-focus imaging, which can reduce the impact of trailing wave artifacts on full-focus imaging. Compared with other ultrasonic buffer rod imaging methods, the trailing wave is significantly weakened, artifacts are almost completely eliminated, defect contrast is significantly improved, positioning accuracy is higher, and it is more suitable for online monitoring under conditions where the buffer rod volume is limited.

[0046] Furthermore, the dual-medium imaging algorithm of the present invention significantly improves the signal-to-noise ratio and positioning reliability of imaging without significantly increasing computational complexity. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1This is a schematic diagram of a full-focus imaging method based on a metasurface-modified stacked plate buffer rod according to an embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of the dual-media imaging mechanism provided in an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of a single-layer stacked board simulation model provided according to an embodiment of the present invention;

[0051] Figure 4 This is a schematic diagram of a wave field snapshot provided according to an embodiment of the present invention; wherein, Figure 4 (a) in the image is a snapshot of the wave field at 7.1 μs; Figure 4 (b) in the image is a snapshot of the wave field at 14 μs; Figure 4 (c) in the figure is a snapshot of the wave field at 21.4 μs;

[0052] Figure 5 This is an A-wave time-domain signal diagram provided according to an embodiment of the present invention; wherein, Figure 5 (a) in the figure is the time-domain signal diagram of the wave field of the stacked plate; Figure 5 (b) in the figure is a time-domain signal diagram of the wave field using an absorbing boundary;

[0053] Figure 6 This is a schematic diagram comparing the root mean square error of the metasurface boundary and the normal boundary according to an embodiment of the present invention;

[0054] Figure 7 This is a schematic diagram comparing the convergence of evaluation functions under different parameter combinations of various metaheuristic algorithms provided in embodiments of the present invention; wherein, Figure 7 (a) in the figure is a schematic diagram comparing the convergence of the evaluation functions obtained without phase for the four parameters; Figure 7 (b) in the diagram is a comparison of the convergence of the evaluation function obtained with six parameters including phase; Figure 7 (c) in the diagram is a comparison of the convergence of the evaluation function obtained without phase for the six parameters; Figure 7 (d) in the figure is a schematic diagram comparing the convergence of the evaluation function obtained with nine parameters including phase;

[0055] Figure 8 This is a graph showing the optimization results of the MBTOA algorithm provided according to an embodiment of the present invention; wherein, Figure 8 (a) in the figure represents the convergence curve of the MBTOA algorithm. Figure 8 (b) in the figure is a schematic diagram of the optimal metasurface stack structure found by the MBTOA algorithm under 200 budgets; Figure 8 (c) in the diagram is a schematic diagram of a traditional stacked plate structure;

[0056] Figure 9This is a time-domain waveform comparison analysis diagram of a metasurface stacked board and a conventional stacked board according to an embodiment of the present invention;

[0057] Figure 10 This is a schematic diagram of a phased array experimental platform provided according to an embodiment of the present invention;

[0058] Figure 11 This is a schematic diagram of full-focus imaging according to an embodiment of the present invention; wherein, Figure 11 (a) shows the full-focus imaging effect based on metasurface stacked plates; Figure 11 (b) in the image shows the full-focus imaging effect of a traditional stacked plate. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the scope of protection of the present invention.

[0060] In the description of this invention, the use of terms such as "first," "second," etc., is for the purpose of distinguishing technical features only and should not be construed as indicating or implying relative importance, or implicitly indicating the number of technical features indicated, or implicitly indicating the order of the technical features indicated.

[0061] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, etc., are based on the orientation or positional relationship shown in the drawings and are only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0062] In the description of this invention, it should be noted that, unless otherwise explicitly defined, terms such as "setting," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0063] Example 1.

[0064] The reverse design path of metasurfaces can be divided into topology optimization, data-driven machine learning / deep learning methods, and semi-empirical parameterized design methods. Among them, semi-empirical parameterized design has natural advantages for engineering implementation and rapid iteration due to its controllable design space, low computational burden, and ease of incorporating engineering priors. Given the computational infeasibility of full-factor parameter scanning in multi-parameter, wide-range design spaces, and the limitations of pure data-driven methods in terms of sample requirements and interpretability, this invention combines semi-empirical parameterized methods with metaheuristic search. By parameterizing the sidewall structure and combining it with time-domain finite element simulation and experimental verification, it achieves directional suppression of tail-following waves in stacked plates and engineered manufacturable design. This approach maintains the physical interpretability of the design and can automatically search for the optimal set of metasurface structure parameters under actual working environment constraints, thus providing a more feasible design scheme for the widespread application of stacked plate buffer bars (buffer bars composed of stacked plates) in phased array non-destructive testing.

[0065] refer to Figure 1 This embodiment provides a full-focus imaging method based on a metasurface-modified stacked plate buffer bar, including the following steps:

[0066] S1. Verify that the trailing wave originates from reflections on both sides of the stacked plate, and construct the metasurface boundary expression of the metasurface stacked plate sidewall based on the family of sine functions.

[0067] according to Figure 2 As shown in the imaging mechanism diagram, each plate in the stacked plate is separated by air, therefore there is no signal propagation between each thin plate, and each thin plate can be regarded as an independent entity. To further reveal the cause of the trailing wave in traditional buffer rod imaging, this invention constructs a simulation model of a single-layer stacked plate, as follows: Figure 3 As shown, the time-domain propagation process of the sound field was numerically simulated using stainless steel in the solid mechanics module of COMSOL simulation software. A snapshot of the wave field is shown below. Figure 4 As shown:

[0068] like Figure 4 As shown in (a), at 7.1 μs, the main wave reaches the bottom of the discrete structure, and two clear oblique wave lines are visible in the figure (marked with red arrows, omitted hereafter. The yellow arrows indicate the waveguide propagation direction). These oblique wave lines are the propagation paths formed by the refraction and reflection of the wave when it encounters an uneven or tilted interface. They propagate along the inside of the buffer rod at a certain incident angle and produce periodic reflections and interference, thus forming multipath propagation channels and introducing time delay and phase distortion. This type of multipath effect can easily produce delay artifacts in full-focus imaging and interfere with the identification of defect echoes.

[0069] like Figure 4As shown in (a) of the figure, at 14 μs, the main wave approaches the top of the single stacked plate. During propagation, due to interface reflection and scattering, a distinct waveform reaches the bottom of the stacked plate, marked by a red arrow in the figure. This waveform is the trailing wave. The trailing wave is a continuous low-amplitude wave train formed by the main wave undergoing multiple reflections and scatterings inside the structure. It has strong time delay characteristics and is prone to overlapping with the main echo or other signals in the time domain.

[0070] like Figure 4 As shown in (c), at 21.4 μs, the trailing wave reaches the top of the stack, i.e., the signal receiving point, while the main wave peak reaches the bottom of the stack.

[0071] To quantitatively verify the source of the trailing wave, we analyzed the received signal starting with the time-domain waveform, and the results are as follows: Figure 5 As shown in (a) of the diagram, a small, raised peak appears between the first and second bottom echoes, coinciding with the arrival time of the trailing wave at the top. To verify that the main cause of the trailing wave is reflection from the sidewalls of the stacked plate, absorbing boundaries were added to both sides of the stacked plate simulation model, and their time-domain waveforms are shown in Figure 1. Figure 5 As shown in (b) of the diagram. The results show that the trailing wave signal is significantly weakened after the addition of the absorbing boundary, with only a weak disturbance remaining. This disturbance is due to residual reflection caused by incomplete absorption at the boundary. This indicates that the sidewall reflection wave is one of the core mechanisms constituting the trailing wave, and its effective absorption and control are key to optimizing the design of the ultrasound total focusing imaging system.

[0072] COMSOL simulation software is a multiphysics simulation software developed by COMSOL Corporation. The software adopts a modular design and provides a large number of additional modules in addition to the basic modules, such as structural mechanics, heat transfer, fluid flow, chemical reaction engineering, AC / DC, RF, microelectromechanical systems and acoustics modules, to expand its analytical capabilities in various engineering fields.

[0073] Based on the above understanding, this invention employs metasurface structures to modulate or absorb the signal amplitude of trailing waves. To reduce design space while considering scalability and implementation efficiency, this study adopts a semi-parametric empirical design strategy, pre-selecting curved structures as the primary scheme for the sidewalls of the metasurface stack. Candidate forms of the sidewall structures can include polygonal lines, arbitrary curves, or other complex boundaries; among the selectable families of curves, the trigonometric function family has strict periodicity and good splicing ability, which allows local structural designs to be easily extended to longer boundary segments or the entire array, thereby significantly saving time and computational cost in the design and optimization of metasurface stack structures.

[0074] Therefore, based on the above analysis results, this embodiment constructs the metasurface boundary expression of the sidewall of the metasurface stacked plate. The metasurface boundary expression of the sidewall of the metasurface stacked plate adopts the form of a family of sine functions, as shown in formula (1):

[0075] (1),

[0076] in, For the first amplitude, For the second amplitude, The m-th amplitude; For the first phase, For the second phase, For the m-th phase; For the first cycle, For the second cycle, The first period multiple factor indicates that the first period and the second period are integer multiples of each other; For the m-th period, is the (m-1)th period multiple factor, indicating that the first period and the mth period are integer multiples of each other; m is the polynomial order; x represents the abscissa of the hypersurface boundary point; y represents the ordinate of the hypersurface boundary point; the line formed by x and y draws the specific structural shape of the sidewall of the stacked plate, which constitutes the hypersurface boundary expression of the sidewall in this embodiment.

[0077] The expression here resembles a curve of a sine function, usually represented by a sin function, which has phase, period, and amplitude. However, this expression uses a family of functions, not a single sin function. Different parameter values ​​in the formula will result in different curve shapes, and the values ​​can be randomly defined. The goal is to find the expression that minimizes the amplitude of the trailing wave signal. Initially, preliminary adjustments to the parameters are made to find a suitable range.

[0078] S2. Construct a metaheuristic algorithm test model based on the metasurface boundary expression, and set the evaluation function of the metaheuristic algorithm test model. Then, test the metaheuristic algorithm test model in sequence based on multiple metaheuristic optimization algorithms, and select the metaheuristic optimization algorithm with the smallest evaluation function value as the optimal metaheuristic algorithm.

[0079] In the COMSOL simulation environment, this invention replaces the straight boundary of the traditional stacked plate sidewall with a curved boundary generated by the metasurface boundary expression of the sidewall in formula (1) to construct a meta-inspired test model. By parametrically adjusting the geometric parameters such as amplitude and period of the curve, batch simulations of different parameter combinations are performed using the secondary development interface of COMSOL to obtain the corresponding waveform response. For each set of parameters, after extracting the time-domain waveform at the signal receiver, the root mean square error (RMSE) between the waveform of the metasurface stacked plate and the reference waveform of the ideal absorption boundary under the parameter is calculated according to formula (2) as an evaluation index to evaluate the absorption effect of the metasurface structure on the trailing wave. Formula (2) is shown below:

[0080] (2),

[0081] In the formula, The time-frequency values ​​represent the waveforms of the metasurface stacked plate. represents the time-frequency value of the reference waveform for the ideal absorption boundary; s represents the number of sampling points. As can be seen from the expression, the smaller the RMSE value under different parameter combinations, the closer the boundary of this series is to the ideal absorption boundary.

[0082] COMSOL simulation yields waveform signals of the ideal absorbing boundary and a sinusoidal series (i.e., the time-frequency values ​​of the metasurface stack waveform). Time-frequency values ​​of the reference waveform at the ideal absorption boundary (The time-frequency values), calculate the root mean square error between the two, and the smaller the error, the closer the two waveforms are;

[0083] This metasurface boundary can alter the interaction and interference modes of guided waves at the boundary, thereby weakening reflections from the sidewalls and enabling control of the trailing wave. Preliminary parameter scan results ( Figure 6 The results show that, under most parameter combinations, stacked plates with metasurface boundaries can achieve a smaller root mean square error compared to conventional straight boundaries, indicating that adjusting the boundary structure can effectively reduce sidewall reflections and improve trailing wave characteristics.

[0084] This invention uses COMSOL Multiphysics commercial software to numerically simulate a single thin plate of a stacked plate. The solid mechanics module is used for wave field simulation. The plate dimensions are 25×0.5×40mm, the material is stainless steel, and a plane stress model is used to approximate the plate thickness. The excitation signal is a three-period Hanning window pulse. Both the transmit and receive positions are located at the center of the stacked plate, and a linear excitation method is used to simulate the signal transmission and reception of phased array elements, thereby reproducing the signal reception within the stacked plate during total focusing imaging.

[0085] Since the parameter space of the sine function family is relatively large, and a single COMSOL simulation takes about 25 minutes, it is obviously not feasible to exhaustively search for each parameter of the test model of the metaheuristic algorithm one by one. Therefore, a metaheuristic algorithm that has performed well in parameter optimization in recent years is introduced to efficiently find the parameter combination that minimizes the amplitude of the trailing wave (the parameter combination that maximizes the signal variance), thereby changing the propagation characteristics of the trailing wave and reducing the amplitude at the receiver during the stacked board imaging process.

[0086] Metaheuristic algorithms are an improvement on heuristic algorithms. They are general optimization algorithms that search the solution space using heuristic methods. They combine random algorithms and local search strategies to find approximate optimal solutions to optimization problems within acceptable computational costs.

[0087] In order to select a suitable metaheuristic algorithm, an evaluation function as shown in formula (3) is constructed for comparative analysis. The goal is to compare the magnitude of the variance of the expression under each parameter combination and take the negative value so as to observe the convergence process more intuitively in the convergence plot.

[0088] The evaluation function of the metaheuristic algorithm test model described in this embodiment is shown in the following formula:

[0089] (3),

[0090] Where f(x) is the value of the evaluation function, the smaller the value, the better the metaheuristic algorithm's optimization effect; Var represents the variance of the expression in []. The minus sign is to make the convergence graph trend downward, which is convenient for subsequent observation of the convergence of different metaheuristic algorithms.

[0091] The metaheuristic algorithm test model is designed to determine which metaheuristic algorithm finds the parameter value that minimizes the variance of the evaluation function under the same number of iterations. In other words, the metaheuristic algorithm test model aims to find the minimum value of the evaluation function (with a negative sign).

[0092] Metaheuristic algorithms can search for the parameter combination that maximizes the variance of the signal of the family of sinusoidal functions, thereby changing the propagation characteristics of the trailing wave and reducing the amplitude at the receiver during the imaging process of stacked plates.

[0093] In this embodiment, eight metaheuristic optimization algorithms were selected and compared under the same model, namely the metaheuristic algorithm test model. Each metaheuristic optimization algorithm was run repeatedly 30 times on the same model, and the evaluation process of the evaluation function was recorded and the arithmetic mean was calculated each time.

[0094] The various metaheuristic optimization algorithms include Rüppell's fox optimizer (RFO), Mantis Shrimp Optimization Algorithm (MShOA), Status-based Optimization (SBO), Superb Fairy-wren Optimization algorithm (SFOA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Basketball Team Optimization Algorithm (BTOA), and Mutation-based Basketball Team Optimization Algorithm (MBTOA).

[0095] RFO (Real Foraging Field) is inspired by the keen and agile group foraging behavior of the Lüper fox during both day and night. The algorithm uses the fox's vision, hearing, and smell as a prototype, mathematically simulating its reconnaissance, coordinated raiding, and terrain adaptation behaviors. It achieves a balance between global exploration and local exploitation through a perception-driven search operator. RFO emphasizes group cooperation and local raiding mechanisms, possessing broad global search capabilities and strong local refinement capabilities.

[0096] MShOA is inspired by mantis shrimp, a species living in nearshore waters with unique vision and rapid predation behavior. The algorithm mathematically characterizes three types of visual strategies based on detection signals: random navigation foraging, attack dynamics upon prey contact, and defense / retreat decisions. It balances the exploration and development of the solution space through strategy switching. MShOA's operator design emphasizes targeted jumps or refinements upon receiving a "signal," thus exhibiting robustness in multimodal and complex nonlinear problems, making it suitable for optimization scenarios requiring a balance between coarse and fine search.

[0097] SBO: This is a powerful algorithm inspired by the human desire for advancement. By simulating how individuals seek to approach, learn from, or acquire resources from high-status figures, SBO transforms these social patterns into robust computational methods for challenging optimization tasks.

[0098] SFOA draws inspiration from the splendid swan's behaviors in group reproduction, chick rearing, and predator avoidance to construct a search mechanism that incorporates strategies such as nest finding / breeding, chick feeding, and risk avoidance. By simulating the group's coordinated behavior during the breeding and chick rearing periods, as well as its avoidance and dispersal strategies when encountering threats, a balance is achieved between maintaining global diversity and refining local characteristics.

[0099] PSO (Progressive Search): Inspired by the social movement of flocks of birds and schools of fish, PSO treats candidate solutions as "particles." These particles adjust their movement strategies based on their own experience and the experience of the group (or local neighborhood) to search for the optimal solution. The algorithm works collaboratively through three mechanisms: inertia (maintaining the current trend of movement), individual learning (approaching its own historical best), and social learning (approaching the group's historical best). Common practices include gradually reducing inertia with iterations to transition from exploration to utilization, or using local neighborhood structures to enhance diversity.

[0100] DE is a population-based evolutionary search method that constructs mutation candidates by utilizing the differences between individuals in the population and combines crossover and greedy selection to retain better solutions.

[0101] BTOA: A kinematic metaheuristic algorithm inspired by the mechanics of basketball games. BTOA maps concepts such as high-intensity training, fast breaks (rapid breakthroughs), dynamic positioning (player position adjustments), and ball re-entry after going out of bounds (reset mechanism) in games to various cooperative search operators, thereby achieving refinement, rapid jumps, local fine-tuning, and diverse resets of solutions at different search stages.

[0102] MBTOA is an improved version of BTOA. After the ball goes out of bounds and re-enters the system, it introduces the mutation operation of differential evolution. Its core function is to improve the diversity of the population, avoid premature convergence, and enhance the global exploration capability when the local search gets stuck.

[0103] Based on prior simulations, in this embodiment, the range of variable values ​​is set as: phase (including the first phase). Second phase ... and the mth phase The value range of ) is [0, 2π], and the amplitude (including the first amplitude) is... Second amplitude ... and the mth amplitude The value of ) is limited to [-1, 0], first period The value range is [10, 40]; the first period multiple factor ..., the multiplier factor of the (m-1)th period The range of values ​​is [2, 20]; correspondingly, the second period ..., the mth period The value range of x is [0.5, 20]; x takes 8000 points in the interval [0, 40], with the unit being millimeters, which makes the drawn sine curve smoother and more accurate.

[0104] A diagram comparing the convergence of evaluation functions under different parameter combinations in various meta-heuristic algorithms is shown below. Figure 7 As shown;

[0105] In the figure, the horizontal axis represents the cumulative number of evaluation function calls from the start of optimization to the current point, and the vertical axis represents the best model convergence value observed so far by each metaheuristic algorithm in the metaheuristic algorithm test model under the corresponding number of evaluations (the minimum value of the evaluation function during the iteration process).

[0106] As the algorithm iterates and the x-axis changes, the y-axis value gradually decreases. Different parameter values ​​in the expression will result in different variances. The goal is to determine which algorithm finds the parameter values ​​that minimize the variance of the evaluation function within the same number of iterations and x-axis constraints. The faster the curve approaches an approximate horizontal state, the faster its convergence speed; the smaller the value found, the smaller the convergence value.

[0107] The curve in the figure represents the arithmetic mean of 30 independent runs. The results show that, under this family of sine function test models, the Basketball Team Optimization Algorithm (BTOA) outperforms other comparable metaheuristic algorithms in terms of optimal model convergence.

[0108] Based on the analysis of the BTOA algorithm mechanism, a mutation strategy of differential evolution was introduced into the algorithm. The mutated basketball team optimization algorithm (MBTOA) further improved in terms of convergence speed and objective function value, showing faster convergence and better optimal value.

[0109] Furthermore, the convergence results show that the introduction of phase does not improve the convergence performance of each algorithm. Therefore, the expression of the hypersurface boundary of the sidewall can be further simplified. When the number of design parameters involved increases, the optimization efficiency will decrease significantly. In summary, in order to balance efficiency, m is taken as 2 in this embodiment, but it can also be modified according to actual needs.

[0110] Therefore, in this embodiment, the metasurface boundary expression of the sidewall is four parameters without phase, as shown in formula (4):

[0111] (4),

[0112] This step utilizes COMSOL Multiphysics finite element software to determine, through simulation analysis, that the main source of the trailing wave is the wave reflected from the sidewall of the stacked plate. From the perspective of time-domain waveform, the signal amplitude of the trailing wave is quantitatively analyzed. By setting a reasonable metasurface boundary structure, the signal amplitude of the trailing wave is reduced, and the absorption or suppression effect is achieved.

[0113] In this embodiment, the modified basketball team optimization algorithm MBTOA is used as the metaheuristic optimizer.

[0114] It is worth noting that the various metaheuristic optimization algorithms described in this embodiment include, but are not limited to, the Lüper fox optimization algorithm, the mantis shrimp optimization algorithm, the state-based optimization algorithm, the magnificent swan-warbler optimization algorithm, particle swarm optimization, differential evolution algorithm, basketball team optimization algorithm, and the mutated basketball team optimization algorithm;

[0115] Furthermore, the modified basketball team optimization algorithm MBTOA is not limited to the metaheuristic optimizer in this embodiment. In another possible implementation, other metaheuristic optimization algorithms of various kinds can be introduced and evaluated using the evaluation function shown in formula (3) on the metaheuristic algorithm test model. The modified basketball team optimization algorithm MBTOA can be replaced by a metaheuristic optimization algorithm whose optimal model convergence value is smaller than that of the modified basketball team optimization algorithm.

[0116] The modified basketball team optimization algorithm first determines the initial population ( , , , The algorithm generates several candidate parameter sets by constructing an initial population with a range of possible values. Simulations corresponding to each candidate parameter set are run in batches using COMSOL secondary development to obtain time-domain waveforms. Then, the fitness of each candidate parameter set is calculated based on a preset objective function, and the results are fed back to the optimizer. The algorithm generates the next generation of candidate parameter sets through its internal high-intensity training, fast attack, dynamic positioning, ball out-of-bounds re-entry, and mutation mechanisms, thereby improving fitness and iterating continuously towards the objective. This closed-loop process continues until a specified stopping condition is met, ultimately outputting the optimal or near-optimal parameter combination. , , , The preset objective function is defined as the root mean square error between the amplitude of the time-domain waveform of the optimized structure and the amplitude of the time-domain waveform of the ideal absorbing boundary. The smaller the root mean square error, the better the optimized structure under the parameter combination can meet the expected performance of the structure in actual needs.

[0117] S3. Based on the optimal meta-heuristic algorithm and the metasurface boundary expression, perform optimization simulation using COMSOL to obtain the optimal parameter combination of the metasurface boundary expression and construct the final metasurface boundary expression of the metasurface stack sidewall.

[0118] S31. An example of writing an optimal metaheuristic algorithm using MATLAB, wherein the example provides the initial optimization parameter combination for the metasurface boundary expression;

[0119] S32. Then, it is linked with COMSOL, and the boundary structure is changed to the optimized structure constructed by the initial optimization parameter combination, and then the simulation is performed.

[0120] The boundary structure is a specific geometric structure used for COMSOL simulation, and the initial state is a traditional straight line structure; the optimized structure is a specific geometric structure characterized by the hypersurface boundary expression constructed by the combination of initial optimization parameters;

[0121] S33. After the simulation is completed, the time-domain signal of the simulation result is exported through MATLAB and the objective function value is calculated. Then, the time-domain signal and the objective function value are fed back to the program instance. The program instance then automatically predicts the next batch of optimized parameter combinations based on the time-domain signal and the objective function value, and increments the iteration number by 1.

[0122] S34. If the number of iterations is greater than the preset maximum number of iterations, proceed to step S35; otherwise, proceed to step S32 and replace the initial optimal parameter combination in step S32 with the next batch of optimized parameter combinations in step S33.

[0123] S35. Obtain the optimal parameter combination corresponding to the optimal solution in all iteration results as the optimal parameter combination, and construct the final metasurface boundary expression of the metasurface stack sidewall based on the optimal parameter combination.

[0124] The objective function is the root mean square error between the time-domain waveform amplitude of the optimized structure and the time-domain waveform amplitude of the ideal absorbing boundary. Finding the hypersurface boundary expression that minimizes the root mean square error involves calculating the root mean square error (RMSE) of each solution, comparing it with the current optimal solution, and then converting the comparison result into fitness feedback to the optimizer and iterating continuously to find the parameter set that minimizes the root mean square error.

[0125] It is understood that the objective function value is the value calculated using the objective function.

[0126] In this embodiment, the preset maximum number of iterations is 200, which can be modified according to actual needs;

[0127] Example 1 exists, where the optimization budget (preset maximum number of iterations) is limited to 200 objective function evaluations. The final convergence curve is as follows. Figure 8As shown in (a) in the figure; the mutated basketball team optimization algorithm found the optimal solution in the 155th generation, and no better solution appeared in the subsequent 45 generations, indicating that the objective function was close to convergence. The final parameter combination was obtained based on the optimal solution. , , , The final metasurface boundary expression of the metasurface stack plate is obtained based on the optimal parameter combination, as shown in formula (5):

[0128] (5),

[0129] The expression curves are placed on both sides of the stacked board, replacing the straight boundaries of the traditional stacked board, such as... Figure 8 As shown in (b); Figure 8 (c) in the diagram is a schematic diagram of a traditional stacked plate structure.

[0130] Subsequently, to verify the actual absorption effect of the metasurface stacked plate on the trailing wave, its time-domain waveform was compared and analyzed. The RMSE obtained from the simulation results was used to judge the quality of the parameters. The results are as follows: Figure 9 As shown in the comparison results, although the metasurface stack cannot achieve the exact same waveform as the ideal absorption boundary, it can still effectively absorb and suppress trailing waves.

[0131] S4. The stacked plate is processed according to the curve structure corresponding to the final metasurface boundary expression to obtain the metasurface stacked plate;

[0132] Based on the curve structure corresponding to the final metasurface boundary expression, the two sides of the stacked plate are processed by laser cutting to obtain the metasurface stacked plate, such as... Figure 8 As shown in (b);

[0133] S5. According to the probe unit spacing of the linear array probe, the metasurface stack plate is bonded to obtain a buffer rod, which is placed between the linear array probe and the defect test block for detection to obtain full matrix data. Then, the defect test block is fully focused imaged using a dual-media imaging algorithm.

[0134] according to Figure 10 A phased array experimental platform was built based on linear array probes, buffer rods, defect test blocks, and phased array boards.

[0135] In this embodiment, to further verify the performance of the metasurface stack, the present invention uses a 128-element, 2 MHz linear array probe to conduct experimental tests on a stainless steel defect test block (the test block). The probe element length is 0.5 mm, the spacing is 0.1 mm, and the actual number of array elements used is 64.

[0136] Specifically, the linear array probe is a Dopler 2MHz-128 linear array probe; the phased array board is a Dopler 256 / 256 phased array board.

[0137] The experiment used full-focus imaging to compare and analyze the differences in imaging quality between metasurface stacked plates and traditional stacked plates.

[0138] The metasurface stacked plates obtained in step S3 are bonded together with double-sided adhesive tape with a thickness of 0.1 mm (corresponding to the probe unit spacing) to form a buffer rod, ensuring that each stacked plate corresponds one-to-one with the phased array element;

[0139] The phased array board is connected to the computer and the linear array probe respectively. The probe is connected to a buffer rod, which is placed between the linear array probe and the defect test block. Honey coupling agent is used to couple the buffer rod and the defect test block. After the linear array probe is turned on for detection, the computer control board sends a signal to the probe. The probe collects the echo signal and transmits it to the phased array board, which then transmits it to the computer. The computer contains pre-developed adaptation software. By adjusting the relevant imaging parameters in the software, the required full matrix data can be obtained. The full matrix data can be exported for post-processing development, and imaging can be performed using the dual-media imaging algorithm designed in this embodiment.

[0140] according to Figure 2 The imaging mechanism diagram shown illustrates how a suitable dual-medium imaging algorithm can be designed to process the full-matrix acquisition data. From the imaging mechanism, it can be seen that the sound waves within the stacked plates can be approximated as propagating longitudinally with no significant lateral propagation. Therefore, dual-medium full-focus imaging with stacked plates does not require a complex refraction tracking strategy based on refraction point search. For the first layer of the medium, the sound waves can be approximated as propagating in a straight line, and their time delay can be directly calculated by dividing the propagation distance by the speed of sound. The time delay calculation for the second layer is consistent with the mechanism of traditional single-layer media.

[0141] according to Figure 2 The propagation path of mid-sound waves, and the transmitting elements of the linear array probe. An ultrasonic signal is emitted, travels through the stacked plates and into the defective test block, propagates to the defect, reflects back as an echo, and is received by all array elements. The propagation path of the acoustic signal is as follows:

[0142] (6),

[0143] In the formula, d is the length of the propagation path; Indicates the height of the stacked plates; Indicates the height of the defect; Indicates the position of the receiving array element; i is the sequence number of the transmitting array element; j is the sequence number of the receiving array element; i=1,...,n; j=1,...,n; n is the number of phased array elements; the transmitting and receiving array elements are shared, one array element transmits and 64 array elements receive simultaneously;

[0144] Correspondingly, the total propagation time t of the sound wave signal under this propagation path is:

[0145] (7),

[0146] In the formula, The velocity of sound within the stacked plate is calculated based on the frequency-thickness product, as the plate is relatively thin and the waves propagating inside are guided waves. The sound velocity inside the defective test block, i.e., the sound velocity inside the stainless steel.

[0147] Ultrasonic total focusing imaging acquires full matrix data based on a phased array board, and then performs post-processing imaging on the full matrix data.

[0148] The experiment used Figure 10 The experimental platform shown collects full matrix data. The theoretical arrival time t of each transmitting / receiving array element is calculated for each pixel of the imaging grid according to equation (5). The array element signals are aligned and superimposed according to this time delay to obtain the composite amplitude of that point. The composite amplitude is then processed by envelope detection and normalization and displayed using chromaticity mapping. When the sound wave encounters a defect, the scattering generated significantly enhances the amplitude of the corresponding point, thereby achieving defect localization and visualization. Details are as follows:

[0149] By transmitting array elements Flattening all the received data yields an n*n full matrix of data. The amplitude expression of the corresponding scatterer echo signal in the full matrix is ​​shown in formula (8):

[0150] (8),

[0151] In the formula, This indicates that at time t, given by formula (7), the airflow from the channel... Take the position in the full matrix data OK The signal values ​​of the column are called the full matrix signal components (in the full matrix data, i represents the number of channels and j represents the time of the specific sampled signal). denoted as the amplitude of the defect echo signal; x and z are the coordinates of the scatterer (i.e., the defect, scatterer being a more technical term), where x is the abscissa of the scatterer and z is the ordinate of the scatterer; n is the number of phased array elements. Indicates to The Hilbert transform signal component obtained by performing the Hilbert transform is a complex analytic signal;

[0152] In traditional total focusing imaging, poor signal phase consistency leads to large-area artifacts and noise, reducing imaging accuracy and reliability. To mitigate this issue, a phase circular statistical vector weighting method is introduced into the imaging algorithm to achieve total focusing imaging.

[0153] According to Euler's formula, the Hilbert transform signal components It can be represented as:

[0154] (9),

[0155] in, For the full matrix signal components The model after Hilbert transformation; The component phase angle represents the signal components of the entire matrix. The corresponding phase angle; Represents the imaginary unit; cosine Sine These are the component phase angles. Phase information of the real and imaginary parts; e is the natural base; right side of the formula This represents the instantaneous phase.

[0156] Phase Circular Statistics Vector (PCSV) is a statistical measure built based on the circular distribution characteristics of signal phase. It is used to optimize ultrasound imaging quality by fusing phase consistency information with signal amplitude.

[0157] Based on the phase ring statistical vector, the component phase angle in formula (9) is... If we consider the sample points as randomly distributed and sum them vectorwise on the complex plane, then the composite phase vector of the phase angles in the full matrix data is... It can be represented as:

[0158] (10)

[0159] in, Let M be the phase angle of different sample points, M be the number of sample points in the full matrix data, and m be the index of the phase angle. This indicates that there are M composite phase vectors;

[0160] Then the unit vector of the phase angle on the complex plane at time t can be expressed as:

[0161] (11),

[0162] in, The composite phase vector of the phase angle The component with index m; For mold taking operation; The unit vector of phase angle is used to characterize the phase consistency index at time t. The closer its value is to 1, the more coherent the phase is, and the closer it is to 0, the more dispersed the phase is, thereby suppressing the signal amplitude at that time.

[0163] It is understandable that a full matrix signal component of the full matrix data... It corresponds to a phase angle; PCSV is equivalent to reducing all data in the full matrix data between (0, 1) according to the phase angle calculation, thereby suppressing incoherent noise.

[0164] When performing post-processing calculations for full-focus imaging, the values ​​of all channels and all times are calculated using equation (11), and this amplitude is weighted into the echo signal of the scatterer. This yields the signal amplitude expression after phase ring statistical vector weighting, as shown in equation (12).

[0165] (12),

[0166] in, The signal amplitude is the result of weighting the phase ring statistical vector.

[0167] The dual-medium imaging algorithm of this embodiment introduces a phase ring statistical vector weighting method to evaluate and utilize the phase consistency of each channel to improve imaging quality. The dual-medium imaging algorithm first extracts the complex analytic signal of the imaging point at the theoretical arrival time t of each transmit-receive channel of the phased array and estimates the instantaneous phase, as shown in formula (9). Then, it maps the instantaneous phase to a unit phase vector according to the phase ring statistical vector and sums them in the channel dimension to obtain a synthetic phase vector, as shown in formula (10). Then, it uses the phase consistency index obtained by normalizing the magnitude of the synthetic phase vector as the coherence weight of the imaging point, as shown in formula (11). As shown; the coherence weight can be directly used to weight the traditional amplitude superposition result to suppress the phase dispersion component, thereby coherently enhancing the phase-consistent defect scattering echo and suppressing the phase dispersion artifacts generated by interface reflection or multipath. Therefore, the signal amplitude after phase ring statistical vector weighting is obtained by weighting the amplitude superposition result according to the coherence weight, as shown in formula (12); then the signal amplitude after phase ring statistical vector weighting is subjected to envelope detection, normalization, thresholding and chromaticity mapping, and the phase consistency weight can be smoothed in the spatial domain as needed to reduce isolated noise, finally obtaining full-focus imaging, the effect is as follows. Figure 11As shown, the dual-medium imaging algorithm in this embodiment significantly improves the signal-to-noise ratio and positioning reliability of imaging without significantly increasing computational complexity.

[0168] refer to Figure 11 (a) and (b) in the text. Figure 11 (a) shows the full-focus imaging effect based on metasurface stacked plates; Figure 11 (b) shows the full-focus imaging effect of the traditional stacked plate. It can be seen that, under the same imaging parameters, the metasurface stacked plate significantly reduces the trailing wave, almost completely eliminates artifacts, significantly improves the defect contrast, and has higher positioning accuracy compared to the traditional stacked plate. It is more suitable for online monitoring when the buffer rod volume is limited.

[0169] To overcome the limitation of trailing wave artifacts in traditional full-focus imaging of buffer bars, this embodiment proposes a full-focus imaging method based on an improved stacked plate buffer bar with a metasurface, combining the advantages of acoustic metasurface technology and metaheuristic algorithms. The method includes: S1, constructing a metasurface boundary expression for the sidewall of the stacked plate based on a family of sine functions; S2, constructing a metaheuristic algorithm test model based on the metasurface boundary expression, setting an evaluation function for the test model, and then testing the test model sequentially using multiple metaheuristic optimization algorithms, selecting the metaheuristic optimization algorithm with the smallest evaluation function value as the final algorithm. The optimal metaheuristic algorithm is used; S3, based on the optimal metaheuristic algorithm and the metasurface boundary expression, the optimal parameter combination of the metasurface boundary expression is obtained through COMSOL optimization simulation to construct the final metasurface boundary expression of the sidewall of the metasurface stacked plate; S4, the stacked plate is processed according to the curve structure corresponding to the final metasurface boundary expression to obtain the metasurface stacked plate; S5, the metasurface stacked plate is bonded according to the probe unit spacing of the linear array probe to obtain a buffer rod, which is placed between the linear array probe and the defect test block for detection to obtain full matrix data, and then the defect test block is fully focused imaged through the dual-medium imaging algorithm. This embodiment chooses to combine the semi-empirical parameterization method with metaheuristic search, and achieves directional suppression of the tail follower wave of the stacked plate and engineering manufacturable design by parametric design of the sidewall structure and time-domain finite element simulation and experimental verification; this approach of the present invention can maintain the physical interpretability of the design and automatically search for the optimal metasurface structure parameter set under the constraints of the actual working environment, thereby providing a more feasible design scheme for the promotion and application of the stacked plate buffer rod in phased array non-destructive testing;

[0170] Furthermore, this embodiment applies the advantages of metasurface wavefront modulation to full-focus imaging, which can reduce the impact of trailing wave artifacts on full-focus imaging. Compared with other ultrasonic buffer rod imaging methods, the trailing wave is significantly weakened, artifacts are almost completely eliminated, defect contrast is significantly improved, positioning accuracy is higher, and it is more suitable for online monitoring under the condition of limited buffer rod volume.

[0171] Furthermore, the dual-medium imaging algorithm in this embodiment significantly improves the signal-to-noise ratio and positioning reliability of the imaging without significantly increasing computational complexity.

[0172] In addition, one embodiment of the present invention provides a control device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor and the memory can be connected via a bus or other means.

[0173] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0174] The non-transient software program and instructions required to implement the full-focus imaging method based on the metasurface improved stacked plate buffer rod in the above embodiments are stored in the memory. When executed by the processor, the full-focus imaging method based on the metasurface improved stacked plate buffer rod in the above embodiments is executed.

[0175] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; 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.

[0176] Furthermore, one embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions that are executed by a processor or controller, for example, by the processor of the above embodiment, such that the processor performs the full-focus imaging method based on metasurface improved stacked plate buffer rods in the above embodiment.

[0177] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as a microprocessor, such as a central processing unit, a digital signal processor, or software executed by a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0178] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A full-focusing imaging method based on a metasurface-modified stacked plate buffer bar, characterized in that, Includes the following steps: S1. Construct the metasurface boundary expression for the sidewall of the metasurface stack based on the family of sine functions; The expression for the metasurface boundary of the sidewall in step S1 is shown in formula (1): （1）， in, For the first amplitude, For the second amplitude, The m-th amplitude; For the first phase, For the second phase, This is the m-th phase; For the first cycle, For the second cycle, The first-cycle multiple factor; For the m-th period, is the period-1 multiplier factor; m is the polynomial order; x represents the x-coordinate of the hypersurface boundary point; y represents the y-coordinate of the hypersurface boundary point; S2. Construct a metaheuristic algorithm test model based on the metasurface boundary expression, and set the evaluation function of the metaheuristic algorithm test model. Then, test the metaheuristic algorithm test model sequentially based on multiple metaheuristic optimization algorithms, and select the metaheuristic optimization algorithm with the smallest evaluation function value as the optimal metaheuristic algorithm; the optimal metaheuristic algorithm is the mutated basketball team optimization algorithm. S3. Based on the optimal metaheuristic algorithm and the metasurface boundary expression, the optimal parameter combination of the metasurface boundary expression is obtained by performing optimization simulation based on COMSOL to construct the final metasurface boundary expression of the metasurface stack plate sidewall. Step S3 specifically includes the following steps: S31. An example of writing an optimal metaheuristic algorithm using MATLAB, wherein the example provides the initial optimization parameter combination for the metasurface boundary expression; S32. Then, it is linked with COMSOL, and the boundary structure is changed to the optimized structure constructed by the initial optimization parameter combination, and then the simulation is performed. S33. After the simulation is completed, the time-domain signal of the simulation result is exported through MATLAB and the objective function value is calculated. Then, the time-domain signal and the objective function value are fed back to the program instance. The program instance then automatically predicts the next batch of optimized parameter combinations based on the time-domain signal and the objective function value, and increments the iteration number by 1. S34. If the number of iterations is greater than the preset maximum number of iterations, proceed to step S35; otherwise, proceed to step S32 and replace the initial optimal parameter combination in step S32 with the next batch of optimized parameter combinations in step S33. S35. Obtain the optimal parameter combination corresponding to the optimal solution in all iteration results as the optimal parameter combination, and construct the final metasurface boundary expression of the metasurface stack sidewall based on the optimal parameter combination. S4. The stacked plate is processed according to the curve structure corresponding to the final metasurface boundary expression to obtain the metasurface stacked plate; S5. According to the probe unit spacing of the linear array probe, the metasurface stack plate is bonded to obtain a buffer rod, which is placed between the linear array probe and the defect test block for detection to obtain full matrix data. Then, the defect test block is fully focused imaged using the dual-medium imaging algorithm. The dual-medium imaging algorithm specifically includes: firstly, extracting the complex analytical signal of the imaging point at the theoretical arrival time of each transmit-receive channel of the phased array and estimating the instantaneous phase; then, mapping the instantaneous phase to a unit phase vector according to the phase ring statistical vector and summing them in the channel dimension to obtain a synthetic phase vector; then, using the phase consistency index obtained by normalizing the magnitude of the synthetic phase vector as the coherence weight of the imaging point; and then, weighting and suppressing the amplitude superposition result according to the coherence weight to obtain the signal amplitude after phase ring statistical vector weighting; and finally, performing envelope detection, normalization, thresholding, and chromaticity mapping on the signal amplitude after phase ring statistical vector weighting to obtain a fully focused image.

2. The method according to claim 1, characterized in that, The evaluation function of the metaheuristic algorithm test model is shown in formula (3): (3)， Where f(x) is the model convergence value, representing the convergence of the model; Var represents the variance of solving the formula in [].

3. The method according to claim 1, characterized in that, The objective function is the root mean square error between the amplitude of the time-domain waveform of the optimized structure and the amplitude of the time-domain waveform of the ideal absorbing boundary.

4. The method according to claim 1, characterized in that, First, the complex analytical signal of the imaging point is extracted at the theoretical arrival time of each transmit-receive channel of the phased array and the instantaneous phase is estimated. Then, the instantaneous phase is mapped to a unit phase vector according to the phase ring statistical vector and summed in the channel dimension to obtain the synthetic phase vector as shown in formulas (9)-(10): （9）， in, For the full matrix signal components, i represents the number of channels, and j represents the time of the sampled signal; For Hilbert transform signal components; For the full matrix signal components The model after Hilbert transformation; The component phase angle represents the signal components of the entire matrix. The corresponding phase angle; Represents the imaginary unit; cosine Sine These are the component phase angles. The phase information of the real and imaginary parts; e is the natural base; Based on the phase ring statistical vector, the component phase angle in formula (9) is... If we consider the sample points as randomly distributed and sum them vectorwise on the complex plane, then the composite phase vector of the phase angles in the full matrix data is... for: （10）， in, represents the phase angle of different sample points; M is the number of sample points in the full matrix data; m is the index of the phase angle.

5. The method according to claim 4, characterized in that, The phase consistency index obtained by normalizing the magnitude of the synthesized phase vector is shown in formula (11): The unit vector of the phase angle in the complex plane at time t can be expressed as: （11）， in, The composite vector of phase angles The component with index m; For mold taking operation; is a unit vector of phase angles, used to characterize the phase consistency index at time t.

6. The method according to claim 5, characterized in that, The signal amplitude after weighting the amplitude superposition result according to the coherence weight is obtained by weighting and suppressing the phase ring statistical vector, as shown in formula (12): （12）， in, is the signal amplitude after weighting by the phase ring statistical vector; n is the number of phased array elements.

Images