System and Adaptive Total Focusing Method

Abstract

A system and method for an adaptive total focusing method (TFM) includes the steps of: obtaining full matrix capture (FMC) data for a region of interest, which is a region of interest of an article and consists of material; evaluating the FMC data against parameters associated with material properties of the material for each of a set of cells in the article; associating the parameters for each of a set of cells with a material state model to determine the anisotropy state for each of the set of cells in the article; generating a distributed parameter model of the material anisotropy of the article based on the anisotropy state for each of the set of cells in the article; and operating a beamformer using TFM with the distributed parameter model to image the region of interest.

Description

[Technical Field]

[0001] [Cross-reference of related applications] This application incorporates, by reference, U.S. Provisional Application No. 63 / 591235, filed October 18, 2023, and claims its priority and interests.

[0002] This disclosure relates to ultrasound testing, and more particularly to systems and methods for adaptive total focusing methods (TFM). [Background technology]

[0003] There is some confusion between the terms "adaptive TFM" and "iterative TFM" below. Adaptive TFM is distinguished from iterative TFM as follows: Essentially, adaptive TFM modifies an established model of the material based on information derived from a full matrix capture (FMC) dataset before applying TFM beamforming. Iterative TFM, on the other hand, applies separate cycles of the TFM beamformer to identify and address geometric changes in the specimen. Geometric changes can be located at both the reflective and refraction interfaces.

[0004] TFM beamforming capability is problematic when applied to anisotropic materials. The difficulties arise from two main conditions. First, velocity dependence on the path to the reflector. The summation under these conditions leads to poor reconstruction of the reflector image. Second, differences between group and phase velocities that can occur in anisotropic materials. Differences in group and phase velocities result in a signal phase that is no longer coherent with the group. The TFM beamformer relies on the summation of analytical waveforms made possible by the Hilbert transform. The underlying assumption of TFM is that the phase always remains coherent with the group transmission. If the signal phase fluctuates within the group envelope, the TFM signal summation can result in poor performance.

[0005] Adaptive TFM is useful when the material's longitudinal and shear wave velocities are not known to the required degree of precision and / or are variable with respect to location and time within the test specimen. This situation represents a challenge posed by materials in which anisotropy is induced by environmental factors (heat, temperature gradient, molecular incorporation, radiation, etc.) and stress. [Overview of the project] [Means for solving the problem]

[0006] A first aspect of the present disclosure provides a method for an adaptive total focusing method (TFM), comprising the steps of: obtaining full matrix capture (FMC) data for a region of interest of an article, which is made of material; evaluating the FMC data against parameters associated with material properties of the material for each of a set of cells in the article; associating the parameters for each of the set of cells with a material state model to determine the anisotropy state for each of the set of cells in the article; generating a distributed parameter model of the material anisotropy of the article based on the anisotropy state for each of the set of cells in the article; and operating a beamformer using a TFM with the distributed parameter model to image the region of interest.

[0007] In some or all illustrative embodiments of the first aspect, the state model includes one or more Christoffel matrices.

[0008] In some or all illustrative embodiments of the first aspect, the state model includes establishing a range of velocities with respect to the direction of the material, from an isotropic state of the material to a fully anisotropic state of the material.

[0009] In some or all illustrative embodiments of the first aspect, the velocity range is established for longitudinal and shear waves.

[0010] In some or all illustrative embodiments of the first aspect, the generating step includes associating parameters with adjacent cells in a set of cells.

[0011] In some or all illustrative embodiments of the first aspect, the relating step includes solving for the orientation and magnitude of the phase and group velocity in the FMC data for each of the cells in a plurality of cells.

[0012] In some or all illustrative embodiments of the first aspect, the cell size in a plurality of cells is determined based on the gradient of anisotropy in adjacent cells in the plurality of cells.

[0013] In some or all illustrative embodiments of the first aspect, the method further includes the step of merging one of a plurality of cells with an adjacent cell of a plurality having corresponding parameters.

[0014] A second aspect of the present disclosure provides a system for an adaptive total focusing method (TFM) comprising at least one processor and a memory storing computer-readable instructions that, when executed by the at least one processor, cause the system to obtain full matrix capture (FMC) data for a region of interest of an article, which is made of material; evaluate the FMC data against parameters associated with material properties of the material for each of a set of cells in the article; associate the parameters for each of a set of cells in the article with a state model of the material in order to determine the anisotropy state for each of the cells in the article; generate a distributed-parameter model of the material anisotropy of the article based on the anisotropy state for each of the cells in the article; and operate a beamformer using a TFM with a distributed-parameter model to image the region of interest.

[0015] In some or all illustrative embodiments of the second aspect, the state model includes one or more Christoffel matrices.

[0016] In some or all illustrative embodiments of the second aspect, the state model comprises establishing a range of velocities with respect to direction for the material from an isotropic state of the material to a fully anisotropic state of the material.

[0017] In some or all illustrative embodiments of the second aspect, the range of velocities is established for longitudinal waves and shear waves.

[0018] In some or all illustrative embodiments of the second aspect, the computer-readable instructions, when executed by at least one processor, cause the system to associate parameters for adjacent cells in a set of cells.

[0019] In some or all illustrative embodiments of the second aspect, the computer-readable instructions, when executed by at least one processor, cause the system to solve for the orientation and magnitude of phase and group velocities in FMC data for each of the cells in the plurality of cells.

[0020] In some or all illustrative embodiments of the second aspect, the size of a cell in the plurality of cells is determined based on the gradient of anisotropic properties in adjacent cells in the plurality of cells.

[0021] In some or all illustrative embodiments of the second aspect, the computer-readable instructions, when executed by at least one processor, cause the system to merge one of the plurality of cells with an adjacent one of the plurality of cells having corresponding parameters.

[0022] Other aspects and features of the present disclosure may become apparent to those skilled in the art upon review of the following description of specific implementations of the present application in conjunction with the accompanying drawings. [Brief Description of the Drawings]

[0023] [Figure 1]FIG. 2 is a diagram illustrating a process for adaptive TFM according to an embodiment of the present disclosure. [Figure 2] FIG. 3 is a diagram showing a framework for a distributed-parameter model of a hypothetical configuration. [Figure 3] FIG. 6 is a flowchart of a method for adaptive TFM according to an embodiment of the present disclosure. [Figure 4A] FIG. 4A is a diagram showing a Cauchy tensor depicting tensile and shear vectors associated with a unit volume. [Figure 4B] FIG. 4B is a diagram showing a matrix representation of the Cauchy tensor of FIG. 4A. [Figure 5A] FIG. 5A is a diagram showing an example of a phase slowness curve for an isotropic material whose velocity remains constant regardless of direction. [Figure 5B] FIG. 5B is a diagram showing another example of a phase slowness curve for an anisotropic material whose velocity varies with direction. [Figure 6] FIG. 5C is a diagram showing phase and group velocity vectors plotted on a slowness curve, where the magnitude of variation is exaggerated for illustrative purposes. [Figure 7A] FIG. 7 is a diagram showing a hydrogenated isotropic sample on the left and the resulting A-scan in immersion mode. [Figure 7B] FIG. 8 is a diagram showing a hydrogenated anisotropic sample on the left and the resulting A-scan in immersion mode. [Figure 8A] FIG. 9A is a diagram showing an example of a solution condition. [Figure 8B] FIG. 9B is a diagram showing an example of a solution condition. [Figure 8C] FIG. 9C is a diagram showing an example of a solution condition. [Figure 9] FIG. 10 is a block diagram illustrating a system for adaptive TFM according to an embodiment of the present disclosure. [Mode for Carrying Out the Invention]

[0024] In the following, by way of example, reference is made to the accompanying drawings which illustrate exemplary embodiments of the present application.

[0025] Similar reference numerals may be used in different drawings to indicate similar components. Unless otherwise specified, items depicted in drawings are not necessarily drawn to a fixed proportion.

[0026] This disclosure is made with reference to the accompanying drawings illustrating embodiments. However, many different embodiments may be used, and for that reason the description should not be construed as being limited to the embodiments described herein. Rather, embodiments referenced herein are provided so as to be exhaustive and complete. Wherever possible, the same reference numerals and symbols are used in the drawings and the following description to refer to the same elements, and prime symbols are used to indicate similar elements, operations or steps in alternative embodiments. The separate boxes or exemplary separations of functional elements of the exemplary systems and devices do not necessarily require physical separation of such functions, as communication between such elements may occur via messaging, function calls, shared memory space, etc., without any such physical separation. For that reason, functions do not need to be implemented on physically or logically separated platforms, but such functions are illustrated separately herein for the sake of clarity. Different devices may have different designs, so that some devices implement some functions with fixed-function hardware, while others implement such functions with a programmable processor by code obtained from a machine-readable medium. Finally, elements mentioned in the singular form may also be plural unless explicitly indicated otherwise or implicitly by the context, and vice versa.

[0027] Adaptive TFM is useful when the material's longitudinal and shear wave velocities are not known to the required degree of precision and / or are variable with respect to location and time within the test specimen. This situation describes the challenges posed by materials in which anisotropy is induced by environmental factors (heat, temperature gradient, molecular incorporation, radiation, etc.) and stress.

[0028] Figure 1 illustrates a process for adaptive TFM according to one embodiment. The main inputs are obtained from the metallurgical research-Christoffel matrix section and the signal analysis section. These parts are described below. In the process, adaptive TFM is implemented as follows: An FMC dataset is acquired. The FMC dataset is then evaluated for parameters associated with material properties. The identified parameters are associated with a material model. The distributed parameter model is updated, and the TFM beamformer is applied.

[0029] Figure 2 shows a weld region 200 in article 204 to be tested. The weld region 200 is sensitized and exposed to an anisotropic induced species. Article 204 has isotropic or known material properties in the region 208 distal to the weld region 200. The material of article 204 exhibits variable anisotropic properties in the weld region 200. The method disclosed herein enables compensation for the anisotropic properties of the weld region 200 when testing article 204 for impurities, defects, etc., during non-destructive testing (NDT).

[0030] Figure 3 shows a method flow 300 for adaptive TFM according to one embodiment, illustrating a method corresponding to the process shown in Figure 1. Method flow 300 begins with the performance of a detailed metallurgical study of the material to be examined (step 310). The study has two objectives. The first objective is to establish a range of velocities with respect to the direction of the material, starting from its nominal state and going up to a fully anisotropic state. This requirement applies to both longitudinal and shear wave modes. The second objective is to determine the direction and rate or change (gradient) of anisotropy with respect to the geometry of the sample. The gradient and the expected potential change in the gradient over the operational life are inputs to the model. Alternatively, a predictive or statistical model of the material can be applied instead of a metallurgical study, if the properties over a range of states have been determined based on other efforts.

[0031] Next, a Christoffel matrix representation of the material is generated (step 320). The Christoffel matrix representation is a state model of the material that represents the range of material states from isotropic to anisotropic. Anisotropic properties in a material can arise from the operating environment due to the introduction of foreign species into the target material matrix. The concentration of foreign species may vary with exposure time and similarly with distance along the target component. Anisotropic behavior can propagate over time and eventually reach an equilibrium state. Similarly, the anisotropy gradient may vary with exposure time to the same environmental factor. The model is required to fit the range of anisotropy from the initial state to the maximum anisotropic state. The cell size and spacing in the model will be determined based on the gradient of anisotropic properties.

[0032] In this explanation, one or more Christoffel matrices are mentioned, but other types of state models may be used to represent the range of material states.

[0033] For material properties and wave propagation in an elastic medium, the general form of the elastic wave equation is expressed as follows:

[0034]

number

[0035] Furthermore, it can be shown that the material properties are related to wave propagation by either the first or second equation below.

[0036]

number

[0037] In the above equation, E is the tensile modulus, G is the shear modulus, and ρ is the material density.

[0038] Based on the aforementioned formula, the following relationship is established.

[0039]

number

[0040] In the above equation, V L and V S These are the longitudinal and shear phase velocities, respectively. If the material properties are constant with respect to direction, i.e., isotropic, then the phase velocity is the same as the group velocity.

[0041] In practice, the measured velocity is typically within the range of 0.995 ≤ Vn ≤ 1.005. Processing methods, heat treatment, slight changes in alloy composition, etc., often explain the nominal variation in velocity.

[0042] Once the Christoffel matrix representation of the material is obtained, cells in the region of interest of the article are selected (step 330). The region of interest can be a part of the article or the entire article. For example, in article 204 illustrated in Figure 2, the region of interest may include, for example, the weld region 200 and the surrounding region. The surrounding region may be selected to provide a buffer region at a specific distance from the weld region.

[0043] FMC data is obtained for the selected cells (step 340). The FMC data may be acquired before or during the execution of method flow 300. The FMC dataset can be acquired using conventional methods such as contact testing or local immersion testing. Any suitable conventional method for adaptive FMC / TFM data acquisition may be used. However, the selection of transducer parameters may be influenced by the distributed parameter model of material anisotropy developed to describe the inspection area. In this embodiment, the FMC data is acquired by the system, but in other embodiments, it may be received from another system.

[0044] The distributed-parameter model of the region of interest is then updated (step 350). The model evolution process begins with known properties, i.e., locations on the test specimen that have or are assumed to have nominal material properties. The distributed-parameter model is developed based on the maximum gradient of anisotropic properties identified in metallurgical studies and represented by the Christoffel matrix. The model cell size is determined by the step that will induce a change at the detection threshold, where the cell is in the region of maximum gradient. That is, the location of subsequent cells is determined based on the detected properties of the material. Cell attributes are key parameters obtained as solutions from the Christoffel matrix.

[0045] It is determined whether there are any cells remaining in the region of interest that should be analyzed (step 360). If there are any cells remaining that should be analyzed, the method flow 300 returns to step 330, and an adjacent cell is selected for analysis.

[0046] A distributed parameter model for a material is updated or adapted by evaluating an unknown cell directly adjacent to a known region. The process solves for the phase and group velocity and vector orientation and magnitude in the unknown cell, in a problem where the waveform passes through both the known region and the unknown cell. Note that a single measurement is insufficient to enable the determination of parameter values. There are two or more points on the slowness surface that can give a valid solution. The solution is constrained by giving separate estimates of complementary parameters from independent paths passing through the same cell and / or the same cell. This evaluation can be derived from the same wave mode passing through at an angle separated in direction from the first, or via complementary wave modes if the relationship between the two wave modes is known. As the cell is evaluated and found to have properties corresponding to the known region, the cell is encompassed within the known region and the model is obsolete. If the cell is separate from the known region, the model is updated with parameter values ​​determined in a path-dependent adaptation process.

[0047] In most situations, group velocities are observed in ultrasonic testing (UT) data acquired by inspection systems. Group and phase velocities are equal when the material is isotropic (in a given plane) or when there are maximum and minimum phase velocities. These conditions are known as stationary points. Under conditions where the phase velocity is neither maximum nor minimum, the difference between phase velocity and group velocity is observed as a shift in the signal phase with respect to the group. The detection of anisotropy depends on the nature of the influence and is material-specific. In some materials, the shear component may be the most affected, while in others, the longitudinal modes may be the most affected. To obtain a reliable assessment of material anisotropy, it is desirable that the detection strategy isolates the most sensitive mode.

[0048] Longitudinal group velocity measurements are typically obtained via autocorrelation methods over known distances. These measurements provide information in both the group velocity and phase vector directions. Similarly, shear wave group velocity can be measured relative to longitudinal group velocity via autocorrelation along the same path, assuming no phase change occurs for the group.

[0049] The velocity ratio between longitudinal and shear wave modes can be used to improve the sensitivity of anisotropic property detection to the detection of a change in one constituent velocity. Situations where tensile and shear moduli change in opposite directions are common. These occurrences have effects corresponding to longitudinal and shear wave velocities. Monitoring the ratio between the two modes allows for more immediate detection of property changes.

[0050] Another procedure for evaluating local changes in wave propagation properties is to detect and measure the amount of phase displacement within a group. A reference signal and a target signal are selected, and a Fourier transform is applied. The difference in the weighted phase components of the two signals gives the angle between the phase vector and the group vector. The phase velocity relative to the group velocity can also be calculated.

[0051] The Cauchy tensor or Cauchy matrix is an effective tool for representing the distribution of mechanical properties of a material. Based on information derived from metallurgical research, the representation of the material may be expressed in the form of a Cauchy tensor, as illustrated in Figure 4A. In the case of an isotropic material, the material properties are constant with respect to direction. All tensile elastic modulus values are equal, i.e., T1=T2=T3, and all shear elastic modulus values are equal, i.e., S 12 =S 21 =S 13 =S 31 =S 23 =S 32 . In the case of an anisotropic material, generally T1≠T2≠T3, and S 12 ≠S 21 ≠S 13 ≠S 31 ≠S 23 ≠S 32 .

[0052] Since the relationship between the elastic modulus and the corresponding velocity is established, it is possible to derive expressions for the longitudinal and shear velocities as a function of the tensile and shear elastic modulus values of the material. In the case of shear waves, the polarization for each shear wave vector is accounted for. The resulting expression is constructed in a notation known as the Christoffel matrix, an example of which is illustrated in Figure 4B, corresponding to the Cauchy tensor illustrated in Figure 4A. The inspection plane is not necessarily aligned with the principal axis of the Christoffel matrix, and may be displaced by any arbitrary angle relative to the basis of the Christoffel matrix. As the material properties evolve with increasing exposure to anisotropy-inducing species, the Christoffel representation will change accordingly. In other words, the Christoffel matrix represents one state of the material properties.

[0053] Slowness surfaces and their corresponding slowness curves are effective means of visualizing phase and group vectors. Phase slowness is defined as the reciprocal of the phase velocity. Faster wave modes are closer to the origin than slower wave modes. Wave slowness can be plotted as a three-dimensional volume. These three-dimensional shapes are known as slowness surfaces and can take various shapes, generally deformations of ellipsoids and spheroids, and in unique circumstances, can fold over themselves. Slowness curves represent the phase velocity with respect to a plane passing through the slowness surface. Nominally, the plane lies on the primary axes of the coordinate system, i.e., the XY, XZ, and YZ planes.

[0054] Figures 5A and 5B show examples of phase slowness curves. In particular, Figure 5A shows the phase slowness curve for isotropic materials where the velocity remains constant regardless of direction, and Figure 5B shows the phase slowness curve for anisotropic materials where the velocity varies with direction.

[0055] When dealing with anisotropic materials, four parameters are determined: the phase vector, phase velocity, group vector, and group velocity. The phase vector is an independent variable that represents the direction through which waves pass through the material. The group vector is the curl of the phase velocity perpendicular to the phase velocity slowness curve. In practice, the phase vector represents the direction of wave propagation, and the group vector represents the orientation of the wavefront. Based on the expansion of the two-dimensional elastic wave equation, the phase velocity in a medium can be expressed by the following equation:

[0056]

number

[0057] In the above equation, ω is the angular frequency expressed in radians / s, and k is the wave vector expressed in radians / m.

[0058] The above relationship holds for both longitudinal and shear wave modes when the vector quantity k is such that it is true.

[0059] The group velocity is defined by the following equation:

[0060]

number

[0061] The vectors and magnitudes for both the phase and the group can be visualized by plotting them on the slowness curve, as shown in Figure 6. The magnitude of the changes is exaggerated for illustrative purposes.

[0062] The hydrogenation isotropic sample is shown on the left side of Figure 7A, and the resulting vertical beam A-scan response in immersion mode is shown in the center of Figure 7A. Note that the phase remains consistent at each multiple. Following the interface signal, the first reflection is the reference signal. All subsequent other echoes have the same phase-group relationship as the reference signal. On the right side of Figure 7A, the grayscale time domain of a single pulse emitted from one element and received through multiple elements is represented.

[0063] The sample material is shown on the left of Figure 7B, and the resulting vertical beam A-scan response is shown in the center of Figure 7B. The A-scan in the center of Figure 7B represents the pitch-catch configuration. The scan includes both the LL signal and the LS signal. Note that the reference signal, the first LL reflection, maintains the same phase-group relationship as shown in Figure 7A, but the subsequent LS signal shows a significant change in the phase-group relationship. This behavior indicates anisotropy.

[0064] The results from the detection process for a given cell are applied equivalently to the corresponding variables in the Christoffel matrix, such as the L-wave phase vector, L-wave group velocity, and group-pair phase vector angle. The eigenvector solution identifies states corresponding to the degree of developed anisotropy, in addition to the orientation of the Christoffel matrix applicable to that cell. Each application has different degrees of freedom arising from the material properties. Furthermore, due to the symmetry of the Christoffel matrix, more than one potential solution may be valid for a cell. Further evaluation (input) is required to solve the conditions that encounter symmetry.

[0065] The process is repeated using parameters from both the underlying material and the solved cells, combined with parameters obtained from the FMC dataset to determine the properties of the new cells. If the cell parameters are unique, the cell is retained as a separate entity. If the parameters correspond to adjacent materials, the cell is contained within a neighboring adjacent structure. In this way, the model progression from the microstructure involves many small cells and leads to a structure of fewer but larger cells. The behavior of cell merging mimics the annealing of materials when exposed to high heat.

[0066] As described above, several independent paths are needed through the cell under consideration to identify the parameters linked to the material properties. Paths are considered independent if they do not traverse parallel or nearly parallel vectors, or if they are in different modes. Figure 8A illustrates the paths under poor conditions, where the L waves are nearly parallel vectors. Figure 8B shows the paths under good conditions, where the L waves are nearly perpendicular to each other. Figure 8C shows the paths under good conditions, where the L and S waves are in different modes.

[0067] This requirement imposes constraints on transducer pitch-array size, specimen geometry, scanning pattern, and any scanning constraints that may be encountered. If the material properties are highly sensitive to direction for all wave modes, or if the range of anisotropic behavior from the fundamental state to perfect anisotropy is remarkably wide, the degrees of freedom for solving various states increase. The range of degrees of freedom varies depending on the application.

[0068] Under adaptive TFM, the TFM process remains largely unchanged compared to the conventional TFM process. The transit time through each cell is governed by the group velocity relative to the vector being evaluated. The correct path conforms to Fermat's principle, i.e., the minimum transit time. If the group and phase velocities are not equal, a phase displacement occurs relative to the group. The difference between the phase and group velocities must be explained and compensated for before the summation of the phase displacements across any pixel through which the signal passes through this type of cell. Otherwise, the various phase displacements will sum up, resulting in poor imaging performance.

[0069] Alternatively, once all cells in the region of interest have been analyzed, the distributed parameter model of material anisotropy is complete, and the TFM beamformer can be operated to analyze the article (step 370).

[0070] Figure 9 shows various physical and logical components of an exemplary system 400 for adaptive TFM according to one embodiment of the present disclosure. While one embodiment of system 400 is illustrated and described below, other embodiments may be used to implement the examples disclosed herein and may include components different from those illustrated. Figure 9 shows a single instance of each component of system 400, but there may be multiple instances of each illustrated component.

[0071] The system 400 includes one or more processors 404, such as a central processing unit, a microprocessor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a dedicated logic network, a tensor processing unit, a neural processing unit, a dedicated artificial intelligence processing unit, or a combination thereof. One or more processors 404 may be collectively referred to as processor 404. The system 400 may include a display 408 for outputting data and / or information in some applications, but may not include one in some other applications.

[0072] The system 400 includes one or more memories 412 (collectively referred to as “memories 412”), which may include volatile or non-volatile memory (e.g., flash memory, random access memory (RAM), and / or read-only memory (ROM)). Non-temporary memory 412 may store machine-executable instructions for execution by processor 404. A set of machine-executable instructions 416 defining a process for adaptive TFM is shown to be stored in memory 412 and may be executed by processor 404 to perform steps of the method for adaptive TFM described herein. Memory 412 may also include other machine-executable instructions for execution by processor 404, such as machine-executable instructions for implementing the operating system and other applications or functions.

[0073] Memory 412 stores or can store metallurgical data 420 from metallurgical research. In addition, memory 412 stores material anisotropy distributed parameter models 420.

[0074] The processor 404 can be connected to the scanning array 424, enabling the processor 404 to control the scanning array 424 and acquire ultrasonic data from the region of interest of an object. The processor 404 can acquire TFM data, FMC data, etc., by controlling the scanning array 424. Alternatively, the processor 404 can be connected to an external scanning array and control the external scanning array to acquire data from the scanning array.

[0075] In some examples, system 400 may include one or more electronic storage units (not shown), such as solid-state drives, hard disk drives, magnetic disk drives, and / or optical disk drives. In some examples, one or more datasets and / or modules may be provided by external memory (e.g., an external drive connected to system 400 via wired or wireless communication) or by temporary or non-temporary computer-readable media. Examples of non-temporary computer-readable media include RAM, ROM, erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, CD-ROM, or other portable memory storage. The storage units and / or external memory may be used in conjunction with memory 412 to implement the data storage, retrieval, and caching functions of system 400.

[0076] The components of system 400 may communicate with each other, for example, via a bus. In some embodiments, system 400 is a distributed computing system and may include one or more optional additional components in addition to multiple computing devices communicating with each other through a network. Various operations described herein may be performed by different computing devices of the distributed system in some embodiments. In some embodiments, system 400 is a virtual machine provided by a cloud computing platform.

[0077] The steps (also referred to as operations or processes) in the flowcharts and drawings described herein are for illustrative purposes only. Many modifications are possible to these steps / processes without departing from the teachings of this disclosure. For example, the steps may be performed in a different order, or steps may be added, deleted, or modified as appropriate.

[0078] In other embodiments, the same techniques described herein can be used for other modalities.

[0079] [General principles] Through the description of the embodiments described above, the present invention may be implemented by using hardware alone, by using software and the necessary general-purpose hardware platform, or by a combination of hardware and software. Coding software to carry out the methods described above is within the scope of those skilled in the art who consider this disclosure. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product. The software product may be stored on a non-volatile or non-temporary storage medium, which may be an optical storage medium, a flash drive, or a hard disk. The software product includes several instructions that enable a computing device (personal computer, server, or network device) to perform the methods provided in the embodiments of this disclosure.

[0080] All values ​​and sub-ranges within the disclosed scope are also disclosed. Furthermore, while the systems, apparatus, and processes disclosed and illustrated herein may include certain elements, the systems, apparatus, and assemblies may be modified to include additional or subtractive such elements. Several examples of embodiments are described herein, but modifications, adaptations, and other implementations are possible. For example, elements illustrated in the drawings may be substituted, added, or modified, and the method examples described herein may be modified by substituting, rearranging, or adding steps of the disclosed method.

[0081] Features from one or more of the above embodiments may be selected to create alternative embodiments consisting of sub-combinations of features that do not need to be explicitly described above. In addition, features from one or more of the above embodiments may be selected and combined to create alternative embodiments consisting of sub-combinations of features that do not need to be explicitly described above. Features suitable for such combinations and sub-combinations will be immediately apparent to those skilled in the art in light of a consideration of the disclosure as a whole.

[0082] In addition, numerous specific details are provided to provide a complete understanding of the embodiments described herein. However, it will be understood by those skilled in the art that the embodiments described herein can be carried out without these specific details. Furthermore, well-known methods, procedures, and elements are not described in detail so as not to obscure the embodiments described herein. The subject matter described herein and in the enumerated claims is intended to encompass and include all appropriate technological variations.

[0083] Although the present invention and its advantages have been described in detail, it should be understood that various modifications, substitutions, and alterations can be made herein without departing from the present invention as defined by the appended claims.

[0084] The present invention may be embodied in other specific forms without departing from the subject matter of the claims. The embodiments described are to be considered merely illustrative and not restrictive in all respects. This disclosure is intended to encompass and include all appropriate technological variations. Accordingly, the scope of this disclosure is described more by the appended claims than by the foregoing description. The claims should not be limited by the embodiments described exemplary, but should be described in the broadest possible interpretation that fits the description as a whole. [Explanation of symbols]

[0085] 200 welding area 204 Goods 208 Distal region 400 System 404 Processor 408 displays 412 memory 416 Machine-Executable Instructions 420 Metallurgical Data 420 Material Anisotropy Distribution Constant Model 424 scanning array

Claims

1. A method for the Adaptive Total Focusing Method (TFM), The steps include obtaining full matrix capture (FMC) data for a region of interest consisting of a material, which is a region of interest of an article, A step of evaluating the FMC data with respect to parameters associated with the material properties of the material for each of a set of cells in the article, The steps of relating the parameters for each of the set of cells in the article to a state model of the material in order to determine the anisotropy state of each of the set of cells, A step of generating a distributed parameter model of the material anisotropy of the article using the anisotropy state for each of the set of cells in the article, The steps include operating a beamformer using a TFM based on the distributed parameter model to image the region of interest, Methods that include...

2. The method according to claim 1, wherein the state model includes one or more Christoffel matrices.

3. The method according to claim 2, wherein the state model includes establishing a range of velocities with respect to the direction of the material, from an isotropic state of the material to a completely anisotropic state of the material.

4. The method according to claim 3, wherein the velocity range is established for longitudinal waves and shear waves.

5. The method according to claim 1, wherein the generating step includes associating the parameters with adjacent cells in the set of cells.

6. The method according to claim 1, wherein the association step includes solving for the orientation and magnitude of the phase and group velocity in the FMC data for each of the cells in a plurality of cells.

7. The method according to claim 6, wherein the size of the cells in the plurality of cells is determined based on the gradient of anisotropy in adjacent cells in the plurality of cells.

8. The method according to claim 7, further comprising the step of merging one of the plurality of cells with an adjacent cell of the plurality having a corresponding parameter.

9. A system for the Adaptive Total Focusing Method (TFM), At least one processor, When executed by the at least one processor, the system The region of interest of an article is to obtain full matrix capture (FMC) data for the said region of interest, which consists of materials. Evaluating the FMC data with respect to parameters associated with the material properties of the material for each of a set of cells in the article, In order to determine the anisotropy state for each of the set of cells in the article, the parameters for each of the set of cells are associated with the state model of the material, Using the anisotropy state for each of the set of cells in the article, a distributed parameter model of the material anisotropy of the article is generated. A memory that stores computer-readable instructions to operate a beamformer using a TFM based on the distributed-parameter model to image the region of interest, A system that includes these features.

10. The system according to claim 9, wherein the state model includes one or more Christoffel matrices.

11. The system according to claim 10, wherein the state model includes establishing a range of velocities with respect to the direction of the material, from an isotropic state of the material to a completely anisotropic state of the material.

12. The system according to claim 11, wherein the velocity range is established for longitudinal waves and shear waves.

13. The system according to claim 9, wherein, when the computer-readable instruction is executed by the at least one processor, the system causes the system to associate the parameters with adjacent cells in the set of cells.

14. The system according to claim 9, wherein the computer-readable instruction, when executed by the at least one processor, causes the system to solve for the orientation and magnitude of the phase and group velocity in the FMC data for each of the cells in a plurality of cells.

15. The system according to claim 14, wherein the size of the cells in the plurality of cells is determined based on the gradient of anisotropy in adjacent cells in the plurality of cells.

16. The system according to claim 15, wherein, when the computer-readable instruction is executed by the at least one processor, the system causes one of the plurality of cells to merge with an adjacent cell of the plurality having the corresponding parameter.

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