Structural inconsistency detection using distance data

Abstract

To perform non-destructive inspection, more specifically, to detect structural inconsistency using non-destructive inspection and design distance data.SOLUTION: A scan surface that represents an inspection area of a structure is identified. A plurality of sample points on an outer surface identified from a model of the structure and a corresponding plurality of projected points on an inner surface identified from the model of the structure are generated by using the scan surface, a first geometric representation of the outer surface, and a second geometric representation of the inner surface. Distance data is computed by using the plurality of sample points and the corresponding plurality of projected points. The distance data identifies a distance between a pair of points formed of one sample point of the plurality of sample points and one corresponding projected point of the corresponding plurality of projected points. Sensor data generated for the inspection area of the structure is analyzed by using the distance data for detecting the presence of inconsistency in the structure.SELECTED DRAWING: Figure 7

Description

Technical Field

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[0001] The present disclosure generally relates to nondestructive inspection, and more specifically to detecting structural inconsistencies using nondestructive inspection and design distance data.

Background Art

[0002] Nondestructive inspection (NDI) is a testing and analysis technique used to evaluate the characteristics of a structure without damaging the structure. Nondestructive inspection may also be referred to as nondestructive testing (NDT), nondestructive examination method (NDE), and nondestructive evaluation (NDE). Evaluating data generated through currently available nondestructive inspection techniques can be difficult for large structures with complex shapes, such as fuselage structures, wing structures, and other types of aircraft structures. For example, ultrasonic devices may be used to generate measurements of a fuselage structure (e.g., distance measurements between different surfaces, thickness measurements, etc.). However, currently available methodologies may not provide an accurate and reliable method for analyzing these measurements with respect to the design data of the fuselage structure. Manual techniques for comparing such measurements with design data can be overly cumbersome and time-consuming. <​​​​​​​​​In one or more examples, the system comprises memory for storing machine-readable media containing machine-executable code, and a processor coupled to the memory. The processor is connected to the memory and configured to execute the machine-executable code to cause the processor to implement an analysis tool, the analysis tool being configured to: identify a scanning plane representing an inspection area of ​​a structure; generate a plurality of sample points on an outer surface identified from a model of the structure and a plurality of corresponding projection points on an inner surface identified from a model of the structure, using the scanning plane, a first geometric representation of the outer surface, and a second geometric representation of the inner surface; and calculate distance data using the plurality of sample points and the corresponding plurality of projection points, wherein the distance data identifies the distance between pairs of points formed by one sample point of the plurality of sample points and one projection point of the corresponding plurality of projection points.

[0005] In one or more examples, a method for calculating distance data for an aircraft structure is provided. A scanning plane representing the inspection area of ​​the aircraft structure is identified. Using a model of the aircraft structure, the outer surface of the aircraft structure and a set of inner surfaces of the aircraft structure are identified. Multiple sample points on the outer surface and, for each selected inner surface of the set of inner surfaces, multiple corresponding projection points on the selected inner surface are generated using the scanning plane, a first geometric representation of the outer surface, a second geometric representation of the selected inner surfaces, and a spatial indexing algorithm. Distance data is calculated using the multiple sample points and the multiple corresponding projection points generated for each selected inner surface of the set of inner surfaces. The distance data provides as-design data to be used when compared with sensor data generated for the aircraft structure to detect inconsistencies in the aircraft structure.

[0006] These features and functions can be realized individually in various embodiments of this disclosure, or in combination in yet another embodiment, which can be understood in further detail by referring to the following description and drawings.

[0007] Novel features that may be considered characteristics of exemplary embodiments are specified in the appended claims. However, exemplary embodiments, preferred modes of use, and further objectives and features thereof will be best understood by referring to the following detailed description of exemplary embodiments of this disclosure, together with the accompanying drawings. [Brief explanation of the drawing]

[0008] [Figure 1] This is a block diagram of an inspection system according to one or more exemplary embodiments. [Figure 2] This is a schematic diagram of a process for generating a scanning surface according to one or more exemplary embodiments. [Figure 3] This is a schematic diagram of a process for generating multiple sample points on the scanning plane from Figure 2, according to one or more exemplary embodiments. [Figure 4] This is a schematic diagram of a geometric representation generated using the structural model from Figure 1, according to one or more exemplary embodiments. [Figure 5] This is a schematic diagram of a relevant surface region identified from the geometric representation in Figure 4, according to one or more exemplary embodiments. [Figure 6] This is a diagram of a thickness map according to one or more exemplary embodiments. [Figure 7] This is a flowchart of a process for inspecting a structure, according to one or more exemplary embodiments. [Figure 8] This is a flowchart of the process for identifying a scanning plane, according to one or more exemplary embodiments. [Figure 9] This is a flowchart of a process for generating sample points according to one or more exemplary embodiments. [Figure 10] This is a flowchart of the process for generating a projection point, according to one or more exemplary embodiments. [Figure 11] This is a flowchart of a process for identifying relevant surface regions of a surface, according to one or more exemplary embodiments. [Figure 12] This is a flowchart of the process for generating a set of functions for a structure, according to one or more exemplary embodiments. [Figure 13] This is a flowchart of a process for identifying multiple scan points, according to one or more exemplary embodiments. [Figure 14] This is a flowchart of a process for calculating distance data for an aircraft structure, according to one or more exemplary embodiments. [Figure 15] This is a block diagram of a data processing system according to an exemplary embodiment. [Figure 16] This is a diagram illustrating a method for manufacturing and maintaining an aircraft according to an exemplary embodiment. [Figure 17] This is a block diagram of an aircraft according to an exemplary embodiment. [Modes for carrying out the invention]

[0009] The exemplary embodiments described below provide a method and system for generating as-design data to be used in conjunction with sensor data to detect the presence of structural inconsistencies. The as-design data includes as-design distance data, which identifies the distances between various surface layers within a structure as designed for that structure. As an example, for an aircraft structure, the as-design data may provide theoretical distances between the outer and inner mold lines of the aircraft structure at various locations along the aircraft structure.

[0010] The exemplary embodiments described in this book provide methods and systems for automating the process of calculating as-design data. These methods and systems result in cost and time savings compared to using currently available manual techniques. As-design data includes distance data that identifies the distance for each of several sample points. In some examples, these distances include thicknesses such as part thickness and material thickness.

[0011] For example, distance data may be calculated for each of several inspection areas designed for a structure. An inspection area can be a region, section, or zone of a structure designed for inspection in a single pass. Within this inspection area, an inspection device (e.g., a non-destructive testing device) may follow a pattern that includes multiple paths and multiple scanning locations along each path of those paths. In one or more examples, a sample point is a theoretical or abstract representation of the scanning location where non-destructive testing was performed or is being performed.

[0012] Furthermore, the exemplary embodiments described in this book provide a method and system for generating a function that allows the calculation of these types of design-defined distances for any location on a structure, regardless of the scanning pattern used to inspect the structure. This function may, for example, be a continuous function. Thus, an automated process for calculating distance data may not need to be repeated for various types of scanning patterns or scanning locations that have not been previously observed.

[0013] Referring here to the figure, Figure 1 is a block diagram of an inspection system according to one or more exemplary embodiments. The inspection system 100 may be used to inspect a structure 101. In one or more examples, the structure 101 is a composite structure, and the inspection system 100 is used to perform non-destructive testing of the composite structure.

[0014] The structure 101 can take any number of different forms. In one or more examples, the structure 101 takes the form of an aircraft structure. For example, the structure 101 can take the form of a fuselage structure 103 (e.g., a barrel fuselage structure). The fuselage structure 103 may be the entire fuselage of an aircraft, such as aircraft 1700 described below with respect to Figure 17, or a part of the fuselage. In other examples, the structure 101 may take the form of a wing structure, tail section, nose section, or control surface structure (e.g., flaps, stabilizers, ailerons, etc.). Thus, depending on the embodiment, the structure 101 may be a part, assembly, system, surface aggregate, or other type of structural entity.

[0015] In one or more examples, inspection system 100 includes sensor system 102 and analysis tool 104. Sensor system 102 may include one or more sensors used to generate sensor data 106. In one or more examples, sensor system 102 includes at least one non-destructive inspection (NDI) device. The NDI device may include one or more sensors for performing non-destructive inspection.

[0016] For example, without limitation, the NDI device may include an ultrasonic device (e.g., an ultrasonic transducer) for performing an ultrasonic inspection of structure 101. During the inspection, the ultrasonic device may move (or scan) over structure 101. The ultrasonic device may be separated from outer surface 105 of structure 101 by a coupling medium (e.g., oil) or water.

[0017] Sensor system 102 generates sensor data 106 used to determine whether there are inconsistencies in structure 101. As used herein, an inconsistency may be an undesirable feature or a feature outside the designed or selected tolerance range of structure 101. For example, an inconsistency may be a void, crack, a particular level of porosity, delamination, other types of inconsistencies, or a combination thereof. In one or more examples, sensor data 106 includes measurements or data that can be used to calculate distances between different surfaces of structure 101. At least some of these distances may identify or be used to identify the thicknesses of various parts or layers of parts of structure 101.

[0018] In one or more examples, analysis tool 104 may be communicatively coupled to sensor system 102. For example, analysis tool 104 may be communicable with sensor system 102 via at least one of a wireless communication link, a wired communication link, an optical communication link, or a combination thereof.

[0019] The analysis tool 104 may be implemented using hardware, software, firmware, or a combination thereof. When using software, the operations performed by the analysis tool 104 may be implemented using, for example, program code configured to run on a processor unit. When using firmware, the operations performed by the analysis tool 104 may be executed using, for example, program code and data configured to run on a processor unit and stored in persistent memory.

[0020] If hardware is used, the hardware may include one or more circuits that operate to perform the operations performed by the analysis tool 104. Depending on the embodiment, the hardware may take the form of a circuit system, an integrated circuit, an application-specific integrated circuit (ASIC), a programmable logic device, or any other suitable type of hardware device configured to perform any number of operations.

[0021] A programmable logic device can be configured to perform specific operations. This device may be permanently configured to perform these operations, or it may be reconfigurable. A programmable logic device may take the form of, for example, a programmable logic array, a programmable array logic, a field-programmable logic array, a field-programmable gate array, or any other type of programmable hardware device.

[0022] In one or more examples, the analysis tool 104 is implemented within a computer system 108. The computer system 108 can take the form of any number of different types of computing platforms. For example, the computer system 108 may include a single computer or multiple computers communicating with each other. In other examples, the computer system 108 may take the form of a cloud computing system, a smartphone, a tablet, or some other type of computing platform.

[0023] The analysis tool 104 generates distance data 110 that can be used in conjunction with the sensor data 106 and is used to detect inconsistencies in the structure 101. In some examples, the analysis tool 104 can itself perform an analysis of the sensor data 106 using the distance data 110 to detect inconsistencies in the structure 101. In one or more examples, the analysis tool 104 may receive inputs 111 used to generate the distance data 110. Inputs 111 may include user input, input from a program, input from a different computing platform, data obtained from a database or other data store, other types of input, or a combination thereof. Inputs 111 may, for example, identify a user (or customer) request.

[0024] Distance data 110 includes data that specifies various distances between one or more surface pairs associated with the structure 101. A surface pair includes two aligned (e.g., overlapping) surfaces. For example, distance data 110 may specify the distance between a first location along the outer surface of the fuselage skin and a second location on the inner surface of the fuselage skin. The second location on the inner surface lies along a vector substantially perpendicular to the outer surface of the first location. As used in this text, "substantially normal" means perpendicular or nearly perpendicular with a selected tolerance. Distance data 110 may include any number of distances for any number of surface pairs.

[0025] Distance data 110 is generated based on the design of the structure 101. Therefore, distance data 110 may be referred to as design distance data, design-based distance data, or as-design distance data. In one or more examples, the distances specified in distance data 110 may be the thickness of a part or portion of the structure 101. Therefore, in some cases, at least a portion of distance data 110 may include thickness data. This thickness data may also be referred to as design thickness data, design-based thickness data, or as-design thickness data.

[0026] In these examples, the analysis tool 104 generates distance data 110 based on a model 112 of the structure 101. The model 112 may be a database representation of the structure 101. For example, the model 112 may be a computer-aided design (CAD) model of the structure 101. In some examples, the model 112 contains data that can be used to construct a three-dimensional model of the structure 101. In one or more examples, the model 112 is received in the input 111.

[0027] The analysis tool 104 uses the model 112 to identify several surfaces 113 for the structure 101. These surfaces 113 are the as-designed surfaces for the structure 101. In one or more examples, the surfaces 113 include an outer surface 114 and a pair of inner surfaces 116. The pair of inner surfaces 116 include one or more inner surfaces of the structure 101. Each of the surfaces 113 may be a continuous or discontinuous surface.

[0028] If the structure 101 takes the form of an aircraft structure such as a fuselage structure 103, the outer surface 114 may be the outermost surface of the fuselage structure 103, which is the outer mold line (OML) of the fuselage structure 103. This outer mold line may be formed, for example, by at least the outer surface of the fuselage skin.

[0029] In this example, the inner surface 118 is the inner surface of one example of a set of inner surfaces 116 for the fuselage structure 103. The inner surface 118 may be a surface located closer to the central axis of the fuselage structure 103 compared to the outer surface 114. For example, the fuselage structure 103 may have a barrel shape, a cylindrical shape, a cylindrical shape, or a similar shape. In some examples, the inner surface 118 is the surface of the fuselage skin facing inward and forms at least a portion of the inner mold line (IML) of the fuselage structure 103. In some examples, the inner surface 118 is a surface formed by the filler material used in the fuselage structure 103. In other examples, the inner surface 118 is a surface formed by various outer surfaces or various inner surfaces of the stringer of the fuselage structure 103.

[0030] The analysis tool 104 further identifies the scanning surface 120. The scanning surface 120 is a representation of an area of ​​the structure 101 that has been inspected (or "scanned") using the sensor system 102, or will be inspected using the sensor system 102. This area may be referred to as the inspection area of ​​the inspection zone. For example, the scanning surface 120 may represent an inspection area 121 on the outer surface 105 of the structure 101.

[0031] The analysis tool 104 generates multiple sample points 122 and, for each set of inner surfaces 116, generates multiple corresponding projection points 123 using the scanning plane 120, the outer surface 114, the set of inner surfaces 116, and the spatial alignment algorithm 124. The sample points 122 are located along the outer surface 114. As used in this document, a “sample point” is a point (e.g., defined in three dimensions) that is projected from the scanning plane 120 in a vector substantially perpendicular to the scanning plane 120 and lies on or coincides with the outer surface 114.

[0032] The projection point 123 is located along the inner surface of a pair of inner surfaces 116 (e.g., inner surface 118). As used in this book, a “projection point” on an inner surface is a point that lies on or coincides with an inner surface (e.g., defined in three dimensions) and is projected from the corresponding point of sample point 122 along a vector substantially perpendicular to the outer surface 114.

[0033] Sample points 122 and projection points 123 can be generated using a spatial alignment algorithm 124. In one or more examples, the spatial alignment algorithm 124 is used to identify the parts of model 112 most relevant to the generation of sample points 122 and projection points 123. Thus, the spatial alignment algorithm 124 can be used to reduce the amount of data that needs to be processed to generate sample points 122 and projection points 123. This reduces the overall time and processing resources required to generate the distance data 110.

[0034] For example, the analysis tool 104 may use a spatial alignment algorithm 124 to narrow down a portion of the inner surface 118 that is aligned with or overlapped by the scanning surface 120. Therefore, it is not necessary to process the entire inner surface 118 to generate the projection point 123. The spatial alignment algorithm 124 may include, for example, one or more algorithms for constructing a k-dimensional tree. Examples of how the sample point 122 and the projection point 123 may be generated are illustrated in more detail in Figures 9 and 10 below, respectively.

[0035] Using the sample point 122 and projection point 123, the analysis tool 104 calculates distance data 110. The distance data 110 includes the distance between each corresponding point pair (sample point and projection point pair) from the sample point 122 and projection point 123. Thus, the distance data 110 provides information based on the model 112 of the structure 101, which can be used to authenticate and / or analyze the sensor data 106 generated by the sensor system 102.

[0036] For example, sensor data 106 may be compared with distance data 110 to determine whether any actual distance between surfaces of the structure 101 falls outside the tolerance range selected from the designed distance when the distance data 110 identifies the distance. Such a difference may signal the detection of one or more inconsistencies in the structure 101.

[0037] In one or more examples, the analysis tool 104 uses distance data 110 to generate a function 126. Function 126 makes it possible to determine the designed distance between surfaces (e.g., design thickness) calculated at other points along the outer surface 114 beyond the sample point 122. For example, function 126 may be a continuous function that makes it possible to generate the distance between the outer surface 114 and the inner surface 118 for any point along the outer surface 114, including those points not included in the sample point 122.

[0038] In some cases, function 126 may be output from the analysis tool 104 to another computing platform (e.g., a cloud computing platform, another computer system, etc.) so that one or more different users can use function 126 to calculate the designed distance. Depending on the embodiment, function 126 may alternatively be referred to as a distance function or a thickness function.

[0039] While distance data 110 has been described as being generated for a single scanning plane 120, distance data 110 may include data generated for multiple scanning planes. For example, model 112 may be divided into various inspection areas (or inspection zones). Distance data 110 may include data generated for each of these various inspection areas, so that data is generated for the entire structure 101.

[0040] In some examples, the analysis tool 104 generates a visualization output 128 of the distance data 110. The visualization output 128 may be sent to, for example, a display device 130 for display to a user. The visualization output 128 may take different forms. In one or more examples, the visualization output 128 takes the form of a three-dimensional distance map or distance model that visually presents at least a portion of the distance data 110. In one or more examples, the visualization output 128 is a thickness map or thickness model that visually presents thickness.

[0041] The block diagram in Figure 1 is not intended to imply any physical or structural limitations on how the exemplary embodiment may be implemented. Other components may be used in addition to or instead of those shown. Some components may be optional. Furthermore, the blocks are presented to illustrate functional components. When implemented in the exemplary embodiment, one or more of these blocks may be combined or divided, or one or more of these blocks may be combined with and divided into other blocks.

[0042] Figure 2-6 illustrates the various steps that may be involved in generating distance data, such as the distance data 110 in Figure 1. Therefore, Figure 2-6 is explained with continuous reference to Figure 1.

[0043] Figure 2 is a schematic diagram of a process for generating a scanning surface according to one or more exemplary embodiments. In particular, the process shown in Figure 2 can be implemented by the analysis tool 104 in Figure 1.

[0044] First, coordinate 200 is identified. Coordinate 200 can define an area of ​​the structure 101 that has been or will be inspected or scanned using the sensor system 102 in Figure 1. For example, coordinate 200 can define the inspection area 121 of the structure 101 in Figure 1. Coordinate 200 is on a reference coordinate system. This reference coordinate system could be, for example, the coordinate system of model 112 of the structure 101 in Figure 1.

[0045] In one or more examples, coordinate 200 is identified from input 111 in Figure 1. For example, input 111 may include a program used to control the sensor system 102 and move the sensor system 102 along the structure 101, or coordinate 200 extracted from this program. In some cases, input 111 may include the program itself, and the analysis tool 104 may identify coordinate 200 from that program.

[0046] In another example, input 111 includes user input that identifies coordinates 200. In some cases, input 111 includes data received from a sensor system 102 that identifies coordinates 200. In yet another example, the analysis tool 104 may receive input 111 that includes initial coordinates corresponding to a different coordinate system. The analysis tool 104 processes these initial coordinates (e.g., transforms the initial coordinates) and generates coordinates 200 on the reference coordinate system.

[0047] The coordinate 200 is processed to identify the initial surface 202 representing the inspection area 121 in Figure 1. In particular, the coordinate 200 is used to identify the boundary 201 for use in defining the initial surface 202. The initial surface 202 may be represented as a three-dimensional domain.

[0048] In some cases, when processing a two-dimensional domain, one or more portions of the boundary 201 of the initial surface 202 may be formed by one or more convex curves, concave curves, or both. To reduce processing time associated with the initial surface 202 having this type of boundary 201, a convex shape 204 may be generated with respect to the initial surface 202 of the two-dimensional domain. The convex shape 204 includes one or more local convex shells that ensure the initial surface 202 is completely or generally contained within the convex shape 204. Thus, the convex shape 204 can be convex or nearly convex. In one or more examples, the convex shape 204 is generated such that the boundary 201 of the initial surface 202 completely overlaps with or is contained within the convex shape 204.

[0049] Next, the scanning surface 206 is generated in a three-dimensional domain based on the convex shape 204. The scanning surface 206 is one example of an implementation for the scanning surface 120 in Figure 1. The scanning surface 206 has a substantial convex shape or boundary. As used in this document, "substantially convex" means convex or nearly convex. In one or more examples, the scanning surface 206 represents the global convex or nearly convex shape of the inspection area 121. In these examples, the scanning surface 206 corresponds to the same reference coordinate system as the initial surface 202.

[0050] In other cases, the boundary 201 of the initial surface 202 may be substantially convex in itself. Thus, the initial surface 202 can be used as the scanning surface 206. In some examples, the initial surface 202 is used as the scanning surface 206 if the shape of the boundary 201 is simple enough that it does not increase the amount of processing time or resources required beyond what is desired.

[0051] Figure 3 is a schematic diagram of a process for generating multiple sample points on the scanning plane 206 from Figure 2, according to one or more exemplary embodiments. In particular, the process shown in Figure 3 can be implemented by the analysis tool 104 of Figure 1.

[0052] First, along the scanning plane 206, several reference curves 300 are identified. Each of the reference curves 300 represents a path, along which the sensor system 102 moves within the inspection area 121 in Figure 1 to inspect or "scan" the structure 101. In one or more examples, the reference curves 300 are generated based on the same portion of input 111 to which coordinates 200 in Figure 2 are identified. In other examples, the reference curves 300 are generated based on a different portion of input 111 received in the analysis tool 104.

[0053] The reference curves 300 can be equally spaced. For example, input 111 may identify a first reference curve (e.g., a spine curve). The analysis tool 104 uses this first reference curve to generate additional reference curves that are parallel to it and equally spaced from one another. In other examples, not all of the reference curves 300 may be equally spaced.

[0054] Next, a set of points 302 along the scanning plane 206 is identified using the reference curve 300 and the spacing distance. Each of the points 302 may be defined in three dimensions (e.g., x, y, z points). The points 302 may be spaced apart along each of the reference curves 300 according to the spacing distance. This spacing distance may be provided by input 111. In some examples, this spacing distance is provided by the same part of input 111 from which the coordinates 200 in Figure 2, the reference curve 300, or both are generated. In other examples, the spacing distance is provided by another part of input 111.

[0055] Multiple scanning points 304 are selected from points 302 based on the initial surface 202, boundary 201, or both of these in Figure 2. The scanning points 304 are some of the points 302 that are entirely contained within the boundary 201, or that overlap with the initial surface 202 if the initial surface 202 is placed on or superimposed on the scanning surface 206. The scanning points 304 may be projected onto an outer surface, such as the outer surface 114 in Figure 1, to generate the sample points 122 in Figure 1.

[0056] Figure 4 is a schematic diagram of geometric representations generated using Model 112 of the structure 101 of Figure 1, according to one or more exemplary embodiments. The geometric representation 400 may be generated with respect to one of the surfaces 133 identified from Model 112 of Figure 1. In the illustrated example, the geometric representation 400 is generated with respect to the outer surface 114 of Figure 1.

[0057] Geometric representation 400 includes multiple patches 402. As used in this text, a “patch” is a geometric representation of a portion or section of a corresponding surface. In some cases, a patch may also be referred to as a surface path. This abstract geometric representation may have curved shapes, polygonal shapes, irregular shapes, or other types of shapes. In one or more examples, patches 402 may have the same shape and size (e.g., the same polygonal shape that forms a polygonal mesh), or they may have different shapes, different sizes, or both. Alternatively, patches may be referred to as “faces” or “surface sections.” In some examples, geometric representation 400 is referred to as a patched or patch-based representation.

[0058] The analysis tool 104 in Figure 1 may sample patch 402 to identify at least one patch in each patch 402. This sampling may be performed to construct at least one k-dimensional tree that organizes these patch points into a three-dimensional domain.

[0059] Figure 5 is a schematic diagram of a relevant surface region identified from the geometric representation 400 of Figure 4, according to one or more exemplary embodiments. The relevant surface region 500 includes a selected portion 502 of patch 402 from Figure 4, determined to overlap, coincide with, and / or be adjacent to the scanning surface 206 when the scanning surface 206 is aligned with or placed on the geometric representation 400 of Figure 4. Thus, the relevant surface 500 corresponds to a surface (e.g., outer surface 114) created by the geometric representation 400 representing the most relevant portion of the scanning surface 206 of that surface.

[0060] One or more k-dimensional trees generated with respect to the geometric representation 400 are used to narrow down the patch 402 to be narrowed and to identify the relevant surface region 500. The relevant surface region 500 provides a more focused space to identify the sample point 122 in Figure 1. By using the relevant surface region 500 to generate the sample point 122, the overall amount of processing time required to generate the distance data 110 and the amount of processing resources used to generate the distance data 110 can be reduced. Although it has been described that the relevant surface region 500 is generated using a k-dimensional tree, in other examples other types of techniques may be used to accelerate the process of identifying the relevant surface region 500.

[0061] Although the geometric representation 400 and associated surface region 500 in Figure 4 are described with respect to the outer surface 114 of Figure 1, similar steps may be performed with respect to each inner surface of a set of inner surfaces 116 of Figure 1. For example, similar steps may be used to generate the projection point 123 of Figure 1.

[0062] Figure 6 shows a thickness map according to one or more exemplary embodiments. Thickness map 600 is one example of an embodiment of the visualization output 128 in Figure 1. Thickness map 600 is a three-dimensional mapping of distance data 110 calculated with respect to the fuselage structure 103 in Figure 1. In one or more examples, thickness map 600 is color-coded to provide a visualization of various ranges of thickness for the fuselage structure 103.

[0063] In other examples, the thickness map 600 may provide various types of visual indications for a range of thicknesses of the fuselage structure 103. For example, various shades of a single color may be used. In another example, different patterns may be used.

[0064] Figure 7 is a flowchart of a process for inspecting a structure according to one or more exemplary embodiments. Process 700 in Figure 7 may be performed using the analysis tool 104 in Figure 1.

[0065] Process 700 begins by identifying a scanning plane representing the inspection area of ​​a structure (step 702). The structure may be, for example, structure 101 in Figure 1. In one or more examples, the structure takes the form of the fuselage structure 103 in Figure 1. In other examples, the structure may be another type of aircraft structure (e.g., wing structure, tail section, nose section, control surface structure, etc.). The inspection area of ​​the structure may be, for example, inspection area 121 in Figure 1. The scanning plane may be, for example, scanning plane 120 in Figure 1. In one or more examples, the scanning plane may have a substantial convex shape.

[0066] Multiple sample points on the outer surface identified from the model of the structure, and multiple corresponding projection points on the inner surface identified from the model of the structure, are generated using a scanning plane, a first geometric representation of the outer surface, a second geometric representation of the inner surface, and a spatial alignment algorithm (step 704). The multiple sample points and the multiple corresponding projection points may be, for example, sample point 122 and projection point 123 in Figure 1, respectively.

[0067] Distance data is calculated using multiple sample points and corresponding multiple projection points (step 706). The distance data is, for example, the distance data 110 in Figure 1. Optionally, process 700 may further include generating a visualization output using the distance data (step 708). This visualization output may be the visualization output 128 in Figure 1, but can take several different forms. For example, the visualization output may include one or more 2D distance maps, one or more 3D distance maps, or a combination thereof.

[0068] Sensor data generated for the inspection area of ​​the structure is analyzed using distance data to detect the presence of inconsistencies in the structure (step 710), after which the process terminates. The sensor data may be, for example, sensor data 106 in Figure 1. In one or more examples, the distance data includes the distance between each corresponding point pair from multiple sample points and corresponding multiple projection points. In short, the distance data includes the distance between each pair of corresponding sample points and projection points.

[0069] Figure 8 is a flowchart of the process for identifying a scanning plane according to one or more exemplary embodiments. Process 800 in Figure 8 may be performed using the analysis tool 104 in Figure 1. Furthermore, process 800 may be one example of how step 702 in Figure 7 may be performed.

[0070] Process 800 begins by identifying the coordinates of the inspection area of ​​the structure (step 802). Coordinates 200 in Figure 2 may be one example of an embodiment of the coordinates identified in step 802. The coordinates may be received by user input, identified by the program, or identified by other means.

[0071] Subsequently, the initial surface is created using coordinates (step 804). The initial surface is three-dimensional. Initial surface 202 in Figure 2 is one example of an embodiment of the initial surface created in step 804.

[0072] It is determined whether the initial surface is substantially convex (step 806). If the initial surface is substantially convex (i.e., substantially contained within a convex shape), the initial surface is used as a scanning surface representing the inspection area of ​​the structure (step 808), and then the process ends.

[0073] Otherwise, a convex shape is generated on the initial surface (step 810). The convex shape includes one or more localized convex shells that ensure the initial surface 202 is completely contained within the convex shape. This convex shape is substantially convex. In one or more examples, the convex shape is generated in a two-dimensional domain. The convex shape 204 in Figure 2 is one example of an embodiment of the convex shape generated in step 810.

[0074] The scanning surface representing the inspection area of ​​the structure is formed using a convex shape (step 812). In one or more examples, step 812 may include transforming the convex shape from a two-dimensional domain to a three-dimensional domain in order to form the scanning surface. Scanning surface 206 in Figure 2 is one example of an embodiment of the scanning surface produced in step 812.

[0075] Figure 9 is a flowchart of a process for generating sample points according to one or more exemplary embodiments. Process 900 in Figure 9 may be carried out using the analysis tool 104 in Figure 1. Furthermore, process 900 may be one example of a method in which at least part of step 704 in Figure 7 may be carried out.

[0076] Process 900 may be initiated by identifying a relevant surface region (step 902) that represents a portion of the outer surface corresponding to the scanning plane. The relevant surface region 500 in Figure 5 is one example of an embodiment of the relevant surface region identified in step 902. If the structure is a fuselage structure, the outer surface may be the outer mold line of the fuselage structure. The outer surface may be identified from a model of the structure.

[0077] A scanning point is identified on a scanning plane representing the inspection area of ​​the structure (step 904). The scanning plane may be, for example, scanning plane 120 in Figure 1. The scanning plane may be generated using, for example, process 800 in Figure 8.

[0078] For processing, a scan point is selected (step 906). The relevant surface regions are processed to identify the patch of relevant surface region closest to the selected scan point (step 908). For example, step 908 may be performed using one or more k-dimensional trees constructed about the relevant surface regions, about the interiors, or both.

[0079] A sample point is formed using a point on the patch that intersects a vector substantially perpendicular to the scanning plane at the location of the selected scanning point (step 910). In one or more examples, step 910 may be performed by identifying the point on the patch that is the shortest distance to the selected scanning point as the sample point. In some cases, this sample point is selected from a set of points that sample the points generated for the patch.

[0080] A determination is made as to whether there are any unprocessed scan points remaining (step 912). If there are any unprocessed scan points remaining, process 900 returns to step 906 above. Otherwise, this process terminates.

[0081] Figure 10 is a flowchart of a process for generating a projection point according to one or more exemplary embodiments. Process 1000 in Figure 10 may be carried out using the analysis tool 104 in Figure 1. Furthermore, process 1000 may be one example of a method in which at least part of step 704 in Figure 7 may be carried out.

[0082] Process 1000 can be initiated by identifying a relevant surface region that represents a portion of the inner surface corresponding to the scanning plane (step 1002). This relevant region may be performed in a manner similar to the relevant surface region 500 in Figure 5. The inner surface in step 1002 may be, for example, the inner surface 118 in Figure 1. The inner surface may be identified from a model of the structure.

[0083] From multiple sample points, one sample point is selected for processing (step 1004). The multiple sample points may be, for example, sample points generated by process 900 described above.

[0084] The relevant surface region is processed to identify the patch of the relevant surface region closest to the selected sample point (step 1006). A projection point is formed using a point on the patch that intersects a vector substantially perpendicular to the outer surface at the location of the selected sample point (step 1008). In one or more examples, step 1008 may be performed by identifying the projection point as a point in the patch corresponding to the inner surface that is the shortest distance to the sample point. In some cases, this projection point is selected from a set of sampling points generated for the patch.

[0085] A decision is made (step 1010) as to whether any unprocessed sample points remain. If unprocessed sample points remain, process 1000 returns to step 1004 above. Otherwise, this process terminates.

[0086] Figure 11 is a flowchart of a process for identifying relevant surface regions of a surface according to one or more exemplary embodiments. Process 1100 in Figure 11 may be performed using the analysis tool 104 in Figure 1. Furthermore, process 1100 may be one example of how steps 902 in Figure 9 and 1002 in Figure 10 may be performed.

[0087] Process 1100 can be initiated by generating a geometric representation of the surface (step 1102). The geometric representation includes multiple patches, each of which is an abstract geometric representation of a portion of the surface. Geometric representation 400 in Figure 4 may be one example of an embodiment of the geometric representation in step 1102.

[0088] Subsequently, the geometric representation is sampled to generate sampling points (step 1104). A set of k-dimensional trees is constructed using the sampling points (step 1106). Using the set of k-dimensional trees, the sampling points are narrowed down to a focused set of sampling points (step 1108). Based on the focused set of sampling points, a related surface region containing a selected portion of the patch is formed (step 1110), after which the process terminates. Although process 1100 is described using a k-dimensional tree, in other examples, a different type of spatial alignment algorithm may be used to perform steps 1106 and 1108 above.

[0089] Figure 12 is a flowchart of a process for generating a set of functions for a structure, according to one or more exemplary embodiments. The process 1200 in Figure 12 may be performed using the inspection system 100 in Figure 1. As one example, the process 1200 may be performed using the analysis tool 104 in Figure 1. While the process 1200 is described in relation to an aircraft structure, the process 1200 may also be used for other types of structures.

[0090] Process 1200 is initiated by identifying a set of surfaces of interest from a model of the aircraft structure, the set of surfaces including an exterior surface and a set of interior surfaces (process 1202). The aircraft structure may be a composite aircraft structure.

[0091] A set of scanning planes representing corresponding sets of inspection areas of an aircraft structure is identified (step 1204). For example, an aircraft structure may be a large fuselage structure with multiple inspection areas (or zones) identified. For each of these various inspection areas, a scanning plane is identified. In one or more examples, these inspection areas do not overlap. In other examples, two or more inspection areas may overlap at least partially.

[0092] For processing, a scanning plane is selected (step 1206). Using a spatial alignment algorithm, an associated surface region corresponding to the selected scanning plane, representing a portion of the outer surface, is identified (step 1208). The associated surface region may include a portion of a patch selected from the geometric representation of the outer surface. The associated surface region corresponds to the selected scanning plane by including these patches, which would at least completely overlap with the selected scanning plane if the selected scanning plane were placed on the outer surface. The spatial alignment algorithm described in step 1200 may be, for example, the spatial alignment algorithm 124 in Figure 1.

[0093] Multiple sample points on the outer surface are generated using the selected scanning plane, the associated surface region representing a portion of the outer surface, and a spatial alignment algorithm (step 1210).

[0094] For processing, one interior is selected from a set of interiors (step 1212). Using a spatial alignment algorithm, an associated surface region corresponding to the selected scanning plane is identified, which represents a portion of the selected interior (step 1214). The associated surface region may include a portion of a selected patch from the geometric representation of the selected interior. The associated surface region corresponds to the selected scanning plane by including these patches, which would at least completely overlap the selected scanning plane if the selected scanning plane were placed on the selected interior.

[0095] Multiple projection points on the selected inner surface are generated using the selected scanning plane, the associated surface region representing a portion of the selected inner surface, and the spatial indexing tree algorithm (step 1216).

[0096] Distance data is calculated using multiple sample points and multiple projection points to determine the distance between the outer surface and the selected inner surface (step 1218). A decision is made as to whether any untreated inner surfaces remain in a pair of inner surfaces (step 1220). If untreated inner surfaces remain, process 1200 returns to step 1212 above. Otherwise, a determination is made as to whether any untreated scanning surfaces remain in a pair of scanning surfaces (step 1222). If untreated scanning surfaces remain, process 1200 returns to step 1206 above.

[0097] Otherwise, the collected distance data is used to generate a set of functions for the structure (step 1224). In one or more examples, each function in the set of functions may be a continuous function. In step 1222, each function in the set of functions may correspond to a different interior of a pair of interiors. Each function can be used to determine the as-designed distance between the outer surface and the corresponding interior at any point along the outer surface.

[0098] With respect to steps 1208 and 1210, the overall processing time and resources required to generate sample points for each scanning surface are reduced by generating associated surface regions that represent a portion of the outer surface corresponding to each scanning surface of a set of scanning surfaces. Similarly, with respect to steps 1214 and 1216, the overall processing time and resources required to generate projection points for each inner surface are reduced by generating associated surfaces that represent a portion of the inner surface of a set of inner surfaces corresponding to the selected scanning surface.

[0099] Figure 13 is a flowchart of the process for identifying multiple scan points according to one or more exemplary embodiments. Process 1300 in Figure 13 may be performed using the analysis tool 104 in Figure 1. Process 1300 may be one example of how step 904 in Figure 9 may be performed.

[0100] Process 1300 may be initiated by identifying multiple reference curves of the scanning plane corresponding to multiple paths used by the sensor system to scan the inspection area of ​​the structure (step 1302). In step 1302, multiple reference curves may be identified based on the program used or used to control the sensor system. In one or more examples, the reference curves are substantially parallel to each other and spaced equally apart. As used in this document, “substantially parallel” means parallel or approximately parallel. The sensor system in step 1302 may be, for example, the sensor system 102 in Figure 1.

[0101] Next, multiple scan points are formed using multiple reference curves and interval distances used by the sensor system (step 1304), after which the process ends. The interval distances provide the distances between scan points identified along the same reference curve.

[0102] Step 1304 can be performed in various ways. In one or more examples, step 1304 includes identifying points along each of several reference curves based on interval distances in order to form a set of points. Step 1304 may further include selecting a portion of a set of points that lie within the boundary corresponding to the inspection area in order to form several scan points.

[0103] Figure 14 is a flowchart of a process for calculating distance data for an aircraft structure according to one or more exemplary embodiments. Process 1400 in Figure 14 may be carried out using the analysis tool 104 in Figure 1. Although process 1400 is described in relation to an aircraft structure, process 1400 may also be used in relation to other types of structures, including various types of composite structures.

[0104] Process 1400 may be initiated by identifying scanning surfaces representing inspection areas of the aircraft structure (step 1402). Using a model of the aircraft structure, the outer surfaces and a set of inner surfaces of the aircraft structure are identified (step 1404).

[0105] Subsequently, using the scanning plane, the first geometric representation of the outer surface, the second geometric representation of the selected inner surface, and a spatial indexing algorithm, multiple sample points on the outer surface and, for each selected inner surface of a set of inner surfaces, multiple corresponding projection points on the selected inner surface are generated (step 1406). Using a spatial indexing algorithm can help speed up processing. In one or more examples, the spatial indexing algorithm uses one or more k-dimensional trees to provide indexing to the first and second geometric representations.

[0106] Using multiple sample points and corresponding projection points generated for each selected inner surface of a pair of inner surfaces, distance data is calculated, and the distance data provides as-designed data to be used in conjunction with sensor data generated for the aircraft structure to detect inconsistencies in the aircraft structure (step 1408), after which the process ends. In some examples, step 1408 includes generating a function (e.g., a continuous function) for each pairing of an outer surface and an inner surface of a pair of inner surfaces.

[0107] This function, which could be function 126 in Figure 1, can be used to quickly and reliably determine the as-design distance between an outer surface and a specific inner surface at any point on the outer surface. This as-design distance can be compared to measurements generated by a sensor system to assess whether the actual aircraft structure conforms to the aircraft structure design. The difference between the as-design distance and measurements generated by the sensor system that fall outside a selected tolerance range may indicate the presence of inconsistencies in the aircraft structure. Inconsistencies may include voids, delamination, cracks, undesirable levels of porosity, other types of inconsistencies, or a combination thereof.

[0108] Referring now to Figure 15, the data processing system is shown in block diagram form according to an exemplary embodiment. The data processing system 1500 may be used to implement the computer system 108 of Figure 1. As illustrated, the data processing system 1500 includes a communication framework 1502 that provides communication between a processor unit 1504, a storage device 1506, a communication unit 1508, an input / output unit 1510, and a display 1512. In some cases, the communication framework 1502 may be implemented as a bus system.

[0109] The processor unit 1504 is configured to execute software instructions to perform several operations. Depending on the embodiment, the processor unit 1504 may comprise several processors, one multiprocessor core, and / or some other type of processor. In some cases, the processor unit 1504 may take the form of a hardware unit such as a circuit system, an application-specific integrated circuit (ASIC), a programmable logic device, or some other suitable type of hardware unit.

[0110] Instructions for operating systems, applications, and / or programs executed by processor unit 1504 may be stored in storage device 1506. Storage device 1506 may communicate with processor unit 1504 through communication framework 1502. A storage device, also referred to as a computer-readable storage device as used herein, is any hardware capable of storing information temporarily and / or permanently. This information may include, but is not limited to, data, program code, and / or other information.

[0111] Memory 1514 and fixed storage device 1516 are examples of storage devices 1506. Memory 1514 may take the form of, for example, random access memory or some kind of volatile or non-volatile storage device. Fixed storage device 1516 may include any number of components or devices. For example, fixed storage device 1516 may include a hard drive, flash memory, rewritable optical disk, rewritable magnetic tape, or any combination thereof. The medium used by fixed storage device 1516 may be removable or not.

[0112] The communication unit 1508 enables the data processing system 1500 to communicate with other data processing systems and / or devices. The communication unit 1508 may provide communication using physical and / or wireless communication links.

[0113] The input / output unit 1510 enables the reception of input from other devices connected to the data processing system 1500 and the transmission of output to such devices. For example, the input / output unit 1510 may enable the reception of user input through a keyboard, mouse, and / or any other type of input device. Another example is that the input / output unit 1510 may enable the transmission of output to a printer connected to the data processing system 1500.

[0114] The display 1512 is configured to display information to the user. The display 1512 may include, but is not limited to, a monitor, touchscreen, laser display, holographic display, virtual display device, and / or any other type of display device.

[0115] In one or more examples, processes of various exemplary embodiments may be executed by the processor unit 1504 using instructions implemented in the computer. These instructions may be referred to as program code, computer-readable program code, or computer-available program code, and may be read and executed by one or more processors within the processor unit 1504.

[0116] In these examples, the program code 1518 is arranged in a functional form on a selectively removable computer-readable medium 1520 and can be loaded into or transmitted to a data processing system 1500 for execution by a processor unit 1504. The program code 1518 and the computer-readable medium 1520 together form a computer program product 1522. In one or more examples, the computer-readable medium 1520 may be a computer-readable storage medium 1524 or a computer-readable signaling medium 1526.

[0117] The computer-readable storage medium 1524 is a physical or tangible storage device used to store the program code 1518, rather than a medium for propagating or transmitting the program code 1518. The computer-readable storage medium 1524 may, but are not limited to, an optical disk or magnetic disk connected to the data processing system 1500, or a fixed storage device.

[0118] Alternatively, program code 1518 may be transmitted to the data processing system 1500 using a computer-readable signaling medium 1526. The computer-readable signaling medium 1526 may be, for example, a propagated data signal containing program code 1518. This data signal may be an electromagnetic signal, an optical signal, and / or any other type of signal that can be transmitted via a physical and / or wireless communication link.

[0119] The diagram of the data processing system 1500 in Figure 15 is not intended to present any structural limitations on the embodiments in which the exemplary embodiments may be implemented. Various exemplary embodiments may be implemented in a data processing system that includes additional or alternative components to the components shown in relation to the data processing system 1500. Furthermore, the components shown in Figure 15 may differ from the examples shown.

[0120] Exemplary embodiments of this disclosure may be described in relation to the aircraft manufacturing and maintenance method 1600 shown in Figure 16 and the aircraft 1700 shown in Figure 17. Referring first to Figure 16, the aircraft manufacturing and maintenance method is shown according to an exemplary embodiment. In the pre-manufacturing stage, the aircraft manufacturing and maintenance method 1600 may include the specifications and design 1602 of the aircraft 1700 shown in Figure 17 and the procurement of materials 1604.

[0121] During the manufacturing phase, the components and subassemblies of the aircraft 1700 shown in Figure 17 are manufactured 1606, and system integration 1608 is carried out. Subsequently, the aircraft 1700 shown in Figure 17 may be put into service 1612 after certification and delivery 1610. During the period of customer operation 1612, the aircraft 1700 shown in Figure 17 is scheduled for periodic maintenance and upkeep 1614, which may include improvements, reconfiguration, modification, and other maintenance or repairs.

[0122] Each of the processes of the Aircraft Manufacturing and Maintenance Method 1600 may be performed or carried out by a system integrator, a third party, and / or an operator. In these embodiments, the operator may be a customer. In this specification, a system integrator may include, but is not limited to, any number of aircraft manufacturers and major system subcontractors; a third party may include, but is not limited to, any number of vendors, subcontractors, and suppliers; and an operator may be an airline, leasing company, military organization, service organization, etc.

[0123] Referring now to Figure 17, a diagram of an aircraft on which an exemplary embodiment may be implemented is shown. In this example, the aircraft 1700 may include a fuselage 1702 manufactured by the aircraft manufacturing and maintenance method 1600 of Figure 16, and equipped with several systems 1704 and interior 1706. Examples of systems 1704 include one or more of the propulsion system 1708, electrical system 1710, hydraulic system 1712, and environmental system 1714. Any number of other systems may be included. Although an example from the aerospace industry is shown here, various exemplary embodiments may be applied to other industries such as the automotive industry.

[0124] The apparatus and methods embodied in this book may be used in at least one of the stages of the aircraft manufacturing and maintenance method 1600 shown in Figure 16. In particular, the manufacturing system 164 from Figure 1 may be used to manufacture the tool 162 during any one of the stages of the aircraft manufacturing and maintenance method 1600. For example, but not limited to, the inspection system 100, sensor system 102, or analytical tool 104 from Figure 1 may be used during the manufacturing of components and subassemblies 1606, system integration 1608, certification and delivery 1610, periodic manufacturing and maintenance 1614, and / or at least one of any other stages in the aircraft manufacturing and maintenance method 1600. Furthermore, the inspection system 100 may be used to inspect one or more aircraft structures of aircraft 1700, such as the aircraft fuselage 1702, interior 1706, or one or more of both, shown in Figure 17. The analysis tool 104 in Figure 1 can be used to calculate distance data 110 of these aircraft structures of aircraft 1700.

[0125] In one or more examples, components or subassemblies manufactured in the manufacturing of components and subassemblies 1606 of Figure 16 may be manufactured or produced in a manner similar to components or subassemblies manufactured during the operation of aircraft 1700 in Figure 1612. In yet another example, one or more embodiments of devices, embodiments of methods, or combinations thereof may be used in manufacturing stages such as the manufacturing of components and subassemblies 1606 and system integration 1608 of Figure 16. One or more embodiments of devices, embodiments of methods, or combinations thereof may be used during the operation of aircraft 1700 in Figure 1612 and / or in maintenance and servicing 1614. By utilizing a number of different exemplary embodiments, the assembly of aircraft 1700 can be made more efficient and / or the cost of aircraft 1700 can be reduced. Furthermore, one or more embodiments described herein may be used as part of the propulsion system 1708 of aircraft 1700.

[0126] The flowcharts and block diagrams in the various embodiments shown illustrate the architecture, functionality, and operation of several possible embodiments of the apparatus and method in the exemplary embodiments. Therefore, each block in the flowchart or block diagram may represent a module, segment, function, and / or part of an operation or step.

[0127] The flowcharts and block diagrams in the various embodiments shown illustrate the architecture, functionality, and operation of several possible embodiments of the apparatus and method in the exemplary embodiments. For example, in some cases, two blocks shown consecutively may be executed substantially simultaneously, depending on their related functions, and sometimes in reverse order. In addition, other blocks may be added in addition to those shown in the flowcharts or block diagrams.

[0128] In this book, the expression "at least one of" used with the listed items means that one or various combinations of the listed items are usable, but only one of the listed items may be required. An item can be a specific object, article, step, process, or category. In other words, "at least one of" means that any combination of items, or any number of items, from the listed items can be used, but not all of the listed items are required. For example, non-restrictively, "at least one of item A, item B, or item C" or "at least one of item A, item B, and item C" could mean, for example, "item A," "item A and item B," "item B," "item A, item B, and item C," "item B and item C," or "item A and item C." In some cases, "at least one of item A, item B, or item C" or "at least one of item A, item B, and item C" could non-restrictively mean "two items A, one item B, and ten items C," "four items B, seven items C," or any other preferred combination.

[0129] Furthermore, this disclosure includes embodiments as defined below.

[0130] Article 1. A method for inspecting a structure (101), Identifying (702) a scanning plane (120) that represents the inspection area (121) of the structure (101), Using the scanning plane (120), a first geometric representation (400) of the outer surface (114), and a second geometric representation (118) of the inner surface (118), generate a plurality of sample points (122) on the outer surface (114) and a plurality of corresponding projection points (123) on the inner surface (118) identified from the model (112) of the structure (101) (704), Using multiple sample points (122) and corresponding multiple projection points (123), distance data (110) is calculated (706), Using distance data (110) to detect the presence of inconsistencies in the structure (101), and analyzing sensor data (106) generated for the inspection area (121) of the structure (101) (710) Methods that include...

[0131] Article 2. To generate a visualization output (128) of distance data (110)(702) (708) The method described in Clause 1, further including the following:

[0132] Article 3. Display the visualization output (128) on a display device (130). The method according to Clause 2, further comprising a visualization output (128) including a color-coded thickness map (600) representing at least a portion of the distance data (110).

[0133] Article 4. Using distance data (110), generate a function (126) that allows the distance between the outer surface (114) and the inner surface (118) to be calculated via the function (126) at any selected point along the outer surface (114) (1224). The method described in any one of clauses 1 to 3, further including the method described in any one of clauses 1 to 3.

[0134] Clause 5. Identifying the scanning plane (120) (702) Identifying the coordinates (200) of the inspection area (121) of the structure (101) (802), Using coordinates (200), create the initial surface (202) (804), To generate the convex shape (204) of the initial surface (202) (810), The scanning surface (120) is formed using the convex shape (204) so ​​that the scanning surface (120) has a substantially convex shape (812) The method described in any one of the clauses 1 to 4, including the method described in any one of the clauses 1 to 4.

[0135] Article 6. Generating multiple sample points (122) on the outer surface (114) (704) Identifying a related surface region (902) that represents a portion of the outer surface (114) corresponding to the scanning plane. The method described in any one of the clauses 1 to 5, including the method described in any one of the clauses 1 to 5.

[0136] Article 7. Generating the plurality of sample points (122) on the outer surface (114) (704) Identifying multiple scanning points (304) on the scanning plane (904), To identify the patch of the associated surface region closest to the selected scan point among multiple scan points (304), the associated surface region (908) is processed for each selected scan point, Using points on a patch that intersect a vector substantially perpendicular to the scanning plane (120) at the location of the selected scanning point (304), one sample point of the multiple sample points (122) is formed for each selected scanning point (910) and The method described in Clause 6, further including the method described in Clause 6.

[0137] Article 8. Identifying multiple scanning points (304) (904) Identifying multiple reference curves (1302) for a scanning plane (120) corresponding to multiple paths used by a sensor system (102) to scan an inspection area (121) of a structure (101), Using multiple reference curves and intervals used by the sensor system (102), multiple scanning points (304) are formed (1304) The method described in Article 7, including the method described in Article 7.

[0138] Article 9. Forming multiple scanning points (304) (1304) In order to form a set of points, we identify points along each of the multiple reference curves based on the interval distance, To form multiple scanning points (304), select a portion of the set of points that lie within the boundary corresponding to the inspection area (121) and The method described in Article 8, including the method described in Article 8.

[0139] Article 10. Identifying the relevant surface region (902) To generate a first geometric representation of the outer surface (114) including multiple patches (1102), To generate sampling points, the first geometric representation is sampled (1104), Using a spatial alignment algorithm (124), the sampling points are narrowed down to a focused set of sampling points (1108), Based on a focused set of sampling points, a related surface region is formed that includes selected portions of multiple patches (1110) The method described in Article 6, including the method described in Article 6.

[0140] Article 11. Generating (704) a number of corresponding projection points (123) on the inner surface (118) is Identifying a related surface region (1002) that represents a portion of the inner surface (118) corresponding to the scanning surface (120) The method described in any one of the clauses 1 to 10, including the method described in any one of the clauses 1 to 10.

[0141] Article 12. Generating the corresponding plurality of projection points (123) on the inner surface (118) (704) To identify the patch of the relevant surface region closest to the selected sample point among multiple sample points (122), the relevant surface region is processed (1004) for each selected sample point, For each selected sample point of multiple sample points (122), a projection point is formed (1104) using a point on the patch that intersects a vector substantially perpendicular to the outer surface (114) at the location of the selected sample point. The method described in Article 11, further including the method described in Article 11.

[0142] Article 13. Identifying the relevant surface region (1002) To generate a second geometric representation of the interior (118) including multiple patches (1102), To generate sampling points, a second geometric representation is sampled (1104), Using a spatial alignment algorithm (124), the sampling points are narrowed down to a focused set of sampling points (1108), Based on a focused set of sampling points, a related surface region is formed that includes selected portions of multiple patches (1110) The method described in Article 11, including the method described in Article 11.

[0143] Article 14. Multiple sampling points and corresponding multiple projection points (123) form multiple point pairs, and distance data (110) is calculated (706). Calculating the distance between each pair of points in a set of multiple pairs of points. The method described in any one of the clauses 1 to 13, including the method described in any one of the clauses 1 to 13.

[0144] Article 15. Memory (1514) for storing machine-readable media containing machine-executable code, A processor (1504) connected to memory (1514) and configured to execute machine executable code to cause a processor (1504) to implement an analysis tool (104), wherein the analysis tool (104) Identifying the scanning plane (120) that represents the inspection area (121) of the structure (101), Multiple sample points (122) on the outer surface (114) identified from the model (112) of the structure (101), and multiple corresponding projection points (123) on the inner surface (118) identified from the model (112) of the structure (101), are generated using the scanning plane, a first geometric representation of the outer surface (114), and a second geometric representation of the inner surface (118). Calculating distance data (110) using multiple sample points (122) and corresponding multiple projection points (123), wherein the distance data (110) specifies the distance between point pairs formed by one sample point of the multiple sample points (122) and one projection point of the corresponding multiple projection points (123). A system configured to perform the following actions.

[0145] Article 16. The system according to Clause 15, wherein the analysis tool is further configured to analyze sensor data (106) generated for an inspection area (121) of the structure (101) using distance data (110) to detect the presence of inconsistencies in the structure (101).

[0146] Article 17. The system according to Clause 15 or 16, wherein the analysis tool (104) is further configured to generate a visualization output of distance data (110).

[0147] Article 18. The system as described in Clause 17, wherein the analysis tool (104) is further configured to display a visualization output, including a thickness map, on a display device.

[0148] Article 19. The system according to any one of the clauses 15 to 18, wherein the analysis tool (104) is further configured to generate a function that uses distance data (110) to enable the calculation of the distance between an outer surface (114) and an inner surface (118) via the function at any selected point along the outer surface (114).

[0149] Article 20. A method for calculating distance data (110) of an aircraft structure (101,103), Identifying (1402) a scanning plane (120) representing the inspection area (121) of the aircraft structure (101, 103), Using the model of the aircraft structure (112), identify the outer surface (114) of the aircraft structure (101,103) and a pair of inner surfaces (116) of the aircraft structure (101,103) (1404), Using a scanning plane (120), a first geometric representation of the outer surface (114), a second geometric representation of the selected inner surface, and a spatial alignment algorithm (124), generate a plurality of sample points (122) on the outer surface (114) and, for each selected pair of inner surfaces (116), a plurality of corresponding projection points (123) on the selected inner surface (1406), Using multiple sample points (122) and corresponding multiple projection points (123) generated for each selected inner surface of a set of inner surfaces (116), distance data (110) is calculated (1408) and A method comprising distance data (110) to provide as-designed data for use in conjunction with sensor data (106) generated for the aircraft structure (101, 103) in order to detect inconsistencies in the aircraft structure (101, 103).

[0150] The description of various exemplary embodiments is presented for illustrative and explanatory purposes only and is not intended to be exhaustive or limit to the embodiments disclosed. Numerous modifications and variations will be obvious to those skilled in the art. Furthermore, various exemplary embodiments may offer different features compared to other preferred embodiments. The selected one or more embodiments are chosen and described to best illustrate the principles and practical applications of the embodiments, and so that those skilled in the art can understand that the disclosure of various embodiments with various modifications is suitable for specific intended uses.

Claims

1. A method for inspecting a structure (101), comprising: identifying (702) a scan plane (120) representing an inspection area (121) of the structure (101); generating (704) a plurality of sample points (122) on an exterior surface (114) identified from the model (112) of the structure (101) and a corresponding plurality of projection points (123) on an interior surface (118) identified from the model (112) of the structure (101) using the scan plane (120), a first geometric representation (400) of the exterior surface (114), and a second geometric representation of the interior surface (118); calculating (706) distance data (110) using the plurality of sample points (122) and the corresponding plurality of projection points (123); analyzing (710) sensor data (106) generated for the inspection area (121) of the structure (101) using the distance data (110) to detect the presence of an inconsistency in the structure (101); A method comprising:

2. generating (708) a visualization output (128) of the distance data (110) (702), the visualization output (128) including a color-coded thickness map (600) representing at least a portion of the distance data (110), and displaying the visualization output (128); The method of claim 1 further comprising: Displaying the visualization output (128) on a display device (130), the visualization output (128) including a color-coded thickness map (600) representing at least a portion of the distance data (110). The method of claim 2 further comprising: using said distance data (110) to generate (1224) a function (126) that allows a distance between said outer surface (114) and said inner surface (118) to be calculated via said function (126) at any selected point along said outer surface (114). The method of claim 1 , further comprising:

5. Identifying (702) the scan plane (120) comprises: Identifying (802) coordinates (200) of the inspection area (121) of the structure (101); creating (804) an initial surface (202) using said coordinates (200); generating (810) a convex shape (204) of the initial surface (202); forming (812) the scan surface (120) using the convex shape (204) such that the scan surface (120) has a substantially convex shape; The method of any one of claims 1 to 4, comprising:

6. The method of claim 1, further comprising: generating (704) the plurality of sample points (122) on the outer surface (114), Identifying (902) an associated surface area representing a portion of the exterior surface (114) that corresponds to the scan surface. The method of any one of claims 1 to 5, comprising:

7. Generating (704) the plurality of sample points (122) on the exterior surface (114) comprises: Identifying (904) a plurality of scan points (304) on the scan surface; processing (908) the associated surface area for each selected scan point to identify a patch of the associated surface area that is closest to the selected scan point of the plurality of scan points (304); forming (910) one sample point of the plurality of sample points (122) for each selected scan point using a point on the patch that intersects a vector substantially perpendicular to the scan plane (120) at the location of a selected scan point of the plurality of scan points (304); The method of claim 6 further comprising:

8. Identifying (904) the plurality of scanning points (304) identifying (1302) a number of reference curves for the scanning surface (120) corresponding to a number of paths used by a sensor system (102) to scan the inspection area (121) of the structure (101); forming (1304) the plurality of scan points (304) using the plurality of reference curves and a spacing distance used by the sensor system (102); The method of claim 7, comprising:

9. forming (1304) the plurality of scanning points (304) identifying points along each of the plurality of reference curves based on the spacing distances to form a set of points; selecting a portion of said set of points that is within a boundary corresponding to said examination area (121) to form said plurality of scanning points (304); The method of claim 8 , comprising:

10. Identifying 902 the relevant surface regions comprises: Generating (1102) the first geometric representation of the exterior surface (114), the first geometric representation including a plurality of patches; Sampling 1104 the first geometric representation to generate sampling points; Narrowing (1108) the sampling points to a focused set of sampling points using a spatial registration algorithm (124); forming (1110) the relevant surface region including a selected portion of the plurality of patches based on the focused set of sampling points; The method of claim 6, comprising:

11. generating (704) the corresponding plurality of projection points (123) on the inner surface (118); Identifying (1002) an associated surface area representing a portion of the interior surface (118) that corresponds to the scan surface (120). The method of any one of claims 1 to 10, comprising:

12. generating (704) the corresponding plurality of projection points (123) on the inner surface (118), processing (1004) the associated surface area for each selected sample point to identify a patch of the associated surface area that is closest to the selected sample point of the plurality of sample points (122); forming (1104) a projection point for each selected sample point of the plurality of sample points (122) using a point on the patch that intersects a vector substantially perpendicular to the outer surface (114) at the location of the selected sample point; The method of claim 11 further comprising:

13. Identifying 1002 the relevant surface regions, Generating (1102) the second geometric representation of the interior surface (118), the second geometric representation including a plurality of patches; Sampling 1104 the second geometric representation to generate sampling points; Narrowing (1108) the sampling points to a focused set of sampling points using a spatial registration algorithm (124); forming (1110) the relevant surface region including a selected portion of the plurality of patches based on the focused set of sampling points; The method of claim 11 , comprising:

14. The plurality of sampling points and the corresponding plurality of projection points (123) form a plurality of point pairs, and calculating (706) the distance data (110) Calculating the distance between each pair of points in the plurality of pairs of points.

14. The method of any one of claims 1 to 13, comprising:

15. A memory (1514) for storing a machine-readable medium including machine executable code; a processor (1504) coupled to the memory (1514) and configured to execute the machine executable code to cause the processor (1504) to implement an analysis tool (104), the analysis tool (104) comprising: Identifying a scan plane (120) representing an inspection area (121) of a structure (101); generating a plurality of sample points (122) on an exterior surface (114) identified from a model (112) of the structure (101) and a corresponding plurality of projection points (123) on an interior surface (118) identified from the model (112) of the structure (101) using the scan plane, a first geometric representation of the exterior surface (114), and a second geometric representation of the interior surface (118); calculating distance data (110) using said plurality of sample points (122) and said corresponding plurality of projection points (123), said distance data (110) specifying a distance between a point pair formed by a sample point of said plurality of sample points (122) and a projection point of said corresponding plurality of projection points (123); A system that is configured to run

16. 16. The system of claim 15, wherein the analysis tool is further configured to analyze sensor data (106) generated for the inspection area (121) of the structure (101) using the distance data (110) to detect the presence of an inconsistency in the structure (101).

17. The system described in claim 15 or 16, wherein the analysis tool (104) is further configured to generate a visualization output of the distance data (110).

18. The system of claim 17 , wherein the analysis tool (104) is further configured to display the visualization output, including a thickness map, on a display device.

19. 19. The system of claim 15, wherein the analysis tool (104) is further configured to use the distance data (110) to generate a function that allows a distance between the outer surface (114) and the inner surface (118) to be calculated via the function at any selected point along the outer surface (114).

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