Detecting structural inconsistencies 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]

[0001] The present disclosure relates generally to non-destructive testing, and more particularly to detecting structural inconsistencies using non-destructive testing and design distance data. [Background technology]

[0002] Nondestructive testing (NDI) is a testing and analysis technique used to evaluate the properties of a structure without damaging the structure. Nondestructive testing may also be referred to as nondestructive testing (NDT), nondestructive inspection (NDE), and nondestructive evaluation (NDE). Evaluating data generated through currently available nondestructive testing 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 fuselage structures (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 may be unnecessarily tedious and time-consuming. Summary of the Invention

[0003] In one or more examples, a method for inspecting a structure is provided. A scan plane representing an inspection area of ​​the structure is identified. A plurality of sample points on an exterior surface identified from a model of the structure and a corresponding plurality of projected points on an interior surface identified from the model of the structure are generated using the scan plane, a first geometric representation of the exterior surface, and a second geometric representation of the interior surface. Range data is calculated using the plurality of sample points and the corresponding plurality of projected points. Sensor data generated for the inspection area of ​​the structure is analyzed using the range data to detect the presence of an inconsistency in the structure.

[0004] In one or more examples, a system includes a memory for storing a machine-readable medium including machine-executable code and a processor coupled to the memory, the processor configured to execute the machine-executable code to cause the processor to implement an analysis tool configured to: identify a scan plane representing an inspection area of ​​a structure; generate, using the scan plane, a first geometric representation of the exterior surface, and a second geometric representation of the interior surface, a plurality of sample points on an exterior surface identified from a model of the structure and a corresponding plurality of projected points on an interior surface identified from the model of the structure; and calculate distance data using the plurality of sample points and the corresponding plurality of projected points, the distance data identifying a distance between a point pair formed by a sample point of the plurality of sample points and a corresponding one of the plurality of projected points.

[0005] In one or more examples, a method for calculating range data for an aircraft structure is provided. A scan plane representing an inspection area of ​​the aircraft structure is identified. An exterior surface of the aircraft structure and a set of interior surfaces of the aircraft structure are identified using a model of the aircraft structure. A plurality of sample points on the exterior surface and a corresponding plurality of projected points on the selected interior surface for each selected interior surface of the set of interior surfaces are generated using the scan plane, a first geometric representation of the exterior surface, a second geometric representation of the selected interior surface, and a spatial indexing algorithm. Range data is calculated using the plurality of sample points and the corresponding plurality of projected points generated for each selected interior surface of the set of interior surfaces. The range data provides as-design data for use in comparison with sensor data generated for the aircraft structure to detect misalignments of the aircraft structure.

[0006] These features and functions may be realized alone in various embodiments of the present disclosure or may be combined in yet further embodiments, further details of which can be seen with reference to the following description and drawings.

[0007] The novel features believed characteristic of the illustrative embodiments are set forth in the appended claims. However, the illustrative embodiments, preferred modes of use, and further objects and features thereof will best be understood by reference to the following detailed description of illustrative embodiments of the present disclosure, taken in conjunction with the accompanying drawings. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram of an inspection system in accordance with one or more exemplary embodiments. [Figure 2] FIG. 1 is a schematic diagram of a process for generating a scan plane in accordance with one or more exemplary embodiments. [Figure 3] 3 is a schematic diagram of a process for generating multiple sample points on the scan plane from FIG. 2 in accordance with one or more exemplary embodiments. [Figure 4] 2 is a schematic diagram of a geometric representation generated using the model of the structure from FIG. 1 in accordance with one or more exemplary embodiments. [Figure 5] 5 is a schematic diagram of relevant surface regions identified from the geometric representation of FIG. 4, in accordance with one or more exemplary embodiments. [Figure 6] FIG. 1 is an illustration of a thickness map according to one or more exemplary embodiments. [Figure 7] 1 is a flowchart of a process for inspecting a structure in accordance with one or more exemplary embodiments. [Figure 8] 1 is a flowchart of a process for identifying a scan plane in accordance with one or more exemplary embodiments. [Figure 9] 1 is a flowchart of a process for generating sample points in accordance with one or more exemplary embodiments. [Figure 10] 1 is a flowchart of a process for generating projection points in accordance with one or more exemplary embodiments. [Figure 11] 1 is a flowchart of a process for identifying relevant surface regions of a surface in accordance with one or more exemplary embodiments. [Figure 12] 1 is a flowchart of a process for generating a set of functions for a structure in accordance with one or more exemplary embodiments. [Figure 13] 1 is a flowchart of a process for identifying multiple scan points in accordance with one or more exemplary embodiments. [Figure 14] 1 is a flowchart of a process for calculating range data for aircraft structures in accordance with one or more exemplary embodiments. [Figure 15] FIG. 1 is an illustration of a block diagram of a data processing system in accordance with an illustrative embodiment. [Figure 16] FIG. 1 is an illustration of an aircraft manufacturing and service method in accordance with an illustrative embodiment. [Figure 17] FIG. 1 is an illustration of a block diagram of an aircraft in accordance with an illustrative embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] The exemplary embodiments described below provide methods and systems for generating as-design data for use with sensor data to detect the presence of structural misalignments. The as-design data includes as-design distance data. This as-design data identifies distances between various surface layers within the structure that are designed for the structure. As one example, for an aircraft structure, the as-design data may provide the theoretical distances between the outer and inner mold lines of the aircraft structure at various locations along the aircraft structure.

[0010] The illustrative embodiments described herein 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. The as-design data includes distance data that specifies distances for each of a plurality of sample points. In some examples, these distances include thicknesses, such as part thicknesses, material thicknesses, etc.

[0011] For example, distance data may be calculated for each of multiple inspection regions designed for the structure. An inspection region may be a region, section, or zone of the structure designed for inspection in a single pass. Within this inspection region, an inspection device (e.g., a non-destructive inspection device) may follow a pattern that includes multiple paths and multiple scan locations along each path of the multiple paths. In one or more examples, the sample points are theoretical or abstract representations of scan locations where non-destructive inspection has been or will be performed.

[0012] Additionally, the exemplary embodiments described herein provide methods and systems for generating a function that allows these types of design distances to be calculated for any location on a structure, regardless of the scan pattern used to inspect the structure. The function may be, for example, a continuous function. Thus, the automated process for calculating distance data may not need to be repeated for different types of scan patterns or previously undisplayed scan locations.

[0013] Referring now to the figures, Figure 1 is a block diagram of an inspection system in accordance with one or more example 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] Structure 101 may take any of a number of different forms. In one or more examples, structure 101 takes the form of an aircraft structure. For example, structure 101 may take the form of a fuselage structure 103 (e.g., a barrel fuselage structure). Fuselage structure 103 may be the entire fuselage of an aircraft, such as aircraft 1700 described below with respect to FIG. 17, or a portion of the fuselage. In other examples, structure 101 takes the form of a wing structure, a tail section, a nose section, a control surface structure (e.g., a flap, a stabilizer, an aileron, etc.). Thus, depending on the implementation, structure 101 may be a part, an assembly, a system, a collection of surfaces, or any other type of structural entity.

[0015] In one or more examples, the inspection system 100 includes a sensor system 102 and an analysis tool 104. The sensor system 102 may include one or more sensors used to generate sensor data 106. In one or more examples, the 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 ultrasonic inspection of the structure 101. During inspection, the ultrasonic device may move (or scan) over the structure 101. The ultrasonic device may be separated from the exterior surface 105 of the structure 101 by a couplant (e.g., oil) or water.

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

[0018] In one or more examples, the analysis tool 104 may be communicatively coupled to the sensor system 102. For example, the analysis tool 104 may be able to communicate with the 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, but not limited to, program code configured to run on a processor unit. When using firmware, the operations performed by the analysis tool 104 may be implemented using, for example, but not limited to, program code and data configured to run on a processor unit and stored in persistent memory.

[0020] When hardware is employed, the hardware may include one or more circuits that operate to perform the operations performed by analysis tool 104. Depending on the implementation, the hardware may take the form of a circuit system, an integrated circuit, an application specific integrated circuit (ASIC), a programmable logic device, or some 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. The device can be permanently configured to perform these operations or can be reconfigurable. A programmable logic device can take the form of, for example, but not limited to, a programmable logic array, programmable array logic, field programmable logic array, field programmable gate array, or some 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 may take the form of any of a number of different types of computing platforms. For example, the computer system 108 may include a single computer or multiple computers in communication with each other. In other examples, the computer system 108 may take the form of a cloud computing system, a smartphone, a tablet, or 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 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 can receive input 111 that is used to generate the distance data 110. The input 111 can 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. The input 111 can, for example, identify a user (or customer) request.

[0024] Distance data 110 includes data identifying various distances between one or more surface pairs associated with structure 101. A surface pair includes two surfaces that are aligned (e.g., overlapping). For example, distance data 110 may identify the distance between a first location along the exterior surface of a fuselage skin and a second location on the interior surface of the fuselage skin. The second location on the interior surface lies along a vector that is substantially normal to the exterior surface of the first location. As used herein, "substantially normal" means perpendicular or nearly perpendicular within 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 a design of structure 101. Accordingly, 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 identified in distance data 110 may be thicknesses of parts or portions of structure 101. Thus, 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, analysis tool 104 generates distance data 110 based on model 112 of structure 101. Model 112 may be a database representation of structure 101. For example, model 112 may be a computer-aided design (CAD) model of structure 101. In some examples, model 112 includes data that can be used to construct a three-dimensional model of structure 101. In one or more examples, model 112 is received at input 111.

[0027] Analysis tool 104 uses model 112 to identify a number of surfaces 113 for structure 101. These surfaces 113 are as-designed surfaces for structure 101. In one or more examples, surfaces 113 include an exterior surface 114 and a set of interior surfaces 116. Set of interior surfaces 116 includes one or more interior surfaces of structure 101. Each of surfaces 113 may be a continuous surface or a discontinuous surface.

[0028] When structure 101 takes the form of an aircraft structure, such as fuselage structure 103, exterior surface 114 may be the outer mold line (OML) of fuselage structure 103, which is the outermost surface of 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 an 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 that is located closer to the central axis of the fuselage structure 103 than the outer surface 114. For example, the fuselage structure 103 may have a barrel shape, a cylindrical shape, a cylindrical-type shape, or a similar shape. In some examples, the inner surface 118 is a surface of a fuselage skin that faces inwardly of the fuselage structure and forms at least a portion of an inner mold line (IML) of the fuselage structure 103. In some examples, the inner surface 118 is a surface formed by a 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 stringers of the fuselage structure 103.

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

[0031] The analysis tool 104 generates a plurality of sample points 122 and, for each surface of the set of inner surfaces 116, generates a corresponding plurality of projected points 123 using the scan plane 120, the outer surface 114, the set of inner surfaces 116, and a spatial registration algorithm 124. The sample points 122 are located along the outer surface 114. As used herein, a "sample point" is a point (e.g., defined in three dimensions) that is projected from the scan plane 120 in a vector that is substantially perpendicular to the scan plane 120 and that is on or coincident with the outer surface 114.

[0032] Projection point 123 is located along an inner surface (e.g., inner surface 118) of set of inner surfaces 116. As used herein, a "projection point" on an inner surface is a point (e.g., defined in three dimensions) that is projected from a corresponding point in sample points 122 along a vector that is substantially perpendicular to outer surface 114 and that is on or coincident with the inner surface.

[0033] The sample points 122 and the projection points 123 may be generated using a spatial registration algorithm 124. In one or more examples, the spatial registration algorithm 124 is used to identify the portions of the model 112 that are most relevant for generating the sample points 122 and the projection points 123. Thus, the spatial registration algorithm 124 may be used to reduce the data that needs to be processed to generate the sample points 122 and the projection points 123. This reduces the overall time and processing resources required to generate the range data 110.

[0034] For example, analysis tool 104 may use a spatial registration algorithm 124 to narrow down the portion of interior surface 118 that is aligned with or overlapped by scan plane 120. Thus, the entire interior surface 118 need not be processed to generate projection points 123. Spatial registration algorithm 124 may include, for example, without limitation, one or more algorithms for constructing a k-dimensional tree. Examples of how sample points 122 and projection points 123 may be generated are described in more detail below in FIGS. 9 and 10, respectively.

[0035] Using the sample points 122 and the projection points 123, the analysis tool 104 calculates distance data 110. The distance data 110 includes the distance between each corresponding point pair (sample point, projection point pair) from the sample points 122 and the projection points 123. Thus, the distance data 110 provides information based on the model 112 of the structure 101 that 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 to distance data 110 to determine whether any actual distances between surfaces of structure 101, as determined by distance data 110, are outside a selected tolerance from the designed distances. Such differences may signal the detection of one or more inconsistencies in structure 101.

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

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

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

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

[0041] The block diagram of Figure 1 is not intended to suggest physical or architectural limitations to the manner in which an example embodiment may be implemented. Other components in addition to or in place of the illustrated components may be used. Some components may be optional. Moreover, the blocks are presented to illustrate functional components. When implemented in an example embodiment, one or more of these blocks may be combined or divided, or one or more of these blocks may be combined and divided into different blocks.

[0042] 2-6 are diagrams illustrating various steps that may be involved in generating distance data, such as distance data 110 of FIG. 1. Thus, FIGS. 2-6 are described with continued reference to FIG.

[0043] 2 is a schematic diagram of a process for generating a scan plane according to one or more exemplary embodiments. In particular, the process shown in FIG. 2 may be implemented by analysis tool 104 of FIG.

[0044] First, coordinates 200 are identified. Coordinates 200 may define an area of ​​structure 101 that has been or will be inspected or "scanned" using sensor system 102 of FIG. 1. For example, coordinates 200 may define inspection area 121 of structure 101 of FIG. 1. Coordinates 200 are in a reference coordinate system. This reference coordinate system may be, for example, the coordinate system of model 112 of structure 101 of FIG. 1.

[0045] 1. For example, input 111 may include a program used to control sensor system 102 and move sensor system 102 along structure 101, or coordinates 200 extracted from that program. In some cases, input 111 may include the program itself, and analysis tool 104 may determine coordinates 200 from that program.

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

[0047] The coordinates 200 are processed to identify an initial surface 202 that represents the inspection region 121 of Figure 1. In particular, the coordinates 200 are used to identify a boundary 201 for use in defining the initial surface 202. The initial surface 202 may be represented in a three-dimensional domain.

[0048] In some cases, when processed with respect to 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 an initial surface 202 having this type of boundary 201, a convex shape 204 may be generated with respect to the initial surface 202 in the two-dimensional domain. The convex shape 204 includes one or more local convex shells that ensure that the initial surface 202 is completely or generally contained within the convex shape 204. As such, the convex shape 204 may 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] A scanplane 206 is then generated in the three-dimensional domain based on the convex shape 204. The scanplane 206 is one example of an implementation for the scanplane 120 of FIG. 1. The scanplane 206 has a substantially convex shape or boundary. As used herein, "substantially convex" means convex or nearly convex. In one or more examples, the scanplane 206 represents the global convex or nearly convex shape of the examination region 121. In these examples, the scanplane 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 itself be substantially convex. Thus, the initial surface 202 may be used as the scan surface 206. In some examples, the initial surface 202 is used as the scan surface 206 when the shape of the boundary 201 is simple enough so as not to increase the amount of processing time or resources required more than desired.

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

[0052] First, multiple reference curves 300 are identified along the scan plane 206. Each of the reference curves 300 represents a path along which the sensor system 102 moves within the inspection region 121 of FIG. 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 the input 111 from which the coordinates 200 of FIG. 2 are identified. In other examples, the reference curves 300 are generated based on a different portion of the input 111 received at the analysis tool 104.

[0053] The reference curves 300 may be equally spaced. For example, the 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 and equally spaced from one another. In other examples, all of the reference curves 300 may not be equally spaced.

[0054] A set of points 302 along the scan plane 206 is then 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 along each of the reference curves 300 according to a spacing distance. This spacing distance may be provided by the input 111. In some examples, this spacing distance is provided by the same portion of the input 111 from which the coordinates 200 of FIG. 2, the reference curve 300, or both, are generated. In other examples, the spacing distance is provided by a different portion of the input 111.

[0055] A plurality of scan points 304 are selected from points 302 based on initial surface 202, boundary 201, or both of Figure 2. Scan points 304 are some of the points 302 that are either completely contained within boundary 201 or that overlap initial surface 202 when initial surface 202 is placed or overlaid on scan plane 206. Scan points 304 may be projected onto an outer surface, such as outer surface 114 of Figure 1, to generate sample points 122 of Figure 1.

[0056] Figure 4 is a schematic diagram of a geometric representation generated using model 112 of structure 101 of Figure 1, in accordance with one or more exemplary embodiments. Geometric representation 400 may be generated for one of surfaces 133 identified from model 112 of Figure 1. In the illustrated example, geometric representation 400 is generated for exterior surface 114 of Figure 1.

[0057] The geometric representation 400 includes a plurality of patches 402. As used herein, 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 a curved shape, a polygonal shape, an irregular shape, or other types of shapes. In one or more examples, the patches 402 may have the same shape and size (e.g., the same polygonal shapes forming a polygonal mesh), or may have different shapes, different sizes, or both. Alternatively, the patches may be referred to as "faces" or "surface sections." In some examples, the geometric representation 400 is referred to as a patched or patch-based representation.

[0058] 1 may sample the patches 402 to identify at least one patch point for each of the patches 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 an associated surface region identified from the geometric representation 400 of Figure 4, according to one or more exemplary embodiments. The associated surface region 500 includes a selected portion 502 of the patch 402 from Figure 4 that is determined to overlap, coincide with, and / or be close to the scan surface 206 when the scan surface 206 is aligned with or placed on the geometric representation 400 of Figure 4. The associated surface 500 thus corresponds to a surface (e.g., the outer surface 114) created by the geometric representation 400 representing the portion of the scan surface 206 that is most associated with that surface.

[0060] The one or more k-dimensional trees generated for the geometric representation 400 are used to refine the refined patches 402 and identify associated surface regions 500. The associated surface regions 500 provide a more focused space in which to identify the sample points 122 of FIG. 1. By using the associated surface regions 500 to generate the sample points 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 may be reduced. Although the associated surface regions 500 have been described as being generated using k-dimensional trees, in other examples, other types of techniques for accelerating the process of identifying the associated surface regions 500 may be used.

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

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

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

[0064] 7 is a flowchart of a process for inspecting a structure according to one or more example embodiments. Process 700 of FIG. 7 may be implemented using analysis tool 104 of FIG.

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

[0066] A plurality of sample points on the outer surface identified from the model of the structure and a corresponding plurality of projected points on the inner surface identified from the model of the structure are generated using the scan plane, the first geometric representation of the outer surface, the second geometric representation of the inner surface, and a spatial registration algorithm (step 704). The plurality of sample points and the corresponding plurality of projected points may be, for example, sample points 122 and projected points 123, respectively, of FIG.

[0067] Using the plurality of sample points and the corresponding plurality of projection points, distance data is calculated (step 706). The distance data is, for example, distance data 110 of FIG. 1. Optionally, process 700 may further include generating a visualization output (step 708) using the distance data. This visualization output may be visualization output 128 of FIG. 1, but may take several different forms. For example, the visualization output may include one or more two-dimensional distance maps, one or more three-dimensional distance maps, or a combination thereof.

[0068] The sensor data generated for the inspection area of ​​the structure is analyzed using the distance data to detect the presence of inconsistencies in the structure (step 710), with the process then terminating. The sensor data may be, for example, sensor data 106 of FIG. 1. In one or more examples, the distance data includes distances between each corresponding point pair from a plurality of sample points and a corresponding plurality of projection points. In essence, the distance data includes the distance for each pair of corresponding sample points and projection points.

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

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

[0071] The coordinates are then used to create an initial surface (step 804). The initial surface is three-dimensional. Initial surface 202 in Figure 2 is one example of an implementation 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 scan plane representing the inspection area of ​​the structure (step 808), with the process terminating thereafter.

[0073] Otherwise, a convex shape is generated for the initial surface (step 810). The convex shape includes one or more local convex hulls that ensure that 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. Convex shape 204 in FIG. 2 is one example of an implementation of a convex shape generated in step 810.

[0074] A scanplane representing the inspection area of ​​the structure is formed using the 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 to form the scanplane. Scanplane 206 of Figure 2 is one example implementation of a scanplane generated in step 812.

[0075] 9 is a flowchart of a process for generating sample points according to one or more example embodiments. Process 900 of FIG. 9 may be implemented using analysis tool 104 of FIG. 1. Furthermore, process 900 may be one example of how at least a portion of step 704 of FIG. 7 may be implemented.

[0076] Process 900 may begin by identifying an associated surface region (step 902) that represents a portion of the exterior surface corresponding to the scan plane. Associated surface region 500 in FIG. 5 is one example of an implementation of the associated surface region identified in step 902. If the structure is a fuselage structure, the exterior surface may be an exterior mold line of the fuselage structure. The exterior surface may be identified from a model of the structure.

[0077] Scan points are identified on a scan plane representing an inspection area of ​​the structure (step 904). The scan plane may be, for example, scan plane 120 of Figure 1. The scan plane may be generated, for example, using process 800 of Figure 8.

[0078] A scan point is selected for processing (step 906). The associated surface region is processed to identify the patch of associated surface region that is closest to the selected scan point (step 908). For example, step 908 may be performed using one or more k-dimensional trees constructed for the associated surface region, constructed for the interior surfaces, or constructed for both.

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

[0080] A determination is made as to whether any unprocessed scan points remain (step 912). If so, process 900 returns to step 906 above. Otherwise, the process ends.

[0081] 10 is a flowchart of a process for generating projection points according to one or more example embodiments. Process 1000 of FIG. 10 may be implemented using analysis tool 104 of FIG. 1. Furthermore, process 1000 may be one example of how at least a portion of step 704 of FIG. 7 may be implemented.

[0082] Process 1000 may begin by identifying a surface region of interest that represents a portion of the interior surface corresponding to the scan surface (step 1002). This surface region of interest may be implemented in a manner similar to surface region of interest 500 of Figure 5. The interior surface of step 1002 may be, for example, interior surface 118 of Figure 1. The interior surface may be identified from a model of the structure.

[0083] From the plurality of sample points, one sample point is selected for processing, step 1004. The plurality of sample points may be, for example, sample points generated by process 900 above.

[0084] The associated surface region is processed to identify a patch of the associated surface region that is closest to the selected sample point (step 1006). A projection point is formed (step 1008) using a point on the patch that intersects a vector that is substantially normal to the exterior surface at the location of the selected sample point. In one or more examples, step 1008 may be performed by identifying as the projection point a point in the patch that corresponds to the interior surface that has the smallest 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 determination is made as to whether any unprocessed sample points remain (step 1010). If so, process 1000 returns to step 1004 above. Otherwise, the process ends.

[0086]

[0013] 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 of Figure 11 may be implemented using analysis tool 104 of Figure 1. Furthermore, process 1100 may be one example of how step 902 of Figure 9 and step 1002 of Figure 10 may be implemented.

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

[0088] The geometric representation is then sampled to generate sampling points (step 1104). A set of k-dimensional trees is constructed using the sampling points (step 1106). The set of k-dimensional trees is used to narrow the sampling points to a focused set of sampling points (step 1108). Based on the focused set of sampling points, an associated surface region is formed that includes a selected portion of the patch (step 1110), after which the process ends. Although process 1100 is described using k-dimensional trees, in other examples, other types of spatial registration algorithms 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 in accordance with one or more example embodiments. Process 1200 of Figure 12 may be implemented using inspection system 100 of Figure 1. As an example, process 1200 may be implemented using analysis tool 104 of Figure 1. Although process 1200 is described with respect to an aircraft structure, process 1200 may also be used with other types of structures.

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

[0091] A set of scan planes representing a corresponding set of inspection areas of the aircraft structure are identified (operation 1204). For example, the aircraft structure may be a large fuselage structure in which multiple inspection areas (or zones) have been identified. A scan plane is identified for each of these various inspection areas. In one or more examples, these inspection areas do not overlap. In other examples, two or more of the inspection areas may at least partially overlap.

[0092] A scan plane is selected for processing (step 1206). A spatial registration algorithm is used to identify an associated surface region corresponding to the selected scan plane, which represents a portion of the exterior surface (step 1208). The associated surface region may include a portion of patches selected from the geometric representation of the exterior surface. The associated surface region corresponds to the selected scan plane by including at least those patches that would completely overlap the selected scan plane if the selected scan plane were placed on the exterior surface. The spatial registration algorithm described in process 1200 may be, for example, spatial registration algorithm 124 of FIG. 1.

[0093] Using the selected scan plane, an associated surface region representing a portion of the exterior surface, and a spatial registration algorithm, a plurality of sample points of the exterior surface are generated (step 1210).

[0094] An interior surface is selected from the set of interior surfaces for processing (step 1212). A spatial registration algorithm is used to identify an associated surface region corresponding to the selected scan plane, which represents a portion of the selected interior surface (step 1214). The associated surface region may include a portion of patches selected from the geometric representation of the selected interior surface. The associated surface region corresponds to the selected scan plane by including at least those patches that would completely overlap the selected scan plane if the selected scan plane were placed over the selected interior surface.

[0095] Using the selected scan plane, an associated surface region representing a portion of the selected interior surface, and a spatial indexing tree algorithm, a plurality of projection points on the selected interior surface are generated (step 1216).

[0096] Distance data specifying the distance between the outer surface and the selected inner surface is calculated using the plurality of sample points and the plurality of projection points (step 1218). A determination is made as to whether any unprocessed inner surfaces remain in the set of inner surfaces (step 1220). If unprocessed inner surfaces remain, process 1200 returns to step 1212 above. Otherwise, a determination is made as to whether any unprocessed scan surfaces remain in the set of scan surfaces (step 1222). If unprocessed scan 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 inner surface of the set of inner surfaces. Each function may be used to determine, at any point along the outer surface, the design distance between the outer surface and the corresponding inner surface.

[0098] For steps 1208 and 1210, generating associated surface regions representing a portion of the exterior surface corresponding to each scanplane in the set of scanplanes reduces the overall processing time and resources required to generate sample points for each scanplane. Similarly, for steps 1214 and 1216, generating associated surfaces representing a portion of each interior surface in the set of interior surfaces corresponding to the selected scanplanes reduces the overall processing time and resources required to generate projection points for each interior surface.

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

[0100] Process 1300 may begin by identifying multiple reference curves of a scan plane (step 1302) corresponding to multiple paths used by a sensor system to scan an inspection area of ​​a structure. In step 1302, the multiple reference curves may be identified based on a program used or being used to control the sensor system. In one or more examples, the reference curves are substantially parallel to one another and equally spaced apart. As used herein, "substantially parallel" means parallel or nearly parallel. The sensor system of step 1302 may be, for example, sensor system 102 of FIG. 1.

[0101] Next, a plurality of scan points are generated (step 1304) using the plurality of reference curves and the spacing distance used by the sensor system, after which the process ends. The spacing distance provides the distance of the identified scan points along the same reference curve.

[0102] Step 1304 may be performed in various ways. In one or more examples, step 1304 includes identifying points along each of the plurality of reference curves based on a spacing distance to form a set of points. Step 1304 may further include selecting a portion of the set of points that are within a boundary corresponding to the examination region to form a plurality of scan points.

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

[0104] Process 1400 may begin by identifying a scan plane representing an inspection area of ​​an aircraft structure (operation 1402). Using a model of the aircraft structure, an exterior surface of the aircraft structure and a set of interior surfaces of the aircraft structure are identified (operation 1404).

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

[0106] Using the plurality of sample points and the corresponding plurality of projection points generated for each selected inner surface of the set of inner surfaces, distance data is calculated, which provides as-design data for use with sensor data generated for the aircraft structure to detect misalignments of the aircraft structure (operation 1408), with the process terminating thereafter. In some examples, operation 1408 includes generating a function (e.g., a continuous function) for each pairing of an exterior surface with an inner surface of the set of inner surfaces.

[0107] This function, which may be, for example, function 126 in FIG. 1 , may be used to quickly and reliably identify the as-designed distance between an exterior surface and a particular interior surface for any location on the exterior surface. This as-designed distance may be compared to measurements generated by a sensor system to assess whether the actual aircraft structure matches the aircraft structure design. A difference between the as-designed distance and measurements generated by the sensor system that fall outside a selected tolerance may indicate the presence of an inconsistency in the aircraft structure. The inconsistency may include voids, delaminations, cracks, undesirable levels of porosity, other types of inconsistencies, or a combination thereof.

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

[0109] Processor unit 1504 is configured to execute software instructions to perform certain operations. Processor unit 1504 may comprise several processors, a multi-processor core, and / or some other type of processor, depending on the implementation. In some cases, 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 the operating system, applications, and / or programs executed by processor unit 1504 may be located in storage devices 1506. Storage devices 1506 may be in communication with processor unit 1504 through communications framework 1502. As used herein, a storage device, also referred to as a computer-readable storage device, is any hardware capable of storing information on a temporary and / or permanent basis. This information may include, but is not limited to, data, program code, and / or other information.

[0111] Memory 1514 and persistent storage 1516 are examples of storage device(s) 1506. Memory 1514 may take the form of, for example, random access memory or any type of volatile or nonvolatile storage device. Persistent storage 1516 may include any number of components or devices. For example, persistent storage 1516 may include a hard drive, a flash memory, a rewritable optical disk, a rewritable magnetic tape, or some combination of these. The media used by persistent storage 1516 may or may not be removable.

[0112] Communications unit 1508 allows data processing system 1500 to communicate with other data processing systems and / or devices. Communications unit 1508 may provide communications using physical and / or wireless communications links.

[0113] Input / output unit 1510 allows for receiving input from, and sending output to, other devices connected to data processing system 1500. For example, input / output unit 1510 may allow for receiving user input through a keyboard, a mouse, and / or some other type of input device. As another example, input / output unit 1510 may allow for sending output to a printer connected to data processing system 1500.

[0114] Display 1512 is configured to display information to a user and may include, for example, but not limited to, a monitor, a touch screen, a laser display, a holographic display, a virtual display device, and / or some other type of display device.

[0115] In one or more examples, the processes of the different illustrative embodiments may be performed by processor unit 1504 using computer-implemented instructions, which may be referred to as program code, computer-usable program code, or computer-readable program code, and may be read and executed by one or more processors within processor unit 1504.

[0116] In these examples, program code 1518 is operably disposed on a selectively removable computer readable medium 1520 and may be loaded onto or transmitted over to data processing system 1500 for execution by processor unit 1504. Program code 1518 and computer readable medium 1520 together form computer program product 1522. In one or more examples, computer readable medium 1520 may be computer readable storage medium 1524 or computer readable signal medium 1526.

[0117] Computer readable storage media 1524 is a physical or tangible storage device used to store program code 1518, rather than a medium that propagates or transmits program code 1518. By way of example, and not limitation, computer readable storage media 1524 may be an optical or magnetic disk or a persistent storage device connected to data processing system 1500.

[0118] Alternatively, program code 1518 may be transmitted to data processing system 1500 using computer readable signal medium 1526. Computer readable signal medium 1526 may be, for example, a propagated data signal embodied with program code 1518. This data signal may be an electromagnetic, optical, and / or some other type of signal that may be transmitted over a physical and / or wireless communications link.

[0119] The illustration of data processing system 1500 in Figure 15 is not meant to provide architectural limitations to the manner in which illustrative embodiments may be implemented. Different illustrative embodiments may be implemented in a data processing system including components in addition to or instead of those illustrated with respect to data processing system 1500. Additionally, the components illustrated in Figure 15 may differ from the depicted example.

[0120] An example embodiment of the present disclosure may be described with reference to aircraft manufacturing and service method 1600 shown in Figure 16 and aircraft 1700 shown in Figure 17. Referring initially to Figure 16, an aircraft manufacturing and service method is illustrated in accordance with an example embodiment. During pre-production, aircraft manufacturing and service method 1600 may include specification and design 1602 and material procurement 1604 of aircraft 1700 in Figure 17.

[0121] During production, component and subassembly manufacturing 1606 and systems integration 1608 of the aircraft 1700 of Figure 17 takes place. The aircraft 1700 of Figure 17 may then go through certification and delivery 1610 and be placed into service 1612. While in customer service 1612, the aircraft 1700 of Figure 17 is scheduled for routine maintenance and service 1614, which may include modification, reconfiguration, refurbishment, and other maintenance or service.

[0122] Each of the processes in aircraft manufacturing and service method 1600 may be performed or carried out by a system integrator, a third party, and / or an operator. In these examples, the operator may be a customer. As used herein, 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, a leasing company, a military organization, a service organization, etc.

[0123] Referring now to Figure 17, a diagram of an aircraft is shown in which illustrative embodiments may be implemented. In this example, aircraft 1700 is manufactured by aircraft manufacturing and service method 1600 in Figure 16 and may include airframe 1702 with a number of systems 1704 and interior 1706. Example systems 1704 include one or more of propulsion system 1708, electrical system 1710, hydraulic system 1712, and environmental system 1714. Any number of other systems may be included. While an aerospace example is provided here, various illustrative embodiments may also be applied to other industries, such as the automotive industry.

[0124] Apparatus and methods embodied herein may be used during at least one of the stages of aircraft manufacturing and service method 1600 in Figure 16. In particular, manufacturing system 164 from Figure 1 may be used to manufacture tool 162 during any one of the stages of aircraft manufacturing and service method 1600. For example, without limitation, inspection system 100, sensor system 102, or analysis tool 104 from Figure 1 may be used during at least one of component and subassembly manufacturing 1606, system integration 1608, certification and delivery 1610, routine production and service 1614, and / or any other stage in aircraft manufacturing and service method 1600. Furthermore, inspection system 100 may be used to inspect one or more aircraft structures of aircraft 1700, such as, without limitation, one or more structures of fuselage 1702, interior 1706, or both of aircraft 1700 in Figure 17. The analysis tool 104 of FIG. 1 may be used to calculate range data 110 for these aircraft structures of the aircraft 1700 .

[0125] In one or more examples, components or subassemblies produced in component and subassembly manufacturing 1606 in FIG. 16 may be manufactured or produced in a manner similar to components or subassemblies produced while aircraft 1700 is in service 1612 in FIG. 16 . As yet another example, one or more apparatus embodiments, method embodiments, or a combination thereof may be utilized during the manufacturing stages of component and subassembly manufacturing 1606 and system integration 1608 in FIG. 16 . One or more apparatus embodiments, method embodiments, or a combination thereof may be utilized during aircraft 1700 in service 1612 in FIG. 16 and / or during maintenance and service 1614. Utilizing various different illustrative embodiments may substantially streamline assembly of aircraft 1700 and / or reduce costs of aircraft 1700. Furthermore, one or more embodiments described herein may be used as part of propulsion system 1708 of aircraft 1700.

[0126] The flowcharts and block diagrams in the various depicted embodiments illustrate the architecture, functionality, and operation of some possible implementations of apparatuses and methods in the illustrative embodiments. As such, each block in the flowcharts or block diagrams may represent a module, a segment, a function, and / or a portion of an operation or step.

[0127] The flowcharts and block diagrams in the various depicted embodiments illustrate the architecture, functionality, and operation of some possible implementations of apparatus and methods in the illustrative embodiments. For example, in some cases, two blocks shown in succession may be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. Also, other blocks may be added in addition to the blocks shown in the flowcharts or block diagrams.

[0128] As used herein, the phrase "at least one of" when used in conjunction with a list of items means that various combinations of one or more of the listed items can be used and that only one of the listed items may be required. An item may refer to a particular object, article, step, operation, process, or category. In other words, "at least one of" means that any combination of items or any number of items from the list can be used, but not all of the listed items are required. For example, without limitation, "at least one of item A, item B, or item C" or "at least one of item A, item B, and item C" may mean, e.g., "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" may mean, without limitation, "two item A, one item B, and ten item C," "four item B and seven item C," or some other suitable combination.

[0129] The present disclosure further includes embodiments according to the following clauses:

[0130] Article 1. A method for inspecting a structure (101), comprising: Identifying (702) a scan plane (120) representing an inspection area (121) of a structure (101); generating (704) a plurality of sample points (122) on the outer surface (114) identified from the model (112) of the structure (101) and a corresponding plurality of projected points (123) on the inner surface (118) identified from the model (112) of the structure (101) using the scan plane (120), the first geometric representation (400) of the outer surface (114), and the second geometric representation of the inner surface (118); calculating (706) distance data (110) using the plurality of sample points (122) and the corresponding plurality of projection points (123); analyzing (710) the 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:

[0131] Article 2. generating (708) a visualization output (128) of the distance data (110) (702); 2. The method of clause 1, further comprising:

[0132] Article 3. Displaying the visualization output (128) on a display device (130). 3. The method of claim 2, further comprising: wherein the visualization output includes a color-coded thickness map representing at least a portion of the distance data.

[0133] Article 4. generating (1224) a function (126) using the distance data (110) such that the distance between the outer surface (114) and the inner surface (118) can be calculated via the function (126) at any selected point along the outer surface (114); 4. The method of any one of clauses 1 to 3, further comprising:

[0134] Clause 5. Identifying (702) the scan plane (120) Identifying (802) coordinates (200) of an inspection area (121) of a structure (101); Creating (804) an initial surface (202) using the coordinates (200); generating (810) a convex shape (204) of an 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; 5. The method of any one of clauses 1 to 4, comprising:

[0135] Article 6. generating (704) a plurality of sample points (122) on the exterior surface (114); Identifying (902) a relevant surface area representing a portion of the exterior surface (114) corresponding to the scan plane. 6. The method of any one of clauses 1 to 5, comprising:

[0136] Article 7. Generating (704) the plurality of sample points (122) on the exterior surface (114) includes: Identifying (904) a plurality of scan points (304) on the scan plane; processing (908) the associated surface area for each selected scan point to identify a patch of the associated surface area 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 with a vector substantially normal to the scan plane (120) at the location of the selected scan point of the plurality of scan points (304); 7. The method of clause 6, further comprising:

[0137] Article 8. Identifying (904) a plurality of scan points (304) identifying (1302) a plurality of reference curves for a scan plane (120) corresponding to a plurality of paths used by a sensor system (102) to scan an inspection area (121) of a structure (101); forming (1304) a plurality of scan points (304) using the plurality of reference curves and the spacing distance used by the sensor system (102); 7. The method according to clause 7, comprising:

[0138] Article 9. forming (1304) a plurality of scan points (304); identifying points along each of the plurality of reference curves based on a spacing distance to form a set of points; selecting a portion of the set of points within a boundary corresponding to the examination area (121) to form a plurality of scan points (304); 9. The method according to clause 8, comprising:

[0139] Article 10. Identifying (902) relevant surface regions generating (1102) a first geometric representation of an exterior surface (114) 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) a related surface region including a selected portion of the plurality of patches based on the focused set of sampling points; 6. The method according to clause 5, comprising:

[0140] Article 11. generating (704) a corresponding plurality of projection points (123) on the inner surface (118); Identifying (1002) a relevant surface area representing a portion of the interior surface (118) corresponding to the scan plane (120). 11. The method of any one of clauses 1 to 10, comprising:

[0141] Article 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 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 with a vector substantially normal to the outer surface (114) at the location of the selected sample point; 12. The method of clause 11, further comprising:

[0142] Article 13. Identifying (1002) relevant surface regions generating (1102) a second geometric representation of the interior surface (118) 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) a related surface region including a selected portion of the plurality of patches based on the focused set of sampling points; 12. The method according to clause 11, comprising:

[0143] Article 14. A plurality of sampling points and a corresponding plurality of projection points (123) form a plurality of point pairs, and calculating (706) distance data (110) Calculating the distance between each pair of points among a plurality of pairs of points 14. The method of any one of clauses 1 to 13, comprising:

[0144] Article 15. a memory (1514) for storing a machine-readable medium containing machine-executable code; a processor (1504) coupled to the memory (1514) and configured to execute 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 the structure (101); generating a plurality of sample points (122) on an outer surface (114) identified from a model (112) of the structure (101) and a corresponding plurality of projected points (123) on an inner surface (118) identified from the model (112) of the structure (101) using the scan 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 the plurality of sample points (122) and the corresponding plurality of projection points (123), the distance data (110) specifying a distance between a point pair formed by a sample point of the plurality of sample points (122) and a projection point of the corresponding plurality of projection points (123); The system is configured to run

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

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

[0147] Article 18. 18. The system of clause 17, wherein the analysis tool (104) is further configured to display a visualization output comprising the thickness map on a display device.

[0148] Article 19. 19. The system of any one of clauses 15 to 18, 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 at any selected point along the outer surface (114) via the function.

[0149] Article 20. A method for calculating distance data (110) of an aircraft structure (101, 103), comprising: identifying (1402) a scan plane (120) representing an inspection area (121) of an aircraft structure (101, 103); identifying (1404) an exterior surface (114) of the aircraft structure (101, 103) and a set of interior surfaces (116) of the aircraft structure (101, 103) using the model (112) of the aircraft structure; generating (1406) a plurality of sample points (122) on the outer surface (114) and a corresponding plurality of projected points (123) on the selected inner surfaces for each inner surface (116) of the selected set of inner surfaces using the scan plane (120), the first geometric representation (114) of the outer surface, the second geometric representation of the selected inner surfaces, and a spatial registration algorithm (124); calculating (1408) distance data (110) using the plurality of sample points (122) and the corresponding plurality of projection points (123) generated for each selected inner surface of the set of inner surfaces (116); wherein the distance data (110) provides as-designed data for use with sensor data (106) generated for the aircraft structure (101, 103) to detect inconsistencies in the aircraft structure (101, 103).

[0150] The description of various exemplary embodiments has been presented for purposes of illustration and description and is not intended to be exhaustive or to limit the embodiments to the form disclosed. Numerous modifications and variations will be apparent to those skilled in the art. Moreover, various exemplary embodiments may offer different features as compared to other preferred embodiments. The selected embodiment or embodiments have been chosen and described in order to best explain the principles and practical applications of the embodiments and to enable others skilled in the art to understand that the disclosure of the various embodiments, with various modifications, is suitable for the particular use contemplated.

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 outer surface (114) identified from a model (112) of design data of the structure (101) and a corresponding plurality of projection points (123) on an inner surface (118) identified from the model (112) of the structure (101) using the scan plane (120), a first geometric representation (400) of the outer surface (114), and a second geometric representation of the inner 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 inconsistencies in the structure (101); A method comprising:

2. Displaying the visualization output (128), including generating (708) a visualization output (128) of the distance data (110). The method of claim 1 further comprising:

3. 3. The method of claim 2, further comprising displaying the visualization output on a display device, wherein the visualization output comprises a color-coded thickness map representing at least a portion of the distance data.

4. generating (1224) a function (126) using the distance data (110) 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); The method of claim 1 , further comprising:

5. Identifying (702) the scan plane (120) Identifying (802) the 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; 5. The method of claim 1, comprising:

6. Generating (704) the plurality of sample points (122) on the exterior surface (114) comprises: Identifying (902) an associated surface area representing a portion of the exterior surface (114) corresponding to the scan surface.

6. The method of claim 1, 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 plane; 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 with a vector substantially normal to the scan plane (120) at the location of the 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) comprises: identifying (1302) a plurality of reference curves for the scanning plane (120) corresponding to a plurality 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 spacing distances 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 distance 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 the relevant surface regions (902) generating (1102) the first geometric representation of the exterior surface (114) 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 associated 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) corresponding to the scan surface (120).

11. The method of claim 1, 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 normal to the outer surface (114) at the location of the selected sample point; The method of claim 11 further comprising:

13. Identifying the relevant surface regions (1002) generating (1102) the second geometric representation of the interior surface (118) 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 associated 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 sample points (122) 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 among 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 containing machine-executable code; a processor (1504) coupled to the memory (1514) and configured to execute the machine-executable code, the machine-executable code causing 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 outer surface (114) identified from a model (112) of the structure (101) and a corresponding plurality of projected points (123) on an inner surface (118) identified from the model (112) of the structure (101) using the scan 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 the plurality of sample points (122) and the corresponding plurality of projection points (123), the distance data (110) specifying a distance between a point pair formed by a sample point of the plurality of sample points (122) and a projection point of the corresponding plurality of projection points (123); The system 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 inconsistencies in the structure (101).

17. The system of 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 is further configured to use the distance data to generate a function that allows a distance between the outer surface and the inner surface to be calculated at any selected point along the outer surface via the function.

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