Characterizing ratio of dissolved veil strand in ply of fiber material
Patent Information
- Application Number
- JP2024200248
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2018-07-23
- Filing Date
- 2024-11-18
- Publication Date
- 2025-03-26
Smart Images
Figure 2025041592000001 
Figure 2025041592000002 
Figure 2025041592000003
Abstract
Description
[Technical field]
[0001] The present disclosure relates to the field of composite materials, and in particular to fiber-reinforced composites that include thermoset veils. [Background technology]
[0002] Dry carbon fiber material typically includes a thermoplastic veil (containing multiple filaments) melted onto a strand of carbon fiber. The strand of carbon fiber provides reinforcement for the material for stability and toughness, while the thermoplastic veil bonds the strands together. Dry carbon fiber material is used as an input for the manufacture of composite parts. To ensure consistent manufacturing quality, it is desirable for the thermoplastic veil of the dry carbon fiber material to be melted into the strand of carbon fiber in a specific amount. However, inspection of the dry carbon fiber material remains a manual and labor intensive process, increasing the cost of manufacturing a composite part. This is particularly noteworthy since thousands of feet of dry carbon fiber material may be utilized in a single composite part (e.g., an aircraft wing skin).
[0003] It would therefore be desirable to have a method and apparatus that takes into account at least some of the above issues, as well as other possible issues. Summary of the Invention
[0004] The embodiments described herein utilize a feature detection process to characterize the amount of melting of a thermoplastic veil within a fibrous material (e.g., unidirectional dry carbon fiber material not impregnated with a thermosetting or thermoplastic resin, fiberglass material, material having metal or even ceramic fibers, etc.). The amount of melting can be characterized by comparing the number of melted veil filaments (resulting in a change in color / brightness) to the number of unmelted filaments. For example, the machine learning process described herein divides an image of the fibrous material into multiple slices and utilizes a trained convolutional neural network to detect the melted and unmelted filaments within the veil and determine the ratio of melted to unmelted filaments for each slice. An overall metric of the amount of melting can then be determined for the image based on the ratio determined for each slice.
[0005] One embodiment is a method for characterizing a fiber ply having a thermoplastic veil, the method including obtaining an image of a fiber material including strands of fiber and further including a veil of thermoplastic filaments, subdividing the image into a plurality of slices, determining an amount of melted filaments represented in each slice, and determining an amount of unmelted filaments represented in each slice.
[0006] A further embodiment is a non-transitory computer readable medium embodied with programmed instructions that, when executed by a processor, are operable to perform a method for characterizing a ply of fiber having a thermoplastic veil, the method including acquiring an image of a fiber material including strands of carbon fiber and further including a veil of thermoplastic filaments, subdividing the image into a plurality of slices, determining an amount of melted filaments exhibited in each slice, and determining an amount of unmelted filaments exhibited in each slice.
[0007] A further embodiment is an apparatus for characterizing a ply of fiber having a thermoplastic veil, the apparatus including an interface for receiving an image of a fiber material including strands of fiber and further including a veil of thermoplastic filaments, and a controller for subdividing the image into a plurality of slices, determining an amount of melted filaments represented in each slice, and determining an amount of unmelted filaments represented in each slice.
[0008] The apparatus and methods of the present disclosure are also referred to in the following clauses, which should not be confused with the claims:
[0009] A1 1. A method for characterizing a ply of fiber having a thermoplastic veil, comprising: Obtaining an image of a textile material including strands of fiber and further including a veil of filaments of thermoplastic material (202); Segmenting the image into a plurality of slices (204); determining (206) an amount of dissolved filaments represented in each said slice; and determining (208) the amount of undissolved filaments represented in each of said slices; and The method includes:
[0010] A2 Quantifying (210) the amount of dissolution of the veil in each slice based on the number of dissolved filaments and the number of undissolved filaments in each slice; Quantifying (212) the amount of dissolution of the veil in the image based on the amount of dissolution of the veil in each slice; There is further provided a method as set forth in paragraph A1, further comprising:
[0011] A3 determining the amount of dissolved filaments includes identifying a portion of each slice that includes dissolved filaments; Further provided is the method of paragraph A1, wherein determining the amount of undissolved filaments includes identifying a portion of each slice that includes undissolved filaments.
[0012] A4 There is further provided a method as recited in paragraph A1, wherein the determining step is performed via a trained neural network.
[0013] A5 Further provided is the method of paragraph A4, wherein the neural network comprises a convolutional neural network.
[0014] A6 comparing the amount of dissolution of the veil in the image to a design tolerance; sending a notification in response to determining that the amount of dissolution of the veil in the image is not within the design tolerance; There is further provided a method as set forth in paragraph A1, further comprising:
[0015] A7 Further provided is the method of paragraph A1, further comprising distinguishing the dissolved filaments from the undissolved filaments based on differences in at least one of brightness or color.
[0016] A8 Further provided is the method of paragraph A1, wherein the strands of fiber are selected from the group consisting of carbon fibers, glass fibers, metal fibers, and ceramic fibers.
[0017] A9 Further provided is the method of paragraph A8, wherein the strands of fiber are strands of carbon fiber.
[0018] A10 (c) a part of an aircraft assembled in accordance with the method described in paragraph A1;
[0019] According to a further aspect of the medium of the present disclosure, the following is provided:
[0020] B1 1. A non-transitory computer readable medium having programmed instructions embodied therein, the instructions, when executed by a processor, operable to perform a method for characterizing a ply of fiber having a thermoplastic veil, the method comprising: Obtaining an image of a textile material including strands of fiber and further including a veil of filaments of thermoplastic material (202); Segmenting the image into a plurality of slices (204); determining (206) an amount of dissolved filaments represented in each said slice; and determining (208) the amount of undissolved filaments represented in each of said slices; and 16. A non-transitory computer readable medium comprising:
[0021] B2 The method further comprising: Quantifying (210) the amount of dissolution of the veil in each slice based on the number of dissolved filaments and the number of undissolved filaments in each slice; Quantifying (212) the amount of dissolution of the veil in the image based on the amount of dissolution of the veil in each slice; There is further provided a medium as described in paragraph B1, further comprising:
[0022] B3 determining the amount of dissolved filaments includes identifying a portion of each slice that includes dissolved filaments; The medium of paragraph B1 is further provided, wherein determining the amount of undissolved filaments includes identifying a portion of each slice that includes undissolved filaments.
[0023] B4 The medium of paragraph B1 is further provided, wherein the determining step is performed via a trained neural network.
[0024] B5 Further provided is the medium of paragraph B2, wherein the neural network comprises a convolutional neural network.
[0025] B6 comparing the amount of dissolution of the veil in the image to a design tolerance; sending a notification in response to determining that the amount of dissolution of the veil in the image is not within the design tolerance; There is further provided a medium as described in paragraph B1, further comprising:
[0026] B7 The medium of paragraph B1 is further provided, wherein the machine learning model distinguishes dissolved filaments from non-dissolved filaments based on differences in at least one of brightness or color.
[0027] B8 There is further provided the medium of paragraph B1, wherein the strands of fiber are selected from the group consisting of carbon fibers, glass fibers, metal fibers, and ceramic fibers.
[0028] B9 Further provided is the medium of paragraph B8, wherein the strands of fiber are strands of carbon fiber.
[0029] B10 A part of an aircraft assembled by a method prescribed by said Directive stored on a computer readable medium as described in paragraph B1.
[0030] According to further aspects of the apparatus of the present disclosure, there is provided:
[0031] C1 1. An apparatus for characterizing a ply of fiber having a thermoplastic veil, comprising: an interface (716) for receiving an image (742) of a fibrous material (750) including a strand of fiber (752) and further including a veil (754) of a thermoplastic filament (760); and a controller (712) for dividing the image into a plurality of slices (744), determining an amount of dissolved filament represented in each of the slices, and determining an amount of undissolved filament represented in each of the slices. An apparatus comprising:
[0032] C2 The apparatus of paragraph C1 is further provided, wherein the controller quantifies an amount of dissolution of the veil in each slice based on a number of the dissolved filaments and the number of the undissolved filaments in each slice, and quantifies an amount of dissolution of the veil in the image based on the amount of dissolution of the veil in each slice.
[0033] C3 the controller running a neural network (724) to determine a quantity of the dissolved filament by identifying a portion of each slice that contains a dissolved filament; The apparatus of paragraph C1 is further provided, wherein the controller determines the amount of undissolved filament by identifying a portion of each slice that includes undissolved filament.
[0034] C4 The apparatus of paragraph C1 is further provided, wherein the controller determines the amount of the dissolved filament and the amount of the undissolved filament via a trained neural network.
[0035] C5 Further provided is the method of paragraph C4, wherein the neural network comprises a convolutional neural network.
[0036] C6 comparing the amount of dissolution of the veil in the image to a design tolerance; sending a notification in response to determining that the amount of dissolution of the veil in the image is not within the design tolerance; There is further provided the apparatus of paragraph C1, further comprising:
[0037] C7 The apparatus of paragraph C1 is further provided, wherein the strands of fiber are selected from the group consisting of carbon fibers, glass fibers, metal fibers, and ceramic fibers.
[0038] C8 Further provided is the apparatus of paragraph C7, wherein the strands of fiber are strands of carbon fiber.
[0039] C9 (c) The manufacture of a part of an aircraft using the equipment described in paragraph C1.
[0040] Other exemplary embodiments (e.g., methods and computer-readable media related to the above-mentioned embodiments) may be described below. The above-mentioned features, functions, and advantages may be realized alone in various embodiments or may be combined in yet other embodiments. Further details of these embodiments may be ascertained by reference to the following description and the accompanying drawings. [Brief description of the drawings]
[0041] Some embodiments of the present disclosure will now be described, by way of example only, with reference to the accompanying drawings in which the same reference numbers represent the same elements or same types of elements in all the drawings.
[0042] [Figure 1] FIG. 1 is a block diagram of a fiber characterization system according to an illustrative embodiment. [Diagram 2] FIG. 1 is a flow diagram illustrating a method for evaluating a fibrous material according to an illustrative embodiment. [Diagram 3] FIG. 1 illustrates an imaging of a fibrous material in accordance with an illustrative embodiment. [Figure 4] FIG. 1 illustrates a slice from an image according to an exemplary embodiment. [Diagram 5] FIG. 1 illustrates a slice from an image according to an exemplary embodiment. [Figure 6] FIG. 1 illustrates a slice from an image according to an exemplary embodiment. [Figure 7] FIG. 1 is a block diagram of a fiber material characterization system in accordance with an illustrative embodiment. [Figure 8] FIG. 1 is a flow diagram of an aircraft production and service method in accordance with an illustrative embodiment. [Figure 9] FIG. 1 is a block diagram of an aircraft in accordance with an illustrative embodiment; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0043] The drawings and the following description show specific exemplary embodiments of the present disclosure. Therefore, it should be understood that those skilled in the art can devise various arrangements not expressly described or shown in the present specification to embody the principles of the present disclosure, but they are included in the scope of the present disclosure. Furthermore, it should be understood that any examples described in this specification are intended to help understand the principles of the present disclosure, and are not limited to the specifically described examples and conditions. As a result, the present disclosure is not limited to the specific embodiments or examples below, but is limited by the scope of the claims and their equivalents.
[0044] Composite parts, such as carbon fiber reinforced polymer (CFRP) parts, are first laid up as multiple plies that together form a laminate. The individual fibers within each layer of the laminate are aligned parallel to one another, but the various plies may exhibit different fiber orientations to enhance the strength of the resulting composite along various directions. To solidify the laminate into a composite part (e.g., for use in an aircraft), the laminate may include a viscous resin that solidifies. Carbon fibers impregnated with uncured thermosetting or thermoplastic resins are called "prepregs." Other types of carbon fibers include "dry fibers" that are not impregnated with thermosetting resins, but may include a tackifier or binder. Dry fibers may be infused with resin before curing. In thermosetting resins, hardening is a unidirectional process called curing, but in the case of thermoplastic resins, the resin may reach a viscous state when reheated. The systems and methods described herein describe the evaluation of dry fiber materials that include a binder in the form of a thermoplastic veil.
[0045] 1 is a block diagram of a fiber characterization system 100 according to an exemplary embodiment. The fiber characterization system 100 comprises any system, device, or component operable to automatically evaluate images of a fiber material (e.g., unidirectional CFRP, fiberglass material, material having metal or even ceramic fibers, etc.) to determine (measure) the ratio of molten and non-molten thermoplastic filaments. In this embodiment, the fiber characterization system 100 includes a characterization unit 110 and an imaging system 130.
[0046] The imaging system 130 acquires images of a sheet 140 of fibrous material 142 (e.g., a ply of unidirectional CFRP, fiberglass material, material having metal fibers or even ceramic fibers, etc.). These images may be acquired at various positions along the sheet 140 and / or at various orientations. The images show not only strands 150 of fibers 152 (e.g., carbon fibers, fiberglass, metal fibers, ceramic fibers, etc.) within the sheet 140, but also a veil 160 of thermoplastic filaments 162 acting as a binder or tackifier for the strands 150. Each thermoplastic filament 162 may be, for example, 7000ths of an inch thick or even thinner. Each strand 150 may be even smaller, such that there are 12,000 to 40,000 strands in a single linear inch. The imaging system 130 may include a camera (e.g., a color camera, a stereo camera, etc.) or other non-destructive imaging components such as ultrasound or laser imaging devices.
[0047] Images acquired via the imaging system 130 are received at an interface (I / F) 116. These images may be stored by the controller 112 in memory 114 (e.g., hard disk, flash memory, etc.) for subsequent analysis. The controller 112 manages the operation of the characterization unit 110 to facilitate the receipt, analysis, and reporting of the images. For example, the controller 112 may access a neural network 124 in the memory 114 when evaluating the images. The neural network 124 may include, for example, a convolutional neural network trained based on the training data 122 to detect undissolved and dissolved filaments in the images.
[0048] The training data 122 may include a set of images or slices (e.g., thousands of images) and associated tags that indicate features found within these elements. For example, the training data 122 may include images with regions already classified as melted or unmelted. The training data 122 may further include images taken of dry carbon material, where the in-plane fiber angles are varied (e.g., 0 degrees, +45 degrees, -45 degrees, 90 degrees, etc.). This may be important to train the neural network 124 to examine the differences in brightness or contrast found at various fiber angles. Thus, the training data 122 may be used to test and refine the process by which the neural network 124 detects both melted and unmelted filaments. The controller 112 may be implemented, for example, as a custom circuit, as a hardware processor executing programmed instructions, or some combination thereof.
[0049] Exemplary details of the operation of fiber characterization system 100 are described in relation to Figure 2. In this embodiment, assume that an engineer desires to characterize a sheet 140 of fiber material 142 to determine whether the ratio of dissolved to undissolved filaments in sheet 140 is within a desired tolerance.
[0050] 2 is a flow diagram illustrating a method 200 for characterizing a textile material, according to an exemplary embodiment. The steps of the method 200 are described with reference to the textile characterization system 100 of FIG. 1, however, one of ordinary skill in the art will appreciate that the method 200 may be implemented in other systems. The steps of the flow diagrams described herein are not exhaustive and may include other steps not shown. The steps described herein may also be performed in a different order.
[0051] In step 202, the imaging system 130 acquires an image of the sheet 140 of the fibrous material 142. The fibrous material 142 includes strands 150 of fibers 152 and further includes a veil 160 of thermoplastic filaments 162 of thermoplastic material. The image may be generated in any suitable format, and a digital version of the image may be acquired by the I / F 116 for storage in the memory 114. In one embodiment, an image is acquired every few hundred meters of the length of the fibrous material 142. The image represents a small portion of the fibrous material 142 (e.g., a 2 inch by 2 inch portion). However, the image may show any suitable area to any suitable size desired.
[0052] Once the image is acquired, the controller 112 then proceeds to subdivide the image into slices (step 204). As used herein, a "slice" may include any suitable portion of an image. For example, a slice may include a portion that occupies the entire width of the image but only a portion of the height of the image, may include a portion that occupies the entire height of the image but only a portion of the width of the image, may include a triangular section, etc. Ideally, the size (e.g., narrow dimension) of a slice is small enough so as not to show multiple filaments, yet large enough so that a convolutional neural network trained for region detection can operate effectively when attempting to classify portions of the slice that represent dissolved or undissolved filaments. For example, the size of a slice may be between 60 and 160 pixels (e.g., 100 pixels). Each slice may be expected to show a number of individual filaments (e.g., 50 to 100 filaments).
[0053] In further embodiments, the size of the slice may be selected such that the height of the image is evenly divisible by the size of the slice, or the image may be pre-processed (e.g., cropped, scaled, filtered, etc.) to enhance image quality and / or slicing.
[0054] In step 206, for each slice, the controller 112 determines the amount of dissolved filament depicted. As used herein, "dissolved filament" (e.g., as indicated by dissolved portion 324 in FIG. 3) includes dissolved filament in the strand 150. This may be performed by the controller 112 running the neural network 124 to detect features within the slice that indicate the presence of dissolved filament. For example, dissolved filament may be associated with a particular curvature, brightness, color, etc., and the neural network 124 may be trained via the training data 122 to recognize such features. In one embodiment, the neural network 124 performs a feature analysis on the slice to detect the presence of one or more features associated with dissolved filament. The size of each region examined by the neural network 124 may be equal in dimension to the size of the slice being examined. If enough features are detected with sufficient confidence, the neural network 124 may indicate the presence of dissolved filament within the region of the slice being analyzed.
[0055] Additionally, for each slice, the controller 112 determines the amount of undissolved filament depicted (i.e., step 208). As used herein, "undissolved filament" (e.g., as illustrated by undissolved portion 326 in FIG. 3) includes filament that does not dissolve into the strand 150 and remains on the strand 150. This may be performed by the controller 112 running the neural network 124 to detect features within the slice that are indicative of the presence of undissolved filament. For example, undissolved filaments may be associated with a particular curvature, brightness, color, etc., and the neural network 124 may be trained via the training data 122 to recognize such features. When making a determination regarding an undissolved filament, the neural network 124 may perform feature recognition tasks similar to those described above in connection with step 206.
[0056] Certain portions of the filament may appear to be partially melted, in which case the controller 112 may classify the filament as either melted or unmelted depending on how the neural network used by the controller 112 was trained. In further embodiments, certain regions may represent both melted and unmelted filaments, and the controller 112 may classify such regions as either melted or unmelted based on how the neural network used by the controller 112 was trained.
[0057] In steps 206 and 208, not all regions within a slice necessarily contain filaments (e.g., dissolved or undissolved filaments). These regions may be reported as void regions. The size of the void regions may be important when determining whether the sheet 140 is within an acceptable range not related to the melting rate.
[0058] The quantities determined in steps 206 and 208 may indicate the size of the area in which filaments of a given type are determined to be present (e.g., linear or flat area), may indicate the number of filaments of a given type, or may include other suitable indicators (e.g., the number of pixels that represent filaments of a given type). These quantities are used to determine the ratio of dissolved to undissolved filaments.
[0059] In step 210, the controller 112 quantifies the amount of melting of the thermoplastic filaments 162 of the veil 160 for each slice. This may be performed, for example, by summing the amount of melted and unmelted filaments in the slice and then dividing the amount of melted filaments by this sum. In further embodiments, this may include determining the ratio of melted to unmelted filaments. The percentage of melting desired in the design specifications may vary depending on the application, but one example percentage of melting is between 30 and 50 percent.
[0060] Step 212 includes the controller 112 quantifying the amount of dissolution of the veil 160 in the image based on the amount of dissolution of the veil 160 in each slice. For example, the controller 112 may average the amount of dissolution determined in step 210 for each slice to determine the amount of dissolution found in the image as a whole.
[0061] In further embodiments, method 200 may be repeated on the same image by slicing the image differently (e.g., wide "slices", as well as "tall" slices, slices of various sizes, inverted or rotated slices, slices with adjusted color, brightness, or contrast, etc.). In yet another embodiment, method 200 may be repeated on multiple images to quantify the entire sheet 140. For example, in an embodiment where an entire roll containing hundreds of feet of dry carbon fiber material 142 is to be characterized, it may be desirable to acquire and analyze a large number of images of the material.
[0062] Method 200 provides advantages over prior techniques and systems because it replaces manual inspection techniques with an automated process that is cheaper and more accurate, giving engineers more time to focus on other aspects of manufacturing and enhancing the process of producing composite parts from dry carbon fiber material.
[0063] The techniques of method 200 can be utilized to inspect the surfaces of large numbers of plies to verify the desired melt ratio of each ply before layup begins. If the melt ratio is known for each ply, then any resulting preform made from the plies will also have a known melt ratio, even for plies placed within the preform after inspection.
[0064] Working Example In the following examples, additional processes, systems, and methods are described in the context of characterizing dry carbon fiber material. That is, while systems and methods for analyzing dry carbon fiber material are described above, the following figures provide examples showing how images can be sliced and characterized.
[0065] FIG. 3 is a diagram illustrating a fibrous material, according to an exemplary embodiment. Specifically, FIG. 3 is a top view of a single ply of unidirectional dry fibrous material in the form of a CFRP. FIG. 3 shows one or more of the strands 310 of carbon fiber bonded together via a veil 320 of filaments 322. FIG. 3 shows minor non-uniformity in the distance between the strands 310 of carbon fiber, because the uniform distribution of the strands 310 in the bundle 300 is rough and not complete. Some filaments have melted portions 324, while other filaments have unmelted portions 326. A single filament may have both melted and unmelted regions.
[0066] In this example, image 300 is subdivided into multiple slices at boundary 330, resulting in slices 342, 344, and 346. FIG. 4 shows slice 342 in detail. As shown by bars 400 in FIG. 4, portions of slice 342 are labeled with different identifiers based on analysis by the neural network. "E" indicates an area that is free of filaments of any kind, "UM" indicates an area occupied by unmelted filaments, and "M" (melted) indicates an area occupied by melted filaments. To determine the amount of melted filaments in slice 342, the size of the portion of M (in this case, linear distance) may be compared to the size of the area UM.
[0067] 5 shows slice 344 and illustrates, through bar 500, region M, region UM, and sky region. In slice 344, the size of region M is substantially smaller than the size of region UM. FIG 6 shows slice 346. Slice 346 has the highest ratio of region M to region UM, as indicated by bar 600.
[0068] FIG. 7 is a block diagram of a fiber material characterization system 700, according to an exemplary embodiment. According to FIG. 7, the system includes a carbon fiber material 750 made of a strand 752 of carbon fiber and a veil 754 of a filament 760. The filament 760 includes a melted portion 764 and an unmelted portion 766. An imaging system 730 generates images of the carbon fiber material 750, which are acquired (in digital form) by an interface (I / F) 716. A controller 712 may direct these images to a memory 714 for storage. The memory 714 stores a neural network 724, training data 722, and an objective function 726 used to score the output from the neural network 724 during training. The memory 714 further stores images 742, slices 744, and slice data 746. The slice data 746 may indicate, for example, the location and amount of melted and unmelted portions detected in each slice. While the above-described systems have focused on dry fibrous materials, in further embodiments they may be utilized to perform similar functions on prepreg materials.
[0069] Referring more specifically to the drawings, embodiments of the disclosure may be described with reference to an aircraft manufacturing and service method 800 as shown in Figure 8 and an aircraft 802 as shown in Figure 9. During pre-production, the method 800 may include specification and design 804 and material procurement 806 of the aircraft 802. During production, component and subassembly manufacturing 808 and systems integration 810 of the aircraft 802 takes place. The aircraft 802 may then go through certification and delivery 812 and be placed into service 814. While in operation by a customer, the aircraft 802 is scheduled for periodic work in maintenance and service 816, which may include modification, reconfiguration, refurbishment, etc. Apparatus and methods embodied herein may be employed at any one or more suitable stages of manufacturing and service described in method 800 (e.g., specification and design 804, materials procurement 806, component and subassembly manufacturing 808, systems integration 810, certification and delivery 812, operation 814, maintenance and maintenance 816), and / or at any suitable component of aircraft 802 (e.g., airframe 818, systems 820, interior 822, propulsion system 824, electrical system 826, hydraulic system 828, environmental system 830).
[0070] Each process of method 800 may be performed or carried out by a system integrator, a third party, and / or an operator (e.g., a customer). For purposes of 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, a leasing company, a military entity, a service organization, etc.
[0071] 9, aircraft 802 produced by method 800 may include an airframe 818 with a number of systems 820 and an interior 822. Examples of systems 820 include one or more of a propulsion system 824, an electrical system 826, a hydraulic system 828, and an environmental system 830. Any number of other systems may also be included. Although an aerospace example is shown, the principles of the invention may be applied to other industries, such as the automotive industry.
[0072] As mentioned above, the apparatus and methods embodied herein may be employed during any one or more stages of manufacturing and service described in method 800. For example, components or subassemblies corresponding to component and subassembly manufacturing 808 may be fabricated or manufactured in a similar manner as components or subassemblies manufactured while aircraft 802 is in service. Further, one or more apparatus embodiments, method embodiments, or combinations thereof may be utilized during subassembly manufacturing 808 and system integration 810, for example, by substantially streamlining the assembly of aircraft 802 or reducing the cost of aircraft 802. Similarly, one or more of the apparatus embodiments, method embodiments, or combinations thereof may be utilized during the operational life of aircraft 802, for example, and during, but not limited to, maintenance and service 816. For example, the techniques and systems described herein may be used in material procurement 806, component and subassembly manufacturing 808, system integration 810, operation 814, and / or maintenance and service 816, and / or may be used in airframe 818 and / or interior 822. These techniques and systems may also be utilized for systems 820 including, for example, propulsion system 824 , electrical system 826 , hydraulic system 828 , and / or environmental system 830 .
[0073] In one embodiment, a part comprises a portion of the airframe 818 and is manufactured during component and subassembly manufacturing 808. The part is then installed into the aircraft in system integration 810, but is then utilized in service 814 until wear renders the part unusable. The part may then be scrapped and replaced with a newly manufactured part in maintenance and service 816. Inventive components and methods may be utilized throughout component and subassembly manufacturing 808 to manufacture new parts.
[0074] Any of the various control elements (e.g., electrical or electronic components) shown in the figures or described herein may be implemented as hardware, software implemented by a processor, firmware implemented by a processor, or some combination thereof. For example, an element may be implemented as dedicated hardware. A dedicated hardware element may be referred to as a "processor," "controller," or some similar terminology. When a function is performed by a processor, it may be performed by a single dedicated processor, by a single shared processor, or by multiple individual processors (some of which may be shared). Furthermore, explicit use of the terms "processor" or "controller" should not be construed to refer only to hardware capable of executing software, but may implicitly include, without limitation, digital signal processor (DSP) hardware, network processors, application specific integrated circuits (ASICs) or other circuitry, field programmable gate arrays (FPGAs), read only memory (ROM) for storing software, random access memory (RAM), non-volatile storage, logic, or any other physical hardware element or module.
[0075] Additionally, a control element may be implemented as instructions executable by a processor or computer to perform the functions of that element. Some examples of instructions are software, program code, and firmware.
[0076] Although specific embodiments have been described herein, the scope of the disclosure is not limited to those specific embodiments. The scope of the disclosure is defined by the following claims and any equivalents thereof.
Claims
1. 1. A method for characterizing a ply of fiber having a thermoplastic veil, comprising: Obtaining an image of a textile material (202) including strands of textile and further including a veil of filaments of thermoplastic material; Segmenting the image into a number of slices (204); determining (206) an amount of dissolved filaments represented in each said slice; determining (208) the amount of undissolved filaments represented in each of said slices; The method includes:
2. Quantifying (210) the amount of dissolution of the veil in each slice based on the number of dissolved filaments and the number of undissolved filaments in each slice; Quantifying (212) the amount of dissolution of the veil in the image based on the amount of dissolution of the veil in each slice; The method of claim 1 further comprising:
3. determining the amount of dissolved filaments includes identifying a portion of each slice that includes dissolved filaments; 2. The method of claim 1, wherein determining the amount of undissolved filaments comprises identifying a portion of each slice that includes undissolved filaments.
4. The method of claim 1 , wherein the determining step is performed via a trained neural network.
5. comparing the amount of dissolution of the veil in the image to a design tolerance; sending a notification in response to determining that the amount of dissolution of the veil in the image is not within the design tolerance; The method of claim 1 further comprising:
6. 10. The method of claim 1, further comprising distinguishing molten filaments from unmolten filaments based on differences in at least one of brightness or color.
7. The method of claim 1 , wherein the strands of fiber are selected from the group consisting of carbon fibers, glass fibers, metal fibers, and ceramic fibers.
8. 13. An aircraft section assembled by the method of claim 1.
9. 1. A non-transitory computer readable medium having programmed instructions embodied therein, the instructions, when executed by a processor, operable to perform a method for characterizing a ply of fiber having a thermoplastic veil, the method comprising: determining (208) the amount of undissolved filaments represented in each of said slices; 16. A non-transitory computer readable medium comprising:
10. 1. An apparatus for characterizing a ply of fiber having a thermoplastic veil, comprising: an interface (716) for receiving an image (742) of a fibrous material (750) including a strand of fiber (752) and further including a veil (754) of a thermoplastic filament (760); and a controller (712) for dividing the image into a plurality of slices (744), determining an amount of dissolved filament represented in each of the slices, and determining an amount of undissolved filament represented in each of the slices. An apparatus comprising:
11. 11. The apparatus of claim 10, wherein the controller quantifies an amount of dissolution of the veil in each slice based on a number of the dissolved filaments and a number of the undissolved filaments in each slice, and quantifies an amount of dissolution of the veil in the image based on the amount of dissolution of the veil in each slice.
12. the controller running a neural network (724) to determine a quantity of the dissolved filament by identifying a portion of each slice that contains a dissolved filament; 11. The apparatus of claim 10, wherein the controller determines the amount of unmelted filament by identifying a portion of each slice that includes unmelted filament.
13. 11. The apparatus of claim 10, wherein the controller determines the amount of the dissolved filament and the amount of the undissolved filament via a trained neural network.
14. comparing the amount of dissolution of the veil in the image to a design tolerance; sending a notification in response to determining that the amount of dissolution of the veil in the image is not within the design tolerance; The apparatus of claim 10 further comprising:
15. The apparatus of claim 10 , wherein the strands of fiber are selected from the group consisting of carbon fibers, glass fibers, metal fibers, and ceramic fibers.