Porosity of the part

Abstract

A method for determining porosity of a part is provided. The method includes determining scan data of the part, the scan data including a plurality of contiguous segments of data; determining a background model of the part, the scan data, or both; and determining a bulk porosity based on a difference between the scan data and the background model.

Description

Technical Field

[0001] This disclosure relates to a non-destructive method for determining the bulk porosity of a part, such as a composite material part for a gas turbine engine. Background Technology

[0002] Industrial inspections are increasingly utilizing three-dimensional (3D) volumes. Volumetric computed tomography (VCT) scans can be performed, for example, on composite aircraft parts being inspected, to generate 2D images or 3D stacks or "volumes" of the parts. Human operators can then examine each 2D slice individually to identify common defect indications in composite parts, such as porosity issues.

[0003] Piece-by-piece 2D inspection of 3D models can be time-consuming, laborious, and / or error-prone. Operators may need to individually inspect and correlate numerous 2D slices of a 3D volume to determine if defects exist throughout the entire volume. For example, an operator may need to observe subtle variations in grayscale across multiple 2D images. This process is time-consuming, tedious, and error-prone. Furthermore, analyses can vary significantly between operators and between shifts due to operator fatigue.

[0004] Previous attempts to automate defect indication detection have encountered various problems. For example, to reduce beam hardening and scattering artifacts, the pixels or voxels of the 3D volume of a part have been "normalized" to a "standard," such as an aluminum rod. However, adding a rod to the field of view can degrade image quality, and this approach is only suitable for linear computed tomography (CT) scans, not VCT. Furthermore, this approach requires little or no geometric difference between the part shape and the standard shape.

[0005] Attempts to automate inspection processes have resulted in procedures estimating porosity far higher than the actual porosity, at least in part due to the VCT scan's resolution being lower than the size of the pores in the part. This could lead to the rejection of parts with acceptable porosity levels.

[0006] In view of the above challenges and problems, improved automated or partially automated systems and methods for determining the porosity of parts will be welcome. Attached Figure Description

[0007] The complete and effective disclosure of the invention, including its best mode, to those skilled in the art is set forth in the description with reference to the accompanying drawings, wherein:

[0008] Figure 1 This is a flowchart illustrating the operation of a VCT-based method for notifying an operator of indications of potential defects in a part, according to various embodiments.

[0009] Figure 2 This is a flowchart for calculating the porosity of a part according to an exemplary aspect of this disclosure.

[0010] Figure 3 It is a voxel array according to an exemplary aspect of this disclosure.

[0011] Figure 4 It is the pixel and the surrounding area of ​​the affected pixel according to this disclosure.

[0012] Figure 5 It refers to the pixel and the surrounding area affecting the pixel according to another embodiment of this disclosure.

[0013] Figure 6 These are high-resolution images from a high-resolution CT scanner according to embodiments of this disclosure.

[0014] Figure 7 It is a low-resolution image from a low-resolution CT scanner according to an embodiment of this disclosure.

[0015] Figure 8 It is a graph of the region of interest of the part, where the scan intensity is represented by the y-axis and the continuous segments of the part are represented by the x-axis.

[0016] Figure 9 It is a computing system based on exemplary aspects of this disclosure. Detailed Implementation

[0017] Reference will now be made in detail to the present embodiments of the invention, one or more examples of which are illustrated in the accompanying drawings. The detailed description uses numerals and letter reference numerals to denote features in the drawings. Similar or analogous reference numerals in the drawings and description have been used to denote similar or analogous portions of the invention.

[0018] The term "exemplary" is used herein to mean "used as an example, instance, or illustration." Any implementation described herein as "exemplary" is not necessarily to be construed as superior or better than other implementations. Furthermore, unless explicitly stated otherwise, all embodiments described herein should be considered exemplary.

[0019] As used herein, the terms “first,” “second,” and “third” are used interchangeably to distinguish one component from another and are not intended to indicate the location or importance of the components.

[0020] The terms "front" and "rear" refer to relative positions within a gas turbine engine or vehicle, and specifically to the normal operating posture of the gas turbine engine or vehicle. For example, in the case of a gas turbine engine, "front" refers to the position closer to the engine inlet, while "rear" refers to the position closer to the engine nozzle or exhaust port.

[0021] The terms "upstream" and "downstream" refer to the relative directions of fluid flow within a fluid path. For example, "upstream" refers to the direction from which the fluid flows, and "downstream" refers to the direction from which the fluid flows.

[0022] Unless otherwise stated herein, the terms “connection,” “fixed,” “attached to,” etc., refer to both direct connection, fixation, or attachment, and indirect connection, fixation, or attachment via one or more intermediate components or features.

[0023] Unless the context clearly indicates otherwise, the singular forms “a,” “a,” and “the” include plural references.

[0024] As used throughout the specification and claims, approximate language is applied to modify any quantitative expression that may allow for variation without altering its underlying function. Therefore, values ​​modified by terms such as “about,” “approximately,” and “substantially” are not limited to specified precise values. In at least some cases, approximate language may correspond to the precision of the instrument used to measure the value, or the precision of the method or machine used to construct or manufacture the component and / or system. For example, approximate language may refer to margins of 1%, 2%, 4%, 10%, 15%, or 20%. These approximate margins may be applied to a single value, to either end of a range defining a numerical value, or to margins between two ends, and / or between the ends.

[0025] Throughout this specification and claims, scope limitations are combined and interchanged, and unless the context or language otherwise indicates otherwise, such scopes are identified and include all subscopes contained herein. For example, all scopes disclosed herein include endpoints, and endpoints may be combined independently of each other.

[0026] This disclosure generally relates to systems and methods that can more accurately determine the porosity of a part. The systems and methods of this disclosure do not rely solely on the resolution of the scanned image to determine in a binary manner whether a single pixel or voxel represents a void or solid portion of the material. As will be understood from the description herein, this can be problematic, particularly for lower-resolution scanning systems, because voids may be much smaller than the area of ​​influence of a pixel or voxel, causing any material actually present within the pixel or voxel surrounding the void to be overlooked. In contrast, the system of this disclosure uses the average scan intensity within the region of interest to determine the porosity of the region of interest, allowing for more accurate porosity determination even when using lower-resolution scanners.

[0027] For example, certain aspects of this disclosure relate to a method for determining the porosity of a part. This method typically includes determining scan data of the part, which comprises data from multiple consecutive segments (e.g., multiple consecutive pixels or voxels). For example, the method may use 2D or 3D computed tomography images to determine scan data from multiple consecutive pixels or voxels. The scan data may refer to the scan intensity or scan density of each individual pixel or voxel, which may indicate the density or porosity of a portion of the part represented by that pixel or voxel, and the density or porosity of a portion of the part material surrounding the portion represented by that pixel or voxel (e.g., the scan data of a particular pixel or voxel may be influenced by the porosity in one or more surrounding pixels or voxels). Furthermore, the method may determine a background model of the part, the scan data, or both. In particular, the method may determine background data that may indicate the expected density of the part at multiple consecutive segments. For example, for a portion of a part intended to be solid, background data may define a portion of the part as having no porosity (e.g., less than 0.1% porosity) or substantially no porosity (e.g., less than 5% porosity, such as less than 2.5% porosity, such as less than 1% porosity). Background data may be determined at least in part using scan data (e.g., by one or more auxiliary defect identification algorithms). Additionally or alternatively, background data may be determined before determining scan data based on an ideal or perfect part.

[0028] Optionally, this method can normalize scan data across, for example, a part or a portion of a part, by subtracting background data. This ensures that the values ​​of the normalized scan data are consistent with a common baseline across the part or a portion of the part, despite, for example, different manufacturing methods across the part, different materials across the part, etc. Using the normalized scan data, this method can further determine which areas of the part are below a porosity threshold, thus allowing assumption that such portions have sufficiently low porosity. This step can further identify regions of interest that require more specific analysis to determine porosity.

[0029] Furthermore, this method can determine volumetric porosity based on the difference between scan data and a background model within one or more regions of interest. For example, the method can utilize scan data from multiple consecutive segments, background data from a background model, and then determine data indicating the signal-to-background ratio (or "SBR") that correlates the scan data with the background data. The SBR can be calculated by using the Riemann sum to calculate the volume between the scan data and the background data. It should be understood that, as used in this context, the term "volume" does not refer to physical volume but to volume in CT space, which is defined by three spatial dimensions plus the amplitude of the CT measurement signal at those locations. Furthermore, the term "scan data" generally refers to data received as part of one or more scans according to this disclosure. The term "scan data" can refer to an energy level indicating the porosity determined or derived by scanning a part. For example, scan data may include data indicating the density of the part at multiple consecutive segments, and background data may include data indicating the expected density of the part at multiple consecutive segments (as determined by a background model).

[0030] Referring now to the accompanying drawings, where the same numbers indicate the same elements throughout all the drawings. Figure 1 An example of a computed tomography (“CT”) based method 100 for informing a user of potential defects in a part is schematically depicted at a relatively high level. Specifically, for the illustrated embodiment, method 100 is a volumetric computed tomography (“VCT”) based method 100. Various aspects of method 100 will now be described and illustrated in more detail.

[0031] At box 102, VCT data can be obtained, for example, by feeding one or more parts through a VCT scanning system. The VCT scanning system can be any suitable VCT scanning system. For example, the VCT scanning system can utilize an X-ray source, X-ray tube, X-ray detector, etc., to generate a three-dimensional image of the part. For example, the VCT scanning system can use a fan-beam X-ray source, which can be detected by a linear detector array when the part is rotated relative to the X-ray source and detector array. Alternatively, the VCT scanning system can use a cone-beam X-ray source, which can be detected by a zone detector array when the part is rotated relative to the X-ray source and detector array. With this configuration, a three-dimensional image can be formed from multiple two-dimensional images / slices of the part.

[0032] However, alternatively, method 100 can be a two-dimensional CT method, such that method 100 can receive CT data including two-dimensional images of the part at block 102 (e.g., using a cone-beam X-ray source).

[0033] At box 104, VCT data can be imported into a computing system configured with selected aspects of this disclosure (e.g., Figure 9 The computing system 400 accesses a database or other storage. At box 106, the imported data can be segmented, for example, by part if multiple parts are scanned, and / or by sub-parts. For example, data associated with 3D volumes can be separated from or otherwise distinguished from data representing the volumes of other parts. In some embodiments, connected volumes can be selected, extracted, and automatically trimmed.

[0034] At box 108, a background model of the part is determined. More specifically, for the illustrated embodiment, determining the background model includes using scan data to determine the background model. More specifically, determining the background model also includes utilizing assisted defect identification (ADR) processing. In various embodiments, ADR may include normalizing voxels of the 3D volume to themselves, denoising the volume using various techniques, and using techniques such as region growing to detect and / or classify indications of potential defects. At box 110, it can be determined whether the part being inspected meets predetermined criteria based on the analysis performed at box 108. If the answer is yes (e.g., the part does not show indications of potential defects (e.g., low porosity)), the indication that the part passed can be stored, for example, in a "pass" database.

[0035] On the other hand, if the answer at box 110 is no, the area can be marked as a "region of interest," and further checks can be initiated at box 112 to allow for a more thorough review of the part, determining whether it truly failed or whether the automatically sensed indication was small enough for the part to pass. In at least some cases, at box 112, metadata about porosity levels, local porosity levels, location, part area, etc., can be provided to the operator for evaluation along with the scan data.

[0036] At box 114, based on the data provided at block 112, method 100 may use one or more inspection methods (such as method 200 described below) to determine whether a part should pass (e.g., indicating a non-critical nature or an artifact of the scan data) or whether a part should fail. The evaluation results at box 114 may be provided to the pass database and / or the fail database.

[0037] However, it should be understood that Figure 1 The CT-based examination method 100 is provided by way of example only, and any other suitable examination method may be used in other exemplary aspects.

[0038] Now for reference Figure 2A flowchart of a method 200 for determining the volume porosity of a part or part region (e.g., region of interest) is provided. Figure 2 The method described in 200 can be combined with Figure 1 In method 100, for example, it is used as a detection at box 108. However, additionally or alternatively, Figure 2 The method 200 described herein may be used in conjunction with any other suitable method and / or system for determining information about a part, or otherwise incorporated into any other suitable method and / or system for determining information about a part.

[0039] like Figure 2 As schematically depicted, method 200 includes determining scan data of a part. Specifically, method 200 includes operating a CT or VCT scanning machine at (202) to scan the part (using CT processing or VCT processing). For the illustrated embodiment, the part is a composite material part. As used herein, the term "composite material" can be defined as a material containing reinforcements (e.g., fibers or particles supported in an adhesive or matrix material). Composite materials include metallic and non-metallic composite materials. One embodiment of a composite material part is made of a unidirectional tape material and an epoxy resin matrix. Composite material parts can include non-metallic types of composite materials made of materials containing fibers (e.g., carbonaceous, silica, metals, metal oxides, or ceramic fibers embedded in resin materials (e.g., epoxy resin, PMR15, BMI, PEEU, etc.)). More specifically, materials include fibers unidirectionally arranged in tape, impregnated with resin to form a part shape, and cured by autoclaving or compression molding to form a lightweight, rigid, relatively homogeneous article having a laminate therein. However, these are merely examples without limitation.

[0040] In some embodiments, the composite material part may be a composite material part for a gas turbine engine. Specifically, for the illustrated embodiment, the part is a composite material airfoil for a gas turbine engine, such as a fan blade, compressor rotor blade, turbine rotor blade, stator blade, guide vane, etc. However, it should be understood that in other exemplary embodiments, aspects of this disclosure can be used with any other suitable part for a gas turbine engine (e.g., one or more of a shield, bushing, dome, etc.). Furthermore, although described as being used with composite material parts, in other embodiments, aspects of this disclosure can be used with parts formed of any other suitable material that can be scanned with a CT scanner or VCT scanner. For example, in other exemplary aspects, the systems and processes described herein can be used to discover porosity in any other material (e.g., metals and metal alloys).

[0041] Furthermore, as part of determining the scan data for the part, method 200 also includes receiving scan data at (204). As will be understood, the scan data received at (204) may include data from multiple consecutive segments. These multiple consecutive segments may be multiple consecutive pixels or voxels. For example, in the case of a method utilizing a two-dimensional CT scanning system, the multiple consecutive segments may be multiple consecutive pixels. In contrast, in the case of a method utilizing a three-dimensional CT or VCT scanning system, the multiple consecutive segments may be multiple consecutive voxels. For example, briefly refer to... Figure 3 Provided is a three-dimensional schematic diagram of multiple voxels 302. It should be understood that multiple continuous voxels 302 can be multiple voxels extending along the x-axis 304, along the y-axis 306, along the z-axis 308, or along any other vector.

[0042] Furthermore, it should be understood that the data received at (204) can typically indicate the porosity of each of a plurality of consecutive segments. For example, the data of a plurality of consecutive segments may include data indicating the porosity of each of the plurality of consecutive segments. For example, the data of a plurality of consecutive segments may include data indicating the scan intensity at each of the plurality of consecutive segments, such as data indicating the scan density at such a segment of a plurality of consecutive segments.

[0043] It is worth noting that, as can be understood from the description herein, the porosity data of the indicator segment may be influenced by the porosity of one or more surrounding segments (including the immediately adjacent segment and / or other nearby segments). For example, briefly refer now to Figure 4 and Figure 5 For illustrative purposes, scan data for the first segment 310 and the second segment 312 are provided according to exemplary aspects of this disclosure. For illustrative purposes, segments 310 and 312 are shown as pixels (i.e., two-dimensional images), but the same principle applies to three-dimensional segments / voxels.

[0044] In particular, Figure 4 and Figure 5 Each of these sections graphically depicts scan data from segments 310, 312 generated from a scan of a part, wherein the part defines one or more holes or voids 314. The intensity of the scan data returned as part of the scan at segments 310, 312 (typically representing the density of the scanned part) is influenced by one or more voids in segments adjacent to the area represented by segments 310, 312 (referred to herein as the surrounding area 316). For example, see specific references. Figure 4 The region represented by the depicted segment 310 has no pores 314, but since segment 310 is not perfectly white, but rather a light gray in grayscale, segment 310 representing this region still indicates some degree of porosity. This is a result of including the pores 314 in the surrounding region 316. For comparison, see specific reference. Figure 5The region represented by the depicted segment 312 includes holes 314 partially within the segment 312, and the surrounding region 316 of the segment 312 also includes a plurality of holes 314. Therefore, it represents... Figure 5 Segment 312 in the region is a darker gray in terms of grayscale.

[0045] As will be understood from the description herein, the data values ​​of a particular segment, and the influence of the aperture 314 in the region represented by the particular segment and the corresponding surrounding region, can respond to the diffusion function of the aperture 314 junction in the segment and the surrounding region 316.

[0046] For details, please refer to the return reference. Figure 2 After receiving scan data at (204), method 200 further includes subtracting a preprocessing parameter from the scan data received at (206). The preprocessing parameter can be a bias, such as a constant bias. The constant bias can be the average value of the air signal in the scan data received at (204) relative to the actual portion. For example, in a fully porous part, it will never truly appear as 100% void because there is at least some mass in the air detected in the voids and in the air between the part and the scanner.

[0047] Constant deviation can be part-specific information, CT scanner / VCT scanner-specific information, or both. For example, a brief reference is provided below. Figure 6 and Figure 7 The image shows a sample image of the part obtained from a CT scanner. Figure 6 The image shown is a high-resolution image of sample part 318 from a high-resolution CT scanner. Figure 7 The image shown is a low-resolution image of sample part 318 from a low-resolution CT scanner. It is worth noting that... Figure 6 and Figure 7 Each image in the document is the same cross-section of the same sample part 318. It should be understood that, as used herein, regarding... Figure 6 and Figure 7 The terms “high” and “low” used to describe the resolution of images in this context are relative terms and do not require or imply any absolute resolution.

[0048] like Figure 6 As shown, the image includes dark shadows 320 on some parts of the part, particularly in the upper left corner. Similarly, as... Figure 7As shown, the images include dark shading 322 on some portions of the part, particularly on the right-center portion of the part. While the sample part 318 depicted in each of these images does indeed include porous regions (e.g., porous regions 324, 326, 328, 330), the aforementioned dark regions 320, 322 do not necessarily indicate additional porosity within the part. Rather, the aforementioned dark regions 320, 322 are artifacts, such as those caused by the scanner, the scanned material, beam hardening, scattering, partial volume effects, etc. Therefore, method 200 depicted at (206) takes into account these dark regions 320, 322 to ensure that they do not affect the accuracy of the porosity determination described below.

[0049] Therefore, it should be understood that the preprocessing parameter subtracted at (204) may additionally or alternatively refer to any other suitable preprocessing parameter that may be useful. For example, preprocessing parameters may refer to linearity correction (which some CT scanners may require), image registration, image transformation, etc.

[0050] Preprocessing parameters used to process images can be determined using empirical data deviation information from the part, the scanner, or both (e.g., to accommodate artifacts from the scanner, the part, environmental conditions, etc.). For example, method 200 can scan and analyze one or more parts with known porosity and can determine a constant deviation based on the known porosity.

[0051] Now back Figure 2 Method 200 further includes determining a background model of the part, scan data, or both at (208). The background model determined at (208) may be an expected value of the scan data of the part defining a region with no or substantially no porosity. For example, the background model determined at (208) may be a baseline scan intensity, which represents the density within the part defining a region with no or substantially no porosity. For example, if Figure 4 and 5 The same grayscale indication method 200 shown uses scan data, so the background model of the part determined at (208) can be a value at the very light gray or white end indicating grayscale.

[0052] In some exemplary embodiments, determining the background model at (208) may include using at least part of the scan data received at (204) to determine the background model, wherein preprocessing parameters are subtracted at (206). More specifically, determining the background model at (208) may include utilizing assisted defect identification (ADR) processing, as discussed in more detail above. In this way, the background model may take into account variations in manufacturing, etc.

[0053] However, in other exemplary aspects, determining the background model of the part, scan data, or both at (208) may include utilizing data stored in memory associated with the part, scanner, or both. For example, the background model of the part may be based on an ideal or near-perfect part, which is expected to contain little or no unexpected porosity.

[0054] Furthermore, it should be understood that, for the exemplary aspects depicted, the method includes scaling the background model at (209). More specifically, it should be understood that, when determined using scan data, the step of determining the background model at (208) effectively fills all air and voids within the model of the scanned part to obtain a part that should be complete and pore-free. As will be understood from the following discussion, determining porosity according to this method may include using the average value of background data from a background model at, for example, a specific region of interest. Furthermore, since the average value of the background data is based on a background model that has all voids filled, the average value of the background data may differ from the true average value of the part. Therefore, Figure 2 The method can scale the background model at (209) to account for this difference. Scale the background model at (209) can include applying a transfer function to the background data of the background model, which applies an energy adjustment to the background data (e.g., multiplying the values ​​of the background data by an energy adjustment factor) so that the background data more closely matches the expected average scan data of the part.

[0055] In one of the exemplary aspects depicted, the method further includes normalizing the scan data received at (204) across, for example, a part or a portion of a part at (210). Normalizing the scan data at (204) may include subtracting background data from a background model of the part or that portion of the part. Thus, the normalized scan data can indicate the porosity of the part, independent of certain fundamental characteristics of the part (e.g., the material forming that portion of the part, the method of manufacturing that portion of the part, etc.). This configuration enables consistent analysis of the part, or more precisely, consistent analysis of the normalized scan data of the part, independent of said certain fundamental characteristics of the part.

[0056] Still referencing Figure 2 The method may further include, at (212), determining one or more regions of interest based on the normalized scan data determined at (210). Specifically, at (212), the method may determine a portion of data below a porosity threshold, indicating that the baseline confidence in said portion is sufficiently non-porous. Using normalized scan data for such determination can facilitate consistent analysis across parts. The determined regions of interest may be areas requiring more specific analysis to determine porosity.

[0057] Figure 2The method further includes determining the bulk porosity of the part at (214). More specifically, for Figure 2 As illustrated in the exemplary aspect, determining the volume porosity of a part at (214) typically involves determining the volume porosity based on the difference between the scan data received at (204) and the background model determined at (208) (and optionally modified based on the preprocessing parameters at (206)).

[0058] Still referencing Figure 2 The method of determining the volume porosity of a part at (214) typically includes determining the volume porosity of the part at a specific region of interest determined at (212) such that the difference between the scan data received at (204) and the background model determined at (208) indicates the volume porosity at the region of interest determined at (212).

[0059] Specifically, for the exemplary aspect shown, determining the volume porosity at (214) includes determining, at (216) data at a signal-to-background ratio indicating the correlation between the scan data and the background data using scan data from multiple consecutive segments and background data from a background model. The term "scan data" may refer to data determined or derived from a scan of the part.

[0060] The data used to determine the signal-to-background ratio at (216) may include using Riemann summation to calculate the volume between the scan data and the background data. In this context, the term "volume" refers to the volume in CT space, which is defined by three spatial dimensions plus the amplitude of the CT measurement signals at those locations.

[0061] For example, now refer to Figure 8 A graph 331 showing the region of interest 332 of the part is shown, where the scan intensity is represented along the y-axis 334 and the continuous segments of the part are represented along the x-axis 336. (See 204) Figure 2 The scan data received at point ) is represented as signal line 338 in graph 331, which indicates the scan intensity at each of the multiple consecutive segments of the part. The background model is also plotted at reference line 340 in the figure. Although in Figure 8 The background model is depicted as a straight line, but it may vary due to various reasons (such as artifacts within the part). Furthermore, although for clarity... Figure 8 Only two dimensions (x-axis 336 and y-axis 334) are depicted, but it should be understood that a third dimension (hence the reference volume) can further exist along the z-axis to similarly represent continuous segments of the part when determining the signal-to-background ratio. Furthermore, although the scan data and background data are... Figure 8The curve is depicted graphically in 331, but when determining the signal-to-background ratio, the method can determine the “volume” without plotting the data, instead of graphically representing the data, by using various algorithms that utilize the data.

[0062] As discussed in this article (e.g., from the above references) Figure 4 and 5 As will be understood in the discussion, the scan data of each individual segment is affected by the porosity within the part of the part represented by that individual segment (e.g., a pixel or voxel), and by the porosity in the part of the part represented by the segment surrounding the individual segment (e.g., see the reference above). Figure 4 and Figure 5 (Discussion to be continued). In this way, it should be understood that the signal-to-background ratio, represented by the volume between reference line 340 and signal line 338, can represent or otherwise indicate the average porosity of the part at the segment within the region of interest 332. In this way, although a segment (e.g., a pixel or voxel) can be larger than a single hole within the segment, especially when using a low-resolution scanner, examining the region of interest as a whole can provide a more accurate representation of the total porosity / volume porosity of the part within the region of interest 332.

[0063] More specifically, for the exemplary aspects shown and returning to the reference Figure 2 As described above, determining the volume porosity at (210) further includes determining at (216) data indicating the signal-to-background ratio that correlates the scan data (represented by, for example, signal line 338) with the background data (represented by reference line 340). The signal-to-background ratio can be visualized as calculating the volume 342 (and, for example, further extending in the z-direction) between the scan data along signal line 338 and the background model drawn along reference line 340 within the region of interest 332, divided by the volume under the background model drawn along reference line 340. In this way, it will be understood more specifically that the signal-to-background ratio refers to the difference between the scan data and the background data within a particular region of interest 332 divided by the background data.

[0064] Furthermore, determining the volume porosity of the part at (214) includes determining the volume porosity of the part at (218) based on the signal-to-background ratio (represented by volume 342). For example, the signal-to-background ratio within the region of interest 332 (represented by dividing the volume 342 between the plotted scan data and the plotted background model by the plotted background model within the region of interest 332) can indicate the volume porosity of the region of interest 332, and a transfer function can be used to convert the signal-to-background ratio within the region of interest 332 into a volume porosity reading for the region of interest 332.

[0065] In some exemplary aspects, as described above, calculating the volume 342 between the drawn scan data and the drawn background model may include using Riemann summation to measure the volume between the scan data drawn along signal line 338 and the background model drawn along reference line 340 (by...). Figure 8 (The volume in the text is represented by 342).

[0066] Alternatively or additionally, other means or methods may be used to determine the volume between signal line 338 and reference line 340 within region of interest 332.

[0067] The volume between signal line 338 and reference line 340 can be represented by rectangle 344, which has a height 346 along the y-axis 334 relative to the average signal level of the average background data / reference line 340 within the region of interest 332, and a width along the x-axis 336 of rectangle 344 (which corresponds to...). Figure 8 The signal-to-background ratio (SPR) can be associated with a comparison of a volume having a height of 346, a width of 332, and a depth (not depicted) with the volume below reference line 340 (having the same width of 332 and depth (not depicted)) divided by the volume below reference line 340. The SPR of a particular region of interest 332 can indicate the volume porosity of the region of interest 332.

[0068] In this manner, it should be understood that the signal-to-background ratio (SBR) within a specific region can be determined by (a) determining the SBR on a segment-by-segment basis or (b) determining the SBR on an average basis within the region of interest (ROI). For option (a), the SBR can be determined by subtracting the scan data from the background data and dividing by the background data at each segment (e.g., (value at reference line 340 - value at signal line 338) / (value at reference line 340)). The SBR data for each segment can then be averaged across the ROI to obtain the ROI's SBR. In contrast, for option (b), the SBR can be determined by subtracting the average scan data across the ROI from the average background data within the ROI and dividing by the average background data within the ROI (e.g., (height 346 / height 348)).

[0069] Furthermore, it should be understood that in at least some exemplary embodiments, the size of the region of interest may be as large or as small as desired. For example, in some exemplary embodiments, the region of interest may correspond to a single segment (e.g., a single pixel or a single voxel). Alternatively, the region of interest may be associated with a larger portion of the part (e.g., at least 1%, at least 2%, at least 5%, and up to, for example, 100% (for relatively small parts), up to, for example, 70%, up to, for example, 50%, up to, for example, 30%, up to, for example, 20%, up to, for example, 10%).

[0070] Further information Figure 2 It should be understood that method 200 can also be used to adjust the background model indicated at (208), the preprocessing parameters indicated at (206), or both. Specifically, method 200 can provide information at (220) to the background model and / or preprocessing parameters indicating the volume porosity determined at (218), the volume calculated at (216), etc., to allow for updating and / or calibration of the background model and / or preprocessing parameters. This allows method 200 to consider different scan parameters or different part geometries that may correlate differently with the true porosity under those scan parameters (e.g., voltage, current, integration time, filtering, etc.).

[0071] Furthermore, it should be understood that method 200 may also provide the user with a visualization of the volume porosity at (222) based on the volume porosity determined at (218). The visualization may be the actual volume porosity of the region of interest, or it may be a "pass" / "fail" indication (e.g., a green or red light). The results may be provided to a database, which is then provided to the user. Any other indicators may be provided additionally or alternatively.

[0072] Furthermore, method 200 can further initiate actions based on volume porosity at (224). In cases where porosity is above a threshold and the part is discarded, the action could be to remove the part from use (i.e., indicating that its porosity is below a minimum threshold or other threshold), or it could be to discontinue the use of the part (e.g., sales, distribution, installation, circulation, etc.). Additionally or alternatively, the action could be to reduce the quality of the part based on the volume porosity determined at (218). The action might be similar to the above regarding… Figure 1 The described "pass" / "fail" action.

[0073] Now for reference Figure 9 A schematic diagram of a computing system 400 according to an exemplary aspect of this disclosure is provided. Figure 9 An exemplary computing system 400 may be configured to receive scan data from one or more scanners (e.g., one or more CT / VCT scanners) and, for example, make decisions based on the received data.

[0074] In one or more exemplary embodiments, Figure 9 The computing system 400 depicted may be a standalone computing system 400, or alternatively may be integrated into one or more other computing systems.

[0075] Referring specifically to the operation of computing system 400, in at least some embodiments, computing system 400 may include one or more computing devices 402. Computing device 402 may include one or more processors 402A and one or more memory devices 402B. The one or more processors 402A may include any suitable processing means, such as a microprocessor, microcontroller, integrated circuit, logic device, and / or other suitable processing means. The one or more memory devices 402B may include one or more computer-readable media, including but not limited to non-transitory computer-readable media, RAM, ROM, hard disk drives, flash drives, and / or other memory devices.

[0076] One or more memory devices 402B may store information accessible by one or more processors 402A, including computer-readable instructions 402C executable by one or more processors 402A. Instructions 402C may be any set of instructions that, when executed by one or more processors 402A, cause one or more processors 402A to operate. In some embodiments, instructions 402C may be executed by one or more processors 402A to cause one or more processors 402A to operate, for example, any operations and functions for which computing system 400 and / or computing device 402 are constructed, operations for operating a porosity system as described herein (e.g., methods 100, 200), and / or any other operations or functions of one or more computing devices 402. Instructions 402C may be written in any suitable programming language or may be implemented in hardware. Additionally and / or alternatively, instructions 402C may be executed in logically and / or virtually decoupled threads on one or more processors 402A. One or more memory devices 402B may further store data 402D accessible by one or more processors 402A. For example, data 402D may include data indicating power flow, data indicating engine / aircraft operating conditions, and / or any other data and / or information described herein.

[0077] The computing device 402 may also include a network interface 402E for communicating, for example, with other components. The network interface 402E may include any suitable components for communicating with one or more network interfaces, including, for example, a transmitter, receiver, port, controller, antenna, and / or other suitable components.

[0078] The techniques discussed herein refer to computer-based systems, actions taken by computer-based systems, information sent to computer-based systems, and information received from computer-based systems. Those skilled in the art will recognize that the inherent flexibility of computer-based systems allows for a wide variety of possible configurations, combinations, and divisions of tasks and functions between and within components. For example, the processes discussed herein can be implemented using a single computing device or multiple computing devices working in combination. Databases, memories, instructions, and applications can be implemented on a single system or distributed across multiple systems. Distributed components can operate sequentially or in parallel.

[0079] This written description uses examples to disclose the invention, including the best mode, and also enables any person skilled in the art to practice the invention, including making and using any device or system and any method of combination. The patent scope of the invention is defined by the claims, but may include other examples that would occur to a person skilled in the art. Such other examples are intended to fall within the scope of the claims if they include structural elements that are not indistinguishable from the literal language of the claims, or if they include equivalent structural elements that are not substantially different from the literal language of the claims.

[0080] Further details are provided by the following topics:

[0081] A method for determining the porosity of a part includes: determining scan data of the part, the scan data including data of a plurality of consecutive segments; determining a background model of the part, the scan data, or both; and determining the volume porosity based on the difference between the scan data and the background model.

[0082] According to one or more of these clauses, determining the scan data of the part includes determining scan data within a region of interest based on the scan data of the plurality of consecutive segments, determining a background model of the part, the scan data, or both includes determining background data within the region of interest based on the background model, and determining the volume porosity based on the difference between the scan data and the background model includes determining data indicating the signal-to-background ratio within the region of interest that associates the scan data with the background data.

[0083] According to one or more of these methods, determining the data indicating the signal-to-background ratio includes using Riemann summation to calculate the volume between the scan data and the background data.

[0084] According to one or more of these provisions, determining the data indicating the signal-to-background ratio includes determining the average signal level.

[0085] According to one or more of these terms, the scanning data includes data indicating the density of the part at the plurality of consecutive segments, and the background data includes data indicating the expected density of the part at the plurality of consecutive segments.

[0086] According to one or more of these clauses, the method wherein the scan data includes data indicating the density of the part at the plurality of consecutive segments, wherein the plurality of consecutive segments are a plurality of pixels or voxels, and wherein the data indicates that the density of each pixel or voxel is affected by the porosity in one or more surrounding pixels or voxels.

[0087] According to one or more of these methods, determining the scan data of the part includes scanning the part using computed tomography (CT) processing.

[0088] The method according to one or more of these clauses, wherein the computed tomography process is a volumetric computed tomography process.

[0089] According to one or more of these terms, the data of the plurality of consecutive segments includes data indicating the porosity in each of the plurality of consecutive segments.

[0090] According to one or more of these terms, each of the plurality of consecutive segments is a pixel or voxel.

[0091] According to one or more of these terms, the part is a composite material part or a metal part for a gas turbine engine.

[0092] According to one or more of these methods, determining the scan data of the part includes receiving the scan data and subtracting preprocessing parameters from the scan data.

[0093] The method according to one or more of these clauses further includes: providing the user with an indication of the determined volumetric porosity.

[0094] The method according to one or more of these clauses further includes: initiating an action based on the determined volumetric porosity.

[0095] According to one or more of these clauses, determining the background model of the part, the scan data, or both includes determining the background model of the part based on the scan data.

[0096] According to one or more of these terms, determining the background model of the part, the scan data, or both includes determining the background model of the part based on the pre-scanned part.

[0097] A system for determining the porosity of a part, the system comprising: one or more processors; and a memory operatively coupled to the one or more processors, the memory containing instructions that, in response to execution by the one or more processors, cause the one or more processors to: determine scan data of the part, the scan data comprising data of a plurality of consecutive segments; determine a background model of the part, the scan data, or both; and determine the volume porosity based on the difference between the scan data and the background model.

[0098] According to one or more of these clauses, the system wherein determining the scan data of the part includes scanning the part using volumetric computed tomography (CT) processing.

[0099] According to one or more of these clauses, in a system wherein determining scan data of the part includes determining scan data within a region of interest based on the scan data of the plurality of consecutive segments, wherein determining a background model of the part, the scan data, or both includes determining background data within the region of interest based on the background model, and wherein determining volume porosity based on the difference between the scan data and the background model includes determining data indicating the signal-to-background ratio within the region of interest that associates the scan data with the background data.

[0100] According to one or more of these clauses, the system wherein calculating the signal-to-background ratio includes using Riemann summation to calculate the volume between the scan data and the background data within the region of interest.

[0101] The method described according to one or more of these clauses uses the system described according to one or more of these clauses.

[0102] The system described according to one or more of these clauses uses the method described according to one or more of these clauses.

Claims

1. A method for determining the porosity of a part, characterized in that, include: Determine the scan data of the part, the scan data comprising multiple consecutive segments of data, wherein determining the scan data of the part includes determining scan data within a region of interest based on the scan data of the multiple consecutive segments; Determine a background model of the part, the scan data, or both, wherein determining the background model of the part, the scan data, or both includes determining background data within the region of interest based on the background model; as well as The volume porosity is determined based on the difference between the scan data and the background model, wherein determining the volume porosity based on the difference between the scan data and the background model includes determining data indicating the signal-to-background ratio within the region of interest that associates the scan data with the background data.

2. The method according to claim 1, characterized in that, in, Determining the data indicating the signal-to-background ratio includes using Riemann summation to calculate the volume between the scan data and the background data.

3. The method according to claim 1, characterized in that, in, Determining the data indicating the signal-to-background ratio includes determining the average signal level.

4. The method according to claim 1, characterized in that, in, The scan data includes data indicating the density of the part at the plurality of consecutive segments, and the background data includes data indicating the expected density of the part at the plurality of consecutive segments.

5. The method according to claim 1, characterized in that, in, The scan data includes data indicating the density of the part at the plurality of consecutive segments, wherein the plurality of consecutive segments are a plurality of pixels or voxels, and wherein the data indicates that the density of each pixel or voxel is affected by the porosity in one or more surrounding pixels or voxels.

6. The method according to claim 1, characterized in that, in, Determining the scan data of the part includes scanning the part using computed tomography (CT) scan.

7. The method according to claim 6, characterized in that, in, The computed tomography (CT) process is a volumetric CT process.

8. The method according to claim 1, characterized in that, in, The data for the plurality of consecutive segments includes data indicating the porosity in each of the plurality of consecutive segments.

9. The method according to claim 1, characterized in that, in, Each of the plurality of consecutive segments is a pixel or voxel.

10. The method according to claim 1, characterized in that, in, The part is a composite material part or a metal part used in a gas turbine engine.

11. The method according to claim 1, characterized in that, in, Determining the scan data of the part includes receiving the scan data and subtracting preprocessing parameters from the scan data.

12. The method according to claim 1, characterized in that, Further includes: Provides the user with an indication of the determined volumetric porosity.

13. The method according to claim 1, characterized in that, Further includes: The action is initiated based on the determined volumetric porosity.

14. The method according to claim 1, characterized in that, in, Determining the background model of the part, the scan data, or both includes determining the background model of the part based on the scan data.

15. The method according to claim 1, characterized in that, in, Determining the background model of the part, the scan data, or both includes determining the background model of the part based on the pre-scanned part.

16. A system for determining the porosity of a part, characterized in that, The system includes: One or more processors; and A memory operatively coupled to the one or more processors, the memory containing instructions that, in response to execution by the one or more processors, cause the one or more processors to: Determine the scan data of the part, the scan data comprising multiple consecutive segments of data, wherein determining the scan data of the part includes determining scan data within a region of interest based on the scan data of the multiple consecutive segments; Determine a background model of the part, the scan data, or both, wherein determining the background model of the part, the scan data, or both includes determining background data within the region of interest based on the background model; and The volume porosity is determined based on the difference between the scan data and the background model, wherein determining the volume porosity based on the difference between the scan data and the background model includes determining data indicating the signal-to-background ratio within the region of interest that associates the scan data with the background data.

17. The system according to claim 16, characterized in that, in, Determining the scan data for the part includes scanning the part using volumetric computed tomography (CT) processing.

18. The system according to claim 16, characterized in that, in, Calculating the signal-to-background ratio involves using Riemann summation to calculate the volume between the scan data and the background data within the region of interest.

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